UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D.C. 20460

Office of Prevention, Pesticides and Toxic Substances

January 5, 2006
SUBJECT: Iodomethane: Revised HED Human Health Risk Assessment; DP Barcode: D325080, PC Code: 000011
FROM: Elizabeth Mendez, Ph.D, Toxicologist/Risk Assessor Jeffrey L. Dawson, Chemist/Risk Assessor Reregistration Branch 1 Health Effects Division (7509C)
THRU: Whang Phang, PhD, Branch Senior Scientist Reregistration Branch 1 Health Effects Division (7509C)
TO: Mary Waller, Product Manager Registration Division
Attached is HED’s risk assessment of the fumigant, iodomethane. HED has evaluated the hazard and exposure data and conducted exposure assessments, as needed, to estimate the risk to human health that will result from the proposed uses of iodomethane.
This risk assessment addresses both exposures in general population and for those occupationally exposed. Exposures in the general population occur primarily via inhalation for those in proximity to treated fields(i.e., bystanders). Occupational exposures also occur via inhalation. The Agency has addressed these scenarios in this assessment. Drinking water exposure is also anticipated at some level. Although iodomethane is used as an agricultural pesticide, it is considered a non-food use chemical since it is quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The levels of iodide released from iodomethane degradation/metabolism are lower than those expected to cause toxic effects. Furthermore, enforcement of tolerances would not be possible since no iodide-free samples are available and residue field trials show evidence of control samples with higher iodide residues than iodomethane treated samples. Moreover, iodide is ubiquitous in the environment and a required nutrient. Finally, iodomethane residues must dissipate in the soil prior to planting. Accordingly, HED concluded tolerances are not required for iodomethane. As a result, a dietary risk assessment has not been conducted.
This risk assessment used the guidance (RfC methodology) developed by the Agency’s Office of Research and Development (ORD) as well as a chemical-specific physiologically-based pharmacokinetic (PBPK) model for the derivation of human equivalent concentrations (HECs) for use in margin of exposure (MOE) calculations.Furthermore, both deterministic and probabilistic methods were used to estimate exposures to bystanders around treated fields which are the key concern. The deterministic approach is based on monitoring data and the use of the EPA’s Industrial Source Complex: Short-Term Model (ISCST3). Use of ISCST3 is thought to provide upper-bound estimates of likely exposures predominantly because its outputs are based on a constant wind vector (speed and direction) flowing directly downwind to those exposed. Since defining the ISCST3 method, OPP has considered three additional modeling systems that allow for the incorporation of actual meteorological data into assessments (i.e., PERFUM, FEMS & SOFEA©). The FIFRA Science Advisory Panel (SAP) evaluated these systems in August and September of 2004. PERFUM (i.e., Probabilistic Exposure and Risk model for Fumigants) has been used herein in addition to ISCST3 in order to evaluate distributional bystander exposure. The SAP provided very similar comments for each distributional model. The Agency has used PERFUM for this assessment because modifications suggested by the SAP were complete, the inputs were readily available, and the comparative computer processing time was lower than FEMS and SOFEA©. The ISCST3 and PERFUM model runs have been summarized in the appendices of this document. Additional information can be provided other than that included in this document that can be used to detail all of the modeling analyses that have been completed (e.g., input and output files for each). Arvesta error correction comments (12/21/05) have been incorporated. No significant changes outside of minor error corrections as noted specifically in their comments have been made.

# HUMAN HEALTH RISK ASSESSMENT Iodomethane

U.S. Environmental Protection Agency Office of Pesticide Programs Health Effects Division (7509C) Elizabeth Mendez, Ph. D., Toxicologist/Risk Assessor Jeffrey L. Dawson, Chemist/Risk Assessor Date: January 5, 2006

### HUMAN HEALTH RISK ASSESSMENT

Iodomethane



## Risk Assessment Team :

Risk Assessor : Elizabeth Mendez, Ph.D. Jeffrey L. Dawson
Residue Chemistry Christine Olinger
Occupational and Residential Exposure: Jeffrey L. Dawson
Epidemiology: Elizabeth Mendez, Ph.D
Toxicology: Elizabeth Mendez, Ph.D
PBPK Model Evaluation: Hugh Barton, Ph.D. Paul Schlosser, Ph.D.
Drinking Water Estimates: Faruque Khan1.0 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2.0 Ingredient Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.0 Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1 Description of Primary Crop Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Description of Livestock Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 Description of Rat Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4.0 Hazard Characterization/Assessment

................................................ 7 4.1 Hazard Characterization

.................................................... 7 4.1.1 Database Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4.1.2 Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.3 Dose-response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.3.1 Inhalation Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.3.2 Dietary Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.3.3 Dermal Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.1.3.4 Classification of Carcinogenic Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.1.4 Endocrine Disruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2 Uncertainty Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Summary of Toxicological Endpoint Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.0 Public Health Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.0 Non-Occupational Exposure Assessment and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6.1 Residential Bystander Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 6.1.1 Bystander Exposure From Known Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 6.1.1.1 Bystander Exposures From Known Area Sources Calculated Using The Monitoring Data Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6.1.1.2 Bystander Exposures From Known Area Sources Calculated Using The ISCST3 Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 6.1.1.3 Bystander Exposures From Known Area Sources Calculated Using The PERFUM Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.1.2 Ambient Bystander Exposure From Multiple Area Sources . . . . . . . . . . . . . . . . . . . 43 6.2 Bystander Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.3 Residue Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.4 Water Exposure/Risk Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
7.0 Aggregate Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

8.0 Cumulative Risk Assessment and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
9.0 Occupational Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
10.0 Data Needs and Label Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 10.1 Toxicology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 10.2 Residue Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 10.3 Occupational and Residential Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Appendix A: Review Of PBPK/PD Model
Appendix B: Toxicity Profile
Appendix C: Methodologies for Inhalation Risk Calculations and Human Equivalent Concentration Arrays
Appendix D: Analysis Of Field Volatility Data For Pre-plant Field Uses
Appendix E: Downwind Air Concentrations Calculated With ISCST3 For PrePlant Field Uses
Appendix F: Downwind Air Concentrations Calculated With PERFUM For PrePlant Field Uses
Appendix G: Occupational Risk Associated with Agricultural Fumigations1 1.0 Executive Summary
The Health Effects Division (HED) of EPA’s Office of Pesticide Programs has conducted a human health risk assessment for the active ingredient, iodomethane, also referred to as methyl iodide. The proposed use of iodomethane is as a pre-plant soil fumigant in strawberries, tomatoes, peppers, perennial crop ornamentals, nurseries, cut flowers, turf, and tree and vines. Iodomethane has been identified as a possible replacement for methyl bromide (MeBr), a fumigant with numerous registered uses.
There are no registered pesticidal uses of iodomethane at present. There are, however, some industrial and commercial uses. Currently, it is used as an intermediate in the manufacture of some pharmaceuticals, in methylation processes and in the field of microscopy.
Although iodomethane will be used as an agricultural pesticide, it is considered a non-food use chemical since it is quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The levels of iodide released from iodomethane degradation/metabolism are lower than those expected to cause toxic effects. Furthermore, iodomethane residues must dissipate in the soil prior to planting to prevent phytotoxicity. Accordingly, HED concludes that tolerances are not required for iodomethane at this time. As a result, a risk assessment has not been conducted for the dietary exposure scenario. The U.S. population, however, may be exposed to iodomethane through drinking water; therefore, a qualitative drinking water risk assessment has been conducted and no risks have been identified from this potential source of exposure.
In the general population, exposure to iodomethane is anticipated to occur via inhalation or oral (drinking water) routes but not through the dermal route. Dermal exposure to iodomethane of any significance is not expected based on the delivery systems used (e.g., soil injection or drip irrigation), packaging (i.e., pressurized cylinders), and emission reduction technologies (e.g., tarping). The high vapor pressure of iodomethane also makes significant dermal exposure unlikely. The general public, however, may be exposed to fumigants in air because of their volat ility following application. Specifically, fumigants can off-gas into air and be transported off-site by winds to those in proximity to treated fields (i.e., bystanders). Consequently, the Agency conducted a quantitative human health risk assessment for nondietary exposure only via the inhalation route. For the purpose of conducting inhalation risk assessments, the current iodomethane database provides sufficient information to assess risks to the human population following iodomethane exposure via the inhalation route. Exposures may be acute (less than 24 hours), short-term (1-30 days), intermediate-term (1 month-6 months), or long-term in duration. Acute exposures have been quantitatively assessed because this duration is the key concern due to the anticipated use pattern of iodomethane, its emission profile, and the nature of its toxicity. Additionally, for these same reasons, it is believed that acute assessments are health protective for other durations of exposure.
Iodomethane has a severe to moderate acute toxicity profile; it is severely toxic via the oral route (Toxicity Category II), corrosive to the eye (Toxicity Category I ) and a severe dermal irritant (Toxicity Category II). Via the inhalation route, it has been classified as a Category IV chemical (slightly toxic) with an LC 50of 4 mg/L.
The pattern of toxicity attributed to iodomethane exposure via the inhalation route includes developmental toxicity (manifested as fetal losses and decreased live births), histopathology findings (respiratory tract lesions and salivary gland squamous cell metaplasia ), thyroid toxicity, neurotoxicity and generalized systemic toxic effects (body weight and body weight gain decreases). The critical effects of iodomethane exposure via the inhalation route are the fetal losses observed in two developmental toxicity studies in rabbits, the2 histopathological lesions reported in three studies, and the generalized systemic toxicity seen throughout the database. The guideline inhalation chronic toxicity/carcinogenicity study in rats and the carcinogenicity study in mice revealed that chronic exposure to iodomethane resulted in an increased incidence of thyroid follicular cell tumors. The sustained perturbation of thyroid hormone homeostasis characteristic of iodomethane exposure (observed in rats, mice, and rabbits) has been established as the operative mode of action (MOA) for this tumorigenic response. As a result, HED’s Cancer Assessment Review Committee (CARC) has identified iodomethane as “not likely to be carcinogenic to humans at doses that do not alter rat thyroid hormone homeostasis.”
An extensive mechanistic data set as well as a physiologically-based pharmacokinetic (PBPK) model are available for iodomethane. These data and model constitute a sophisticated effort to better characterize the toxicity profile for this compound in terms of developmental toxicity, respiratory tract lesions, and thyroid hormone perturbations identified as the critical effects of iodomethane exposure. In addition, the use of a PBPK model that takes into consideration the toxicokinetic aspect of iodomethane exposure enables the Agency to use chemical-specific parameters to determine the most appropriate dose metric and internal dose in calculating human equivalent concentrations (HECs) instead of the default inputs used in the Agency’s Reference Concentration (RfC) methodology. The Agency has reviewed these data and their usefulness to calculate human equivalent concentrations (HECs) based on chemical-specific data. In general, the model and the mechanistic studies used to provide its inputs are considered adequate and their results have been incorporated into this risk assessment.
Based on the toxicity profile and the major exposure routes of iodomethane, endpoints have been selected for the residential/bystanders and occupational human health risk assessments. HED is currently using the reference concentration (RfC) methodology along with a PBPK to derive the human equivalent concentration (HEC) for inhalation exposures in this risk assessment. Under the RfC methodology and the PBPK model approach, endpoint selection is based on the HECs which are derived from the NOAELs of the selected studies. Different HECs may be derived for non-occupational and occupational assessments due to the time adjustments conducted for the exposure scenarios. While non-occupational exposure is presumed to potentially occur 24 hours/day, 7days/week; occupational exposure is presumed to occur for 8 hours/day and 5 days/week. The specific concentrations and endpoints for the exposure scenarios are summarized below:

• Acute inhalation: Two critical endpoints have been identified for this risk assessment: nasal histopathology in the subchronic inhalation toxicity study in rats and the fetal losses in the developmental toxicity study in rabbits. The purpose of this approach is to bracket the range of possible effects associated with iodomethane from the least to most adverse effects which have been identified. For the nasal histopathology endpoint, HED selected an HEC of 2.9 or 3.7 ppm (bystander and occupational risk assessment, respectively) from the NOAEL of 21 ppm based on degeneration of the olfactory epithelium. For the developmental endpoint, HED selected an HEC of 4.0 ppm(non-occupational risk assessment) from the NOAEL of 10 ppmbased on fetal losses and decreased fetal weights in a developmental toxicity study in rabbits at the LOAEL of 20 ppm. An uncertainty factor (UF) of 30X defines the HED level of concern.
In the case of the nasal histopathology lesions, sustained glutathione (GSH) depletion has been identified as a key event in the toxicity pathway leading to the lesions. Consequently, GSH depletion ( 

### # 50%) is the dose metric used in the PBPK model for the interspecies extrapolation to determine the NOAEL for the nasal lesions. Using the HECs of 2.9 or 3.7 ppm (bystander and occupational risk assessment, respectively) results in a time weighted average GSH depletion of 

### . 25% which is below3 the level of GSH depletion commonly cited in the literature as critical for development of nasal histopathology.
The dose metric used for the fetal losses is the level of fetal serum inorganic iodide after a single day of exposure. Based on this dose metric an HEC of 17 ppm could be used for the bystander risk assessment. However, this HEC would be based on the assumption that human fetal serum iodide levels are 

### . 75% of maternal levels. Although there is some evidence in support of this assumption in the peer reviewed literature, the Agency has concluded that the evidence is not sufficiently robust ( i.e., limited and often indirect) to establish an HEC of 17 ppm as a point of departure for the risk assessment. As a result, an HEC of 4.0 ppm is used in the risk assessment based on the assumption that there is an equivalent distribution of fetal serum iodide in rabbits and humans. The biomedical literature indicate that the “true” value is likely towards the high end of this range ( i.e. , 4-17 ppm). However, there does not seem to be adequate information to establish a particular value in the higher part of that range is the most accurate value. Consequently, there is higher confidence in the use of the 4.0 ppm HEC as a point of departure for this assessment based on the endpoint of fetal losses.

• Short-term and Intermediate inhalation (bystander): HED selected an HEC of 1.25 ppm from the NOAEL of 5 ppmbased on decreased pup weight and weight gain, decreased thymus weights, and delays in vaginal patency acquisition seen in the multigeneration reproduction toxicity study at the LOAEL of 20 ppm. An uncertainty factor (UF) of 30X defines the HED level of concern.

• Short-, Intermediate- term inhalation (occupational): HED selected an HEC = 3.7 ppmfrom the NOAEL of 21 ppm based on minimal-mild degeneration of the olfactory epithelium seen at the LOAEL of 70 ppm in the subchronic inhalation toxicity study in rats. An uncertainty factor (UF) of 30X defines the HED level of concern.
• Long- term inhalation: HED selected an HEC = 0.89 ppm or 3.75 ppm (bystander and occupational risk assessments, respectively) from the NOAEL of 5 ppmbased increased incidence of salivary gland squamous cell metaplasia seen at the LOAEL of 20 ppm from the chronic toxicity/carcinogenicity study in rats. An uncertainty factor (UF) of 30X defines the HED level of

concern. Releases of fumigants such as iodomethane can be categorized in two distinct manners that include addressing exposures from known area sources (e.g., a treated agricultural field) and also by evaluating available ambient air levels from multiple area sources that could occur from many applications in a region (e.g., several farms in a specific valley).
The evaluations of bystander exposures that can result from known area sources considered field volatility data directly from several studies, results from an Agency developed Gaussian air plume model (Industrial Source Complex Short-Term Model, ISCST3), and results from PERFUM (i.e., Probabilistic Exposure and Risk model for Fumigants) which is a new modeling system based on ISCST3 that allows for incorporation of actual meteorological data into probabilistic assessments. In fact, the FIFRA Science Advisory Panel (SAP) evaluated three such modeling systems for fumigants (i.e., PERFUM, FEMS & SOFEA©) in August and September of 2004. The Agency used PERFUM for this assessment because modifications suggested by the SAP were complete, the inputs were readily available, and the comparative computer processing time was lower than FEMS and SOFEA©.4 For known area sources (i.e., treated agricultural fields), HED first used monitoring data to assess bystander exposures to iodomethane. Risks exceeded HED’s level of concern based on these data. In addition, the Industrial Source Complex - Short Term model (ISCST3) was used to further characterize exposures by extrapolating to conditions under which empirical data are not be available. In the ISCST3 analysis, varied meteorological conditions, field sizes, and emission rates were considered. Results demonstrate that for the cases considered, many risks exceed HED’s level of concern (MOEs <30) for distances less than 100 meters downwind of the treated fields larger than 1 acre especially when the atmosphere is relatively stable and where wind speeds < 5 mph. MOEs decrease as field sizes increase while MOEs increase as the atmosphere becomes less stable leading to conditions where more off-target drift can occur. There is not a significant impact in the results due to the two different HECs that were considered.
At the upper percentiles of the exposure distributions generated with PERFUM, the results presented in are markedly similar to those calculated with ISCST3 which supports the construct that the ISCST3 results in conservative estimates of exposure. If maximum buffer distances at the 95 thpercentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 65 meters (at HEC = 4 ppm) and 120 meters (at HEC = 2.9 ppm) while values range up to approximately 400 meters (at HEC = 4 ppm) and 600 meters (at HEC = 2.9 ppm). If whole field buffer distances at the 95 thpercentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 5 meters (at HEC = 4 ppm) and 45 meters (at HEC = 2.9 ppm) while values range up to approximately 140 meters (at HEC = 4 ppm) and 230 meters (at HEC = 2.9 ppm). Generally, Ventura California and Bradenton Florida meteorological data result in the largest buffer distances regardless of flux profile. Likewise, Flint Michigan meteorological data seems to generally result in the lowest buffer distances which is logical given its routine climate compared to the California and Florida locations (e.g., typically cooler with potentially less atmospheric turbulence). For 40 acre fields at the 99.9 thpercentile, maximum buffer distances ranged as high as 875 meters (HEC = 4 ppm) and 1380 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California drip irrigation flux information. For 40 acre fields at the 99.9 thpercentile, whole buffer distances ranged as high as 495 meters (HEC = 4 ppm) and 710 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California raised bed flux information. For 1 acre fields, the vast majority of buffer distances at all percentiles of exposure considered (up to 99.9 thpercentile) were less than 100 meters with most being in the 5 to 50 meter range at the upper percentiles of exposure (i.e., 99 thpercentile plus) regardless of whether maximum or whole field buffers are considered. Changes in risk estimates with percentiles of exposure were examined and these analyses indicate that even at buffer distances based on the 95 thpercentile of exposure risks were generally not significantly less even at higher percentiles of exposure which indicates slight changes in risks with percentile of exposure (e.g., MOEs ~10 were common at the 99.9 thpercentile of exposure with MOEs of 30 at the 95 thpercentile). Other factors which could potentially impact results were examined including the size and shapes of fields. As expected, larger fields yielded greater buffer distances. Ventura California meteorological data were the basis of the field shape analysis which indicates very little impact on buffer distances but it is possible different meteorological data could yield different results.
Exposures from ambient sources were qualitatively evaluated based on physical-chemical properties and environmental fate characteristics. Ambient air monitoring data were not available since iodomethane is not currently widely used. Exposures to bystanders from ambient air resulting from multiple area sources (e.g., multiple iodomethane applications in a geographic area) would typically be calculated based on ambient monitoring data if available. Ambient-air exposures could potentially occur in proximity to agricultural areas where there is significant use during a particular growin g season on a regional basis (e.g., in coastal areas of California during field fumigation prior to strawberry growing season). Currently, no ambient air data are5 available for iodomethane; however, HED does not believe that ambient air exposures to bystanders are likely to be a significant concern based on a comparison of the characteristics of iodomethane with those of methyl bromide and the ambient air monitoring data available for methyl bromide.
Iodomethane is very soluble in water, so there is the possibility of leaching to ground water and/or transporting to surface water through runoff, if slicing or removal of the tarpaulin coincides with, or is followed soon by, a rain event. Based on environmental fate data, the residual contents in soils, and Tier I and II models estimated concentrations, HED does not expect iodomethane to adversely impact ground water or surface water.
Risks to occupational handlers, including (tractor drivers, co-pilots, shovelers, soil sealers, and tarp removers), involved in pre-plant field fumigation were evaluated using iodomethane-specific handler monitoring data. The data indicate that exposures exceed HED’s level of concern for some workers involved in the application of iodomethane when no respiratory protection is used (e.g., tractor drivers, co-pilots, and shovelers). Air purifying organic vapor removing respirators (APRs) which reduce exposure levels by a factor of 10 were also considered and exposures were reduced below HED’s level of concern for all workers involved in application with these devices although for some application tasks APRs are not required to achieve acceptable exposure levels. Respirators would be the most practical protective equipment choice for reducing exposures for most workers in this case. This was because the field monitoring data used for this analysis already reflected the use of some engineering co ntrols such as tarps, tractor cabs, deep injection, or other devices including fans in proximity to drivers. The duration of exposure had no impact on the results of this assessment.
For workers who entered fields days after application to prepare for planting (e.g., tarp cutters or hole punchers), exposures were not of concern 5 days after application (which reflects the available data) without any sort of respiratory protection. This was also the case for planters where exposures were not of concern 7 days after application without any sort of respiratory protection (which also reflects the available data).6 2.0 Ingredient Profile

Iodomethane Empirical Formula: CH 3 I Molecular Weight: 141.95 CAS Registry No.: 74-88-4 PC Code: 000011 Chemical Class: Alkyl Iodide
Iodomethane is a colorless, liquid at normal temperatures and pressures and has a sweet ethereal odor. Iodomethane has a specific gravity of 2.28 at 20 °C, vapor pressure of 375 torr at 20 °C, bo iling point of 42.5 °C, and octanol/water partition coefficient (log P ow ) of 1.51. It is soluble in water at 1.75 g/100 mL at 20 C, and is miscible in alcohols and ether.
Iodomethane is a pre-plant soil biocide used to control insects, plant parasitic nematodes, soil borne pathogens, and weed seeds. The proposed uses are fo r growing strawberries, fresh market tomatoes, peppers, perennial crop ornamentals, nurseries, cut flowers, turf, and tree and vines.
Iodomethane is stored as a liquid under pressure but volatilizes rapidly following soil injection. It is applied by shallow shank broadcast flat fume (flat fume); raised bed shallow shank injection (raised bed); or raised bed drip irrigation (drip irrigation). A maximum application rate of 175 lb ai/acre has been proposed for use in various crops. This proposed application rate is the basis for the modeling completed in this assessment. [Note: The field volatility and worker exposure monitoring data were generated at application rates which ranged between 234 and 259 lb ai/acre. The PERFUM model calculations in this assessment accounted for these differences by scaling results based on the rate differences. Worker exposure monitoring results were not scaled based on application rate.]

3.0 Metabolism
3.1 Description of Primary Crop Metabolism
Plant metabolism studies conducted on strawberries and tomatoes found iodomethane to be extensively metabolized and incorporated into the plant constituents, primarily carbohydrates. Iodide levels in the raw commodities were comparable to background levels found in control samples.1 Robinson, DA et al. (2003). “Three-dimensional mapping of the lesions induced by beta-beta’- iminodiproprionitrile, methyl iodide and methyl methacrylate in the rat nasal cavity.” Toxicol. Pathol. 31 (3):340- 347. Chamberlain, MP et al. (1999). Methyl iodide toxicity in rat cerebellar granule cells in vitro: the role of glutathione.” Toxicology 139 (2-3):27-37. Chamberlain, MP et al. (1998). “Investigations of the pathways of toxicity of methyl iodide in the rat nasal cavity.” Toxicology 129 (2-3):169-181 Reed, CJ et al. (1995). “Olfactory toxicity of methyl iodide in the rat.” Arch. Toxicol. 70 (1):51-56 Bonnefoi, MS (1992). “Mitochondrial glutathione and methyl iodide-induced neurotoxicity in primary neural cell cultures.” Neurotoxicology 13 (2):401-412

7 3.2 Description of Livestock Metabolism
There are no significant livestock feed items concerned with this assessment so livestock metabolism studies are not required.
3.3 Description of Rat Metabolism
A rat metabolism study comparing absorption after oral and inhalation administration is available. The data in this study indicate that iodomethane is quickly absorbed through both routes of exposure (maximum blood concentration at 2-4 hours). In contrast, the elimination profile indicates that excretion of 14 C-labeled iodomethane is biphasic with the initial half-life of 5-7 hours and a terminal half-life of approximately 116-136 hours. These half-lives, however, are measured on the basis of the 14 C radiolabel and may not accurately reflect the amount of iodomethane or iodide remaining in the body since the methyl and iodide moieties of iodomethane are expected to quickly dissociate after administration. Radioactivity accumulates in a variety of tissues including the thyroid (radioactivity concentration of 106-198 

### : g/g tissue). A second rat metabolism study was conducted to quantify the levels of inorganic iodide in rat serum after a two-day exposure (6 hrs/day) via the inhalation route. The results of this study indicate that inorganic iodide serum levels increase dramatically ( 

### 8 300-1000 fold) during the exposure period and remained elevated during the 18 hours following exposure ( 

### 8 63-400 fold).

4.0 Hazard Characterization/Assessment

4.1 Hazard Characterization
4.1.1 Database Summary

Studies available and acceptable (animal, human, general literature)
The registrant has submitted a complete database via the inhalation route including an acute neurotoxicity study, developmental studies in rats and rabbits, subchronic inhalation toxicity study in rats, as well as a multigeneration reproductive toxicity study and a combined chronic/carcinogenicity study in rats. All of the inhalation studies received to date have been classified as acceptable. At this time, subchronic oral toxicity studies have been submitted to the Agency. Since iodomethane has been classified as a non-food usechemical, only a screening level assessment of the oral toxicity studies has been completed. In the peerreviewed literature there are several reports indicating that iodomethane is toxic to the central nervous system, as well as the respiratory tract. 1 Moreover, numerous published articles indicate that methyl iodide is2 Bolt, HM and Gansewendt, B. (1993) “Mechanisms of carcinogenicity of methyl halides.” Crit. Rev. Toxicol. 23 (3):237-253. Xu, DG et al. (1993). “DNA methylation of monohalo genated methanes of F344 rats.” J. Tongji Med. Univ. 13 (2):100-104

8 genotoxic due to its methylating capabilities. 2Interestingly, the only evidence of genotoxicity observed in the guideline studies submitted to the Agency is an induction of structural chromosome aberrations (clastogenesis).
Since iodomethane is a new proposed pesticidal active ingredient, there are no incident reports related to agricultural uses. However, this compound is us ed as an intermediate in the manufacture of some pharmaceuticals, in methylation processes, and in the field of microscopy, thus sporadic reports of iodomethane poisonings are available in the open literature.
Metabolism, toxicokinetic , mode of action data
As stated above, a rat metabolism study comparing absorption after oral and inhalation administration is available. The data indicate that iodomethane or its metabolites accumulate in a variety of tissues including the thyroid (radioactivity concentration of 106-198 

### : g/g tissue) and is quickly absorbed through both oral and inhalation routes of exposure (maximum blood concentration at 2-4 hours). Also available is a rat metabolism study intended to quantify the levels of inorganic iodide in the rat serum and describe the kinetics for serum iodide accumulation/elimination after iodomethane exposure. As was noted in the guideline metabolism study, this special study indicated that while accumulation of iodide is rapid the elimination profile is slow and biphasic in nature.
Toxicokinetic and mode of action data have been submitted by the registrant in support of a physiologically based pharmacokinetic (PBPK) model including: i) Mode of Action Study for Iodomethane-related Fetotoxicity Study in Rabbits; ii) Combined Baseline Inhalation Exposure Study of Iodomethane-Related Fetotoxicity in Rabbits; iii) In vivo Two Day Inhalation Mechanistic Toxicity Study in the Rat; iv) Iodomethane: Analysis of Select Biomarkers in Rabbit Tissue; v) Iodomethane: Pulmonary Function Study in Rabbits; vi) Iodomethane: In vitro Partition Coefficients in Rat and Rabbit Tissues and Human Blood; vii) Iodomethane: Select Biomarkers in Rabbit Tissues after Inhalation Exposure; viii) Effects of Methyl Iodide on Deiodinase Activity; ix) Derivation of Human Reference Toxicity Values for Methyl Iodide using Physiologically Based Pharmacokinetic (PBPK) Modeling; x) Magnetic Resonance Imaging and Computational Fluid Dynamics Simulations of Rabbit Nasal Airflows; xi) Uptake of MeI by the Rabbit Nasal Cavity; xii) Uptake of MeI by the Rat Nasal Cavity; xiii) In vivo Gas Uptake in Rabbits; xiv) The Pharmacokinetics of Sodium Iodide (NaI) in Pregnant Rabbits; xv) In vitro GSH Conjugation Study in Rat, Rabbit, and Human Blood and Tissues with MeI.
The Agency has reviewed these data and its usefulness to calculate human equivalent concentrations (HECs) based on chemical-specific data. The mechanistic studies were intended to either define the dose metric or provide compound-specific inputs for the PBPK model. The model is a sophisticated effort to describe the kinetics of methyl iodide following inhalation exposure and the kinetics of iodide as a metabolite. It describes nasal tract dosimetry and glutathione (GSH) depletion in the rat to evaluate nasal toxicity, distribution of methyl iodide to tissues including brain, and iodide kinetics in the pregnant rabbit to address developmental toxicity. The model has also been parameterized for the human and Monte Carlo analyses that were9 performed to describe human variability. The review was carried out using the framework described in Clark et al., 2004. The results of the evaluation are described focusing on the rat and human nasal modeling , the rabbit and human pregnancy modeling, modeling human variability, and model documentation. The strengths and limitations of the modeling were identified. The nasal modeling for rat and human was concluded to be adequate to estimate a human equivalent concentration. Selection of the appropriate degree of GSH depletion to predict nasal olfactory toxicity is dependent on additional factors beyond the PBPK/PD modeling, including judgments about the relationship of this measure with toxicity and the linkage of the time-course of exposure concentrations with the prediction of GSH depletion. The pregnancy modeling was found to be adequate to estimate a range of human equivalent concentrations. The human variability analysis was considered to provide perspective on the default value of 3 to address human pharmacokinetic variability. In general, the model and mechanistic studies used to provide its inputs are considered adequate and their results have been incorporated into this risk assessment. For a more detailed description of the model evaluation, the reader is referred to Appendix A of this document
Sufficiency of studies/data
At this time, the Agency is conducting a quantitative human health risk assessment for exposure via the inhalation route only. For the purpose of conducting inhalation risk assessments, the current iodomethane database provides sufficient information to assess risks to the human population following iodomethane exposure via the inhalation route.
4.1.2 Endpoints
The general public may be exposed to fumigants in air because of their volat ility following application. Specifically, fumigants can off-gas into air and be transported by diffusion and wind off-site. In addition, the U.S. population may be exposed to iodomethane through the drinking water.
The pattern of toxicity attributed to iodomethane exposure via the inhalation route includes developmental toxicity (manifested as fetal losses and decreased live births), histopathology findings (respiratory tract lesions and salivary gland squamous cell metaplasia ), thyroid toxicity, neurotoxicity and generalized systemic toxic effects (body weight and body weight gain decreases).
Developmental and/or offspring toxicity is observed in both rats and rabbits. Two developmental toxicity studies in rabbits conducted via the inhalation route have been reviewed by the Agency. In the guideline study, an increase in fetal losses was noted at the highest exposure concentration. Subsequently, the registrant conducted a phased exposure rabbit developmental toxicity study in which animals were exposed for different time periods. This second study reproduced the fetal losses seen in the guideline study and defined a narrow dosing window which may elicit this effect. Only exposure on gestation days (GD) 23-24 or GD 25-26 resulted in fetal losses. It is noteworthy, that the time of fetal loss coincides with the time of ontogeny of fetal thyroid function in the rabbit (GD22). Given the essential role of iodine in the proper function of the thyroid gland (both iodine deficiency and excess can have profound effects on thyroid function and thyroid hormone biosynthesis) and the fact that iodomethane exposure may lead to an excess accumulation of iodine in the thyroid, a mode of action (MOA) for the fetal losses involving perturbations of fetal thyroid function as a result of excess iodidehas been proposed. In the case of rats, no fetal losses were reported in the developmental toxicity study yet a decrease in the number of live births was reported in the multigeneration reproduction toxicity study. It is interesting to note, however, that while iodomethane exposure in the3 Amiodarone printed label. Food and Drug Administration

4 Zhu, Y. et al. “Excess iodine induces the expression of thyroid solid cel nests in lymphocytic thyroiditis- prone BB/W rats.” Autoimmunity (1995) 20 :201-106 Kanno, J. et al. “Tumor-promoting effects of both iodine deficiency and iodine excess in the rat thyroid” Toxicol. Path. (1992) 20 (2):226-235

10 developmental study ceased on GD17 (before ontogeny of rat fetal thyroid function), in utero exposure during the multigeneration toxicity continued until GD20 ( i.e. during ontogeny of fetal thyroid function). Thus, the data suggest that fetal losses may have occurred in the rat developmental study had exposure continued beyond GD17. Similar effects have been reported for another iodine-rich compound, amiodarone (an antiarrythmic drug), after treatment of pregnant rabbits and rats. 3
The histopathological changes caused by iodomethane exposure occurred in the respiratory tract, and the salivary and thyroid glands. The respiratory tract histopathology was characterized by lesions of the nasal cavity described as degeneration of the olfactory epithelium (portal of entry effects). These lesions were identified in the 13-week inhalation toxicity study, the multigeneration reproductive toxicity study, and the combined chronic toxicity/carcinogenicity study in rats and were limited to the extrathoracic region with no involvement of the tracheobronchial or pulmonary regions. Furthermore, they did not appear to progress with time ( i.e. nasal lesions of comparable severity were seen after 4, 13, and 52 weeks of exposure at the same concentration) thus suggesting the nasal lesions were the result of reaching a critical concentration (C max ) rather than time-dependent ( i.e. C x t; Haber’s law). In contrast, a C x t relationship is assumed for all systemic effects.
Frank evidence of thyroid toxicity was reported in the combined chronic toxicity/carcinogenicity study in rats, the MOA study in rabbits, and the carcinogenicity study in mice. Indications of thyroid toxicity included enlarged thyroids, increased thyroid weights, increased incidence of ultimobranchial thyroid cysts, follicular cell hyperplasia, follicular cell adenomas, and thyroid cytoplasmic vacuolation, as well as perturbations of the thyroid-pituitary axis (decreases in T3 and T4 in conj unction with increases in TSH and rT3). These results are consistent with reports in the open literature linking excess iodine to thyroid hormone perturbations and eventually thyroid tumor formation. 4
In regards to the potential role of iodomethane as a neurotoxicant, the inhalation acute neurotoxicity study in rats revealed that iodomethane exposure elicited clonic convulsions (repetitive mouth and jaw movement), a 2-3 o C decrease in body temperature, and an 80% decrease in motor activity in the absence of neuropathology. No additional guideline studies are available to evaluate the potential neurotoxicant properties of iodomethane. This effect, however, appears to be a less sensitive endpoint than the fetal losses, and nasal histopathological lesions.5 At this time, CDPR has not conducted a risk assessment for iodomethane; thus, CDPR HECs are not available for iodomethane.

11 4.1.3 Dose-response
The primary exposure pathway for iodomethane is via inhalation. Exposures may be acute (less than 24 hours), short-term (1-30 days), intermediate-term (1 month-6 months), or long-term in duration.
4.1.3.1 Inhalation Exposure
The critical effects of iodomethane exposure via the inhalation route are the fetal losses observed in two developmental toxicity studies in rabbits, the histopathological lesions reported in three studies, and the generalized systemic toxicity seen throughout the database. In evaluating the risks that a compound may pose to human health after exposure via the inhalation route, different methodologies have been historically used by the USEPA and the California Department of Pesticide Regulation (CDPR). An example of CDPR’s methodology, and the species-specific parameters used in this approach can be found in the CDPR website and their MeBr risk assessment, Appendix G at the following web address ( www.cdpr.ca.gov/docs/dprdocs/methbrom/append_g.pdf ). As OPP understands the importance to harmonize with other regulatory agencies, fumigant risk assessments will present HECs derived using EPA’s RfC methodology as well as CDPR’s methodology, when available. 5 Endpoint selection will be based on the endpoints occurring at the lowest HECs (which may or may not be the lowest animal NOAEL).
In this risk assessment, endpoint selection will be based on the endpoints occurring at the lowest HECs (which may or may not be the lowest animal NOAEL) derived using the RfC methodology or PBPK model. In both approaches, different HECs may be calculated for the same experimental NOAEL due to: 1) the different algorithms used to derive HECs for systemic versusportal of entry effects; 2) different dose metrics used in the PBPK model or 3) the time adjustments conducted for non-occupational versusoccupational exposure scenarios. The differences between systemic versusportal of entry effects, arise from the use of different calculations to estimate the inhalation risk to humans which are dependent on the regional gas dose ratio (RGDR). In the case of systemic versusportal of entry effects, different RGDRs are derived for each type of toxicity. For non-occupational versusoccupational exposure, the differences arise because while it is presumed that non-occupational exposure may occur 24 hours/day, 7 days/week; occupational exposure occurs only during the course of an average workweek (8 hours/day and 5 days/week). The iodomethane PBPK model, on the other hand, uses MOA data to derive internal dose metrics in the test species which are then used to extrapolate to humans and calculate HECs. A more detailed description and evaluation of the PBPK model are available in Appendix A of this document. For further details on the critical studies used for endpoint selection and the iodomethane toxicity profile the reader is referred to Appendix B. For additional information on the methodologies used in this risk assessment and the HEC arrays, please refer to Appendix C. The toxicity endpoints selected for risk assessment are presented below.
Acute Inhalation Exposure
Endpoint selection for acute inhalation exposures was based on three co-critical studies: a subchronic inhalation toxicity study in rats and two developmental toxicity studies in rabbits briefly described below:
Subchronic Inhalation Toxicity Study in rats6 Reed, CJ et al. (1995). “Olfactory Toxicity of Methyl Iodide in the Rat” Arch Toxicol. 70: 51-56

12 In a subchronic inhalation toxicity study (MRID 45593810), iodomethane (99.7% a.i.; Lot/batch # 007403/02) was administered via whole-body inhalation to Crl:CD~(SD)IGS BR rats (20/sex/concentration) for 6 hours/day, 5 days/week for 13 weeks at analytical concentrations of 0, 5, 21, or 70 ppm (0, 0.029, 0.12, or 0.41 mg/L/day). Ten rats/sex/concentration were sacrificed after 4 weeks, and the remaining 10 rats/sex/concentration were sacrificed after 13 weeks. There were no effects of treatment on mortality, ophthalmology, urinalysis, hematology, organ weights, or gross pathology .
The systemic LOAEL for this study is 70 ppm based on initial decreases in body weights, body weightgains, and food consumption (males). The NOAEL is 21 ppm (HEC = 3.8 or 15.8 ppm for nonoccupational and occupational risk assessments, respectively).
The port-of-entry LOAEL is 70 ppm based on degenera tion of the olfactory epithelium

. The NOAEL is 21 ppm (HEC = 2.9 or 3.7 ppm for non-occupational and occupational risk assessments, respectively). In a developmental toxicity study (MRID 45593811), groups of 24 female New Zealand White rabbits were dynamically exposed to iodomethane vapor (Lot/batch # 007403/02; 99.6% a.i.) in whole-body inhalation chambers at analytical concentrations of 0, 2, 10, or 20 ppm (0, 0.012, 0.058, or 0.12 mg/L/day) six hours per day on gestation days (GDs) 6 through 28.
The maternal NOAEL is 20 ppm; no maternal LOAEL was identified. The developmental toxicity LOAEL is 20 ppm based on increased fetal losses and decreased fetal weights ( 

### 9 20%). The developmental toxicity NOAEL is 10 ppm (HEC = 4.0 for non-occupational).
In a developmental toxicity study (MRID 46077001) iodomethane (99.7% a.i., Batch# 02/Lot# 007403) was administered via the inhalation route (whole body) to 24 New Zealand White rabbits/group at concentrations of 0 or 20 ppm during GD 6-28 (Control and Group 2), GD 6-14 (Group 3), GD 15-22 (Group 4), GD 23-24 (Group 5), GD 25-26 (Group 6), or GD 27-28 (Group 7) for 6 hours/exposure day

. This study was not intended to fulfill the guideline requirement or establish NOAELs and LOAELs but rather was conducted to determine the critical period of exposure during gestation that resulted in fetal loss as observed in a previously evaluated guideline developmental toxicity study in rabbits.
Dose and Endpoint for Risk Assessment: Two critical endpoints have been identified for this risk assessment: nasal histopathology in the Subchronic Inhalation Toxicity Study in rats and fetal losses in two developmental toxicity studies in rabbits. Using the iodomethane PBPK model developed by Arysta (iodomethaneregistrant) and reviewed by the Agency, the HEC for nasal histopathology is 2.9 or 3.7 ppm for nonoccupational and occupational risk assessments, respectively. In the case of fetal losses, the PBPK model indicates that an HEC of 4.0 would be appropriate for the non-occupational risk assessment.
The nasal histopathology was reported after a 13-week exposure to iodomethane, however, data from the published literature indicate that nasal lesions can occur after acute exposures ( 

### . 2 hrs. at 100 ppm) if the time profile of the exposure concentration leads to an overall iodomethane exposure of 

### $ 200 ppm/hr. 6 Based on this information in conjunction with the iodomethane PBPK and PERFUM models, HED and ORD scientists have concluded that an HEC of 2.9 or 3.7 ppm for non-occupational and occupational risk assessments,13 respectively, is appropriate for this risk assessment.
The proposed MOA for nasal histopathology involves glutathione (GSH) depletion as a key event in the toxicity pathway leading to damage of the nasal olfactory epithelium. Consequently, GSH depletion ( 

### # 50%) is the dose metric used in the PBPK model for interspecies extrapolation to determine the NOAEL for thenasal lesions. Using the HEC of 2.9 ppm for non-occupational risk assessments results in a 24-hr timeweighted average GSH depletion of 

### . 25% ( i.e. below the level of GSH depletion commonly cited in the literature as critical for development of nasal histopathology). It should be noted, however, that the emission profile of iodomethane suggests that during peak emissions GSH depletion may be higher than 25% ( e.g. 38%). However, it is the sustaineddecrease in GSH levels that is critical thus and HEC of 2.9 ppm is considered protective of the effects of concern in the respiratory tract. Similarly, using an HEC of 3.7 ppm for the occupational risk assessment results in an 8-hr time-weighted-average GSH depletion of 

### . 25%.
The endpoint of fetal losses identified in the developmental toxicity studies in rabbits is also considered appropriate for this risk assessment since it is presumed that developmental effects may be the outcome of an acute exposure. In the case of iodomethane, this presumption has been substantiated by the results of the phased developmental toxicity study in rabbits in which fetal losses were observed after two 6 hr exposures. Excess serum iodide has been implicated as a critical element in the MOA proposed for this endpoint. In a MOA study submitted by the registrant, excess iodide has been shown to lead to fetal thyroid hormone disruptions (Wolff-Chaikoff effect) resulting in fetal loss. Consequently, the dose metric used for this assessment is the area under the concentration curve (AUC) for fetal serum inorganic iodide during a single day of exposure. Based on this dose metric, an HEC of 17 ppm is calculated for the non-occupational risk assessment. This HEC, however, is based on the assumption that human fetal serum iodide levels are 75% of the maternal levels. Although data in support of this presumption are available from the peer reviewed literature, the Agency has concluded that the evidence is not sufficiently robust ( i.e. limited and often indirect) to establish an HEC of 17 ppm as a point of departure for the assessment. Thus, although the HEC of 17 ppm may represent a high or perhaps even a reasonable estimate of the human fetal serum iodide AUC, an HEC of 4 ppm which assumes an equivalent distribution of fetal serum iodide in rabbits and humans is used in this risk assessment. This HEC likely represents a lower bound estimate of the true dose metric. The bulk of the biomedical literature indicate that within the range of these estimate ( i.e. 4-17 ppm), the “true” value is likely towards the higher part of the range. But, there does not appear to be adequate information to establish that a particular value in the higher part of that range is the most accurate value. Consequently, there is higher confidence in the use of the 4 ppm HEC as a point of departure for this assessment based on the endpoint of fetal losses. An UF of 30X defines HED’s level of concern for both endpoints.
Note: For a more detailed description of the PBPK evaluation, refer to Appendix A of this risk assessment.14 Short-, and Intermediate-term Inhalation Exposure
Non-occupational Exposure
In a two-generation reproduction toxicity study, iodomethane (99.7% a.i.; Lot/batch # 007403/02) was administered via whole-body inhalation to Crl:CD ® (SD)IGS BR rats (30/sex/concentration) for 6 hours/day at nominal concentration levels of 0, 5, 20, or 50 ppm (equivalent to analytical concentrations of 0, 5, 21, and 50 ppm). The P animals were exposed to the test article for at least 70 days prior to mating to produce the F 1 litters. Exposure of the P males continued throughout mating and until the day prior to euthanasia. The P females continued to be exposed throughout mating and through gestation day (GD) 20, at which point exposure was discontinued. Daily exposure of the P females was reinitiated on lactation day (LD) 5 and continued until the day prior to euthanasia. After weaning, F 1 animals (30/sex/concentration) were selected, equalized by sex, to become the parents of the F 2generation and, beginning on post-natal day (PND) 28, were exposed to the same concentration test atmosphere as their dam.
The systemic parental NOAEL is 20 ppm (HEC = 5 ppm) and the LOAEL is established at 50 ppm based on decreases in body weight, body weight gain, changes in organ weights (adrenal glands, testis, cauda epidymis, epidydimis, and thymus) as well as gross pathology and histopathology findings.
The port of entry NOAEL is 20 ppm (HEC = 3.2 ppm) and the LOAEL is 50 ppm based on minimalmild degeneration of the olfactory epithelium.
The offspring NOAEL is 5 ppm (HEC = 1.25 ppm) and the LOAEL is 20 ppm based on decreases in body weight, body weight gain, as well as lower absolute and relative thymus weights.
The reproductive NOAEL is 5 ppm (HEC = 1.25 ppm) and the LOAEL is 20 ppm based on delays in attainment of vaginal patency.
Dose and Endpoint for Risk Assessment : HEC of 1.25 ppm based on decreased pup weight and weight gain, decreased thymus weights, and delays in vaginal patency acquisition. The duration of exposure in the multigeneration reproduction toxicity is appropriate for short- and intermediate-term risk assessments and it yields the lowest HEC ( ie. most health protective exposure concentration) for these exposure scenarios. An UF of 30X defines HED’s level of concern in accordance with guidance provided in the RfC methodology (see section 4.2 below).
Occupational Exposure
See non-occupational exposure above for brief executive summary. Different HECs have been calculated for occupational exposures due to the time adjustments made for the exposure scenarios.
Systemic parental NOAEL is 20 ppm (HEC = 15 ppm). Port of entry NOAEL is 20 ppm (HEC = 3.7 ppm). Offspring NOAEL is 5 ppm (HEC = 3.75 ppm). Reproductive NOAEL is 5 ppm (HEC = 3.75 ppm).15 Dose and Endpoint for Risk Assessment : HEC of 3.7 ppm based on minimal-mild degeneration of the olfactory epithelium. The duration of exposure in the multigeneration reproduction toxicity is appropriate for short- and intermediate-term risk assessments and it yields the lowest HEC ( ie. most health protective exposure concentration) for these exposure scenarios. An UF of 30X defines HED’s level of concern in accordance with guidance provided in the RfC methodology (see section 4.2 below).
Long-term Inhalation Exposure
Non-occupational and Occupational Exposure
In a combined chronic toxicity/carcinogenicity study in rats (MRID 45612401),iodomethane (99.7% a.i., Batch No. 02/Lot # 007403) was administered to Crl:CD®(SD)IGS BR rats via whole body inhalation at concentrations of 0, 5, 20, or 60 ppm for 6 hours/day 5 days/week. Sixty animals/sex/concentration were exposed to 0, 5, or 20 ppm iodomethane while 70/sex were exposed at the 60 ppm level. Animals were observed for moribundity and mortality twice daily and clinical observations once daily. Once a week a detailed physical examination was conducted including but not limited to evaluations of changes in appearance, autonomic activity ( e.g. lacrimation, piloerection, pupil size, breathing patterns), gait, posture, response to handling, stereotypic and/or bizarre behavior. In addition, evaluations of clinical chemistry, hematology, urinalysis, gross pathology and histopathology parameters were conducted.
The systemic NOAEL is 5 ppm (HEC = 0.89 or 3.75 ppm for non-occupational and occupational risk assessments, respectively); the LOAEL is established at 20 ppm based on increased incidence of salivary gland squamous cell metaplasia.
The NOAEL for port of entry effects (respiratory tract) is 20 ppm (HEC = 3.2 or 4.2 ppm for nonoccupational and occupational risk assessments, respectively) and the LOAEL is 60 ppm based on degeneration of the olfactory epithelium.
Dose and Endpoint for Risk Assessment : HEC of 0.89 ppm or 3.75 ppm for non-occupational and occupational risk assessments, respectively based on increased incidence of salivary gland squamous cell metaplasia. This is the study of the longest duration available in the iodomethane database and it yields the lowest HEC ( ie. most health-protective) for this exposure scenario. An UF of 30X defines HED’s level of concern in accordance with guidance provided in the RfC methodology (see section 4.2 below).

4.1.3.2 Dietary Exposure
Although iodomethane is used as an agricultural pesticide, it is considered a non-food use chemical since it is quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The levels of iodide released from iodomethane degradation/metabolism are lower than those expected to cause toxic effects. Furthermore, enforcement of tolerances would not be possible since no iodide-free samples are available and residue field trials show evidence of control samples with higher iodide residues than iodomethane treated samples. Moreover, iodide is ubiquitous in the environment and a required nutrient. Finally, iodomethane residues must dissipate in the soil prior to planting. Accordingly, HED concluded tolerances are not required for iodomethane. As a result, a risk assessment has not been conducted16 for this exposure scenario. The U.S. population, however, may be exposed to iodomethane through drinking water; therefore, a qualitative drinking water risk assessment was conducted and no risks were identified from this potential exposure.

4.1.3.3 Dermal Exposure
Exposure to iodomethane is anticipated via inhalation or oral (drinking water) routes but not through the dermal route. Dermal exposure to iodomethane of any significance is not expected based on the delivery systems used (e.g., soil injection or drip irrigation ), packaging (i.e., pressurized cylinders), and emission reduction technologies (e.g., tarping). The high vapor pressure of iodomethane also makes significant dermal exposure unlikely and quantifying any potential low level exposures very difficult. Therefore, a quantitative dermal exposure assessment has not been completed. Since HED does not have adequate data to quantify dermal risk, PPE for dermal protection should be based on the acute toxicity of the end-use product as described in the Worker Protection Standard and mitigation measures for dermal exposure described in PR Notice 93-7.
4.1.3.4 Classification of Carcinogenic Potential
The Cancer Assessment Review Committee (CARC) evaluated the rodent bioassays and mechanistic data available for iodomethane. Evidence of carcinogenicity in the iodomethane database manifested as an increased incidence of thyroid follicular cell tumors observed in both the Inhalation Chronic Toxicity/Carcinogenicity Study in Rats and the Carcinogenicity Study in Mice. The committee concluded that the key event influencing the thyroid tumor response is the sustained stimulation of cell proliferation by TSH, consistent with the increase in thyroid follicular cell tumors only . Based on the evidence that rats are substantially more sensitive than humans to the development of thyroid follicular cell tumors in response to thyroid hormone imbalance, the CARC classified iodomethane as “not likely to be carcinogenic to humans at doses that do not alter rat thyroid hormone homeostasis.”
4.1.4 Endocrine Disruption
Following the recommendations of its Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC), EPA determined that there was scientific bases for including, as part of the endocrine disruption screening program, the androgen and thyroid hormone systems, in addition to the estrogen hormone system. EPA also adopted EDSTAC’s recommendation that the Program include evaluations of potential effects in wildlife. As the science develops and resources allow, screening of additional hormone systems may be added to the Endocrine Disruptor Screening Program (EDSP).
Evidence of thyroid hormone perturbation is available in the iodomethane database. A chronic toxicity/carcinogenicity study in rats, as well as a MOA study in rabbits, and a carcinogenicity study in mice indicate that iodomethane exposure elicits sustained perturbation of thyroid hormone homeostasis. When the appropriate screening and/or testing protocols being considered under the Agency’s EDSP have been developed, iodomethane may be subjected to additional screening and/or testing to better characterize effects related to endocrine disruption.7 A 3X UF for interspecies extrapolation is retained to account for the PD differences between animals and humans which are not accounted for in the RfC methodology or the PBPK model.

17 4.2 Uncertainty Factors
Iodomethane has been classified as a non-food use pesticide. Consequently, this chemical is not subject to the FQPA (1996) and the 10X FQPA factor does not apply.
When conducting inhalation risk assessments, the magnitude of the UFs applied is dependent on the methodology used to calculate risk. This risk assessment is based on the RfC methodology developed by the Office of Research and Development (ORD) and the PBPK model developed by the registrant for the derivation of inhalation reference concentrations (RfCs) and human equivalent concentrations (HECs) for use in margin of exposure (MOE) calculations. Since both of these approaches take into consideration the pharmacokinetic (PK) but not pharmacodynamic (PD) differences between test species and humans, the UF for interspecies extrapolation may be reduced to 3X while the UF for intraspecies variation is retained at 10X. 7 Thus, when using the RfC methodology the overall UF is customarily 30X.
4.3 Summary of Toxicological Endpoint Selection

Table 1: Summary of Toxicological Dose and Endpoints for Use in Iodomethane Human Health Inhalation Risk Assessment Risk Assessment Study NOAEL/LOAEL Endpoint HED HECs CPDR HECs ¶

Acute Nonoccupational Subchronic Inhalation Toxicity Study in Rat NOAEL = 21 ppm LOAEL = 70 ppm Degeneration of the olfactory epithelium 2.9 ppm UF=30 N.A.

Developmental Study in Rabbits NOAEL = 10 ppm LOAEL = 20 ppm Developmental effects: fetal loss 4.0 ppm UF = 30

Occupational Subchronic Inhalation Toxicity Study in Rat NOAEL = 21 ppm LOAEL = 70 ppm Degeneration of the olfactory epithelium 3.7 ppm UF=30

Short-,IntermediateTerm, Inhalation (1-6 months exposure) Nonoccupational Multigeneration Reproductive Toxicity Study in Rats NOAEL = 5 ppm LOAEL = 20 ppm Offspring effects: decreased body weight, weight gain, and thymus weights Reproductive effects: Delays in vaginal patency 1.25 ppm UF = 30 N.A.

Occupational Subchronic Inhalation Toxicity Study in Rats NOAEL = 20 ppm LOAEL = 50 ppm Degeneration of the olfactory epithelium 3.7 ppm UF = 30Table 1: Summary of Toxicological Dose and Endpoints for Use in Iodomethane Human Health Inhalation Risk Assessment Risk Assessment Study NOAEL/LOAEL Endpoint HED HECs CPDR HECs ¶

8 Hermouet, C. et al. “Methyl iodide poisoning: Report of two cases” Am. J. Ind. Medicine (1996) 30 : 759- 764 & Appel, G.B. et al. “Methyl iodide intoxication” Annals of Int. Med (1975) 82 :534-536

18 Long-term (> 6 months) Nonoccupational Chronic/ Carcinogenicity Study in Rats NOAEL = 5 ppm LOAEL = 20 ppm Squamous cell metaplasia 0.89 ppm UF = 30 N.A.

Occupational Chronic/ Carcinogenicity Study in Rats NOAEL = 5 ppm LOAEL = 20 ppm Squamous cell metaplasia 3.75 ppm UF = 30

Cancer Not likely to be carcinogenic to humans at doses that do not alter rat thyroid hormone homeostasis
¶At this time, CDPR has not conducted a risk assessment for iodomethane (pending submission of additional data).

5.0 Public Health Data
Over the past century, only 11 incidents of iodomethane poisoning have been reported in the published literature 8 . In general, symptoms of iodomethane intoxication in humans were related to effects on the nervous system ranging from somnolence to ataxia, seizures, delirium and coma in severe cases. In some patients, cerebellar lesions and damage of the third, fourth, or sixth cranial nerve pathways as well as spinal cord lesions producing motor and sensory disturbances have been reported. Latent symptoms of iodomethane intoxication include psychological disorders such as depression. In addition to neurological effects, iodomethane exposure has also been linked to congestive changes in the lungs and oliguric renal failure. It is noteworthy, however, that in most of these incidents the precise iodomethane exposure concentration is unknown though it appears that exposure was to high levels (due in part to use of inadequate protective devices) resulting from industr ial uses and far exceeding those proposed for regulatory purposes in this risk assessment or anticipated agricultural uses.
6.0 Non-Occupational Exposure Assessment and Characterization
This section describes the potential exposure scenarios associated with the use of iodomethane. These include residential bystander exposure from two key sources: a known area source (e.g., at the edge of a treated field), as well as from multiple area sources within a region (e.g., ambient air). There are no residential uses of iodomethane by homeowners so this aspect of the risk assessment focuses on those types of exposures that may occur from the proposed commercial uses of iodomethane. HED also considered the potential for dietary exposures from food and drinking water.19 6.1 Residential Bystander Exposure
Residential bystander exposure may occur because of emissions from treated fields. These emissions can travel to non-target areas which could lead to negative impacts on human health and are referred to as bystander or off-target risks.
Exposures from known uses or point sources have been estimated based on controlled field volatility studies conducted in agricultural fields. Exposures have been calculated in three distinct ways involving direct use of the monitoring data and through two modeling approaches including the Industrial Source Complex: Short-Term model (ISCST3) and the Probabilistic Exposure and Risk Model For Fumigants (PERFUM) which was developed and evaluated by the FIFRA Science Advisory Panel (SAP) in August of 2004. ISCST3 is a standard Agency model which was used in conjunction with the monitoring data available for iodomethane, and other inputs such as a series of standard weather conditions. ISCST3 is a Gaussian plume model developed by the Office of Air and Radiation which allows better characterization of air concentrations that result from the application of iodomethane at different distances from the emission source that is not possible with direct use of the data. However, ISCST3 outputs are also limited because they are based on a constant wind vector (speed and direction) flowing directly downwind to those exposed. Since the initial assessment using ISCST3 was completed, OPP has also considered three different modeling systems that allow for the incorporation of actual meteorological data into assessments (i.e., PERFUM, FEMS & SOFEA©). The FIFRA Science Advisory Panel (SAP) evaluated these modeling systems in August and September of 2004. PERFUM (i.e., Probab ilistic Exposure and Risk model for Fumigants) has been used herein in addition to ISCST3 in order to better evaluate distributional bystander exposure. The SAP provided very similar comments for each. The Agency has used PERFUM for this assessment because modifications suggested by the SAP have been completed, the inputs were readily available, and the comparative computer processing time was lower than FEMS and SOFEA©. Complete descriptions of each new probab ilistic model along with the associated SAP reports are posted at www.epa.gov/scipoly/sap/index.htm

. For exposures from multiple area sources or ambient air, air concentrations of iodomethane have not been quantitatively assessed due to a lack of appropriate data. However, a qualitative discussion is provided below.
Exposures for bystanders from known area sources are described below in Section 6.1.1 while exposures from ambient or multiple area sources are described below in Section 6.1.2.
6.1.1 Bystander Exposure From Known Area Sources
As stated above, residential bystander exposure from known area sources may occur because of emissions from treated fields. The following sectio ns describe the data and modeling approaches used to estimate these types of exposures.
HED considered several field volatility studies in the development of the bystander assessment for iodomethane. Two studies quantified emissions from tarped broadcast, flat fume applications in California (i.e., Manteca and Watsonville, California). Three studies quantified emissions from tarped20 drip irrigation application in California (i.e., La Selva, Camarillo, and Guadalupe, California). The three remaining studies quantified emissions from tarped, raised bed shallow shank injection applications in California and Florida (i.e., Plant City, Florida and Oxnard and Guadalupe, California). The citations and summary of the data are included in Appendix D. Each of these studies was considered to be a sufficient quality for risk assessment purposes. [Note: All flux studies included the use of tarps; for brevity, the use of tarps is implied and will no longer be noted further in this assessment as it is implied in the results.]
As indicated above, three methods were used for estimating air concentration of fumigants from area sources such as a treated field: direct use of air monitoring data from controlled volatility studies in treated fields referred to as the Monitoring Data Method , the use of ISCST3 referred to as the ISCST3 Modeling Method , and the use of PERFUM referred to as the PERFUM Modeling Method . Each of these are described separately below.
Monitoring Data Method : In the monitoring data method, air concentrations are estimated using actual air monitoring data from controlled volatility studies. In these studies, the fumigant is applied to a field and air samplers positioned in and around the treated area continuously sample the air by pulling the air through a filter (e.g., charcoal) or a vacuum chamber is used which captures the chemical for later analysis. Sampling times can vary but generally range from about 4 to 12 hours, so that the samples represent the average air concentrations for the sampling intervals used. Usually shorter times are used at the beginning of the sampling period because fumigants tend to volat ilize soon after application.
There are several uncertainties associated with the use of the direct sampling method which limit its utility. First, the air concentrations represent only those for the conditions under which the study is carried out. Air concentrations around treated fields, buildings, or other areas are influenced by a number of factors including how a chemical is applied, application rate, techniques to control emissions (e.g., tarps), and weather conditions. Varying weather conditions, for example, can significantly change the air concentrations at specific sites around a treated area; and since there is such a large range of potential weather conditions which can exist, it is not possible for these studies to represent the entire range of potential exposures that can result from different weather situations. Second, the air concentrations are measured by fixed samplers positioned at various directions around the treated area, both downwind and upwind, as well as at points in between. Air concentrations downwind will be relatively high since the fumigant plume will be pushed by the wind in that direction, while concentrations upwind will be low or close to zero since the plume is pushed by the wind in the opposite direction. Therefore, there can be a very large difference between upwind and downwind air concentrations. For areas where there is a predominant wind direction, averaging of the air concentrations from these various samplers should not be done since persons around treated areas will generally be in one location relative to the wind and not exposed to an average of these concentrations. Third, samplers are positioned at specific distances from the treated area, and represent air concentrations only at those distances. Since air concentrations vary greatly by distance, the air concentrations estimated from direct measures repr esent a very narrow range of the possible levels to which people can be exposed. Results based on the Monitoring Data Method are presented below in Section 6.1.1.1.21 ISCST3 Modeling Method: The modeling method uses the Agency model, Industrial Source Complex Short Term (ISCST3) together with information about emissions from a treated field, building or structure (i.e., known as flux) to model the range of concentrations which may be found under different conditions of application rate, weather, source size and shape (e.g., field size in acres), and distance from the treated field, building or structure. Before a modeling analysis can be done, one of the most important parameters for ISCST3, the flux rate, which is the quantity of pesticide which is emitted from the treated fields, buildings or structures per unit area per unit time, must be determined. As an example, for field applications it is usually expressed in units of micrograms per square meter per second (ug/m 2/sec). In essence, flux represents how quickly the pesticide moves or volatilizes into the surrounding atmosphere. Numerous factors can influence flux rates such as application rate, depth of soil injection, type of application (e.g., drip vs. soil injection), techniques used to control emissions (e.g., tarps), temperature, wind and weather conditions, soil type, and others. Flux is also difficult to determine. Three general methods are used to calculate flux which are discussed briefly below. The first two of these measure flux from sampling directly in treated fields, and the third is indirect in that it calculates flux using samples from downwind locations. For iodomethane, flux estimates were completed using the indirect back-calculation method.
Method 1, Flux Chamber: The first direct method for estimating flux uses field fumigant emission data measured in a flux chamber. A flux chamber is basically a box which encloses a small defined area of a treated field, from which air samples are obtained representing defined durations (e.g., air is pulled through a charcoal trap collecting emitted pesticide over a continuous length of time such as 4 hours). Since the surface area is defined by the area of the chamber, and the quantity of pesticide emitted per unit time is defined by the air concentration, this method directly measures flux. A possible issue with flux chambers is that the conditions within the chamber (e.g., temperature, wind, air stability) are not generally identical to those outside the chamber in the treated field; since flux rates can be significantly affected by these factors, flux rates measured in these chambers may not always represent actual flux rates in the field. Flux chambers are not often used for estimating flux rates.
Method 2, Aerodynamic Flux Method: The second direct method used is the aerodynamic flux method. In this method, air samplers are set up in the treated field at various heights on a mast (e.g., 15, 30, 90, and 150 cm from the ground). Using measured air concentrations at these various heights, a vertical gradient of concentrations can be estimated for different time points, which can be integrated across all heights to estimate the flux rate at each time point after application. Some studies are available using this method to determine flux rates.
Method 3, Back-Calculation: The method most often used to determine flux rates is an indirect method known as the back-calcu lation method. This method uses measured air concentrations taken in a typical field fumigation study in which air samplers are located at various positions around the field. The measured air concentrations, together with information about weather conditions which occurred when the samples were obtained, are used as inputs into the Industrial Source Complex Short Term model (ISCST3). The model assumes that these air concentrations result from a Gaussian22 plume, the plume being distributed around the treated field as a result of the wind and weather conditions measured. The model then calculates the flux rate which would be required to emit the plume in that manner and to obtain the air concentrations measured.
Determination of the flux rate for all situations to be considered in an assessment is necessary before ISCST3 can be run. After these are defined, other key inputs must be defined such as the size and shape of a treated field, wind direction, wind speed, and atmospheric stability. ISCST3 calculates downwind air concentrations using hourly meteorological conditions, that include wind speed and atmospheric stability. The lower the wind speed and the more stable the environment, the higher the air concentrations are going to be close to a treated field. Conversely, if wind speed increases or the atmosphere is less stable, then air concentrations are lower in proximity to the treated field. Atmospheric stability is essentially a measure of how turbulent the atmosphere is at any given time. Stability is affected by solar radiation, wind speed, cloud cover, and temperature, among other factors. If the atmosphere is unstable, then more off-field movement of airborne residues is possible because they are pushed up into the atmosphere and moved away from the field, thereby lowering the air concentration in proximity to the field. To simplify modeling the transport of soil fumigant vapors from a treated field, a single wind direction, wind speed, and stability category are used for a given 24- hour period. The Agency has not determined if a particular set of meteorological conditions should be used for regulatory purposes, so risk assessments generally present exposures and potential risks representing a variety of different conditions.
Modeling with ISCST3 produced high-end estimates of air concentration and resulting risks for a number of reasons. First, only the downwind direction is considered. Most people will not be directly downwind from a treated field. Secondly, the model runs are based on constant wind speed, wind direction, and atmospheric stability for a 24-hour period. This will rarely occur resulting in overestimates of air concentrations and risks. However, the model is useful because it allows for estimation of air concentrations reflecting different conditions based on changing factors such as application rates, field sizes, downwind distances, wind and weather conditions, and other factors, which cannot be done using the monitoring data method described above. Therefore, results using the ISCST3 model should be considered to be potential exposures to the most highly exposed, upper percentile of the population, but are not representative of exposures to most of the population around a treated field. The Agency believes that using ISCST3 to predict exposures over more extended periods is inappropriate because constant meteorological conditions over such periods w ill not occur and will yield highly conservative, physically unlikely results. Results based on theISCST3 Modeling Method are presented below in Section 6.1.1.2.
PERFUM Modeling Method: The monitoring data and ISCST3 methods described above are deterministic methods which provide high-end point estimates of risk. OPP is coordinating with EPA’s Office of Air, the California Department of Pesticide Regulation (CDPR), and other stakeholders to develop modeling approaches which determine the distribution of potential bystander exposures, and thus more fully characterize the range of potential risks to bystanders around treated fields. In this assessment, the PERFUM model has been used in order to calculate differing percentiles of exposure (see Section 6.1 above for more details concerning PERFUM use and other possible models).
ISCST3 is an integral part of the PERFUM model. As a result, many of the inputs used for PERFUM23 Figure 1: Receptor Grid For a 5 Acre Field [Note: 5 Acre field in center, line is a spoke] are similar to those used for the ISCST3 analysis described above (e.g., field sizes and back-calculated flux rates). The key differences are that PERFUM addresses the uncertainty associated with flux profiles in its calculations by sampling the coefficient of variation for those measurements but this does not account for important factors such as field to field differences. It also incorporates 5 years of meteorological data to generate a distribution of daily average concentrations that represent the possible range of downwind air concentrations based on changing wind vectors from the measured data in a series of receptor locations described in Table 2 and Figure 1 below. PERFUM analyses were completed for several field sizes (i.e., 1 to 40 acres) but in most cases only the results for 1 and 40 acres were processed which represent the possible range of outcomes. In other cases, all results were processed so the impacts of changing field sizes could be more readily apparent. It is also thought that the general trend would apply generically to most situations. Field geometry (i.e., shape) was also investigated by completing analyses for 5 acre fields shaped like a square and a rectangle oriented on its side and also top to bottom. [Note: The maximum distance for which calculations are performed on each spoke in PERFUM is 1440 meters from the edge of the treated field.]
Table 2: Receptor Points for Various Field Sizes In PERFUM
Grid Type Field Size (acres) Number of Spokes Number of Rings Number of Receptors (Spokes*Distances)

Fine 1 96 28 2,688 5 132 28 3,696 10 152 28 4,256 20 188 28 5,264 40 232 28 6,496

Coarse 1 24 28 672 5 33 28 924 10 38 28 1,064 20 47 28 1,316 40 58 28 1,624 Notes: Fine grid option was used for iodomethane analysis. The maximum distance used for PERFUM calculations on each spoke is 1440 meters

.24 Figure 2: Distribution of Daily Average Windspeeds At Selected Meteorological Stations Since actual meteorological data are integrated into PERFUM for each analysis, data representative of the locations where iodomethane use is anticipated were identified and used in the analysis. It is anticipated that the initial major uses for iodomethane could be as a methyl bromide alternative in California and Florida on tomatoes, strawberries and similar crops. Some use in Michigan (or elsewhere in that region) is also anticipated. As a result, the following locations and sources of meteorological data were used in this assessment:
• Bakersfield California (Source: ASOS or Automated Surface Observing System operated by the FAA) to represent inland California locations; • Ventura California (Source: CIMIS or California Irrigation Management Information System) to represent coastal California locations; • Flint Michigan (Source: NWS or National Weather Service) to represent central Michigan and other upper midwest locations; • Tallahassee Florida (Source: NWS or National Weather Service) to represent inland Florida locations; and • Bradenton Florida (Source: FAWN or Florida Automated Weather Network) to represent coastal Florida.
In this assessment, 5 years or 1825 days of meteorological data were considered in each calculation (i.e., data are available for all days with no sampling errors). Most data were in the range of 1997 through 2003 but Tallahassee was in the late 1980s through early 1990s. [Note: Please refer to the SAP background documents for PERFUM for further information concerning these data including how they were processed for incorporation into PERFUM and any quality control issues related to these data.] Figure 2 provides a comparison of the distributions of daily average windspeeds for selected stations in California and Florida. These can be used to help characterize the deterministic assessments and to illustrate different PERFUM results for the different stations. [Note: As an example, CADPR regulated methyl bromide at 1.4 m/s windspeed.]25 Flux (i.e., field volatility or emissions) data were treated in a manner similar to that used for the ISCST3 analysis described above. Data from each of the 8 flux studies were used in conjunction with appropriate meteorological information. In some cases, studies that quantified emissions from the same application method were available in both Florida and California. In those cases, only meteorological data from the same state were used in the PERFUM analysis. PERFUM also considers the uncertainties associated with daily flux profiles by probabilistically sampling flux based on the range defined by the coefficient of variation associated with those data. Table 3 below provides a summary of the analyses that were completed using PERFUM. There is a significant difference with PERFUM compared to ISCST3 with regard to flux values. ISCST3 used a single emission ratio which does not account for changes over time in flux. Alternatively, PERFUM samples flux distributions over time periods of interest. The Agency verified the flux distributions based on the monitoring data.

Table 3: Summary Of PERFUM Analyses Completed For Iodomethane

Weather Station Location Flux Study Summary

Watsonville CA

Flat Fume Manteca CA

Flat Fume Plant City FL

Raised Bed Oxnard CA

Raised Bed Guadalupe CA

Raised Bed La Selva CA
Drip Irrigation Camarillo CA Drip Irrigation Guadalupe CA Drip Irrigation

Ventura CA X X NA X X X X X

Bakersfield CA X X NA X X X X X

Flint MI X X X X X X X X

Tallahassee FL X X X NA NA X X X

Bradenton FL X X X NA NA X X X

X = analysis completed, NA = analysis not appropriate; Tarps were used in all cases. Note: All analyses completed using both 2 .9 & 4.0 ppm HECs.
PERFUM calculates outputs based on each day’s worth of meteorological data and the result is illustrated by Figure 3 which shows the distances from the field where airborne concentrations meet a threshold of concern around the entire perimeter of the field for each spoke in the model (i.e., the irregularly shaped line). The concentric circle represents a 95 thpercentile distance value around the perimeter (i.e., MOEs are not of concern for 95% of those exposed). The cross hatch area represents the locations where distances exceed the 95 thpercentile value (i.e., MOEs are of concern at these distances for 5% of those exposed). These exceed ances have been examined using the PERFUM MOE program which was used in conjunction with the air model itself (see SAP site for more details).26 Figure 3: Example Daily PERFUM Output For A 5 Acre Field

PERFUM generates the type of output illustrated by Figure 3 for each day over a 5 year period (i.e., 1825 days) then summarizes the information by providing two types of results including the “Maximum Buffer” distance and the “Whole Field Buffer” distance. Each is reported as a distribution. The “Maximum Buffer” distribution is based on the maximum distance needed to reach a threshold level of concern (i.e., HEC adjusted by uncertainty factor) calculated using PERFUM for each day (i.e., the farthest single point on the irregular line for each day). This results in a distribution that contains 1825 values, and in this assessment, the results have been reported for selected percentiles from those distributions. The “Whole Field Buffer” is based on values from each day as well except the distances on which the distribution is based include those on each spoke where the threshold concentration is achieved for each day (i.e., distances on all spokes where threshold is achieved or the distances on the irregular line where it intersects each spoke). The number of values in these distributions varies and it is based on 1825 days multiplied by the number of spokes around the field which relates to field size (see Table 2 above). As with the “Maximum Buffer” distances, results from selected percentiles from the distribution have been reported.

Results based on the PERFUM Modeling Method are presented below in Section 6.1.1.3 (also refer to analysis plan described in Table 3).

6.1.1.1 Bystander Exposures From Known Area Sources Calculated Using The Monitoring Data Method
Exposures of and the risks to bystanders from pre-plant agricultural field fumigations calculated using monitoring data are presented in this section. Air concentrations based on volatility data were generally reported on a per sample basis (e.g., 4 hour duration air samples) and as 24-hour time-weighted averages (TWAs). These were calculated using field volatility data that were usually collected around the perimeter of treated sites including downwind locations. Samples were collected at mast sites27 located within 150 feet or so from the perimeter of the treated field, depending upon the study design. It is clear, as described above that downwind locations will have higher concentrations associated with them because emitted plumes w ill be pushed in that direction if there is a prevailing wind. For the data considered in this assessment, wind directions varied which makes defining “downwind locations” for the available data complex. There are other micrometeorological and site specific factors (e.g., topography and roughness) which also add to the complexity of the analysis.
Given these difficulties, and to ensure that this assessment is health protective the Agency has calculated acute MOEs using the 24 hour maximum TWA from the monitoring data for each site where data are available. These maximum TWAs were compared to both of the acute HECs to calculate MOEs based on each toxicological endpoint of concern. The highest TWAs were always observed in the first 24 hours after application.
For the majority of the pre-plant field volatility data collected (10 of 18 site/HEC combinations), risks do not exceed HED’s acute level of concern (i.e., MOEs >30). Results for the maximum monitored values from each treated field are presented below in Table 4. The field volat ility monitoring data upon which this analysis is based are presented and summarized in Appendix D: Analysis Of Field Volatility Data For Pre-plant Field Uses contains these data.

Table 4: Iodomethane 24 Hour TWA Concentrations Based On Pre-Plant Agricultural Field Volatility Data

Flux Data Source: Field Location & Application Method Maximum 24 Hour TWA (ppm) Acute MOEs Based On Each HEC 2.9 ppm HEC 4.0 ppm HEC
Manteca CA Broadcast Flat Fume 1.988 (site near/in-field while others on perimeter - represents worst case) 1.5 2.0

Watsonville CA Broadcast Flat Fume 0.061 48 66
Plant City FL*

Raised Bed 0.066 44 60

Oxnard CA Raised Bed 0.347 8.4 12
Guadalupe CA

Raised Bed 0.103 28 39

La Selva CA Drip Irrigation 0.071 41 56

Camarillo CA Drip Irrigation 0.06 48 66
Guadalupe CA Drip Irrigation 0.12 24 33
Note: The Agency is aware that additional field volatility monitoring data have been completed by the registrant for iodomethan e. Also, the application rates for these studies ranged from 234 to 259 lb ai/acre and the data were not adjusted for rate and sam plers were located from 12 to 30 feet from the treated field.
* There was a rain event during this study 24 hours after application or so. The TWAs are presented here for comparative purpo ses.28 6.1.1.2 Bystander Exposures From Known Area Sources Calculated Using The ISCST3 Modeling Method
Exposures to bystanders from pre-plant agricultural field fumigations and their associated risks, calculated using a modeling approach, are presented in this section. The previous section 6.1.1.1 describes exposures based on actual field volatility monitoring data. However, these data are limited because they represent only the conditions in which the studies were actually conducted. Therefore, in order to better characterize the risks associated with the use of iodomethane for various conditions (e.g., distance from emission source, atmospheric conditions, application method, etc.), exposures have also been calculated using the Agency’s ISCST3 model.
The risk estimates (MOEs or Margins of Exposure) presented below represent results for the acute duration of exposure because they compare 24 hour concentrations calculated with ISCST3 to both of the acute HECs of concern (i.e., 2.9 or 4.0 ppm with a total uncertainty factor of 30 applied to both).
Results for various field sizes, application methods (with distinct emission ratios or fraction of the applied material emitted per unit of time); wind speed; atmospheric stability and distances downwind are presented below in Table 5. Table 5 demonstrat es that for the cases considered, many risks exceed HED’s level of concern (MOEs <30) for distances less than 100 meters downwind of the treated field especially when the atmosphere is relatively stable and/or fields are larger than 1 acre. MOEs decrease as field sizes increase while MOEs increase as the atmosphere becomes less stable leading to conditions where more off-target drift can occur. There is not a significant impact in the results due to the two different HECs that were considered. The results in this table summarize the analysis presented in Appendix E: Downwind Air Concentrations Calcul ated With ISCST3 For PrePlant Field Uses. [Note: Appendix E contains a summary of the outputs generated by ISCST3 for more distances downwind and field sizes. It does not contain detailed input and output files needed to complete calculations with ISCST3. If so required, these can be provided for review and validation purposes.]

Table 5: MOEs Calculated for Various Distances Downwind For Pre-Plant Agricultural Field Fumigations Using ISCST3

Flux Data Source App. Method ER Fld Size (A) Distance (M) MOEs For Differing Meteorological Conditions

1 m/s 2.3 mph 1.4 m/s 3.1 mpha

(a) 1.8 m/s 4 mph 2.2 m/s 5 mph 2.7 m/s 6 mph 3.1 m/s 7 mph 3.6 m/s 8 mph 4.0 m/s 9 mph 4.5 m/s 10 mph 4.5 m/s 10 mph

Stab D Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab B
MOEs Based On HEC = 2.9 ppm (16802 μg/m3 )

Manteca

CA Shank Injection, Broadcast, Flat Fume 0.47 1 25 8 17 22 27 33 38 44 49 55 79

100 18 43 55 67 82 94 109 122 137 230

500 99 352 452 553 679 779 905 1006 1131 2644

1000 286 1142 1469 1795 2203 2530 2938 3264 3672 9774

40 25 3 7 9 11 14 16 18 20 23 33

100 5 12 15 19 23 26 30 34 38 56

500 12 30 38 47 57 66 77 85 96 170

1000 18 54 70 86 105 121 140 156 175 395Table 5: MOEs Calculated for Various Distances Downwind For Pre-Plant Agricultural Field Fumigations Using ISCST3

Flux Data Source App. Method ER Fld Size (A) Distance (M) MOEs For Differing Meteorological Conditions

1 m/s 2.3 mph 1.4 m/s 3.1 mpha

(a) 1.8 m/s 4 mph 2.2 m/s 5 mph 2.7 m/s 6 mph 3.1 m/s 7 mph 3.6 m/s 8 mph 4.0 m/s 9 mph 4.5 m/s 10 mph 4.5 m/s 10 mph

Stab D Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab B

29Watsonville CA Shank Injection, Broadcast, Flat Fume 0.35 1 25 11 23 30 36 45 51 60 66 74 106

100 24 57 74 90 111 127 147 164 184 310

500 133 474 6609 744 914 1049 1218 1353 1523 3559

1000 385 1538 1977 2416 2965 3405 3954 4393 4942 13155

40 25 4 10 12 15 19 21 25 28 31 45

100 7 16 21 24 31 35 41 46 51 75

500 16 40 52 63 77 89 103 115 129 228

1000 24 73 94 115 141 162 188 209 236 532

Oxnard

CA Shallow Shank Injection, Raised Bed 0.37 1 25 10 22 28 34 42 48 56 62 70 99

100 23 54 69 84 103 119 138 153 172 290

500 125 443 570 696 854 981 1139 1266 1424 3329

1000 360 1438 1849 2260 2773 3184 3698 4109 4622 12304

40 25 4 9 12 14 17 20 23 26 29 42

100 6 15 19 23 29 33 38 43 48 71

500 15 38 48 59 72 83 96 107 121 213

1000 22 69 88 108 132 152 176 196 220 497

La Selva

CA Drip Irrigation, Raised Bed 0.51 1 25 13 28 36 44 54 62 72 80 90 127

100 29 69 89 108 133 153 177 197 222 373

500 160 571 734 897 1100 1263 1467 1630 1834 4287

1000 464 1852 2381 2910 3572 4101 4763 5292 5953 15846

40 25 5 12 15 18 22 26 30 33 37 54

100 8 19 25 30 37 43 49 55 62 91

500 19 48 62 76 93 107 124 138 155 275

1000 29 88 113 139 170 195 227 252 284 640
MOEs Based On HEC = 4.0 ppm (23176 μg/m3 )

Manteca

CA Shank Injection, Broadcast, Flat Fume 0.47 1 25 11 24 31 37 46 53 61 68 76 108

100 25 59 76 92 113 130 151 168 189 317

500 137 485 624 763 936 1075 1248 1387 1560 3647

1000 395 1576 2026 2476 3039 3489 4052 4502 5065 13482

40 25 4 10 13 16 19 22 25 28 32 46

100 7 16 21 26 32 36 42 47 53 77

500 16 41 53 65 79 91 106 117 132 234

1000 25 75 97 118 145 166 193 215 241 545Table 5: MOEs Calculated for Various Distances Downwind For Pre-Plant Agricultural Field Fumigations Using ISCST3

Flux Data Source App. Method ER Fld Size (A) Distance (M) MOEs For Differing Meteorological Conditions

1 m/s 2.3 mph 1.4 m/s 3.1 mpha

(a) 1.8 m/s 4 mph 2.2 m/s 5 mph 2.7 m/s 6 mph 3.1 m/s 7 mph 3.6 m/s 8 mph 4.0 m/s 9 mph 4.5 m/s 10 mph 4.5 m/s 10 mph

Stab D Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab C Stab B

30Watsonville CA Shank Injection, Broadcast, Flat Fume 0.35 1 25 15 32 41 50 62 71 82 91 103 146

100 33 79 102 124 152 175 203 226 254 427

500 184 653 840 1027 1260 1447 1680 1867 2100 4909

1000 531 2121 2727 3333 4090 4696 5454 6060 6817 18146

40 25 6 13 17 21 26 29 34 38 43 62

100 9 22 28 35 42 49 57 63 71 104

500 21 55 71 87 107 123 142 158 178 315

1000 33 101 130 159 195 224 260 289 325 733

Oxnard

CA Shallow Shank Injection, Raised Bed 0.37 1 25 14 30 38 47 58 66 77 85 96 137

100 31 74 95 116 143 164 190 211 238 399

500 172 611 786 960 1179 1653 1571 1746 1964 4591

1000 497 1984 2550 3117 3826 4392 5101 5668 6376 16972

40 25 6 12 16 20 24 28 32 36 40 58

100 9 21 26 32 40 46 53 59 66 97

500 20 52 67 81 100 115 133 148 166 294

1000 31 95 122 149 182 209 243 270 304 686

La Selva

CA Drip Irrigation, Raised Bed 0.51 1 25 18 38 49 60 74 85 99 110 124 176

100 40 95 122 150 184 211 245 272 306 514

500 221 787 1012 1237 1518 1743 2024 2249 2530 5913

1000 640 2555 3285 4015 4927 5657 6569 7299 8212 21858

40 25 7 16 21 25 31 36 41 46 52 74

100 11 27 34 42 51 59 68 76 85 125

500 26 67 86 105 129 157 171 190 214 379

1000 40 122 157 191 235 270 313 348 391 883 a. Standard atmospheric conditions used by CDPR in their risk assessments. 6.1.1.3 Bystander Exposures From Known Area Sources Calculated Using The PERFUM Modeling Method
Exposures to bystanders from pre-plant agricultural field fumigations and their associated risks, calculated using a modeling approach based on PERFUM , are presented in this section. The previous sections 6.1.1.1 and 6.1.1.2 describe exposures based on actual field volatility monitoring data and a deterministic modeling approach using ISCST3. However, these data are limited because they represent only the conditions in which the studies were actually conducted or the constrained meteorological conditions in ISCST3. Therefore, in order to better characterize the risks associated31 with the use of iodomethane for various conditions (e.g., distance from emission source, actual meteorological conditions, application method, etc.), exposures have also been calculated using PERFUM.
The risk estimates presented below represent results for the acute duration of exposure because they compare 24 hour concentrations calculated with PERFUM to the two acute HECs of concern (i.e., 2.9 ppm or 4.0 ppm, each with a total uncertainty factor of 30). Results for selected percentiles of exposure are reported and in many cases results are based on 95 thpercentile values as an example (e.g., MOE exceedance analysis). Use of the 95 thpercentile (or results for any other percentile) for reporting purposes does not imply a selection of these estimates for regulatory purposes. Additional analysis based on other percentiles of exposure could be completed if so needed. The results presented in Appendix F: Downwind Air Concentrations Calc ulated With PERFUM For PrePlant Field Uses are summarized in this section. Please refer to Appendix F for additional details. [Note: Appendix F itself contains a summary of the outputs generated by PERFUM for both HECs of concern. It does not contain detailed input and output files needed to complete calculations with PERFUM. If so required, these can be provided for review and validation purposes. Also, note in tables specific cells are highlighted for illustrative purposes throughout this section (e.g., 95 th%tile results at maximum rate and maximum buffer distances).]
Appendix F contains several sub-appendices (Appendices F1a through F5f) that include a series of spreadsheets that summarize the results of each analysis described above in Table 3 (i.e., various combinations of meteorological data and flux/application methods). [Note: Appendix F6 contains a series of summary analyses used to develop many of the graphics and tables provided below. Please refer there for further information regarding the details of the summary calculations.] Table 6 and Figure 4 below provide an example of how the results were summarized for each meteorological/flux data combination considered in this analysis using Ventura California and Watsonville flux data. Table 6 contains results for both HECs considered. Figure 4 represents only the results for the HEC = 4 ppm and an uncertainty factor of 30. The general trend is similar for the results based on the HEC of 2.9 ppm

Table 6: Buffer Distances For Ventura CA Weather And Watsonville CA Flat Fume Flux %tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square Maximum Buffer Distances (meters) 50 0 50 0 15 0 5 0 10 0 5 0 0 75 5 90 0 40 0 5 0 35 0 5 0 5 90 5 150 5 80 0 15 5 75 0 30 0 5 95 5 210 5 125 0 40 5 115 0 50 0 5 97 5 265 5 165 0 65 5 155 0 80 0 10 99 15 375 5 275 5 140 5 265 5 170 0 65 99.9 60 700 35 510 5 360 35 495 5 395 5 225 99.99 65 710 45 565 5 385 45 550 5 405 5 275Table 6: Buffer Distances For Ventura CA Weather And Watsonville CA Flat Fume Flux %tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square 1 Acre Square 40 Acres Square

32 **Iod o meth an e: M axim u m D istance B u ffers V entura CA CIM IS & Watsonville Flat Fum e (M RID**  **455937-10)**

0 100 200 300 400 500 600

5 1 5 25 35 4 5 5 5 65 75 8 5 9 5 99 9 9.9

9

**P ercen tile of E xpo su re Buffer Distance (meters)**  1A-Max 40A-Max 1A-75% 40A-75% 1A-50% 40A-50%

Figure 4: Maximum Distance Buffers For Ventura California Weather And Watsonville Flat Fume Flux Based On HEC = 4 ppm & UF = 30 Whole Field Buffer Distances (meters) 50 0 0 0 0 0 0 0 0 0 0 0 0 75 0 5 0 5 0 0 0 5 0 0 0 0 90 0 40 0 10 0 5 0 10 0 5 0 0 95 5 70 0 30 0 5 0 25 0 5 0 5 97 5 90 0 45 0 5 0 40 0 10 0 5 99 5 155 5 90 0 30 5 85 0 40 0 5 99.9 15 350 5 260 5 145 5 250 5 165 0 50 99.99 55 630 30 475 5 350 25 465 5 355 5 220

The information in Table 6 (and Appendix F for each analysis) can be used to determine the range of buffers calculated by PERFUM for each analysis (see Table 3). As expected, 1 acre fields have significantly smaller buffer distances than 40 acre fields. Additionally, reductions in application rate impact the amount of iodomethane volatilizing into the atmosphere from treated fields and also lower buffer distances. The differences between considering “Maximum Buffer” versus “Whole Field Buffer” distances can also be defined. Differences in HECs also impact the results with the higher HEC producing lower buffer distances as expected but it should be noted that the general trends associated with this analysis do not change regardless of which of the two HECs are considered. Figure 4 summarizes the“Maximum Buffer” results for all of the percentiles included in the calculation for 1 and 40 acre fields at varying application rates for Ventura California and using the Watsonville Flat Fume flux data based on an HEC = 4 ppm. The types of results described above can be reviewed in more detail in Appendix F of this document.
PERFUM is also capable of examining the changes observed in MOEs (Margins of Exposure) under different conditions. For example, the cross-hatched area in Figure 3 which represents exceedances can be examined. [Note: The overall PERFUM analysis considered meteorological data from 5 different33 **Iodomethane: M OE Analysis For 95%tile Max Rate Ventura CA CIMIS & Watsonville Flat Fume (MRID 455937-10)** 

1 10 100 1000

90 95 97 99 99.9 **Pe rcentile of Exposure Margins Of Exposure** **(Target = 30)** M ax-100%

W hole-100%

M ax-75%

W hole-75%

M ax-50%

W hole-50%

Figure 5: MOE Analysis For 95 th Percentile Maximum Buffer Distance of 115 Meters Based On Ventura CA Meteorological And Watsonville Flat Fume Flux Data (HEC = 4 ppm) regions of the country including coastal and inland locations in California and Florida as well as a location in Michigan. Several of the trends in the results are expected to be similar regardless of the location considered so for the purposes of this assessment, as such, only results for Ventura California were considered. PERFUM outputs are available for all locations if additional analyses are required.] Table 7 and Figure 5 below present the results of the MOE analysis for Ventura California and Watsonville flux data as an example using both HECs of concern. Also, note that the MOE analyses have only been completed using 40 acre fields which allow for better determination of the trends. The general trend illustrated in Figure 5 is similar for the results based on the HEC of 2.9 ppm.

Table 7: MOE Analysis For 95 th Percentile Buffer Distance( 260 meters) In A 40 Acre Field Using Ventura California Meteorological And Watsonville Flat Fume Flux Data Percentiles Max ( 175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max Buffer Whole Field Max Buffer Whole Field Max Buffer Whole Field Results Based On HEC = 2.9 ppm 50 59 1000 79 1000 118 1000 75 46 1000 62 1000 92 1000 90 36 76 48 102 72 149 95 29 57 38 77 58 115 97 24 49 32 66 49 98 99 17 35 23 48 35 71 99.9 8 18 11 24 17 37 99.99 Not. Calc. 9.9 Not. Calc. 13 Not. Calc. 20 Results Based On HEC = 4.0 ppm 50 57 1000 77 1000 115 1000 75 45 124 60 1000 90 1000 90 35 67 47 90 70 135 95 29 53 39 70 58 105 97 25 45 33 61 50 91 99 17 33 23 45 35 67 99.9 8 17 10 23 16 35 99.99 Not. Calc. 9.4 Not. Calc. 12 Not. Calc. 18 Note: MOE analysis is based on the 95 th percentile maximum buffer distance at the maximum application rate which is 210 and 115 meters, respectively for HECs = 2.9 and 4.0 ppm (see Table 6 above). The results shown in this table are actual MOEs and the target uncertainty factor is 30 (i.e., 29 in this case from rounding in PERFUM) which is reported here at the corresponding 95 th percentile. This table illustrates how MOEs would change if whole field buffers, different application rates, or different percentiles of exposure were considered for risk management purposes.34 The information in Table 7 and Figure 5 (and Appendix F for each analysis) show that MOEs increase at a fixed point (e.g., 115 meters in this example) if application rates decrease or whole field buffers are considered instead of maximum distance buffers. Figure 5 also clearly illustrates the rates of change in MOEs over different percentiles of exposure. Even at percentiles of exposure greater than the 95 th

, MOEs are not substantially lower ( i.e., most MOEs are still 

### $ 10). Thus suggesting that risks are not changing dramatically at the higher percentiles of exposure.
The analyses completed with PERFUM are based on 5 weather station locations and 8 flux studies. In order to broadly understand how differing application methods and the impact of use in different regions (i.e., climates) can impact buffer zone distances, it is necessary to group the outputs from PERFUM for comparative purposes. Table 8 and Figure 6 are examples of the results for both maximum and whole field buffers in Ventura California at different application rates for all flux studies available for iodomethane rather than just based on the Watsonville study as described above in Table 7 and Figure 5. Results are presented based on both acute HECs of concern (i.e., 2.9 and 4.0 ppm) for comparative purposes. An analysis of MOE changes under different conditions was also completed using Ventura Meteorological data and all appropriat e flux studies. These are presented below in Table 9 and Figure 7. The MOE analysis uses the results for the HEC of 4 ppm based on the results in Table 8 which indicate that the trends would be similar for the results of both HECs.

Table 8: Summary Of PERFUM Buffer Results For Ventura California Meteorological Conditions And All Flux Types For 1 And 40 Acre Square Fields

Percen tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Maximum PERFUM Buffers (meters) Whole PERFUM Buffers (meters) Maximum PERFUM Buffers (meters) Whole PERFUM Buffers (meters) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres Flux - Watsonville CA Flat Fume 75 5 90 0 40 0 5 0 5 0 5 0 0 0 35 0 5 0 5 0 5 0 0 0 0 90 5 150 5 80 0 15 0 40 0 10 0 5 5 75 0 30 0 5 0 10 0 5 0 0 95 5 210 5 125 0 40 5 70 0 30 0 5 5 115 0 50 0 5 0 25 0 5 0 5 99 15 375 5 275 5 140 5 155 5 90 0 30 5 265 5 170 0 65 5 85 0 40 0 5 99.9 60 700 35 510 5 360 15 350 5 260 5 145 35 495 5 395 5 225 5 250 5 165 0 80 Flux - Manteca CA Flat Fume 75 5 145 5 75 0 15 0 25 0 5 0 0 5 70 0 25 0 5 0 5 0 5 0 0 90 5 210 5 125 0 45 0 75 0 35 0 5 5 115 0 55 0 5 0 30 0 5 0 5 95 5 275 5 175 0 75 5 110 0 60 0 10 5 165 5 90 0 20 0 55 0 20 0 5 99 35 480 5 340 5 195 5 210 5 130 0 55 5 330 5 225 0 105 5 125 0 70 0 15 99.9 60 800 35 545 5 330 30 420 5 310 5 185 35 525 5 355 5 210 5 300 5 205 0 110 Flux - Oxnard CA Raised Bed 75 5 320 0 200 0 80 0 20 0 5 0 0 0 190 0 100 0 5 0 5 0 5 0 0 90 5 465 0 315 0 155 0 115 0 65 0 10 5 300 0 185 0 65 0 60 0 20 0 5 95 5 565 0 390 0 200 0 215 0 135 0 50 5 370 0 235 0 95 0 125 0 65 0 5 99 45 730 0 515 0 295 5 440 0 300 0 155 5 490 5 330 0 175 5 285 0 180 0 70 99.9 80 960 5 680 0 410 30 665 0 485 0 280 15 655 5 460 0 230 5 465 0 320 0 165 Flux - Guadalupe CA Raised Bed 75 5 335 5 220 0 95 0 40 0 15 0 5 5 205 0 115 0 25 0 15 0 5 0 0 90 5 480 5 330 0 170 0 135 0 80 0 25 5 315 0 195 0 80 0 75 0 35 0 5 95 30 590 5 410 0 225 5 230 0 145 0 60 5 395 5 260 0 115 0 140 0 75 0 10 99 65 775 25 560 5 335 5 445 5 310 0 170 15 540 5 370 0 210 5 300 0 190 0 85 99.9 95 1120 60 805 5 510 50 710 15 510 5 315 55 775 5 560 5 320 10 495 5 350 0 195 Flux - La Selva CA Drip 75 5 220 0 120 0 30 0 10 0 5 0 0 0 115 0 45 0 5 0 5 0 0 0 0 90 5 350 0 220 0 90 0 75 0 35 0 5 0 210 0 110 0 50 0 30 0 5 0 5 95 5 480 5 315 0 145 0 145 0 80 0 20 5 300 0 175 0 85 0 75 0 30 0 5 99 40 660 5 455 0 245 5 320 5 215 0 100 5 435 5 285 0 175 5 205 0 115 0 60 99.9 65 850 5 615 5 345 20 580 5 410 0 225 5 590 5 390 0 260 5 395 5 255 0 165Percen tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Maximum PERFUM Buffers (meters) Whole PERFUM Buffers (meters) Maximum PERFUM Buffers (meters) Whole PERFUM Buffers (meters) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) Max (175 lb/A) 75% (131 lb/A) 50% (88 lb/A) 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres

35 Flux - Camarillo CA Drip Irrigation 75 0 85 0 35 0 5 0 5 0 5 0 0 0 30 0 5 0 5 0 5 0 0 0 0 90 5 145 0 75 0 25 0 35 0 5 0 5 0 65 0 20 0 5 0 5 0 5 0 0 95 5 195 0 105 0 50 0 65 0 25 0 5 0 100 0 40 0 5 0 20 0 5 0 5 99 5 350 5 230 0 145 5 150 0 85 0 35 5 220 0 135 0 70 0 75 0 30 0 5 99.9 50 625 5 460 5 325 5 325 5 225 0 160 5 440 5 305 0 215 5 215 0 140 0 80 Flux - Guadalupe CA Drip Irrigation 75 5 190 5 110 0 35 0 40 0 15 0 5 5 105 0 50 0 5 0 15 0 5 0 0 90 15 270 5 175 5 75 5 100 0 55 0 10 5 165 5 90 0 20 0 50 0 20 0 5 95 20 330 5 220 5 110 5 145 5 85 0 30 5 210 5 125 0 45 5 80 0 35 0 5 99 55 640 30 445 5 280 10 265 5 175 5 85 20 435 5 305 5 160 5 170 5 100 0 35 99.9 105 1380 70 910 35 545 45 545 25 410 5 260 65 875 405 605 5 355 20 395 5 265 5 160

Table 9: Summary Of Ventura California MOEs For All Flux Studies And 40 Acre Fields Using 95 th Percentile Maximum Buffer Distances (HEC = 4 ppm) Percentiles MOEs For Ventura California Meteorological C onditions And All Flux Studies Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max Whole Max Whole Max Whole Watsonville Flat Fume - 95th Percentile Maximum Distance = 115 Meters 75 45 124 60 100 90 1000 90 35 67 47 90 70 135 95 29 53 39 70 58 105 99 17 33 23 45 35 67 99.9 8 17 10 23 16 35 Manteca Flat Fume - 95th Percentile Maximum Distance = 165 Meters 75 43 127 58 1000 87 1000 90 35 65 47 87 70 131 95 29 52 39 69 58 103 99 17 34 24 46 36 69 99.9 9 18 11 24 16 37 Oxnard Raised Bed - 95th Percentile Maximum Distance = 370 Meters 75 45 1000 61 1000 91 1000 90 33 102 45 136 67 1000 95 29 62 39 83 58 124 99 23 35 31 47 46 71 99.9 18 24 24 32 36 49 Guadalupe Raised Bed - 95th Percen tile Maximum Distance = 395 Meters 75 45 1000 61 1000 91 1000 90 34 98 46 131 69 1000 95 29 63 39 84 58 126 99 22 36 30 49 45 73 99.9 16 24 22 32 33 48 La Selva Drip - 95th Percentile Maximum Distance = 300 Meters 75 49 1000 65 1000 98 1000 90 36 111 48 149 73 1000 95 29 70 39 93 58 139 99 22 38 29 51 44 77 99.9 16 24 21 32 31 48 Camarillo Drip Irrigation - 95th Percentile Maximum Distance = 100 Meters 75 43 108 57 145 86 1000 90 33 62 45 83 68 124 95 29 49 39 66 58 98 99 18 32 24 44 37 65 99.9 9 18 11 24 17 36 Guadalupe Drip Irrigation - 95th Percentile Maximum Distance = 210 Meters 75 45 1000 61 1000 91 1000 90 34 98 46 131 69 1000 95 29 63 39 84 58 126 99 22 36 30 49 45 73 99.9 18 24 22 32 33 4836 **9 5 th P e rc e n tile Io d o m e th a n e B u ffe r R e s u lts F o r 4 0 A c re F ie ld s F o r V e n tu ra C A & A ll F lu x P ro file s**  **(H E C 4pp m & U F = 30)**

0 5 0 100 150 200 250 300 350 400 450

M a x R a te /D is t 7 5 % R a te /M a x D is t. 5 0 % R a te /M a x D is t. M a x R a te /W h o le 7 5 % R a te /W h o le 5 0 % R a te /W h o le **A p p lic a tio n R a te & P E R F U M F ie ld A p p ro a c h Buffer Distance (m)** 

F lat F um e - W atson. F lat F um e - M anteca R a is e d B e d - O x nard R a is e d B e d - Guad. D r ip - L a S e lva D rip - C am ar. D rip - G u ad.

Figure 6: 95 th Percentile Iodomethane Buffer Distances For 40 Acre Fields, Ventura California Meteorological Data, All Applicable Flux Profiles, Varied Application Rates, And HEC = 4 ppm.

0 20 40 60 80 100 120 140 160 180 200**MOEs At 95th Percentile Maximum Buffer Distance** 

Watson.115M Manteca 165 M Ox nard 370 M Guad.RB 395 M LaSelva 300 M Camar. 100 M Guad.Dr 210 M
**Flux Source & 95th Percentile M aximum Buffer (m)** Max (175 lb/Acre) Max

Max (175 lb/Acre) W hole

75% (131 lb/Acre) Max

75% (131 lb/Acre) W hole

50% (88 lb/Acre) Max

50% (88 lb/Acre) W hole

Figure 7: 95 th Percentile MOE Analysis For 40 Acre Fields, Ventura California Meteorological Data, All Available Flux Profiles, Varied Application Rates , And HEC = 4 ppm
The information in Table 8 and Figure 6 (and Appendix F for each analysis) shows that buffers decrease if application rates decrease or whole field buffers are considered instead of maximum distance buffers. Figure 6 also clearly illustrates that fields where raised bed flux is used as an application method tend to emit more iodomethane than the other methods considered in this analysis. It also appears that at least for La Selva, California that drip irrigation also emits faster than in the other locations considered. It is unclear, however, that drip irrigation inherently emits faster than flat fume techniques based on the Camarillo drip irrigation study. Table 9 and Figure 7 indicate that similar trends in MOE changes with conditions are noted with the Ventura California meteorological data regardless of the flux profile under consideration. It is likely that similar conclusions would be drawn for other weather station locations but the relative differences might be affected.
Along with evaluating the trends among different types of flux results (i.e., application methods) at a specific location, it is also important to understand how regional weather differences might impact buffer distances. The results for the maximum application rate of 175 lb ai/acre have been summarized in Tables 10 and 11 as well as Figures 8 through 11 below for all weather stations and flux profiles available for this analysis (5 weather stations and 8 flux profiles). Table 10 presents the results for37 maximum buffer distances for each HEC of concern and all flux rates for 1 and 40 acre fields at differing percentiles of exposure. Figures 8 and 9 present for both HECs of concern the maximum buffer results only for 40 acre fields. Table 11 and Figures 10 and 11 are similar except that they present the whole field buffer results. These tables and figures provide the broadest overall results for the PERFUM analysis that has been completed for iodomethane because they can be used to compare differences based on HEC, region, field size, and flux rate.

Table10:Summary Of PERFUM Maximum Buffer Results For All Weather Stations And Flux Types With 1 And 40 Acre Fields At Maximum A pplication Rate Of 175 lb ai/APercentiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Ventura California Tallahassee Florida Flint Michigan Bradenton Florida Bakersfield Calfornia Ventura California Tallahassee Florida Flint Michigan Bradenton Florida Bakersfield Calfornia 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres Flux - Watsonville CA Flat Fume 75 5 90 5 120 0 70 5 155 0 75 0 35 0 60 0 25 5 85 0 30 90 5 150 5 185 5 120 10 235 5 120 5 75 5 100 0 60 5 140 0 60 95 5 210 5 240 5 150 20 305 5 155 5 115 5 135 0 80 5 185 0 90 99 15 375 20 355 5 235 40 460 5 220 5 265 5 230 5 140 15 310 5 130 99.9 60 700 50 560 25 415 60 675 15 350 35 495 20 405 5 265 30 465 5 190 Flux - Manteca CA Flat Fume 75 5 145 5 170 5 120 10 225 5 120 5 70 5 95 0 60 5 135 0 65 90 5 210 5 255 5 165 25 325 5 170 5 115 5 150 5 95 5 210 5 100 95 5 275 15 310 5 210 35 395 5 205 5 165 5 195 5 120 10 260 5 120 99 35 480 30 415 15 305 50 545 15 265 5 330 5 270 5 175 25 385 5 165 99.9 60 800 60 725 35 560 70 745 25 360 35 525 25 500 15 290 45 505 5 245 Flux - Plant City FL Raised Bed 75 NA NA 0 85 0 60 5 160 NA NA NA NA 0 35 0 15 0 85 NA NA 90 NA NA 5 150 0 105 5 255 NA NA NA NA 0 75 0 50 5 150 NA NA 95 NA NA 5 200 5 140 5 315 NA NA NA NA 0 110 0 70 5 195 NA NA 99 NA NA 5 320 5 220 25 455 NA NA NA NA 5 195 0 125 5 290 NA NA 99.9 NA NA 20 520 5 350 55 720 NA NA NA NA 5 355 5 205 15 460 NA NA Flux - Oxnard CA Raised Bed 75 5 320 NA NA 0 180 NA NA 0 205 0 190 NA NA 0 100 NA NA 0 120 90 5 465 NA NA 5 270 NA NA 5 280 5 300 NA NA 0 165 NA NA 0 170 95 5 565 NA NA 5 335 NA NA 5 325 5 370 NA NA 5 215 NA NA 0 200 99 45 730 NA NA 5 505 NA NA 5 415 5 490 NA NA 5 330 NA NA 5 270 99.9 80 960 NA NA 30 730 NA NA 5 525 15 655 NA NA 5 510 NA NA 5 350 Flux - Guadalupe CA Raised Bed 75 5 335 NA NA 5 205 NA NA 5 155 5 205 NA NA 0 120 NA NA 0 135 90 5 480 NA NA 5 295 NA NA 10 210 5 315 NA NA 5 190 NA NA 5 190 95 30 590 NA NA 5 365 NA NA 15 245 5 395 NA NA 5 240 NA NA 5 225 99 65 775 NA NA 25 540 NA NA 30 340 15 540 NA NA 5 350 NA NA 5 295 99.9 95 1120 NA NA 55 785 NA NA 40 445 55 775 NA NA 5 520 NA NA 5 425 Flux - La Selva CA Drip 75 5 220 0 190 0 115 5 300 0 185 0 115 0 100 0 50 0 180 0 105 90 5 350 5 315 5 205 25 475 5 275 0 210 0 185 0 110 5 310 0 160 95 5 480 5 395 5 265 40 625 5 330 5 300 5 240 0 150 5 410 0 205 99 40 660 5 600 5 395 65 875 5 460 5 435 5 360 5 245 15 580 5 285 99.9 65 850 65 840 5 715 95 1175 5 695 5 590 5 585 5 455 55 800 5 445 Flux - Camarillo CA Drip Irrigation 75 0 85 5 100 0 70 5 155 0 70 0 30 0 45 0 25 0 85 0 25 90 5 145 5 165 5 110 5 235 5 100 0 65 0 85 0 50 5 140 0 50 95 5 195 5 210 5 135 10 300 5 120 0 100 5 115 0 75 5 185 0 65 99 5 350 5 310 5 210 30 420 5 165 5 220 5 180 5 120 5 270 5 95 99.9 50 625 20 475 5 415 45 565 5 215 5 440 5 365 5 230 15 380 5 135 Flux - Guadalupe CA Drip Irrigation 75 5 190 5 225 5 170 20 285 5 230 5 105 5 135 0 95 5 180 5 90 90 15 270 20 320 5 230 35 400 5 300 5 185 5 205 5 140 15 270 5 130 95 20 330 30 400 15 275 45 485 5 350 5 210 5 260 5 170 25 330 5 160 99 55 640 50 540 30 455 70 685 5 440 20 435 20 375 10 275 45 495 10 225 99.9 105 1380 90 915 55 1205 90 930 25 670 65 875 50 645 30 555 60 625 20 29038 Table11:Summary Of PERFUM Whole Field Buffer Results For All Weather Stations And Flux Types With 1 And 40 Acre Fields At Maxim um Application Rate Of 175 lb ai/APercentiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm Ventura California Tallahassee Florida Flint Michigan Bradenton Florida Bakersfield Calfornia Ventura California Tallahassee Florida Flint Michigan Bradenton Florida Bakersfield Calfornia 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres 1 Acre 40 Acres Flux - Watsonville CA Flat Fume 75 0 5 0 10 0 5 0 25 0 10 0 5 0 5 0 0 0 5 0 5 90 0 40 0 50 0 30 0 75 0 40 0 10 0 15 0 5 0 40 0 10 95 5 70 0 85 0 55 5 115 0 60 0 25 0 40 0 20 0 65 0 25 99 5 155 5 185 5 115 10 215 5 120 5 85 5 105 0 60 5 135 0 65 99.9 15 350 10 330 5 220 30 380 5 210 5 250 5 215 5 135 15 265 5 125 Flux - Manteca CA Flat Fume 75 0 25 0 25 0 15 0 50 0 30 0 5 0 5 0 5 0 20 0 5 90 0 75 0 80 0 60 5 115 0 70 0 30 0 40 0 25 0 70 0 30 95 5 110 5 125 0 90 5 165 5 95 0 55 0 70 0 45 5 100 0 50 99 5 210 5 245 5 165 20 285 5 165 5 125 5 150 5 95 5 195 5 95 99.9 30 420 25 395 10 290 45 480 10 245 5 300 5 255 5 175 25 335 5 155 Flux - Plant City FL Raised Bed 75 NA NA 0 5 0 5 0 10 NA NA NA NA 0 0 0 0 0 5 NA NA 90 NA NA 0 30 0 20 0 65 NA NA NA NA 0 5 0 5 0 25 NA NA 95 NA NA 0 65 0 45 0 110 NA NA NA NA 0 25 0 5 0 60 NA NA 99 NA NA 5 155 0 110 5 225 NA NA NA NA 0 80 0 50 5 140 NA NA 99.9 NA NA 5 300 5 210 25 395 NA NA NA NA 5 185 0 120 5 260 NA NA Flux - Oxnard CA Raised Bed 75 0 20 NA NA 0 20 NA NA 0 45 0 5 NA NA 0 5 NA NA 0 10 90 0 115 NA NA 0 80 NA NA 0 105 0 60 NA NA 0 35 NA NA 0 55 95 0 215 NA NA 0 130 NA NA 0 155 0 125 NA NA 0 70 NA NA 0 90 99 5 440 NA NA 5 260 NA NA 5 260 5 285 NA NA 0 165 NA NA 0 165 99.9 30 665 NA NA 5 475 NA NA 5 385 5 465 NA NA 5 315 NA NA 5 255 Flux - Guadalupe CA Raised Bed 75 0 40 NA NA 0 35 NA NA 0 60 0 15 NA NA 0 5 NA NA 0 25 90 0 135 NA NA 0 100 NA NA 0 125 0 75 NA NA 0 50 NA NA 0 70 95 5 230 NA NA 5 150 NA NA 0 175 0 140 NA NA 0 90 NA NA 0 105 99 5 445 NA NA 5 285 NA NA 5 280 5 300 NA NA 5 185 NA NA 5 180 99.9 50 710 NA NA 20 495 NA NA 5 415 10 495 NA NA 5 330 NA NA 5 275 Flux - La Selva CA Drip 75 0 10 0 5 0 5 0 25 0 25 0 5 0 5 0 0 0 5 0 5 90 0 75 0 70 0 45 0 115 0 85 0 30 0 30 0 10 0 65 0 40 95 0 145 0 130 0 85 5 185 0 135 0 75 0 70 0 35 0 120 0 70 99 5 320 5 285 5 200 20 385 5 250 5 205 0 175 0 115 5 260 0 155 99.9 20 580 5 535 5 365 55 700 5 405 5 395 5 345 5 235 15 485 5 260 Flux - Camarillo CA Drip Irrigation 75 0 5 0 5 0 5 0 20 0 10 0 5 0 5 0 0 0 5 0 5 90 0 35 0 45 0 30 0 75 0 35 0 5 0 10 0 5 0 35 0 5 95 0 65 0 75 0 55 5 115 0 55 0 20 0 30 0 15 0 65 0 20 99 5 150 5 160 5 110 5 215 5 100 0 75 0 90 0 55 5 135 0 50 99.9 5 325 5 285 5 205 25 360 5 155 5 215 5 170 5 115 5 235 5 90 Flux - Guadalupe CA Drip Irrigation 75 0 40 0 40 0 30 0 65 0 50 0 15 0 15 0 5 0 35 0 20 90 5 100 5 105 0 90 5 145 5 95 0 50 0 60 0 45 5 90 0 50 95 5 145 5 165 5 130 15 205 5 125 5 80 5 100 0 75 5 135 5 75 99 10 265 15 310 5 220 30 345 10 200 5 170 5 200 5 140 15 240 5 125 99.9 45 545 40 505 25 395 60 565 25 305 20 395 15 340 5 265 35 415 5 20539 0 50 100 150 200 250 300 350 400 450

Flat Fume - W atsonville Flat Fum e - Manteca R aised Bed - Plant City Raised Bed - Oxnard Raised Bed - Guadalupe Drip - La Selva Drip - Camarillo Drip - Guadalupe **Source of Flux Profile Buffer Distance (meters)**  Ventura CA T allahassee FL Flint MI Bradenton FL Bakersfield CA

Figure 8: Summary of 95 th Percentile Maximum Buffer Distances For 40 A Fields At Maximum Application Rate, HEC = 4 ppm, UF = 30, All Weather Stations, and All Flux Studies

0 100 200 300 400 500 600 700

Flat Fume - W atsonville Flat Fum e - Manteca R ais ed Bed - Plant C ity Raised Bed - Oxnard Raised Bed - Guadalupe D rip - La Selva D rip - C am arillo D rip - Guadalupe **S ource of Flux P rofile Buffer Distance (meters)**  Ventura CA T alla has see F L F lint MI Bradenton FL Ba ke rsfield CA

Figure 9: Summary of 95 th Percentile Maximum Buffer Distances For 40 A Fields At Maximum Application Rate, HEC = 2.9 ppm, UF = 30, All Weather Stations, and All Flux Studies

0 50 100 150 200 250 300 350

Flat Fum e - W atsonville Flat Fum e - Manteca Raised Bed - Plant City Rais ed Bed - Oxnard Rais ed Bed - Guadalupe Drip - La Selva Drip - Cam arillo Drip - Guadalupe **Source of Flux Profile Buffer Distance (meters)**  Ventura CA T a llah asse e F L F lint MI Bradenton FL Ba ke rsfie ld CA

Figure 10: Summary of 95 th Percentile Whole Buffer Distances For 40A Fields At Maximum Application Rate, HEC=4 ppm, UF=30, All Weather Stations, and All Flux Studies40 0 50 100 150 200 250 300 350

Flat Fum e - W atsonville Flat Fum e - Manteca Raised Bed - P lant City Raised Bed - O xnard Raised Bed - Guadalupe D rip - La Selva D rip - Cam arillo Drip - Guadalupe **Source of Flux Profile Buffer Distance (meters)**  Ventura CA T allahassee F L Flint MI Bradenton FL Bakersfield CA

Figure 11: Summary of 95 th Percentile Whole Buffer Distances For 40 A Fields At Maximum Application Rate, HEC=2.9 ppm, UF=30, All Weather Stations, and All Flux Studies

At the upper percentiles of the exposure distributions generated with PERFUM, the results presented in Tables 10 and 11 and Figures 9 through 12 are markedly similar to those calculated with ISCST3 which supports the construct that the ISCST3 results in conservative estimates of exposure. If maximum buffer distances at the 95 thpercentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 65 meters (at HEC = 4 ppm) and 120 meters (at HEC = 2.9 ppm) while values range up to approximately 400 meters (at HEC = 4 ppm) and 600 meters (at HEC = 2.9 ppm). If whole field buffer distances at the 95 thpercentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 5 meters (at HEC = 4 ppm) and 45 meters (at HEC = 2.9 ppm) while values range up to approximately 140 meters (at HEC = 4 ppm) and 230 meters (at HEC = 2.9 ppm). Generally, Ventura California and Bradenton Florida meteorological data result in the largest buffer distances regardless of flux profile. Likewise, Flint Michigan meteorological data seems to generally result in the lowest buffer distances which is logical given its routine climate compared to the California and Florida locations (e.g., typically cooler in Michigan with potentially less atmospheric turbulence).

For 40 acre fields at the 99.9 thpercentile, maximum buffer distances ranged as high as 875 meters (HEC = 4 ppm) and 1380 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California drip irrigation flux information. For 40 acre fields at the 99.9 thpercentile, whole buffer distances ranged as high as 495 meters (HEC = 4 ppm) and 710 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California raised bed flux information. For 1 acre fields, the vast majority of buffer distances at all percentiles of exposure considered (up to 99.9 thpercentile) were less than 100 meters with most being in the 5 to 50 meter range at the upper percentiles of exposure (i.e., 99 thpercentile plus) regardless of whether maximum or whole field buffers are considered.
Regional climatic differences and differences in application methods (i.e., and associated flux/emissions) can impact buffer distances calculated using PERFUM as shown above. Potentially, the size and shape of a treated field can also impact buffer distances. Using the Ventura California meteorological data, the relative impacts of altering field sizes from 1 to 40 acres were evaluated for each available flux study (Figure 12). Likewise, changing the shape of a treated field can potentially impact buffer distances. This parameter was investigated using differently shaped 5 acre fields (i.e., square and “wide” and “long”41 0 5 0 100 150 200 250 300 350 400 450

1 5 10 20 40 **F ie ld S iz e (a c re s ) Buffer Distance (meters)** 

W a t s o n v ille

M a n te ca

O xn ard
G u a d a lu p e TRB L a S e lv a

C a m a r illo
G u a d a lu p e D rip

Figure 12: Impact Of Changing Field Size On Maximum Buffer Distances, 95 th Percentile Exposures, Maximum Application Rate, Ventura CA Weather, All Available Flux Profiles, HEC=4 ppm and UF=30

0 20 40 60 80 100 120 140

Max Whole Max Whole Max Whole Max Whole Max Whole Max Whole Max Whole

Watsonville Flat Fume Manteca Flat Fume Oxnard Raised Bed Guadalupe Raised Bed La Selva Drip Camarillo Drip Guadalupe Drip 5A Square

5A Wide

5A Long

Figure 13: Impact Of Changing Field Shape On Buffer Distances, 95 th Percentile Exposures, Maximum Application Rate, Ventura CA Weather, All Available Flux Profiles, HEC=4 ppm and UF=30 rectangles oriented horizontally and vertically) and results for all flux studies in conjunction with the Ventura California meteorological data (Figure 13). [Note: Appendix F contains numerical inputs for Figures 12 and 13 if required.]

The results of the analysis which examined the impact of changing field sizes were expected in that buffer distances increased with larger fields. The slope of the curves for each flux study varied because of the different emission profiles associated with each. The higher the emissions, the larger the buffer distance required (see Tables 10 and 11 for additional information). The impact of changing field shapes was also investigated. It appears that at least for the Ventura California meteorological data and 5 acre fields that the shape did not significantly impact buffer distances. This probably indicates that there is not a prevailing wind direction for the Ventura California weather station and that wind vectors occurred in several directions over the timeframe of the analysis. It is possible that results would differ for different weather station locations and that more site specific analyses could be needed if this parameter requires further investigation. It is also likely that results would be more pronounced with larger fields or if a weather station has a definite prevailing wind direction.42 0 50 100 150 200 250 300 350 400 450

15 22.5 30 **MOEs Buffer Distance (m)**  Flat Fume - Watson.

Flat Fume - Manteca

Raised Bed - Oxnard

Raised Bed - Guad.

Drip - La Selva

Drip - Camarillo

Drip - Guadalupe

Figure 14: MOE Changes With Buffer Distance For Maximum Buffer Results, 95 th Percentile Exposures, Ventura CA Weather, All Available Flux Profiles, Maximum Application Rates, 40 A Fields, and HEC = 4 ppm. Finally, changes in MOEs with distance were examined using results based on all available flux profiles appropriate for Ventura California (Figure 14). MOEs of 30 which is the total uncertainty factor used as the basis for the PERFUM calculations are reported as well as MOEs of 22.5 and 15. PERFUM does not readily supply information that tracks MOE changes with distances which is the cause of providing results in this manner. The results in Figure 14 indicate that as locations get closer to a treated field, MOEs decrease (i.e., risks get worse) as expected. Buffer di stances also increase when flux rates increase which has been noted as well above (see Tables 10 and 11). The relationship between MOEs and distance illustrated in Figure 14 is linear. However, the slopes of the lines presented in Figure15 differ b ecause of the differing flux profiles used for the analyses and the Gaussian nature of the PERFUM model.

At the upper percentiles of the exposure distributions generated with PERFUM, the results presented in are markedly similar to those calculated with ISCST3 which supports the construct that the ISCST3 results in conservative estimates of exposure. If maximum buffer distances at the 95 thpercentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 65 meters (at HEC = 4 ppm) and 120 meters (at HEC = 2.9 ppm) while values range up to approximately 400 meters (at HEC = 4 ppm) and 600 meters (at HEC = 2.9 ppm). If whole field buffer distances at the 95 th percentile of exposure are considered, the lowest predicted buffer distance for 40 acre fields is approximately 5 meters (at HEC = 4 ppm) and 45 meters (at HEC = 2.9 ppm) while values range up to approximately 140 meters (at HEC = 4 ppm) and 230 meters (at HEC = 2.9 ppm). Generally, Ventura California and Bradenton Florida meteorological data result in the largest buffer distances regardless of flux profile. Likewise, Flint Michigan meteorological data seems to generally result in the lowest buffer distances which is logical given its routine climate compared to the California and Florida locations (e.g., typically cooler with potentially less atmospheric turbulence). For 40 acre fields at the 99.9 thpercentile, maximum buffer distances ranged as high as 875 meters (HEC = 4 ppm) and 1380 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California drip irrigation flux information. For 40 acre fields at the 99.9 thpercentile, whole buffer distances ranged as high as 495 meters (HEC = 4 ppm) and 710 meters (HEC = 2.9 ppm) which both are based on the Guadalupe California raised bed flux information. For 1 acre fields, the vast majority of buffer distances at all percentiles of exposure considered (up to 99.9 thpercentile) were less than 100 meters with most being in the 5 to 50 meter range at the upper43 percentiles of exposure (i.e., 99 thpercentile plus) regardless of whether maximum or whole field buffers are considered.
Changes in risk estimates with percentiles of exposure were examined and these analyses indicate that even at buffer distances based on the 95 thpercentile of exposure risks were generally not significantly greater even at higher percentiles of exposure which indicates slight changes in risks with percentile of exposure (e.g., MOEs ~10 were common at the 99.9 thpercentile of exposure with MOEs of 30 at the 95 th percentile). Other factors which could potentially impact results were examined including the size and shapes of fields. As expected, larger fields yielded greater buffer distances. Ventura California meteorological data were the basis of the field shape analysis which indicates very little impact on buffer distances but it is possible different meteorological data could yield different results.
6.1.2 Ambient Bystander Exposure From Multiple Area Sources
No direct ambient air monitoring data are available for iodomethane. Although there are low volume industrial and commercial uses of iodomethane, it is not routinely screened for by CARB (California Air Resources Board) or similar organizations which would be expected. As such, to qualitatively characterize the potential for exposure from ambient air, HED has compared iodomethane to the ambient air levels that were quantified for methyl bromide using physical chemical properties and environmental fate characteristics. Based on this comparison, HED believes there is less potential for exposure with iodomethane than with MeBr because of the environmental fate characteristics of iodomethane relative to MeBr (i.e., iodomethane dissipates/degrades faster in the environment).
6.2 Bystander Risk Characterization
There are several issues that should be considered in the interpretation of the above assessments for offtarget releases of iodomethane. The first is that the flux data used for this analysis have been generated in California with some in Florida; however, iodomethane could be used in other regions of the country. Therefore, the results based on California and Florida flux data were used to represent the rest of the country due to a lack of adequate information for any other region. It is unclear what the potential impacts of this extrapolation might be although it is believed it likely will overestimate risks since conditions in those states are generally warmer than elsewhere. For example, it may be possible that other factors such as soil type and other environmental conditions might also affect the rate at which iodomethane is emitted from treated fields or other sources.
Meteorological conditions such as differences in humidity, levels of solar radiation, and atmospheric stability also impact ambient concentrations. In this analysis, coastal and inland stations in Florida and California as well as a station in central were used to complete the PERFUM calculations. This information can also be used to characterize the risks calculated using ISCST3 (i.e., range of windspeeds used for ISCST3 were similar to those used for PERFUM). The selected regions in California, Florida, and Michigan are thought to represent the likely use regions for iodomethane so it is believed these data are representative for the proposed uses. Another factor that should be considered in the interpretation of these results is the data quality associated with the inputs and other factors used in the calculations. For most of the data, HED believes that the data and other information used are of reasonable quality for risk assessment purposes.44 Several factors also need to be considered in the interpretation of the results associated with a known area source. There were two distinct analyses that were completed, the first based on data that considered studies of disparate design, and the second based on the ISCST3 or PERFUM models. Analysis of the data itself indicated risks exceeding HED’s level of concern at the sampling locations close to the treated fields and the conclusions would not significantly be altered due to data quality issues or other uncertainties. The second type of analysis was based on the use of the Agency’s ISCST3 or the PERFUM models which provide much more flexibility in that it allowed extrapolations from an area source to different distances downwind depending upon the conditions of the assessment. These types of results can be used for risk management purposes. The ISCST3 model itself is a publically vetted tool that is currently used by the Agency’s Office of Air for regulatory decision making. A number of support documents for this tool can be found at the associated Agency website Technology Transfer Network Support Center for Regulatory Air Models( www.epa.gov/scram001/tt22.htm#isc ). These should be explored in detail for a more complete examination of the uncertainties associated with this model. Additionally, the results of the analysis of the PERFUM model should be reviewed in detail for a more complete understanding of its associated uncertainties ( www.epa.gov/scipoly/sap ).
The specific inputs for the ISCST3 model calculations drove the associated uncertainties in the results. For example, the key input factors for pre-plant agricultural uses were field size, flux/emission rates, atmospheric stability, and windspeed. Wind direction is another factor which also should be considered. The field sizes used by HED in this assessment were 1 to 40 acres which is well within the range of what could be treated on a daily basis. There are uncertainties associated with estimates of flux/emission rates for specific application techniques which is another varying factor. The flux rates which were used have been calculated by HED and they compare reasonably well with those calculated by the study investigators. The reality is that there is a large distribution of flux rates which is a phenomenon inherent in the nature of these types of data. The values used for this assessment yield conservative air concentration estimates because considering a constant flux rate does not allow for diurnal/nocturnal changes that may occur, which when coupled with the appropriate wind speed and stability category, can result in lower concentrations. Additionally, the maximum application rate of 175 lb ai/acre was considered coupled with the median emission rate which also provided a conservative estimate for flux. The meteorological inputs also will provide a conservative estimate of exposure b ecause the wind direction is considered to be perpendicular (pointed downwind) to the treated field for the entire 24 hours represented in the calculation. This is not a normal situation in the atmosphere for most locations. There is normally a prevailing wind with directional changes over the course of a typical day, especially when diurnal and nocturnal differences are noted. HED did not recommend a specific set of meteorological conditions for this assessment but instead provided a range of results for different conditions. Different meteorological databases were evaluated including SAMSON and CIMIS for comparative purposes to actual weather data. The lower 10 thpercentile windspeeds for a 24 hour period in that analysis ranged from approximately 2 to 5.5 mph depending upon the location. The windspeeds used by HED ranged from approximately 2 to 10 mph. Overall, HED believes that the approach used to evaluate potential exposures from a known area source can be considered conservative. It is believed, however, that the range of selected input values and outputs represent what could reasonably occur in agriculture given proper field and climatological conditions.45 Again, as with the ISCST3 analysis described above, much of the uncertainties associated with the PERFUM results are those inherently associated with the inputs used. In addition to the uncertainties described above for ISCST3 associated with flux rates, field sizes, and application rates there are other issues which should be considered. These include the uncertainties associated with the selection of the weather stations to represent each region of proposed iodomethane use, sampling issues associated with weather data, and use of the uncertainties associated with the flux measurements. HED has concluded that the weather data used for this assessment represent conditions that would occur in the regions were iodomethane use is expected. Weather data were obtained from a variety of sources including NWS and FAA which have a rigorous quality control program. These data, however, are generally collected in airports and/or urban areas leading to differences in terrain from agricultural lands ( i.e., surface roughness differs) thus creating an uncertainty. HED also used data from two weather networks (CIMIS and FAWN). Though these data are collected in agricultural areas ( i.e., they are more representative locations), the quality control program for these networks is not as rigorous. Thus, there is uncertainty around these data as well due to the quality control issue. When the two uncertainties were balanced (QC & more/less representative locations), it was concluded that all were reasonable sources of weather data for the analysis. Additionally, the manner in which PERFUM probabilistically addresses the uncertainties associated with the flux measurements seems reasonable in light of the FIFRA SAP comments. Additional information (e.g., more flux and weather data) would be useful for completing a more robust analysis for other regions of the country. The Agency attempted to determine the sensitivity of PERFUM outputs to varying parameters in its analysis of the results described above (e.g., field size and shape). However, there are clearly additional analyses that can potentially be completed to explore how different parameters alone or in conjunction with others impact results. In summary, HED believes that the use of PERFUM provides for a more informed characterization of the risks for bystanders from use of iodomethane than ISCST3. PERFUM results also support the notion that the results calculated using ISCST3 are conservative estimates of exposure. Finally, it should be reiterated that results for selected percentiles of exposure are reported and in many cases (e.g., MOE exceedance analysis) results are based on 95 th percentile values as an example. Use of the 95 thpercentile for reporting the results does not imply a selection of these estimates for regulatory purposes. Additional analysis based on other percentiles of exposure could be completed if so needed. Risk managers are also cautioned that the uncertainties and representativeness associated with the inputs used to calculate any PERFUM results under consideration for regulatory purposes should be integrated into the ultimate decision making process.
Finally, it should be noted that the Agency has identified a number of scientific issues which it intends to address in a more refined manner pending the outcomes of several ongoing analyses. These issues include the following:

• Treatment Of Calm Periods Due To No/Low Windspeeds And High Atmospheric Stability: The Agency is aware that in many situations that field conditions are calm and can result in very high near field concentrations that the Gaussian plume approach used in ISCST3 and PERFUM may not predict in the most accurate manner because of the slow meandering nature of the emissions. However, it should be noted that the analyses completed by the Agency were in compliance with the then current Appendix W of 40CFR51 for treatment of calms in modeling. The Agency is also considering possible approaches for better characterizing the impact that calm periods may have on modeling results which will be made available upon its completion.46 • Modification of Core Air Model Platform From ISCST3 To Either AERMOD or CALPUFF: On November 9, 2005 the Agency promulgated a revision to Appendix W of 40CFR51 that essentially recommends the use of AERMOD instead of ISCST3. General guidance included in Appendix W provides for 1 year for grandfathering in the use of updated models. The Agency is considering the implications of the use of AERMOD instead of ISCST3 although it is believed that the treatment of calm periods in AERMOD is essentially similar to ISCST3 with the only discernible difference is that is has a low-end windspeed limit of 0.5 instead of 1.0 mph. The major change in AERMOD is the inclusion of an aerodynamic downwash algorithm (i.e., PRIME) which does not significantly impact results for area sources such as treated fields. The use of CALPUFF is also being evaluated by the Agency at this time. CALPUFF has the benefit of being able to better track slowly meandering and even stalled emissions over time and when subjected to changing wind directions. The attributes of each system is currently under consideration as are the pragmatic implications of continuing to use ISCST3 (on its own or as incorporated into PERFUM at this time) because the current results, depending upon which scenarios are considered, already suggest risks of concern may be present at what may be considered viable label limitations for common use in many agricultural settings.

• Representativeness of Meteorological and Emissions Data: In this current assessment, meteorological data were selected to represent a range of geographical areas where iodomethane could potentially be used in agriculture. The results of this assessment could be more refined and applicable for smaller agricultural if additional source of such data were used. Additionally, regional level analyses could be done to incorpor ate many sources of data simultaneously. These options have been considered by the Agency but it is not clear if the variability and uncertainty around the results would be reduced since most meteorological data are not collected directly from affected fields. Similarly, the Agency has used all available emissions data in this current assessment. Consideration of additional regions or application methods would depend on additional emissions data coming available. The Agency is examining the value of additional analyses to address these issues.

• Consideration of Multiple, Successive Applications: The Agency is aware that many applications to large tracts of agricultural lands occurs over a several day process which is limited based on the amount of acreage that can be treated in a day by users and also other logistical issues such as timing related to variety and market issues. As such, the Agency did not consider sequential application events in this assessment pending further investigation into defining appropriate modeling scenarios for possible key agricultural uses (e.g., tomatoes and strawberries in FL and CA) and possible implications for modeling based on possible risk management decisions for single-field scenarios.
[Note: A number of policy and risk management issues have been identified by the Agency pertaining to its current, focused efforts on soil fumigant issues. Th ese types of comments have not been identified or addressed in this document as the intent of this document is to focus on the scientific issues pertaining to the risk estimates that have been calculated herein and not on risk management or mitigation strategies.]47 6.3 Residue Profile
There is no reasonable expectation of finite residues to be incurred in/on food and feed crops when iodomethane is used as a preplant soil fumigant in/on strawberries and tomatoes, so this use is considered to be a non-food use, and tolerances are not needed. (Refer to Section 3.1.)
6.4 Water Exposure/Risk Pathway
Iodomethane is very soluble in water, so there is the possibility of leaching to ground water and/or transporting to surface water through runoff, if slicing or removal of the tarpaulin coincides with, or is followed soon by, a rain event. Therefore, a qualitative drinking water assessment was performed for this risk assessment.
Tier II PRZM/EXAMS for surface water and Tier I SCIGROW for ground water were used to estimate iodomethane concentrations in drinking water. Since iodomethane is a volatile compound, additional input parameters like DAIR (vapor phase diffusion coefficient) and ENPY (enthalpy of vaporization) were activated during the PRZM-EXAMS simulation. In the absence of monitoring data, the concentration of iodomethane in ground water was estimated using SCIGROW, which has limited capability to perform vapor phase transport of iodomethane to groundwater. The assessments were based on maximum application rate of iodomethane for pepper in Florida and generally represent upper-bound estimates of iodomethane concentrations that might be found in surface water and groundwater. Based on environmental fate data, the residual contents in soils, and Tier I and II models estimated concentrations, Agency does not expect iodomethane to adversely impact ground water or surface water.
7.0 Aggregate Risk Assessment
The physical/chemical characteristics, the environmental fate data, and results of metabolism studies in plants assure that there is no reasonable expectation of finite residues to be incurred in/on food and drinking water when iodomethane is applied according to label directions. Therefore, this fumigant does not require food tolerances, is considered to be a ‘non-food use’ chemical, and is not subject to the amendments to the Federal Food, Drug, and Cosmetic Act (FFDCA) promulgated under the Food Quality Protection Act (FQPA) of 1996, and an aggregate risk assessment is not required.

8.0 Cumulative Risk Assessment and Characterization
Unlike other pesticides for which EPA has followed a cumulative risk approach based on a common mechanism of toxicity, EPA has not made a common mechanism of toxicity finding as to iodomethane and any other substances and iodomethane does not appear to produce a toxic metabolite produced by other substances. For the purposes of this tolerance action, therefore, EPA has not assumed that iodomethane has a common mechanism of toxicity with other substances. For information regarding EPA’s efforts to determine which chemicals have a common mechanism of toxicity and to evaluate the cumulative effects of such chemicals, see the policy statements released by EPA’s Office of Pesticide Programs concerning common mechanism determinations and procedures for cumulating effects from substances found to have a common mechanism on EPA’s website at www.epa.gov/pesticides/cumulative
9.0 Occupational Exposure48 Overall, the data indicate that exposures exceed HED’s level of concern for some workers involved in the application of iodomethane when no respiratory protection is used (e.g., tractor drivers, co-pilots, shovelers). Air purifying organic vapor removing respirators (APRs) which reduce exposure levels by a factor of 10 were also considered and exposures were reduced below HED’s level of concern for all workers involved in application with these devices although for some application tasks APRs are not required to achieve acceptable exposure levels. For workers who enter fields days after application to prepare for planting (e.g., tarp cutters or hole punchers), exposures were not of concern 5 days after application (which reflects the available data) without any sort of respiratory protection. This is also the case for planters where exposures were not of concern 7 days after application without any sort of respiratory protection (which also reflects the available data). Respirators would be the most practical protective equipment choice for reducing exposures for most workers in this case. This is because the field monitoring data used for this analysis already reflect the use of some engineering controls such as tarps, tractor cabs, deep injection, or other devices including fans in proximity to drivers.
In a typical pesticide handler assessment, the Agency uses normalized estimates of exposures based on similar equipment and with similar levels of protective equipment or clothing. Additionally, in typical post-application worker assessments, exposures are scaled based on how residues decay over time. These approaches have not been used in the occupational assessments presented below due to methodological issues. For example, it is not clear how changes in various parameters or conditions (e.g., temperature, emission reduction methods such as tarps or applicatio n methods) may impact exposures. It is also not clear how time after application can be used for scaling exposures from one day to the next because worker exposures may be inherently related to the conditions of the field under which monitoring has occurred. It should also be noted that the currently proposed maximum application rate for iodomethane is 175 lb ai/acre and that most of the occupational monitoring data were collected at an approximate application rate of 235 lb ai/acre based on the proposed labels at the time the data were collected. No scaling of the occupational exposure data were completed for the purposes of this assessment. Under actual field conditions, it is likely that exposure levels could be lower for some workers based on application rate and all other factors being equal.
Current requirements for entry of post-application workers into previously treated fields are dictated by the Worker Protection Standard as described in PR 93-7 for various other fumigants. Similar requirements are recommended for iodomethane. Refinement of time-based entry requirements is pending related to the investigation of factors that may impact exposures over time and development of an appropriate methodology for such analyses.
Occupational exposures were quantified in six worker monitoring studies which used iodomethane under field conditions. [Note: As a reminder, all studies involved the use of a tarp during application which should be considered in the interpretation of this assessment.] The application techniques monitored include:49 • MRID 455938-20: flat fume application in Manteca CA; • MRID 463852-04: shank raised bed application in Guadalupe CA; • MRID 458791-02: shank raised bed application in Marina CA (near Oxnard); • MRID 462037-02: drip irrigation application in LaSelva CA; • MRID 463852-03: drip irrigation application in Camar illo CA; and • MRID 464636-02: drip irrigation application in Guadalupe CA.

The tasks that were monitored in these studies are liste d below. Planting in all cases occurred 7 days after application and post-application, pre-plant activities (e.g., hole punching or tarp removal) occurred 5 days after application.
a) Tractor Driver b) Co-pilot (reported as 1 st Tarp Monitor in MRID 458791-02) c) Drip Applicator d) Drip Line Tender (sometimes reported as 2 ndApplicator) e) Planter f) Shoveler g) Tarp Monitor h) Hole Puncher i) Tarp Cutter j) Tarp Remover k) Tarp Remover Driver
The corresponding exposure and risk estimates associated with these activities are presented in Table 12. [Note: Only risks based on the acute duration of exposure are presented because the same endpoint and HEC apply to both the acute and short-/intermediate-term durations of exposure. As such, acute estimates are health protective for all durations of exposure because they represent higher risks since maximum exposure concentrations have been used to calculate them.] Table 12 indicates that if no respiratory protection is considered, exposures exceed HED’s level of concern for many worker tasks associated with application. Exposures were not of concern for those involved with post-application field preparation and planting activities. HED evaluated how the use of an APR (PF 10) would impact worker risks. Exposures for all scenarios with the use of an APR (PF 10) did not exceed HED’s level of concern (with MOE >30). See Appendix G for more details.
It is important to consider that in this assessment all available worker exposure monitoring data have been used directly for risk assessment purposes. The studies used are thought to be of reasonable quality for use in the risk assessment process. The data were used as conducted with no adjustment for application rate although it is likely that typical use rates for iodomethane will likely be less than those considered in the occupational exposure monitoring studies. The data included in these studies also reflect the use of tarps and various types of emission controls. As such, the results of the monitoring data are specific to those conditions but likely represent what would be encountered in agricultural use situations. It is clear, however, that the elements of any application can impact exposure levels based on several factors (e.g., care of operator, equipment condition, field preparation). Finally, it should be noted that the duration of most exposure monitoring periods ranged from 5 to over 8 hours which could be expected to represent what could happen in typical agriculture.50 Table 12: Iodomethane Worker Exposure Associated With Pre-Plant Agricultural Field Fumigation Scenario Application Method (N) Acute Duration

Max. Conc. 1 Monitored

(ppm) Acute MOE For Max. Conc. 1 Monitored

Max. Conc. With PF10 Resp. Applied (ppm) Acute MOE For Max. Conc. With PF10 Resp. Applied

Tractor Driver Raised Bed (4) 1.029 3.6

0.103 36

Broadcast Flat Fume (2) 0.024 154

0.0024 1542

Co-Pilot Raised Bed & Broadcast Flat Fume (4) 0.648 5.7

0.0648 57

Drip Applicator Drip Application (6) 0.240 15

0.024 154

Drip Line Tender Drip Application (6) 0.147 25

0.0147 252

Planter Raised Bed, Broadcast Flat Fume & Drip (18) 0.007 529

0.00070 5286

Shoveler Raised Bed (8) 0.76 4.9

0.076 49

Broadcast Flat Fume (4) 0.117 32

0.0117 316

Tarp Monitor Raised Bed (6) 1.11 3.3

0.111 33

Hole Puncher Raised Bed (4) 0.07 53

0.007 529
Drip Application (6) 0.017 224

0.0017 2242

Tarp Cutter Broadcast Flat Fume (2) 0.006 617

0.0006 6167Table 12: Iodomethane Worker Exposure Associated With Pre-Plant Agricultural Field Fumigation Scenario Application Method (N) Acute Duration

Max. Conc. 1 Monitored

(ppm) Acute MOE For Max. Conc. 1 Monitored

Max. Conc. With PF10 Resp. Applied (ppm) Acute MOE For Max. Conc. With PF10 Resp. Applied

51 Tarp Remover Broadcast Flat Fume (2) 0.013 284

0.0013 2846

Tarp Remover Driver Broadcast Flat Fume (2) 0.024 154

0.0024 1542 Nominal flow rate for all samples was 0.05 liters/minute. No samples reported as <LOD or <LOQ. For some scenarios, results are presented based on application equipment due to differences in exposure rates for each techniqu e. Sample durations ranged from approximately 1 to 7 hours. Most samples were 3 hours in duration or longer.

10.0 Data Needs and Label Requirements
10.1 Toxicology
There are no additional data required at this time.
10.2 Residue Chemistry
There are no additional data required at this time.
10.3 Occupational and Residential Exposure
The assessment of occupational and residential risks associated with the use of iodomethane is complex. There was a significant amount of data available, but additional data are still required. These include both occupational monitoring of various workers and data to better assess exposures in the general population. The types of data, guideline citations, and examples of the scenarios which need to be addressed are presented below. Final determination of the scenarios should be made in consultation with the Agency.
OPPTS Guideline 835.8100 - Field volatility from soil
Volatility studies to determine flux for ISCST3 modeling purposes in major use regions of country for significant application methods (e.g., Florida for raised beds or drip irrigation). Exact studies to be determined after direct consultation with the Agency.52 OPPTS Guideline 875.1300 - Inhalation exposure for applicators (outdoors)
Pre-Plant Field - (e.g., rig drivers & tenders, tarpers, tarp removers). Exact studies to be determined after direct consultation with the Agency.
OPPTS Guideline 875.2500 - Inhalation exposure for postapplication workers
Pre-Plant Field - (e.g., planters, irrigators). Exact studies to be determined after direct consultation with the Agency.
Requirements For Special Studies
Meteorological data for probabilistic modeling purposes
Projections for product use by major use region, frequency, application parameters (e.g., rate, acres treated, data, application equipment and emission control technologies used)
Measurements of indoor air concentrations for residences in proximity of treated areas.
Ambient air monitoring in key growing regions.
Exact studies to be determined after direct consultation with the Agency.

# Appendix A

Review Of PBPK/PD ModelUNITED STATES ENVIRONMENTAL PROTECTION AGENCY NATIONAL CENTER for COMPUTATIONAL TOXICOLOGY Research Triangle Park, NC 27711
November 22, 2005

Office of Research and Development

TXR #: 0053105

MEMORANDUM

SUBJECT: IODOMETHANE: Review of Methyl Iodide PBPK/PD Model. DP Barcode: D312630; PC Code: 000011.

FROM: Hugh Barton, Ph.D. Toxicologist (B205-01)
Paul Schlosser, Ph.D. Environmental Health Scientist National Center for Environmental Assessment (B243-01)

TO: Elizabeth Méndez, Ph.D. Toxicologist, Reregistration Branch I Health Effects Division (7509C)

1. SUMMARY
A physiologically-based pharmacokinetic (PBPK)/pharmacodynamic (PD) model for methyl iodide (Sweeney et al., 2005; MRID # 46446901) submitted by Arysta LifeScience to the Office of Pesticides Program (OPP) has been reviewed and its utility in risk assessment evaluated. The model is a sophisticated effort to describe the kinetics of methyl iodide following inhalation exposure and the kinetics of iodide as a metabolite. It describes nasal tract dosimetry and glutathione (GSH) depletion in the rat to evaluate nasal toxicity, distribution of methyl iodide to tissues including brain, and iodide kinetics in the pregnant rabbit to address developmental toxicity. The model has also been parameterized for the human and Monte Carlo analyses were performed to describe human variability. The review was carried out using the framework described in Clark et al., 2004. The results of the evaluation are described focusing on the rat and human nasal modeling (Section 2), the rabbit and human pregnancy modeling (Section 3), modeling human variability (Section 4), and model documentation (Section 5). The strengths and limitations of the modeling were identified. The OPP indicated the acute neurotoxicity did not appear a critical endpoint either in the PBPK-based or the default analysis, so it was not evaluated. The nasal modeling for rat and human was concluded to be adequate to estimate a human equivalent concentration. Selection of the appropriate degree of GSH depletion to predict nasal olfactory toxicity is dependent on additional factors beyond the PBPK/PD modeling, including judgments about the relationship of this measure with toxicity and the linkage of the time-course of exposure concentrations with the prediction of GSH depletion. The pregnancy modeling was found to be adequate to estimate a range of human equivalent concentrations. The human variability analysis was considered to provide perspective on thedefault value of 3 to address human pharmacokinetic variability. Finally, the last section discusses potential improvements in model documentation to improve the transparency of the modeling efforts.
A model of this degree of complexity requires a substantial effort to review and evaluate. Our review was greatly facilitated by the willingness of the model developers, Drs Lisa Sweeney and Mike Gargas, to answer questions and respond to issues that were raised. Many of these communications are summarized in the memos that have described our review as it was ongoing (Jan 20, 2005; March 31, 2005) and their comments on the issues we raised [Feb 22, 2005; March 29, 2005 (MRID 46559301); April 29, 2005; May 23, 2005 (MRID 46559303), Aug 24, 2005 (MRID 46631401)]. This memo documents our evaluation of the model at the current time, informed by these previous communications. This memo supersedes all previous interim evaluations.

2. NASAL MODELING
Model Purpose Modeling of methyl iodide absorption and metabolism in the nasal tissues of the rat and human is proposed to obtain an HEC for the acute risk assessment of the effects observed following inhalation exposure in the rat as well as for establishing a chronic HEC for nasal effects. The proposed mode of action involves the depletion of GSH due to methyl iodide conjugation as a key event in the toxicity pathway leading to damage of the nasal olfactory epithelium. The proposed metric for cross-species extrapolation is a reduction in GSH concentration in the olfactory epithelium tissue. The reduction is calculated from the modeled average GSH concentration in the five tissue layers (including the blood exchange layer). The extent of GSH reduction considered appropriate is discussed below.
Model Structure and Biological Characterization While the complete model is large and complex, the predictions of dosimetry in the nasal olfactory region and developing fetus are largely independent of each other, so the model can be viewed as two separate submodels. While each of these submodels is somewhat complex in itself, the structure overall is sound and supported by scientific data. The nasal/olfactory model can be further divided into the airphase/CFD component and the tissue-phase/metabolic component. The nasal/olfactory model is a slightly modified implementation of the model developed for ester vapors (Frederick et al., 2002). Vapor breathed in through the nose splits into two airstreams, which pass over respiratory and olfactory epithelial tissues prior to recombining in the nasopharynx and proceeding to the lungs. The epithelial tissue compartments consist of a mucus layer, tissue layers, and a blood exchange region, so vapor equilibrates from the gas phase through the mucus and tissues to the blood.
Glutathione transferase mediated conjugation of methyl iodide occurs in all the respiratory and olfactory epithelial tissue layers (including the gas exchange layer) as well as in tissues throughout the body, e.g., liver, stomach, and kidney. These compartments also have basal glutathione turnover and re-synthesis. This model does not describe inducible re-synthesis, i.e., increased synthesis rate in response to GSH depletion (D'Souza et al., 1988). Limited non-protein-dependent degradation of GSH occurs, so for simplicity additional terms for this degradation were not included in the many nasal tissue compartments, though they were included for several tissues including liver, brain, and kidney. Note that enzymatic rates observed in Chamberlain et al., 1998a are in the presence of cytosol diluted by comparison with in vivo, so the non-protein-dependent degradation will be a much smaller fraction oftotal GSH utilization under physiological conditions than reported by Chamberlain et al. It is possible that non-enzymatic methylation of proteins and other macromolecules is a component of the mode of action for methyl iodide induced nasal toxicity.
Cross-species differences in the respiratory tract play an important role in the model structure and parameterization. The model structure includes all the regions appropriate for the rat nose, but the human nose has only one region for olfactory epithelium (Frederick et al., 1998). The human parameterization addresses this by making the posterior epithelial stack (ethmoid olfactory tissue stack) very small as well as setting the gas transfer to be negligibly small. However, while the impact of this region on the behavior of the modeled gas is extremely limited, the model still calculates values for this region in both species, leading to the incorrect use of dose metrics in the ethmoid olfactory for humans in the original submissions. The potential for confusion is particularly great in this specific case because the relevant region in the rat nose is considered to be the ethmoid olfactory epithelium, while the appropriate region in the human parameterization is the dorsal olfactory epithelium.
Simplifications of the model would be useful in the future notably due to the computational intensity of running the model to simulate inhalation and exhalation; it does not appear that this level of detail is necessary. It is also not clear that the large number of nasal tissue layers in each tissue stack is necessary. While these compartments probably have a limited impact on computational intensity, they make the model more complex to understand and check. However, these considerations do not impact making a decision concerning the acceptab ility of this version of the model.
Mathematical Descriptions and Computational Implementation The model (mei2epa.csl) is coded in ACSL version 11.8 (Aegis Technologies, Huntsville, AL). With limited editing it can be run in acslXtreme version 1.4, a more current version of the software used in this evaluation (mei2epa7-11-05.csl). In ACSL, the code for the model provides a complete listing of the equations in the CSL file; parameter values are specified in the CMD file (mei2epa.cmd submitted; mei2epa7-11-05.cmd EPA revision). The equations have previously been described in publications cited in Sweeney et al., 2005, particularly the peer-review ed publications of Frederick et al. 1998, 2002. A screening level assessment of the equations and computer implementation for the nasal modeling has been conducted since it is assumed that they were consistent with the previous modeling published in peer-reviewed journals. One potential error has been noted in the parameter values used in the computer code for the nose, but it is not expected to have a significant impact on the model.
Parameter Analysis and Model Simulations
The nasal model has a significant number of parameters a substantial fraction of which had to be estimated for the human model without support of data for methyl iodide in humans. These estimates appear to be made in reasonable ways, but still they carry uncertainty. The parameter values for rats are largely obtained from direct experimental measurements.
Airphase/CFD parameters: The parameters for the airphase/CFD component of the rat were derived from an incomplete computational mesh of the rat nose -- the best that was available when the model on which this was based was developed. In particular the gas-phase mass transfer resistance for the olfactory region, which is the target tissue, was estimated based on assumptions about that region. There is also an inconsistency in how two other resistances were determined and are implemented in the model. The rat nasal CFD model has since been completed, and we recommend that the set of CFD parameters for the rat be updated based on the current model, and/or the code be corrected for the application of theparameters for the vestibule and pharynx. There are also now several human computational meshes that could be used to characterize the variability in the human CFD parameters. These activities, while desirable for a future version of the model, are not considered essential to deciding the acceptab ility of the current version of the model. Our simulations with the rat model indicate that the predictions are not sensitive to the CFD-derived parameters, given the current values of the other model parameters. In particular, uptake by the nasal tissues appears to be largely controlled by the mucus:air partition coefficient, estimated diffusivity of iodomethane in mucus and tissue, and the estimated thickness of the mucus and epithelium.
Nasal tissue metabolism-related parameters: In the absence of human pharmacokinetic studies, extrapolation to humans of a relatively simple model for a volatile organic like iodomethane can be addressed by using human physiological/anatomic parameters (e.g., blood flow rates and tissue volumes), in vitro measurements of at least human blood:air partition coefficients for the chemical (because tissue:air partition coefficients are considered to be relatively similar across species), and in vitro/in vivo extrapolation of human and rodent metabolism (e.g., cytosolic glutathione transferase metabolism). These approaches were used to some degree in this model, but no in vitro measurements were made using human nasal tissue, so rat values were assumed for human glutathione transferase activity.
Due to the role of glutathione (GSH) as a cofactor and the proposal that depletion of GSH be used as a dose metric, this model also has biochemical parameters (i.e. GSH turnover rates in tissues) that are not chemical specific. For rats, values for GSH concentrations in tissues and turnover rate constants were obtained from the literature or estimated from data collected for the development of the model. For humans, some values for GSH concentrations in tissues were obtained from the literature, but others were assumed the same as rats. Turnover rate constants were assumed the same in rats and humans.
Sensitivity analysis found, not surprisingly, that model predictions of olfactory GSH depletion were sensitive to the glutathione concentration and turnover rate in the olfactory epithelium along with the parameters for glutathione transferase metabolic activity (see Table 10 in Sweeney et al., 2005). The most sensitive parameter was the glutathione concentration, which is based upon measured values reported in the literature for humans and rats. The turnover rate and glutathione transferase metabolic parameters (Vmax, Km) use experimentally determined values for rat and assume the same values for humans. We noted this issue in the Jan. 20, 2005 memo and responses were provided in a memo from Lisa Sweeney and Mike Gargas dated February 22, 2005. The responses were carefully reviewed. They include a citation to a very useful new study (Shokeer et al 2005) and discussion of a number of ways to try to estimate human nasal glutathione transfer activity for methyl iodide metabolism. These various estimates indicate that the estimates based upon the rat are likely a modest overestimate of human metabolic capability s upporting the original parameterization. Shokeer et al., 2005 mutated a critical amino acid in the human glutathione transferase T1-1 from tryptophan to arginine, which is the amino acid found in the rodent protein. This resulted in a 13-fold increase in the specific activity of the enzyme and a 2.7-fold increase in the catalytic efficiency. Therefore, if the concentration of enzymes were the same in human and rat nasal tissues, the enzymatic activity would be at least 3-fold lower in humans (and the HEC would be increased, though not necessarily in direct proportion). While we judge the current parameterization to be acceptable for use at this time, measurement of methyl iodide conjugation in human nasal tissues would be desirable in future iterations of the model. Human nasal tissues are not readily available, but special arrangements can be made as evidenced by in vitro studies with other chemicals (Frederick et al., 2002).The information on GSH turnover across species is far less compelling, so it is unclear if there is a consistent relationship in GSH concentrations across species. Potter and Tran (1995) show in Figure 2 of their paper a correlation of GSH concentration with GSH half-lives, though the correlation is not a strong one and some efforts to reproduce this figure have been unsuccessful (memo dated April 29, 2005). There are additional antioxidants present in nasal tissue and urea appears to play a larger role in humans than in rats, so this might be a significant factor increasing protection in the humans but not incorporated in the model as described in the memo of February 22, 2005. The GSH turnover and glutathione transferase metabolic rate work in opposite directions, so the use of a relatively high rat GSH turnover and a relatively high rat glutathione transferase-mediated metabolism rate would be expected to essentially cancel each other out. However, this indicates the importance of having better data on both parameters, not just one, to strengthen the model parameterization for humans. To fairly consider the proposed model, one should also ask how the assumptions regarding metabolic constants compare to the assumptions implicit in the default method if the model wasn't used, rather than evaluating them alone. Since the Category 1 default doesn't address metabolism at all, it effectively assumes it to be identical across species, the same assumption currently made in the modeling.
Model simulation of rat data: Figure 9 in Sweeney et al., 2005 illustrates the simulation of the rat glutathione depletion data during and following methyl iodide exposure at two concentrations. There are no universally accepted statistical methods for characterizing the quality of PBPK model simulations of experimental data. Visual examination indicates that the simulations are reasonable. The acceptable quality of this fit was achieved by changing the value of KDGSHOE, the glutathione turnover rate constant in the olfactory epithelium from the published value of 0.016 hr-1 (Potter et al., 1995) to a visually fitted value of 0.19 hr-1 as described in the report. Olfactory epithelial GSH depletion measured during and after the 25 ppm methyl iodide exposure is well simulated with this value, though depletion at 100 ppm is overpredicted. The good description of the data at 25 ppm is particularly relevant for simulating the NOAEL conditions in the longer duration toxicity studies.
Proposed Risk Assessment Application It is proposed to calculate acute HECs for the rat nasal toxicity based upon the reduction in concentration of GSH in the ethmoid olfactory tissue stack (CGSHEO) in the rat and the olfactory tissue in human (CGSHDO). Nasal damage was only observed in the olfactory epithelium and not in the respiratory epithelium leading to the localization of the proposed dose metric. (Note that pathologically the regenerating tissue in the olfactory epithelium was referred to as having respiratory epithelial characteristics, but this is distinct from damage occurring in the regions of the nose normally consisting of respiratory epithelium.) The basis for the damage occu rring specifically in the olfactory epithelium is not entirely known.
In contrast to a dose metric directly describing the presence or metabolism of methyl iodide, GSH depletion represents an event in the toxicodynamic process leading to the toxicity arising from methyl iodide metabolism. Nasal toxicity is not believed to arise from GSH depletion alone but rather from a combination of depletion with other damage such as methylation of cell macromolecules and other constituents by the parent compound, formation of reactive metabolites, or oxidative stress (Chamberlain et al., 1998a). Direct evidence for the involvement of GSH as a protective factor against methyl iodide toxicity was obtained by pre-treating animals with substances that deplete or replenish GSH (Chamberlain et al., 1998b). The extent of glutathione depletion consistent with observing toxicity is not clearly defined (nor necessarily the same for every tissue and toxicity). Depletion 50% or greater from normal levels tends to be associated with toxicity, for example as described for effects of propylene oxide in rat nasal tissues (Lee et al., 2005). Lesser depletion appears to reflect the role of GSH in limiting the extent ofdamage. This is consistent with the 35% depletion observed in nasal tissues of rats exposed to 25 ppm methyl iodide, approximating the 21 ppm NOAEL for olfactory lesions, which the model predicted to give 30% depletion (Table 1).
Table 1: Model predictions for the NOAEL and LOAEL in the rat subchronic toxicity study (Kirkpatrick, 2002). Modeled using mei2epa7-11-05.csl by implementing the procedure "rat025" in the CMD file and setting TSTOP=6, TCHNG=6, and CONC=21 or 70.

CONC (ppm) (hr duration) TIME (hr) CGSHEO %GSH depletion (% remaining)
RAT 0 0.00000 2.35000 0 (100) 21 ppm 6 hr 6.00000 1.64934 30 (70) 70 ppm 6 hr 6.00000 0.450259 81 (19)
One important complication in developing HECs for an acute biochemical event is that the time profile of the exposure concentration is important. This is particularly obvious with GSH depletion because GSH synthesis is modeled as the constant or zero-order rate required to obtain the measured GSH concentration in the tissue (assumed to represent a steady-state concentration) given the first order basal GSH elimination rate (estimated from data for rats and assumed the same in humans). This is a standard approach for modeling steady state turnover. A consequence, however, is that the maximum GSH depletion achieved is dependent upon the rate of delivery of the methyl iodide to the nasal tissue. Because there was not a simple way to link the exposure modeling using the PERFUM model, with the PBPK modeling, the issue is illustrated with examples in Table 2.
Table 2: Simulation of GSH depletion in human dorsal olfactory epithelium for constant continuous inhalation (Scenario 1) and variable continuous inhalation (Scenarios 2 and 3). All scenarios result in an average exposure of 2.9 ppm for 24 hr. Time and GSH concentrations (CGSHDO) were obtained using the PBPK model.

CONC (ppm) (hr duration) TIME (hr) CGSHDO %GSH depletion (% control GSH) Pre-Exposure 0 0 0.800000 0 (100)
Scenario 1 2.9 (24 hr) 24 0.598396 25 (75)
Scenario 2 5.9 (0-4 hr) 4 0.59205 26 (74) 2.9 (4-12 hr) 12 0.59328 26 (74)CONC (ppm) (hr duration) TIME (hr) CGSHDO %GSH depletion (% control GSH) 1.93 (12-24 hr) 24 0.65562 18 (82)
Scenario 3 8.7 (0-4 hr) 4 0.4942 38 (62) 2.9 (4-8 hr) 8 0.5419 32 (68) 1.45 (8-24 hr) 24 0.6889 14 (86)

The scenarios in Table 2 were selected to illustrate the issues arising from different time profiles that all give an average HEC=2.9 ppm over 24 hours. The scenarios were intended to approximate the release characteristics from treated fields (i.e., higher concentrations earlier and lower concentrations at later times) absent modeled exposure concentrations as outputs from PERFUM.
The HECs that resulted in 25% GSH depletion were estimated for a constant continuous exposure lasting 8 hours (worker scenario) or 24 hour (bystanderscenario). These values are 2.9 ppm for 24 hr and 3.7 ppm for 8 hr exposures. The selection of 25% GSH depletion for the constant continuous exposure was felt to provide reasonable protection for exposure scenarios that would involve higher concentrations dropping off over time as appear likely for the proposed use of this compound as a soil fumigant. Such scenarios might result in GSH depletion between 25 and 50%. Due to the proposed application of an uncertainty factor of 3 for human pharmacokinetic variability, greater than 95% of the population is predicted to have significantly less GSH depletion. The exact percentage of the population that might experience GSH depletion towards 50% cannot be estimated at this time because it is dependent on modeling both the time course for methyl iodide inhalation (which is dependent upon the emissions from the treated field and meteorological conditions) and human variability. It is possible that linking the exposure and pharmacokinetic modeling could demonstrate that appropriate protection is obtained with different buffer zones than those estimated in PERFUM using an 8- or 24-hour average HEC.
As described previously, there are several factors that could result in lower estimates of GSH depletion for humans for a given exposure, notably the lower specific activity of the human glutathione transferase T1-1 for methyl iodide and the greater importance of other antioxidants in human nasal tissues. It is also worth noting that these HECs are significantly more protective than the HECs that would be derived using the default RfC methodology for a Category 1 gas.
The model can also be used to predict the GSH depletion at the NOAEL and LOAEL in the chronic rat toxicity study ("24-Month Inhalation Combined Chronic Toxicity/Carcinogenicity Study of Iodomethane in Rats." MRID 46512401). These predictions are shown in Table 3.
Table 3: Model predictions for the NOAEL and LOAEL in the rat chronic toxicity study. Modeled using mei2epa7-11-05.csl by implementing the procedure "rat025" in the CMD file and setting TSTOP=6, TCHNG=6, and CONC=20 or 60.CONC (ppm) (hr duration) TIME (hr) CGSHEO %GSH depletion (% remaining)
RAT
0 0.00000 2.35000 - 20 ppm 6 hr 6.00000 1.68223 28 (72) 60 ppm 6 hr 6.00000 0.623069 74 (26)
The human model was run for conditions of continuous constant exposures to methyl iodide at a range of concentrations. The extent of depletion is of course dependent upon the exposure duration (e.g., 8 or 24 hr), but is linear as a function of methyl iodide concentration until very high GSH depletion has occurred. Therefore, model outputs were fitted with straight lines in EXCEL to obtain equations to predict the equivalent of the NOAEL and LOAEL for the rat toxicity study. The HEC values were reconfirmed by running them in the PBPK/PD model.
Table 4: HECs predicted with human PBPK model. Modeled using mei2epa7-11-05.csl by implementing the procedure "preghum" in the CMD file and setting TSTOP=8 or 24, TCHNG=24, and CONC = value in HEC column.

Acute Effects (24 or 8 hr exposure) HEC (ppm) 24 hr (25% GSH depletion) 2.9 24 hr (30% GSH depletion) - NOAEL (Kirkpatrick, 2002) 3.4 24 hr (81% GSH depletion) - LOAEL (Kirkpatrick, 2002) 10

8 hr (25% GSH depletion) 3.7 8 hr (30% GSH depletion) - NOAEL (Kirkpatrick, 2002) 4.5 8 hr (81% GSH depletion) - LOAEL (Kirkpatrick, 2002) 13

Chronic Effects (24 or 8 hr exposure) 24 hr (NOAEL - 28% GSH depletion) 3.2 24 hr (LOAEL - 74% GSH depletion) 8.9

8 hr (NOAEL - 28% GSH depletion) 4.2 8 hr (LOAEL - 74% GSH depletion) 11.73. PREGNANCY MODELING
Model Purpose Modeling of methyl iodide metabolism to iodide during rabbit and human pregnancy was proposed to obtain an HEC for the acute risk assessment of the effects observed following in utero exposure. The proposed mode of action involves production of iodide and disruption of the developing fetal thyroid due to excessively high iodide levels in rabbits. The proposed dose metric is the area under the concentration curve for fetal serum inorganic iodide during a single day exposure. This dose metric would account for differences in fetal distribution of iodide that might exist between rabbits and humans. Some analysis with a two-day fetal serum AUC as the dose metric from the rabbit study has also been described. An alternative dose metric is the maternal AUC, which would assume that the distribution to the fetus was similar in rabbit and humans.

3.1. Rabbit Model
Model Structure and Biological Characterization The rabbit model has versions with different structures for the parent compound in the doe; the same description is used for fetal methyl iodide and maternal and fetal iodide. The full model has the extensive nasal tract description described above for the rat and human models (model code file: mei2epa.csl). Simulations with a simplified model in which the nose was eliminated so all absorption was alveolar produced very similar predictions for serum iodide levels (see Fig 4 in Sweeney et al., 2005) (model code file: meinoepa.csl). It is unclear the degree to which the models differ in predictions of serum levels of parent methyl iodide, but that is not important for this dose metric. For purposes of predicting iodide levels, the simplified model provides computational efficiencies. Finally, an iodide only model with maternal and fetal submodels was used to simulate the intravenous iodide dosing data (model code file: bunny.csl).
The pregnancy model is appropriate for describing a short period of pregnancy, perhaps a day or two, because it does not describe the complex changes during the course of gestation (e.g., growth of the fetus and its organs). Given the short duration of the critical window in the rabbits (gestational days 23 - 26 or some portion of that), the model can be appropriately calibrated for a relevant period, generally one day, using fixed parameter values to estimate dose metrics. This period is relatively late in rabbit gestation, so the model assumes the same structure for the fetal submodels as used for the maternal submodels (except for the elaborated maternal nasal description in the full version) with the addition of placental compartments to interface the fetal and maternal submodels.
Mathematical Descriptions and Computational Implementation The model is coded in ACSL version 11.8 (Aegis Technologies, Huntsville, AL), though with limited editing it can be run in acslXtreme version 1.4, a more current version of the software. In ACSL, the code for the model provides a complete listing of the equations in the CSL file; parameter values are specified in the CMD file. The equations have previously been described in publications cited in Sweeney et al., 2005, particularly the peer-reviewed publicatio ns on iodide from the group of authors including R.A. Clewell and EA Merrill (including Clewell et al. 2001; Merr ill et al., 2005 and others). The equations and computer implementation for the pregnancy modeling were only screened, assuming that they were consistent with the previous modeling published in peer-reviewed journals.Parameter Analysis and Model Fit The rabbit parameter values are intended to be representative of the pregnant animal during the critical window (gestational days 23 - 26) in the toxicity study. Much of the pharmacokinetic data collected for methyl iodide or inorganic iodide in the rabbits also used pregnant rabbits during this gestational period. Studies of the nasal tract and methyl iodide extraction used nonpregnant female rabbits. Because nasal toxicity was not evaluated in the rabbits, the similarity of iodide serum predictions in the model without the nose compartments and limited resources, the parameter values and model fits for the nasal extraction were not reviewed.
The intravenous dosing with inorganic iodide was carried out to obtain iodide-specific parameter values for the doe and fetus. The data were generally well simulated. The simulations show substantial accumulation of iodide in the mammary gland for which no data were available to validate that prediction. Furthermore, simulation of the maternal and fetal serum iodide concentrations following methyl iodide exposures (20 or 25 ppm 6 hr/day for 4 days) required adjusting the iodide parameters estimated from the intravenous inorganic iodide exposures resulting in a poorer simulation of the intravenous data. The data from methyl iodide exposures were appropriately considered the more critical to simulate, so the adjusted parameters were used in subsequent analyses.
There are a large number of parameters in the model (even without the nose) and the sensitivity analysis for the maternal and fetal serum iodide AUC was reported in a memo dated May 23, 2005 (MRID 46559303). A relatively few parameters reflect the delivery of methyl iodide, while the majority are associated with inorganic iodide. Reasonable simulations of the maternal and fetal serum iodide following methyl iodide exposures are obtained using the parameter values so in total the calibration appears reasonable, though it is not clear which individual parameters are significantly constrained by the data.

3.2. Human Model
Model Structure and Biological Characterization The model structure and characterization of the biology is the same for the human and the rabbit except that only a full version of the model with the complete nasal tract was used.
Mathematical Descriptions and Computational Implementation As previously noted, the full model (model code file: mei2epa.csl) is used to simulate humans.
Parameter Analysis and Model Fit The human period corresponding to the rabbit developmental window for toxicity is described as gestation week 18. Due to the large number of parameters in the model, the focus of the evaluation was on those to which the dose metric was most sensitive. The results of the sensitivity analysis for serum iodide AUC in the mother and fetus was described in a memo dated May 23, 2005 (MRID 46559303). Fewer parameters are identified as important and except for three (fraction blood flow to the liver, the affinity and maximum capacity of the sodium-iodide symporter in the thyroid follicle), the parameters are the same as found in the rabbit sensitivity analysis. The differences in parameters determining the serum iodide AUC between species likely reflect some combination of the higher shorter exposure used in the rabbit analysis versus lower continuous human exposure along with potentially some interspecies differences.
For both the rabbit and human sensitivity analyses, placental parameters are important determinantsof the fetal serum iodide AUC. These parameters are: the placenta:plasma partition coefficient for iodide; the permeability-area product diffusion constant for pl acental tissue; the affinity constant and maximum capacity of the placental sodium-iodide symporter; and the iodide transfer rates between the mother and the fetus. While for the rabbit there are direct data on fetal and maternal serum levels, no such data exist for humans. The human iodide parameter values reflect a combination of published parameters from iodide modeling of the adult human (Merrill et al., 2005), scaling parameters based upon differences between male and pregnant female rats, and parameter values reported in preliminary human modeling (Clewell et al., 2001). Maternal and fetal iodide data are available for a number of tissues, particularly maternal serum, amniotic fluid, and maternal and fetal thyroid (Clewell et al., 2001, Aboul-Khair et al., 1966). These data likely bound maternal and fetal serum iodide levels, but that has not been directly demonstrated using the current set of parameter values.
Proposed Risk Assessment Application The proposed application of the model is to derive a human HEC based upon the rabbit study in which late term resorptions were observed (Gargas et al., 2005). The proposal is to use the fetal serum iodide AUC as the dose metric. This dose metric would reflect cross-species differences that might exist in the maternal-fetal distribution of iodide. An alternative possibility would be to use the maternal serum iodide AUC, which would assume fetal distribution was similar across humans and rabbits. This alternative might be appropriate because it would be more strongly supported by the available adult human iodide modeling (Merrill et al., 2005) and would not be dependent upon the placental transfer parameters described above, which vary in the degree to which they are supported.
Therefore, the issue becomes whether there are differences between human and rabbits in maternal versus fetal serum iodide concentrations. Differences have been reported between rabbits, guinea pig, and sheep versus rats indicating the former species have higher fetal than maternal serum levels (Logothetopoulos and Scott, 1965; Roti et al., 1983; McGuire and Berman, 1978). Similar differences are observed in the submitted intravenous iodide and the methyl iodide exposures of rabbits in which iodide concentrations in fetal serum are approximately 3-fold higher than in maternal serum. Rats, in contrast, appear to have similar maternal and fetal serum levels (Logothetopoulos and Scott, 1965).
As noted previously, no references were identified with comparative maternal and fetal serum data for humans in contrast to the relatively well documented information about the development of the fetal thyroid to accumulate iodide and synthesize thyroid hormones. A number of review articles in the biomedical literature describe the perspectives of their authors on serum levels. Roti et al. (1983) indicate that humans do not appear to have the same placental ab ility to transfer and concentrate iodide in the fetus as sheep or rabbits. Iodide concentrations in fetal blood are indicated to be 75% of maternal levels, but no references are cited in Gorman (1999) and an incorrect citation is provided in Pauwels et al., 1999. Another review indicates that the extent of iodide exchange between mother and fetus is uncertain (Glinoer, 1997). Concerns about fetal exposure to radioactive iodide either from treatment of maternal thyroid conditions, which are now considered to be contraindicated during pregnancy, or from environmental exposures, have led to a substantial literature including mathematical models to estimate fetal radiation dosimetry. This literature tends to focus on the thyroid and iodide-containing thyroid hormones, which does not necessarily require modeling even maternal serum (Russell et al., 1997). A recent paper estimated human fetal doses from radioiodine using tissue distribution based upon data theauthors collected in guinea pigs (Millard et al., 2001). They argue that their previous work has demonstrated the guinea pig is a good model for human placental transfer. This model predicts fetal serum levels approximately 3-times maternal levels (i.e., similar to those measured for the rabbits exposed to methyl iodide).
Finally, the human placenta is described as freely permeable to iodide (e.g., Fisher 1997) while a recent review indicates that it is unresolved whether it is passive transfer or an active pump (Glinoer, 1997). One possib ility would be that fetal and maternal serum could have similar physical chemical properties so given a placenta that was freely permeable the concentrations would be similar in the fetal and maternal serum. Since iodide is a charged ion, it is not clear how it crosses the placenta though the sodium/iodide symporter has been found in specific placental cell types (Bidart et al., 2000). It is also possible that other anion transporters are involved. Finally, it should be noted that statements about iodide placental transport may largely reflect a contrast to the situation for thyroid hormones (thyroxine or T4 and triiodothyroxine or T3). The placenta has very active deiodinase enzymes, which dramatically limit the distribution of maternal T4 and T3 to the fetus. It should also be noted that much of the focus of biomedical publications is on iodide insufficiency as opposed to the potential for iodide excess that is the focus here.
The limited and often indirect information available suggest that human fetal serum likely does not have as high concentrations relative to maternal serum as are observed in a number of animal species. Unfortunately, the information is too limited and indirect to accurately identify what the relative concentrations would be. The HEC of 17 ppm modeled using the assumption that human fetal serum levels are 75% of maternal levels likely represents a high or perhaps reasonable estimate, while the HEC of 4 ppm based upon assuming equivalent distribution to fetal serum in rabbits and humans likely represents a lower bound above which the "true" value would be found. The HEC values were obtained by: 1) estimating the AUC for maternal (aucca_i in the model) or fetal (auccaf_i in the model) serum iodide using the rabbit parameterization of the model at the NOAEL of 10 ppm methyl iodide exposure for 6 hours, and 2) running the human parameterization of the model to obtain the 24-hr constant continuous exposure concentration matching that maternal or fetal AUC. The bulk of the biomedical literature suggests that within the range of these estimates (i.e., 4 to 17 ppm); the "true" value is likely towards the higher part of the range. But, there does not appear to be adequate information to establish that a particular value in the higher part of that range is the most accurate value.

4. HUMAN VARIABILITY MODELING
Analyses of human variability for relevant dose metrics, e.g. nasal GSH depletion, fetal serum iodide were undertaken. Monte Carlo simulations used estimates of the distribution of values for parameters with normalized sensitivity coefficients the absolute value of which were greater than or equal to 0.05 (for the given dose metric being evaluated). Even with this limitation, there are a large number of parameters and it would require substantial effort to evaluate the documentation of the distributions (i.e. citation of publications) supporting the choices made. The analyses provide some useful perspective on the default uncertainty factor (UF) of 3.0 for human pharmacokinetic variability s uggesting that it is a reasonable value. It is important to note that concerns exist for all endpoints to insure that life stages (e.g., infants, children, aged) and potential susceptible subpopulations are protected. Life stages differ from susceptible subpopulations because all of the population over time may go through a given life stage, while a susceptible subpopulation may represent only a portion of the population (see Section 3.5 in US EPA 2005).The following comments identify issues with the distributions used that might increase or decrease the resulting estimated variability.
• CV estimates for parameters were obtained in a variety of ways. Those based upon experimental measurements tend to overestimate the variability b ecause experimental error was generally not separated from true variability. This is likely to remain a limitation that rarely can be addressed.
• Matching appropriate parameter CVs with the toxicological endpoint and population is not necessarily straightforward or easily documented. Where appropriate, analyses need to include multiple life-stages (e.g., adults, children, pregnant women). o Analyses for the nasal dose metric were carried out for adults and additional analyses evaluated children's variability for a subset of parameters related to the CFD/airflow aspect of the model (memo dated March 29, 2005, MRID #46559301). Analyzing children's variability for the full range of parameters (including metabolic parameters) would be needed to understand variability during that life-stage. It also should be noted that these analyses were carried out using CGSHEO, which is inappropriate for humans, so it is not clear whether the resulting distributions would be the same for the human dorsal olfactory GSH levels (CGSHDO). o For the developmental endpoint, the analysis considered the maternal variability for serum levels of iodide; placental and fetal variab ility would largely require assumptions about the distributions and likely lead to a somewhat higher estimate of variability.
• The report and memos document the sources of all the CVs used in the analysis, but it would require checking each one to understand whether it appears to appropriately estimate human variability. This was not done. Many CVs were based upon the estimates in Allen et al., 1996 which used CVs of 0.15 and 0.3 for parameter considered to have lesser or greater variability (see justification of values in the paper). It is notable that several of the key metabolic parameters are estimated to have larger CVs than 0.3. The extent to which this reflects limitations of propagating error in multiple terms to estimate the metabolic parameters or that the original justification resulted in too small a CV for highly variable parameters is unclear. The authors have made a substantial effort to evaluate human variability, but it isn't easy to draw conclusions about all the distributions used.
• The analysis for nasal effects does not include a description for the portion of the population that does not express the glutathione transferase theta-1 form involved in methyl iodide metabolism. To the extent that the nasal toxicity is dependent upon GSH depletion, these people would be expected to be at decreased risk due to their inability to carry out GSH-dependent metabolism of methyl iodide.
Finally, the portion of the population upon which to evaluate the variability is an important issue. The report and memos use the 95th-percentile. However, this leaves 1 in 20 people outside the range covered by such a factor accounting for human variab ility. If it were considered appropriate to only have 1 in 100 or 1 in 10,000 estimated outside the covered range of the distribution, a larger value would be needed. The reports and memos do not describe the values needed to be protective for higher percentiles of the populations. Thus, these analyses suggest that the default UF of 3.0 is likely protective of a higherpercentile of the population than the 95th-percentile, perhaps protective of the 99th or even 99.9th percentiles. Basing the UF directly on the human PK variability modeling would be desirable, but information characterizing the population distribution overall were not available to permit the Agency to decide the appropriate percentile and value.

5. MODEL DOCUMENTATION
Model documentation is intended to communicate the modeling clearly so the risk assessment using the model is transparent and reproducible. Documentation includes a report providing an overall description of the modeling and its results, documentation of parameter values, the computer implementation of the model equations (called CSL files in ACSL), and procedures (coded in CMD files in ACSL).
Modeling Results Replication: Procedures that permit the reproduction of all essential model simulations should be provided in the computer code (the CMD file for ACSL version 11.8). These procedures would permit easy replication of 1) simulations of data used to calibrate or validate the model, 2) toxicity study exposures to estimate internal dose metrics in animals, 3) human exposures to obtain internal dose metrics, and 4) other specialized procedures such as sensitivity analyses. For example, the calculation of the AUC for iodide in the maternal and fetal serum is incompletely documented, which a procedure in the CMD file could easily remedy. The relevant HECs are reported but the actual AUCs and the methods used to obtain them are not described (e.g., duration over which the AUC was calculated). The human nasal modeling reasonably used the pregnant human parameterization of the model, but this was not specified.
Model parameters: Model parameters are described in Sweeney et al., 2005. In addition, they are specified in the CMD files for the models. Reviews of the model parameters found that it was difficult to confirm values in cited references, partly due to conve rsions made from the original references values. It is recommended that for those parameters that have significant normalized sensitivity coefficients (>0.1 or <-0.1 as described in Sweeney et al., 2005), a clear and detailed explanation of how they were estimated or derived should be presented in an Appendix or other supporting document so the reader knows exactly how the values in the model were obtained from values presented in publications. Since the sensitivity coefficients can vary with the exposure conditions and species, ideally those parameters with significant sensitivity for simulating the animal point of departure (e.g., LOAEL, NOAEL, BMD) in the toxicity study as well as those parameters with significant sensitivity for estimating the human equivalent concentration should be fully documented. Note that such sensitivity analyses were reported in memos for the rabbit and human maternal and fetal serum iodide, but only for the human for the nasal glutathione depletion.

Cc: Dr. Robert Kavlock Dr. Jerry Blancato Dr. Paul White Dr. Jack Fowle Dr. Mike Metzger5. REFERENCES
Aboul-Khair SA, Buchanan TJ, Crooks J, Turnbull AC. Structural and functional development of the human foetal thyroid. Clin Sci. 1966 Dec;31(3):415-24 Bidart JM, Lacroix L, Evain-Brion D, Caillou B, L azar V, Frydman R, Bellet D, Filetti S, Schlumberger M. Expression of Na+/I- symporter and Pendred syndrome genes in trophoblast cells. J Clin Endocrinol Metab. 2000 Nov;85(11):4367-72 Chamberlain MP, Lock EA, Reed CJ. Investigations of the pathways of toxicity of methyl iodide in the rat nasal cavity. Toxicology. 1998a Aug 21;129(2-3):169-81 Chamberlain MP, Lock EA, Gaskell BA, Reed CJ. The role of glutathione S-transferase- and cytochrome P450-dependent metabolism in the olfactory toxicity of methyl iodide in the rat. Arch Toxicol. 1998b Jun;72(7):420-8. Clark LH, Setzer RW, Barton HA. Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment. Risk Anal. 2004 Dec;24(6):1697-717 Clewell RA, Merrill EA, Robinson PJ. The use of physiologically based models to integrate diverse data sets and reduce uncertainty in the prediction of perchlorate and iodide kinetics across life stages and species. Toxicol Ind Health. 2001 Jun;17(5-10):210-22 D'Souza RW, Francis WR, Andersen ME. Physiological model for tissue glutathione depletion and increased resynthesis after ethylene dichloride exposure. J Pharmacol Exp Ther. 1988 May;245(2):563-8 Fisher DA. Fetal thyroid function: diagnosis and management of fetal thyroid disorders. Clin Obstet Gynecol. 1997 Mar;40(1):16-31 Frederick CB, Lomax LG, Black KA, Finch L, Scribner HE, Kimbell JS, Morgan KT, Subramaniam RP, Morris JB. Use of a hybrid computational fluid dynamics and physiologically based inhalation model for interspecies dosimetry comparisons of ester vapors. Toxicol Appl Pharmacol. 2002 Aug 15;183(1):23-40 Frederick CB, Bush ML, Lomax LG, Black KA, Finch L, Kimbell JS, Morgan KT, Subramaniam RP, Morris JB, Ultman JS. Application of a hybrid computational fluid dynamics and physiologically based inhalation model for interspecies dosimetry extrapolation of acidic vapors in the upper airways. Toxicol Appl Pharmacol. 1998 Sep;152(1):211-31 Gargas, M.; Sweeney, L.; Kirman, C. (2005) Weight of Evidence for Evaluation of the HEC for Acute Developmental Toxicity of Methyl Iodide. Project Number: 34501. Unpublished study prepared by Sapphire Group, Inc. 274 p. MRID #46593801. Glinoer D. The regulation of thyroid function in pregnancy: pathways of endocrine adaptation from physiology to pathology. Endocr Rev. 1997 Jun;18(3):404-33 Gorman CA. Radioiodine and pregnancy. Thyroid. 1999 Jul;9(7):721-6 Logothetopoulos J, Scott RF. Active iodide transport across the placenta of the guinea-pig, rabbit and rat. J Physiol. 1956 May 28;132(2):365-71 McGuire RA, Berman M. Maternal, fetal, and amniotic fluid transport of thyroxine, triiodothyronine, and iodide in sheep: a kinetic model. Endocrinology. 1978 Aug;103(2):567-76 Merrill EA, Clewell RA, Robinson PJ, Jarabek AM, Gearhart JM, Sterner TR, Fisher JW. PBPK model for radioactive iodide and perchlorate kinetics and perchlorate-induced inhibition of iodide uptake in humans. Toxicol Sci. 2005 Jan;83(1):25-43 Millard RK, Saunders M, Palmer AM, Pr eece AW. Approximate distribution of dose among foetal organs for radioiodine uptake via placenta transfer. Phys Med Biol. 2001 Nov;46(11):2773-83.Pauwels EK, Thomson WH, Blokland JA, Schmidt ME, Bourguignon M, El-Maghraby TA, Broerse JJ, Harding LK. Aspects of fetal thyroid dose following iodine-131 administration during early stages of pregnancy in patients suffering from benign thyroid disorders. Eur J Nucl Med. 1999 Nov;26(11):1453-7 Potter DW, Finch L, Udinsky JR. Glutathione content and turnover in rat nasal epithelia. Toxicol Appl Pharmacol. 1995 Dec;135(2):185-91 Roti E, Gnudi A, Braverman LE. The placental transport, synthesis and metabolism of hormones and drugs which affect thyroid function. Endocr Rev. 1983 Spring;4(2):131-49 Russell JR, Stabin MG, Sparks RB. Placental transfer of radiopharmaceuticals and dosimetry in pregnancy. Health Phys. 1997 Nov;73(5):747-55 Sweeney, L.; Kirman, C.; Gargas, M. (2005) Derivation of Human Toxicity Reference Values for Methyl Iodide Using Physiologically Based Pharmacokinetic (PBPK) Modeling (Revised Report). Project Number: 34501. Unpublished study prepared by Sapphire Group, Inc. 103 p. MRID # 46446901 Sweeney, L.; Gargas, M. (2005) Age Specific HEC's for Potential Nasal Effects of MeI in Children: Supplement to (PBPK) Modeling (MRID 46446901). Project Number: 34503. Unpublished study prepared by Sapphire Group, Inc. 46 p. MRID #46559301 Sweeney, L. (2004) Revision of HEC for Nasal Effects of Methyl Iodide (MeI): Supplement to (PBPK) Modeling (MRID 46408801). Project Number: 45603. Unpublished study prepared by Sapphire Group, Inc. 68 p. MRID #46559302 Sweeney, L.; Gargas, M. (2005) Supplemental Information Regarding Human Fetal and Maternal Iodide AUC Sensitivity and a Rabbit Model Sensitivity Analysis: Supplement to (PBPK) Modeling (MRID 46446901). Project Number: 45603. Unpublished study prepared by Sapphire Group, Inc. 65 p. MRID #46559303 Sweeney, L.; Gargas, M. (2005) Revaluation of the HEC for Acute Nasal Toxicity of Methyl Iodide. Project Number: 34510. Unpublished study prepared by Sapphire Group, Inc. 417 p. MRID #46631401

# Appendix B
Toxicity ProfileIodomethane Toxicity Profile Guideline No./Study Type MRID No. (year)/Classification/Exposure Conditions Results

870.1100 Acute Oral - Rat 45593803 (2001) Acceptable/Guideline. LD 50 = 79.8 mg/kg (males); 131.9 mg/kg (females) Clinical signs: decreased activity, breathing abnormalities, salivation, nasal/ocular discharge, dark material around facial area and eyes, partially closed eyelids, pale skin, soft feces, prostration, tremors, and wobbly gait. Gross pathology: red lungs (congestion) and abnormal GI contents in decedents. The Up/Down procedure was used in SD rats. Toxicity Category II

870.1100 Acute Oral - Mouse 45593804 (2001) Acceptable/Guideline LD 50 = 155 mg/kg (males); 214 mg/kg (females) Clinical signs: urine and fecal stain, decreased food consumption, salivation, decreased or no defecation, dilated pupils, piloerection, rough hair coat, prostration, hypothermia, decreased activity, breathing abnormalities, skin blue in color over the entire body (anoxemia), hunched posture and wobbly gait. Gross pathology: red fluid in thoracic cavity, abnormal GI contents, red glandular mucosa (congestion) of the stomach, and dilated kidney pelvis in decedents. The Up/Down procedure was used in CD-1 mice. Toxicity Category II

870.1200 Acute Dermal - Rat 45593805 (2001) Acceptable/Guideline LD 50 >2000 mg/kg (limit test) There were no deaths. Clinical signs: severe dermal irritation (at 500 and 2000 mg/kg) and hemorrhage (2000 mg/kg) at the dosing site. Decreased defecation, soft stools, decreased food consumption, breathing abnormalities, and dark material around the facial area. Gross pathology: Unremarkable Toxicity Category IIIGuideline No./Study Type MRID No. (year)/Classification/Exposure Conditions Results

870.1300 Acute Inhalation - Rat 45593806 (2001) Acceptable/Guideline 581, 710, 797 or 1198 ppm LC 50 = 691 ppm = 4 mg/L (combined sexes) The test article was administered as a vapor in a dynamic whole-body chamber for 4 hours. Clinical signs: gasping, ataxia, hypoactivity, nasal discharge, labored respiration, rales, and red material around nose. Gross pathology: dark pituitary, dark red lungs, distended gas-filled and congested stomach, hemorrhagic thymus, dark red adrenal glands, and distended intestines in decedents. Toxicity Category IV
870.2400 Primary Eye Irritation - Rabbit 45593807 (2001) Acceptable/Guideline Corrosive: Corneal opacity, conjunctivitis, iritis, corneal neo-vascularization, sloughing of corneal epithelium, blanching of nictitating membrane, and corneal bulging. Toxicity Category I
870.2500 Primary Skin Irritation - Rabbit 45593808 (2001 A cceptable/Guideline Well defined erythema and blanching, slightsevere edema, lightening, extended erythema beyond the test sites and desquamation. Toxicity Category II
870.2600 Dermal Sensitization (Magnassun-Kligman Maximization Test) - Guinea Pig 45593809 (2001) Acceptable/Guideline Not a dermal sensitizer

870.3100 Subchronic Feeding - Rat Not required by the Agency

870.3100 Subchronic Feeding - Mice Not required by the Agency

870.3100 Subchronic Feeding - Mice Not required by the Agency

870.3150 Subchronic Feeding - Dog Not required by the Agency

870.3200 21-Day Dermal - Rat Not required by the Agency

870.3465 13-Week Inhalation - Rat 45593810 (2002) Acceptable/Guideline 0, 5, 21, or 70 ppm in a whole-body chamber, 6 h/day, 5 days/week for 4 weeks (interim sacrifice) or 13 weeks NOAEL = 21 ppm (0.12 mg/L/day) LOAEL = 70 ppm (0.41 mg/L/day) based on initial decreases in body weights, body weight gains, and food consumption (males); and nasal degeneration.870.3700 Inhalation Developmental Toxicity - Rat 45593812 (2002) Acceptable/Guideline 0, 5, 20, 60 ppm in a whole-body inhalation chamber, 6 h/day on GDs 6- 19. Maternal NOAEL = 20 ppm (0.12 mg/L/day) Maternal LOAEL = 60 ppm (0.35 mg/L/day) based on decreased body weight gain ( 9 19%; 9 5-6% absolute body weight). Developmental NOAEL = 60 ppm (0.35 mg/L/day) Developmental LOAEL was not observed

870.3700 Inhalation Developmental Toxicity - Rabbit 45593811 (2002) Acceptable/Guideline 0, 2, 10, or 20 ppm in a whole-body inhalation chamber, 6 h/day, on GDs 6- 28. Maternal NOAEL = 20 ppm Maternal LOAEL: Not identified Developmental NOAEL = 10 ppm Developmental LOAEL = 20 ppm based on increased fetal losses and decreased fetal weights ( 9 20%). Guideline No./Study Type MRID No. (year)/Classification/Exposure Conditions Results

Non-guidelineInhalation PhasedExposure Developmental Toxicity - Rabbit 46077001 (2003) Acceptable/non-guideline 0 or 20 ppm from GD6-28; 20 ppm from GDs 6-14, 15-22, 23-24, 25-26, or 27-28 in a whole-body inhalation chamber 6hrs/day This study was not intended to fulfill the guideline requirement or establish NOAELs and LOAELs but rather was conducted to determine the critical period of exposure during gestation that resulted in fetal loss as observed in a previously evaluated guideline developmental toxicity study in rabbits. Increased fetal losses at 20 ppm on GD 6-28 ( 8 21%), 23-24 ( 8 9%), and 25-26 ( 8 11%)

870.3800 Inhalation 2-Generation Reproductive Toxicity - Rat 45710301 (2001) Acceptable/guideline 0, 5, 20, or 50 ppm in whole body inhalation chamber Note: Offspring not directly exposed until PND 28 Parental systemic NOAEL = 20 ppm Parental systemic LOAEL = 50 ppm based on decreased body weight gain, body weight, organ weight changes, gross pathology, and histopathology findings. Portal of entry NOAEL = 20 ppm Portal of entry LOAEL = 50 ppm based on degeneration of the olfactory epithelium Offspring NOAEL = 5 ppm Offspring LOAEL = 20 ppm based on decreases in body weight gain, body weight, and thymus weights Reproductive NOAEL = 5 ppm Reproductive LOAEL = 20 ppm based on delays in vaginal patency

870.4100 Chronic Feeding Toxicity - Dog – Not required by the Agency

870.4200 Carcinogenicity Feeding - Mouse (18 months) – Not required by the Agency.Guideline No./Study Type MRID No. (year)/Classification/Exposure Conditions Results

870.4300 Chronic FeedingToxicity/CarcinogenicityRat. 46512401 (2005) Acceptable/non-guideline 0, 5, 10, 60 ppm in a whole body inhalation chamber for 6 hrs/day, 5days/week Systemic NOAEL = 5 ppm Systemic LOAEL = 20 ppm based on increased incidence of salivary gland squamous cell metaplasia. Portal of entry NOAEL = 20 ppm Portal of entry LOAEL = 60 ppm based on degeneration of the olfactory epithelium.At 60 ppm, perturbations of the thyroidpituitary axis as well thyroid histopathology findings were reported.

870.5100 Bacterial Reverse Mutation Test (Ames Assay) 45593813 (2001) Nonmutagenic in Salmonella typhimurium strains TA98, TA100, TA1535, and TA1537; and in Escherichi coli

.

870.5300 In Vitro Mammalian Cell Mutation Test in Chinese Hamster Ovary Cells 45593815 (2001) Negative

870.5375 In Vitro Chromosomal Aberration in Chinese Hamster Ovary 45593814 (2001) Pos itive for the induction of structural chromosome aberrations (clastogenesis), but negative for induction of numerical aberrations in CHO cells in this assay.

870.5395 In Vivo Micronucleus Assay in Mice 45593816 (2001) Negative

870.6200 Inhalation Acute Neurotoxicity - Rats 45593817 (2002) Acceptable/Guideline 0, 27, 93, 401 ppm whole-body, 6-hour exposure. Systemic NOAEL = 27 ppm. Systemic LOAEL = 93 ppm based on FOB findings (clonic convulsions in 1/12 females, decreased body temperature), and decreased motor activity ( 9 75-78% in males, 81-84% in females). Portal of entry effects not assessed

870.6200 Feeding Subchronic Neurotoxicity - Rats – Not required by the AgencyGuideline No./Study Type MRID No. (year)/Classification/Exposure Conditions Results

870.7485 Metabolism - Rat 45641401 (2002) Sprague-Dawley rats were orally dosed or exposed via inhalation with [ 14 C] CH 3 I. Maximum blood concentrations were achieved within 4 hours (oral) and 0-2 hours (inhalation), and were proportional to dose/concentration. Initial t 1⁄2was 5.1-7.2 hours, and terminal t 1⁄2was 116-136 hours. Radioactivity recovery was low in the main test due to inefficient CO 2trapping. Overall recovery in the supplementary test was increased due to increased recovery of carbon dioxide. Recovered radioactivity was primarily as CO 2 (39.40-60.81% dose) and in the urine (26.50- 33.40% dose) in all treated groups, while feces accounted for <2% dose. Radioactivity remained in the carcasses (11.92-14.39% dose) of all treated animals 168 hours following treatment in the main test. Elimination t 1⁄2were 17.8-22.3 hours for urine and 29.7-38.0 hours for feces in all treatment groups of the main test. The elimination t 1⁄2was 5.8-6.8 hours for CO 2in all treatment groups of the supplementary test. At 0-1 hour post-treatment in orally treated rats and 233 ppm inhalation exposed rats, relatively high levels of radioactivity were observed in the liver and GI tract. Radioactivity was relatively high in the kidney, lung, and nasal turbinates of the 25 ppm inhalation exposed rats and in the kidney, thyroid, and lung of the 233 ppm inhalation exposed rats. At 6 hours post-oral dosing, tissue concentrations increased in the spleen (at 1.5 mg/kg only), kidney, brain, thyroid, lung, nasal turbinates, and fat (at 1.5 mg/kg only). Tissue concentrations decreased in all tissues of the inhalation exposed rats at 6 hours after exposure. At 168 hours post-dose, radioactivity had declined in all tissues and was highest in the kidney, liver, and thyroid. Tissue concentrations increased (not proportionally) with dose. The major metabolites were expired CO 2 , and N-(methylthioacetyl) glycine and S-methyl glutathione which were excreted in the urine. Minor metabolites were methylthioacetic acid, methyl mercapturic acid, and S-methyl cysteine.

870.7600 Dermal Penetration - Rat – Not required by the Agency

# Appendix C Methodologies for Inhalation Risk Calculations and Human Equivalent Concentration ArraysMETHODOLOGIES FOR INHALA TION RISK CALCULATIONS
In evaluating the risks that a compound may pose to human health after exposure via the inhalation route, different methodologies have been historically used by the USEPA and the California Department of Pesticide Regulation (CDPR). The Agency’s approach to calculating risks due to inhalation exposure is based on the guidance methodology developed by the Office of Research and Development (ORD) for the derivation of inhalation reference concentrations (RfCs) and human equivalent concentrations (HECs) for use in margin of exposure (MOE) calculations (RfC methodology). An example of CDPR’s methodology , and the species-specific parameters used in this approach can be found in the CDPR methyl bromide risk assessment, Appendix G ( www.cdpr.ca.gov/docs/dprdocs/methbrom/append_g.pdf ). As OPP understands the importance to harmonize, to the extent possible, with other regulatory agencies, this risk assessment will present HECs derived using both methodologies. Furthermore, in the case of iodomethane, a chemical-specific PBPK model has been developed by the registrant and reviewed by Agency experts. Hence, the PBPK model has been used to calculate HECs for those endpoints where appropriate mechanistic data are available to identify a suitable chemical-specific dose metric. A more detailed explanation of the review of this PBPK model is available in Appendix A of this document.
The RfC methodology applies a dosimetric adjustment that takes into consideration not only the differences in ventilation rate (MV) but also the physicochemical properties of the inhaled compound, the type of toxicity observed ( e.g. systemic vs. port of entry) and the pharmacokinetic (PK) but not pharmacodynamic (PD) differences between animals and humans. Based on the RfC guidance (1994), the methodology for RfCs derivation is an estimate of the quantitative dose-response assessment of chronic non-cancer toxicity for individual inhaled chemicals and includes dosimetric adjustment to account for the species-specific relationships of exposure concentration to deposited/delivered dose. This adjustment is influenced by the physicochemical properties of the inhaled compound as well as the type of toxicity observed ( e.g. systemic vs. port of entry), and takes into consideration the PK differences between animals and humans. Though the RfC methodology was developed to estimate toxicity of inhaled chemicals over a lifetime, it can be used for other inhalation exposures ( e.g. acute and short-term exposures) since the dosimetric adjustment incorporates mechanistic determinants of disposition that can be applied to shorter duration of exposures provided the assumptions underlying the methodology are still valid . These assumptions, in turn, vary depending on the type of toxicity observed and will be discussed later on in this document. Thus the derivation of a HEC for inhaled gases is described by the following equation:

HEC = POD * D D * W W * RGDR study animal exposure (hrs / day)
human exposure (hrs / day) animal exposure (days / wk)
human exposure (days / wk)

Where:

POD study : Point of departure identified in the critical toxicology study D animal exposure : Duration of animal exposure (hrs/day; days/wk) D anticipated exposure : Anticipated human duration of exposure (hrs/day; days/wk) RGDR: Regional Gas Dose Ratio
For gases eliciting both port of entry and systemic effects, calculations to estimate the inhalation risk to humans are dependent on the regional gas dose ratio (RGDR). In the case of systemiceffects, the RGDR is defined as the ratio of the blood:gas partition coefficient of the chemical for the test species to humans (H b/g animal /H b/g human ). When this ratio is unknown or when the H b/g animal > H b/g humana default value of 1.0 is used as the RGDR. This default is based on the observation that for chemicals where partition coefficient data are available in both rats and humans the RGDR value has usually been comparable or slightly higherthan 1. Thus, the use of an RGDR of 1 results in a protective calculation of the inhalation risk. Some of the key assumptions fundamental to the use of the RfC methodology to derive a HEC based on systemic effects include:
1) all the concentrations of inhaled gas within the animal’s body are periodic with respect to time ( i.e. periodic steady state - the concentration vstime profile is the same for every week). Periodicity must be attained for at least 90% of the exposure. 2) in the respiratory tract, the air, tissue, capillary blood concentration are in equilibrium with respect to each other. 3)systemically, the blood and tissue concentrations are in equilibrium with respect to each other.
In the case of iodomethane, the physicochemical properties and metabolism data for the compound indicate that these conditions ( i.e. periodicity and equilibrium between different compartments) will be achieved in a very short period of time. Under these conditions, therefore, the use of the RfC methodology to estimate acute inhalation risk is appropriate.
When the critical toxic effect in a study occurs in the respiratory tract ( i.e port of entry effects), the RGDR is not related to the blood:gas partition coefficient of the compound but rather the ratio of the minute volume (MV) to the surface area (SA) of the affected region. In these instances, attaining periodicity or equilibrium between the compartments is not critical (since the effect is a function of the direct interaction between the inhaled compound and the affected region in the respiratory tract) and the RGDR may be calculated using the following equation:



### RGDR = MV SA MV SA animal animal
human human

Where: MV animal : Minute volume for the test species (varies depending on body weight) SA animal : Surface area of the affected region in animals MV human : Minute volume for humans (default value is 13.8 l/min) SA human : Surface area of the affected region in humans

The MV animal is calculated using the allometric scaling provided in USEPA (1988a). The equation for calculation of the MV animal is:

lnMV animal = b 0+ b 1 ln(BW)Where: ln MV animal : natural logarithm of the minute volume b 0: species specific intercept used in the algorithm to calculate minute volumes based on body weight b 1 : species specific coefficient used in the algorithm to calculate minute volumes based on body weight ln BW: natural logarithm of the body weight (expressed in kg)
The values for the species-specific parameters used to calculate the MV animal based on body weight and the values for the surface areas of various regions of the respiratory tract (extrathoracic, thoracic, and pulmonary) are provided in the EPA document “Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry” (1994).
The magnitude of the UFs applied is dependent on the methodology used to calculate risk. When using the methodology developed by CDPR, a 100X UF is applied (10X for interspecies extrapolation and 10X for intraspecies variation). In contrast, the RfC methodology and the PBPK model take into consideration the PK differences but not the PD differences. Consequently, the UF for interspecies extrapolation may be reduced to 3X (to account for the PD differences) while the UF for intraspecies variation is retained at 10X. Thus, the UF when using the RfC methodology or the PBPK model is 30X.

Hazard Assessment Array HEC Array for Non-Occupational Risk Assessment §

Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR* HEC (ppm) inter Intra UF

ACUTE EXPOSURE ACN- Rat Systemic 93 27 6 24 1 1 1 6.75 3 10 1

Dev Rat Maternal Systemic 60 20 Not Applicable; the LOAEL for the dams is based on decreases in body weight and body weight gain which are not expected to occur as the result of a single exposure Developmental Not identified 60 6 24 1 1 1 15 3 10 1

Dev Rabbit ‡ Maternal Not identified 20 6 24 1 1 1 5 3 10 1 Developmental 20 10 6 24 1 1 N.A. 4.00 3 10 1
Subchronic Inhalation Study - Rat Local 70 21 6 24 1 1 N.A. 2.90 3 10 1HEC Array for Non-Occupational Risk Assessment §

Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR* HEC (ppm) inter Intra UF

SHORT TERM EXPOSURE ACN-Rat Systemic 93 27 6 24 1 1 1 6.75 3 10 1

Devel Rat Maternal Systemic 60 20 6 24 7 7 1 5.0 3 10 1
Developmental Not identified 60 6 24 7 7 1 15 3 10 1

Dev Rabbit Maternal Systemic Not identified 20 6 24 7 7 1 5.0 3 10 1
Developmental 20 10 6 24 7 7 N.A. 4.0 3 10 1

Subchronic Inhalation Study - Rat Systemic 70 21 6 24 5 7 1 3.75 3 10 1
Local ¶ 70 21 2.90 3 10 1

MultiGen Repro: Rat Parental Systemic 50 20 6 24 7 7 1 5 3 10 1

Parental

Local ¶ 50 20 Not applicable 3.20 3 10 1
Offspring 20 5 6 24 7 7 1 1.25 3 10 1 Reproductive Effects 20 5 6 24 7 7 1 1.25 3 10 1

INTERMEDIATE TERM EXPOSURE
Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR HEC (ppm) Inter Intra UF
ACN-Rat Systemic 93 27 6 24 1 1 1 6.75 3 10 1

Devel Rat Maternal Systemic 60 20 6 24 7 7 1 5.0 3 10 1
Developmental Not identified 60 6 24 7 7 1 15 3 10 1

Dev Rabbit Maternal Systemic Not identified 20 6 24 7 7 1 5.0 3 10 1
Developmental 10 20 6 24 7 7 N.A 4.0 3 10 1

Subchronic Inhalation Study - Rat Systemic 70 21 6 24 5 7 1 3.75 3 10 1
Local ¶ 70 21 Not applicable 2.90 3 10 1

MultiGen Repro: Rat Parental Systemic 50 20 6 24 7 7 1 5.00 3 10 1

Parental

Local ¶ 50 20 Not applicable 3.20 3 10 1
Offspring 20 5 6 24 7 7 1 1.25 3 10 1 Reproductive 20 5 6 24 7 7 1 1.25 3 10 1HEC Array for Non-Occupational Risk Assessment §

Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR* HEC (ppm) inter Intra UF

LONG TERM EXPOSURE

MultiGen Repro: Rat Parental Systemic 50 20 6 24 7 7 1 5.00 3 10 1

Parental

Local ¶ 50 20 Not applicable 3.20 3 10 1
Offspring 20 5 6 24 7 7 1 1.25 3 10 1 Reproductive 20 5 6 24 7 7 1 1.25 3 10 1 Chronic/Carcinogenicit y: Rat Systemic 20 5 6 24 5 7 1 0.89 3 10 1

Local ¶ 60 20 Not applicable 3.20 3 10 1

§ Bolded studies used for endpoint selection. ‡ Italicized HECs derived from PBPK model N.A. = not applicable ¶Local effects (nasal lesions) did not progress with time ( ( i.e. nasal lesions of comparable severity were seen after 4, 13, and 52 weeks of exposure at the same concentration). Therefore, it appears that this effects is not a function of C x t thus a time a djustment is not appropriate. * Input parameters for the derivation of RGDRs were obtained from “Methods for Derivation of Inhalation Reference Concentration s and Application of Inhalation Dosimetry” (USEPA, 1994) Tables 4-4, 4-5, and 4-6.

Key for Array Table LOAEL: Lowest observed adverse effect level NOAEL: No observed adverse effect level Da: Daily animal exposure (hrs/day) Dh: Anticipated daily human exposure (hrs/day) Wa: Weekly animal exposure (days/week) Wh: Anticipated weekly human exposure (days/week) RGDR: Regional Gas Dose Ratio HEC: Human Equivalent Concentration inter: interspecies extrapolation uncertainty factor intra: intraspecies variation uncertainty factor UF: Other uncertainty factor(s)HEC Array for Occupational risk assessments §

Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR* HEC (ppm) inter Intra UF

ACUTE EXPOSURE ACN-Rat Systemic 93 27 6 8 1 1 1 20.25 3 10 1

Dev Rat Maternal Systemic 60 20 Not Applicable; the LOAEL for the dams is based on decreases in body weight and body weight gain which are not expected to occur as the result of a single exposure
Developmental Not identified 60 6 8 1 1 1 45 3 10 1

Dev Rabbit ‡ Maternal Systemic Not identified 20 6 8 1 1 1 15 3 10 1

Subchronic Inhalation Study - Rat Local 70 21 6 8 1 1 N.A. 3.70 3 10 1

SHORT TERM EXPOSURE ACN-Rat Systemic 93 27 6 8 1 1 1 20.25 3 10 1

Dev Rat Maternal Systemic 60 20 6 8 7 7 1 15 3 10 1

Developmental Not identified 60 6 8 7 7 1 45 3 10 1

Dev Rabbit Maternal Systemic Not identified 20 6 8 1 1 1 15 3 10 1

Subchronic Inhalation Study - Rat Systemic 70 21 6 8 5 5 1 15.75 3 10 1
Local ¶ 70 21 Not applicable 3.70 3 10 1

MultiGen Repro: Rat Parental Systemic 50 20 6 8 5 5 1 15 3 10 1

Parental

Local ¶ 50 20 Not applicable 4.20 3 10 1
Offspring 20 5 6 8 5 5 1 3.75 3 10 1 Reproductive 20 5 6 8 5 5 1 3.75 3 10 1
INTERMEDIATE TERM EXPOSURE ACN-Rat Systemic 93 27 6 8 1 1 1 20.25 3 10 1

Devel Rat Maternal Systemic 60 20 6 8 7 7 1 15 3 10 1

Developmental Not identified 60 6 8 7 7 1 45 3 10 1

Dev Rabbit Maternal Systemic Not identified 20 6 8 1 1 1 15 3 10 1

Subchronic Inhalation Study - Rat Systemic 70 21 6 8 5 5 1 15.75 3 10 1
Local ¶ 70 21 Not applicable 3.70 3 10 1

MultiGen Repro: Rat Parental Systemic 50 20 6 8 5 5 1 15.00 3 10 1

Parental

Local ¶ 50 20 Not applicable 4.20 3 10 1
Offspring 20 5 6 8 5 5 1 3.75 3 10 1HEC Array for Occupational risk assessments §

Relevant Study LOAEL (ppm) NOAEL (ppm) Da Dh Wa Wh RGDR* HEC (ppm) inter Intra UF
Reproductive 20 5 6 8 5 5 1 3.75 3 10 1
Chronic/Carcinogenicity: Rat Systemic 20 5 6 8 5 5 1 3.75 3 10 1
Local ¶ 60 20 Not applicable 4.20 3 10 1
LONG TERM EXPOSURE

MultiGen Repro: Rat Parental Systemic 50 20 6 8 5 5 1 15.00 3 10 1

Parental Local ¶‡ 50 20 Not applicable 4.20 3 10 1
Offspring 20 5 6 8 5 5 1 3.75 3 10 1 Reproductive 20 5 6 8 5 5 1 3.75 3 10 1
Chronic/Carcinogenicity : Rat Systemic 20 5 6 8 5 5 1 3.75 3 10 1
Local ¶ 60 20 Not applicable 4.20 3 10 1
§ Bolded studies used for endpoint selection. ‡ Italicized HECs derived from PBPK model N.A. = not applicable ¶Local effects (nasal lesions) did not progress with time ( ( i.e. nasal lesions of comparable severity were seen after 4, 13, and 52 weeks of exposure at the same concentration). Therefore, it appears that this effects is not a function of C x t thus a time adjustment is not appropriate. ‡ An uncertainty factor for extrapolation from subchronic to chronic exposure is not recommended since the endpoint, nasal lesions, did not progress with time. * Input parameters for the derivation of RGDRs were obtained from “Methods for Derivation of Inhalation Reference Concentration s and Application of Inhalation Dosimetry” (USEPA, 1994) Tables 4-4, 4-5, and 4-6.

Key for Array Table LOAEL: Lowest observed adverse effect level NOAEL: No observed adverse effect level Da: Daily animal exposure (hrs/day) Dh: Anticipated daily human exposure (hrs/day) Wa: Weekly animal exposure (days/week) Wh: Anticipated weekly human exposure (days/week) RGDR: Regional Gas Dose Ratio HEC: Human Equivalent Concentration inter: interspecies extrapolation uncertainty factor intra: intraspecies variation uncertainty factor UF: Other uncertainty factor(s)

# Appendix D
Analysis Of Field Volatility Data For Pre-plant Field Uses

# Appendix E

Downwind Air Concentrations Calculated With ISCST3 For PrePlant Field Uses

# Appendix F

Downwind Air Concentrations Calculated With PERFUM For PrePlant Field Uses

# Appendix G
Occupational RiskAssociated with Agricultural Fumigations