Table10: Summary Of PERFUM Maximum Buffer Results For All Weather Stations And Flux Types With 1 And 40 Acre Fields At Maximum Application Rate Of 175 lb ai/ A Percentiles 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 20 290 38 Table11: Summary Of PERFUM Whole Field Buffer Results For All Weather Stations And Flux Types With 1 And 40 Acre Fields At Maximum Application Rate Of 175 lb ai/ A Percentiles 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 5 205 39 0 50 100 150 200 250 300 350 400 450 Flat Fume  Watsonville Flat Fume  Manteca Raised 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 Tallahassee FL Flint MI Bradenton FL Bakersfield CA Figure 8: Summary of 95th 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 10 0 20 0 30 0 40 0 50 0 60 0 70 0 Flat Fume  Watsonville Flat Fume  Manteca Rais ed Bed  Plant C ity Raised Bed  Oxnard Raised Bed  Guadalupe Drip  La Selva Drip  Camarillo Drip  Guadalupe Source of Flux P rofile Buffer Distance ( meters) Ven tura CA Tallahassee F L F lint MI Bradenton FL Ba ke rsfield CA Figure 9: Summary of 95th 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 Fume  Watsonville Flat Fume  Mantec a Rais ed Bed  Plant City Raised Bed  Oxnard Raised Bed  Guadalupe Drip  La Selva Drip  Camarillo Drip  Guadalupe Source of Flux Profile Buffer Distance ( meters) Ventu ra CA Tallahassee F L F lint MI Bradenton FL Bakersfield CA Figure 10: Summary of 95th Percentile Whole Buffer Distances For 40A Fields At Maximum Application Rate, HEC= 4 ppm, UF= 30, All Weather Stations, and All Flux Studies 40 0 50 100 150 200 250 300 350 Flat Fume  Watsonville Flat Fume  Manteca Raised Bed  Plant City Raised Bed  Oxnard Raised Bed  Guadalupe Drip  La Selva Drip  Camarillo Dr ip  Guadalupe Source of Flux Profile Buffer Distance ( meters) Ventura CA Tallahassee FL Flint MI Bradenton FL Bakersfield CA Figure 11: Summary of 95th 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 95th percentile 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 95th 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 in Michigan with potentially less atmospheric turbulence).

For 40 acre fields at the 99.9th percentile, 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 99.9th percentile, 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 99.9th percentile) were less than 100 meters with most being in the 5 to 50 meter range at the upper percentiles of exposure ( i. e., 99th percentile 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 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0 4 5 0 1 5 1 0 2 0 4 0 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 c a O xn a rd G u a d a lu p e T R B L a S e lv a C am a r illo G u a d a lu p e D rip Figure 12: Impact Of Changing Field Size On Maximum Buffer Distances, 95th 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, 95th 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, 95th 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 distances 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 because 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 95th percentile 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 95th 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 99.9th percentile, 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 99.9th percentile, 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 99.9th percentile) were less than 100 meters with most being in the 5 to 50 meter range at the upper 43 percentiles of exposure ( i. e., 99th percentile 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 95th percentile 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 99.9th percentile of exposure with MOEs of 30 at the 95th 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 because 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 10th percentile 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 95th percentile values as an example. Use of the 95th percentile 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 incorporate 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. These 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 Exposure 48 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 application 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 Camarillo CA; and ° MRID 464636 02: drip irrigation application in Guadalupe CA.

The tasks that were monitored in these studies are listed 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 1st Tarp Monitor in MRID 458791 02) c) Drip Applicator d) Drip Line Tender ( sometimes reported as 2nd Applicator) 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 6167 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 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 technique. 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 Model UNITED 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 the default 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 of total 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 acceptability 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 reviewed 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 the parameters 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 acceptability 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 supporting 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 occurring 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 of damage. 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 ( bystander scenario). 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) 11.7 3. 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 publications on iodide from the group of authors including R. A. Clewell and EA Merrill ( including Clewell et al. 2001; Merrill 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 determinants of the fetal serum iodide AUC. These parameters are: the placenta: plasma partition coefficient for iodide; the permeability area product diffusion constant for placental 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 ability 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 the authors 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 possibility 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 suggesting 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 because 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 variability 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 variability. 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 higher percentile 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 conversions 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 Metzger 5. 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, Lazar 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, Preece 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 Profile Iodomethane Toxicity Profile Guideline No./ Study Type MRID No. ( year)/ Classification/ Exposure Conditions Results 870.1100 Acute Oral  Rat 45593803 ( 2001) Acceptable/ Guideline. LD50 = 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 LD50 = 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 LD50 > 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 III Guideline 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 LC50 = 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 Acceptable/ 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 ( 919%; 95 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 ( 920%).

Guideline No./ Study Type MRID No. ( year)/ Classification/ Exposure Conditions Results Non guideline Inhalation Phased Exposure 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 ( 821%), 23 24 ( 89%), and 25 26 ( 811%)

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 Feeding Toxicity/ Carcinogenicity Rat. 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) Positive 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 ( 975 78% in males, 81 84% in females). Portal of entry effects not assessed 870.6200 Feeding Subchronic Neurotoxicity  Rats   Not required by the Agency Guideline 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 [ 14C] CH3I. Maximum blood concentrations were achieved within 4 hours ( oral) and 0 2 hours ( inhalation), and were proportional to dose/ concentration. Initial t ½ was 5.1 7.2 hours, and terminal t ½ was 116 136 hours. Radioactivity recovery was low in the main test due to inefficient CO2 trapping. Overall recovery in the supplementary test was increased due to increased recovery of carbon dioxide. Recovered radioactivity was primarily as CO2 ( 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 ½ were 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 ½ was 5.8 6.8 hours for CO2 in 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 CO2, 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 Arrays METHODOLOGIES FOR INHALATION 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 systemic effects, 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 human a 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 higher than 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 vs time 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 1 HEC 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 1 HEC 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 adjustment is not appropriate. * Input parameters for the derivation of RGDRs were obtained from " Methods for Derivation of Inhalation Reference Concentrations 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 1 HEC 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 Concentrations 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 Risk Associated with Agricultural Fumigations