Document ID: EPA-HQ-OPP-2005-0252-0005
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2006-01-06T05:00Z

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
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1
2.0
Ingredient
Profile
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6
3.0
Metabolism
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6
3.1
Description
of
Primary
Crop
Metabolism
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6
3.2
Description
of
Livestock
Metabolism
.
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7
3.3
Description
of
Rat
Metabolism
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7
4.0
Hazard
Characterization/
Assessment
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7
4.1
Hazard
Characterization
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7
4.1.1
Database
Summary
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7
4.1.2
Endpoints
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9
4.1.3
Dose­
response
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11
4.1.3.1
Inhalation
Exposure
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11
4.1.3.2
Dietary
Exposure
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15
4.1.3.3
Dermal
Exposure
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16
4.1.3.4
Classification
of
Carcinogenic
Potential
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16
4.1.4
Endocrine
Disruption
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16
4.2
Uncertainty
Factors
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17
4.3
Summary
of
Toxicological
Endpoint
Selection
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17
5.0
Public
Health
Data
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18
6.0
Non­
Occupational
Exposure
Assessment
and
Characterization
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18
6.1
Residential
Bystander
Exposure
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19
6.1.1
Bystander
Exposure
From
Known
Area
Sources
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19
6.1.1.1
Bystander
Exposures
From
Known
Area
Sources
Calculated
Using
The
Monitoring
Data
Method
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26
6.1.1.2
Bystander
Exposures
From
Known
Area
Sources
Calculated
Using
The
ISCST3
Modeling
Method
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28
6.1.1.3
Bystander
Exposures
From
Known
Area
Sources
Calculated
Using
The
PERFUM
Modeling
Method
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30
6.1.2
Ambient
Bystander
Exposure
From
Multiple
Area
Sources
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43
6.2
Bystander
Risk
Characterization
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43
6.3
Residue
Profile
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47
6.4
Water
Exposure/
Risk
Pathway
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47
7.0
Aggregate
Risk
Assessment
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47
8.0
Cumulative
Risk
Assessment
and
Characterization
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47
9.0
Occupational
Exposure
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48
10.0
Data
Needs
and
Label
Requirements
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51
10.1
Toxicology
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51
10.2
Residue
Chemistry
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51
10.3
Occupational
and
Residential
Exposure
.
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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
LC
50
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:
CH
3
I
Molecular
Weight:
141.95
CAS
Registry
No.:
74­
88­
4
PC
Code:
000011
Chemical
Class:
Alkyl
Iodide
Iodomethane
is
a
colorless,
liquid
at
normal
temperatures
and
pressures
and
has
a
sweet
ethereal
odor.
Iodomethane
has
a
specific
gravity
of
2.28
at
20
°
C,
vapor
pressure
of
375
torr
at
20
°
C,
boiling
point
of
42.5
°
C,
and
octanol/
water
partition
coefficient
(
log
P
ow
)
of
1.51.
It
is
soluble
in
water
at
1.75
g/
100
mL
at
20
C,
and
is
miscible
in
alcohols
and
ether.

Iodomethane
is
a
pre­
plant
soil
biocide
used
to
control
insects,
plant
parasitic
nematodes,
soil
borne
pathogens,
and
weed
seeds.
The
proposed
uses
are
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
4Zhu,
Y.
et
al.
"
Excess
iodine
induces
the
expression
of
thyroid
solid
cel
nests
in
lymphocytic
thyroiditisprone
BB/
W
rats."
Autoimmunity
(
1995)
20:
201­
106
Kanno,
J.
et
al.
"
Tumor­
promoting
effects
of
both
iodine
deficiency
and
iodine
excess
in
the
rat
thyroid"
Toxicol.
Path.
(
1992)
20(
2):
226­
235
10
developmental
study
ceased
on
GD17
(
before
ontogeny
of
rat
fetal
thyroid
function),
in
utero
exposure
during
the
multigeneration
toxicity
continued
until
GD20
(
i.
e.
during
ontogeny
of
fetal
thyroid
function).
Thus,
the
data
suggest
that
fetal
losses
may
have
occurred
in
the
rat
developmental
study
had
exposure
continued
beyond
GD17.
Similar
effects
have
been
reported
for
another
iodine­
rich
compound,
amiodarone
(
an
antiarrythmic
drug),
after
treatment
of
pregnant
rabbits
and
rats.
3
The
histopathological
changes
caused
by
iodomethane
exposure
occurred
in
the
respiratory
tract,
and
the
salivary
and
thyroid
glands.
The
respiratory
tract
histopathology
was
characterized
by
lesions
of
the
nasal
cavity
described
as
degeneration
of
the
olfactory
epithelium
(
portal
of
entry
effects).
These
lesions
were
identified
in
the
13­
week
inhalation
toxicity
study,
the
multigeneration
reproductive
toxicity
study,
and
the
combined
chronic
toxicity/
carcinogenicity
study
in
rats
and
were
limited
to
the
extrathoracic
region
with
no
involvement
of
the
tracheobronchial
or
pulmonary
regions.
Furthermore,
they
did
not
appear
to
progress
with
time
(
i.
e.
nasal
lesions
of
comparable
severity
were
seen
after
4,
13,
and
52
weeks
of
exposure
at
the
same
concentration)
thus
suggesting
the
nasal
lesions
were
the
result
of
reaching
a
critical
concentration
(
C
max
)
rather
than
time­
dependent
(
i.
e.
C
x
t;
Haber's
law).
In
contrast,
a
C
x
t
relationship
is
assumed
for
all
systemic
effects.

Frank
evidence
of
thyroid
toxicity
was
reported
in
the
combined
chronic
toxicity/
carcinogenicity
study
in
rats,
the
MOA
study
in
rabbits,
and
the
carcinogenicity
study
in
mice.
Indications
of
thyroid
toxicity
included
enlarged
thyroids,
increased
thyroid
weights,
increased
incidence
of
ultimobranchial
thyroid
cysts,
follicular
cell
hyperplasia,
follicular
cell
adenomas,
and
thyroid
cytoplasmic
vacuolation,
as
well
as
perturbations
of
the
thyroid­
pituitary
axis
(
decreases
in
T3
and
T4
in
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
3oC
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
F
1
litters.
Exposure
of
the
P
males
continued
throughout
mating
and
until
the
day
prior
to
euthanasia.
The
P
females
continued
to
be
exposed
throughout
mating
and
through
gestation
day
(
GD)
20,
at
which
point
exposure
was
discontinued.
Daily
exposure
of
the
P
females
was
reinitiated
on
lactation
day
(
LD)
5
and
continued
until
the
day
prior
to
euthanasia.
After
weaning,
F
1
animals
(
30/
sex/
concentration)
were
selected,
equalized
by
sex,
to
become
the
parents
of
the
F
2
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,
Intermediate­
Term,
Inhalation
(
1­
6
months
exposure)
Nonoccupational
Multigeneration
Reproductive
Toxicity
Study
in
Rats
NOAEL
=
5
ppm
LOAEL
=
20
ppm
Offspring
effects:
decreased
body
weight,
weight
gain,
and
thymus
weights
Reproductive
effects:
Delays
in
vaginal
patency
1.25
ppm
UF
=
30
N.
A.

Occupational
Subchronic
Inhalation
Toxicity
Study
in
Rats
NOAEL
=
20
ppm
LOAEL
=
50
ppm
Degeneration
of
the
olfactory
epithelium
3.7
ppm
UF
=
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
¶

8Hermouet,
C.
et
al.
"
Methyl
iodide
poisoning:
Report
of
two
cases"
Am.
J.
Ind.
Medicine
(
1996)
30:
759­
764
&
Appel,
G.
B.
et
al.
"
Methyl
iodide
intoxication"
Annals
of
Int.
Med
(
1975)
82:
534­
536
18
Long­
term
(>
6
months)
Nonoccupational
Chronic/
Carcinogenicity
Study
in
Rats
NOAEL
=
5
ppm
LOAEL
=
20
ppm
Squamous
cell
metaplasia
0.89
ppm
UF
=
30
N.
A.

Occupational
Chronic/
Carcinogenicity
Study
in
Rats
NOAEL
=
5
ppm
LOAEL
=
20
ppm
Squamous
cell
metaplasia
3.75
ppm
UF
=
30
Cancer
Not
likely
to
be
carcinogenic
to
humans
at
doses
that
do
not
alter
rat
thyroid
hormone
homeostasis
¶
At
this
time,
CDPR
has
not
conducted
a
risk
assessment
for
iodomethane
(
pending
submission
of
additional
data).

5.0
Public
Health
Data
Over
the
past
century,
only
11
incidents
of
iodomethane
poisoning
have
been
reported
in
the
published
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
µ
g/
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
µ
g/
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
Iodomethane:
M
aximum
Distance
Buffers
Ventura
CA
C
IM
IS
&
Watsonville
F
lat
Fume
(
M
RID
455937­
10)

0
100
200
300
400
500
600
5
15
25
35
45
55
65
75
85
95
99
99.9
9
Percentile
of
Exposure
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:
MOE
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
Percentile
o
f
Exposure
Margins
Of
Exposure
(
Target
=
30)
Max­
100%

Whole­
100%

Max­
75%

Whole­
75%

Max­
50%

Whole­
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
r
c
e
n
t
i
le
Io
d
o
m
e
th
a
n
e
B
u
f
fe
r
R
e
s
u
lts
F
o
r
4
0
A
c
r
e
F
ie
ld
s
F
o
r
V
e
n
tu
r
a
C
A
&
A
ll
F
lu
x
P
ro
f
ile
s
(
H
E
C
4
p
p
m
&
U
F
=
3
0
)

0
5
0
1
0
0
1
5
0
2
0
0
2
5
0
3
0
0
3
5
0
4
0
0
4
5
0
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
la
t
F
um
e
­
W
a
ts
o
n
.
F
la
t
F
um
e
­
M
a
n
t
e
c
a
R
a
is
e
d
B
e
d
­
O
x
n
a
rd
R
a
is
e
d
B
e
d
­
G
u
a
d
.
D
r
i
p
­
L
a
S
e
l
va
D
r
ip
­
C
a
m
a
r
.

D
r
ip
­
G
u
a
d
.

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
Buffe
r
(
m)
Max
(
175
lb/
Acre)
Max
Max
(
175
lb/
Acre)
Whole
75%
(
131
lb/
Acre)
Max
75%
(
131
lb/
Acre)
Whole
50%
(
88
lb/
Acre)
Max
50%
(
88
lb/
Acre)
Whole
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
Percentiles
Results
Based
On
HEC
=
2.9
ppm
Results
Based
On
HEC
=
4.0
ppm
Ventura
California
Tallahassee
Florida
Flint
Michigan
Bradenton
Florida
Bakersfield
Calfornia
Ventura
California
Tallahassee
Florida
Flint
Michigan
Bradenton
Florida
Bakersfield
Calfornia
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
Flux
­
Watsonville
CA
Flat
Fume
75
5
90
5
120
0
70
5
155
0
75
0
35
0
60
0
25
5
85
0
30
90
5
150
5
185
5
120
10
235
5
120
5
75
5
100
0
60
5
140
0
60
95
5
210
5
240
5
150
20
305
5
155
5
115
5
135
0
80
5
185
0
90
99
15
375
20
355
5
235
40
460
5
220
5
265
5
230
5
140
15
310
5
130
99.9
60
700
50
560
25
415
60
675
15
350
35
495
20
405
5
265
30
465
5
190
Flux
­
Manteca
CA
Flat
Fume
75
5
145
5
170
5
120
10
225
5
120
5
70
5
95
0
60
5
135
0
65
90
5
210
5
255
5
165
25
325
5
170
5
115
5
150
5
95
5
210
5
100
95
5
275
15
310
5
210
35
395
5
205
5
165
5
195
5
120
10
260
5
120
99
35
480
30
415
15
305
50
545
15
265
5
330
5
270
5
175
25
385
5
165
99.9
60
800
60
725
35
560
70
745
25
360
35
525
25
500
15
290
45
505
5
245
Flux
­
Plant
City
FL
Raised
Bed
75
NA
NA
0
85
0
60
5
160
NA
NA
NA
NA
0
35
0
15
0
85
NA
NA
90
NA
NA
5
150
0
105
5
255
NA
NA
NA
NA
0
75
0
50
5
150
NA
NA
95
NA
NA
5
200
5
140
5
315
NA
NA
NA
NA
0
110
0
70
5
195
NA
NA
99
NA
NA
5
320
5
220
25
455
NA
NA
NA
NA
5
195
0
125
5
290
NA
NA
99.9
NA
NA
20
520
5
350
55
720
NA
NA
NA
NA
5
355
5
205
15
460
NA
NA
Flux
­
Oxnard
CA
Raised
Bed
75
5
320
NA
NA
0
180
NA
NA
0
205
0
190
NA
NA
0
100
NA
NA
0
120
90
5
465
NA
NA
5
270
NA
NA
5
280
5
300
NA
NA
0
165
NA
NA
0
170
95
5
565
NA
NA
5
335
NA
NA
5
325
5
370
NA
NA
5
215
NA
NA
0
200
99
45
730
NA
NA
5
505
NA
NA
5
415
5
490
NA
NA
5
330
NA
NA
5
270
99.9
80
960
NA
NA
30
730
NA
NA
5
525
15
655
NA
NA
5
510
NA
NA
5
350
Flux
­
Guadalupe
CA
Raised
Bed
75
5
335
NA
NA
5
205
NA
NA
5
155
5
205
NA
NA
0
120
NA
NA
0
135
90
5
480
NA
NA
5
295
NA
NA
10
210
5
315
NA
NA
5
190
NA
NA
5
190
95
30
590
NA
NA
5
365
NA
NA
15
245
5
395
NA
NA
5
240
NA
NA
5
225
99
65
775
NA
NA
25
540
NA
NA
30
340
15
540
NA
NA
5
350
NA
NA
5
295
99.9
95
1120
NA
NA
55
785
NA
NA
40
445
55
775
NA
NA
5
520
NA
NA
5
425
Flux
­
La
Selva
CA
Drip
75
5
220
0
190
0
115
5
300
0
185
0
115
0
100
0
50
0
180
0
105
90
5
350
5
315
5
205
25
475
5
275
0
210
0
185
0
110
5
310
0
160
95
5
480
5
395
5
265
40
625
5
330
5
300
5
240
0
150
5
410
0
205
99
40
660
5
600
5
395
65
875
5
460
5
435
5
360
5
245
15
580
5
285
99.9
65
850
65
840
5
715
95
1175
5
695
5
590
5
585
5
455
55
800
5
445
Flux
­
Camarillo
CA
Drip
Irrigation
75
0
85
5
100
0
70
5
155
0
70
0
30
0
45
0
25
0
85
0
25
90
5
145
5
165
5
110
5
235
5
100
0
65
0
85
0
50
5
140
0
50
95
5
195
5
210
5
135
10
300
5
120
0
100
5
115
0
75
5
185
0
65
99
5
350
5
310
5
210
30
420
5
165
5
220
5
180
5
120
5
270
5
95
99.9
50
625
20
475
5
415
45
565
5
215
5
440
5
365
5
230
15
380
5
135
Flux
­
Guadalupe
CA
Drip
Irrigation
75
5
190
5
225
5
170
20
285
5
230
5
105
5
135
0
95
5
180
5
90
90
15
270
20
320
5
230
35
400
5
300
5
185
5
205
5
140
15
270
5
130
95
20
330
30
400
15
275
45
485
5
350
5
210
5
260
5
170
25
330
5
160
99
55
640
50
540
30
455
70
685
5
440
20
435
20
375
10
275
45
495
10
225
99.9
105
1380
90
915
55
1205
90
930
25
670
65
875
50
645
30
555
60
625
20
290
38
Table11:
Summary
Of
PERFUM
Whole
Field
Buffer
Results
For
All
Weather
Stations
And
Flux
Types
With
1
And
40
Acre
Fields
At
Maximum
Application
Rate
Of
175
lb
ai/
A
Percentiles
Results
Based
On
HEC
=
2.9
ppm
Results
Based
On
HEC
=
4.0
ppm
Ventura
California
Tallahassee
Florida
Flint
Michigan
Bradenton
Florida
Bakersfield
Calfornia
Ventura
California
Tallahassee
Florida
Flint
Michigan
Bradenton
Florida
Bakersfield
Calfornia
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
1
Acre
40
Acres
Flux
­
Watsonville
CA
Flat
Fume
75
0
5
0
10
0
5
0
25
0
10
0
5
0
5
0
0
0
5
0
5
90
0
40
0
50
0
30
0
75
0
40
0
10
0
15
0
5
0
40
0
10
95
5
70
0
85
0
55
5
115
0
60
0
25
0
40
0
20
0
65
0
25
99
5
155
5
185
5
115
10
215
5
120
5
85
5
105
0
60
5
135
0
65
99.9
15
350
10
330
5
220
30
380
5
210
5
250
5
215
5
135
15
265
5
125
Flux
­
Manteca
CA
Flat
Fume
75
0
25
0
25
0
15
0
50
0
30
0
5
0
5
0
5
0
20
0
5
90
0
75
0
80
0
60
5
115
0
70
0
30
0
40
0
25
0
70
0
30
95
5
110
5
125
0
90
5
165
5
95
0
55
0
70
0
45
5
100
0
50
99
5
210
5
245
5
165
20
285
5
165
5
125
5
150
5
95
5
195
5
95
99.9
30
420
25
395
10
290
45
480
10
245
5
300
5
255
5
175
25
335
5
155
Flux
­
Plant
City
FL
Raised
Bed
75
NA
NA
0
5
0
5
0
10
NA
NA
NA
NA
0
0
0
0
0
5
NA
NA
90
NA
NA
0
30
0
20
0
65
NA
NA
NA
NA
0
5
0
5
0
25
NA
NA
95
NA
NA
0
65
0
45
0
110
NA
NA
NA
NA
0
25
0
5
0
60
NA
NA
99
NA
NA
5
155
0
110
5
225
NA
NA
NA
NA
0
80
0
50
5
140
NA
NA
99.9
NA
NA
5
300
5
210
25
395
NA
NA
NA
NA
5
185
0
120
5
260
NA
NA
Flux
­
Oxnard
CA
Raised
Bed
75
0
20
NA
NA
0
20
NA
NA
0
45
0
5
NA
NA
0
5
NA
NA
0
10
90
0
115
NA
NA
0
80
NA
NA
0
105
0
60
NA
NA
0
35
NA
NA
0
55
95
0
215
NA
NA
0
130
NA
NA
0
155
0
125
NA
NA
0
70
NA
NA
0
90
99
5
440
NA
NA
5
260
NA
NA
5
260
5
285
NA
NA
0
165
NA
NA
0
165
99.9
30
665
NA
NA
5
475
NA
NA
5
385
5
465
NA
NA
5
315
NA
NA
5
255
Flux
­
Guadalupe
CA
Raised
Bed
75
0
40
NA
NA
0
35
NA
NA
0
60
0
15
NA
NA
0
5
NA
NA
0
25
90
0
135
NA
NA
0
100
NA
NA
0
125
0
75
NA
NA
0
50
NA
NA
0
70
95
5
230
NA
NA
5
150
NA
NA
0
175
0
140
NA
NA
0
90
NA
NA
0
105
99
5
445
NA
NA
5
285
NA
NA
5
280
5
300
NA
NA
5
185
NA
NA
5
180
99.9
50
710
NA
NA
20
495
NA
NA
5
415
10
495
NA
NA
5
330
NA
NA
5
275
Flux
­
La
Selva
CA
Drip
75
0
10
0
5
0
5
0
25
0
25
0
5
0
5
0
0
0
5
0
5
90
0
75
0
70
0
45
0
115
0
85
0
30
0
30
0
10
0
65
0
40
95
0
145
0
130
0
85
5
185
0
135
0
75
0
70
0
35
0
120
0
70
99
5
320
5
285
5
200
20
385
5
250
5
205
0
175
0
115
5
260
0
155
99.9
20
580
5
535
5
365
55
700
5
405
5
395
5
345
5
235
15
485
5
260
Flux
­
Camarillo
CA
Drip
Irrigation
75
0
5
0
5
0
5
0
20
0
10
0
5
0
5
0
0
0
5
0
5
90
0
35
0
45
0
30
0
75
0
35
0
5
0
10
0
5
0
35
0
5
95
0
65
0
75
0
55
5
115
0
55
0
20
0
30
0
15
0
65
0
20
99
5
150
5
160
5
110
5
215
5
100
0
75
0
90
0
55
5
135
0
50
99.9
5
325
5
285
5
205
25
360
5
155
5
215
5
170
5
115
5
235
5
90
Flux
­
Guadalupe
CA
Drip
Irrigation
75
0
40
0
40
0
30
0
65
0
50
0
15
0
15
0
5
0
35
0
20
90
5
100
5
105
0
90
5
145
5
95
0
50
0
60
0
45
5
90
0
50
95
5
145
5
165
5
130
15
205
5
125
5
80
5
100
0
75
5
135
5
75
99
10
265
15
310
5
220
30
345
10
200
5
170
5
200
5
140
15
240
5
125
99.9
45
545
40
505
25
395
60
565
25
305
20
395
15
340
5
265
35
415
5
205
39
0
50
100
150
200
250
300
350
400
450
Flat
Fume
­
Watsonville
Flat
Fume
­
Manteca
Raised
Bed
­
Plant
City
Raised
Bed
­
Oxnard
Raised
Bed
­
Guadalupe
Drip
­
La
Selva
Drip
­
Camarillo
Drip
­
Guadalupe
Source
of
Flux
Profile
Buffer
Distance
(
meters)
Ventura
CA
Tallahassee
FL
Flint
MI
Bradenton
FL
Bakersfield
CA
Figure
8:
Summary
of
95th
Percentile
Maximum
Buffer
Distances
For
40
A
Fields
At
Maximum
Application
Rate,
HEC
=
4
ppm,
UF
=
30,
All
Weather
Stations,
and
All
Flux
Studies
0
10
0
20
0
30
0
40
0
50
0
60
0
70
0
Flat
Fume
­
Watsonville
Flat
Fume
­
Manteca
Rais
ed
Bed
­
Plant
C
ity
Raised
Bed
­
Oxnard
Raised
Bed
­
Guadalupe
Drip
­
La
Selva
Drip
­
Camarillo
Drip
­
Guadalupe
Source
of
Flux
P
rofile
Buffer
Distance
(
meters)
Ven
tura
CA
Tallahassee
F
L
F
lint
MI
Bradenton
FL
Ba
ke
rsfield
CA
Figure
9:
Summary
of
95th
Percentile
Maximum
Buffer
Distances
For
40
A
Fields
At
Maximum
Application
Rate,
HEC
=
2.9
ppm,
UF
=
30,
All
Weather
Stations,
and
All
Flux
Studies
0
50
100
150
200
250
300
350
Flat
Fume
­
Watsonville
Flat
Fume
­
Mantec
a
Rais
ed
Bed
­
Plant
City
Raised
Bed
­
Oxnard
Raised
Bed
­
Guadalupe
Drip
­
La
Selva
Drip
­
Camarillo
Drip
­
Guadalupe
Source
of
Flux
Profile
Buffer
Distance
(
meters)
Ventu
ra
CA
Tallahassee
F
L
F
lint
MI
Bradenton
FL
Bakersfield
CA
Figure
10:
Summary
of
95th
Percentile
Whole
Buffer
Distances
For
40A
Fields
At
Maximum
Application
Rate,
HEC=
4
ppm,
UF=
30,
All
Weather
Stations,
and
All
Flux
Studies
40
0
50
100
150
200
250
300
350
Flat
Fume
­
Watsonville
Flat
Fume
­
Manteca
Raised
Bed
­
Plant
City
Raised
Bed
­
Oxnard
Raised
Bed
­
Guadalupe
Drip
­
La
Selva
Drip
­
Camarillo
Dr
ip
­
Guadalupe
Source
of
Flux
Profile
Buffer
Distance
(
meters)
Ventura
CA
Tallahassee
FL
Flint
MI
Bradenton
FL
Bakersfield
CA
Figure
11:
Summary
of
95th
Percentile
Whole
Buffer
Distances
For
40
A
Fields
At
Maximum
Application
Rate,
HEC=
2.9
ppm,
UF=
30,
All
Weather
Stations,
and
All
Flux
Studies
At
the
upper
percentiles
of
the
exposure
distributions
generated
with
PERFUM,
the
results
presented
in
Tables
10
and
11
and
Figures
9
through
12
are
markedly
similar
to
those
calculated
with
ISCST3
which
supports
the
construct
that
the
ISCST3
results
in
conservative
estimates
of
exposure.
If
maximum
buffer
distances
at
the
95th
percentile
of
exposure
are
considered,
the
lowest
predicted
buffer
distance
for
40
acre
fields
is
approximately
65
meters
(
at
HEC
=
4
ppm)
and
120
meters
(
at
HEC
=
2.9
ppm)
while
values
range
up
to
approximately
400
meters
(
at
HEC
=
4
ppm)
and
600
meters
(
at
HEC
=
2.9
ppm).
If
whole
field
buffer
distances
at
the
95th
percentile
of
exposure
are
considered,
the
lowest
predicted
buffer
distance
for
40
acre
fields
is
approximately
5
meters
(
at
HEC
=
4
ppm)
and
45
meters
(
at
HEC
=
2.9
ppm)
while
values
range
up
to
approximately
140
meters
(
at
HEC
=
4
ppm)
and
230
meters
(
at
HEC
=
2.9
ppm).
Generally,
Ventura
California
and
Bradenton
Florida
meteorological
data
result
in
the
largest
buffer
distances
regardless
of
flux
profile.
Likewise,
Flint
Michigan
meteorological
data
seems
to
generally
result
in
the
lowest
buffer
distances
which
is
logical
given
its
routine
climate
compared
to
the
California
and
Florida
locations
(
e.
g.,
typically
cooler
in
Michigan
with
potentially
less
atmospheric
turbulence).

For
40
acre
fields
at
the
99.9th
percentile,
maximum
buffer
distances
ranged
as
high
as
875
meters
(
HEC
=
4
ppm)
and
1380
meters
(
HEC
=
2.9
ppm)
which
both
are
based
on
the
Guadalupe
California
drip
irrigation
flux
information.
For
40
acre
fields
at
the
99.9th
percentile,
whole
buffer
distances
ranged
as
high
as
495
meters
(
HEC
=
4
ppm)
and
710
meters
(
HEC
=
2.9
ppm)
which
both
are
based
on
the
Guadalupe
California
raised
bed
flux
information.
For
1
acre
fields,
the
vast
majority
of
buffer
distances
at
all
percentiles
of
exposure
considered
(
up
to
99.9th
percentile)
were
less
than
100
meters
with
most
being
in
the
5
to
50
meter
range
at
the
upper
percentiles
of
exposure
(
i.
e.,
99th
percentile
plus)
regardless
of
whether
maximum
or
whole
field
buffers
are
considered.

Regional
climatic
differences
and
differences
in
application
methods
(
i.
e.,
and
associated
flux/
emissions)
can
impact
buffer
distances
calculated
using
PERFUM
as
shown
above.
Potentially,
the
size
and
shape
of
a
treated
field
can
also
impact
buffer
distances.
Using
the
Ventura
California
meteorological
data,
the
relative
impacts
of
altering
field
sizes
from
1
to
40
acres
were
evaluated
for
each
available
flux
study
(
Figure
12).
Likewise,
changing
the
shape
of
a
treated
field
can
potentially
impact
buffer
distances.
This
parameter
was
investigated
using
differently
shaped
5
acre
fields
(
i.
e.,
square
and
"
wide"
and
"
long"
41
0
5
0
1
0
0
1
5
0
2
0
0
2
5
0
3
0
0
3
5
0
4
0
0
4
5
0
1
5
1
0
2
0
4
0
F
ie
ld
S
iz
e
(
a
c
re
s
)
Buffer
Distance
(
meters)

W
a
t
s
o
n
v
ille
M
a
n
te
c
a
O
xn
a
rd
G
u
a
d
a
lu
p
e
T
R
B
L
a
S
e
lv
a
C
am
a
r
illo
G
u
a
d
a
lu
p
e
D
rip
Figure
12:
Impact
Of
Changing
Field
Size
On
Maximum
Buffer
Distances,
95th
Percentile
Exposures,
Maximum
Application
Rate,
Ventura
CA
Weather,
All
Available
Flux
Profiles,
HEC=
4
ppm
and
UF=
30
0
20
40
60
80
100
120
140
Max
Whole
Max
Whole
Max
Whole
Max
Whole
Max
Whole
Max
Whole
Max
Whole
Watsonville
Flat
Fume
Manteca
Flat
Fume
Oxnard
Raised
Bed
Guadalupe
Raised
Bed
La
Selva
Drip
Camarillo
Drip
Guadalupe
Drip
5A
Square
5A
Wide
5A
Long
Figure
13:
Impact
Of
Changing
Field
Shape
On
Buffer
Distances,
95th
Percentile
Exposures,
Maximum
Application
Rate,
Ventura
CA
Weather,
All
Available
Flux
Profiles,
HEC=
4
ppm
and
UF=
30
rectangles
oriented
horizontally
and
vertically)
and
results
for
all
flux
studies
in
conjunction
with
the
Ventura
California
meteorological
data
(
Figure
13).
[
Note:
Appendix
F
contains
numerical
inputs
for
Figures
12
and
13
if
required.]

The
results
of
the
analysis
which
examined
the
impact
of
changing
field
sizes
were
expected
in
that
buffer
distances
increased
with
larger
fields.
The
slope
of
the
curves
for
each
flux
study
varied
because
of
the
different
emission
profiles
associated
with
each.
The
higher
the
emissions,
the
larger
the
buffer
distance
required
(
see
Tables
10
and
11
for
additional
information).
The
impact
of
changing
field
shapes
was
also
investigated.
It
appears
that
at
least
for
the
Ventura
California
meteorological
data
and
5
acre
fields
that
the
shape
did
not
significantly
impact
buffer
distances.
This
probably
indicates
that
there
is
not
a
prevailing
wind
direction
for
the
Ventura
California
weather
station
and
that
wind
vectors
occurred
in
several
directions
over
the
timeframe
of
the
analysis.
It
is
possible
that
results
would
differ
for
different
weather
station
locations
and
that
more
site
specific
analyses
could
be
needed
if
this
parameter
requires
further
investigation.
It
is
also
likely
that
results
would
be
more
pronounced
with
larger
fields
or
if
a
weather
station
has
a
definite
prevailing
wind
direction.
42
0
50
100
150
200
250
300
350
400
450
15
22.5
30
MOEs
Buffer
Distance
(
m)

Flat
Fume
­
Watson.

Flat
Fume
­
Manteca
Raised
Bed
­
Oxnard
Raised
Bed
­
Guad.

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

At
the
upper
percentiles
of
the
exposure
distributions
generated
with
PERFUM,
the
results
presented
in
are
markedly
similar
to
those
calculated
with
ISCST3
which
supports
the
construct
that
the
ISCST3
results
in
conservative
estimates
of
exposure.
If
maximum
buffer
distances
at
the
95th
percentile
of
exposure
are
considered,
the
lowest
predicted
buffer
distance
for
40
acre
fields
is
approximately
65
meters
(
at
HEC
=
4
ppm)
and
120
meters
(
at
HEC
=
2.9
ppm)
while
values
range
up
to
approximately
400
meters
(
at
HEC
=
4
ppm)
and
600
meters
(
at
HEC
=
2.9
ppm).
If
whole
field
buffer
distances
at
the
95th
percentile
of
exposure
are
considered,
the
lowest
predicted
buffer
distance
for
40
acre
fields
is
approximately
5
meters
(
at
HEC
=
4
ppm)
and
45
meters
(
at
HEC
=
2.9
ppm)
while
values
range
up
to
approximately
140
meters
(
at
HEC
=
4
ppm)
and
230
meters
(
at
HEC
=
2.9
ppm).
Generally,
Ventura
California
and
Bradenton
Florida
meteorological
data
result
in
the
largest
buffer
distances
regardless
of
flux
profile.
Likewise,
Flint
Michigan
meteorological
data
seems
to
generally
result
in
the
lowest
buffer
distances
which
is
logical
given
its
routine
climate
compared
to
the
California
and
Florida
locations
(
e.
g.,
typically
cooler
with
potentially
less
atmospheric
turbulence).
For
40
acre
fields
at
the
99.9th
percentile,
maximum
buffer
distances
ranged
as
high
as
875
meters
(
HEC
=
4
ppm)
and
1380
meters
(
HEC
=
2.9
ppm)
which
both
are
based
on
the
Guadalupe
California
drip
irrigation
flux
information.
For
40
acre
fields
at
the
99.9th
percentile,
whole
buffer
distances
ranged
as
high
as
495
meters
(
HEC
=
4
ppm)
and
710
meters
(
HEC
=
2.9
ppm)
which
both
are
based
on
the
Guadalupe
California
raised
bed
flux
information.
For
1
acre
fields,
the
vast
majority
of
buffer
distances
at
all
percentiles
of
exposure
considered
(
up
to
99.9th
percentile)
were
less
than
100
meters
with
most
being
in
the
5
to
50
meter
range
at
the
upper
43
percentiles
of
exposure
(
i.
e.,
99th
percentile
plus)
regardless
of
whether
maximum
or
whole
field
buffers
are
considered.

Changes
in
risk
estimates
with
percentiles
of
exposure
were
examined
and
these
analyses
indicate
that
even
at
buffer
distances
based
on
the
95th
percentile
of
exposure
risks
were
generally
not
significantly
greater
even
at
higher
percentiles
of
exposure
which
indicates
slight
changes
in
risks
with
percentile
of
exposure
(
e.
g.,
MOEs
~
10
were
common
at
the
99.9th
percentile
of
exposure
with
MOEs
of
30
at
the
95th
percentile).
Other
factors
which
could
potentially
impact
results
were
examined
including
the
size
and
shapes
of
fields.
As
expected,
larger
fields
yielded
greater
buffer
distances.
Ventura
California
meteorological
data
were
the
basis
of
the
field
shape
analysis
which
indicates
very
little
impact
on
buffer
distances
but
it
is
possible
different
meteorological
data
could
yield
different
results.

6.1.2
Ambient
Bystander
Exposure
From
Multiple
Area
Sources
No
direct
ambient
air
monitoring
data
are
available
for
iodomethane.
Although
there
are
low
volume
industrial
and
commercial
uses
of
iodomethane,
it
is
not
routinely
screened
for
by
CARB
(
California
Air
Resources
Board)
or
similar
organizations
which
would
be
expected.
As
such,
to
qualitatively
characterize
the
potential
for
exposure
from
ambient
air,
HED
has
compared
iodomethane
to
the
ambient
air
levels
that
were
quantified
for
methyl
bromide
using
physical
chemical
properties
and
environmental
fate
characteristics.
Based
on
this
comparison,
HED
believes
there
is
less
potential
for
exposure
with
iodomethane
than
with
MeBr
because
of
the
environmental
fate
characteristics
of
iodomethane
relative
to
MeBr
(
i.
e.,
iodomethane
dissipates/
degrades
faster
in
the
environment).

6.2
Bystander
Risk
Characterization
There
are
several
issues
that
should
be
considered
in
the
interpretation
of
the
above
assessments
for
offtarget
releases
of
iodomethane.
The
first
is
that
the
flux
data
used
for
this
analysis
have
been
generated
in
California
with
some
in
Florida;
however,
iodomethane
could
be
used
in
other
regions
of
the
country.
Therefore,
the
results
based
on
California
and
Florida
flux
data
were
used
to
represent
the
rest
of
the
country
due
to
a
lack
of
adequate
information
for
any
other
region.
It
is
unclear
what
the
potential
impacts
of
this
extrapolation
might
be
although
it
is
believed
it
likely
will
overestimate
risks
since
conditions
in
those
states
are
generally
warmer
than
elsewhere.
For
example,
it
may
be
possible
that
other
factors
such
as
soil
type
and
other
environmental
conditions
might
also
affect
the
rate
at
which
iodomethane
is
emitted
from
treated
fields
or
other
sources.

Meteorological
conditions
such
as
differences
in
humidity,
levels
of
solar
radiation,
and
atmospheric
stability
also
impact
ambient
concentrations.
In
this
analysis,
coastal
and
inland
stations
in
Florida
and
California
as
well
as
a
station
in
central
were
used
to
complete
the
PERFUM
calculations.
This
information
can
also
be
used
to
characterize
the
risks
calculated
using
ISCST3
(
i.
e.,
range
of
windspeeds
used
for
ISCST3
were
similar
to
those
used
for
PERFUM).
The
selected
regions
in
California,
Florida,
and
Michigan
are
thought
to
represent
the
likely
use
regions
for
iodomethane
so
it
is
believed
these
data
are
representative
for
the
proposed
uses.
Another
factor
that
should
be
considered
in
the
interpretation
of
these
results
is
the
data
quality
associated
with
the
inputs
and
other
factors
used
in
the
calculations.
For
most
of
the
data,
HED
believes
that
the
data
and
other
information
used
are
of
reasonable
quality
for
risk
assessment
purposes.
44
Several
factors
also
need
to
be
considered
in
the
interpretation
of
the
results
associated
with
a
known
area
source.
There
were
two
distinct
analyses
that
were
completed,
the
first
based
on
data
that
considered
studies
of
disparate
design,
and
the
second
based
on
the
ISCST3
or
PERFUM
models.
Analysis
of
the
data
itself
indicated
risks
exceeding
HED's
level
of
concern
at
the
sampling
locations
close
to
the
treated
fields
and
the
conclusions
would
not
significantly
be
altered
due
to
data
quality
issues
or
other
uncertainties.
The
second
type
of
analysis
was
based
on
the
use
of
the
Agency's
ISCST3
or
the
PERFUM
models
which
provide
much
more
flexibility
in
that
it
allowed
extrapolations
from
an
area
source
to
different
distances
downwind
depending
upon
the
conditions
of
the
assessment.
These
types
of
results
can
be
used
for
risk
management
purposes.
The
ISCST3
model
itself
is
a
publically
vetted
tool
that
is
currently
used
by
the
Agency's
Office
of
Air
for
regulatory
decision
making.
A
number
of
support
documents
for
this
tool
can
be
found
at
the
associated
Agency
website
Technology
Transfer
Network
Support
Center
for
Regulatory
Air
Models
(
www.
epa.
gov/
scram001/
tt22.
htm#
isc).
These
should
be
explored
in
detail
for
a
more
complete
examination
of
the
uncertainties
associated
with
this
model.
Additionally,
the
results
of
the
analysis
of
the
PERFUM
model
should
be
reviewed
in
detail
for
a
more
complete
understanding
of
its
associated
uncertainties
(
www.
epa.
gov/
scipoly/
sap).

The
specific
inputs
for
the
ISCST3
model
calculations
drove
the
associated
uncertainties
in
the
results.
For
example,
the
key
input
factors
for
pre­
plant
agricultural
uses
were
field
size,
flux/
emission
rates,
atmospheric
stability,
and
windspeed.
Wind
direction
is
another
factor
which
also
should
be
considered.
The
field
sizes
used
by
HED
in
this
assessment
were
1
to
40
acres
which
is
well
within
the
range
of
what
could
be
treated
on
a
daily
basis.
There
are
uncertainties
associated
with
estimates
of
flux/
emission
rates
for
specific
application
techniques
which
is
another
varying
factor.
The
flux
rates
which
were
used
have
been
calculated
by
HED
and
they
compare
reasonably
well
with
those
calculated
by
the
study
investigators.
The
reality
is
that
there
is
a
large
distribution
of
flux
rates
which
is
a
phenomenon
inherent
in
the
nature
of
these
types
of
data.
The
values
used
for
this
assessment
yield
conservative
air
concentration
estimates
because
considering
a
constant
flux
rate
does
not
allow
for
diurnal/
nocturnal
changes
that
may
occur,
which
when
coupled
with
the
appropriate
wind
speed
and
stability
category,
can
result
in
lower
concentrations.
Additionally,
the
maximum
application
rate
of
175
lb
ai/
acre
was
considered
coupled
with
the
median
emission
rate
which
also
provided
a
conservative
estimate
for
flux.
The
meteorological
inputs
also
will
provide
a
conservative
estimate
of
exposure
because
the
wind
direction
is
considered
to
be
perpendicular
(
pointed
downwind)
to
the
treated
field
for
the
entire
24
hours
represented
in
the
calculation.
This
is
not
a
normal
situation
in
the
atmosphere
for
most
locations.
There
is
normally
a
prevailing
wind
with
directional
changes
over
the
course
of
a
typical
day,
especially
when
diurnal
and
nocturnal
differences
are
noted.
HED
did
not
recommend
a
specific
set
of
meteorological
conditions
for
this
assessment
but
instead
provided
a
range
of
results
for
different
conditions.
Different
meteorological
databases
were
evaluated
including
SAMSON
and
CIMIS
for
comparative
purposes
to
actual
weather
data.
The
lower
10th
percentile
windspeeds
for
a
24
hour
period
in
that
analysis
ranged
from
approximately
2
to
5.5
mph
depending
upon
the
location.
The
windspeeds
used
by
HED
ranged
from
approximately
2
to
10
mph.
Overall,
HED
believes
that
the
approach
used
to
evaluate
potential
exposures
from
a
known
area
source
can
be
considered
conservative.
It
is
believed,
however,
that
the
range
of
selected
input
values
and
outputs
represent
what
could
reasonably
occur
in
agriculture
given
proper
field
and
climatological
conditions.
45
Again,
as
with
the
ISCST3
analysis
described
above,
much
of
the
uncertainties
associated
with
the
PERFUM
results
are
those
inherently
associated
with
the
inputs
used.
In
addition
to
the
uncertainties
described
above
for
ISCST3
associated
with
flux
rates,
field
sizes,
and
application
rates
there
are
other
issues
which
should
be
considered.
These
include
the
uncertainties
associated
with
the
selection
of
the
weather
stations
to
represent
each
region
of
proposed
iodomethane
use,
sampling
issues
associated
with
weather
data,
and
use
of
the
uncertainties
associated
with
the
flux
measurements.
HED
has
concluded
that
the
weather
data
used
for
this
assessment
represent
conditions
that
would
occur
in
the
regions
were
iodomethane
use
is
expected.
Weather
data
were
obtained
from
a
variety
of
sources
including
NWS
and
FAA
which
have
a
rigorous
quality
control
program.
These
data,
however,
are
generally
collected
in
airports
and/
or
urban
areas
leading
to
differences
in
terrain
from
agricultural
lands
(
i.
e.,
surface
roughness
differs)
thus
creating
an
uncertainty.
HED
also
used
data
from
two
weather
networks
(
CIMIS
and
FAWN).
Though
these
data
are
collected
in
agricultural
areas
(
i.
e.,
they
are
more
representative
locations),
the
quality
control
program
for
these
networks
is
not
as
rigorous.
Thus,
there
is
uncertainty
around
these
data
as
well
due
to
the
quality
control
issue.
When
the
two
uncertainties
were
balanced
(
QC
&
more/
less
representative
locations),
it
was
concluded
that
all
were
reasonable
sources
of
weather
data
for
the
analysis.
Additionally,
the
manner
in
which
PERFUM
probabilistically
addresses
the
uncertainties
associated
with
the
flux
measurements
seems
reasonable
in
light
of
the
FIFRA
SAP
comments.
Additional
information
(
e.
g.,
more
flux
and
weather
data)
would
be
useful
for
completing
a
more
robust
analysis
for
other
regions
of
the
country.
The
Agency
attempted
to
determine
the
sensitivity
of
PERFUM
outputs
to
varying
parameters
in
its
analysis
of
the
results
described
above
(
e.
g.,
field
size
and
shape).
However,
there
are
clearly
additional
analyses
that
can
potentially
be
completed
to
explore
how
different
parameters
alone
or
in
conjunction
with
others
impact
results.
In
summary,
HED
believes
that
the
use
of
PERFUM
provides
for
a
more
informed
characterization
of
the
risks
for
bystanders
from
use
of
iodomethane
than
ISCST3.
PERFUM
results
also
support
the
notion
that
the
results
calculated
using
ISCST3
are
conservative
estimates
of
exposure.
Finally,
it
should
be
reiterated
that
results
for
selected
percentiles
of
exposure
are
reported
and
in
many
cases
(
e.
g.,
MOE
exceedance
analysis)
results
are
based
on
95th
percentile
values
as
an
example.
Use
of
the
95th
percentile
for
reporting
the
results
does
not
imply
a
selection
of
these
estimates
for
regulatory
purposes.
Additional
analysis
based
on
other
percentiles
of
exposure
could
be
completed
if
so
needed.
Risk
managers
are
also
cautioned
that
the
uncertainties
and
representativeness
associated
with
the
inputs
used
to
calculate
any
PERFUM
results
under
consideration
for
regulatory
purposes
should
be
integrated
into
the
ultimate
decision
making
process.

Finally,
it
should
be
noted
that
the
Agency
has
identified
a
number
of
scientific
issues
which
it
intends
to
address
in
a
more
refined
manner
pending
the
outcomes
of
several
ongoing
analyses.
These
issues
include
the
following:

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

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

°
Consideration
of
Multiple,
Successive
Applications:
The
Agency
is
aware
that
many
applications
to
large
tracts
of
agricultural
lands
occurs
over
a
several
day
process
which
is
limited
based
on
the
amount
of
acreage
that
can
be
treated
in
a
day
by
users
and
also
other
logistical
issues
such
as
timing
related
to
variety
and
market
issues.
As
such,
the
Agency
did
not
consider
sequential
application
events
in
this
assessment
pending
further
investigation
into
defining
appropriate
modeling
scenarios
for
possible
key
agricultural
uses
(
e.
g.,
tomatoes
and
strawberries
in
FL
and
CA)
and
possible
implications
for
modeling
based
on
possible
risk
management
decisions
for
single­
field
scenarios.

[
Note:
A
number
of
policy
and
risk
management
issues
have
been
identified
by
the
Agency
pertaining
to
its
current,
focused
efforts
on
soil
fumigant
issues.
These
types
of
comments
have
not
been
identified
or
addressed
in
this
document
as
the
intent
of
this
document
is
to
focus
on
the
scientific
issues
pertaining
to
the
risk
estimates
that
have
been
calculated
herein
and
not
on
risk
management
or
mitigation
strategies.]
47
6.3
Residue
Profile
There
is
no
reasonable
expectation
of
finite
residues
to
be
incurred
in/
on
food
and
feed
crops
when
iodomethane
is
used
as
a
preplant
soil
fumigant
in/
on
strawberries
and
tomatoes,
so
this
use
is
considered
to
be
a
non­
food
use,
and
tolerances
are
not
needed.
(
Refer
to
Section
3.1.)

6.4
Water
Exposure/
Risk
Pathway
Iodomethane
is
very
soluble
in
water,
so
there
is
the
possibility
of
leaching
to
ground
water
and/
or
transporting
to
surface
water
through
runoff,
if
slicing
or
removal
of
the
tarpaulin
coincides
with,
or
is
followed
soon
by,
a
rain
event.
Therefore,
a
qualitative
drinking
water
assessment
was
performed
for
this
risk
assessment.

Tier
II
PRZM/
EXAMS
for
surface
water
and
Tier
I
SCIGROW
for
ground
water
were
used
to
estimate
iodomethane
concentrations
in
drinking
water.
Since
iodomethane
is
a
volatile
compound,
additional
input
parameters
like
DAIR
(
vapor
phase
diffusion
coefficient)
and
ENPY
(
enthalpy
of
vaporization)
were
activated
during
the
PRZM­
EXAMS
simulation.
In
the
absence
of
monitoring
data,
the
concentration
of
iodomethane
in
ground
water
was
estimated
using
SCIGROW,
which
has
limited
capability
to
perform
vapor
phase
transport
of
iodomethane
to
groundwater.
The
assessments
were
based
on
maximum
application
rate
of
iodomethane
for
pepper
in
Florida
and
generally
represent
upper­
bound
estimates
of
iodomethane
concentrations
that
might
be
found
in
surface
water
and
groundwater.
Based
on
environmental
fate
data,
the
residual
contents
in
soils,
and
Tier
I
and
II
models
estimated
concentrations,
Agency
does
not
expect
iodomethane
to
adversely
impact
ground
water
or
surface
water.

7.0
Aggregate
Risk
Assessment
The
physical/
chemical
characteristics,
the
environmental
fate
data,
and
results
of
metabolism
studies
in
plants
assure
that
there
is
no
reasonable
expectation
of
finite
residues
to
be
incurred
in/
on
food
and
drinking
water
when
iodomethane
is
applied
according
to
label
directions.
Therefore,
this
fumigant
does
not
require
food
tolerances,
is
considered
to
be
a
`
non­
food
use'
chemical,
and
is
not
subject
to
the
amendments
to
the
Federal
Food,
Drug,
and
Cosmetic
Act
(
FFDCA)
promulgated
under
the
Food
Quality
Protection
Act
(
FQPA)
of
1996,
and
an
aggregate
risk
assessment
is
not
required.

8.0
Cumulative
Risk
Assessment
and
Characterization
Unlike
other
pesticides
for
which
EPA
has
followed
a
cumulative
risk
approach
based
on
a
common
mechanism
of
toxicity,
EPA
has
not
made
a
common
mechanism
of
toxicity
finding
as
to
iodomethane
and
any
other
substances
and
iodomethane
does
not
appear
to
produce
a
toxic
metabolite
produced
by
other
substances.
For
the
purposes
of
this
tolerance
action,
therefore,
EPA
has
not
assumed
that
iodomethane
has
a
common
mechanism
of
toxicity
with
other
substances.
For
information
regarding
EPA's
efforts
to
determine
which
chemicals
have
a
common
mechanism
of
toxicity
and
to
evaluate
the
cumulative
effects
of
such
chemicals,
see
the
policy
statements
released
by
EPA's
Office
of
Pesticide
Programs
concerning
common
mechanism
determinations
and
procedures
for
cumulating
effects
from
substances
found
to
have
a
common
mechanism
on
EPA's
website
at
www.
epa.
gov/
pesticides/
cumulative
9.0
Occupational
Exposure
48
Overall,
the
data
indicate
that
exposures
exceed
HED's
level
of
concern
for
some
workers
involved
in
the
application
of
iodomethane
when
no
respiratory
protection
is
used
(
e.
g.,
tractor
drivers,
co­
pilots,
shovelers).
Air
purifying
organic
vapor
removing
respirators
(
APRs)
which
reduce
exposure
levels
by
a
factor
of
10
were
also
considered
and
exposures
were
reduced
below
HED's
level
of
concern
for
all
workers
involved
in
application
with
these
devices
although
for
some
application
tasks
APRs
are
not
required
to
achieve
acceptable
exposure
levels.
For
workers
who
enter
fields
days
after
application
to
prepare
for
planting
(
e.
g.,
tarp
cutters
or
hole
punchers),
exposures
were
not
of
concern
5
days
after
application
(
which
reflects
the
available
data)
without
any
sort
of
respiratory
protection.
This
is
also
the
case
for
planters
where
exposures
were
not
of
concern
7
days
after
application
without
any
sort
of
respiratory
protection
(
which
also
reflects
the
available
data).
Respirators
would
be
the
most
practical
protective
equipment
choice
for
reducing
exposures
for
most
workers
in
this
case.
This
is
because
the
field
monitoring
data
used
for
this
analysis
already
reflect
the
use
of
some
engineering
controls
such
as
tarps,
tractor
cabs,
deep
injection,
or
other
devices
including
fans
in
proximity
to
drivers.

In
a
typical
pesticide
handler
assessment,
the
Agency
uses
normalized
estimates
of
exposures
based
on
similar
equipment
and
with
similar
levels
of
protective
equipment
or
clothing.
Additionally,
in
typical
post­
application
worker
assessments,
exposures
are
scaled
based
on
how
residues
decay
over
time.
These
approaches
have
not
been
used
in
the
occupational
assessments
presented
below
due
to
methodological
issues.
For
example,
it
is
not
clear
how
changes
in
various
parameters
or
conditions
(
e.
g.,
temperature,
emission
reduction
methods
such
as
tarps
or
application
methods)
may
impact
exposures.
It
is
also
not
clear
how
time
after
application
can
be
used
for
scaling
exposures
from
one
day
to
the
next
because
worker
exposures
may
be
inherently
related
to
the
conditions
of
the
field
under
which
monitoring
has
occurred.
It
should
also
be
noted
that
the
currently
proposed
maximum
application
rate
for
iodomethane
is
175
lb
ai/
acre
and
that
most
of
the
occupational
monitoring
data
were
collected
at
an
approximate
application
rate
of
235
lb
ai/
acre
based
on
the
proposed
labels
at
the
time
the
data
were
collected.
No
scaling
of
the
occupational
exposure
data
were
completed
for
the
purposes
of
this
assessment.
Under
actual
field
conditions,
it
is
likely
that
exposure
levels
could
be
lower
for
some
workers
based
on
application
rate
and
all
other
factors
being
equal.

Current
requirements
for
entry
of
post­
application
workers
into
previously
treated
fields
are
dictated
by
the
Worker
Protection
Standard
as
described
in
PR
93­
7
for
various
other
fumigants.
Similar
requirements
are
recommended
for
iodomethane.
Refinement
of
time­
based
entry
requirements
is
pending
related
to
the
investigation
of
factors
that
may
impact
exposures
over
time
and
development
of
an
appropriate
methodology
for
such
analyses.

Occupational
exposures
were
quantified
in
six
worker
monitoring
studies
which
used
iodomethane
under
field
conditions.
[
Note:
As
a
reminder,
all
studies
involved
the
use
of
a
tarp
during
application
which
should
be
considered
in
the
interpretation
of
this
assessment.]
The
application
techniques
monitored
include:
49
°
MRID
455938­
20:
flat
fume
application
in
Manteca
CA;
°
MRID
463852­
04:
shank
raised
bed
application
in
Guadalupe
CA;
°
MRID
458791­
02:
shank
raised
bed
application
in
Marina
CA
(
near
Oxnard);
°
MRID
462037­
02:
drip
irrigation
application
in
LaSelva
CA;
°
MRID
463852­
03:
drip
irrigation
application
in
Camarillo
CA;
and
°
MRID
464636­
02:
drip
irrigation
application
in
Guadalupe
CA.

The
tasks
that
were
monitored
in
these
studies
are
listed
below.
Planting
in
all
cases
occurred
7
days
after
application
and
post­
application,
pre­
plant
activities
(
e.
g.,
hole
punching
or
tarp
removal)
occurred
5
days
after
application.

a)
Tractor
Driver
b)
Co­
pilot
(
reported
as
1st
Tarp
Monitor
in
MRID
458791­
02)
c)
Drip
Applicator
d)
Drip
Line
Tender
(
sometimes
reported
as
2nd
Applicator)
e)
Planter
f)
Shoveler
g)
Tarp
Monitor
h)
Hole
Puncher
i)
Tarp
Cutter
j)
Tarp
Remover
k)
Tarp
Remover
Driver
The
corresponding
exposure
and
risk
estimates
associated
with
these
activities
are
presented
in
Table
12.
[
Note:
Only
risks
based
on
the
acute
duration
of
exposure
are
presented
because
the
same
endpoint
and
HEC
apply
to
both
the
acute
and
short­/
intermediate­
term
durations
of
exposure.
As
such,
acute
estimates
are
health
protective
for
all
durations
of
exposure
because
they
represent
higher
risks
since
maximum
exposure
concentrations
have
been
used
to
calculate
them.]
Table
12
indicates
that
if
no
respiratory
protection
is
considered,
exposures
exceed
HED's
level
of
concern
for
many
worker
tasks
associated
with
application.
Exposures
were
not
of
concern
for
those
involved
with
post­
application
field
preparation
and
planting
activities.
HED
evaluated
how
the
use
of
an
APR
(
PF
10)
would
impact
worker
risks.
Exposures
for
all
scenarios
with
the
use
of
an
APR
(
PF
10)
did
not
exceed
HED's
level
of
concern
(
with
MOE
>
30).
See
Appendix
G
for
more
details.

It
is
important
to
consider
that
in
this
assessment
all
available
worker
exposure
monitoring
data
have
been
used
directly
for
risk
assessment
purposes.
The
studies
used
are
thought
to
be
of
reasonable
quality
for
use
in
the
risk
assessment
process.
The
data
were
used
as
conducted
with
no
adjustment
for
application
rate
although
it
is
likely
that
typical
use
rates
for
iodomethane
will
likely
be
less
than
those
considered
in
the
occupational
exposure
monitoring
studies.
The
data
included
in
these
studies
also
reflect
the
use
of
tarps
and
various
types
of
emission
controls.
As
such,
the
results
of
the
monitoring
data
are
specific
to
those
conditions
but
likely
represent
what
would
be
encountered
in
agricultural
use
situations.
It
is
clear,
however,
that
the
elements
of
any
application
can
impact
exposure
levels
based
on
several
factors
(
e.
g.,
care
of
operator,
equipment
condition,
field
preparation).
Finally,
it
should
be
noted
that
the
duration
of
most
exposure
monitoring
periods
ranged
from
5
to
over
8
hours
which
could
be
expected
to
represent
what
could
happen
in
typical
agriculture.
50
Table
12:
Iodomethane
Worker
Exposure
Associated
With
Pre­
Plant
Agricultural
Field
Fumigation
Scenario
Application
Method
(
N)
Acute
Duration
Max.
Conc.
1
Monitored
(
ppm)
Acute
MOE
For
Max.
Conc.
1
Monitored
Max.
Conc.
With
PF10
Resp.
Applied
(
ppm)
Acute
MOE
For
Max.
Conc.
With
PF10
Resp.
Applied
Tractor
Driver
Raised
Bed
(
4)
1.029
3.6
0.103
36
Broadcast
Flat
Fume
(
2)
0.024
154
0.0024
1542
Co­
Pilot
Raised
Bed
&
Broadcast
Flat
Fume
(
4)
0.648
5.7
0.0648
57
Drip
Applicator
Drip
Application
(
6)
0.240
15
0.024
154
Drip
Line
Tender
Drip
Application
(
6)
0.147
25
0.0147
252
Planter
Raised
Bed,
Broadcast
Flat
Fume
&
Drip
(
18)
0.007
529
0.00070
5286
Shoveler
Raised
Bed
(
8)
0.76
4.9
0.076
49
Broadcast
Flat
Fume
(
4)
0.117
32
0.0117
316
Tarp
Monitor
Raised
Bed
(
6)
1.11
3.3
0.111
33
Hole
Puncher
Raised
Bed
(
4)
0.07
53
0.007
529
Drip
Application
(
6)
0.017
224
0.0017
2242
Tarp
Cutter
Broadcast
Flat
Fume
(
2)
0.006
617
0.0006
6167
Table
12:
Iodomethane
Worker
Exposure
Associated
With
Pre­
Plant
Agricultural
Field
Fumigation
Scenario
Application
Method
(
N)
Acute
Duration
Max.
Conc.
1
Monitored
(
ppm)
Acute
MOE
For
Max.
Conc.
1
Monitored
Max.
Conc.
With
PF10
Resp.
Applied
(
ppm)
Acute
MOE
For
Max.
Conc.
With
PF10
Resp.
Applied
51
Tarp
Remover
Broadcast
Flat
Fume
(
2)
0.013
284
0.0013
2846
Tarp
Remover
Driver
Broadcast
Flat
Fume
(
2)
0.024
154
0.0024
1542
Nominal
flow
rate
for
all
samples
was
0.05
liters/
minute.
No
samples
reported
as
<
LOD
or
<
LOQ.
For
some
scenarios,
results
are
presented
based
on
application
equipment
due
to
differences
in
exposure
rates
for
each
technique.
Sample
durations
ranged
from
approximately
1
to
7
hours.
Most
samples
were
3
hours
in
duration
or
longer.

10.0
Data
Needs
and
Label
Requirements
10.1
Toxicology
There
are
no
additional
data
required
at
this
time.

10.2
Residue
Chemistry
There
are
no
additional
data
required
at
this
time.

10.3
Occupational
and
Residential
Exposure
The
assessment
of
occupational
and
residential
risks
associated
with
the
use
of
iodomethane
is
complex.
There
was
a
significant
amount
of
data
available,
but
additional
data
are
still
required.
These
include
both
occupational
monitoring
of
various
workers
and
data
to
better
assess
exposures
in
the
general
population.
The
types
of
data,
guideline
citations,
and
examples
of
the
scenarios
which
need
to
be
addressed
are
presented
below.
Final
determination
of
the
scenarios
should
be
made
in
consultation
with
the
Agency.

OPPTS
Guideline
835.8100
­
Field
volatility
from
soil
Volatility
studies
to
determine
flux
for
ISCST3
modeling
purposes
in
major
use
regions
of
country
for
significant
application
methods
(
e.
g.,
Florida
for
raised
beds
or
drip
irrigation).
Exact
studies
to
be
determined
after
direct
consultation
with
the
Agency.
52
OPPTS
Guideline
875.1300
­
Inhalation
exposure
for
applicators
(
outdoors)

Pre­
Plant
Field
­
(
e.
g.,
rig
drivers
&
tenders,
tarpers,
tarp
removers).
Exact
studies
to
be
determined
after
direct
consultation
with
the
Agency.

OPPTS
Guideline
875.2500
­
Inhalation
exposure
for
postapplication
workers
Pre­
Plant
Field
­
(
e.
g.,
planters,
irrigators).
Exact
studies
to
be
determined
after
direct
consultation
with
the
Agency.

Requirements
For
Special
Studies
Meteorological
data
for
probabilistic
modeling
purposes
Projections
for
product
use
by
major
use
region,
frequency,
application
parameters
(
e.
g.,
rate,
acres
treated,
data,
application
equipment
and
emission
control
technologies
used)

Measurements
of
indoor
air
concentrations
for
residences
in
proximity
of
treated
areas.

Ambient
air
monitoring
in
key
growing
regions.

Exact
studies
to
be
determined
after
direct
consultation
with
the
Agency.
Appendix
A
Review
Of
PBPK/
PD
Model
UNITED
STATES
ENVIRONMENTAL
PROTECTION
AGENCY
NATIONAL
CENTER
for
COMPUTATIONAL
TOXICOLOGY
Research
Triangle
Park,
NC
27711
November
22,
2005
Office
of
Research
and
Development
TXR
#:
0053105
MEMORANDUM
SUBJECT:
IODOMETHANE:
Review
of
Methyl
Iodide
PBPK/
PD
Model.
DP
Barcode:
D312630;
PC
Code:
000011.

FROM:
Hugh
Barton,
Ph.
D.
Toxicologist
(
B205­
01)

Paul
Schlosser,
Ph.
D.
Environmental
Health
Scientist
National
Center
for
Environmental
Assessment
(
B243­
01)

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

1.
SUMMARY
A
physiologically­
based
pharmacokinetic
(
PBPK)/
pharmacodynamic
(
PD)
model
for
methyl
iodide
(
Sweeney
et
al.,
2005;
MRID
#
46446901)
submitted
by
Arysta
LifeScience
to
the
Office
of
Pesticides
Program
(
OPP)
has
been
reviewed
and
its
utility
in
risk
assessment
evaluated.
The
model
is
a
sophisticated
effort
to
describe
the
kinetics
of
methyl
iodide
following
inhalation
exposure
and
the
kinetics
of
iodide
as
a
metabolite.
It
describes
nasal
tract
dosimetry
and
glutathione
(
GSH)
depletion
in
the
rat
to
evaluate
nasal
toxicity,
distribution
of
methyl
iodide
to
tissues
including
brain,
and
iodide
kinetics
in
the
pregnant
rabbit
to
address
developmental
toxicity.
The
model
has
also
been
parameterized
for
the
human
and
Monte
Carlo
analyses
were
performed
to
describe
human
variability.
The
review
was
carried
out
using
the
framework
described
in
Clark
et
al.,
2004.
The
results
of
the
evaluation
are
described
focusing
on
the
rat
and
human
nasal
modeling
(
Section
2),
the
rabbit
and
human
pregnancy
modeling
(
Section
3),
modeling
human
variability
(
Section
4),
and
model
documentation
(
Section
5).
The
strengths
and
limitations
of
the
modeling
were
identified.
The
OPP
indicated
the
acute
neurotoxicity
did
not
appear
a
critical
endpoint
either
in
the
PBPK­
based
or
the
default
analysis,
so
it
was
not
evaluated.
The
nasal
modeling
for
rat
and
human
was
concluded
to
be
adequate
to
estimate
a
human
equivalent
concentration.
Selection
of
the
appropriate
degree
of
GSH
depletion
to
predict
nasal
olfactory
toxicity
is
dependent
on
additional
factors
beyond
the
PBPK/
PD
modeling,
including
judgments
about
the
relationship
of
this
measure
with
toxicity
and
the
linkage
of
the
time­
course
of
exposure
concentrations
with
the
prediction
of
GSH
depletion.
The
pregnancy
modeling
was
found
to
be
adequate
to
estimate
a
range
of
human
equivalent
concentrations.
The
human
variability
analysis
was
considered
to
provide
perspective
on
the
default
value
of
3
to
address
human
pharmacokinetic
variability.
Finally,
the
last
section
discusses
potential
improvements
in
model
documentation
to
improve
the
transparency
of
the
modeling
efforts.

A
model
of
this
degree
of
complexity
requires
a
substantial
effort
to
review
and
evaluate.
Our
review
was
greatly
facilitated
by
the
willingness
of
the
model
developers,
Drs
Lisa
Sweeney
and
Mike
Gargas,
to
answer
questions
and
respond
to
issues
that
were
raised.
Many
of
these
communications
are
summarized
in
the
memos
that
have
described
our
review
as
it
was
ongoing
(
Jan
20,
2005;
March
31,
2005)
and
their
comments
on
the
issues
we
raised
[
Feb
22,
2005;
March
29,
2005
(
MRID
46559301);
April
29,
2005;
May
23,
2005
(
MRID
46559303),
Aug
24,
2005
(
MRID
46631401)].
This
memo
documents
our
evaluation
of
the
model
at
the
current
time,
informed
by
these
previous
communications.
This
memo
supersedes
all
previous
interim
evaluations.

2.
NASAL
MODELING
Model
Purpose
Modeling
of
methyl
iodide
absorption
and
metabolism
in
the
nasal
tissues
of
the
rat
and
human
is
proposed
to
obtain
an
HEC
for
the
acute
risk
assessment
of
the
effects
observed
following
inhalation
exposure
in
the
rat
as
well
as
for
establishing
a
chronic
HEC
for
nasal
effects.
The
proposed
mode
of
action
involves
the
depletion
of
GSH
due
to
methyl
iodide
conjugation
as
a
key
event
in
the
toxicity
pathway
leading
to
damage
of
the
nasal
olfactory
epithelium.
The
proposed
metric
for
cross­
species
extrapolation
is
a
reduction
in
GSH
concentration
in
the
olfactory
epithelium
tissue.
The
reduction
is
calculated
from
the
modeled
average
GSH
concentration
in
the
five
tissue
layers
(
including
the
blood
exchange
layer).
The
extent
of
GSH
reduction
considered
appropriate
is
discussed
below.

Model
Structure
and
Biological
Characterization
While
the
complete
model
is
large
and
complex,
the
predictions
of
dosimetry
in
the
nasal
olfactory
region
and
developing
fetus
are
largely
independent
of
each
other,
so
the
model
can
be
viewed
as
two
separate
submodels.
While
each
of
these
submodels
is
somewhat
complex
in
itself,
the
structure
overall
is
sound
and
supported
by
scientific
data.
The
nasal/
olfactory
model
can
be
further
divided
into
the
airphase/
CFD
component
and
the
tissue­
phase/
metabolic
component.
The
nasal/
olfactory
model
is
a
slightly
modified
implementation
of
the
model
developed
for
ester
vapors
(
Frederick
et
al.,
2002).
Vapor
breathed
in
through
the
nose
splits
into
two
airstreams,
which
pass
over
respiratory
and
olfactory
epithelial
tissues
prior
to
recombining
in
the
nasopharynx
and
proceeding
to
the
lungs.
The
epithelial
tissue
compartments
consist
of
a
mucus
layer,
tissue
layers,
and
a
blood
exchange
region,
so
vapor
equilibrates
from
the
gas
phase
through
the
mucus
and
tissues
to
the
blood.

Glutathione
transferase
mediated
conjugation
of
methyl
iodide
occurs
in
all
the
respiratory
and
olfactory
epithelial
tissue
layers
(
including
the
gas
exchange
layer)
as
well
as
in
tissues
throughout
the
body,
e.
g.,
liver,
stomach,
and
kidney.
These
compartments
also
have
basal
glutathione
turnover
and
re­
synthesis.
This
model
does
not
describe
inducible
re­
synthesis,
i.
e.,
increased
synthesis
rate
in
response
to
GSH
depletion
(
D'Souza
et
al.,
1988).
Limited
non­
protein­
dependent
degradation
of
GSH
occurs,
so
for
simplicity
additional
terms
for
this
degradation
were
not
included
in
the
many
nasal
tissue
compartments,
though
they
were
included
for
several
tissues
including
liver,
brain,
and
kidney.
Note
that
enzymatic
rates
observed
in
Chamberlain
et
al.,
1998a
are
in
the
presence
of
cytosol
diluted
by
comparison
with
in
vivo,
so
the
non­
protein­
dependent
degradation
will
be
a
much
smaller
fraction
of
total
GSH
utilization
under
physiological
conditions
than
reported
by
Chamberlain
et
al.
It
is
possible
that
non­
enzymatic
methylation
of
proteins
and
other
macromolecules
is
a
component
of
the
mode
of
action
for
methyl
iodide
induced
nasal
toxicity.

Cross­
species
differences
in
the
respiratory
tract
play
an
important
role
in
the
model
structure
and
parameterization.
The
model
structure
includes
all
the
regions
appropriate
for
the
rat
nose,
but
the
human
nose
has
only
one
region
for
olfactory
epithelium
(
Frederick
et
al.,
1998).
The
human
parameterization
addresses
this
by
making
the
posterior
epithelial
stack
(
ethmoid
olfactory
tissue
stack)
very
small
as
well
as
setting
the
gas
transfer
to
be
negligibly
small.
However,
while
the
impact
of
this
region
on
the
behavior
of
the
modeled
gas
is
extremely
limited,
the
model
still
calculates
values
for
this
region
in
both
species,
leading
to
the
incorrect
use
of
dose
metrics
in
the
ethmoid
olfactory
for
humans
in
the
original
submissions.
The
potential
for
confusion
is
particularly
great
in
this
specific
case
because
the
relevant
region
in
the
rat
nose
is
considered
to
be
the
ethmoid
olfactory
epithelium,
while
the
appropriate
region
in
the
human
parameterization
is
the
dorsal
olfactory
epithelium.

Simplifications
of
the
model
would
be
useful
in
the
future
notably
due
to
the
computational
intensity
of
running
the
model
to
simulate
inhalation
and
exhalation;
it
does
not
appear
that
this
level
of
detail
is
necessary.
It
is
also
not
clear
that
the
large
number
of
nasal
tissue
layers
in
each
tissue
stack
is
necessary.
While
these
compartments
probably
have
a
limited
impact
on
computational
intensity,
they
make
the
model
more
complex
to
understand
and
check.
However,
these
considerations
do
not
impact
making
a
decision
concerning
the
acceptability
of
this
version
of
the
model.

Mathematical
Descriptions
and
Computational
Implementation
The
model
(
mei2epa.
csl)
is
coded
in
ACSL
version
11.8
(
Aegis
Technologies,
Huntsville,
AL).
With
limited
editing
it
can
be
run
in
acslXtreme
version
1.4,
a
more
current
version
of
the
software
used
in
this
evaluation
(
mei2epa7­
11­
05.
csl).
In
ACSL,
the
code
for
the
model
provides
a
complete
listing
of
the
equations
in
the
CSL
file;
parameter
values
are
specified
in
the
CMD
file
(
mei2epa.
cmd
submitted;
mei2epa7­
11­
05.
cmd
EPA
revision).
The
equations
have
previously
been
described
in
publications
cited
in
Sweeney
et
al.,
2005,
particularly
the
peer­
reviewed
publications
of
Frederick
et
al.
1998,
2002.
A
screening
level
assessment
of
the
equations
and
computer
implementation
for
the
nasal
modeling
has
been
conducted
since
it
is
assumed
that
they
were
consistent
with
the
previous
modeling
published
in
peer­
reviewed
journals.
One
potential
error
has
been
noted
in
the
parameter
values
used
in
the
computer
code
for
the
nose,
but
it
is
not
expected
to
have
a
significant
impact
on
the
model.

Parameter
Analysis
and
Model
Simulations
The
nasal
model
has
a
significant
number
of
parameters
a
substantial
fraction
of
which
had
to
be
estimated
for
the
human
model
without
support
of
data
for
methyl
iodide
in
humans.
These
estimates
appear
to
be
made
in
reasonable
ways,
but
still
they
carry
uncertainty.
The
parameter
values
for
rats
are
largely
obtained
from
direct
experimental
measurements.

Airphase/
CFD
parameters:
The
parameters
for
the
airphase/
CFD
component
of
the
rat
were
derived
from
an
incomplete
computational
mesh
of
the
rat
nose
­­
the
best
that
was
available
when
the
model
on
which
this
was
based
was
developed.
In
particular
the
gas­
phase
mass
transfer
resistance
for
the
olfactory
region,
which
is
the
target
tissue,
was
estimated
based
on
assumptions
about
that
region.
There
is
also
an
inconsistency
in
how
two
other
resistances
were
determined
and
are
implemented
in
the
model.
The
rat
nasal
CFD
model
has
since
been
completed,
and
we
recommend
that
the
set
of
CFD
parameters
for
the
rat
be
updated
based
on
the
current
model,
and/
or
the
code
be
corrected
for
the
application
of
the
parameters
for
the
vestibule
and
pharynx.
There
are
also
now
several
human
computational
meshes
that
could
be
used
to
characterize
the
variability
in
the
human
CFD
parameters.
These
activities,
while
desirable
for
a
future
version
of
the
model,
are
not
considered
essential
to
deciding
the
acceptability
of
the
current
version
of
the
model.
Our
simulations
with
the
rat
model
indicate
that
the
predictions
are
not
sensitive
to
the
CFD­
derived
parameters,
given
the
current
values
of
the
other
model
parameters.
In
particular,
uptake
by
the
nasal
tissues
appears
to
be
largely
controlled
by
the
mucus:
air
partition
coefficient,
estimated
diffusivity
of
iodomethane
in
mucus
and
tissue,
and
the
estimated
thickness
of
the
mucus
and
epithelium.

Nasal
tissue
metabolism­
related
parameters:
In
the
absence
of
human
pharmacokinetic
studies,
extrapolation
to
humans
of
a
relatively
simple
model
for
a
volatile
organic
like
iodomethane
can
be
addressed
by
using
human
physiological/
anatomic
parameters
(
e.
g.,
blood
flow
rates
and
tissue
volumes),
in
vitro
measurements
of
at
least
human
blood:
air
partition
coefficients
for
the
chemical
(
because
tissue:
air
partition
coefficients
are
considered
to
be
relatively
similar
across
species),
and
in
vitro/
in
vivo
extrapolation
of
human
and
rodent
metabolism
(
e.
g.,
cytosolic
glutathione
transferase
metabolism).
These
approaches
were
used
to
some
degree
in
this
model,
but
no
in
vitro
measurements
were
made
using
human
nasal
tissue,
so
rat
values
were
assumed
for
human
glutathione
transferase
activity.

Due
to
the
role
of
glutathione
(
GSH)
as
a
cofactor
and
the
proposal
that
depletion
of
GSH
be
used
as
a
dose
metric,
this
model
also
has
biochemical
parameters
(
i.
e.
GSH
turnover
rates
in
tissues)
that
are
not
chemical
specific.
For
rats,
values
for
GSH
concentrations
in
tissues
and
turnover
rate
constants
were
obtained
from
the
literature
or
estimated
from
data
collected
for
the
development
of
the
model.
For
humans,
some
values
for
GSH
concentrations
in
tissues
were
obtained
from
the
literature,
but
others
were
assumed
the
same
as
rats.
Turnover
rate
constants
were
assumed
the
same
in
rats
and
humans.

Sensitivity
analysis
found,
not
surprisingly,
that
model
predictions
of
olfactory
GSH
depletion
were
sensitive
to
the
glutathione
concentration
and
turnover
rate
in
the
olfactory
epithelium
along
with
the
parameters
for
glutathione
transferase
metabolic
activity
(
see
Table
10
in
Sweeney
et
al.,
2005).
The
most
sensitive
parameter
was
the
glutathione
concentration,
which
is
based
upon
measured
values
reported
in
the
literature
for
humans
and
rats.
The
turnover
rate
and
glutathione
transferase
metabolic
parameters
(
Vmax,
Km)
use
experimentally
determined
values
for
rat
and
assume
the
same
values
for
humans.
We
noted
this
issue
in
the
Jan.
20,
2005
memo
and
responses
were
provided
in
a
memo
from
Lisa
Sweeney
and
Mike
Gargas
dated
February
22,
2005.
The
responses
were
carefully
reviewed.
They
include
a
citation
to
a
very
useful
new
study
(
Shokeer
et
al
2005)
and
discussion
of
a
number
of
ways
to
try
to
estimate
human
nasal
glutathione
transfer
activity
for
methyl
iodide
metabolism.
These
various
estimates
indicate
that
the
estimates
based
upon
the
rat
are
likely
a
modest
overestimate
of
human
metabolic
capability
supporting
the
original
parameterization.
Shokeer
et
al.,
2005
mutated
a
critical
amino
acid
in
the
human
glutathione
transferase
T1­
1
from
tryptophan
to
arginine,
which
is
the
amino
acid
found
in
the
rodent
protein.
This
resulted
in
a
13­
fold
increase
in
the
specific
activity
of
the
enzyme
and
a
2.7­
fold
increase
in
the
catalytic
efficiency.
Therefore,
if
the
concentration
of
enzymes
were
the
same
in
human
and
rat
nasal
tissues,
the
enzymatic
activity
would
be
at
least
3­
fold
lower
in
humans
(
and
the
HEC
would
be
increased,
though
not
necessarily
in
direct
proportion).
While
we
judge
the
current
parameterization
to
be
acceptable
for
use
at
this
time,
measurement
of
methyl
iodide
conjugation
in
human
nasal
tissues
would
be
desirable
in
future
iterations
of
the
model.
Human
nasal
tissues
are
not
readily
available,
but
special
arrangements
can
be
made
as
evidenced
by
in
vitro
studies
with
other
chemicals
(
Frederick
et
al.,
2002).
The
information
on
GSH
turnover
across
species
is
far
less
compelling,
so
it
is
unclear
if
there
is
a
consistent
relationship
in
GSH
concentrations
across
species.
Potter
and
Tran
(
1995)
show
in
Figure
2
of
their
paper
a
correlation
of
GSH
concentration
with
GSH
half­
lives,
though
the
correlation
is
not
a
strong
one
and
some
efforts
to
reproduce
this
figure
have
been
unsuccessful
(
memo
dated
April
29,
2005).
There
are
additional
antioxidants
present
in
nasal
tissue
and
urea
appears
to
play
a
larger
role
in
humans
than
in
rats,
so
this
might
be
a
significant
factor
increasing
protection
in
the
humans
but
not
incorporated
in
the
model
as
described
in
the
memo
of
February
22,
2005.
The
GSH
turnover
and
glutathione
transferase
metabolic
rate
work
in
opposite
directions,
so
the
use
of
a
relatively
high
rat
GSH
turnover
and
a
relatively
high
rat
glutathione
transferase­
mediated
metabolism
rate
would
be
expected
to
essentially
cancel
each
other
out.
However,
this
indicates
the
importance
of
having
better
data
on
both
parameters,
not
just
one,
to
strengthen
the
model
parameterization
for
humans.
To
fairly
consider
the
proposed
model,
one
should
also
ask
how
the
assumptions
regarding
metabolic
constants
compare
to
the
assumptions
implicit
in
the
default
method
if
the
model
wasn't
used,
rather
than
evaluating
them
alone.
Since
the
Category
1
default
doesn't
address
metabolism
at
all,
it
effectively
assumes
it
to
be
identical
across
species,
the
same
assumption
currently
made
in
the
modeling.

Model
simulation
of
rat
data:
Figure
9
in
Sweeney
et
al.,
2005
illustrates
the
simulation
of
the
rat
glutathione
depletion
data
during
and
following
methyl
iodide
exposure
at
two
concentrations.
There
are
no
universally
accepted
statistical
methods
for
characterizing
the
quality
of
PBPK
model
simulations
of
experimental
data.
Visual
examination
indicates
that
the
simulations
are
reasonable.
The
acceptable
quality
of
this
fit
was
achieved
by
changing
the
value
of
KDGSHOE,
the
glutathione
turnover
rate
constant
in
the
olfactory
epithelium
from
the
published
value
of
0.016
hr­
1
(
Potter
et
al.,
1995)
to
a
visually
fitted
value
of
0.19
hr­
1
as
described
in
the
report.
Olfactory
epithelial
GSH
depletion
measured
during
and
after
the
25
ppm
methyl
iodide
exposure
is
well
simulated
with
this
value,
though
depletion
at
100
ppm
is
overpredicted.
The
good
description
of
the
data
at
25
ppm
is
particularly
relevant
for
simulating
the
NOAEL
conditions
in
the
longer
duration
toxicity
studies.

Proposed
Risk
Assessment
Application
It
is
proposed
to
calculate
acute
HECs
for
the
rat
nasal
toxicity
based
upon
the
reduction
in
concentration
of
GSH
in
the
ethmoid
olfactory
tissue
stack
(
CGSHEO)
in
the
rat
and
the
olfactory
tissue
in
human
(
CGSHDO).
Nasal
damage
was
only
observed
in
the
olfactory
epithelium
and
not
in
the
respiratory
epithelium
leading
to
the
localization
of
the
proposed
dose
metric.
(
Note
that
pathologically
the
regenerating
tissue
in
the
olfactory
epithelium
was
referred
to
as
having
respiratory
epithelial
characteristics,
but
this
is
distinct
from
damage
occurring
in
the
regions
of
the
nose
normally
consisting
of
respiratory
epithelium.)
The
basis
for
the
damage
occurring
specifically
in
the
olfactory
epithelium
is
not
entirely
known.

In
contrast
to
a
dose
metric
directly
describing
the
presence
or
metabolism
of
methyl
iodide,
GSH
depletion
represents
an
event
in
the
toxicodynamic
process
leading
to
the
toxicity
arising
from
methyl
iodide
metabolism.
Nasal
toxicity
is
not
believed
to
arise
from
GSH
depletion
alone
but
rather
from
a
combination
of
depletion
with
other
damage
such
as
methylation
of
cell
macromolecules
and
other
constituents
by
the
parent
compound,
formation
of
reactive
metabolites,
or
oxidative
stress
(
Chamberlain
et
al.,
1998a).
Direct
evidence
for
the
involvement
of
GSH
as
a
protective
factor
against
methyl
iodide
toxicity
was
obtained
by
pre­
treating
animals
with
substances
that
deplete
or
replenish
GSH
(
Chamberlain
et
al.,
1998b).
The
extent
of
glutathione
depletion
consistent
with
observing
toxicity
is
not
clearly
defined
(
nor
necessarily
the
same
for
every
tissue
and
toxicity).
Depletion
50%
or
greater
from
normal
levels
tends
to
be
associated
with
toxicity,
for
example
as
described
for
effects
of
propylene
oxide
in
rat
nasal
tissues
(
Lee
et
al.,
2005).
Lesser
depletion
appears
to
reflect
the
role
of
GSH
in
limiting
the
extent
of
damage.
This
is
consistent
with
the
35%
depletion
observed
in
nasal
tissues
of
rats
exposed
to
25
ppm
methyl
iodide,
approximating
the
21
ppm
NOAEL
for
olfactory
lesions,
which
the
model
predicted
to
give
30%
depletion
(
Table
1).

Table
1:
Model
predictions
for
the
NOAEL
and
LOAEL
in
the
rat
subchronic
toxicity
study
(
Kirkpatrick,
2002).
Modeled
using
mei2epa7­
11­
05.
csl
by
implementing
the
procedure
"
rat025"
in
the
CMD
file
and
setting
TSTOP=
6,
TCHNG=
6,
and
CONC=
21
or
70.

CONC
(
ppm)
(
hr
duration)
TIME
(
hr)
CGSHEO
%
GSH
depletion
(%
remaining)

RAT
0
0.00000
2.35000
0
(
100)

21
ppm
6
hr
6.00000
1.64934
30
(
70)

70
ppm
6
hr
6.00000
0.450259
81
(
19)

One
important
complication
in
developing
HECs
for
an
acute
biochemical
event
is
that
the
time
profile
of
the
exposure
concentration
is
important.
This
is
particularly
obvious
with
GSH
depletion
because
GSH
synthesis
is
modeled
as
the
constant
or
zero­
order
rate
required
to
obtain
the
measured
GSH
concentration
in
the
tissue
(
assumed
to
represent
a
steady­
state
concentration)
given
the
first
order
basal
GSH
elimination
rate
(
estimated
from
data
for
rats
and
assumed
the
same
in
humans).
This
is
a
standard
approach
for
modeling
steady
state
turnover.
A
consequence,
however,
is
that
the
maximum
GSH
depletion
achieved
is
dependent
upon
the
rate
of
delivery
of
the
methyl
iodide
to
the
nasal
tissue.
Because
there
was
not
a
simple
way
to
link
the
exposure
modeling
using
the
PERFUM
model,
with
the
PBPK
modeling,
the
issue
is
illustrated
with
examples
in
Table
2.

Table
2:
Simulation
of
GSH
depletion
in
human
dorsal
olfactory
epithelium
for
constant
continuous
inhalation
(
Scenario
1)
and
variable
continuous
inhalation
(
Scenarios
2
and
3).
All
scenarios
result
in
an
average
exposure
of
2.9
ppm
for
24
hr.
Time
and
GSH
concentrations
(
CGSHDO)
were
obtained
using
the
PBPK
model.

CONC
(
ppm)
(
hr
duration)
TIME
(
hr)
CGSHDO
%
GSH
depletion
(%
control
GSH)

Pre­
Exposure
0
0
0.800000
0
(
100)

Scenario
1
2.9
(
24
hr)
24
0.598396
25
(
75)

Scenario
2
5.9
(
0­
4
hr)
4
0.59205
26
(
74)

2.9
(
4­
12
hr)
12
0.59328
26
(
74)
CONC
(
ppm)
(
hr
duration)
TIME
(
hr)
CGSHDO
%
GSH
depletion
(%
control
GSH)

1.93
(
12­
24
hr)
24
0.65562
18
(
82)

Scenario
3
8.7
(
0­
4
hr)
4
0.4942
38
(
62)

2.9
(
4­
8
hr)
8
0.5419
32
(
68)

1.45
(
8­
24
hr)
24
0.6889
14
(
86)

The
scenarios
in
Table
2
were
selected
to
illustrate
the
issues
arising
from
different
time
profiles
that
all
give
an
average
HEC=
2.9
ppm
over
24
hours.
The
scenarios
were
intended
to
approximate
the
release
characteristics
from
treated
fields
(
i.
e.,
higher
concentrations
earlier
and
lower
concentrations
at
later
times)
absent
modeled
exposure
concentrations
as
outputs
from
PERFUM.

The
HECs
that
resulted
in
25%
GSH
depletion
were
estimated
for
a
constant
continuous
exposure
lasting
8
hours
(
worker
scenario)
or
24
hour
(
bystander
scenario).
These
values
are
2.9
ppm
for
24
hr
and
3.7
ppm
for
8
hr
exposures.
The
selection
of
25%
GSH
depletion
for
the
constant
continuous
exposure
was
felt
to
provide
reasonable
protection
for
exposure
scenarios
that
would
involve
higher
concentrations
dropping
off
over
time
as
appear
likely
for
the
proposed
use
of
this
compound
as
a
soil
fumigant.
Such
scenarios
might
result
in
GSH
depletion
between
25
and
50%.
Due
to
the
proposed
application
of
an
uncertainty
factor
of
3
for
human
pharmacokinetic
variability,
greater
than
95%
of
the
population
is
predicted
to
have
significantly
less
GSH
depletion.
The
exact
percentage
of
the
population
that
might
experience
GSH
depletion
towards
50%
cannot
be
estimated
at
this
time
because
it
is
dependent
on
modeling
both
the
time
course
for
methyl
iodide
inhalation
(
which
is
dependent
upon
the
emissions
from
the
treated
field
and
meteorological
conditions)
and
human
variability.
It
is
possible
that
linking
the
exposure
and
pharmacokinetic
modeling
could
demonstrate
that
appropriate
protection
is
obtained
with
different
buffer
zones
than
those
estimated
in
PERFUM
using
an
8­
or
24­
hour
average
HEC.

As
described
previously,
there
are
several
factors
that
could
result
in
lower
estimates
of
GSH
depletion
for
humans
for
a
given
exposure,
notably
the
lower
specific
activity
of
the
human
glutathione
transferase
T1­
1
for
methyl
iodide
and
the
greater
importance
of
other
antioxidants
in
human
nasal
tissues.
It
is
also
worth
noting
that
these
HECs
are
significantly
more
protective
than
the
HECs
that
would
be
derived
using
the
default
RfC
methodology
for
a
Category
1
gas.

The
model
can
also
be
used
to
predict
the
GSH
depletion
at
the
NOAEL
and
LOAEL
in
the
chronic
rat
toxicity
study
("
24­
Month
Inhalation
Combined
Chronic
Toxicity/
Carcinogenicity
Study
of
Iodomethane
in
Rats."
MRID
46512401).
These
predictions
are
shown
in
Table
3.

Table
3:
Model
predictions
for
the
NOAEL
and
LOAEL
in
the
rat
chronic
toxicity
study.
Modeled
using
mei2epa7­
11­
05.
csl
by
implementing
the
procedure
"
rat025"
in
the
CMD
file
and
setting
TSTOP=
6,
TCHNG=
6,
and
CONC=
20
or
60.
CONC
(
ppm)
(
hr
duration)
TIME
(
hr)
CGSHEO
%
GSH
depletion
(%
remaining)

RAT
0
0.00000
2.35000
­

20
ppm
6
hr
6.00000
1.68223
28
(
72)

60
ppm
6
hr
6.00000
0.623069
74
(
26)

The
human
model
was
run
for
conditions
of
continuous
constant
exposures
to
methyl
iodide
at
a
range
of
concentrations.
The
extent
of
depletion
is
of
course
dependent
upon
the
exposure
duration
(
e.
g.,
8
or
24
hr),
but
is
linear
as
a
function
of
methyl
iodide
concentration
until
very
high
GSH
depletion
has
occurred.
Therefore,
model
outputs
were
fitted
with
straight
lines
in
EXCEL
to
obtain
equations
to
predict
the
equivalent
of
the
NOAEL
and
LOAEL
for
the
rat
toxicity
study.
The
HEC
values
were
reconfirmed
by
running
them
in
the
PBPK/
PD
model.

Table
4:
HECs
predicted
with
human
PBPK
model.
Modeled
using
mei2epa7­
11­
05.
csl
by
implementing
the
procedure
"
preghum"
in
the
CMD
file
and
setting
TSTOP=
8
or
24,
TCHNG=
24,
and
CONC
=
value
in
HEC
column.

Acute
Effects
(
24
or
8
hr
exposure)
HEC
(
ppm)

24
hr
(
25%
GSH
depletion)
2.9
24
hr
(
30%
GSH
depletion)
­
NOAEL
(
Kirkpatrick,
2002)
3.4
24
hr
(
81%
GSH
depletion)
­
LOAEL
(
Kirkpatrick,
2002)
10
8
hr
(
25%
GSH
depletion)
3.7
8
hr
(
30%
GSH
depletion)
­
NOAEL
(
Kirkpatrick,
2002)
4.5
8
hr
(
81%
GSH
depletion)
­
LOAEL
(
Kirkpatrick,
2002)
13
Chronic
Effects
(
24
or
8
hr
exposure)

24
hr
(
NOAEL
­
28%
GSH
depletion)
3.2
24
hr
(
LOAEL
­
74%
GSH
depletion)
8.9
8
hr
(
NOAEL
­
28%
GSH
depletion)
4.2
8
hr
(
LOAEL
­
74%
GSH
depletion)
11.7
3.
PREGNANCY
MODELING
Model
Purpose
Modeling
of
methyl
iodide
metabolism
to
iodide
during
rabbit
and
human
pregnancy
was
proposed
to
obtain
an
HEC
for
the
acute
risk
assessment
of
the
effects
observed
following
in
utero
exposure.
The
proposed
mode
of
action
involves
production
of
iodide
and
disruption
of
the
developing
fetal
thyroid
due
to
excessively
high
iodide
levels
in
rabbits.
The
proposed
dose
metric
is
the
area
under
the
concentration
curve
for
fetal
serum
inorganic
iodide
during
a
single
day
exposure.
This
dose
metric
would
account
for
differences
in
fetal
distribution
of
iodide
that
might
exist
between
rabbits
and
humans.
Some
analysis
with
a
two­
day
fetal
serum
AUC
as
the
dose
metric
from
the
rabbit
study
has
also
been
described.
An
alternative
dose
metric
is
the
maternal
AUC,
which
would
assume
that
the
distribution
to
the
fetus
was
similar
in
rabbit
and
humans.

3.1.
Rabbit
Model
Model
Structure
and
Biological
Characterization
The
rabbit
model
has
versions
with
different
structures
for
the
parent
compound
in
the
doe;
the
same
description
is
used
for
fetal
methyl
iodide
and
maternal
and
fetal
iodide.
The
full
model
has
the
extensive
nasal
tract
description
described
above
for
the
rat
and
human
models
(
model
code
file:
mei2epa.
csl).
Simulations
with
a
simplified
model
in
which
the
nose
was
eliminated
so
all
absorption
was
alveolar
produced
very
similar
predictions
for
serum
iodide
levels
(
see
Fig
4
in
Sweeney
et
al.,
2005)
(
model
code
file:
meinoepa.
csl).
It
is
unclear
the
degree
to
which
the
models
differ
in
predictions
of
serum
levels
of
parent
methyl
iodide,
but
that
is
not
important
for
this
dose
metric.
For
purposes
of
predicting
iodide
levels,
the
simplified
model
provides
computational
efficiencies.
Finally,
an
iodide
only
model
with
maternal
and
fetal
submodels
was
used
to
simulate
the
intravenous
iodide
dosing
data
(
model
code
file:
bunny.
csl).

The
pregnancy
model
is
appropriate
for
describing
a
short
period
of
pregnancy,
perhaps
a
day
or
two,
because
it
does
not
describe
the
complex
changes
during
the
course
of
gestation
(
e.
g.,
growth
of
the
fetus
and
its
organs).
Given
the
short
duration
of
the
critical
window
in
the
rabbits
(
gestational
days
23
­
26
or
some
portion
of
that),
the
model
can
be
appropriately
calibrated
for
a
relevant
period,
generally
one
day,
using
fixed
parameter
values
to
estimate
dose
metrics.
This
period
is
relatively
late
in
rabbit
gestation,
so
the
model
assumes
the
same
structure
for
the
fetal
submodels
as
used
for
the
maternal
submodels
(
except
for
the
elaborated
maternal
nasal
description
in
the
full
version)
with
the
addition
of
placental
compartments
to
interface
the
fetal
and
maternal
submodels.

Mathematical
Descriptions
and
Computational
Implementation
The
model
is
coded
in
ACSL
version
11.8
(
Aegis
Technologies,
Huntsville,
AL),
though
with
limited
editing
it
can
be
run
in
acslXtreme
version
1.4,
a
more
current
version
of
the
software.
In
ACSL,
the
code
for
the
model
provides
a
complete
listing
of
the
equations
in
the
CSL
file;
parameter
values
are
specified
in
the
CMD
file.
The
equations
have
previously
been
described
in
publications
cited
in
Sweeney
et
al.,
2005,
particularly
the
peer­
reviewed
publications
on
iodide
from
the
group
of
authors
including
R.
A.
Clewell
and
EA
Merrill
(
including
Clewell
et
al.
2001;
Merrill
et
al.,
2005
and
others).
The
equations
and
computer
implementation
for
the
pregnancy
modeling
were
only
screened,
assuming
that
they
were
consistent
with
the
previous
modeling
published
in
peer­
reviewed
journals.
Parameter
Analysis
and
Model
Fit
The
rabbit
parameter
values
are
intended
to
be
representative
of
the
pregnant
animal
during
the
critical
window
(
gestational
days
23
­
26)
in
the
toxicity
study.
Much
of
the
pharmacokinetic
data
collected
for
methyl
iodide
or
inorganic
iodide
in
the
rabbits
also
used
pregnant
rabbits
during
this
gestational
period.
Studies
of
the
nasal
tract
and
methyl
iodide
extraction
used
nonpregnant
female
rabbits.
Because
nasal
toxicity
was
not
evaluated
in
the
rabbits,
the
similarity
of
iodide
serum
predictions
in
the
model
without
the
nose
compartments
and
limited
resources,
the
parameter
values
and
model
fits
for
the
nasal
extraction
were
not
reviewed.

The
intravenous
dosing
with
inorganic
iodide
was
carried
out
to
obtain
iodide­
specific
parameter
values
for
the
doe
and
fetus.
The
data
were
generally
well
simulated.
The
simulations
show
substantial
accumulation
of
iodide
in
the
mammary
gland
for
which
no
data
were
available
to
validate
that
prediction.
Furthermore,
simulation
of
the
maternal
and
fetal
serum
iodide
concentrations
following
methyl
iodide
exposures
(
20
or
25
ppm
6
hr/
day
for
4
days)
required
adjusting
the
iodide
parameters
estimated
from
the
intravenous
inorganic
iodide
exposures
resulting
in
a
poorer
simulation
of
the
intravenous
data.
The
data
from
methyl
iodide
exposures
were
appropriately
considered
the
more
critical
to
simulate,
so
the
adjusted
parameters
were
used
in
subsequent
analyses.

There
are
a
large
number
of
parameters
in
the
model
(
even
without
the
nose)
and
the
sensitivity
analysis
for
the
maternal
and
fetal
serum
iodide
AUC
was
reported
in
a
memo
dated
May
23,
2005
(
MRID
46559303).
A
relatively
few
parameters
reflect
the
delivery
of
methyl
iodide,
while
the
majority
are
associated
with
inorganic
iodide.
Reasonable
simulations
of
the
maternal
and
fetal
serum
iodide
following
methyl
iodide
exposures
are
obtained
using
the
parameter
values
so
in
total
the
calibration
appears
reasonable,
though
it
is
not
clear
which
individual
parameters
are
significantly
constrained
by
the
data.

3.2.
Human
Model
Model
Structure
and
Biological
Characterization
The
model
structure
and
characterization
of
the
biology
is
the
same
for
the
human
and
the
rabbit
except
that
only
a
full
version
of
the
model
with
the
complete
nasal
tract
was
used.

Mathematical
Descriptions
and
Computational
Implementation
As
previously
noted,
the
full
model
(
model
code
file:
mei2epa.
csl)
is
used
to
simulate
humans.

Parameter
Analysis
and
Model
Fit
The
human
period
corresponding
to
the
rabbit
developmental
window
for
toxicity
is
described
as
gestation
week
18.
Due
to
the
large
number
of
parameters
in
the
model,
the
focus
of
the
evaluation
was
on
those
to
which
the
dose
metric
was
most
sensitive.
The
results
of
the
sensitivity
analysis
for
serum
iodide
AUC
in
the
mother
and
fetus
was
described
in
a
memo
dated
May
23,
2005
(
MRID
46559303).
Fewer
parameters
are
identified
as
important
and
except
for
three
(
fraction
blood
flow
to
the
liver,
the
affinity
and
maximum
capacity
of
the
sodium­
iodide
symporter
in
the
thyroid
follicle),
the
parameters
are
the
same
as
found
in
the
rabbit
sensitivity
analysis.
The
differences
in
parameters
determining
the
serum
iodide
AUC
between
species
likely
reflect
some
combination
of
the
higher
shorter
exposure
used
in
the
rabbit
analysis
versus
lower
continuous
human
exposure
along
with
potentially
some
interspecies
differences.

For
both
the
rabbit
and
human
sensitivity
analyses,
placental
parameters
are
important
determinants
of
the
fetal
serum
iodide
AUC.
These
parameters
are:
the
placenta:
plasma
partition
coefficient
for
iodide;
the
permeability­
area
product
diffusion
constant
for
placental
tissue;
the
affinity
constant
and
maximum
capacity
of
the
placental
sodium­
iodide
symporter;
and
the
iodide
transfer
rates
between
the
mother
and
the
fetus.
While
for
the
rabbit
there
are
direct
data
on
fetal
and
maternal
serum
levels,
no
such
data
exist
for
humans.
The
human
iodide
parameter
values
reflect
a
combination
of
published
parameters
from
iodide
modeling
of
the
adult
human
(
Merrill
et
al.,
2005),
scaling
parameters
based
upon
differences
between
male
and
pregnant
female
rats,
and
parameter
values
reported
in
preliminary
human
modeling
(
Clewell
et
al.,
2001).
Maternal
and
fetal
iodide
data
are
available
for
a
number
of
tissues,
particularly
maternal
serum,
amniotic
fluid,
and
maternal
and
fetal
thyroid
(
Clewell
et
al.,
2001,
Aboul­
Khair
et
al.,
1966).
These
data
likely
bound
maternal
and
fetal
serum
iodide
levels,
but
that
has
not
been
directly
demonstrated
using
the
current
set
of
parameter
values.

Proposed
Risk
Assessment
Application
The
proposed
application
of
the
model
is
to
derive
a
human
HEC
based
upon
the
rabbit
study
in
which
late
term
resorptions
were
observed
(
Gargas
et
al.,
2005).
The
proposal
is
to
use
the
fetal
serum
iodide
AUC
as
the
dose
metric.
This
dose
metric
would
reflect
cross­
species
differences
that
might
exist
in
the
maternal­
fetal
distribution
of
iodide.
An
alternative
possibility
would
be
to
use
the
maternal
serum
iodide
AUC,
which
would
assume
fetal
distribution
was
similar
across
humans
and
rabbits.
This
alternative
might
be
appropriate
because
it
would
be
more
strongly
supported
by
the
available
adult
human
iodide
modeling
(
Merrill
et
al.,
2005)
and
would
not
be
dependent
upon
the
placental
transfer
parameters
described
above,
which
vary
in
the
degree
to
which
they
are
supported.

Therefore,
the
issue
becomes
whether
there
are
differences
between
human
and
rabbits
in
maternal
versus
fetal
serum
iodide
concentrations.
Differences
have
been
reported
between
rabbits,
guinea
pig,
and
sheep
versus
rats
indicating
the
former
species
have
higher
fetal
than
maternal
serum
levels
(
Logothetopoulos
and
Scott,
1965;
Roti
et
al.,
1983;
McGuire
and
Berman,
1978).
Similar
differences
are
observed
in
the
submitted
intravenous
iodide
and
the
methyl
iodide
exposures
of
rabbits
in
which
iodide
concentrations
in
fetal
serum
are
approximately
3­
fold
higher
than
in
maternal
serum.
Rats,
in
contrast,
appear
to
have
similar
maternal
and
fetal
serum
levels
(
Logothetopoulos
and
Scott,
1965).

As
noted
previously,
no
references
were
identified
with
comparative
maternal
and
fetal
serum
data
for
humans
in
contrast
to
the
relatively
well
documented
information
about
the
development
of
the
fetal
thyroid
to
accumulate
iodide
and
synthesize
thyroid
hormones.
A
number
of
review
articles
in
the
biomedical
literature
describe
the
perspectives
of
their
authors
on
serum
levels.
Roti
et
al.
(
1983)
indicate
that
humans
do
not
appear
to
have
the
same
placental
ability
to
transfer
and
concentrate
iodide
in
the
fetus
as
sheep
or
rabbits.
Iodide
concentrations
in
fetal
blood
are
indicated
to
be
75%
of
maternal
levels,
but
no
references
are
cited
in
Gorman
(
1999)
and
an
incorrect
citation
is
provided
in
Pauwels
et
al.,
1999.
Another
review
indicates
that
the
extent
of
iodide
exchange
between
mother
and
fetus
is
uncertain
(
Glinoer,
1997).
Concerns
about
fetal
exposure
to
radioactive
iodide
either
from
treatment
of
maternal
thyroid
conditions,
which
are
now
considered
to
be
contraindicated
during
pregnancy,
or
from
environmental
exposures,
have
led
to
a
substantial
literature
including
mathematical
models
to
estimate
fetal
radiation
dosimetry.
This
literature
tends
to
focus
on
the
thyroid
and
iodide­
containing
thyroid
hormones,
which
does
not
necessarily
require
modeling
even
maternal
serum
(
Russell
et
al.,
1997).
A
recent
paper
estimated
human
fetal
doses
from
radioiodine
using
tissue
distribution
based
upon
data
the
authors
collected
in
guinea
pigs
(
Millard
et
al.,
2001).
They
argue
that
their
previous
work
has
demonstrated
the
guinea
pig
is
a
good
model
for
human
placental
transfer.
This
model
predicts
fetal
serum
levels
approximately
3­
times
maternal
levels
(
i.
e.,
similar
to
those
measured
for
the
rabbits
exposed
to
methyl
iodide).

Finally,
the
human
placenta
is
described
as
freely
permeable
to
iodide
(
e.
g.,
Fisher
1997)
while
a
recent
review
indicates
that
it
is
unresolved
whether
it
is
passive
transfer
or
an
active
pump
(
Glinoer,
1997).
One
possibility
would
be
that
fetal
and
maternal
serum
could
have
similar
physical
chemical
properties
so
given
a
placenta
that
was
freely
permeable
the
concentrations
would
be
similar
in
the
fetal
and
maternal
serum.
Since
iodide
is
a
charged
ion,
it
is
not
clear
how
it
crosses
the
placenta
though
the
sodium/
iodide
symporter
has
been
found
in
specific
placental
cell
types
(
Bidart
et
al.,
2000).
It
is
also
possible
that
other
anion
transporters
are
involved.
Finally,
it
should
be
noted
that
statements
about
iodide
placental
transport
may
largely
reflect
a
contrast
to
the
situation
for
thyroid
hormones
(
thyroxine
or
T4
and
triiodothyroxine
or
T3).
The
placenta
has
very
active
deiodinase
enzymes,
which
dramatically
limit
the
distribution
of
maternal
T4
and
T3
to
the
fetus.
It
should
also
be
noted
that
much
of
the
focus
of
biomedical
publications
is
on
iodide
insufficiency
as
opposed
to
the
potential
for
iodide
excess
that
is
the
focus
here.

The
limited
and
often
indirect
information
available
suggest
that
human
fetal
serum
likely
does
not
have
as
high
concentrations
relative
to
maternal
serum
as
are
observed
in
a
number
of
animal
species.
Unfortunately,
the
information
is
too
limited
and
indirect
to
accurately
identify
what
the
relative
concentrations
would
be.
The
HEC
of
17
ppm
modeled
using
the
assumption
that
human
fetal
serum
levels
are
75%
of
maternal
levels
likely
represents
a
high
or
perhaps
reasonable
estimate,
while
the
HEC
of
4
ppm
based
upon
assuming
equivalent
distribution
to
fetal
serum
in
rabbits
and
humans
likely
represents
a
lower
bound
above
which
the
"
true"
value
would
be
found.
The
HEC
values
were
obtained
by:
1)
estimating
the
AUC
for
maternal
(
aucca_
i
in
the
model)
or
fetal
(
auccaf_
i
in
the
model)
serum
iodide
using
the
rabbit
parameterization
of
the
model
at
the
NOAEL
of
10
ppm
methyl
iodide
exposure
for
6
hours,
and
2)
running
the
human
parameterization
of
the
model
to
obtain
the
24­
hr
constant
continuous
exposure
concentration
matching
that
maternal
or
fetal
AUC.
The
bulk
of
the
biomedical
literature
suggests
that
within
the
range
of
these
estimates
(
i.
e.,
4
to
17
ppm);
the
"
true"
value
is
likely
towards
the
higher
part
of
the
range.
But,
there
does
not
appear
to
be
adequate
information
to
establish
that
a
particular
value
in
the
higher
part
of
that
range
is
the
most
accurate
value.

4.
HUMAN
VARIABILITY
MODELING
Analyses
of
human
variability
for
relevant
dose
metrics,
e.
g.
nasal
GSH
depletion,
fetal
serum
iodide
were
undertaken.
Monte
Carlo
simulations
used
estimates
of
the
distribution
of
values
for
parameters
with
normalized
sensitivity
coefficients
the
absolute
value
of
which
were
greater
than
or
equal
to
0.05
(
for
the
given
dose
metric
being
evaluated).
Even
with
this
limitation,
there
are
a
large
number
of
parameters
and
it
would
require
substantial
effort
to
evaluate
the
documentation
of
the
distributions
(
i.
e.
citation
of
publications)
supporting
the
choices
made.
The
analyses
provide
some
useful
perspective
on
the
default
uncertainty
factor
(
UF)
of
3.0
for
human
pharmacokinetic
variability
suggesting
that
it
is
a
reasonable
value.
It
is
important
to
note
that
concerns
exist
for
all
endpoints
to
insure
that
life
stages
(
e.
g.,
infants,
children,
aged)
and
potential
susceptible
subpopulations
are
protected.
Life
stages
differ
from
susceptible
subpopulations
because
all
of
the
population
over
time
may
go
through
a
given
life
stage,
while
a
susceptible
subpopulation
may
represent
only
a
portion
of
the
population
(
see
Section
3.5
in
US
EPA
2005).
The
following
comments
identify
issues
with
the
distributions
used
that
might
increase
or
decrease
the
resulting
estimated
variability.

°
CV
estimates
for
parameters
were
obtained
in
a
variety
of
ways.
Those
based
upon
experimental
measurements
tend
to
overestimate
the
variability
because
experimental
error
was
generally
not
separated
from
true
variability.
This
is
likely
to
remain
a
limitation
that
rarely
can
be
addressed.

°
Matching
appropriate
parameter
CVs
with
the
toxicological
endpoint
and
population
is
not
necessarily
straightforward
or
easily
documented.
Where
appropriate,
analyses
need
to
include
multiple
life­
stages
(
e.
g.,
adults,
children,
pregnant
women).
o
Analyses
for
the
nasal
dose
metric
were
carried
out
for
adults
and
additional
analyses
evaluated
children's
variability
for
a
subset
of
parameters
related
to
the
CFD/
airflow
aspect
of
the
model
(
memo
dated
March
29,
2005,
MRID
#
46559301).
Analyzing
children's
variability
for
the
full
range
of
parameters
(
including
metabolic
parameters)
would
be
needed
to
understand
variability
during
that
life­
stage.
It
also
should
be
noted
that
these
analyses
were
carried
out
using
CGSHEO,
which
is
inappropriate
for
humans,
so
it
is
not
clear
whether
the
resulting
distributions
would
be
the
same
for
the
human
dorsal
olfactory
GSH
levels
(
CGSHDO).
o
For
the
developmental
endpoint,
the
analysis
considered
the
maternal
variability
for
serum
levels
of
iodide;
placental
and
fetal
variability
would
largely
require
assumptions
about
the
distributions
and
likely
lead
to
a
somewhat
higher
estimate
of
variability.

°
The
report
and
memos
document
the
sources
of
all
the
CVs
used
in
the
analysis,
but
it
would
require
checking
each
one
to
understand
whether
it
appears
to
appropriately
estimate
human
variability.
This
was
not
done.
Many
CVs
were
based
upon
the
estimates
in
Allen
et
al.,
1996
which
used
CVs
of
0.15
and
0.3
for
parameter
considered
to
have
lesser
or
greater
variability
(
see
justification
of
values
in
the
paper).
It
is
notable
that
several
of
the
key
metabolic
parameters
are
estimated
to
have
larger
CVs
than
0.3.
The
extent
to
which
this
reflects
limitations
of
propagating
error
in
multiple
terms
to
estimate
the
metabolic
parameters
or
that
the
original
justification
resulted
in
too
small
a
CV
for
highly
variable
parameters
is
unclear.
The
authors
have
made
a
substantial
effort
to
evaluate
human
variability,
but
it
isn't
easy
to
draw
conclusions
about
all
the
distributions
used.

°
The
analysis
for
nasal
effects
does
not
include
a
description
for
the
portion
of
the
population
that
does
not
express
the
glutathione
transferase
theta­
1
form
involved
in
methyl
iodide
metabolism.
To
the
extent
that
the
nasal
toxicity
is
dependent
upon
GSH
depletion,
these
people
would
be
expected
to
be
at
decreased
risk
due
to
their
inability
to
carry
out
GSH­
dependent
metabolism
of
methyl
iodide.

Finally,
the
portion
of
the
population
upon
which
to
evaluate
the
variability
is
an
important
issue.
The
report
and
memos
use
the
95th­
percentile.
However,
this
leaves
1
in
20
people
outside
the
range
covered
by
such
a
factor
accounting
for
human
variability.
If
it
were
considered
appropriate
to
only
have
1
in
100
or
1
in
10,000
estimated
outside
the
covered
range
of
the
distribution,
a
larger
value
would
be
needed.
The
reports
and
memos
do
not
describe
the
values
needed
to
be
protective
for
higher
percentiles
of
the
populations.
Thus,
these
analyses
suggest
that
the
default
UF
of
3.0
is
likely
protective
of
a
higher
percentile
of
the
population
than
the
95th­
percentile,
perhaps
protective
of
the
99th
or
even
99.9th
percentiles.
Basing
the
UF
directly
on
the
human
PK
variability
modeling
would
be
desirable,
but
information
characterizing
the
population
distribution
overall
were
not
available
to
permit
the
Agency
to
decide
the
appropriate
percentile
and
value.

5.
MODEL
DOCUMENTATION
Model
documentation
is
intended
to
communicate
the
modeling
clearly
so
the
risk
assessment
using
the
model
is
transparent
and
reproducible.
Documentation
includes
a
report
providing
an
overall
description
of
the
modeling
and
its
results,
documentation
of
parameter
values,
the
computer
implementation
of
the
model
equations
(
called
CSL
files
in
ACSL),
and
procedures
(
coded
in
CMD
files
in
ACSL).

Modeling
Results
Replication:
Procedures
that
permit
the
reproduction
of
all
essential
model
simulations
should
be
provided
in
the
computer
code
(
the
CMD
file
for
ACSL
version
11.8).
These
procedures
would
permit
easy
replication
of
1)
simulations
of
data
used
to
calibrate
or
validate
the
model,
2)
toxicity
study
exposures
to
estimate
internal
dose
metrics
in
animals,
3)
human
exposures
to
obtain
internal
dose
metrics,
and
4)
other
specialized
procedures
such
as
sensitivity
analyses.
For
example,
the
calculation
of
the
AUC
for
iodide
in
the
maternal
and
fetal
serum
is
incompletely
documented,
which
a
procedure
in
the
CMD
file
could
easily
remedy.
The
relevant
HECs
are
reported
but
the
actual
AUCs
and
the
methods
used
to
obtain
them
are
not
described
(
e.
g.,
duration
over
which
the
AUC
was
calculated).
The
human
nasal
modeling
reasonably
used
the
pregnant
human
parameterization
of
the
model,
but
this
was
not
specified.

Model
parameters:
Model
parameters
are
described
in
Sweeney
et
al.,
2005.
In
addition,
they
are
specified
in
the
CMD
files
for
the
models.
Reviews
of
the
model
parameters
found
that
it
was
difficult
to
confirm
values
in
cited
references,
partly
due
to
conversions
made
from
the
original
references
values.
It
is
recommended
that
for
those
parameters
that
have
significant
normalized
sensitivity
coefficients
(>
0.1
or
<­
0.1
as
described
in
Sweeney
et
al.,
2005),
a
clear
and
detailed
explanation
of
how
they
were
estimated
or
derived
should
be
presented
in
an
Appendix
or
other
supporting
document
so
the
reader
knows
exactly
how
the
values
in
the
model
were
obtained
from
values
presented
in
publications.
Since
the
sensitivity
coefficients
can
vary
with
the
exposure
conditions
and
species,
ideally
those
parameters
with
significant
sensitivity
for
simulating
the
animal
point
of
departure
(
e.
g.,
LOAEL,
NOAEL,
BMD)
in
the
toxicity
study
as
well
as
those
parameters
with
significant
sensitivity
for
estimating
the
human
equivalent
concentration
should
be
fully
documented.
Note
that
such
sensitivity
analyses
were
reported
in
memos
for
the
rabbit
and
human
maternal
and
fetal
serum
iodide,
but
only
for
the
human
for
the
nasal
glutathione
depletion.

Cc:
Dr.
Robert
Kavlock
Dr.
Jerry
Blancato
Dr.
Paul
White
Dr.
Jack
Fowle
Dr.
Mike
Metzger
5.
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reduce
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the
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Use
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fluid
dynamics
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physiologically
based
inhalation
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ester
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15;
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L,
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JS,
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KT,
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RP,
Morris
JB,
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JS.
Application
of
a
hybrid
computational
fluid
dynamics
and
physiologically
based
inhalation
model
for
interspecies
dosimetry
extrapolation
of
acidic
vapors
in
the
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Appl
Pharmacol.
1998
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152(
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C.
(
2005)
Weight
of
Evidence
for
Evaluation
of
the
HEC
for
Acute
Developmental
Toxicity
of
Methyl
Iodide.
Project
Number:
34501.
Unpublished
study
prepared
by
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Inc.
274
p.
MRID
#
46593801.
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D.
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pathology.
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1997
Jun;
18(
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33
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9(
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6
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J,
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J
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2):
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71
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RA,
Berman
M.
Maternal,
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amniotic
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thyroxine,
triiodothyronine,
and
iodide
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sheep:
a
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model.
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1978
Aug;
103(
2):
567­
76
Merrill
EA,
Clewell
RA,
Robinson
PJ,
Jarabek
AM,
Gearhart
JM,
Sterner
TR,
Fisher
JW.
PBPK
model
for
radioactive
iodide
and
perchlorate
kinetics
and
perchlorate­
induced
inhibition
of
iodide
uptake
in
humans.
Toxicol
Sci.
2005
Jan;
83(
1):
25­
43
Millard
RK,
Saunders
M,
Palmer
AM,
Preece
AW.
Approximate
distribution
of
dose
among
foetal
organs
for
radioiodine
uptake
via
placenta
transfer.
Phys
Med
Biol.
2001
Nov;
46(
11):
2773­
83.
Pauwels
EK,
Thomson
WH,
Blokland
JA,
Schmidt
ME,
Bourguignon
M,
El­
Maghraby
TA,
Broerse
JJ,
Harding
LK.
Aspects
of
fetal
thyroid
dose
following
iodine­
131
administration
during
early
stages
of
pregnancy
in
patients
suffering
from
benign
thyroid
disorders.
Eur
J
Nucl
Med.
1999
Nov;
26(
11):
1453­
7
Potter
DW,
Finch
L,
Udinsky
JR.
Glutathione
content
and
turnover
in
rat
nasal
epithelia.
Toxicol
Appl
Pharmacol.
1995
Dec;
135(
2):
185­
91
Roti
E,
Gnudi
A,
Braverman
LE.
The
placental
transport,
synthesis
and
metabolism
of
hormones
and
drugs
which
affect
thyroid
function.
Endocr
Rev.
1983
Spring;
4(
2):
131­
49
Russell
JR,
Stabin
MG,
Sparks
RB.
Placental
transfer
of
radiopharmaceuticals
and
dosimetry
in
pregnancy.
Health
Phys.
1997
Nov;
73(
5):
747­
55
Sweeney,
L.;
Kirman,
C.;
Gargas,
M.
(
2005)
Derivation
of
Human
Toxicity
Reference
Values
for
Methyl
Iodide
Using
Physiologically
Based
Pharmacokinetic
(
PBPK)
Modeling
(
Revised
Report).
Project
Number:
34501.
Unpublished
study
prepared
by
Sapphire
Group,
Inc.
103
p.
MRID
#
46446901
Sweeney,
L.;
Gargas,
M.
(
2005)
Age
Specific
HEC's
for
Potential
Nasal
Effects
of
MeI
in
Children:
Supplement
to
(
PBPK)
Modeling
(
MRID
46446901).
Project
Number:
34503.
Unpublished
study
prepared
by
Sapphire
Group,
Inc.
46
p.
MRID
#
46559301
Sweeney,
L.
(
2004)
Revision
of
HEC
for
Nasal
Effects
of
Methyl
Iodide
(
MeI):
Supplement
to
(
PBPK)
Modeling
(
MRID
46408801).
Project
Number:
45603.
Unpublished
study
prepared
by
Sapphire
Group,
Inc.
68
p.
MRID
#
46559302
Sweeney,
L.;
Gargas,
M.
(
2005)
Supplemental
Information
Regarding
Human
Fetal
and
Maternal
Iodide
AUC
Sensitivity
and
a
Rabbit
Model
Sensitivity
Analysis:
Supplement
to
(
PBPK)
Modeling
(
MRID
46446901).
Project
Number:
45603.
Unpublished
study
prepared
by
Sapphire
Group,
Inc.
65
p.
MRID
#
46559303
Sweeney,
L.;
Gargas,
M.
(
2005)
Revaluation
of
the
HEC
for
Acute
Nasal
Toxicity
of
Methyl
Iodide.
Project
Number:
34510.
Unpublished
study
prepared
by
Sapphire
Group,
Inc.
417
p.
MRID
#
46631401
Appendix
B
Toxicity
Profile
Iodomethane
Toxicity
Profile
Guideline
No./
Study
Type
MRID
No.
(
year)/
Classification/
Exposure
Conditions
Results
870.1100
Acute
Oral
­
Rat
45593803
(
2001)
Acceptable/
Guideline.
LD50
=
79.8
mg/
kg
(
males);
131.9
mg/
kg
(
females)
Clinical
signs:
decreased
activity,
breathing
abnormalities,
salivation,
nasal/
ocular
discharge,
dark
material
around
facial
area
and
eyes,
partially
closed
eyelids,
pale
skin,
soft
feces,
prostration,
tremors,
and
wobbly
gait.
Gross
pathology:
red
lungs
(
congestion)
and
abnormal
GI
contents
in
decedents.
The
Up/
Down
procedure
was
used
in
SD
rats.
Toxicity
Category
II
870.1100
Acute
Oral
­
Mouse
45593804
(
2001)
Acceptable/
Guideline
LD50
=
155
mg/
kg
(
males);
214
mg/
kg
(
females)
Clinical
signs:
urine
and
fecal
stain,
decreased
food
consumption,
salivation,
decreased
or
no
defecation,
dilated
pupils,
piloerection,
rough
hair
coat,
prostration,
hypothermia,
decreased
activity,
breathing
abnormalities,
skin
blue
in
color
over
the
entire
body
(
anoxemia),
hunched
posture
and
wobbly
gait.
Gross
pathology:
red
fluid
in
thoracic
cavity,
abnormal
GI
contents,
red
glandular
mucosa
(
congestion)
of
the
stomach,
and
dilated
kidney
pelvis
in
decedents.
The
Up/
Down
procedure
was
used
in
CD­
1
mice.
Toxicity
Category
II
870.1200
Acute
Dermal
­
Rat
45593805
(
2001)
Acceptable/
Guideline
LD50
>
2000
mg/
kg
(
limit
test)
There
were
no
deaths.
Clinical
signs:
severe
dermal
irritation
(
at
500
and
2000
mg/
kg)
and
hemorrhage
(
2000
mg/
kg)
at
the
dosing
site.
Decreased
defecation,
soft
stools,
decreased
food
consumption,
breathing
abnormalities,
and
dark
material
around
the
facial
area.
Gross
pathology:
Unremarkable
Toxicity
Category
III
Guideline
No./
Study
Type
MRID
No.
(
year)/
Classification/
Exposure
Conditions
Results
870.1300
Acute
Inhalation
­
Rat
45593806
(
2001)
Acceptable/
Guideline
581,
710,
797
or
1198
ppm
LC50
=
691
ppm
=
4
mg/
L
(
combined
sexes)
The
test
article
was
administered
as
a
vapor
in
a
dynamic
whole­
body
chamber
for
4
hours.
Clinical
signs:
gasping,
ataxia,
hypoactivity,
nasal
discharge,
labored
respiration,
rales,
and
red
material
around
nose.
Gross
pathology:
dark
pituitary,
dark
red
lungs,
distended
gas­
filled
and
congested
stomach,
hemorrhagic
thymus,
dark
red
adrenal
glands,
and
distended
intestines
in
decedents.
Toxicity
Category
IV
870.2400
Primary
Eye
Irritation
­
Rabbit
45593807
(
2001)
Acceptable/
Guideline
Corrosive:
Corneal
opacity,
conjunctivitis,
iritis,
corneal
neo­
vascularization,
sloughing
of
corneal
epithelium,
blanching
of
nictitating
membrane,
and
corneal
bulging.
Toxicity
Category
I
870.2500
Primary
Skin
Irritation
­
Rabbit
45593808
(
2001
Acceptable/
Guideline
Well
defined
erythema
and
blanching,
slightsevere
edema,
lightening,
extended
erythema
beyond
the
test
sites
and
desquamation.
Toxicity
Category
II
870.2600
Dermal
Sensitization
(
Magnassun­
Kligman
Maximization
Test)
­
Guinea
Pig
45593809
(
2001)
Acceptable/
Guideline
Not
a
dermal
sensitizer
870.3100
Subchronic
Feeding
­
Rat
Not
required
by
the
Agency
870.3100
Subchronic
Feeding
­
Mice
Not
required
by
the
Agency
870.3100
Subchronic
Feeding
­
Mice
Not
required
by
the
Agency
870.3150
Subchronic
Feeding
­
Dog
Not
required
by
the
Agency
870.3200
21­
Day
Dermal
­
Rat
Not
required
by
the
Agency
870.3465
13­
Week
Inhalation
­
Rat
45593810
(
2002)
Acceptable/
Guideline
0,
5,
21,
or
70
ppm
in
a
whole­
body
chamber,
6
h/
day,
5
days/
week
for
4
weeks
(
interim
sacrifice)
or
13
weeks
NOAEL
=
21
ppm
(
0.12
mg/
L/
day)
LOAEL
=
70
ppm
(
0.41
mg/
L/
day)
based
on
initial
decreases
in
body
weights,
body
weight
gains,
and
food
consumption
(
males);
and
nasal
degeneration.
870.3700
Inhalation
Developmental
Toxicity
­
Rat
45593812
(
2002)
Acceptable/
Guideline
0,
5,
20,
60
ppm
in
a
whole­
body
inhalation
chamber,
6
h/
day
on
GDs
6­
19.
Maternal
NOAEL
=
20
ppm
(
0.12
mg/
L/
day)
Maternal
LOAEL
=
60
ppm
(
0.35
mg/
L/
day)
based
on
decreased
body
weight
gain
(
919%;
95­
6%
absolute
body
weight).
Developmental
NOAEL
=
60
ppm
(
0.35
mg/
L/
day)
Developmental
LOAEL
was
not
observed
870.3700
Inhalation
Developmental
Toxicity
­
Rabbit
45593811
(
2002)
Acceptable/
Guideline
0,
2,
10,
or
20
ppm
in
a
whole­
body
inhalation
chamber,
6
h/
day,
on
GDs
6­
28.
Maternal
NOAEL
=
20
ppm
Maternal
LOAEL:
Not
identified
Developmental
NOAEL
=
10
ppm
Developmental
LOAEL
=
20
ppm
based
on
increased
fetal
losses
and
decreased
fetal
weights
(
920%).

Guideline
No./
Study
Type
MRID
No.
(
year)/
Classification/
Exposure
Conditions
Results
Non­
guideline
Inhalation
Phased­
Exposure
Developmental
Toxicity
­
Rabbit
46077001
(
2003)
Acceptable/
non­
guideline
0
or
20
ppm
from
GD6­
28;
20
ppm
from
GDs
6­
14,
15­
22,
23­
24,
25­
26,
or
27­
28
in
a
whole­
body
inhalation
chamber
6hrs/
day
This
study
was
not
intended
to
fulfill
the
guideline
requirement
or
establish
NOAELs
and
LOAELs
but
rather
was
conducted
to
determine
the
critical
period
of
exposure
during
gestation
that
resulted
in
fetal
loss
as
observed
in
a
previously
evaluated
guideline
developmental
toxicity
study
in
rabbits.
Increased
fetal
losses
at
20
ppm
on
GD
6­
28
(
821%),
23­
24
(
89%),
and
25­
26
(
811%)

870.3800
Inhalation
2­
Generation
Reproductive
Toxicity
­
Rat
45710301
(
2001)
Acceptable/
guideline
0,
5,
20,
or
50
ppm
in
whole
body
inhalation
chamber
Note:
Offspring
not
directly
exposed
until
PND
28
Parental
systemic
NOAEL
=
20
ppm
Parental
systemic
LOAEL
=
50
ppm
based
on
decreased
body
weight
gain,
body
weight,
organ
weight
changes,
gross
pathology,
and
histopathology
findings.
Portal
of
entry
NOAEL
=
20
ppm
Portal
of
entry
LOAEL
=
50
ppm
based
on
degeneration
of
the
olfactory
epithelium
Offspring
NOAEL
=
5
ppm
Offspring
LOAEL
=
20
ppm
based
on
decreases
in
body
weight
gain,
body
weight,
and
thymus
weights
Reproductive
NOAEL
=
5
ppm
Reproductive
LOAEL
=
20
ppm
based
on
delays
in
vaginal
patency
870.4100
Chronic
Feeding
Toxicity
­
Dog
 
Not
required
by
the
Agency
870.4200
Carcinogenicity
Feeding
­
Mouse
(
18
months)
 
Not
required
by
the
Agency.
Guideline
No./
Study
Type
MRID
No.
(
year)/
Classification/
Exposure
Conditions
Results
870.4300
Chronic
Feeding
Toxicity/
Carcinogenicity­
Rat.
46512401
(
2005)
Acceptable/
non­
guideline
0,
5,
10,
60
ppm
in
a
whole
body
inhalation
chamber
for
6
hrs/
day,
5days/
week
Systemic
NOAEL
=
5
ppm
Systemic
LOAEL
=
20
ppm
based
on
increased
incidence
of
salivary
gland
squamous
cell
metaplasia.
Portal
of
entry
NOAEL
=
20
ppm
Portal
of
entry
LOAEL
=
60
ppm
based
on
degeneration
of
the
olfactory
epithelium.
At
60
ppm,
perturbations
of
the
thyroidpituitary
axis
as
well
thyroid
histopathology
findings
were
reported.

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

870.5300
In
Vitro
Mammalian
Cell
Mutation
Test
in
Chinese
Hamster
Ovary
Cells
45593815
(
2001)
Negative
870.5375
In
Vitro
Chromosomal
Aberration
in
Chinese
Hamster
Ovary
45593814
(
2001)
Positive
for
the
induction
of
structural
chromosome
aberrations
(
clastogenesis),
but
negative
for
induction
of
numerical
aberrations
in
CHO
cells
in
this
assay.

870.5395
In
Vivo
Micronucleus
Assay
in
Mice
45593816
(
2001)
Negative
870.6200
Inhalation
Acute
Neurotoxicity
­
Rats
45593817
(
2002)
Acceptable/
Guideline
0,
27,
93,
401
ppm
whole­
body,
6­
hour
exposure.
Systemic
NOAEL
=
27
ppm.
Systemic
LOAEL
=
93
ppm
based
on
FOB
findings
(
clonic
convulsions
in
1/
12
females,
decreased
body
temperature),
and
decreased
motor
activity
(
975­
78%
in
males,
81­
84%
in
females).
Portal
of
entry
effects
not
assessed
870.6200
Feeding
Subchronic
Neurotoxicity
­
Rats
 
Not
required
by
the
Agency
Guideline
No./
Study
Type
MRID
No.
(
year)/
Classification/
Exposure
Conditions
Results
870.7485
Metabolism
­
Rat
45641401
(
2002)
Sprague­
Dawley
rats
were
orally
dosed
or
exposed
via
inhalation
with
[
14C]
CH3I.
Maximum
blood
concentrations
were
achieved
within
4
hours
(
oral)
and
0­
2
hours
(
inhalation),
and
were
proportional
to
dose/
concentration.
Initial
t
½
was
5.1­
7.2
hours,
and
terminal
t
½
was
116­
136
hours.
Radioactivity
recovery
was
low
in
the
main
test
due
to
inefficient
CO2
trapping.
Overall
recovery
in
the
supplementary
test
was
increased
due
to
increased
recovery
of
carbon
dioxide.
Recovered
radioactivity
was
primarily
as
CO2
(
39.40­
60.81%
dose)
and
in
the
urine
(
26.50­
33.40%
dose)
in
all
treated
groups,
while
feces
accounted
for
<
2%
dose.
Radioactivity
remained
in
the
carcasses
(
11.92­
14.39%
dose)
of
all
treated
animals
168
hours
following
treatment
in
the
main
test.
Elimination
t
½
were
17.8­
22.3
hours
for
urine
and
29.7­
38.0
hours
for
feces
in
all
treatment
groups
of
the
main
test.
The
elimination
t
½
was
5.8­
6.8
hours
for
CO2
in
all
treatment
groups
of
the
supplementary
test.
At
0­
1
hour
post­
treatment
in
orally
treated
rats
and
233
ppm
inhalation
exposed
rats,
relatively
high
levels
of
radioactivity
were
observed
in
the
liver
and
GI
tract.
Radioactivity
was
relatively
high
in
the
kidney,
lung,
and
nasal
turbinates
of
the
25
ppm
inhalation
exposed
rats
and
in
the
kidney,
thyroid,
and
lung
of
the
233
ppm
inhalation
exposed
rats.
At
6
hours
post­
oral
dosing,
tissue
concentrations
increased
in
the
spleen
(
at
1.5
mg/
kg
only),
kidney,
brain,
thyroid,
lung,
nasal
turbinates,
and
fat
(
at
1.5
mg/
kg
only).
Tissue
concentrations
decreased
in
all
tissues
of
the
inhalation
exposed
rats
at
6
hours
after
exposure.
At
168
hours
post­
dose,
radioactivity
had
declined
in
all
tissues
and
was
highest
in
the
kidney,
liver,
and
thyroid.
Tissue
concentrations
increased
(
not
proportionally)
with
dose.
The
major
metabolites
were
expired
CO2,
and
N­(
methylthioacetyl)
glycine
and
S­
methyl
glutathione
which
were
excreted
in
the
urine.
Minor
metabolites
were
methylthioacetic
acid,
methyl
mercapturic
acid,
and
S­
methyl
cysteine.

870.7600
Dermal
Penetration
­
Rat
 
Not
required
by
the
Agency
Appendix
C
Methodologies
for
Inhalation
Risk
Calculations
and
Human
Equivalent
Concentration
Arrays
METHODOLOGIES
FOR
INHALATION
RISK
CALCULATIONS
In
evaluating
the
risks
that
a
compound
may
pose
to
human
health
after
exposure
via
the
inhalation
route,
different
methodologies
have
been
historically
used
by
the
USEPA
and
the
California
Department
of
Pesticide
Regulation
(
CDPR).
The
Agency's
approach
to
calculating
risks
due
to
inhalation
exposure
is
based
on
the
guidance
methodology
developed
by
the
Office
of
Research
and
Development
(
ORD)
for
the
derivation
of
inhalation
reference
concentrations
(
RfCs)
and
human
equivalent
concentrations
(
HECs)
for
use
in
margin
of
exposure
(
MOE)
calculations
(
RfC
methodology).
An
example
of
CDPR's
methodology
,
and
the
species­
specific
parameters
used
in
this
approach
can
be
found
in
the
CDPR
methyl
bromide
risk
assessment,
Appendix
G
(
www.
cdpr.
ca.
gov/
docs/
dprdocs/
methbrom/
append_
g.
pdf).
As
OPP
understands
the
importance
to
harmonize,
to
the
extent
possible,
with
other
regulatory
agencies,
this
risk
assessment
will
present
HECs
derived
using
both
methodologies.
Furthermore,
in
the
case
of
iodomethane,
a
chemical­
specific
PBPK
model
has
been
developed
by
the
registrant
and
reviewed
by
Agency
experts.
Hence,
the
PBPK
model
has
been
used
to
calculate
HECs
for
those
endpoints
where
appropriate
mechanistic
data
are
available
to
identify
a
suitable
chemical­
specific
dose
metric.
A
more
detailed
explanation
of
the
review
of
this
PBPK
model
is
available
in
Appendix
A
of
this
document.

The
RfC
methodology
applies
a
dosimetric
adjustment
that
takes
into
consideration
not
only
the
differences
in
ventilation
rate
(
MV)
but
also
the
physicochemical
properties
of
the
inhaled
compound,
the
type
of
toxicity
observed
(
e.
g.
systemic
vs.
port
of
entry)
and
the
pharmacokinetic
(
PK)
but
not
pharmacodynamic
(
PD)
differences
between
animals
and
humans.
Based
on
the
RfC
guidance
(
1994),
the
methodology
for
RfCs
derivation
is
an
estimate
of
the
quantitative
dose­
response
assessment
of
chronic
non­
cancer
toxicity
for
individual
inhaled
chemicals
and
includes
dosimetric
adjustment
to
account
for
the
species­
specific
relationships
of
exposure
concentration
to
deposited/
delivered
dose.
This
adjustment
is
influenced
by
the
physicochemical
properties
of
the
inhaled
compound
as
well
as
the
type
of
toxicity
observed
(
e.
g.
systemic
vs.
port
of
entry),
and
takes
into
consideration
the
PK
differences
between
animals
and
humans.
Though
the
RfC
methodology
was
developed
to
estimate
toxicity
of
inhaled
chemicals
over
a
lifetime,
it
can
be
used
for
other
inhalation
exposures
(
e.
g.
acute
and
short­
term
exposures)
since
the
dosimetric
adjustment
incorporates
mechanistic
determinants
of
disposition
that
can
be
applied
to
shorter
duration
of
exposures
provided
the
assumptions
underlying
the
methodology
are
still
valid.
These
assumptions,
in
turn,
vary
depending
on
the
type
of
toxicity
observed
and
will
be
discussed
later
on
in
this
document.
Thus
the
derivation
of
a
HEC
for
inhaled
gases
is
described
by
the
following
equation:

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

human
exposure
(
hrs
/
day)
animal
exposure
(
days
/
wk)

human
exposure
(
days
/
wk)

Where:

POD
study
:
Point
of
departure
identified
in
the
critical
toxicology
study
D
animal
exposure
:
Duration
of
animal
exposure
(
hrs/
day;
days/
wk)
D
anticipated
exposure
:
Anticipated
human
duration
of
exposure
(
hrs/
day;
days/
wk)
RGDR:
Regional
Gas
Dose
Ratio
For
gases
eliciting
both
port
of
entry
and
systemic
effects,
calculations
to
estimate
the
inhalation
risk
to
humans
are
dependent
on
the
regional
gas
dose
ratio
(
RGDR).
In
the
case
of
systemic
effects,
the
RGDR
is
defined
as
the
ratio
of
the
blood:
gas
partition
coefficient
of
the
chemical
for
the
test
species
to
humans
(
H
b/
g
animal
/
H
b/
g
human
).
When
this
ratio
is
unknown
or
when
the
H
b/
g
animal
>
H
b/
g
human
a
default
value
of
1.0
is
used
as
the
RGDR.
This
default
is
based
on
the
observation
that
for
chemicals
where
partition
coefficient
data
are
available
in
both
rats
and
humans
the
RGDR
value
has
usually
been
comparable
or
slightly
higher
than
1.
Thus,
the
use
of
an
RGDR
of
1
results
in
a
protective
calculation
of
the
inhalation
risk.
Some
of
the
key
assumptions
fundamental
to
the
use
of
the
RfC
methodology
to
derive
a
HEC
based
on
systemic
effects
include:

1)
all
the
concentrations
of
inhaled
gas
within
the
animal's
body
are
periodic
with
respect
to
time
(
i.
e.
periodic
steady
state
­
the
concentration
vs
time
profile
is
the
same
for
every
week).
Periodicity
must
be
attained
for
at
least
90%
of
the
exposure.
2)
in
the
respiratory
tract,
the
air,
tissue,
capillary
blood
concentration
are
in
equilibrium
with
respect
to
each
other.
3)
systemically,
the
blood
and
tissue
concentrations
are
in
equilibrium
with
respect
to
each
other.

In
the
case
of
iodomethane,
the
physicochemical
properties
and
metabolism
data
for
the
compound
indicate
that
these
conditions
(
i.
e.
periodicity
and
equilibrium
between
different
compartments)
will
be
achieved
in
a
very
short
period
of
time.
Under
these
conditions,
therefore,
the
use
of
the
RfC
methodology
to
estimate
acute
inhalation
risk
is
appropriate.

When
the
critical
toxic
effect
in
a
study
occurs
in
the
respiratory
tract
(
i.
e
port
of
entry
effects),
the
RGDR
is
not
related
to
the
blood:
gas
partition
coefficient
of
the
compound
but
rather
the
ratio
of
the
minute
volume
(
MV)
to
the
surface
area
(
SA)
of
the
affected
region.
In
these
instances,
attaining
periodicity
or
equilibrium
between
the
compartments
is
not
critical
(
since
the
effect
is
a
function
of
the
direct
interaction
between
the
inhaled
compound
and
the
affected
region
in
the
respiratory
tract)
and
the
RGDR
may
be
calculated
using
the
following
equation:

RGDR
=
MV
SA
MV
SA
animal
animal
human
human
Where:
MV
animal
:
Minute
volume
for
the
test
species
(
varies
depending
on
body
weight)
SA
animal
:
Surface
area
of
the
affected
region
in
animals
MV
human
:
Minute
volume
for
humans
(
default
value
is
13.8
l/
min)
SA
human
:
Surface
area
of
the
affected
region
in
humans
The
MV
animal
is
calculated
using
the
allometric
scaling
provided
in
USEPA
(
1988a).
The
equation
for
calculation
of
the
MV
animal
is:

lnMV
animal
=
b
0
+
b
1
ln(
BW)
Where:
ln
MV
animal
:
natural
logarithm
of
the
minute
volume
b
0
:
species
specific
intercept
used
in
the
algorithm
to
calculate
minute
volumes
based
on
body
weight
b
1
:
species
specific
coefficient
used
in
the
algorithm
to
calculate
minute
volumes
based
on
body
weight
ln
BW:
natural
logarithm
of
the
body
weight
(
expressed
in
kg)

The
values
for
the
species­
specific
parameters
used
to
calculate
the
MV
animal
based
on
body
weight
and
the
values
for
the
surface
areas
of
various
regions
of
the
respiratory
tract
(
extrathoracic,
thoracic,
and
pulmonary)
are
provided
in
the
EPA
document
"
Methods
for
Derivation
of
Inhalation
Reference
Concentrations
and
Application
of
Inhalation
Dosimetry"
(
1994).

The
magnitude
of
the
UFs
applied
is
dependent
on
the
methodology
used
to
calculate
risk.
When
using
the
methodology
developed
by
CDPR,
a
100X
UF
is
applied
(
10X
for
interspecies
extrapolation
and
10X
for
intraspecies
variation).
In
contrast,
the
RfC
methodology
and
the
PBPK
model
take
into
consideration
the
PK
differences
but
not
the
PD
differences.
Consequently,
the
UF
for
interspecies
extrapolation
may
be
reduced
to
3X
(
to
account
for
the
PD
differences)
while
the
UF
for
intraspecies
variation
is
retained
at
10X.
Thus,
the
UF
when
using
the
RfC
methodology
or
the
PBPK
model
is
30X.

Hazard
Assessment
Array
HEC
Array
for
Non­
Occupational
Risk
Assessment
§

Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR*
HEC
(
ppm)
inter
Intra
UF
ACUTE
EXPOSURE
ACN­
Rat
Systemic
93
27
6
24
1
1
1
6.75
3
10
1
Dev
Rat
Maternal
Systemic
60
20
Not
Applicable;
the
LOAEL
for
the
dams
is
based
on
decreases
in
body
weight
and
body
weight
gain
which
are
not
expected
to
occur
as
the
result
of
a
single
exposure
Developmental
Not
identified
60
6
24
1
1
1
15
3
10
1
Dev
Rabbit*
Maternal
Not
identified
20
6
24
1
1
1
5
3
10
1
Developmental
20
10
6
24
1
1
N.
A.
4.00
3
10
1
Subchronic
Inhalation
Study
­
Rat
Local
70
21
6
24
1
1
N.
A.
2.90
3
10
1
HEC
Array
for
Non­
Occupational
Risk
Assessment
§

Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR*
HEC
(
ppm)
inter
Intra
UF
SHORT
TERM
EXPOSURE
ACN­
Rat
Systemic
93
27
6
24
1
1
1
6.75
3
10
1
Devel
Rat
Maternal
Systemic
60
20
6
24
7
7
1
5.0
3
10
1
Developmental
Not
identified
60
6
24
7
7
1
15
3
10
1
Dev
Rabbit
Maternal
Systemic
Not
identified
20
6
24
7
7
1
5.0
3
10
1
Developmental
20
10
6
24
7
7
N.
A.
4.0
3
10
1
Subchronic
Inhalation
Study
­
Rat
Systemic
70
21
6
24
5
7
1
3.75
3
10
1
Local
¶
70
21
2.90
3
10
1
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
24
7
7
1
5
3
10
1
Parental
Local
¶
50
20
Not
applicable
3.20
3
10
1
Offspring
20
5
6
24
7
7
1
1.25
3
10
1
Reproductive
Effects
20
5
6
24
7
7
1
1.25
3
10
1
INTERMEDIATE
TERM
EXPOSURE
Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR
HEC
(
ppm)
Inter
Intra
UF
ACN­
Rat
Systemic
93
27
6
24
1
1
1
6.75
3
10
1
Devel
Rat
Maternal
Systemic
60
20
6
24
7
7
1
5.0
3
10
1
Developmental
Not
identified
60
6
24
7
7
1
15
3
10
1
Dev
Rabbit
Maternal
Systemic
Not
identified
20
6
24
7
7
1
5.0
3
10
1
Developmental
10
20
6
24
7
7
N.
A
4.0
3
10
1
Subchronic
Inhalation
Study
­
Rat
Systemic
70
21
6
24
5
7
1
3.75
3
10
1
Local
¶
70
21
Not
applicable
2.90
3
10
1
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
24
7
7
1
5.00
3
10
1
Parental
Local
¶
50
20
Not
applicable
3.20
3
10
1
Offspring
20
5
6
24
7
7
1
1.25
3
10
1
Reproductive
20
5
6
24
7
7
1
1.25
3
10
1
HEC
Array
for
Non­
Occupational
Risk
Assessment
§

Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR*
HEC
(
ppm)
inter
Intra
UF
LONG
TERM
EXPOSURE
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
24
7
7
1
5.00
3
10
1
Parental
Local
¶
50
20
Not
applicable
3.20
3
10
1
Offspring
20
5
6
24
7
7
1
1.25
3
10
1
Reproductive
20
5
6
24
7
7
1
1.25
3
10
1
Chronic/
Carcinogenicit
y:
Rat
Systemic
20
5
6
24
5
7
1
0.89
3
10
1
Local
¶
60
20
Not
applicable
3.20
3
10
1
§
Bolded
studies
used
for
endpoint
selection.
*
Italicized
HECs
derived
from
PBPK
model
N.
A.
=
not
applicable
¶
Local
effects
(
nasal
lesions)
did
not
progress
with
time
(
(
i.
e.
nasal
lesions
of
comparable
severity
were
seen
after
4,
13,
and
52
weeks
of
exposure
at
the
same
concentration).
Therefore,
it
appears
that
this
effects
is
not
a
function
of
C
x
t
thus
a
time
adjustment
is
not
appropriate.
*
Input
parameters
for
the
derivation
of
RGDRs
were
obtained
from
"
Methods
for
Derivation
of
Inhalation
Reference
Concentrations
and
Application
of
Inhalation
Dosimetry"
(
USEPA,
1994)
Tables
4­
4,
4­
5,
and
4­
6.

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

Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR*
HEC
(
ppm)
inter
Intra
UF
ACUTE
EXPOSURE
ACN­
Rat
Systemic
93
27
6
8
1
1
1
20.25
3
10
1
Dev
Rat
Maternal
Systemic
60
20
Not
Applicable;
the
LOAEL
for
the
dams
is
based
on
decreases
in
body
weight
and
body
weight
gain
which
are
not
expected
to
occur
as
the
result
of
a
single
exposure
Developmental
Not
identified
60
6
8
1
1
1
45
3
10
1
Dev
Rabbit*
Maternal
Systemic
Not
identified
20
6
8
1
1
1
15
3
10
1
Subchronic
Inhalation
Study
­
Rat
Local
70
21
6
8
1
1
N.
A.
3.70
3
10
1
SHORT
TERM
EXPOSURE
ACN­
Rat
Systemic
93
27
6
8
1
1
1
20.25
3
10
1
Dev
Rat
Maternal
Systemic
60
20
6
8
7
7
1
15
3
10
1
Developmental
Not
identified
60
6
8
7
7
1
45
3
10
1
Dev
Rabbit
Maternal
Systemic
Not
identified
20
6
8
1
1
1
15
3
10
1
Subchronic
Inhalation
Study
­
Rat
Systemic
70
21
6
8
5
5
1
15.75
3
10
1
Local
¶
70
21
Not
applicable
3.70
3
10
1
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
8
5
5
1
15
3
10
1
Parental
Local
¶
50
20
Not
applicable
4.20
3
10
1
Offspring
20
5
6
8
5
5
1
3.75
3
10
1
Reproductive
20
5
6
8
5
5
1
3.75
3
10
1
INTERMEDIATE
TERM
EXPOSURE
ACN­
Rat
Systemic
93
27
6
8
1
1
1
20.25
3
10
1
Devel
Rat
Maternal
Systemic
60
20
6
8
7
7
1
15
3
10
1
Developmental
Not
identified
60
6
8
7
7
1
45
3
10
1
Dev
Rabbit
Maternal
Systemic
Not
identified
20
6
8
1
1
1
15
3
10
1
Subchronic
Inhalation
Study
­
Rat
Systemic
70
21
6
8
5
5
1
15.75
3
10
1
Local
¶
70
21
Not
applicable
3.70
3
10
1
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
8
5
5
1
15.00
3
10
1
Parental
Local
¶
50
20
Not
applicable
4.20
3
10
1
Offspring
20
5
6
8
5
5
1
3.75
3
10
1
HEC
Array
for
Occupational
risk
assessments
§

Relevant
Study
LOAEL
(
ppm)
NOAEL
(
ppm)
Da
Dh
Wa
Wh
RGDR*
HEC
(
ppm)
inter
Intra
UF
Reproductive
20
5
6
8
5
5
1
3.75
3
10
1
Chronic/
Carcinogenicity:
Rat
Systemic
20
5
6
8
5
5
1
3.75
3
10
1
Local
¶
60
20
Not
applicable
4.20
3
10
1
LONG
TERM
EXPOSURE
MultiGen
Repro:
Rat
Parental
Systemic
50
20
6
8
5
5
1
15.00
3
10
1
Parental
Local
¶
*
50
20
Not
applicable
4.20
3
10
1
Offspring
20
5
6
8
5
5
1
3.75
3
10
1
Reproductive
20
5
6
8
5
5
1
3.75
3
10
1
Chronic/
Carcinogenicity
:
Rat
Systemic
20
5
6
8
5
5
1
3.75
3
10
1
Local
¶
60
20
Not
applicable
4.20
3
10
1
§
Bolded
studies
used
for
endpoint
selection.
*
Italicized
HECs
derived
from
PBPK
model
N.
A.
=
not
applicable
¶
Local
effects
(
nasal
lesions)
did
not
progress
with
time
(
(
i.
e.
nasal
lesions
of
comparable
severity
were
seen
after
4,
13,
and
52
weeks
of
exposure
at
the
same
concentration).
Therefore,
it
appears
that
this
effects
is
not
a
function
of
C
x
t
thus
a
time
adjustment
is
not
appropriate.
*
An
uncertainty
factor
for
extrapolation
from
subchronic
to
chronic
exposure
is
not
recommended
since
the
endpoint,
nasal
lesions,
did
not
progress
with
time.
*
Input
parameters
for
the
derivation
of
RGDRs
were
obtained
from
"
Methods
for
Derivation
of
Inhalation
Reference
Concentrations
and
Application
of
Inhalation
Dosimetry"
(
USEPA,
1994)
Tables
4­
4,
4­
5,
and
4­
6.

Key
for
Array
Table
LOAEL:
Lowest
observed
adverse
effect
level
NOAEL:
No
observed
adverse
effect
level
Da:
Daily
animal
exposure
(
hrs/
day)
Dh:
Anticipated
daily
human
exposure
(
hrs/
day)
Wa:
Weekly
animal
exposure
(
days/
week)
Wh:
Anticipated
weekly
human
exposure
(
days/
week)
RGDR:
Regional
Gas
Dose
Ratio
HEC:
Human
Equivalent
Concentration
inter:
interspecies
extrapolation
uncertainty
factor
intra:
intraspecies
variation
uncertainty
factor
UF:
Other
uncertainty
factor(
s)
Appendix
D
Analysis
Of
Field
Volatility
Data
For
Pre­
plant
Field
Uses
Appendix
E
Downwind
Air
Concentrations
Calculated
With
ISCST3
For
PrePlant
Field
Uses
Appendix
F
Downwind
Air
Concentrations
Calculated
With
PERFUM
For
PrePlant
Field
Uses
Appendix
G
Occupational
Risk
Associated
with
Agricultural
Fumigations