UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460

Office of Prevention, Pesticides 

and Toxic Substances

January 5, 2006

SUBJECT: Iodomethane: Revised HED Human Health Risk Assessment; DP Barcode: D325080, PC

Code: 000011

FROM: Elizabeth Mendez, Ph.D, Toxicologist/Risk Assessor

Jeffrey L. Dawson, Chemist/Risk Assessor

Reregistration Branch 1

Health Effects Division (7509C)

THRU: Whang Phang, PhD, Branch Senior Scientist 

Reregistration Branch 1

Health Effects Division (7509C)

TO: Mary Waller, Product Manager

Registration Division

Attached is HED’s risk assessment of the fumigant, iodomethane. HED has evaluated the hazard and

exposure data and conducted exposure assessments, as needed, to estimate the risk to human health that

will result from the proposed uses of iodomethane. 

This risk assessment addresses both exposures in general population and for those occupationally

exposed. Exposures in the general population occur primarily via inhalation for those in proximity to

treated fields(i.e., bystanders). Occupational exposures also occur via inhalation. The Agency has

addressed these scenarios in this assessment. Drinking water exposure is also anticipated at some level. 

Although iodomethane is used as an agricultural pesticide, it is considered a non-food use chemical since

it is quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The

levels of iodide released from iodomethane degradation/metabolism are lower than those expected to

cause toxic effects. Furthermore, enforcement of tolerances would not be possible since no iodide-free

samples are available and residue field trials show evidence of control samples with higher iodide residues

than iodomethane treated samples. Moreover, iodide is ubiquitous in the environment and a required

nutrient. Finally, iodomethane residues must dissipate in the soil prior to planting. Accordingly, HED

concluded tolerances are not required for iodomethane. As a result, a dietary risk assessment has not

been conducted. 

This risk assessment used the guidance (RfC methodology) developed by the Agency’s Office of

Research and Development (ORD) as well as a chemical-specific physiologically-based pharmacokinetic

(PBPK) model for the derivation of human equivalent concentrations (HECs) for use in margin of

exposure (MOE) calculations. 
Furthermore, both deterministic and probabilistic methods were used to estimate exposures to bystanders

around treated fields which are the key concern. The deterministic approach is based on monitoring data

and the use of the EPA’s Industrial Source Complex: Short-Term Model (ISCST3). Use of ISCST3 is

thought to provide upper-bound estimates of likely exposures predominantly because its outputs are

based on a constant wind vector (speed and direction) flowing directly downwind to those exposed. 

Since defining the ISCST3 method, OPP has considered three additional modeling systems that allow for

the incorporation of actual meteorological data into assessments (i.e., PERFUM, FEMS & SOFEA©). 

The FIFRA Science Advisory Panel (SAP) evaluated these systems in August and September of 2004. 

PERFUM (i.e., Probabilistic Exposure and Risk model for Fumigants) has been used herein in addition to

ISCST3 in order to evaluate distributional bystander exposure. The SAP provided very similar comments

for each distributional model. The Agency has used PERFUM for this assessment because modifications

suggested by the SAP were complete, the inputs were readily available, and the comparative computer

processing time was lower than FEMS and SOFEA©. The ISCST3 and PERFUM model runs have been

summarized in the appendices of this document. Additional information can be provided other than that

included in this document that can be used to detail all of the modeling analyses that have been completed

(e.g., input and output files for each). Arvesta error correction comments (12/21/05) have been

incorporated. No significant changes outside of minor error corrections as noted specifically in their

comments have been made.
HUMAN HEALTH 

RISK ASSESSMENT

Iodomethane

U.S. Environmental Protection Agency

Office of Pesticide Programs

Health Effects Division (7509C)

Elizabeth Mendez, Ph. D., Toxicologist/Risk Assessor

Jeffrey L. Dawson, Chemist/Risk Assessor

Date: January 5, 2006
HUMAN HEALTH RISK ASSESSMENT

Iodomethane

Risk Assessment Team:

Risk Assessor: Elizabeth Mendez, Ph.D.

Jeffrey L. Dawson

Residue Chemistry Christine Olinger

Occupational and Residential Exposure: Jeffrey L. Dawson

Epidemiology: Elizabeth Mendez, Ph.D

Toxicology: Elizabeth Mendez, Ph.D

PBPK Model Evaluation: Hugh Barton, Ph.D.

Paul Schlosser, Ph.D.

Drinking Water Estimates: Faruque Khan
1.0 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.0 Ingredient Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.0 Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.1 Description of Primary Crop Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.2 Description of Livestock Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.3 Description of Rat Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4.0 Hazard Characterization/Assessment ................................................ 7

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

4.1.1 Database Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4.1.2 Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1.3 Dose-response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.1.3.1 Inhalation Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.1.3.2 Dietary Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.1.3.3 Dermal Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1.3.4 Classification of Carcinogenic Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1.4 Endocrine Disruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2 Uncertainty Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3 Summary of Toxicological Endpoint Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.0 Public Health Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

6.0 Non-Occupational Exposure Assessment and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

6.1 Residential Bystander Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

6.1.1 Bystander Exposure From Known Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

6.1.1.1 Bystander Exposures From Known Area Sources Calculated Using The

Monitoring Data Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

6.1.1.2 Bystander Exposures From Known Area Sources Calculated Using The

ISCST3 Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6.1.1.3 Bystander Exposures From Known Area Sources Calculated Using The

PERFUM Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.1.2 Ambient Bystander Exposure From Multiple Area Sources . . . . . . . . . . . . . . . . . . . 43

6.2 Bystander Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.3 Residue Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.4 Water Exposure/Risk Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7.0 Aggregate Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

8.0 Cumulative Risk Assessment and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

9.0 Occupational Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

10.0 Data Needs and Label Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

10.1 Toxicology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

10.2 Residue Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

10.3 Occupational and Residential Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Appendix A: Review Of PBPK/PD Model

Appendix B: Toxicity Profile

Appendix C: Methodologies for Inhalation Risk Calculations and Human Equivalent Concentration Arrays

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

Appendix E: Downwind Air Concentrations Calculated With ISCST3 For PrePlant Field Uses

Appendix F: Downwind Air Concentrations Calculated With PERFUM For PrePlant Field Uses

Appendix G: Occupational Risk Associated with Agricultural Fumigations
1

1.0 Executive Summary

The Health Effects Division (HED) of EPA’s Office of Pesticide Programs has conducted a human health risk

assessment for the active ingredient, iodomethane, also referred to as methyl iodide. The proposed use of

iodomethane is as a pre-plant soil fumigant in strawberries, tomatoes, peppers, perennial crop ornamentals,

nurseries, cut flowers, turf, and tree and vines. Iodomethane has been identified as a possible replacement for

methyl bromide (MeBr), a fumigant with numerous registered uses. 

There are no registered pesticidal uses of iodomethane at present. There are, however, some industrial and

commercial uses. Currently, it is used as an intermediate in the manufacture of some pharmaceuticals, in

methylation processes and in the field of microscopy.

Although iodomethane will be used as an agricultural pesticide, it is considered a non-food use chemical since

it is quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The levels

of iodide released from iodomethane degradation/metabolism are lower than those expected to cause toxic

effects. Furthermore, iodomethane residues must dissipate in the soil prior to planting to prevent

phytotoxicity. Accordingly, HED concludes that tolerances are not required for iodomethane at this time. As

a result, a risk assessment has not been conducted for the dietary exposure scenario. The U.S. population,

however, may be exposed to iodomethane through drinking water; therefore, a qualitative drinking water risk

assessment has been conducted and no risks have been identified from this potential source of exposure. 

In the general population, exposure to iodomethane is anticipated to occur via inhalation or oral (drinking

water) routes but not through the dermal route. Dermal exposure to iodomethane of any significance is not

expected based on the delivery systems used (e.g., soil injection or drip irrigation), packaging (i.e.,

pressurized cylinders), and emission reduction technologies (e.g., tarping). The high vapor pressure of

iodomethane also makes significant dermal exposure unlikely. The general public, however, may be exposed

to fumigants in air because of their volatility following application. Specifically, fumigants can off-gas into air

and be transported off-site by winds to those in proximity to treated fields (i.e., bystanders). Consequently,

the Agency conducted a quantitative human health risk assessment for nondietary exposure only via the

inhalation route. For the purpose of conducting inhalation risk assessments, the current iodomethane database

provides sufficient information to assess risks to the human population following iodomethane exposure via

the inhalation route. Exposures may be acute (less than 24 hours), short-term (1-30 days), intermediate-term

(1 month-6 months), or long-term in duration. Acute exposures have been quantitatively assessed because

this duration is the key concern due to the anticipated use pattern of iodomethane, its emission profile, and the

nature of its toxicity. Additionally, for these same reasons, it is believed that acute assessments are health

protective for other durations of exposure. 

Iodomethane has a severe to moderate acute toxicity profile; it is severely toxic via the oral route (Toxicity

Category II), corrosive to the eye (Toxicity Category I ) and a severe dermal irritant (Toxicity Category II). 

Via the inhalation route, it has been classified as a Category IV chemical (slightly toxic) with an LC50 of 4

mg/L.

The pattern of toxicity attributed to iodomethane exposure via the inhalation route includes developmental

toxicity (manifested as fetal losses and decreased live births), histopathology findings (respiratory tract lesions

and salivary gland squamous cell metaplasia ), thyroid toxicity, neurotoxicity and generalized systemic toxic

effects (body weight and body weight gain decreases). The critical effects of iodomethane exposure via the

inhalation route are the fetal losses observed in two developmental toxicity studies in rabbits, the
2

histopathological lesions reported in three studies, and the generalized systemic toxicity seen throughout the

database. The guideline inhalation chronic toxicity/carcinogenicity study in rats and the carcinogenicity study

in mice revealed that chronic exposure to iodomethane resulted in an increased incidence of thyroid follicular

cell tumors. The sustained perturbation of thyroid hormone homeostasis characteristic of iodomethane

exposure (observed in rats, mice, and rabbits) has been established as the operative mode of action (MOA) for

this tumorigenic response. As a result, HED’s Cancer Assessment Review Committee (CARC) has identified

iodomethane as “not likely to be carcinogenic to humans at doses that do not alter rat thyroid hormone

homeostasis.”

An extensive mechanistic data set as well as a physiologically-based pharmacokinetic (PBPK) model are

available for iodomethane. These data and model constitute a sophisticated effort to better characterize the

toxicity profile for this compound in terms of developmental toxicity, respiratory tract lesions, and thyroid

hormone perturbations identified as the critical effects of iodomethane exposure. In addition, the use of a

PBPK model that takes into consideration the toxicokinetic aspect of iodomethane exposure enables the

Agency to use chemical-specific parameters to determine the most appropriate dose metric and internal dose

in calculating human equivalent concentrations (HECs) instead of the default inputs used in the Agency’s

Reference Concentration (RfC) methodology. The Agency has reviewed these data and their usefulness to

calculate human equivalent concentrations (HECs) based on chemical-specific data. In general, the model and

the mechanistic studies used to provide its inputs are considered adequate and their results have been

incorporated into this risk assessment.

Based on the toxicity profile and the major exposure routes of iodomethane, endpoints have been selected for

the residential/bystanders and occupational human health risk assessments. HED is currently using the

reference concentration (RfC) methodology along with a PBPK to derive the human equivalent concentration

(HEC) for inhalation exposures in this risk assessment. Under the RfC methodology and the PBPK model

approach, endpoint selection is based on the HECs which are derived from the NOAELs of the selected

studies. Different HECs may be derived for non-occupational and occupational assessments due to the time

adjustments conducted for the exposure scenarios. While non-occupational exposure is presumed to

potentially occur 24 hours/day, 7days/week; occupational exposure is presumed to occur for 8 hours/day and

5 days/week. The specific concentrations and endpoints for the exposure scenarios are summarized below: 

• Acute inhalation: Two critical endpoints have been identified for this risk assessment: nasal

histopathology in the subchronic inhalation toxicity study in rats and the fetal losses in the

developmental toxicity study in rabbits. The purpose of this approach is to bracket the range of

possible effects associated with iodomethane from the least to most adverse effects which have been

identified. For the nasal histopathology endpoint, HED selected an HEC of 2.9 or 3.7 ppm

(bystander and occupational risk assessment, respectively) from the NOAEL of 21 ppm based on

degeneration of the olfactory epithelium. For the developmental endpoint, HED selected an HEC of

4.0 ppm (non-occupational risk assessment) from the NOAEL of 10 ppm based on fetal losses and

decreased fetal weights in a developmental toxicity study in rabbits at the LOAEL of 20 ppm. An

uncertainty factor (UF) of 30X defines the HED level of concern.

In the case of the nasal histopathology lesions, sustained glutathione (GSH) depletion has been

identified as a key event in the toxicity pathway leading to the lesions. Consequently, GSH depletion

(#50%) is the dose metric used in the PBPK model for the interspecies extrapolation to determine the

NOAEL for the nasal lesions. Using the HECs of 2.9 or 3.7 ppm (bystander and occupational risk

assessment, respectively) results in a time weighted average GSH depletion of .25% which is below
3

the level of GSH depletion commonly cited in the literature as critical for development of nasal

histopathology.

The dose metric used for the fetal losses is the level of fetal serum inorganic iodide after a single day of

exposure. Based on this dose metric an HEC of 17 ppm could be used for the bystander risk

assessment. However, this HEC would be based on the assumption that human fetal serum iodide

levels are .75% of maternal levels. Although there is some evidence in support of this assumption in

the peer reviewed literature, the Agency has concluded that the evidence is not sufficiently robust (i.e.,

limited and often indirect) to establish an HEC of 17 ppm as a point of departure for the risk

assessment. As a result, an HEC of 4.0 ppm is used in the risk assessment based on the assumption

that there is an equivalent distribution of fetal serum iodide in rabbits and humans. The biomedical

literature indicate that the “true” value is likely towards the high end of this range (i.e., 4-17 ppm). 

However, there does not seem to be adequate information to establish a particular value in the higher

part of that range is the most accurate value. Consequently, there is higher confidence in the use of

the 4.0 ppm HEC as a point of departure for this assessment based on the endpoint of fetal losses.

• Short-term and Intermediate inhalation (bystander): HED selected an HEC of 1.25 ppm from

the NOAEL of 5 ppm based on decreased pup weight and weight gain, decreased thymus weights,

and delays in vaginal patency acquisition seen in the multigeneration reproduction toxicity study at the

LOAEL of 20 ppm. An uncertainty factor (UF) of 30X defines the HED level of concern.

• Short-, Intermediate- term inhalation (occupational): HED selected an HEC = 3.7 ppm from the

NOAEL of 21 ppm based on minimal-mild degeneration of the olfactory epithelium seen at the

LOAEL of 70 ppm in the subchronic inhalation toxicity study in rats. An uncertainty factor (UF) of

30X defines the HED level of concern.

• Long- term inhalation: HED selected an HEC = 0.89 ppm or 3.75 ppm (bystander and

occupational risk assessments, respectively) from the NOAEL of 5 ppm based increased incidence

of salivary gland squamous cell metaplasia seen at the LOAEL of 20 ppm from the chronic

toxicity/carcinogenicity study in rats. An uncertainty factor (UF) of 30X defines the HED level of

concern.

Releases of fumigants such as iodomethane can be categorized in two distinct manners that include addressing

exposures from known area sources (e.g., a treated agricultural field) and also by evaluating available ambient

air levels from multiple area sources that could occur from many applications in a region (e.g., several farms in

a specific valley). 

The evaluations of bystander exposures that can result from known area sources considered field volatility

data directly from several studies, results from an Agency developed Gaussian air plume model (Industrial

Source Complex Short-Term Model, ISCST3), and results from PERFUM (i.e., Probabilistic Exposure and

Risk model for Fumigants) which is a new modeling system based on ISCST3 that allows for incorporation of

actual meteorological data into probabilistic assessments. In fact, the FIFRA Science Advisory Panel (SAP)

evaluated three such modeling systems for fumigants (i.e., PERFUM, FEMS & SOFEA©) in August and

September of 2004. The Agency used PERFUM for this assessment because modifications suggested by the

SAP were complete, the inputs were readily available, and the comparative computer processing time was

lower than FEMS and SOFEA©. 
4

For known area sources (i.e., treated agricultural fields), HED first used monitoring data to assess bystander

exposures to iodomethane. Risks exceeded HED’s level of concern based on these data. In addition, the

Industrial Source Complex - Short Term model (ISCST3) was used to further characterize exposures by

extrapolating to conditions under which empirical data are not be available. In the ISCST3 analysis, varied

meteorological conditions, field sizes, and emission rates were considered. Results demonstrate that for the

cases considered, many risks exceed HED’s level of concern (MOEs <30) for distances less than 100 meters

downwind of the treated fields larger than 1 acre especially when the atmosphere is relatively stable and where

wind speeds < 5 mph. MOEs decrease as field sizes increase while MOEs increase as the atmosphere

becomes less stable leading to conditions where more off-target drift can occur. There is not a significant

impact in the results due to the two different HECs that were considered.

At the upper percentiles of the exposure distributions generated with PERFUM, the results presented in are

markedly similar to those calculated with ISCST3 which supports the construct that the ISCST3 results in

conservative estimates of exposure. If maximum buffer distances at the 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 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 less even at higher percentiles of exposure which indicates slight changes

in risks with percentile of exposure (e.g., MOEs ~10 were common at the 99.9th 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.

Exposures from ambient sources were qualitatively evaluated based on physical-chemical properties and

environmental fate characteristics. Ambient air monitoring data were not available since iodomethane is not

currently widely used. Exposures to bystanders from ambient air resulting from multiple area sources (e.g.,

multiple iodomethane applications in a geographic area) would typically be calculated based on ambient

monitoring data if available. Ambient-air exposures could potentially occur in proximity to agricultural areas

where there is significant use during a particular growing season on a regional basis (e.g., in coastal areas of

California during field fumigation prior to strawberry growing season). Currently, no ambient air data are
5

available for iodomethane; however, HED does not believe that ambient air exposures to bystanders are likely

to be a significant concern based on a comparison of the characteristics of iodomethane with those of methyl

bromide and the ambient air monitoring data available for methyl bromide.

Iodomethane is very soluble in water, so there is the possibility of leaching to ground water and/or

transporting to surface water through runoff, if slicing or removal of the tarpaulin coincides with, or is

followed soon by, a rain event. Based on environmental fate data, the residual contents in soils, and Tier I and

II models estimated concentrations, HED does not expect iodomethane to adversely impact ground water or

surface water.

Risks to occupational handlers, including (tractor drivers, co-pilots, shovelers, soil sealers, and tarp

removers), involved in pre-plant field fumigation were evaluated using iodomethane-specific handler

monitoring data. The data indicate that exposures exceed HED’s level of concern for some workers involved

in the application of iodomethane when no respiratory protection is used (e.g., tractor drivers, co-pilots, and

shovelers). Air purifying organic vapor removing respirators (APRs) which reduce exposure levels by a factor

of 10 were also considered and exposures were reduced below HED’s level of concern for all workers

involved in application with these devices although for some application tasks APRs are not required to

achieve acceptable exposure levels. Respirators would be the most practical protective equipment choice for

reducing exposures for most workers in this case. This was because the field monitoring data used for this

analysis already reflected the use of some engineering controls such as tarps, tractor cabs, deep injection, or

other devices including fans in proximity to drivers. The duration of exposure had no impact on the results of

this assessment.

For workers who entered fields days after application to prepare for planting (e.g., tarp cutters or hole

punchers), exposures were not of concern 5 days after application (which reflects the available data) without

any sort of respiratory protection. This was also the case for planters where exposures were not of concern 7

days after application without any sort of respiratory protection (which also reflects the available data).
6

2.0 Ingredient Profile

 

Iodomethane

Empirical Formula: CH3I

Molecular Weight: 141.95

CAS Registry No.: 74-88-4

PC Code: 000011

Chemical Class: Alkyl Iodide

Iodomethane is a colorless, liquid at normal temperatures and pressures and has a sweet ethereal odor. 

Iodomethane has a specific gravity of 2.28 at 20 °C, vapor pressure of 375 torr at 20 °C, boiling point of 42.5

°C, and octanol/water partition coefficient (log Pow) of 1.51. It is soluble in water at 1.75 g/100 mL at 20 C,

and is miscible in alcohols and ether. 

Iodomethane is a pre-plant soil biocide used to control insects, plant parasitic nematodes, soil borne

pathogens, and weed seeds. The proposed uses are for growing strawberries, fresh market tomatoes, peppers,

perennial crop ornamentals, nurseries, cut flowers, turf, and tree and vines.

Iodomethane is stored as a liquid under pressure but volatilizes rapidly following soil injection. It is applied by

shallow shank broadcast flat fume (flat fume); raised bed shallow shank injection (raised bed); or raised bed

drip irrigation (drip irrigation). A maximum application rate of 175 lb ai/acre has been proposed for use in

various crops. This proposed application rate is the basis for the modeling completed in this assessment. 

[Note: The field volatility and worker exposure monitoring data were generated at application rates which

ranged between 234 and 259 lb ai/acre. The PERFUM model calculations in this assessment accounted for

these differences by scaling results based on the rate differences. Worker exposure monitoring results were

not scaled based on application rate.]

3.0 Metabolism

3.1 Description of Primary Crop Metabolism

Plant metabolism studies conducted on strawberries and tomatoes found iodomethane to be extensively

metabolized and incorporated into the plant constituents, primarily carbohydrates. Iodide levels in the raw

commodities were comparable to background levels found in control samples. 
1

 Robinson, DA et al. (2003). “Three-dimensional mapping of the lesions induced by beta-beta’-

iminodiproprionitrile, methyl iodide and methyl methacrylate in the rat nasal cavity.” Toxicol. Pathol. 31(3):340-

347.

 Chamberlain, MP et al. (1999). Methyl iodide toxicity in rat cerebellar granule cells in vitro: the role of

glutathione.” Toxicology 139(2-3):27-37.

 Chamberlain, MP et al. (1998). “Investigations of the pathways of toxicity of methyl iodide in the rat

nasal cavity.” Toxicology 129(2-3):169-181

 Reed, CJ et al. (1995). “Olfactory toxicity of methyl iodide in the rat.” Arch. Toxicol. 70(1):51-56

 Bonnefoi, MS (1992). “Mitochondrial glutathione and methyl iodide-induced neurotoxicity in primary

neural cell cultures.” Neurotoxicology 13(2):401-412

7

3.2 Description of Livestock Metabolism

There are no significant livestock feed items concerned with this assessment so livestock metabolism studies

are not required.

3.3 Description of Rat Metabolism

A rat metabolism study comparing absorption after oral and inhalation administration is available. The data in

this study indicate that iodomethane is quickly absorbed through both routes of exposure (maximum blood

concentration at 2-4 hours). In contrast, the elimination profile indicates that excretion of 14C-labeled

iodomethane is biphasic with the initial half-life of 5-7 hours and a terminal half-life of approximately 116-136

hours. These half-lives, however, are measured on the basis of the 14C radiolabel and may not accurately

reflect the amount of iodomethane or iodide remaining in the body since the methyl and iodide moieties of

iodomethane are expected to quickly dissociate after administration. Radioactivity accumulates in a variety of

tissues including the thyroid (radioactivity concentration of 106-198 :g/g tissue). A second rat metabolism

study was conducted to quantify the levels of inorganic iodide in rat serum after a two-day exposure (6

hrs/day) via the inhalation route. The results of this study indicate that inorganic iodide serum levels increase

dramatically (8300-1000 fold) during the exposure period and remained elevated during the 18 hours

following exposure (863-400 fold).

4.0 Hazard Characterization/Assessment

4.1 Hazard Characterization

4.1.1 Database Summary

Studies available and acceptable (animal, human, general literature)

The registrant has submitted a complete database via the inhalation route including an acute neurotoxicity

study, developmental studies in rats and rabbits, subchronic inhalation toxicity study in rats, as well as a

multigeneration reproductive toxicity study and a combined chronic/carcinogenicity study in rats. All of the

inhalation studies received to date have been classified as acceptable. At this time, subchronic oral toxicity

studies have been submitted to the Agency. Since iodomethane has been classified as a non-food use

chemical, only a screening level assessment of the oral toxicity studies has been completed. In the peerreviewed literature there are several reports indicating that iodomethane is toxic to the central nervous system,

as well as the respiratory tract.1

 Moreover, numerous published articles indicate that methyl iodide is
2

 Bolt, HM and Gansewendt, B. (1993) “Mechanisms of carcinogenicity of methyl halides.” Crit. Rev.

Toxicol. 23(3):237-253.

 Xu, DG et al. (1993). “DNA methylation of monohalogenated methanes of F344 rats.” J. Tongji Med.

Univ. 13(2):100-104

8

genotoxic due to its methylating capabilities.2

 Interestingly, the only evidence of genotoxicity observed in the

guideline studies submitted to the Agency is an induction of structural chromosome aberrations

(clastogenesis).

Since iodomethane is a new proposed pesticidal active ingredient, there are no incident reports related to

agricultural uses. However, this compound is used as an intermediate in the manufacture of some

pharmaceuticals, in methylation processes, and in the field of microscopy, thus sporadic reports of

iodomethane poisonings are available in the open literature. 

Metabolism, toxicokinetic, mode of action data

As stated above, a rat metabolism study comparing absorption after oral and inhalation administration is

available. The data indicate that iodomethane or its metabolites accumulate in a variety of tissues including

the thyroid (radioactivity concentration of 106-198 :g/g tissue) and is quickly absorbed through both oral and

inhalation routes of exposure (maximum blood concentration at 2-4 hours). Also available is a rat metabolism

study intended to quantify the levels of inorganic iodide in the rat serum and describe the kinetics for serum

iodide accumulation/elimination after iodomethane exposure. As was noted in the guideline metabolism study,

this special study indicated that while accumulation of iodide is rapid the elimination profile is slow and

biphasic in nature.

Toxicokinetic and mode of action data have been submitted by the registrant in support of a physiologically

based pharmacokinetic (PBPK) model including: i) Mode of Action Study for Iodomethane-related

Fetotoxicity Study in Rabbits; ii) Combined Baseline Inhalation Exposure Study of Iodomethane-Related

Fetotoxicity in Rabbits; iii) In vivo Two Day Inhalation Mechanistic Toxicity Study in the Rat; iv)

Iodomethane: Analysis of Select Biomarkers in Rabbit Tissue; v) Iodomethane: Pulmonary Function Study in

Rabbits; vi) Iodomethane: In vitro Partition Coefficients in Rat and Rabbit Tissues and Human Blood; vii)

Iodomethane: Select Biomarkers in Rabbit Tissues after Inhalation Exposure; viii) Effects of Methyl Iodide

on Deiodinase Activity; ix) Derivation of Human Reference Toxicity Values for Methyl Iodide using

Physiologically Based Pharmacokinetic (PBPK) Modeling; x) Magnetic Resonance Imaging and

Computational Fluid Dynamics Simulations of Rabbit Nasal Airflows; xi) Uptake of MeI by the Rabbit Nasal

Cavity; xii) Uptake of MeI by the Rat Nasal Cavity; xiii) In vivo Gas Uptake in Rabbits; xiv) The

Pharmacokinetics of Sodium Iodide (NaI) in Pregnant Rabbits; xv) In vitro GSH Conjugation Study in Rat,

Rabbit, and Human Blood and Tissues with MeI. 

The Agency has reviewed these data and its usefulness to calculate human equivalent concentrations (HECs)

based on chemical-specific data. The mechanistic studies were intended to either define the dose metric or

provide compound-specific inputs for the PBPK model. The model is a sophisticated effort to describe the

kinetics of methyl iodide following inhalation exposure and the kinetics of iodide as a metabolite. It describes

nasal tract dosimetry and glutathione (GSH) depletion in the rat to evaluate nasal toxicity, distribution of

methyl iodide to tissues including brain, and iodide kinetics in the pregnant rabbit to address developmental

toxicity. The model has also been parameterized for the human and Monte Carlo analyses that were
9

performed to describe human variability. The review was carried out using the framework described in Clark

et al., 2004. The results of the evaluation are described focusing on the rat and human nasal modeling , the

rabbit and human pregnancy modeling, modeling human variability, and model documentation. The strengths

and limitations of the modeling were identified. The nasal modeling for rat and human was concluded to be

adequate to estimate a human equivalent concentration. Selection of the appropriate degree of GSH depletion

to predict nasal olfactory toxicity is dependent on additional factors beyond the PBPK/PD modeling, including

judgments about the relationship of this measure with toxicity and the linkage of the time-course of exposure

concentrations with the prediction of GSH depletion. The pregnancy modeling was found to be adequate to

estimate a range of human equivalent concentrations. The human variability analysis was considered to

provide perspective on the default value of 3 to address human pharmacokinetic variability. In general, the

model and mechanistic studies used to provide its inputs are considered adequate and their results have been

incorporated into this risk assessment. For a more detailed description of the model evaluation, the reader is

referred to Appendix A of this document

Sufficiency of studies/data

At this time, the Agency is conducting a quantitative human health risk assessment for exposure via the

inhalation route only. For the purpose of conducting inhalation risk assessments, the current iodomethane

database provides sufficient information to assess risks to the human population following iodomethane

exposure via the inhalation route.

4.1.2 Endpoints

The general public may be exposed to fumigants in air because of their volatility following application. 

Specifically, fumigants can off-gas into air and be transported by diffusion and wind off-site. In addition, the

U.S. population may be exposed to iodomethane through the drinking water.

The pattern of toxicity attributed to iodomethane exposure via the inhalation route includes developmental

toxicity (manifested as fetal losses and decreased live births), histopathology findings (respiratory tract lesions

and salivary gland squamous cell metaplasia ), thyroid toxicity, neurotoxicity and generalized systemic toxic

effects (body weight and body weight gain decreases).

Developmental and/or offspring toxicity is observed in both rats and rabbits. Two developmental toxicity

studies in rabbits conducted via the inhalation route have been reviewed by the Agency. In the guideline

study, an increase in fetal losses was noted at the highest exposure concentration. Subsequently, the registrant

conducted a phased exposure rabbit developmental toxicity study in which animals were exposed for different

time periods. This second study reproduced the fetal losses seen in the guideline study and defined a narrow

dosing window which may elicit this effect. Only exposure on gestation days (GD) 23-24 or GD 25-26

resulted in fetal losses. It is noteworthy, that the time of fetal loss coincides with the time of ontogeny of 

fetal thyroid function in the rabbit (GD22). Given the essential role of iodine in the proper function of the

thyroid gland (both iodine deficiency and excess can have profound effects on thyroid function and thyroid

hormone biosynthesis) and the fact that iodomethane exposure may lead to an excess accumulation of iodine

in the thyroid, a mode of action (MOA) for the fetal losses involving perturbations of fetal thyroid function as

a result of excess iodidehas been proposed. In the case of rats, no fetal losses were reported in the

developmental toxicity study yet a decrease in the number of live births was reported in the multigeneration

reproduction toxicity study. It is interesting to note, however, that while iodomethane exposure in the
3

 Amiodarone printed label. Food and Drug Administration

4

Zhu, Y. et al. “Excess iodine induces the expression of thyroid solid cel nests in lymphocytic thyroiditis- 

 prone BB/W rats.” Autoimmunity (1995) 20:201-106

 Kanno, J. et al. “Tumor-promoting effects of both iodine deficiency and iodine excess in the rat thyroid” 

 Toxicol. Path. (1992) 20(2):226-235

10

developmental study ceased on GD17 (before ontogeny of rat fetal thyroid function), in utero exposure during

the multigeneration toxicity continued until GD20 (i.e. during ontogeny of fetal thyroid function). Thus, the

data suggest that fetal losses may have occurred in the rat developmental study had exposure continued

beyond GD17. Similar effects have been reported for another iodine-rich compound, amiodarone (an

antiarrythmic drug), after treatment of pregnant rabbits and rats.3

The histopathological changes caused by iodomethane exposure occurred in the respiratory tract, and the

salivary and thyroid glands. The respiratory tract histopathology was characterized by lesions of the nasal

cavity described as degeneration of the olfactory epithelium (portal of entry effects). These lesions were

identified in the 13-week inhalation toxicity study, the multigeneration reproductive toxicity study, and the

combined chronic toxicity/carcinogenicity study in rats and were limited to the extrathoracic region with no

involvement of the tracheobronchial or pulmonary regions. Furthermore, they did not appear to progress with

time (i.e. nasal lesions of comparable severity were seen after 4, 13, and 52 weeks of exposure at the same

concentration) thus suggesting the nasal lesions were the result of reaching a critical concentration (Cmax)

rather than time-dependent (i.e. C x t; Haber’s law). In contrast, a C x t relationship is assumed for all

systemic effects.

Frank evidence of thyroid toxicity was reported in the combined chronic toxicity/carcinogenicity study in rats,

the MOA study in rabbits, and the carcinogenicity study in mice. Indications of thyroid toxicity included

enlarged thyroids, increased thyroid weights, increased incidence of ultimobranchial thyroid cysts, follicular

cell hyperplasia, follicular cell adenomas, and thyroid cytoplasmic vacuolation, as well as perturbations of the

thyroid-pituitary axis (decreases in T3 and T4 in conjunction with increases in TSH and rT3). These results

are consistent with reports in the open literature linking excess iodine to thyroid hormone perturbations and

eventually thyroid tumor formation.4

In regards to the potential role of iodomethane as a neurotoxicant, the inhalation acute neurotoxicity study in

rats revealed that iodomethane exposure elicited clonic convulsions (repetitive mouth and jaw movement), a

2-3o

C decrease in body temperature, and an 80% decrease in motor activity in the absence of neuropathology. 

No additional guideline studies are available to evaluate the potential neurotoxicant properties of

iodomethane. This effect, however, appears to be a less sensitive endpoint than the fetal losses, and nasal

histopathological lesions.
5

 At this time, CDPR has not conducted a risk assessment for iodomethane; thus, CDPR HECs are not

available for iodomethane.

11

4.1.3 Dose-response

The primary exposure pathway for iodomethane is via inhalation. Exposures may be acute (less than 24

hours), short-term (1-30 days), intermediate-term (1 month-6 months), or long-term in duration. 

4.1.3.1 Inhalation Exposure

The critical effects of iodomethane exposure via the inhalation route are the fetal losses observed in two

developmental toxicity studies in rabbits, the histopathological lesions reported in three studies, and the

generalized systemic toxicity seen throughout the database. In evaluating the risks that a compound may pose

to human health after exposure via the inhalation route, different methodologies have been historically used by

the USEPA and the California Department of Pesticide Regulation (CDPR). An example of CDPR’s

methodology, and the species-specific parameters used in this approach can be found in the CDPR website

and their MeBr risk assessment, Appendix G at the following web address

(www.cdpr.ca.gov/docs/dprdocs/methbrom/append_g.pdf). As OPP understands the importance to

harmonize with other regulatory agencies, fumigant risk assessments will present HECs derived using EPA’s

RfC methodology as well as CDPR’s methodology, when available.5

 Endpoint selection will be based on the

endpoints occurring at the lowest HECs (which may or may not be the lowest animal NOAEL). 

In this risk assessment, endpoint selection will be based on the endpoints occurring at the lowest HECs (which

may or may not be the lowest animal NOAEL) derived using the RfC methodology or PBPK model. In both

approaches, different HECs may be calculated for the same experimental NOAEL due to: 1) the different

algorithms used to derive HECs for systemic versus portal of entry effects; 2) different dose metrics used in

the PBPK model or 3) the time adjustments conducted for non-occupational versus occupational exposure

scenarios. The differences between systemic versus portal of entry effects, arise from the use of different

calculations to estimate the inhalation risk to humans which are dependent on the regional gas dose ratio

(RGDR). In the case of systemic versus portal of entry effects, different RGDRs are derived for each type of

toxicity. For non-occupational versus occupational exposure, the differences arise because while it is

presumed that non-occupational exposure may occur 24 hours/day, 7 days/week; occupational exposure

occurs only during the course of an average workweek (8 hours/day and 5 days/week). The iodomethane

PBPK model, on the other hand, uses MOA data to derive internal dose metrics in the test species which are

then used to extrapolate to humans and calculate HECs. A more detailed description and evaluation of the

PBPK model are available in Appendix A of this document. For further details on the critical studies used for

endpoint selection and the iodomethane toxicity profile the reader is referred to Appendix B. For additional

information on the methodologies used in this risk assessment and the HEC arrays, please refer to Appendix

C. The toxicity endpoints selected for risk assessment are presented below.

Acute Inhalation Exposure

Endpoint selection for acute inhalation exposures was based on three co-critical studies: a subchronic

inhalation toxicity study in rats and two developmental toxicity studies in rabbits briefly described below:

Subchronic Inhalation Toxicity Study in rats
6 Reed, CJ et al. (1995). “Olfactory Toxicity of Methyl Iodide in the Rat” Arch Toxicol. 70:51-56

12

In a subchronic inhalation toxicity study (MRID 45593810), iodomethane (99.7% a.i.; Lot/batch #

007403/02) was administered via whole-body inhalation to Crl:CD~(SD)IGS BR rats (20/sex/concentration)

for 6 hours/day, 5 days/week for 13 weeks at analytical concentrations of 0, 5, 21, or 70 ppm (0, 0.029, 0.12,

or 0.41 mg/L/day). Ten rats/sex/concentration were sacrificed after 4 weeks, and the remaining 10

rats/sex/concentration were sacrificed after 13 weeks. There were no effects of treatment on mortality,

ophthalmology, urinalysis, hematology, organ weights, or gross pathology .

The systemic LOAEL for this study is 70 ppm based on initial decreases in body weights, body weight

gains, and food consumption (males). The NOAEL is 21 ppm (HEC = 3.8 or 15.8 ppm for nonoccupational and occupational risk assessments, respectively).

The port-of-entry LOAEL is 70 ppm based on degeneration of the olfactory epithelium. The NOAEL

is 21 ppm (HEC = 2.9 or 3.7 ppm for non-occupational and occupational risk assessments,

respectively). 

In a developmental toxicity study (MRID 45593811), groups of 24 female New Zealand White rabbits were

dynamically exposed to iodomethane vapor (Lot/batch # 007403/02; 99.6% a.i.) in whole-body inhalation

chambers at analytical concentrations of 0, 2, 10, or 20 ppm (0, 0.012, 0.058, or 0.12 mg/L/day) six hours per

day on gestation days (GDs) 6 through 28.

The maternal NOAEL is 20 ppm; no maternal LOAEL was identified. The developmental toxicity

LOAEL is 20 ppm based on increased fetal losses and decreased fetal weights (920%). The

developmental toxicity NOAEL is 10 ppm (HEC = 4.0 for non-occupational).

In a developmental toxicity study (MRID 46077001) iodomethane (99.7% a.i., Batch# 02/Lot# 007403) was

administered via the inhalation route (whole body) to 24 New Zealand White rabbits/group at concentrations

of 0 or 20 ppm during GD 6-28 (Control and Group 2), GD 6-14 (Group 3), GD 15-22 (Group 4), GD 23-24

(Group 5), GD 25-26 (Group 6), or GD 27-28 (Group 7) for 6 hours/exposure day. This study was not

intended to fulfill the guideline requirement or establish NOAELs and LOAELs but rather was conducted to

determine the critical period of exposure during gestation that resulted in fetal loss as observed in a previously

evaluated guideline developmental toxicity study in rabbits.

 

Dose and Endpoint for Risk Assessment: Two critical endpoints have been identified for this risk assessment:

nasal histopathology in the Subchronic Inhalation Toxicity Study in rats and fetal losses in two developmental

toxicity studies in rabbits. Using the iodomethane PBPK model developed by Arysta (iodomethane

registrant) and reviewed by the Agency, the HEC for nasal histopathology is 2.9 or 3.7 ppm for nonoccupational and occupational risk assessments, respectively. In the case of fetal losses, the PBPK

model indicates that an HEC of 4.0 would be appropriate for the non-occupational risk assessment.

 

The nasal histopathology was reported after a 13-week exposure to iodomethane, however, data from the

published literature indicate that nasal lesions can occur after acute exposures (.2 hrs. at 100 ppm) if the time

profile of the exposure concentration leads to an overall iodomethane exposure of $200 ppm/hr.6 Based on

this information in conjunction with the iodomethane PBPK and PERFUM models, HED and ORD scientists

have concluded that an HEC of 2.9 or 3.7 ppm for non-occupational and occupational risk assessments,
13

respectively, is appropriate for this risk assessment.

The proposed MOA for nasal histopathology involves glutathione (GSH) depletion as a key event in the

toxicity pathway leading to damage of the nasal olfactory epithelium. Consequently, GSH depletion (#50%)

is the dose metric used in the PBPK model for interspecies extrapolation to determine the NOAEL for the

nasal lesions. Using the HEC of 2.9 ppm for non-occupational risk assessments results in a 24-hr timeweighted average GSH depletion of .25% (i.e. below the level of GSH depletion commonly cited in the

literature as critical for development of nasal histopathology). It should be noted, however, that the emission

profile of iodomethane suggests that during peak emissions GSH depletion may be higher than 25% (e.g.

38%). However, it is the sustained decrease in GSH levels that is critical thus and HEC of 2.9 ppm is

considered protective of the effects of concern in the respiratory tract. Similarly, using an HEC of 3.7 ppm

for the occupational risk assessment results in an 8-hr time-weighted-average GSH depletion of .25%.

The endpoint of fetal losses identified in the developmental toxicity studies in rabbits is also considered

appropriate for this risk assessment since it is presumed that developmental effects may be the outcome of an

acute exposure. In the case of iodomethane, this presumption has been substantiated by the results of the

phased developmental toxicity study in rabbits in which fetal losses were observed after two 6 hr exposures. 

Excess serum iodide has been implicated as a critical element in the MOA proposed for this endpoint. In a

MOA study submitted by the registrant, excess iodide has been shown to lead to fetal thyroid hormone

disruptions (Wolff-Chaikoff effect) resulting in fetal loss. Consequently, the dose metric used for this

assessment is the area under the concentration curve (AUC) for fetal serum inorganic iodide during a single

day of exposure. Based on this dose metric, an HEC of 17 ppm is calculated for the non-occupational risk

assessment. This HEC, however, is based on the assumption that human fetal serum iodide levels are 75% of

the maternal levels. Although data in support of this presumption are available from the peer reviewed

literature, the Agency has concluded that the evidence is not sufficiently robust (i.e. limited and often

indirect) to establish an HEC of 17 ppm as a point of departure for the assessment. Thus, although the HEC

of 17 ppm may represent a high or perhaps even a reasonable estimate of the human fetal serum iodide AUC,

an HEC of 4 ppm which assumes an equivalent distribution of fetal serum iodide in rabbits and humans is used

in this risk assessment. This HEC likely represents a lower bound estimate of the true dose metric. The bulk

of the biomedical literature indicate that within the range of these estimate (i.e. 4-17 ppm), the “true” value is

likely towards the higher part of the range. But, there does not appear to be adequate information to establish

that a particular value in the higher part of that range is the most accurate value. Consequently, there is higher

confidence in the use of the 4 ppm HEC as a point of departure for this assessment based on the endpoint of

fetal losses. An UF of 30X defines HED’s level of concern for both endpoints.

Note: For a more detailed description of the PBPK evaluation, refer to Appendix A of this risk

assessment.
14

Short-, and Intermediate-term Inhalation Exposure

Non-occupational Exposure 

In a two-generation reproduction toxicity study, iodomethane (99.7% a.i.; Lot/batch # 007403/02) was

administered via whole-body inhalation to Crl:CD®(SD)IGS BR rats (30/sex/concentration) for 6 hours/day at

nominal concentration levels of 0, 5, 20, or 50 ppm (equivalent to analytical concentrations of 0, 5, 21, and 50

ppm). The P animals were exposed to the test article for at least 70 days prior to mating to produce the F1

litters. Exposure of the P males continued throughout mating and until the day prior to euthanasia. The P

females continued to be exposed throughout mating and through gestation day (GD) 20, at which point

exposure was discontinued. Daily exposure of the P females was reinitiated on lactation day (LD) 5 and

continued until the day prior to euthanasia. After weaning, F1 animals (30/sex/concentration) were selected,

equalized by sex, to become the parents of the F2 generation and, beginning on post-natal day (PND) 28, were

exposed to the same concentration test atmosphere as their dam. 

The systemic parental NOAEL is 20 ppm (HEC = 5 ppm) and the LOAEL is established at 50 ppm

based on decreases in body weight, body weight gain, changes in organ weights (adrenal glands, testis,

cauda epidymis, epidydimis, and thymus) as well as gross pathology and histopathology findings. 

The port of entry NOAEL is 20 ppm (HEC = 3.2 ppm) and the LOAEL is 50 ppm based on minimalmild degeneration of the olfactory epithelium. 

The offspring NOAEL is 5 ppm (HEC = 1.25 ppm) and the LOAEL is 20 ppm based on decreases in

body weight, body weight gain, as well as lower absolute and relative thymus weights.

The reproductive NOAEL is 5 ppm (HEC = 1.25 ppm) and the LOAEL is 20 ppm based on delays in

attainment of vaginal patency.

Dose and Endpoint for Risk Assessment: HEC of 1.25 ppm based on decreased pup weight and weight gain,

decreased thymus weights, and delays in vaginal patency acquisition. The duration of exposure in the

multigeneration reproduction toxicity is appropriate for short- and intermediate-term risk assessments and it

yields the lowest HEC (ie. most health protective exposure concentration) for these exposure scenarios. An

UF of 30X defines HED’s level of concern in accordance with guidance provided in the RfC methodology

(see section 4.2 below). 

Occupational Exposure

See non-occupational exposure above for brief executive summary. Different HECs have been calculated for

occupational exposures due to the time adjustments made for the exposure scenarios.

Systemic parental NOAEL is 20 ppm (HEC = 15 ppm).

Port of entry NOAEL is 20 ppm (HEC = 3.7 ppm).

Offspring NOAEL is 5 ppm (HEC = 3.75 ppm).

Reproductive NOAEL is 5 ppm (HEC = 3.75 ppm).
15

Dose and Endpoint for Risk Assessment: HEC of 3.7 ppm based on minimal-mild degeneration of the

olfactory epithelium. The duration of exposure in the multigeneration reproduction toxicity is appropriate for

short- and intermediate-term risk assessments and it yields the lowest HEC (ie. most health protective

exposure concentration) for these exposure scenarios. An UF of 30X defines HED’s level of concern in

accordance with guidance provided in the RfC methodology (see section 4.2 below). 

Long-term Inhalation Exposure

Non-occupational and Occupational Exposure

In a combined chronic toxicity/carcinogenicity study in rats (MRID 45612401),iodomethane (99.7% a.i.,

Batch No. 02/Lot # 007403) was administered to Crl:CD®(SD)IGS BR rats via whole body inhalation at

concentrations of 0, 5, 20, or 60 ppm for 6 hours/day 5 days/week. Sixty animals/sex/concentration were

exposed to 0, 5, or 20 ppm iodomethane while 70/sex were exposed at the 60 ppm level. Animals were

observed for moribundity and mortality twice daily and clinical observations once daily. Once a week a

detailed physical examination was conducted including but not limited to evaluations of changes in

appearance, autonomic activity (e.g. lacrimation, piloerection, pupil size, breathing patterns), gait, posture,

response to handling, stereotypic and/or bizarre behavior. In addition, evaluations of clinical chemistry,

hematology, urinalysis, gross pathology and histopathology parameters were conducted. 

The systemic NOAEL is 5 ppm (HEC = 0.89 or 3.75 ppm for non-occupational and occupational risk

assessments, respectively); the LOAEL is established at 20 ppm based on increased incidence of

salivary gland squamous cell metaplasia. 

The NOAEL for port of entry effects (respiratory tract) is 20 ppm (HEC = 3.2 or 4.2 ppm for nonoccupational and occupational risk assessments, respectively) and the LOAEL is 60 ppm based on

degeneration of the olfactory epithelium.

Dose and Endpoint for Risk Assessment: HEC of 0.89 ppm or 3.75 ppm for non-occupational and

occupational risk assessments, respectively based on increased incidence of salivary gland squamous cell

metaplasia. This is the study of the longest duration available in the iodomethane database and it yields the

lowest HEC (ie. most health-protective) for this exposure scenario. An UF of 30X defines HED’s level of

concern in accordance with guidance provided in the RfC methodology (see section 4.2 below). 

4.1.3.2 Dietary Exposure

Although iodomethane is used as an agricultural pesticide, it is considered a non-food use chemical since it is

quickly degraded or metabolized and subsequently incorporated into natural plant constituents. The levels of

iodide released from iodomethane degradation/metabolism are lower than those expected to cause toxic

effects. Furthermore, enforcement of tolerances would not be possible since no iodide-free samples are

available and residue field trials show evidence of control samples with higher iodide residues than

iodomethane treated samples. Moreover, iodide is ubiquitous in the environment and a required nutrient. 

Finally, iodomethane residues must dissipate in the soil prior to planting. Accordingly, HED concluded

tolerances are not required for iodomethane. As a result, a risk assessment has not been conducted 
16

for this exposure scenario. The U.S. population, however, may be exposed to iodomethane through drinking

water; therefore, a qualitative drinking water risk assessment was conducted and no risks were identified from

this potential exposure. 

4.1.3.3 Dermal Exposure

Exposure to iodomethane is anticipated via inhalation or oral (drinking water) routes but not through the

dermal route. Dermal exposure to iodomethane of any significance is not expected based on the delivery

systems used (e.g., soil injection or drip irrigation), packaging (i.e., pressurized cylinders), and emission

reduction technologies (e.g., tarping). The high vapor pressure of iodomethane also makes significant dermal

exposure unlikely and quantifying any potential low level exposures very difficult. Therefore, a quantitative

dermal exposure assessment has not been completed. Since HED does not have adequate data to quantify

dermal risk, PPE for dermal protection should be based on the acute toxicity of the end-use product as

described in the Worker Protection Standard and mitigation measures for dermal exposure described in PR

Notice 93-7.

4.1.3.4 Classification of Carcinogenic Potential

The Cancer Assessment Review Committee (CARC) evaluated the rodent bioassays and mechanistic data

available for iodomethane. Evidence of carcinogenicity in the iodomethane database manifested as an

increased incidence of thyroid follicular cell tumors observed in both the Inhalation Chronic

Toxicity/Carcinogenicity Study in Rats and the Carcinogenicity Study in Mice. The committee concluded that

the key event influencing the thyroid tumor response is the sustained stimulation of cell proliferation by TSH,

consistent with the increase in thyroid follicular cell tumors only. Based on the evidence that rats are

substantially more sensitive than humans to the development of thyroid follicular cell tumors in response to

thyroid hormone imbalance, the CARC classified iodomethane as “not likely to be carcinogenic to humans at

doses that do not alter rat thyroid hormone homeostasis.”

4.1.4 Endocrine Disruption

Following the recommendations of its Endocrine Disruptor Screening and Testing Advisory Committee

(EDSTAC), EPA determined that there was scientific bases for including, as part of the endocrine disruption

screening program, the androgen and thyroid hormone systems, in addition to the estrogen hormone system. 

EPA also adopted EDSTAC’s recommendation that the Program include evaluations of potential effects in

wildlife. As the science develops and resources allow, screening of additional hormone systems may be added

to the Endocrine Disruptor Screening Program (EDSP).

Evidence of thyroid hormone perturbation is available in the iodomethane database. A chronic

toxicity/carcinogenicity study in rats, as well as a MOA study in rabbits, and a carcinogenicity study in mice

indicate that iodomethane exposure elicits sustained perturbation of thyroid hormone homeostasis. When the

appropriate screening and/or testing protocols being considered under the Agency’s EDSP have been

developed, iodomethane may be subjected to additional screening and/or testing to better characterize effects

related to endocrine disruption.
7

 A 3X UF for interspecies extrapolation is retained to account for the PD differences between animals

and humans which are not accounted for in the RfC methodology or the PBPK model.

17

4.2 Uncertainty Factors

Iodomethane has been classified as a non-food use pesticide. Consequently, this chemical is not subject to the

FQPA (1996) and the 10X FQPA factor does not apply.

When conducting inhalation risk assessments, the magnitude of the UFs applied is dependent on the

methodology used to calculate risk. This risk assessment is based on the RfC methodology developed by the

Office of Research and Development (ORD) and the PBPK model developed by the registrant for the

derivation of inhalation reference concentrations (RfCs) and human equivalent concentrations (HECs) for use

in margin of exposure (MOE) calculations. Since both of these approaches take into consideration the

pharmacokinetic (PK) but not pharmacodynamic (PD) differences between test species and humans, the UF

for interspecies extrapolation may be reduced to 3X while the UF for intraspecies variation is retained at

10X.7 Thus, when using the RfC methodology the overall UF is customarily 30X. 

4.3 Summary of Toxicological Endpoint Selection

 Table 1: Summary of Toxicological Dose and Endpoints for Use in Iodomethane Human Health Inhalation Risk

Assessment

Risk Assessment Study NOAEL/LOAEL Endpoint HED

HECs

CPDR 

 HECs¶

Acute Nonoccupational

Subchronic

Inhalation Toxicity

Study in Rat

NOAEL = 21 ppm

LOAEL = 70 ppm

Degeneration

of the olfactory

epithelium

2.9 ppm

UF=30 N.A.

Developmental

Study in Rabbits

NOAEL = 10 ppm

LOAEL = 20 ppm

Developmental

effects: fetal

loss

4.0 ppm 

UF = 30

Occupational

Subchronic

Inhalation Toxicity

Study in Rat

NOAEL = 21 ppm

LOAEL = 70 ppm

Degeneration

of the olfactory

epithelium

3.7 ppm

UF=30

Short-,

IntermediateTerm, 

Inhalation

(1-6 months

exposure)

NonoccupationalMultigeneration

Reproductive

Toxicity Study in

Rats

NOAEL = 5 ppm

LOAEL = 20 ppm

Offspring

effects:

decreased body

weight, weight

gain, and

thymus weights

Reproductive

effects: Delays

in vaginal

patency

1.25

ppm

UF = 30

N.A.

Occupational Subchronic

Inhalation Toxicity

Study in Rats

NOAEL = 20 ppm

LOAEL = 50 ppm

Degeneration

of the olfactory

epithelium

3.7 ppm

UF = 30
 Table 1: Summary of Toxicological Dose and Endpoints for Use in Iodomethane Human Health Inhalation Risk

Assessment

Risk Assessment Study NOAEL/LOAEL Endpoint HED

HECs

CPDR 

 HECs¶

8

Hermouet, C. et al. “Methyl iodide poisoning: Report of two cases” Am. J. Ind. Medicine (1996) 30: 759-

764 & Appel, G.B. et al. “Methyl iodide intoxication” Annals of Int. Med (1975) 82:534-536

18

Long-term (>

6 months)

NonoccupationalChronic/

Carcinogenicity

Study in Rats

NOAEL = 5 ppm

LOAEL = 20 ppm

Squamous cell

metaplasia

0.89

ppm

UF = 30

N.A.

Occupational Chronic/

Carcinogenicity

Study in Rats

NOAEL = 5 ppm

LOAEL = 20 ppm

Squamous cell

metaplasia

3.75

ppm

UF = 30

Cancer Not likely to be carcinogenic to humans at doses that do not

alter rat thyroid hormone homeostasis

¶

 At this time, CDPR has not conducted a risk assessment for iodomethane (pending submission of additional data). 

5.0 Public Health Data

Over the past century, only 11 incidents of iodomethane poisoning have been reported in the published

literature8. In general, symptoms of iodomethane intoxication in humans were related to effects on the

nervous system ranging from somnolence to ataxia, seizures, delirium and coma in severe cases. In

some patients, cerebellar lesions and damage of the third, fourth, or sixth cranial nerve pathways as

well as spinal cord lesions producing motor and sensory disturbances have been reported. Latent

symptoms of iodomethane intoxication include psychological disorders such as depression. In addition

to neurological effects, iodomethane exposure has also been linked to congestive changes in the lungs

and oliguric renal failure. It is noteworthy, however, that in most of these incidents the precise

iodomethane exposure concentration is unknown though it appears that exposure was to high levels

(due in part to use of inadequate protective devices) resulting from industrial uses and far exceeding

those proposed for regulatory purposes in this risk assessment or anticipated agricultural uses.

6.0 Non-Occupational Exposure Assessment and Characterization

This section describes the potential exposure scenarios associated with the use of iodomethane. These

include residential bystander exposure from two key sources: a known area source (e.g., at the edge of

a treated field), as well as from multiple area sources within a region (e.g., ambient air). There are no

residential uses of iodomethane by homeowners so this aspect of the risk assessment focuses on those

types of exposures that may occur from the proposed commercial uses of iodomethane. HED also

considered the potential for dietary exposures from food and drinking water.
19

6.1 Residential Bystander Exposure

Residential bystander exposure may occur because of emissions from treated fields. These emissions

can travel to non-target areas which could lead to negative impacts on human health and are referred

to as bystander or off-target risks.

Exposures from known uses or point sources have been estimated based on controlled field volatility

studies conducted in agricultural fields. Exposures have been calculated in three distinct ways

involving direct use of the monitoring data and through two modeling approaches including the

Industrial Source Complex: Short-Term model (ISCST3) and the Probabilistic Exposure and Risk

Model For Fumigants (PERFUM) which was developed and evaluated by the FIFRA Science Advisory

Panel (SAP) in August of 2004. ISCST3 is a standard Agency model which was used in conjunction

with the monitoring data available for iodomethane, and other inputs such as a series of standard

weather conditions. ISCST3 is a Gaussian plume model developed by the Office of Air and Radiation

which allows better characterization of air concentrations that result from the application of

iodomethane at different distances from the emission source that is not possible with direct use of the

data. However, ISCST3 outputs are also limited because they are based on a constant wind vector

(speed and direction) flowing directly downwind to those exposed. Since the initial assessment using

ISCST3 was completed, OPP has also considered three different modeling systems that allow for the

incorporation of actual meteorological data into assessments (i.e., PERFUM, FEMS & SOFEA©). 

The FIFRA Science Advisory Panel (SAP) evaluated these modeling systems in August and September

of 2004. PERFUM (i.e., Probabilistic Exposure and Risk model for Fumigants) has been used herein

in addition to ISCST3 in order to better evaluate distributional bystander exposure. The SAP

provided very similar comments for each. The Agency has used PERFUM for this assessment because

modifications suggested by the SAP have been completed, the inputs were readily available, and the

comparative computer processing time was lower than FEMS and SOFEA©. Complete descriptions of

each new probabilistic model along with the associated SAP reports are posted at

www.epa.gov/scipoly/sap/index.htm.

For exposures from multiple area sources or ambient air, air concentrations of iodomethane have not

been quantitatively assessed due to a lack of appropriate data. However, a qualitative discussion is

provided below.

Exposures for bystanders from known area sources are described below in Section 6.1.1 while

exposures from ambient or multiple area sources are described below in Section 6.1.2.

6.1.1 Bystander Exposure From Known Area Sources

As stated above, residential bystander exposure from known area sources may occur because of

emissions from treated fields. The following sections describe the data and modeling approaches used

to estimate these types of exposures.

HED considered several field volatility studies in the development of the bystander assessment for

iodomethane. Two studies quantified emissions from tarped broadcast, flat fume applications in

California (i.e., Manteca and Watsonville, California). Three studies quantified emissions from tarped
20

drip irrigation application in California (i.e., La Selva, Camarillo, and Guadalupe, California). The

three remaining studies quantified emissions from tarped, raised bed shallow shank injection

applications in California and Florida (i.e., Plant City, Florida and Oxnard and Guadalupe, California). 

The citations and summary of the data are included in Appendix D. Each of these studies was

considered to be a sufficient quality for risk assessment purposes. [Note: All flux studies included the

use of tarps; for brevity, the use of tarps is implied and will no longer be noted further in this

assessment as it is implied in the results.]

As indicated above, three methods were used for estimating air concentration of fumigants from area

sources such as a treated field: direct use of air monitoring data from controlled volatility studies in

treated fields referred to as the Monitoring Data Method, the use of ISCST3 referred to as the ISCST3

Modeling Method, and the use of PERFUM referred to as the PERFUM Modeling Method. Each of

these are described separately below. 

Monitoring Data Method: In the monitoring data method, air concentrations are estimated using

actual air monitoring data from controlled volatility studies. In these studies, the fumigant is applied to

a field and air samplers positioned in and around the treated area continuously sample the air by pulling

the air through a filter (e.g., charcoal) or a vacuum chamber is used which captures the chemical for

later analysis. Sampling times can vary but generally range from about 4 to 12 hours, so that the

samples represent the average air concentrations for the sampling intervals used. Usually shorter times

are used at the beginning of the sampling period because fumigants tend to volatilize soon after

application.

There are several uncertainties associated with the use of the direct sampling method which limit its

utility. First, the air concentrations represent only those for the conditions under which the study is

carried out. Air concentrations around treated fields, buildings, or other areas are influenced by a

number of factors including how a chemical is applied, application rate, techniques to control

emissions (e.g., tarps), and weather conditions. Varying weather conditions, for example, can

significantly change the air concentrations at specific sites around a treated area; and since there is such

a large range of potential weather conditions which can exist, it is not possible for these studies to

represent the entire range of potential exposures that can result from different weather situations. 

Second, the air concentrations are measured by fixed samplers positioned at various directions around

the treated area, both downwind and upwind, as well as at points in between. Air concentrations

downwind will be relatively high since the fumigant plume will be pushed by the wind in that direction,

while concentrations upwind will be low or close to zero since the plume is pushed by the wind in the

opposite direction. Therefore, there can be a very large difference between upwind and downwind air

concentrations. For areas where there is a predominant wind direction, averaging of the air

concentrations from these various samplers should not be done since persons around treated areas will

generally be in one location relative to the wind and not exposed to an average of these concentrations. 

Third, samplers are positioned at specific distances from the treated area, and represent air

concentrations only at those distances. Since air concentrations vary greatly by distance, the air

concentrations estimated from direct measures represent a very narrow range of the possible levels to

which people can be exposed. Results based on the Monitoring Data Method are presented below in

Section 6.1.1.1.
21

ISCST3 Modeling Method: The modeling method uses the Agency model, Industrial Source

Complex Short Term (ISCST3) together with information about emissions from a treated field,

building or structure (i.e., known as flux) to model the range of concentrations which may be found

under different conditions of application rate, weather, source size and shape (e.g., field size in acres),

and distance from the treated field, building or structure. Before a modeling analysis can be done, one

of the most important parameters for ISCST3, the flux rate, which is the quantity of pesticide which is

emitted from the treated fields, buildings or structures per unit area per unit time, must be determined. 

As an example, for field applications it is usually expressed in units of micrograms per square meter

per second (ug/m2/sec). In essence, flux represents how quickly the pesticide moves or volatilizes into

the surrounding atmosphere. Numerous factors can influence flux rates such as application rate, depth

of soil injection, type of application (e.g., drip vs. soil injection), techniques used to control emissions

(e.g., tarps), temperature, wind and weather conditions, soil type, and others. Flux is also difficult to

determine. Three general methods are used to calculate flux which are discussed briefly 

below. The first two of these measure flux from sampling directly in treated fields, and the third is

indirect in that it calculates flux using samples from downwind locations. For iodomethane, flux

estimates were completed using the indirect back-calculation method.

Method 1, Flux Chamber: The first direct method for estimating flux uses field

fumigant emission data measured in a flux chamber. A flux chamber is basically a box

which encloses a small defined area of a treated field, from which air samples are

obtained representing defined durations (e.g., air is pulled through a charcoal trap

collecting emitted pesticide over a continuous length of time such as 4 hours). Since

the surface area is defined by the area of the chamber, and the quantity of pesticide

emitted per unit time is defined by the air concentration, this method directly measures

flux. A possible issue with flux chambers is that the conditions within the chamber

(e.g., temperature, wind, air stability) are not generally identical to those outside the

chamber in the treated field; since flux rates can be significantly affected by these

factors, flux rates measured in these chambers may not always represent actual flux

rates in the field. Flux chambers are not often used for estimating flux rates. 

Method 2, Aerodynamic Flux Method: The second direct method used is the

aerodynamic flux method. In this method, air samplers are set up in the treated field at

various heights on a mast (e.g., 15, 30, 90, and 150 cm from the ground). Using

measured air concentrations at these various heights, a vertical gradient of

concentrations can be estimated for different time points, which can be integrated

across all heights to estimate the flux rate at each time point after application. Some

studies are available using this method to determine flux rates. 

Method 3, Back-Calculation: The method most often used to determine flux rates is

an indirect method known as the back-calculation method. This method uses measured

air concentrations taken in a typical field fumigation study in which air samplers are

located at various positions around the field. The measured air concentrations, together

with information about weather conditions which occurred when the samples were

obtained, are used as inputs into the Industrial Source Complex Short Term model

(ISCST3). The model assumes that these air concentrations result from a Gaussian
22

plume, the plume being distributed around the treated field as a result of the wind and

weather conditions measured. The model then calculates the flux rate which would be

required to emit the plume in that manner and to obtain the air concentrations

measured. 

Determination of the flux rate for all situations to be considered in an assessment is necessary before

ISCST3 can be run. After these are defined, other key inputs must be defined such as the size and

shape of a treated field, wind direction, wind speed, and atmospheric stability. ISCST3 calculates

downwind air concentrations using hourly meteorological conditions, that include wind speed and

atmospheric stability. The lower the wind speed and the more stable the environment, the higher the

air concentrations are going to be close to a treated field. Conversely, if wind speed increases or the

atmosphere is less stable, then air concentrations are lower in proximity to the treated field. 

Atmospheric stability is essentially a measure of how turbulent the atmosphere is at any given time. 

Stability is affected by solar radiation, wind speed, cloud cover, and temperature, among other factors. 

If the atmosphere is unstable, then more off-field movement of airborne residues is possible because

they are pushed up into the atmosphere and moved away from the field, thereby lowering the air

concentration in proximity to the field. To simplify modeling the transport of soil fumigant vapors

from a treated field, a single wind direction, wind speed, and stability category are used for a given 24-

hour period. The Agency has not determined if a particular set of meteorological conditions should be

used for regulatory purposes, so risk assessments generally present exposures and potential risks

representing a variety of different conditions. 

Modeling with ISCST3 produced high-end estimates of air concentration and resulting risks for a

number of reasons. First, only the downwind direction is considered. Most people will not be directly

downwind from a treated field. Secondly, the model runs are based on constant wind speed, wind

direction, and atmospheric stability for a 24-hour period. This will rarely occur resulting in

overestimates of air concentrations and risks. However, the model is useful because it allows for

estimation of air concentrations reflecting different conditions based on changing factors such as

application rates, field sizes, downwind distances, wind and weather conditions, and other factors,

which cannot be done using the monitoring data method described above. Therefore, results using the

ISCST3 model should be considered to be potential exposures to the most highly exposed, upper

percentile of the population, but are not representative of exposures to most of the population around a

treated field. The Agency believes that using ISCST3 to predict exposures over more extended periods

is inappropriate because constant meteorological conditions over such periods will not occur and will

yield highly conservative, physically unlikely results. Results based on the ISCST3 Modeling Method

are presented below in Section 6.1.1.2.

PERFUM Modeling Method: The monitoring data and ISCST3 methods described above are

deterministic methods which provide high-end point estimates of risk. OPP is coordinating with EPA’s

Office of Air, the California Department of Pesticide Regulation (CDPR), and other stakeholders to

develop modeling approaches which determine the distribution of potential bystander exposures, and

thus more fully characterize the range of potential risks to bystanders around treated fields. In this

assessment, the PERFUM model has been used in order to calculate differing percentiles of exposure

(see Section 6.1 above for more details concerning PERFUM use and other possible models). 

ISCST3 is an integral part of the PERFUM model. As a result, many of the inputs used for PERFUM
23

Figure 1: Receptor Grid For a 5 Acre Field

[Note: 5 Acre field in center, line is a spoke]

are similar to those used for the ISCST3 analysis described above (e.g., field sizes and back-calculated

flux rates). The key differences are that PERFUM addresses the uncertainty associated with flux

profiles in its calculations by sampling the coefficient of variation for those measurements but this does

not account for important factors such as field to field differences. It also incorporates 5 years of

meteorological data to generate a distribution of daily average concentrations that represent the

possible range of downwind air concentrations based on changing wind vectors from the measured data

in a series of receptor locations described in Table 2 and Figure 1 below. PERFUM analyses were

completed for several field sizes (i.e., 1 to 40 acres) but in most cases only the results for 1 and 40 acres

were processed which represent the possible range of outcomes. In other cases, all results were

processed so the impacts of changing field sizes could be more readily apparent. It is also thought that

the general trend would apply generically to most situations. Field geometry (i.e., shape) was also

investigated by completing analyses for 5 acre fields shaped like a square and a rectangle oriented on its

side and also top to bottom. [Note: The maximum distance for which calculations are performed on

each spoke in PERFUM is 1440 meters from the edge of the treated field.]

Table 2: Receptor Points for Various Field Sizes In PERFUM

Grid Type Field Size

(acres)

Number of

Spokes

Number of

Rings

Number of

Receptors

(Spokes*Distances)

Fine

1 96 28 2,688

5 132 28 3,696

10 152 28 4,256

20 188 28 5,264

40 232 28 6,496

Coarse

1 24 28 672

5 33 28 924

10 38 28 1,064

20 47 28 1,316

40 58 28 1,624

Notes: Fine grid option was used for iodomethane analysis.

The maximum distance used for PERFUM calculations on each spoke is 1440 meters.
24

Figure 2: Distribution of Daily Average Windspeeds At Selected

Meteorological Stations

Since actual meteorological data are integrated into PERFUM for each analysis, data representative of

the locations where iodomethane use is anticipated were identified and used in the analysis. It is

anticipated that the initial major uses for iodomethane could be as a methyl bromide alternative in

California and Florida on tomatoes, strawberries and similar crops. Some use in Michigan (or

elsewhere in that region) is also anticipated. As a result, the following locations and sources of

meteorological data were used in this assessment:

• Bakersfield California (Source: ASOS or Automated Surface Observing System operated by the

FAA) to represent inland California locations;

• Ventura California (Source: CIMIS or California Irrigation Management Information System)

to represent coastal California locations;

• Flint Michigan (Source: NWS or National Weather Service) to represent central Michigan and

other upper midwest locations;

• Tallahassee Florida (Source: NWS or National Weather Service) to represent inland Florida

locations; and

• Bradenton Florida (Source: FAWN or Florida Automated Weather Network) to represent

coastal Florida.

In this assessment, 5 years or 1825 days of meteorological data were considered in each calculation

(i.e., data are available for all days with no sampling errors). Most data were in the range of 1997

through 2003 but Tallahassee was in the late 1980s through early 1990s. [Note: Please refer to the

SAP background documents for PERFUM for further information concerning these data including how

they were processed for incorporation into PERFUM and any quality control issues related to these

data.] Figure 2 provides a comparison of the distributions of daily average windspeeds for selected

stations in California and Florida. These can be used to help characterize the deterministic assessments

and to illustrate different PERFUM results for the different stations. [Note: As an example, CADPR

regulated methyl bromide at 1.4 m/s windspeed.]
25

Flux (i.e., field volatility or emissions) data were treated in a manner similar to that used for the ISCST3

analysis described above. Data from each of the 8 flux studies were used in conjunction with

appropriate meteorological information. In some cases, studies that quantified emissions from the same

application method were available in both Florida and California. In those cases, only meteorological

data from the same state were used in the PERFUM analysis. PERFUM also considers the

uncertainties associated with daily flux profiles by probabilistically sampling flux based on the range

defined by the coefficient of variation associated with those data. Table 3 below provides a summary of

the analyses that were completed using PERFUM. There is a significant difference with PERFUM

compared to ISCST3 with regard to flux values. ISCST3 used a single emission ratio which does not

account for changes over time in flux. Alternatively, PERFUM samples flux distributions over time

periods of interest. The Agency verified the flux distributions based on the monitoring data.

Table 3: Summary Of PERFUM Analyses Completed For Iodomethane

Weather

Station

Location

Flux Study Summary 

Watsonville

CA

Flat Fume

Manteca CA

Flat Fume

Plant City FL

Raised Bed

Oxnard CA

Raised Bed

Guadalupe

CA

Raised Bed

La Selva CA

Drip

Irrigation

Camarillo

CA

Drip

Irrigation

Guadalupe

CA

Drip

Irrigation

Ventura CA X X NA X X X X X

Bakersfield 

CA

X X NA X X X X X

Flint MI X X X X X X X X

Tallahassee

FL

X X X NA NA X X X

Bradenton

FL

X X X NA NA X X X

X = analysis completed, NA = analysis not appropriate; Tarps were used in all cases. Note: All analyses completed using both 2.9 & 4.0 ppm HECs. 

PERFUM calculates outputs based on each day’s worth of meteorological data and the result is

illustrated by Figure 3 which shows the distances from the field where airborne concentrations meet a

threshold of concern around the entire perimeter of the field for each spoke in the model (i.e., the

irregularly shaped line). The concentric circle represents a 95th percentile distance value around the

perimeter (i.e., MOEs are not of concern for 95% of those exposed). The cross hatch area represents

the locations where distances exceed the 95th percentile value (i.e., MOEs are of concern at these

distances for 5% of those exposed). These exceedances have been examined using the PERFUM MOE

program which was used in conjunction with the air model itself (see SAP site for more details).
26

Figure 3: Example Daily PERFUM Output For A 5 Acre

Field

PERFUM generates the type of output illustrated by Figure 3 for each day over a 5 year period (i.e.,

1825 days) then summarizes the information by providing two types of results including the “Maximum

Buffer” distance and the “Whole Field Buffer” distance. Each is reported as a distribution. The

“Maximum Buffer” distribution is based on the maximum distance needed to reach a threshold level of

concern (i.e., HEC adjusted by uncertainty factor) calculated using PERFUM for each day (i.e., the

farthest single point on the irregular line for each day). This results in a distribution that contains 1825

values, and in this assessment, the results have been reported for selected percentiles from those

distributions. The “Whole Field Buffer” is based on values from each day as well except the distances

on which the distribution is based include those on each spoke where the threshold concentration is

achieved for each day (i.e., distances on all spokes where threshold is achieved or the distances on the

irregular line where it intersects each spoke). The number of values in these distributions varies and it is

based on 1825 days multiplied by the number of spokes around the field which relates to field size (see

Table 2 above). As with the “Maximum Buffer” distances, results from selected percentiles from the

distribution have been reported.

Results based on the PERFUM Modeling Method are presented below in Section 6.1.1.3 (also refer to 

analysis plan described in Table 3).

6.1.1.1 Bystander Exposures From Known Area Sources Calculated Using

The Monitoring Data Method

Exposures of and the risks to bystanders from pre-plant agricultural field fumigations calculated using

monitoring data are presented in this section. Air concentrations based on volatility data were generally

reported on a per sample basis (e.g., 4 hour duration air samples) and as 24-hour time-weighted

averages (TWAs). These were calculated using field volatility data that were usually collected around

the perimeter of treated sites including downwind locations. Samples were collected at mast sites
27

located within 150 feet or so from the perimeter of the treated field, depending upon the study design. 

It is clear, as described above that downwind locations will have higher concentrations associated with

them because emitted plumes will be pushed in that direction if there is a prevailing wind. For the data

considered in this assessment, wind directions varied which makes defining “downwind locations” for

the available data complex. There are other micrometeorological and site specific factors (e.g.,

topography and roughness) which also add to the complexity of the analysis.

Given these difficulties, and to ensure that this assessment is health protective the Agency has

calculated acute MOEs using the 24 hour maximum TWA from the monitoring data for each site where

data are available. These maximum TWAs were compared to both of the acute HECs to calculate

MOEs based on each toxicological endpoint of concern. The highest TWAs were always observed in

the first 24 hours after application.

For the majority of the pre-plant field volatility data collected (10 of 18 site/HEC combinations), risks

do not exceed HED’s acute level of concern (i.e., MOEs >30). Results for the maximum monitored

values from each treated field are presented below in Table 4. The field volatility monitoring data upon

which this analysis is based are presented and summarized in Appendix D: Analysis Of Field Volatility

Data For Pre-plant Field Uses contains these data.

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

Flux Data Source: Field Location & 

Application Method

 Maximum 24 Hour TWA 

(ppm)

Acute MOEs Based On Each HEC

2.9 ppm HEC 4.0 ppm HEC

Manteca CA

 Broadcast Flat Fume

1.988 (site near/in-field while others on

perimeter - represents worst case) 1.5 2.0

Watsonville CA

 Broadcast Flat Fume 0.061 48 66

Plant City FL*

 Raised Bed 0.066 44 60

Oxnard CA

 Raised Bed 0.347 8.4 12

Guadalupe CA

 Raised Bed 0.103 28 39

La Selva CA

 Drip Irrigation 0.071 41 56

Camarillo CA

 Drip Irrigation 0.06 48 66

Guadalupe CA

Drip Irrigation 0.12 24 33

Note: The Agency is aware that additional field volatility monitoring data have been completed by the registrant for iodomethane. 

Also, the application rates for these studies ranged from 234 to 259 lb ai/acre and the data were not adjusted for rate and samplers

were located from 12 to 30 feet from the treated field.

* There was a rain event during this study 24 hours after application or so. The TWAs are presented here for comparative purposes. 
28

6.1.1.2 Bystander Exposures From Known Area Sources Calculated Using

The ISCST3 Modeling Method

Exposures to bystanders from pre-plant agricultural field fumigations and their associated risks,

calculated using a modeling approach, are presented in this section. The previous section 6.1.1.1

describes exposures based on actual field volatility monitoring data. However, these data are limited

because they represent only the conditions in which the studies were actually conducted. Therefore, in

order to better characterize the risks associated with the use of iodomethane for various conditions

(e.g., distance from emission source, atmospheric conditions, application method, etc.), exposures have

also been calculated using the Agency’s ISCST3 model. 

The risk estimates (MOEs or Margins of Exposure) presented below represent results for the acute

duration of exposure because they compare 24 hour concentrations calculated with ISCST3 to both of

the acute HECs of concern (i.e., 2.9 or 4.0 ppm with a total uncertainty factor of 30 applied to both). 

Results for various field sizes, application methods (with distinct emission ratios or fraction of the

applied material emitted per unit of time); wind speed; atmospheric stability and distances downwind

are presented below in Table 5. Table 5 demonstrates that for the cases considered, many risks exceed

HED’s level of concern (MOEs <30) for distances less than 100 meters downwind of the treated field 

especially when the atmosphere is relatively stable and/or fields are larger than 1 acre. MOEs decrease

as field sizes increase while MOEs increase as the atmosphere becomes less stable leading to conditions

where more off-target drift can occur. There is not a significant impact in the results due to the two

different HECs that were considered. The results in this table summarize the analysis presented in

Appendix E: Downwind Air Concentrations Calculated With ISCST3 For PrePlant Field Uses.

[Note: Appendix E contains a summary of the outputs generated by ISCST3 for more distances

downwind and field sizes. It does not contain detailed input and output files needed to complete

calculations with ISCST3. If so required, these can be provided for review and validation purposes.]

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

Flux

Data

Source

App.

Method

ER Fld

Size

(A)

Distance

(M)

 MOEs For Differing Meteorological Conditions

1 m/s

2.3

mph

1.4 m/s

3.1 mpha

(a)

1.8 m/s

4 mph

2.2 m/s

5 mph

2.7 m/s

6 mph

3.1 m/s

7 mph

3.6 m/s

8 mph

4.0 m/s

9 mph

4.5 m/s

10 mph

4.5 m/s

10 mph

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

MOEs Based On HEC = 2.9 ppm (16802 μg/m3

)

Manteca

CA

Shank

Injection,

Broadcast,

Flat Fume

0.47 1 25 8 17 22 27 33 38 44 49 55 79

100 18 43 55 67 82 94 109 122 137 230

500 99 352 452 553 679 779 905 1006 1131 2644

1000 286 1142 1469 1795 2203 2530 2938 3264 3672 9774

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

100 5 12 15 19 23 26 30 34 38 56

500 12 30 38 47 57 66 77 85 96 170

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

Flux

Data

Source

App.

Method

ER Fld

Size

(A)

Distance

(M)

 MOEs For Differing Meteorological Conditions

1 m/s

2.3

mph

1.4 m/s

3.1 mpha

(a)

1.8 m/s

4 mph

2.2 m/s

5 mph

2.7 m/s

6 mph

3.1 m/s

7 mph

3.6 m/s

8 mph

4.0 m/s

9 mph

4.5 m/s

10 mph

4.5 m/s

10 mph

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

29

Watsonville

CA

Shank

Injection,

Broadcast,

Flat Fume

0.35 1 25 11 23 30 36 45 51 60 66 74 106

100 24 57 74 90 111 127 147 164 184 310

500 133 474 6609 744 914 1049 1218 1353 1523 3559

1000 385 1538 1977 2416 2965 3405 3954 4393 4942 13155

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

100 7 16 21 24 31 35 41 46 51 75

500 16 40 52 63 77 89 103 115 129 228

1000 24 73 94 115 141 162 188 209 236 532

Oxnard

CA

Shallow

Shank

Injection,

Raised Bed

0.37 1 25 10 22 28 34 42 48 56 62 70 99

100 23 54 69 84 103 119 138 153 172 290

500 125 443 570 696 854 981 1139 1266 1424 3329

1000 360 1438 1849 2260 2773 3184 3698 4109 4622 12304

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

100 6 15 19 23 29 33 38 43 48 71

500 15 38 48 59 72 83 96 107 121 213

1000 22 69 88 108 132 152 176 196 220 497

La Selva

CA

Drip

Irrigation,

Raised Bed

0.51 1 25 13 28 36 44 54 62 72 80 90 127

100 29 69 89 108 133 153 177 197 222 373

500 160 571 734 897 1100 1263 1467 1630 1834 4287

1000 464 1852 2381 2910 3572 4101 4763 5292 5953 15846

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

100 8 19 25 30 37 43 49 55 62 91

500 19 48 62 76 93 107 124 138 155 275

1000 29 88 113 139 170 195 227 252 284 640

MOEs Based On HEC = 4.0 ppm (23176 μg/m3

)

Manteca

CA

Shank

Injection,

Broadcast,

Flat Fume

0.47 1 25 11 24 31 37 46 53 61 68 76 108

100 25 59 76 92 113 130 151 168 189 317

500 137 485 624 763 936 1075 1248 1387 1560 3647

1000 395 1576 2026 2476 3039 3489 4052 4502 5065 13482

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

100 7 16 21 26 32 36 42 47 53 77

500 16 41 53 65 79 91 106 117 132 234

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

Flux

Data

Source

App.

Method

ER Fld

Size

(A)

Distance

(M)

 MOEs For Differing Meteorological Conditions

1 m/s

2.3

mph

1.4 m/s

3.1 mpha

(a)

1.8 m/s

4 mph

2.2 m/s

5 mph

2.7 m/s

6 mph

3.1 m/s

7 mph

3.6 m/s

8 mph

4.0 m/s

9 mph

4.5 m/s

10 mph

4.5 m/s

10 mph

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

30

Watsonville

CA

Shank

Injection,

Broadcast,

Flat Fume

0.35 1 25 15 32 41 50 62 71 82 91 103 146

100 33 79 102 124 152 175 203 226 254 427

500 184 653 840 1027 1260 1447 1680 1867 2100 4909

1000 531 2121 2727 3333 4090 4696 5454 6060 6817 18146

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

100 9 22 28 35 42 49 57 63 71 104

500 21 55 71 87 107 123 142 158 178 315

1000 33 101 130 159 195 224 260 289 325 733

Oxnard

CA

Shallow

Shank

Injection,

Raised Bed

0.37 1 25 14 30 38 47 58 66 77 85 96 137

100 31 74 95 116 143 164 190 211 238 399

500 172 611 786 960 1179 1653 1571 1746 1964 4591

1000 497 1984 2550 3117 3826 4392 5101 5668 6376 16972

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

100 9 21 26 32 40 46 53 59 66 97

500 20 52 67 81 100 115 133 148 166 294

1000 31 95 122 149 182 209 243 270 304 686

La Selva

CA

Drip

Irrigation,

Raised Bed

0.51 1 25 18 38 49 60 74 85 99 110 124 176

100 40 95 122 150 184 211 245 272 306 514

500 221 787 1012 1237 1518 1743 2024 2249 2530 5913

1000 640 2555 3285 4015 4927 5657 6569 7299 8212 21858

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

100 11 27 34 42 51 59 68 76 85 125

500 26 67 86 105 129 157 171 190 214 379

1000 40 122 157 191 235 270 313 348 391 883

a. Standard atmospheric conditions used by CDPR in their risk assessments.

6.1.1.3 Bystander Exposures From Known Area Sources Calculated Using

The PERFUM Modeling Method

Exposures to bystanders from pre-plant agricultural field fumigations and their associated risks,

calculated using a modeling approach based on PERFUM, are presented in this section. The previous

sections 6.1.1.1 and 6.1.1.2 describe exposures based on actual field volatility monitoring data and a

deterministic modeling approach using ISCST3. However, these data are limited because they

represent only the conditions in which the studies were actually conducted or the constrained

meteorological conditions in ISCST3. Therefore, in order to better characterize the risks associated
31

with the use of iodomethane for various conditions (e.g., distance from emission source, actual

meteorological conditions, application method, etc.), exposures have also been calculated using

PERFUM. 

The risk estimates presented below represent results for the acute duration of exposure because they

compare 24 hour concentrations calculated with PERFUM to the two acute HECs of concern (i.e., 2.9

ppm or 4.0 ppm, each with a total uncertainty factor of 30). Results for selected percentiles of

exposure are reported and in many cases results are based on 95th percentile values as an example (e.g.,

MOE exceedance analysis). Use of the 95th percentile (or results for any other percentile) for reporting

purposes does not imply a selection of these estimates for regulatory purposes. Additional analysis

based on other percentiles of exposure could be completed if so needed. The results presented in

Appendix F: Downwind Air Concentrations Calculated With PERFUM For PrePlant Field Uses are

summarized in this section. Please refer to Appendix F for additional details. [Note: Appendix F itself

contains a summary of the outputs generated by PERFUM for both HECs of concern. It does not

contain detailed input and output files needed to complete calculations with PERFUM. If so required,

these can be provided for review and validation purposes. Also, note in tables specific cells are

highlighted for illustrative purposes throughout this section (e.g., 95th %tile results at maximum rate and

maximum buffer distances).]

Appendix F contains several sub-appendices (Appendices F1a through F5f) that include a series of

spreadsheets that summarize the results of each analysis described above in Table 3 (i.e., various

combinations of meteorological data and flux/application methods). [Note: Appendix F6 contains a

series of summary analyses used to develop many of the graphics and tables provided below. Please

refer there for further information regarding the details of the summary calculations.] Table 6 and

Figure 4 below provide an example of how the results were summarized for each meteorological/flux

data combination considered in this analysis using Ventura California and Watsonville flux data. Table

6 contains results for both HECs considered. Figure 4 represents only the results for the HEC = 4 ppm

and an uncertainty factor of 30. The general trend is similar for the results based on the HEC of 2.9

ppm

Table 6: Buffer Distances For Ventura CA Weather And Watsonville CA Flat Fume Flux

%tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm

Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

Maximum Buffer Distances (meters)

50 0 50 0 15 0 5 0 10 0 5 0 0

75 5 90 0 40 0 5 0 35 0 5 0 5

90 5 150 5 80 0 15 5 75 0 30 0 5

95 5 210 5 125 0 40 5 115 0 50 0 5

97 5 265 5 165 0 65 5 155 0 80 0 10

99 15 375 5 275 5 140 5 265 5 170 0 65

99.9 60 700 35 510 5 360 35 495 5 395 5 225

99.99 65 710 45 565 5 385 45 550 5 405 5 275
Table 6: Buffer Distances For Ventura CA Weather And Watsonville CA Flat Fume Flux

%tiles Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm

Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre) Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

1 Acre

Square

40 Acres

Square

32

Iod o meth an e: M axim u m D istance B u ffers

V entura CA CIM IS & Watsonville Flat Fum e (M RID 455937-10)

0

100

200

300

400

500

600

5

15253545556575859599

99.99

P ercen tile of E xpo su re

Buffer Distance (meters)

1A-Max

40A-Max

1A-75%

40A-75%

1A-50%

40A-50%

Figure 4: Maximum Distance Buffers For Ventura California Weather And

Watsonville Flat Fume Flux Based On HEC = 4 ppm & UF = 30

Whole Field Buffer Distances (meters)

50 0 0 0 0 0 0 0 0 0 0 0 0

75 0 5 0 5 0 0 0 5 0 0 0 0

90 0 40 0 10 0 5 0 10 0 5 0 0

95 5 70 0 30 0 5 0 25 0 5 0 5

97 5 90 0 45 0 5 0 40 0 10 0 5

99 5 155 5 90 0 30 5 85 0 40 0 5

99.9 15 350 5 260 5 145 5 250 5 165 0 50

99.99 55 630 30 475 5 350 25 465 5 355 5 220

The information in Table 6 (and Appendix F for each analysis) can be used to determine the range of

buffers calculated by PERFUM for each analysis (see Table 3). As expected, 1 acre fields have

significantly smaller buffer distances than 40 acre fields. Additionally, reductions in application rate

impact the amount of iodomethane volatilizing into the atmosphere from treated fields and also lower

buffer distances. The differences between considering “Maximum Buffer” versus “Whole Field

Buffer” distances can also be defined. Differences in HECs also impact the results with the higher HEC

producing lower buffer distances as expected but it should be noted that the general trends associated

with this analysis do not change regardless of which of the two HECs are considered. Figure 4

summarizes the “Maximum Buffer” results for all of the percentiles included in the calculation for 1

and 40 acre fields at varying application rates for Ventura California and using the Watsonville Flat

Fume flux data based on an HEC = 4 ppm. The types of results described above can be reviewed in

more detail in Appendix F of this document. 

PERFUM is also capable of examining the changes observed in MOEs (Margins of Exposure) under

different conditions. For example, the cross-hatched area in Figure 3 which represents exceedances can

be examined. [Note: The overall PERFUM analysis considered meteorological data from 5 different
33

Iodomethane: M OE Analysis For 95%tile Max Rate

Ventura CA CIMIS & Watsonville Flat Fume (MRID 455937-10)

1

10

100

1000

90 95 97 99 99.9

Pe rcentile of Exposure

Margins Of Exposure 

(Target = 30)

M ax-100%

W hole-100%

M ax-75%

W hole-75%

M ax-50%

W hole-50%

Figure 5: MOE Analysis For 95th Percentile Maximum Buffer Distance

of 115 Meters Based On Ventura CA Meteorological And Watsonville

Flat Fume Flux Data (HEC = 4 ppm)

regions of the country including coastal and inland locations in California and Florida as well as a

location in Michigan. Several of the trends in the results are expected to be similar regardless of the

location considered so for the purposes of this assessment, as such, only results for Ventura California

were considered. PERFUM outputs are available for all locations if additional analyses are required.]

Table 7 and Figure 5 below present the results of the MOE analysis for Ventura California and

Watsonville flux data as an example using both HECs of concern. Also, note that the MOE analyses

have only been completed using 40 acre fields which allow for better determination of the trends. The

general trend illustrated in Figure 5 is similar for the results based on the HEC of 2.9 ppm.

Table 7: MOE Analysis For 95th Percentile Buffer Distance(260 meters) In A 40 Acre Field Using Ventura California

Meteorological And Watsonville Flat Fume Flux Data

Percentiles Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

Max Buffer Whole Field Max Buffer Whole Field Max Buffer Whole Field

Results Based On HEC = 2.9 ppm

50 59 1000 79 1000 118 1000

75 46 1000 62 1000 92 1000

90 36 76 48 102 72 149

95 29 57 38 77 58 115

97 24 49 32 66 49 98

99 17 35 23 48 35 71

99.9 8 18 11 24 17 37

99.99 Not. Calc. 9.9 Not. Calc. 13 Not. Calc. 20

Results Based On HEC = 4.0 ppm

50 57 1000 77 1000 115 1000

75 45 124 60 1000 90 1000

90 35 67 47 90 70 135

95 29 53 39 70 58 105

97 25 45 33 61 50 91

99 17 33 23 45 35 67

99.9 8 17 10 23 16 35

99.99 Not. Calc. 9.4 Not. Calc. 12 Not. Calc. 18

Note: MOE analysis is based on the 95th percentile maximum buffer distance at the maximum application rate which is 210 and

115 meters, respectively for HECs = 2.9 and 4.0 ppm (see Table 6 above). The results shown in this table are actual MOEs and

the target uncertainty factor is 30 (i.e., 29 in this case from rounding in PERFUM) which is reported here at the corresponding

95th percentile. This table illustrates how MOEs would change if whole field buffers, different application rates, or different

percentiles of exposure were considered for risk management purposes.
34

The information in Table 7 and Figure 5 (and Appendix F for each analysis) show that MOEs increase

at a fixed point (e.g., 115 meters in this example) if application rates decrease or whole field buffers are

considered instead of maximum distance buffers. Figure 5 also clearly illustrates the rates of change in

MOEs over different percentiles of exposure. Even at percentiles of exposure greater than the 95th,

MOEs are not substantially lower (i.e., most MOEs are still $ 10). Thus suggesting that risks are not

changing dramatically at the higher percentiles of exposure.

The analyses completed with PERFUM are based on 5 weather station locations and 8 flux studies. In

order to broadly understand how differing application methods and the impact of use in different

regions (i.e., climates) can impact buffer zone distances, it is necessary to group the outputs from

PERFUM for comparative purposes. Table 8 and Figure 6 are examples of the results for both

maximum and whole field buffers in Ventura California at different application rates for all flux studies

available for iodomethane rather than just based on the Watsonville study as described above in Table 7

and Figure 5. Results are presented based on both acute HECs of concern (i.e., 2.9 and 4.0 ppm) for

comparative purposes. An analysis of MOE changes under different conditions was also completed

using Ventura Meteorological data and all appropriate flux studies. These are presented below in Table

9 and Figure 7. The MOE analysis uses the results for the HEC of 4 ppm based on the results in Table

8 which indicate that the trends would be similar for the results of both HECs.

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

Percen

tiles

Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm

Maximum PERFUM Buffers

(meters)

Whole PERFUM Buffers

(meters)

Maximum PERFUM Buffers

(meters)

Whole PERFUM Buffers

(meters)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

Flux - Watsonville CA Flat Fume

75 5 90 0 40 0 5 0 5 0 5 0 0 0 35 0 5 0 5 0 5 0 0 0 0

90 5 150 5 80 0 15 0 40 0 10 0 5 5 75 0 30 0 5 0 10 0 5 0 0

95 5 210 5 125 0 40 5 70 0 30 0 5 5 115 0 50 0 5 0 25 0 5 0 5

99 15 375 5 275 5 140 5 155 5 90 0 30 5 265 5 170 0 65 5 85 0 40 0 5

99.9 60 700 35 510 5 360 15 350 5 260 5 145 35 495 5 395 5 225 5 250 5 165 0 80

Flux - Manteca CA Flat Fume

75 5 145 5 75 0 15 0 25 0 5 0 0 5 70 0 25 0 5 0 5 0 5 0 0

90 5 210 5 125 0 45 0 75 0 35 0 5 5 115 0 55 0 5 0 30 0 5 0 5

95 5 275 5 175 0 75 5 110 0 60 0 10 5 165 5 90 0 20 0 55 0 20 0 5

99 35 480 5 340 5 195 5 210 5 130 0 55 5 330 5 225 0 105 5 125 0 70 0 15

99.9 60 800 35 545 5 330 30 420 5 310 5 185 35 525 5 355 5 210 5 300 5 205 0 110

Flux - Oxnard CA Raised Bed

75 5 320 0 200 0 80 0 20 0 5 0 0 0 190 0 100 0 5 0 5 0 5 0 0

90 5 465 0 315 0 155 0 115 0 65 0 10 5 300 0 185 0 65 0 60 0 20 0 5

95 5 565 0 390 0 200 0 215 0 135 0 50 5 370 0 235 0 95 0 125 0 65 0 5

99 45 730 0 515 0 295 5 440 0 300 0 155 5 490 5 330 0 175 5 285 0 180 0 70

99.9 80 960 5 680 0 410 30 665 0 485 0 280 15 655 5 460 0 230 5 465 0 320 0 165

Flux - Guadalupe CA Raised Bed

75 5 335 5 220 0 95 0 40 0 15 0 5 5 205 0 115 0 25 0 15 0 5 0 0

90 5 480 5 330 0 170 0 135 0 80 0 25 5 315 0 195 0 80 0 75 0 35 0 5

95 30 590 5 410 0 225 5 230 0 145 0 60 5 395 5 260 0 115 0 140 0 75 0 10

99 65 775 25 560 5 335 5 445 5 310 0 170 15 540 5 370 0 210 5 300 0 190 0 85

99.9 95 1120 60 805 5 510 50 710 15 510 5 315 55 775 5 560 5 320 10 495 5 350 0 195

Flux - La Selva CA Drip

75 5 220 0 120 0 30 0 10 0 5 0 0 0 115 0 45 0 5 0 5 0 0 0 0

90 5 350 0 220 0 90 0 75 0 35 0 5 0 210 0 110 0 50 0 30 0 5 0 5

95 5 480 5 315 0 145 0 145 0 80 0 20 5 300 0 175 0 85 0 75 0 30 0 5

99 40 660 5 455 0 245 5 320 5 215 0 100 5 435 5 285 0 175 5 205 0 115 0 60

99.9 65 850 5 615 5 345 20 580 5 410 0 225 5 590 5 390 0 260 5 395 5 255 0 165
Percen

tiles

Results Based On HEC = 2.9 ppm Results Based On HEC = 4.0 ppm

Maximum PERFUM Buffers

(meters)

Whole PERFUM Buffers

(meters)

Maximum PERFUM Buffers

(meters)

Whole PERFUM Buffers

(meters)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

Max 

(175 lb/A)

75% 

(131 lb/A)

50% 

(88 lb/A)

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

1 Acre 40

Acres 

35

Flux - Camarillo CA Drip Irrigation

75 0 85 0 35 0 5 0 5 0 5 0 0 0 30 0 5 0 5 0 5 0 0 0 0

90 5 145 0 75 0 25 0 35 0 5 0 5 0 65 0 20 0 5 0 5 0 5 0 0

95 5 195 0 105 0 50 0 65 0 25 0 5 0 100 0 40 0 5 0 20 0 5 0 5

99 5 350 5 230 0 145 5 150 0 85 0 35 5 220 0 135 0 70 0 75 0 30 0 5

99.9 50 625 5 460 5 325 5 325 5 225 0 160 5 440 5 305 0 215 5 215 0 140 0 80

Flux - Guadalupe CA Drip Irrigation

75 5 190 5 110 0 35 0 40 0 15 0 5 5 105 0 50 0 5 0 15 0 5 0 0

90 15 270 5 175 5 75 5 100 0 55 0 10 5 165 5 90 0 20 0 50 0 20 0 5

95 20 330 5 220 5 110 5 145 5 85 0 30 5 210 5 125 0 45 5 80 0 35 0 5

99 55 640 30 445 5 280 10 265 5 175 5 85 20 435 5 305 5 160 5 170 5 100 0 35

99.9 105 1380 70 910 35 545 45 545 25 410 5 260 65 875 405 605 5 355 20 395 5 265 5 160

Table 9: Summary Of Ventura California MOEs For All Flux Studies And 40 Acre Fields Using 95th Percentile Maximum Buffer Distances (HEC = 4 ppm)

Percentiles MOEs For Ventura California Meteorological Conditions And All Flux Studies

Max (175 lb/Acre) 75% (131 lb/Acre) 50% (88 lb/Acre)

Max Whole Max Whole Max Whole

Watsonville Flat Fume - 95th Percentile Maximum Distance = 115 Meters

75 45 124 60 100 90 1000

90 35 67 47 90 70 135

95 29 53 39 70 58 105

99 17 33 23 45 35 67

99.9 8 17 10 23 16 35

Manteca Flat Fume - 95th Percentile Maximum Distance = 165 Meters

75 43 127 58 1000 87 1000

90 35 65 47 87 70 131

95 29 52 39 69 58 103

99 17 34 24 46 36 69

99.9 9 18 11 24 16 37

Oxnard Raised Bed - 95th Percentile Maximum Distance = 370 Meters

75 45 1000 61 1000 91 1000

90 33 102 45 136 67 1000

95 29 62 39 83 58 124

99 23 35 31 47 46 71

99.9 18 24 24 32 36 49

Guadalupe Raised Bed - 95th Percentile Maximum Distance = 395 Meters

75 45 1000 61 1000 91 1000

90 34 98 46 131 69 1000

95 29 63 39 84 58 126

99 22 36 30 49 45 73

99.9 16 24 22 32 33 48

La Selva Drip - 95th Percentile Maximum Distance = 300 Meters

75 49 1000 65 1000 98 1000

90 36 111 48 149 73 1000

95 29 70 39 93 58 139

99 22 38 29 51 44 77

99.9 16 24 21 32 31 48

Camarillo Drip Irrigation - 95th Percentile Maximum Distance = 100 Meters

75 43 108 57 145 86 1000

90 33 62 45 83 68 124

95 29 49 39 66 58 98

99 18 32 24 44 37 65

99.9 9 18 11 24 17 36

Guadalupe Drip Irrigation - 95th Percentile Maximum Distance = 210 Meters

75 45 1000 61 1000 91 1000

90 34 98 46 131 69 1000

95 29 63 39 84 58 126

99 22 36 30 49 45 73

99.9 18 24 22 32 33 48
36

9 5 th P e rc e n tile Io d o m e th a n e B u ffe r R e s u lts F o r 4 0 A c re 

F ie ld s F o r V e n tu ra C A & A ll F lu x P ro file s 

(H E C 4pp m & U F = 30)

0

5 0

100

150

200

250

300

350

400

450

M a x R a te /D is t 7 5 % R a te /M a x D is t. 5 0 % R a te /M a x D is t. M a x R a te /W h o le 7 5 % R a te /W h o le 5 0 % R a te /W h o le

A p p lic a tio n R a te & P E R F U M F ie ld A p p ro a c h

Buffer Distance (m)

F lat F um e -

W atson.

F lat F um e -

M anteca

R a is e d B e d -

O x nard

R a is e d B e d -

Guad.

D r ip - L a S e lva

D rip - C am ar.

D rip - G u ad.

Figure 6: 95th Percentile Iodomethane Buffer Distances For 40 Acre Fields,

Ventura California Meteorological Data, All Applicable Flux Profiles, Varied

Application Rates, And HEC = 4 ppm.

0

20

40

60

80

100

120

140

160

180

200

MOEs At 95th Percentile Maximum Buffer 

Distance

Watson.115M Manteca 165 M Ox nard 370 M Guad.RB 395 M LaSelva 300 M Camar. 100 M Guad.Dr 210 M

Flux Source & 95th Percentile M aximum Buffer (m)

Max (175 lb/Acre)

Max

Max (175 lb/Acre)

W hole

75% (131 lb/Acre)

Max

75% (131 lb/Acre)

W hole

50% (88 lb/Acre)

Max

50% (88 lb/Acre)

W hole

Figure 7: 95th Percentile MOE Analysis For 40 Acre Fields, Ventura

California Meteorological Data, All Available Flux Profiles, Varied

Application Rates , And HEC = 4 ppm

The information in Table 8 and Figure 6 (and Appendix F for each analysis) shows that buffers decrease

if application rates decrease or whole field buffers are considered instead of maximum distance buffers. 

Figure 6 also clearly illustrates that fields where raised bed flux is used as an application method tend to

emit more iodomethane than the other methods considered in this analysis. It also appears that at least

for La Selva, California that drip irrigation also emits faster than in the other locations considered. It is

unclear, however, that drip irrigation inherently emits faster than flat fume techniques based on the

Camarillo drip irrigation study. Table 9 and Figure 7 indicate that similar trends in MOE changes with

conditions are noted with the Ventura California meteorological data regardless of the flux profile under

consideration. It is likely that similar conclusions would be drawn for other weather station locations

but the relative differences might be affected.

Along with evaluating the trends among different types of flux results (i.e., application methods) at a

specific location, it is also important to understand how regional weather differences might impact buffer

distances. The results for the maximum application rate of 175 lb ai/acre have been summarized in

Tables 10 and 11 as well as Figures 8 through 11 below for all weather stations and flux profiles

available for this analysis (5 weather stations and 8 flux profiles). Table 10 presents the results for
37

maximum buffer distances for each HEC of concern and all flux rates for 1 and 40 acre fields at differing

percentiles of exposure. Figures 8 and 9 present for both HECs of concern the maximum buffer results

only for 40 acre fields. Table 11 and Figures 10 and 11 are similar except that they present the whole

field buffer results. These tables and figures provide the broadest overall results for the PERFUM

analysis that has been completed for iodomethane because they can be used to compare differences

based on HEC, region, field size, and flux rate.

Table10:Summary Of PERFUM Maximum Buffer Results For All Weather Stations And Flux Types With 1 And 40 Acre Fields At Maximum Application Rate Of 175 lb ai/A

PercentilesResults 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

PercentilesResults 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 -

W atsonville

Flat Fum e -

Manteca

R aised Bed -

Plant City

Raised Bed -

Oxnard

Raised Bed -

Guadalupe

Drip - La Selva Drip - Camarillo Drip -

Guadalupe

Source of Flux Profile

Buffer Distance (meters)

Ventura CA T allahassee FL Flint MI Bradenton FL Bakersfield CA

Figure 8: Summary of 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

100

200

300

400

500

600

700

Flat Fume -

W atsonville

Flat Fum e -

Manteca

R ais ed Bed -

Plant C ity

Raised Bed -

Oxnard

Raised Bed -

Guadalupe

D rip - La Selva D rip - C am arillo D rip -

Guadalupe

S ource of Flux P rofile

Buffer Distance (meters)

Ventura CA T alla has see F L F lint MI Bradenton FL Ba ke rsfield CA

Figure 9: Summary of 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 Fum e -

W atsonville

Flat Fum e -

Manteca

Raised Bed -

Plant City

Rais ed Bed -

Oxnard

Rais ed Bed -

Guadalupe

Drip - La Selva Drip - Cam arillo Drip -

Guadalupe

Source of Flux Profile

Buffer Distance (meters)

Ventura CA T a llah asse e F L F lint MI Bradenton FL Ba ke rsfie ld CA

Figure 10: Summary of 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 Fum e -

W atsonville

Flat Fum e -

Manteca

Raised Bed -

P lant City

Raised Bed -

O xnard

Raised Bed -

Guadalupe

D rip - La Selva D rip - Cam arillo Drip -

Guadalupe

Source of Flux Profile

Buffer Distance (meters)

Ventura CA T allahassee F L Flint MI Bradenton FL Bakersfield CA

Figure 11: Summary of 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

100

150

200

250

300

350

400

450

1 5 10 20 40

F ie ld S iz e (a c re s )

Buffer Distance (meters)

W a t s o n v ille

M a n te ca

O xn ard

G u a d a lu p e

TRB

L a S e lv a

C a m a r illo

G u a d a lu p e

D rip

Figure 12: Impact Of Changing Field Size On Maximum Buffer Distances,

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 PhasedExposure Developmental

Toxicity - Rabbit

46077001 (2003)

Acceptable/non-guideline

0 or 20 ppm from GD6-28;

20 ppm from GDs 6-14, 15-22, 23-24,

25-26, or 27-28 in a whole-body

inhalation chamber 6hrs/day

This study was not intended to fulfill the

guideline requirement or establish NOAELs and

LOAELs but rather was conducted to determine

the critical period of exposure during gestation

that resulted in fetal loss as observed in a

previously evaluated guideline developmental

toxicity study in rabbits. Increased fetal losses at

20 ppm on GD 6-28 (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/CarcinogenicityRat.

46512401 (2005)

Acceptable/non-guideline

0, 5, 10, 60 ppm in a whole body

inhalation chamber for 6 hrs/day,

5days/week

Systemic NOAEL = 5 ppm

Systemic LOAEL = 20 ppm based on increased

incidence of salivary gland squamous cell

metaplasia.

Portal of entry NOAEL = 20 ppm

Portal of entry LOAEL = 60 ppm based on

degeneration of the olfactory epithelium. 

At 60 ppm, perturbations of the thyroidpituitary axis as well thyroid histopathology

findings were reported.

870.5100

Bacterial Reverse

Mutation Test (Ames

Assay)

45593813 (2001) Nonmutagenic in Salmonella typhimurium

strains TA98, TA100, TA1535, and TA1537;

and in Escherichi coli.

870.5300

In Vitro Mammalian Cell

Mutation Test in Chinese

Hamster Ovary Cells

45593815 (2001) Negative

870.5375

In Vitro Chromosomal

Aberration in Chinese

Hamster Ovary

45593814 (2001) 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 t1⁄2 was 5.1-7.2 hours, and terminal t1⁄2 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 t1⁄2 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 t1⁄2 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 study * RGDR animal exposure (hrs / day)

human exposure (hrs / day)

animal exposure (days / wk)

human exposure (days / wk)

Where:

PODstudy: Point of departure identified in the critical toxicology study

Danimal exposure: Duration of animal exposure (hrs/day; days/wk)

Danticipated 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 (Hb/g animal/Hb/g human). When this ratio is unknown or when the Hb/g animal >

Hb/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 = b0 + b1ln(BW)
Where:

ln MV animal : natural logarithm of the minute volume

b0 : species specific intercept used in the algorithm to calculate minute volumes based on

body weight

b1: 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