Document ID: EPA-HQ-OPP-2006-0202-0002
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2006-03-29T05:00Z

Page
1
of
74
UNITED
STATES
ENVIRONMENTAL
PROTECTION
AGENCY
WASHINGTON,
D.
C.
20460
OFFICE
OF
PREVENTION,
PESTICIDES
AND
TOXIC
SUBSTANCES
Date:
March
8,
2006
MEMORANDUM
SUBJECT:
ACETOCHLOR/
ALACHLOR:
Cumulative
Risk
Assessment
for
the
Chloroacetanilides.
PC
Codes:
121601
&
090501,
DP
Barcode:
D292317
Regulatory
Action:
Tolerance
Reassessment
Risk
Assessment
Type:
Cumulative
Risk
assessment
/
Aggregate
FROM:
Alberto
Protzel,
Ph.
D.,
Branch
Senior
Scientist,
Risk
Assessor
Toxicology
Branch
Health
Effects
Division
(
7509C)
AND
Linnea
Hansen,
Ph.
D.,
Toxicologist
Toxicology
Branch
Health
Effects
Division
AND
Samuel
Ary,
Chemist
Reregistration
Branch
2
Health
Effects
Division
(
7509C)
AND
Michael
R.
Barrett,
Ph.
D,
Senior
Chemist
(
ERB
V)
Marietta
Echeverria,
Environmental
Scientist
(
ERB
IV)
Ronald
Parker,
Ph.
D.,
Senior
Environmental
Scientist
(
ERB
V)
Environmental
Fate
and
Effects
Division
(
7507C)

THRU:
Louis
Scarano,
Ph.
D.,
Chief
Toxicology
Branch
Health
Effects
Division
(
7509C)

TO:
Felicia
Fort,
Chemical
Review
Manager
Reregistration
Branch
III
Special
Review
and
Reregistration
Division
(
7508W)
Page
2
of
74
ACTION:
Complete
the
Chloroacetanilide
Cumulative
Risk
Assessment.

CONCLUSIONS:

1.
A
risk
assessment
of
a
Cumulative
Assessment
Group
(
CAG)
consisting
of
the
Chloroacetanilide
pesticides
acetochlor
and
alachlor
has
been
conducted.
MOE
calculations
have
been
made
based
on
the
endpoint
of
nasal
olfactory
epithelium
tumors
in
rats,
and
using
slightly
refined
values
for
food
and
drinking
water,

2.
Compared
to
a
MOE
of
100,
defined
as
level
of
concern
(
LOC)
for
this
risk
assessment,
the
cumulated
MOE
values,
greater
than
13,000
for
the
subject
CAG
for
all
populations,
are
outside
the
Agency's
level
of
concern.

3.
Because
these
cumulative
MOE
values
were
obtained
using
high­
end
exposures,
they
are
considered
to
be
conservative.
Additional
MOE
calculations
in
Appendixes
1
and
2
of
the
Cumulative
Risk
Assessment
document,
using
more
conservative
approaches
to
estimation
of
drinking­
water
exposure,
support
the
conclusions
of
this
analysis
by
producing
MOE
values
that
exceed
the
LOC
of
100
by
nearly
an
order
of
magnitude
or
more.
Page
3
of
74
N
Cl
O
CH
3
O
R1
R2
CUMULATIVE
RISK
FROM
CHLOROACETANILIDE
PESTICIDES
U.
S.
Environmental
Protection
Agency
Office
of
Pesticide
Programs
Health
Effects
Division
March
8,
2006
Page
4
of
74
CUMULATIVE
RISK
FROM
CHLOROACETANILIDE
PESTICIDES
Executive
Summary
As
part
of
the
tolerance
reassessment
process
under
the
Food
Quality
Protection
Act
(
FQPA)
of
1996,
EPA
must
consider
available
information
concerning
the
cumulative
effects
on
human
health
resulting
from
exposure
to
multiple
chemicals
that
have
a
common
mechanism
of
toxicity.

This
document
contains
the
results
of
a
cumulative
risk
assessment
conducted
for
a
group
of
chloroacetanilide
pesticides
that
have
a
common
mode
of
action
for
the
production
of
tumors
of
the
nasal
olfactory
epithelium
in
rats.

Previously,
a
common
mechanism
group
(
CMG)
of
chloroacetanilide
pesticides
consisting
of
acetochlor,
alachlor
and
butachlor
was
defined
by
the
Agency
for
nasal
tumors,
and
evaluated
by
the
FIFRA
Science
Advisory
Panel
(
SAP,
1997).
After
consideration
of
the
SAP
comments,
OPP's
own
reviews
and
the
data
underlying
these
reviews,
as
well
as
additional
information
received
by
the
Agency
from
registrants
or
presented
in
the
open
literature
since
the
1997
SAP
meeting,
OPP
published
a
paper
in
2001
titled
"
The
Grouping
of
a
Series
of
Chloroacetanilide
Pesticides
Based
on
a
Common
Mechanism
of
Toxicity"
(
http://
www.
epa.
gov/
oppfod01/
cb/
csb_
page/
updates/
commechs.
htm)
(
USEPA
2001).
It
was
concluded
in
that
document
that
Acetochlor,
Alachlor,
and
Butachlor
should
be
considered
as
a
Common
Mechanism
Group
due
to
their
ability
to
cause
nasal
turbinate
tumors
via
the
generation
of
a
common
tissue
reactive
metabolite
that
leads
to
cytotoxicity
and
regenerative
proliferation
in
the
nasal
epithelium.
Sustained
cytotoxicity
and
proliferation
is
needed
to
lead
to
neoplasia.
Thus,
the
common
mechanism
effect
is
a
systemic
chronic
endpoint.

For
purposes
of
a
cumulative
risk
assessment
Acetochlor,
Alachlor,
and
Butachlor,
will
be
considered
as
a
Common
Mechanism
Group.
Butachlor,
however,
has
no
registered
uses
in
the
US
and
has
been
excluded
from
the
risk
assessment.
Thus,
the
Common
Assessment
Group
(
CAG:
a
subset
of
the
CMG),
on
which
the
risk
assessment
was
conducted
consists
of
Acetochlor
and
Alachlor
only.

Development
of
nasal
olfactory
epithelium
tumors
in
rats
has
been
attributed
to
a
nonlinear
non­
mutagenic
mode
of
action
(
USEPA
,
2004).
Thus,
as
per
the
2005
EPA
Cancer
Guidelines
(
USEPA
2005b)
a
Margin­
of­
Exposure
(
MOE)
calculation
has
been
Page
5
of
74
used
for
the
cumulative
risk
assessment,
as
one
would
do
for
a
threshold
noncancer
toxicity
risk
assessment.
Because
of
the
threshold
approach
that
is
being
used
for
risk
assessment,
the
uncertainty
factors
(
UFs)
of
10
(
interspecies)
and
10
(
intraspecies)
are
used.
In
the
absence
of
sensitivity
issues
the
FQPA
factor
is
1.
Thus,
MOEs
above
100
are
considered
to
be
outside
of
the
Agency's
level
of
concern
(
LOC).

Calculations
for
this
document
have
involved:

!
For
each
CAG
member,
determination
of
the
Point­
of­
Departure
(
POD)
for
the
nasal
tumors
and
its
respective
dietary
exposure
(
food
and
drinking
water).

!
Computations
of
the
MOE
value
for
the
cumulative
exposure
using
alachlor
as
the
index
chemical
and
using
a
relative
potency
factor
(
RPF)
to
express
the
contribution
of
acetochlor
in
equivalents
of
the
index
chemical.

For
this
cumulative
assessment,
POD
values
were
determined
as
the
No­
Observed­
Adverse­
Effect­
Level
(
NOAELs)
for
tumor
formation.
NOAELs
for
nasal
tumor
formation
were
found
to
be
10
mg/
kg
bw
per
day
for
acetochlor
and
0.5
mg/
kg
bw
per
day
for
alachlor.
These
values
were
used
in
the
MOE
calculations.
The
POD
value
for
alachlor,
the
index
chemical,
was
0.5
mg/
kg
bw
per
day.
Based
on
comparison
of
tumor
NOAELs,
the
relative
potency
of
acetochlor
was
estimated
as
1/
20th
that
of
alachlor,
yielding
an
RPF
value
of
0.05.
This
RPF
value
was
used
in
subsequent
calculations
to
express
acetochlor
in
alachlor­
equivalent
units.

There
are
no
residential
uses
for
alachlor
or
acetochlor,
thus
this
risk
assessment
involved
only
two
pathways
of
exposure
(
food
and
drinking
water)
and
the
oral
route
of
exposure.
Exposure
was
evaluated,
as
follows,
using
a
limited
degree
of
refinement:

!
Alachlor
values
in
food
were
the
anticipated
residues,
as
estimated
in
the
alachlor
RED
document
of
1998
(
USEPA,
1998),
adjusted
with
current
(
year
2004,
Attachment
2)
values
for
percent
crop
treated.

!
Acetochlor,
values
in
food
were
tolerance
values
corrected
for
processing
factor
and
percent
crop
treated
from
the
Acetochlor
TRED
(
USEPA
2005c).
These
acetochlor
values
were
converted
into
alachlor
equivalents
by
multiplying
them
by
0.05
(
the
RPF
for
acetochlor).
The
alachlor
equivalents
from
acetochlor
were
then
added
to
their
counterparts
for
alachlor.

!
The
water
component
was
obtained
from
a
data
set
generated
by
the
Acetochlor
Registration
Partnership
(
ARP;
the
registrant
for
acetochlor)
which
monitored
both
acetochlor
and
alachlor
occurrence
in
drinking
water
supplies
relying
on
surface
water
sources
over
a
seven
year
period
(
1995
 
2001).
The
single­
year
water
Time­
Weighed­
Annualized­
Mean
(
TWAM)
concentrations
of
Page
6
of
74
acetochlor,
co­
occurrent
with
alachlor,
were
converted
into
alachlor
equivalents
using
RPFs
and
added
to
the
co­
occurrent
alachlor
TWAM
concentration
values.
The
single­
year
monitoring
data
for
each
site,
now
in
alachlor
equivalents,
were
averaged
over
the
years
of
data
availability
(
up
to
7
years)
to
obtain
a
multi­
year
average.
The
multi­
year
average
water
concentrations
were
ranked
from
smallest
to
largest
and
the
largest
value
was
used
for
risk
assessment.
It
is
noted
that
most
of
the
available
data
from
the
ARP
represent
finished
drinking
water;
thus,
exposure
in
the
future
could
be
higher
if
drinking
water
systems
revert
to
treatment
methods
which
less
effectively
reduce
acetochlor
or
alachlor
in
drinking
water.

Groundwater
levels
of
alachlor
and
acetochlor
were
significantly
lower
than
surface
water
sources,
thus
were
not
used
in
risk
assessment.

Because
the
nasal
olfactory
epithelium
tumors
are
a
systemic
chronic
endpoint,
a
chronic
dietary
analysis
was
conducted.
Multi­
year
averages
for
drinking
water
concentrations
were
used,
as
this
is
the
standard
practice
at
HED.

Acetochlor
chronic
dietary
exposure
assessments
were
conducted
using
the
Dietary
Exposure
Evaluation
Model
software
with
the
Food
Commodity
Intake
Database
(
DEEM­
FCID
 
,
Version
2.03).
Results
of
the
DEEM­
FCIDTM
analysis
produced
cumulated
MOEs,
greater
than
13,000
for
all
populations.
Selected
cumulated
MOEs
were:

!
U.
S.
Population
(
Total):
40,119
!
Non­
Nursing
infants:
13,175
(
lowest
MOE)

!
All
Infants
(<
1
year):
16,
464
!
Females
(
13­
19)
not
pregnant
or
nursing:
53,237
(
highest
MOE).

Compared
to
the
MOE
of
100
as
the
LOC
,
the
cumulated
MOE
values
reported
in
this
document
(
in
excess
of
13,000)
for
the
subject
CAG
are
outside
of
the
Agency's
level
of
concern.

Because
these
cumulative
MOE
values
were
obtained
using
high­
end
exposures,
they
may
be
considered
to
be
sufficiently
protective
and
conservative.
This
conclusion
is
supported
by
subsequent
analyses
(
detailed
in
Appendixes
1
and
2)
using
more
conservative
assumptions
for
chloroacetanilide
concentrations
in
drinking
water
that
give
MOEs
outside
of
the
Agency's
LOC:

!
When
monitored
single­
year
TWAM
concentrations
of
chloroacetanilides
in
water
were
used
for
DEEM­
FCIDTM
analysis
MOEs
greater
than
7,700
were
obtained
for
all
populations
(
Appendix
1).
Page
7
of
74
!
When
PRZM­
EXAMS
modeled
estimates
of
environmental
concentrations
of
alachlor
and
acetochlor
in
drinking
water
(
without
correction
for
percent
crop
treated
,
PCT)
were
used
for
DEEM­
FCIDTM
analysis
MOEs
greater
than
640
were
obtained
for
all
populations
(
Appendix
2).
These
values
will
increase
to
several
thousand
if
correction
for
current
values
of
percent
crop
treated
(
PCT)
were
to
be
incorporated
in
the
analysis.
Page
8
of
74
Table
of
Contents
Executive
Summary
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4
I.
Introduction
.
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9
II.
The
Cumulative
Risk
Assessment
Process
.
.
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.
10
III.
Performing
the
Cumulative
Risk
Assessment
.
.
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.
11
A.
Identification
of
the
Common
Mechanism
Group
(
CMG)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
11
B.
Identification
of
the
Candidate
Cumulative
Assessment
group
(
CAG)
.
.
.
.
13
C.
Dose
Response
Analysis
and
Determination
of
Points
of
Departure
.
.
.
.
.
13
D.
Exposure
Analysis
.
.
.
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.
18
i.
Inputs
for
Determination
of
Exposure
from
Foods
and
Water
.
.
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.
.
18
i.
a.
Inputs
From
Foods
.
.
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.
18
i.
b.
Inputs
from
Water
.
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.
18
ii.
DEEM­
FCID
Analysis
of
Exposure
From
Foods
and
Water
.
.
.
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.
.
.
24
E.
The
Cumulative
Risk
assessment
.
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.
26
i.
DEEM
Analysis
Using
the
RPF
Method
.
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.
26
ii.
DEEM
Analysis
for
Acetochlor
and
Alachlor
as
Separate
Chemicals
.
.
.
.
.
26
F.
Characterization
of
the
Risk
Assessment
.
.
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29
IV.
Conclusions
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32
V.
References
.
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34
VI.
Appendices
.
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37
A.
Appendix
1
.
.
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38
B.
Appendix
2
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43
VII.
Attachments
.
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49
Page
9
of
74
Cumulative
Risk
Assessment
of
the
Chloroacetanilides
I.
Introduction
The
passage
of
the
Food
Quality
Protection
Act
(
FQPA)
in
August
1996
led
the
Office
of
Pesticide
Programs
(
OPP)
to
develop
methodology
to
evaluate
the
risk
from
exposure
to
more
than
one
pesticide
acting
through
a
common
mechanism
of
toxicity.
As
defined
in
FQPA,
those
pesticides
that
induce
adverse
effects
by
a
common
mechanism
of
toxicity
must
be
considered
jointly.
In
other
words,
the
exposures
of
concern
are
to
include
all
relevant
routes
and
sources
based
upon
the
use
patterns
of
the
pesticides
in
question.
This
multi­
chemical,
multi­
pathway
risk
is
referred
to
as
cumulative
risk.

The
Agency's
first
step
in
developing
a
cumulative
risk
assessment
was
to
develop
methodologies
and
guidance
on
determining
whether
two
or
more
chemicals
share
a
common
mechanism
of
toxicity.
The
reader
is
referred
to
the
document,
Guidance
for
Identifying
Pesticide
Chemicals
and
Other
Substances
that
Have
a
Common
Mechanism
of
Toxicity
(
1/
29/
99)
for
additional
information
on
this
topic
(
see
http://
www.
epa.
gov/
fedrgstr/
EPA­
PEST/
1999/
February/
Day­
05/
6055.
pdf).

Further
guidance
on
conducting
cumulative
risk
assessment
was
provided
by
EPA
in
1999
and
2002.
The
Guidance
on
Cumulative
Risk
Assessment
of
Pesticide
Chemicals
That
Have
a
Common
Mechanism
of
Toxicity
[
1/
14/
02,
see
http://
www.
epa.
gov/
pesticides/
trac/
science/
cumulative_
guidance.
pdf,
(
USEPA
2002a)]
and
its
precursor
document
General
Principles
for
Performing
Aggregate
Exposure
and
Risk
Assessments
(
10/
29/
99),
see
(
http://
www.
epa.
gov/
pesticides/
trac/
science/
aggregate.
pdf)
describe
aspects
of
the
exposure
assessment
that
must
be
accounted
for
in
developing
an
integrated
cumulative
risk
assessment.
Specifically,
these
guidance
documents
state
that
the
cumulative
assessment
must
account
for
temporal
aspects
of
exposure
such
as
those
related
to
the
time
of
year
during
which
applications
resulting
in
exposures
are
likely
to
occur,
the
frequency
of
application
and
period
of
re­
application.
In
addition,
these
documents
state
that
the
assessment
must
appropriately
consider
demographic
factors
and
patterns.

Based
in
part
on
the
principles
and
suggested
practices
contained
in
the
above
guidance
documents,
the
first
cumulative
risk
assessment
conducted
by
the
Agency
was
for
the
organophosphorus
(
OP)
class
of
pesticides.
EPA
published
a
revised
cumulative
risk
assessment
for
these
pesticides
in
June
2002
(
USEPA
2002b).
In
this
assessment,
OPP
developed
and
demonstrated
in
detail
the
methods,
parameters,
and
issues
that
should
be
considered
in
estimating
cumulative
risk
associated
with
common
mechanism
pesticides
by
multiple
pathways
of
exposure.
Various
aspects
of
the
hazard
Page
10
of
74
and
dose­
response
assessment
and
the
exposure
analyses
were
presented
to
both
the
SAP
and
the
public
for
comment
numerous
times
over
the
course
of
several
years.
Both
the
SAP
and
the
public
provided
helpful
and
insightful
comments
and
ideas
which
were
incorporated
into
the
revised
documents.

Following
publication
of
the
Cumulative
Risk
Assessment
for
the
OP
pesticides
and
in
accordance
with
the
requirements
of
FQPA,
OPP
conducted
a
preliminary
cumulative
risk
assessment
for
the
N­
methyl
carbamate
(
NMC)
class
of
pesticides.
The
results
of
this
effort
appear
in
the
document
Estimation
of
Cumulative
Risk
from
NMethyl
Carbamates:
Preliminary
Assessment
(
USEPA,
2005a).

The
present
document
is
regarded
as
a
screening­
level
cumulative
risk
assessment
of
the
chloroacetanilide
pesticides.
Namely,
this
risk
assessment
has
been
done
using
high­
end
exposure
estimates
and
NOAELs
have
been
used
for
hazard
assessment.

As
presented
below,
the
selected
endpoint
for
risk
assessment
(
development
of
nasal
tumors
tumors
in
rats)
has
been
attributed
to
a
non­
linear,
non
mutagenic
mode
of
action
involving
sustained
cytotoxicity
and
regenerative
cell
proliferation.
Thus,
as
per
the
2005
EPA
Cancer
Guidelines
(
USEPA
2005b)
a
Margin­
of­
Exposure
(
MOE)
calculation
has
been
used
for
the
cumulative
risk
assessment,
as
one
would
do
for
a
threshold
noncancer
toxicity
risk
assessment.
Because
of
the
threshold
approach
that
is
being
used
for
risk
assessment,
the
uncertainty
factors
(
UFs)
of
10
(
interspecies)
and
10
(
intraspecies)
are
used.
In
the
absence
of
sensitivity
issues
the
FQPA
factor
is
1.
Thus,
MOEs
above
100
are
considered
to
be
outside
of
the
Agency's
level
of
concern
(
LOC).

The
high
MOE
values
obtained
in
this
risk
assessment
are,
thus,
outside
the
Agency's
LOC
and
are
considered
to
be
adequate
to
satisfy
any
safety
concerns.
Additional
refinement
of
the
data
could
be
required
if
more
common
mechanism
compounds
are
identified
or
higher
exposures
are
observed.

II.
The
Cumulative
Risk
Assessment
Process
As
elaborated
in
OPP's
cumulative
guidance
document
(
USEPA
2002a),
the
cumulative
risk
assessment
process
unfolds
in
several
steps.
In
brief,
these
include:

A.
Identification
of
the
Common
Mechanism
Group
(
CMG).
B.
Determination
of
the
Candidate
Cumulative
Assessment
Group
(
CAG)
C.
Determination
of
Points
of
Departure
(
dose
response
analysis)
D.
Exposure
analysis
(
exposure
scenarios
for
all
routes
and
durations,
establish
exposure
input
parameters).
Page
11
of
74
E.
Conduct
final
cumulative
risk
assessment.
F.
Characterize
the
cumulative
risk
assessment.

The
following
sections
will
develop
the
process
as
applied
to
the
chloroacetanilide
pesticides.

III.
Performing
the
Cumulative
Risk
Assessment
A.
Identification
of
the
Common
Mechanism
Group
(
CMG)

i.
Introduction
A
cumulative
risk
assessment
begins
with
the
identification
of
a
group
of
chemicals,
called
a
common
mechanism
group
(
CMG),
that
induce
a
common
toxic
effect
by
a
common
mechanism
of
toxicity.
Pesticides
are
determined
to
have
a
"
common
mechanism
of
toxicity"
if
they
act
the
same
way
in
the
body­­
that
is,
the
same
toxic
effect
occurs
in
the
same
organ
or
tissue
by
essentially
the
same
sequence
of
major
biochemical
events.

The
chloroacetanilide
pesticides,
have
been
previously
evaluated
by
the
Agency
to
determine
if
some
of
them
comprise
a
common
mechanism
group.
Details
of
the
analysis
appear
in
the
document
The
Grouping
of
a
Series
of
Chloroacetanilide
Pesticides
Based
on
a
Common
Mechanism
of
Toxicity
(
USEPA
2001).
In
brief,

!
Acetochlor,
Alachlor
and
Butachlor
may
be
grouped
together
based
on
a
common
end­
point
(
nasal
turbinate
tumors
in
rats)
and
a
known
mechanism
of
toxicity
for
this
endpoint.
All
three
compounds
produce
tumors
of
the
nasal
olfactory
epithelium
in
rats
by
way
of
a
non­
linear,
non­
genotoxic
mode
of
action
that
includes
cytotoxicity
of
the
olfactory
epithelium,
followed
by
regenerative
cell
proliferation
of
the
nasal
epithelium
that
can
then
lead
to
neoplasia
if
cytotoxicity
and
proliferation
are
sustained
(
see
more
details
below).

!
Acetochlor,
Alachlor
and
Butachlor
may
also
be
grouped
together
based
on
an
common
end­
point
and
a
known
mechanism
of
toxicity
(
UDPGT
induction).
All
three
compounds
produce
tumors
of
the
thyroid
follicular
cells
in
rats
by
way
of
a
non­
genotoxic
mode
of
action
that
includes
UDPGT
induction,
increased
TSH,
alterations
in
T3/
T4
hormone
production
and
thyroid
hyperplasia.

The
grouping
of
Acetochlor,
Alachlor,
and
Butachlor
based
on
a
common
mechanism
of
action
was
presented
to
the
FIFRA
Scientific
Advisory
Panel
(
SAP)
as
a
draft
on
March
19,
1997.
The
SAP
agreed
with
the
Agency's
conclusion
that
there
is
sufficient
evidence
to
support
the
proposed
grouping
for
the
nasal
turbinate
tumors
and
for
the
thyroid
follicular
tumors
(
USEPA,
1997).
Page
12
of
74
The
FIFRA
SAP
noted
in
their
report
(
USEPA,
1997),
additionally,
that
even
though
the
evidence
illustrated
that
a
common
mechanism
could
be
used
to
group
certain
chemicals
for
the
development
of
thyroid
tumors,
it
was
recommended
that
this
endpoint
not
be
used
in
combining
margins
of
exposure
because
the
toxic
effects
were
noted
at
doses
above
the
Maximum
Tolerated
Dose
(
MTD).
While
the
full
range
of
doses
employed
can
be
used
to
determine
common
mechanisms,
endpoints
occurring
solely
at
doses
above
the
MTD
should
not
be
used
in
risk
assessments.
Furthermore,
humans
are
more
refractory
to
the
induction
of
thyroid
follicular
cells
tumors
due
to
prolong
stimulation
of
thyroid
stimulating
hormone
compared
to
rats.

Thus,
for
the
purposes
of
this
document,
the
induction
of
nasal
olfactory
epithelium
tumors
in
rats
was
regarded
as
the
most
sensitive
and
relevant
common
mechanism
endpoint
to
base
the
cumulative
risk
assessment
of
the
chloracetanilides.

ii.
Determination
of
the
CMG
As
summarized
below,
and
illustrated
for
acetochlor,
there
is
ample
evidence
(
USEPA,
2004)
that
the
development
of
nasal
olfactory
epithelium
tumors
in
rats
dosed
with
chloroacetanilides
involves
the
following
sequence
of
steps,:

!
Acetochlor
conjugates
with
glutathione
(
GSH)
and
is
excreted
in
the
bile.

!
The
conjugate
is
biotransformed
to
a
series
of
sulfur­
containing
products.
Enterohepatic
circulation
of
these
products
creates
a
pool
of
metabolites
that
are
delivered
to
the
nose.

!
Biotransformation
to
tissue­
reactive
and
toxic
metabolites.
Metabolism
by
nasal
enzymes,
results
in
formation
of
a
benzoquinoneimine,
an
electrophile
and
redox­
active
molecule.

!
Binding
of
toxic
metabolite
to
cellular
proteins
plus
possible
generation
of
oxidative
stress
.

!
Cytotoxicity
!
Regenerative
cell
proliferation.

!
Sustained
cytotoxicity
and
cell
proliferation
that
results
in
neoplasia.

The
following
three
events
are
considered
key
events
for
formation
of
nasal
olfactory
epithelium
tumors
by
the
proposed
non­
linear,
non
genotoxic
mode
of
action
(
MOA):

QUINONE
IMINE­
FORMATION
(
PROTEIN
BINDING)
º
CYTOTOXICITY
º
CELL
PROLIFERATION
Based
on
the
FIFRA
SAP's
recommendations
(
USEPA
1997),
on
OPP's
2001
paper
on
the
MOA
of
chloroacetanilides
(
USEPA
2001)
and
in
a
more
recent
evaluation
of
the
MOA
of
acetochlor/
alachlor
(
USEPA
2004),
the
Common
Mechanism
Group
(
CMG)
for
the
present
document
consists
of
acetochlor,
alachlor
and
butachlor
with
formation
of
nasal
olfactory
epithelium
tumors
in
rats
as
the
common
endpoint.
Page
13
of
74
N
CH
3
Cl
O
O
CH
3
C
H
3
N
Cl
O
CH
3
O
C
H
3
C
H
3
Other
chloroacetanilides
were
considered
(
USEPA,
1997),
but
the
evidence
was
found
to
support
only
the
three
compounds
selected.
Although
the
chloroacetanilide
metolachlor
distributes
to
the
nasal
turbinates,
and
might
produce
a
quinoneimine,
it
is
not
apparent
from
currently
available
data
that
it
shares
the
same
target
site
in
the
nasal
tissue
as
acetochlor,
alachlor
and
butachlor.
Although
another
chloroacetanilide,
propachlor,
produces
a
precursor
of
a
quinoneimine,
the
available
data
do
not
support
its
tumorigenicity
to
the
nasal
turbinates.

B.
Identification
of
the
Candidate
Cumulative
Assessment
Group
(
CAG).

Once
the
CMG
is
defined,
a
subset
of
this
group,
the
Common
Assessment
Group
(
CAG)
is
selected,
for
which
the
cumulative
risk
assessment
will
be
performed.
This
final
selection
incorporates
into
the
CAG
those
pesticides
from
the
Common
Mechanism
Group
whose
uses,
routes,
and
pathways
of
exposure
will
present
sufficient
exposure
and
hazard
potential
to
warrant
inclusion
in
the
quantitative
estimates
of
risk.

The
CMG
subject
of
this
document
consists
of
acetochlor,
alachlor
and
butachlor.
At
present
only
alachlor
and
acetochlor
are
Registered
pesticides
in
the
US.
There
are
no
registered
uses
or
import
tolerances
for
butachlor.
Therefore
no
exposure,
and
hence,
no
risk
is
expected
for
butachlor
.
Thus,
a
cumulative
risk
assessment
will
be
performed
using
a
CAG
comprising
only
acetochlor
and
alachlor
(
Figure
1).

Figure
1.
Structures
of
Acetochlor
(
left)
and
Alachlor
(
right)
Page
14
of
74
C.
Dose
Response
Analysis:
Determination
of
Relative
Potency
Factors
and
Points
of
Departure.

The
Agency's
revised
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
2005b)
divide
dose
response
assessment
into
two
parts.
The
first
is
assessment
of
the
dose
response
near
the
lower
end
of
the
observed
range
(
the
point
of
departure
or
POD).
The
second
part
is
extrapolation
of
the
dose­
response
curve
from
the
POD
into
the
lowdose
range.

Once
the
POD
is
determined,
it
is
used
as
the
starting
point
for
subsequent
extrapolations
and
analyses.
If
data
are
available,
biologically
based
dose­
response
(
BBDR)
modeling
may
be
done
to
extrapolate
to
lower
doses
below
the
POD.
In
the
absence
of
BBDR
models,
for
linear
extrapolation
(
i.
e.
genotoxic
carcinogens),
the
POD
may
be
used
to
calculate
a
slope
factor,
and
for
non­
linear
extrapolation
(
the
present
case
for
acetochlor
and
alachlor)
the
POD
may
be
used
in
the
calculation
of
a
Margin
of
Exposure
(
MOE)

The
revised
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
2005b),
discuss
the
relative
advantages
of
several
approaches
to
obtaining
the
POD
for
cancer
risk
assessment:

!
When
tumor
data
are
used,
a
POD
is
obtained
from
the
modeled
tumor
incidences.
Conventional
cancer
bioassays,
with
approximately
50
animals
per
group,
generally
can
support
modeling
down
to
an
increased
incidence
of
1 
10%.
A
noobserved
adverse­
effect
level
(
NOAEL)
generally
is
not
used
for
assessing
the
potential
for
carcinogenic
response
when
one
or
more
models
can
be
fitted
to
the
data.

!
When
good
quality
precursor
data
are
available
and
are
clearly
tied
to
the
mode
of
action
of
the
compound
of
interest,
models
that
include
both
tumors
and
their
precursors
may
be
advantageous
for
deriving
a
POD.
Such
models
can
provide
insight
into
quantitative
relationships
between
tumors
and
precursors,
possibly
suggesting
the
precursor
response
level
that
is
associated
with
a
particular
tumor
response
level.

On
the
other
hand,
the
Guidelines
note,
that
if
the
precursor
data
are
drawn
from
small
samples
or
if
the
quantitative
relationship
between
tumors
and
precursors
is
not
well
defined,
then
the
tumor
data
will
provide
a
more
reliable
POD.

In
this
document,
tumor
incidences
will
be
used
for
POD
determination
because
they
constitute
a
robust
set
of
data
and
use
of
observed
tumor
NOAELs
will
be
used
as
a
conservative
screening
approach.
Since
experimental
NOAELs
are
determined
by
the
doses
selected
by
the
investigator,
the
"
true
NOAEL"
may
actually
be
a
higher
value.
Page
15
of
74
i.
Determination
of
the
POD
using
nasal
tumor
incidences.

Table
1
summarizes
the
incidences
of
nasal
tumors
in
rats
treated
chronically
with
acetochlor
or
alachlor.

Table
1.
Incidence
of
nasal
tumors
in
rat
chronic
studies.

#
Study
(
MRID)
Dose
Level
(
mg/
kg/
day)

Males
Females
Acetochlor
Tumors
(
Sprague­
Dawley
rats)

#
1
PR­
80­
006
(
00131088,
40484801)
0
22
69
250
0
30
93
343
papillary
adenoma
0/
69
1/
70
6/
69*
18/
69**
0/
69
0/
68
2/
70
1/
69
pap.
adenocarcinom.
0/
69
0/
70
0/
69
2/
69
0/
69
0/
69
0/
70
0/
69
Combined
ND
ND
ND
ND
ND
ND
ND
ND
#
2
ML­
83­
200
(
40077601)
0
2
10
50
0
2
10
50
papillary
adenomaa
1/
58**
0/
54
0/
58
12/
59**
0/
69**
0/
69
0/
67
19/
68**

#
3
88/
SUC017/
0348
(
41592004)
0
0.67
6.37
66.9
0
0.88
8.53
92.1
papillary
adenoma
0/
69**
0/
59
0/
59
35/
70**
0/
69**
0/
57
0/
58
36/
63**

carcinom.
0/
69
0/
59
0/
59
2/
70
0/
69
0/
57
0/
58
1/
63
Combined
0/
69**
0/
59
0/
59
37/
70**
0/
69**
0/
57
0/
58
37/
63**

Alachlor
Tumors
(
Long­
Evans
rats)

#
1
BD­
77­
421
(
00091050)
0
14
42
126
0
14
42
126
Adenoma
0/
46**
0/
47
10/
41
23/
40**
0/
47**
0/
41
4/
41
10/
41**

Carcinoma
0/
27
0/
20
1/
21
0/
19
0/
34
0/
28
1/
34
0/
22
Combined
0/
46**
0/
47
11/
41
23/
40**
0/
47**
0/
41
5/
41
10/
41**

#
2
EHL
800218
(
00075709)
0
0.5
2.5
15
0
0.5
2.5
15
Adenoma
0/
45**
0/
47
0/
45
11/
45**
0/
38**
0/
38
1/
43
9/
34**

*
=
p#
0.05;
**
=
p#
0.01.
;
a
Only
adenomas
reported.

For
Acetochlor,
examination
of
the
data
in
Table
1
indicates
that
the
incidence
of
nasal
tumors
in
Sprague­
Dawley
rats
increases
significantly
with
dose
in
all
three
studies.
Page
16
of
74
!
Study
PR­
80­
006
(
MRIDs
00131088
and
40484801),
does
not
define
a
NOAEL
at
22
mg/
kg/
day
for
nasal
olfactory
epithelium
tumors.
Even
though
the
incidence
of
papillary
adenomas
is
only
1/
70
and
is
not
statistically
significant
vs
controls,
it
is
considered
to
be
treatment­
related
due
to
the
rarity
of
the
tumor.
It
is
likely
that
it
is
the
beginning
of
the
dose
response,
which
reaches
statistical
significance
for
the
two
other
higher
doses
in
males.

!
In
study
ML­
83­
200
(
MRID
40077601),
likewise,
the
incidence
of
adenomas
of
the
olfactory
epithelium
at
the
highest
dose
tested
is
statistically
significantly
higher
than
in
controls.
No
carcinomas
were
reported.
This
study
defines
a
NOAEL
for
adenomas
of
10
mg/
kg/
day.

!
In
study
88/
SUC017/
0348
(
MRID
41592004),
the
incidence
of
adenomas
and
combined
adenomas/
carcinomas
of
the
olfactory
epithelium
at
the
highest
dose
tested
is
statistically
significantly
higher
than
in
controls.
No
nasal
tumors
occurred
at
lower
doses.
Thus,
the
NOAEL
for
combined
adenomas/
carcinomas
in
female
rats
is
8.53
mg/
kg
bw/
day.
A
similar
pattern
is
evident
for
male
rats:
yielding
a
NOAEL
for
combined
adenomas/
carcinomas
of
6.37
mg/
kg
bw/
day.

Thus,
the
available
data
define
a
POD
for
acetochlor
of
10
mg/
kg/
day
for
nasal
tumors
in
S­
D
rats.

For
Alachlor,
examination
of
the
data
in
Table
1,
indicates
that
the
incidences
of
nasal
tumors
in
Long­
Evans
rats
increases
significantly
with
dose
in
both
studies.

!
Study
BD­
77­
421
(
MRID
00091050),
in
Long­
Evans
rats,
was
conducted
at
dose
levels
of
approximately
0,
14,
42
or
126
mg/
kg
bw/
day
using
technical
alachlor
stabilized
with
0.5%
epichlorohydrin
for
the
first
eleven
months
of
the
study
before
a
switch
was
made
to
stabilization
with
epoxidized
soybean
oil
for
the
rest
of
the
study.
Epichlorohydrin
is
carcinogenic
for
male
Wistar
and
Sprague­
Dawley
rats:
when
given
in
drinking
water
epichlorohydrin
has
been
found
to
cause
forestomach
tumors
(
squamous
cell
papillomas
and
carcinomas)
in
Wistar
rats
(
Konishi,
et
al.,
1980).
By
the
inhalation
route,
epichlorohydrin
has
caused
squamous
cell
carcinomas
of
the
nasal
cavity
(
Laskin,
et
al.,
1980).
Although
nasal
tumors
were
observed
in
this
study,
these
results
are
confounded
by
the
nasal
tumorigenic
properties
of
epichlorohydrin.
Results
from
the
above
study
involving
the
administration
of
alachlor
in
the
presence
of
epichlorhydrin
will
not
be
used
for
determining
the
POD
for
alachlor
due
to
the
confounding
effect
of
the
epichlorohydrin.

!
In
study
EHL
800218
(
MRID
00075709),
the
incidences
of
adenomas
of
the
nasal
olfactory
epithelium
were
statistically
significantly
increased
in
high­
dose
Long­
Evans
rats
of
both
sexes
(
Table1).
No
carcinomas
were
reported.
Page
17
of
74
Although
the
incidence
of
tumors
in
female
rats
at
the
mid­
dose
(
2%)
is
not
statistically
significant,
it
may
be
considered
toxicologically
significant
in
view
of
the
rarity
of
the
tumors
and
the
significantly
increasing
trend
in
the
incidence
of
nasal
tumors.
Thus,
for
female
rats
the
NOAEL
for
nasal
tumors
is
0.5
mg/
kg
bw/
day.

Thus,
the
available
data
define
a
POD
for
alachlor
of
0.5
mg/
kg/
day
for
nasal
tumors
in
Long­
Evans
rats.

Determination
of
a
Relative
Potency
Factor
for
Acetochlor.

The
POD
values
(
based
on
NOAELs)
used
in
the
risk
assessment
in
this
document
are
summarized
in
Table
2.
The
POD
for
acetochlor
is
10
mg/
kg/
day
and
the
POD
for
alachlor
is
0.5
mg/
kg/
day.
Relative
Potency
Factors
(
USEPA
2002a)
were
calculated
using
the
ratio
of
POD
values
(
based
on
NOAELs)
for
alachlor
as
(
index
chemical)
and
acetochlor.
As
shown
in
Table
2,
the
RPFs
for
alachlor
and
acetochlor
are
1
and
0.05,
respectively.

Table
2.
Summary
of
POD
values
for
Nasal
Tumors
in
Rats
Treated
Chronically
in
the
Diet
with
Acetochlor
or
Alachlor
(
Values
from
Table
1).

Compound
POD
(
Mg/
kg
bw/
day)
RPF1
Rat
Strain/
S
ex
Comments
Alachlor
(
Index
Chemical)
0.5
1
Long­
Evans
/
Female
A
conservative
value,
the
incidence
of
1/
43
at
2.5
may
well
be
the
beginning
of
the
dose
response
of
a
rare
tumor,
and
thus
toxicologically
significant.

Acetochlor
10
0.05
Sprague­
Dawley
/
Male
&
Female
The
incidence
is
1/
70
at
22
mg/
kg/
day
in
study
PR­
80­
006.
This
effect
is
likely
toxicologically
significant.

1
With
Alachlor
as
index
chemical;
RPF
=
POD
of
alachlor
divided
by
the
POD
of
acetochlor.
Acetochlor
(
in
alachlor
equivalents)
=
Concentration
of
acetochlor
x
RPF.
Page
18
of
74
D.
Exposure
Analysis
This
assessment
is
designed
to
determine
if
the
two
chemicals
in
the
chloroacetanilide
CAG
(
Acetochlor
and
Alachlor)
pose
a
cumulative
dietary
risk.
There
are
no
residential
uses
for
these
two
chemicals.
Thus,
this
risk
assessment
involves
:

!
Only
two
pathways
(
food
and
drinking
water)
and
the
oral
route
of
exposure.

!
Because
the
endpoint
of
interest
is
a
cancer
endpoint
that
arises
via
a
mode
of
action
that
requires
prolonged
exposure,
only
a
chronic
analysis
was
performed.

i.
Inputs
for
Determination
of
Exposure
from
Foods
and
Water
i.
a.
Inputs
From
Foods.

Acetochlor.
The
qualitative
nature
of
acetochlor
residues
in
plants
is
understood
based
on
the
adequate
metabolism
studies.
Tolerances
have
been
established
(
see
40
CFR
180.470)
for
residues
of
alachlor
in/
on
a
variety
of
food
and
feed
commodities:

!
Field
corn
(
forage,
grain
and
stover)

!
Sorghum
(
forage,
grain
and
stover)

!
Soybeans(
forage,
grain
and
hay)

!
Wheat
(
forage,
grain
and
straw)

Considering
the
data
from
the
available
animal
metabolism
and
feeding
studies
and
the
calculated
maximum
theoretical
dietary
burdens
(
MTDBs)
of
3.0­
3.8
ppm
for
cattle
and
0.04
ppm
for
poultry
and
swine,
the
Agency
concluded
that
there
is
no
reasonable
expectation
of
quantifiable
residues
of
acetochlor
or
its
metabolites
occurring
in
livestock
commodities,
thus
no
tolerances
have
been
established
for
those
commodities.

Alachlor.
The
qualitative
nature
of
alachlor
residues
in
plants
is
understood
based
on
adequate
metabolism
studies.
Tolerances
have
been
established
(
see
40
CFR
180.249)
for
residues
of
alachlor
in/
on
a
variety
of
food
and
feed
commodities:

!
beans,
which
includes
dry
beans,
lima
beans,
forage
and
fodder;

!
corn,
fresh
sweet,
and
forage,
fodder,
and
grain;

!
eggs;

!
milk;

!
peanuts,
forage,
hay,
and
hulls;

!
sorghum,
fodder,
forage,
and
grain;

!
soybeans,
forage,
and
hay;

!
meat
and
meat
byproducts
of
cattle,
goats,
hogs,
poultry
and
horses.
Page
19
of
74
i.
b.
Inputs
from
Water
Introduction.

The
primary
source
data
for
the
water
component
of
this
exposure
assessment
is
a
data
set
generated
by
the
Acetochlor
Registration
Partnership
(
ARP;
the
registrant
for
acetochlor)
which
directly
evaluated
both
acetochlor
and
alachlor
occurrence
in
drinking
water
supplies
relying
on
surface
water
sources
over
a
7­
year
period
(
1995
 
2001).

This
assessment
does
not
use
ground
water
exposure
levels
because
ground­
water
monitoring
data
show
that
both
parent
acetochlor
and
parent
alachlor
are
less
prevalent
and
usually
at
lower
chronic
levels
in
ground
water
than
in
surface
water
(
USEPA,
2006).

Additionally,
the
ARP
monitored
water
levels
of
the
sulfonic
and
oxanilic
environmental
degradates
of
acetochlor
and
alachlor
shown
in
Figure
2.
These
compounds,
however,
are
not
included
in
this
cumulative
risk
assessment
because
extensive
data
are
available
(
USEPA
2004b)
to
show
that
these
compounds
show
a
different
toxicological
profile
than
the
respective
parents
and
do
not
contribute
to
the
development
of
nasal
olfactory
epithelium
tumors
in
rats.

The
ARP
selected
a
total
of
175
Community
Water
Supplies
(
CWSs)
in
nine
midwestern
and
three
Mid­
Atlantic
States
for
the
acetochlor
and
alachlor
surface
water
monitoring
program.
The
selection
process
was
designed
to
include
a
wide
array
of
CWSs
with
watersheds
in
areas
of
corn
production,
with
an
emphasis
on
including
worst­
case
watersheds
i.
e.,
smaller
watersheds
(
not
on
the
Great
Lakes
and
Continental
Rivers)
in
areas
of
high
corn
production.
These
watersheds
are
expected
to
have
higher
concentrations
of
acetochlor
and
alachlor
after
runoff
events
than
larger
watersheds
which
drain
areas
of
both
high
and
low
corn
production,
because
dilution
would
be
greater
for
CWSs
taking
water
from
the
Great
Lakes
and
Continental
Rivers.
Data
were
collected
to
characterize
each
community
water
system
included
in
the
program.
Since
there
were
some
CWSs
replaced
during
the
course
of
the
7­
year
study,
a
total
of
189
systems
were
included
in
the
study.
Raw
(
pre­
treatment)
water
was
only
collected
and
analyzed
for
selected
systems;
therefore,
only
44
of
the
CWSs
have
monitoring
data
for
residues
in
both
treated
and
untreated
water.
Further
details
on
the
design
of
the
Surface
Drinking
Water
Supply
(
SDWS)
study
by
the
ARP
can
be
found
in
"
Drinking
Water
Exposure
Assessment
for
Acetochlor"
(
M.
Barrett,
OPP/
EFED
Memorandum,
1/
3/
2005)
and
USEPA
(
2006).

The
surface
drinking
water
supply
(
SDWS)
and
state
ground
water
(
SGW)
monitoring
programs
were
designed
to
focus
on
areas
of
high
acetochlor/
alachlor
use.
The
monitoring
does
not
cover
the
entire
geographic
distribution
of
acetochlor
use.
Page
20
of
74
N
O
CH
3
C
H
3
O
O
H
O
C
H
3
N
CH
3
O
CH
3
C
H
3
O
O
H
O
N
CH
3
SO
3
H
O
O
CH
3
C
H
3
N
SO
3
H
O
CH
3
O
C
H
3
C
H
3
Acetochlor
ethane
sulfonic
acid
(
Acetochlor
ESA)

Alachlor
ethane
sulfonic
acid
(
Alachlor
ESA)
Acetochlor
oxanilic
acid
(
Acetochlor
OXA)

Alachlor
oxanilic
acid
(
Alachlor
OXA)
Geographic
analysis
of
the
SDWS
site
locations
and
acetochlor/
alachlor
use
patterns
seems
to
indicate
that
a
number
of
high
acetochlor/
alachlor
use
areas
were
not
monitored.
This
is
especially
true
for
the
SDWS
where
the
lack
of
sampling
of
raw
(
pre­
facility
treatment)
water
at
most
locations
makes
it
difficult
to
isolate
the
effects
of
site­
specific
usage
and
vulnerability
factors
and
water
treatment
processes
on
the
observed
residue
levels.
Additionally,
important
caveats
for
the
monitoring
data
are
described
in
more
detail
in
the
EFED
Memorandum
cited
above.

Figure
2.
Environmental
degradates
of
acetochlor
and
alachlor
Page
21
of
74
Monitored
Water
Concentrations.

A
chronic
toxicity
endpoint
(
nasal
olfactory
epithelium
tumors
)
is
used
in
this
document
for
cumulative
risk
assessment
of
chloroacetanilides.
Thus,
multi­
year
monitored
annual
means
for
drinking
water
appear
most
appropriate
for
evaluation
of
risk
relating
to
the
selected
chronic
endpoint
and
are
used
for
the
calculations
reported
in
this
assessment.
However,
to
further
bracket
the
maximum
potential
risk
associated
with
uncertainties
in
the
cumulative
exposure
to
acetochlor
and
alachlor
in
drinking
water,
two
additional
risk
assessments
using
more
conservative
assumptions
(
one
of
them
using
PRZM/
EXAMS
modeling)
are
detailed
in
the
Appendices.
.
Prior
to
calculating
the
multi­
year
monitored
annual
means
for
drinking
water,
the
single­
year
values
were
examined.
The
single­
year
co­
occurring
Time­
Weighed
Annualized
Mean
(
TWAM)
concentrations
of
acetochlor
and
corresponding
alachlor
in
the
ARP
SDWS
study
were
ranked
separately
in
decreasing
order
of
acetochlor
and
alachlor.
The
top
six
values
for
acetochlor
appear
in
Table
3
and
the
top
six
values
for
alachlor
appear
in
Table
4.

There
were
significant
differences
in
the
community
water
supply
systems
with
the
highest
residues
(
TWAMs)
of
acetochlor
and
alachlor
(
Tables
3
and
4,
respectively).
All
of
the
systems
with
the
highest
residues
of
alachlor
had
finished
water
sampled
and
were
not
among
the
sites
for
which
raw
water
samples
were
collected
and
analyzed.
Although
the
highest
alachlor
exposure
levels
were
lower
than
for
acetochlor,
the
difference
was
not
great.
The
alachlor
TWAM
for
the
518­
US­
OH
site
in
1997
was
0.590
ppb,
slightly
lower
than
the
second
highest
TWAM
observed
for
acetochlor
(
compare
Tables
3
and
4.
Four
of
the
six
highest
alachlor
TWAMs
(
Table
4)
occurred
in
three
different
community
water
supply
systems
in
the
state
of
Kansas;
this
is
a
state
which
has
relatively
little
corn
production
acreage
compared
to
Illinois
and
several
other
Corn
Belt
states.
This
may
reflect
significant
alachlor
usage
on
sorghum,
which
is
a
more
important
crop
in
Kansas.
Five
of
the
highest
acetochlor
TWAMs
(
Table
3)
occurred
in
the
state
of
Illinois.

As
shown
in
Tables
3
and
4,
the
highest
co­
occurring
TWAM
for
Acetochlor
in
surface
waters
was
the
value
from
site
214­
GI­
IL
(
1.428
ppb,
Table
3)
and
for
Alachlor
the
highest
value
was
found
in
site
518­
US­
OH
(
0.590
ppb,
Table
4).
Page
22
of
74
Table
3.
Top
six
co­
occurring
single­
year
Time­
Weighed
Annual
Mean
concentrations
(
TWAM)
of
acetochlor
and
corresponding
alachlor
TWAMs
in
the
ARP
SDWS
study.
1
Site
ID
Year
Water
Type
Acetochlor
TWAM
(
ppb)
Alachlor
TWAM
(
ppb)

214­
GI­
IL
1996
Finished
1.428
0.009
168­
PA­
IL
1998
Raw
0.591
0.015
455­
MO­
OH
1997
Finished
0.584
0.121
166­
NE­
IL
1996
Finished
0.533
0.048
214­
GI­
IL
1998
Finished
0.489
0.009
168­
PA­
IL
1998
Finished
0.475
0.011
1
Co­
occurring
acetochlor/
alachlor
concentrations
were
ranked
in
decreasing
values
for
acetochlor
for
each
year.
The
highest
value
for
acetochlor
(
1.428
ppb)
is
in
bold.

Table
4.
Top
six
co­
occurring
single­
year
Time­
Weighed
Annual
Mean
concentrations
(
TWAM)
of
alachlor
and
corresponding
acetochlor
TWAMs
in
the
ARP
SDWS
study
1
(
No
raw
water
samples
were
in
the
top
six).

Site
ID
Year
Water
Type
Acetochlor
TWAM
(
ppb)
Alachlor
TWAM
(
ppb)

518­
US­
OH
1997
Finished
0.202
0.590
23­
WE­
KS
2001
Finished
0.004
0.406
340­
NV­
IN
1996
Finished
0.372
0.357
114­
RI­
KS
1997
Finished
0.002
0.345
125­
TO­
KS
1996
Finished
0.089
0.269
125­
TO­
KS
1999
Finished
0.115
0.234
1
Co­
occurring
acetochlor/
alachlor
concentrations
were
ranked
in
decreasing
values
for
alachlor
for
each
year.
The
highest
value
for
alachlor
(
0.590
ppb)
is
in
bold.
Page
23
of
74
Combined
Co­
occurring
Acetochlor
and
Alachlor
Concentrations
To
conduct
the
risk
assessment,
the
single­
year,
co­
occurring,
acetochlor
and
alachlor
TWAM
water
concentrations
in
surface
waters
in
the
ARP
SDWS
study,
were
combined
using
Relative
Potency
Factors
(
RPF).
The
concentrations
were
combined
using
the
RPF
factor
of
0.05
(
in
Table
2)
for
acetochlor
with
alachlor
as
the
index
chemical.
The
concentrations,
expressed
as
"
alachlor
equivalents"
,
were
averaged
for
each
site
over
the
years
(
up
to
7
years)
for
which
data
were
available
and
the
averages
were
ranked
in
decreasing
order
(
Table
5).
The
maximum
value
for
this
ranking
(
0.286
ppm)
was
used
for
MOE
calculations
with
DEEM­
FCIDTM
.

Table
5.
Top
ten
co­
occurring
Multi­
Year
Time­
Weighed
Mean
concentrations
(
TWAM)
of
alachlor
and
acetochlor
in
the
ARP
SDWS
study
expressed
as
Alachlor
equivalents,
1
(
No
raw
water
samples
were
in
the
top
ten).

Site
ID
No.
Of
Years2
with
data
Water
Type
Acetochlor
TWAM
(
ppb)
Alachlor
TWAM
(
ppb)
TWAM
in
Alachlor
Equivalents
(
ppb)

340­
NV­
IN
2
Finished
0.205
0.276
0.286
125­
TO­
KS
7
Finished
0.069
0.158
0.155
23­
WE­
KS
4
Finished
0.004
0.147
0.147
114­
RI­
KS
3
Finished
0.001
0.144
0.144
408­
DE­
OH
6
Finished
0.129
0.110
0.104
518­
US­
OH
7
Finished
0.135
0.103
0.096
451­
ML­
OH
7
Finished
0.157
0.093
0.085
330­
LO­
IN
3
Finished
0.232
0.090
0.078
172­
FA­
IL
7
Finished
0.118
0.083
0.077
355­
SC­
IN
7
Finished
0.065
0.082
0.079
1
Co­
occurring
acetochlor/
alachlor
concentrations
(
TWAMs)
were
converted
to
alachlor
equivalents
using
an
RPF
(
0.05)
and
ranked
in
decreasing
values
for
alachlor
for
each
year.
The
highest
value
for
alachlor
equivalents
(
0.286
ppb)
is
in
bold
and
was
used
in
risk
assessment.
2
Number
of
years
for
which
water
monitoring
data
were
available
during
1995­
2001.

Table
6
summarizes
the
surface
water
multi­
year
TWAM
concentrations
(
ppb)
from
the
ARP
SDWS
study
and
their
percentiles
and
median.
The
combined
concentrations
Page
24
of
74
Acetochlor
plus
Alachlor
(
in
Alachlor
equivalents)
were
used
for
the
Margin­
of­
Exposure
(
MOE)
calculations
with
DEEM­
FCIDTM
analysis.

Table
6.
Summary
of
Surface
Water
Exposure
Values
for
Acetochlor
+
Alachlor
(
in
Alachlor
equivalents)
used
for
Risk
Assessment1,2.

Chemical
Maximum
Multi
year
TWAM
(
ppb)
Percentiles
(
ppb)
Median
(
ppb)
99.5th
99th
95th
Acetochlor
0.282
0.235
0.208
0.125
0.015
Alachlor
0.276
0.162
0.148
0.074
0.008
Acetochlor
+
Alachlor
(
in
Alachlor
equivalents)
3
0.286
0.166
0.149
0.078
0.009
1
Multi
year
Time­
Weighed
Annualized
Means
(
TWAM)
in
surface
water
from
the
ARP
monitoring
program
for
Chloroacetanilides
(
SDWS
study).
Values
are
maximum
TWAM
values
(
in
ppb),
99.5th
,
99th
and
95th
percentiles
(
in
ppb)
and
median
(
in
ppb)
observed
for
all
sites
(
189
sites)
.
Represents
predominantly
TWAMs
calculated
from
a
series
of
finished
water
samples,
although
for
a
minority
of
sampled
systems
the
ARP
also
regularly
monitored
raw
(
pre­
treatment)
water.

2
Data
from
EFED's
Cumulative
Drinking
Water
Exposure
Assessment
for
Chloroacetanilides,
USEPA
(
2006).

3
Acetochlor
concentration
(
in
alachlor
equivalents)
=
Acetochlor
concentration
x
RPF.
Where
RPF
=
NOAEL
Alachlor
/
NOAEL
Acetochlor
=
(
0.5
mg/
kg/
day
)
/
(
10
mg/
kg/
day)
=
0.05.
NOAEL
(
i.
e.
POD)
values
were
obtained
from
Table
2.
Each
acetochlor
concentration
was
converted
to
alachlor
equivalents
and
then
added
to
its
respective
co­
occurring
alachlor
concentration.
Then,
the
sums
were
averaged
for
each
site
over
the
years
of
available
data,
ranked
in
descending
order
and
the
maximum
TWAM
was
selected
for
risk
assessment.

ii.
DEEM­
FCIDTM
Analysis
of
Exposure
From
Foods
and
Water.

Acetochlor
chronic
dietary
exposure
assessments
were
conducted
using
the
Dietary
Exposure
Evaluation
Model
software
with
the
Food
Commodity
Intake
Database
(
DEEM­
FCID
 
,
Version
2.03),
which
incorporates
consumption
data
from
USDA's
Continuing
Surveys
of
Food
Intakes
by
Individuals
(
CSFII),
1994­
1996
and
1998.
The
1994­
96
and
98
data
are
based
on
the
reported
consumption
of
more
than
20,000
individuals
over
two
non­
consecutive
survey
days.
Foods
"
as
consumed"
(
e.
g.,
apple
pie)
are
linked
to
EPA­
defined
food
commodities
(
e.
g.
apples,
peeled
fruit
­
cooked;
fresh
or
N/
S;
baked;
or
wheat
flour
­
cooked;
fresh
or
N/
S,
baked)
using
publicly
available
recipe
translation
files
developed
jointly
by
USDA/
ARS
and
EPA.
For
chronic
Page
25
of
74
exposure
assessment,
consumption
data
are
averaged
for
the
entire
U.
S.
population
and
within
population
subgroups,
but
for
acute
exposure
assessment
are
retained
as
individual
consumption
events.

Based
on
analysis
of
the
1994­
96
and
98
CSFII
consumption
data,
which
took
into
account
dietary
patterns
and
survey
respondents,
HED
concluded
that
it
is
most
appropriate
to
report
risk
for
the
following
population
subgroups:
the
general
U.
S.
population,
all
infants
(
less
than
1
year
old),
children
1­
2,
children
3­
5,
children
6­
12,
youth
13­
19,
adults
20­
49,
females
13­
49,
and
adults
50+
years
old.

DEEM­
FCIDTM
Analysis
of
the
Data.

As
summarized
below
,
two
types
of
DEEM­
FCIDTM
runs
were
done:
(
1)
DEEM­
FCIDTM
runs
to
obtain
the
cumulative
Margin­
of
Exposure
(
MOE)
and
(
2)
DEEM­
FCIDTM
runs
with
each
separate
chemical
to
obtain
MOE
values
for
each
chemical
separately,
to
identify
the
risk­
driving
chemical.

1.
Cumulative
Margin­
of­
Exposure
(
MOE)
values
were
obtained
using
the
following
commodity
and
water
inputs:

!
Alachlor
commodity
values
were
the
anticipated
residues,
as
estimated
for
the
alachlor
RED
document
of
1998,
corrected
for
percent
crop
treated.
The
anticipated
residue
values
are
summarized
in
Attachment
1,
obtained
from
USEPA
(
1998).
The
percent
crop
treated
values
that
were
used
are
current
values
(
year
2004)
determined
by
USEPA/
OPP/
BEAD
and
summarized
in
Attachment
2.
It
is
noted
that
the
anticipated
residues
used
in
this
assessment
are
from
field
trial
data,
The
anticipated
residue
values
are
summarized
in
Attachment
1,
obtained
from
USEPA
(
1998).
thus
the
fact
that
they
were
obtained
8­
9
years
ago
does
not
make
them
obsolete
as
would
be
the
case
if
monitoring
data
had
been
used.

!
Acetochlor
commodity
values
were
tolerance
values
refined
through
the
use
of
experimentally
determined
processing
factors
and
average
percent
crop
treated
data
These
values
were
obtained
from
the
acetochlor
TRED
(
USEPA
2005c).
These
acetochlor
values
were
converted
into
alachlor
equivalents
by
multiplying
them
by
0.05
(
the
RPF
for
acetochlor).
The
alachlor
equivalents
from
acetochlor
were
then
added
to
their
counterparts
for
alachlor
(
the
index
chemical).

Detailed
guidance
for
these
calculations
appears
in
Section
9.5
(
Expression
of
Cumulative
Risk
­
Combining
Multiple­
Pathway
Risk)
of
the
Guidance
on
Cumulative
Risk
Assessment
of
Pesticide
Chemicals
(
USEPA
2002b).
Page
26
of
74
!
For
Drinking
Water
inputs
multi­
year
averages
were
used.
The
Single­
Year
Water
TWAM
concentrations
of
acetochlor
co­
occurrent
with
alachlor
from
the
ARP­
SDWS
study
were
converted
into
alachlor
equivalents
using
RPFs
and
added
to
the
co­
occurrent
alachlor
TWAM
concentration
values.
The
monitoring
data
for
each
site
were
averaged
over
the
years
of
data
availability
(
up
to
7
years)
to
obtain
a
multi­
year
average.
The
multi­
year
averages
were
ranked
from
smallest
to
largest
and
the
highest
value
was
used
for
risk
assessment.
The
results
of
such
calculations
are
shown
in
Table
5.
The
value
used
for
risk
assessment,
in
alachlor
equivalents
is
0.286
ppb
from
site
340­
NV­
IN.
Additionally,
various
percentiles
and
the
median
were
calculated
for
the
distribution
of
multi­
year
averages.
These
values
are
shown
in
Table
6.

2.
MOE
values
were
obtained
for
each
chemical
alone
using
the
following
commodity
and
water
inputs:

!
Alachlor
commodity
values
were
the
same
as
above
for
(
1).

!
Acetochlor
commodity
values
were
the
same
above
for
(
1),
except
that
they
were
not
converted
to
alachlor
equivalents.

!
Water
values
were
multi­
year
average
values
for
concentration
for
each
chemical
in
the
ARP
SDWS
study.
For
acetochlor
the
value
was
0.282
ppb,
(
See
Table
6).
For
alachlor
the
value
was
0.276
ppb
(
See
Table
6).

E.
The
Cumulative
Risk
assessment
This
section
contains
the
results
of
the
DEEM­
FCIDTM
runs
performed
as
discussed
in
Section
D.

The
following
Tables
report
MOEs
for
some
populations,
including
the
U.
S.
Population
(
Total)
and
the
results
for
the
population
groups
that
have
the
highest
and
the
lowest
MOE
values.
The
MOE
values
for
additional
populations
appear
in
Attachments
4,
6,
and
8.

i.
Cumulative
DEEM
Analysis
using
the
RPF
Method
(
Attachments
3
and
4):
Acetochlor
expressed
as
Alachlor
equivalents.

Commodity
levels
and
water
concentrations
for
acetochlor
were
converted
into
alachlor
equivalents
using
the
RPF
factor
of
0.05
(
see
Tables
2,
5
and
6)
and
added
to
those
of
alachlor.
The
combined
surface
water
TWAM
concentration
used
was
0.286
ppm,
instead
of
the
separate
concentrations
used
for
each
chemical
in
case
2
below
(
0.282
ppb
for
acetochlor
and
0.276
ppb
for
alachlor).
Page
27
of
74
As
shown
in
Table
7,
the
lowest
MOE
(
non
nursing
infants)
is
13,175
and
the
MOE
for
the
U.
S.
Population
(
Total)
is
40,119.
Results
for
other
populations
not
listed
in
the
Table,
appear
in
Attachment
4.
Table
7.
Cumulative
MOE
for
Alachlor
and
Acetochlor
using
the
RPF
method.
(
Acetochlor
is
expressed
as
Alachlor
equivalents):
Highest
and
Lowest
chronic
MOE
values
obtained
using
DEEM­
FCID
for
various
population
subgroups
exposed
to
Acetochlor
or
Alachlor.

Population
subgroup
Exposure
(
mg/
kg/
day)
Cumulated
MOE
(
MOE
T
)

U.
S.
Population
(
Total)
0.000012
40,119
All
infants
(
less
than
1
year
old)
0.000030
16,
464
Non­
nursing
infants
0.000038
13,175
(
lowest)

Females
(
13­
19)
not
preg.
or
nursing
0.000009
53,237
Children
1­
2
0.000037
13,
595
Children
3­
5
0.000028
17,
815
Children
6­
12
0.000018
27,875
Youth
13­
19
0.000010
47,
799
Adults
20­
49
0.000010
52,
303
Females
13­
49
0.000010
52,
171
Adults
50+
years
old
0.000009
54,
027
1
Acetochlor
and
Alachlor
were
refined
as
described
in
the
text.

2
Acetochlor
was
converted
to
alachlor
equivalents
using
the
RPF
method.
Acetochlor
concentration
(
in
alachlor
equivalents)
=
Acetochlor
concentration
x
RPF.
.
Where
RPF
=
NOAEL
Alachlor
/
NOAEL
Acetochlor
=
(
0.5
mg/
kg/
day
)
/
(
10
mg/
kg/
day),
NOAEL
(
i.
e.
POD)
values
from
Table
2.
For
water,
each
acetochlor
concentration
was
converted
to
alachlor
equivalents
and
then
added
to
its
respective
cooccurring
alachlor
concentration.
Then,
the
sums
were
averaged
for
each
site
over
the
years
of
data
availability
(
up
to
7
years),
ranked
in
descending
order
and
the
maximum
multi
year
average
was
selected
for
risk
assessment.
For
agricultural
commodities,
each
value
was
multiplied
by
the
RPF
of
0.05
(
as
described
above
and
added
to
the
respective
value
for
alachlor.

3
Parameters
used
for
the
chronic
DEEM­
FCID
runs
for
alachlor
as
the
Index
Chemical
were:
(
a)
Water
concentration:
Max.
Multiyear
TWAM,
from
Table
6
for
(
alachlor
+
acetochlor)
in
alachlor
equivalents
=
0.286
ppb.
(
b)
POD
(
i.
e
NOAEL)
for
Alachlor
=
0.5
mg/
kg/
day
(
From
Table
2).
(
c)
Anticipated
residues
for
alachlor
as
summarized
in
USEPA
(
1998)
and
also
in
Attachment
1
and
correction
for
percent
crop
treated
from
Attachment
2.
Page
28
of
74
ii.
DEEM
analysis
for
Acetochlor
(
Attachments
5
and
6)
and
Alachlor
(
Attachments
7
and
8)
as
separate
chemicals.

In
order
to
identify
the
risk­
driving
compound
in
the
cumulative
analysis,
MOE
values
were
also
obtained
each
compound
separately.
As
summarized
above,
anticipated
residues
corrected
for
percent
crop
treated
were
used
for
alachlor
and
tolerance
levels
corrected
for
processing
factors
and
percent
crop
treated
were
used
for
acetochlor.

Water
concentrations
for
each
chemical
were
the
maximum
multiple­
year
average
concentration
for
all
sites
(
0.282
ppb
for
acetochlor
and
0.286
ppb
for
alachlor)
in
the
ARP
SDWS
study.

As
shown
in
Table
8,
under
the
exposure
conditions
used,
the
MOE
values
for
acetochlor
are
much
higher
than
those
for
alachlor
(
nearly
10­
fold).
The
lowest
MOE
for
alachlor
is
13,636
(
Children
1­
2
years)
and
the
U.
S.
Population
(
Total)
has
an
MOE
of
40,813.
All
MOEs
for
acetochlor
exceed
160,000
and
the
U.
S.
Population
Total
has
an
MOE
of
392,207.
From
this
information
one
may
conclude
that
alachlor,
under
the
exposure
levels
covered,
is
the
risk
driving
component
of
the
cumulative
assessment
group
(
CAG).
Page
29
of
74
Table
8.
DEEM
Analysis
for
Acetochlor
alone
and
Alachlor
alone:
Highest
and
Lowest
chronic
MOE
values
obtained
using
DEEM­
FCID
for
various
population
subgroups
exposed
to
Acetochlor
or
Alachlor.

Chemical
Population
subgroup
Exposure
(
mg/
kg/
day)
MOE
Acetochlor
1
U.
S.
Population
(
Total)
0.000025
392,207
Non­
nursing
infants
0.000062
160,914
(
lowest)

Females
(
13­
19)
not
preg.
or
nurs.
0.000026
377,562
Seniors
55+
0.000015
676,613
(
highest)

Alachlor
2
U.
S.
Population
(
Total)
0.000012
40,813
Non­
nursing
infants
0.000035
14,109
Females
(
13­
19)
not
preg.
or
nurs.
0.000009
56,016
(
highest)

Children
1­
2
years
0.000037
13,636
(
lowest)

1
Acetochlor
was
refined
as
follows:
Tolerance
levels
for
RACs
corrected
for
percent
crop
treated
and
for
production
factors,
as
shown
in
Table
11.
Alachlor
was
refined
as
follows:
Anticipated
Residues
[
as
summarized
in
Alachlor
RED,
Tables
18
and
19,
December
1998,
USEPA
(
1998)]
corrected
for
percent
crop
treated,
as
shown
in
Table
12.
2
Parameters
used
for
the
chronic
DEEM­
FCID
runs
for
acetochlor
were:
(
a)
Water
concentration:
Max.
Multi
year
average
concentration
for
Acetochlor
(
alone)
=
0.282
ppb.
(
b)
POD
(
i.
e.
NOAEL)
for
Acetochlor
=
10
mg/
kg/
day
(
From
Table
2)
(
c)
Tolerances
for
acetochlor
from
40CFR(
§
180.470)
July
2004
Edition.
3
Parameters
used
for
the
chronic
DEEM­
FCID
runs
for
alachlor
were:
(
a)
Water
concentration:
Max.
Multi
year
average
concentration
for
alachlor
=
0.276
ppb.
(
b)
POD
(
i.
e
NOAEL)
for
Alachlor
=
0.5
mg/
kg/
day
(
From
Table
2).
(
c)
Anticipated
residues
for
alachlor
as
summarized
in
Attachment
1
(
From
USEPA
1998)
and
correction
for
percent
crop
treated
(
See
Attachment
2).

F.
Characterization
of
the
Risk
Assessment
A
cumulative
risk
assessment
of
a
Cumulative
Assessment
Group
(
CAG)
of
Chloroacetanilide
pesticides
has
been
conducted.
The
CAG
for
this
document
consists
of
two
chemicals:
alachlor
and
acetochlor.
An
original
member
of
the
Common
Mechanism
Group,
butachlor,
has
been
excluded
from
the
present
risk
assessment
because
at
present
there
are
no
registered
tolerances
for
this
chemical.

The
selected
endpoint
for
risk
assessment
(
development
of
nasal
tumors
in
rats)
has
been
attributed
to
a
non­
linear,
non
mutagenic
mode
of
action
involving
sustained
cytotoxicity
and
regenerative
cell
proliferation.
Thus,
as
per
the
2005
EPA
Cancer
Page
30
of
74
Guidelines
(
USEPA
2005b)
a
Margin­
of­
Exposure
(
MOE)
calculation
has
been
used
for
the
cumulative
risk
assessment,
as
one
would
do
for
a
threshold
noncancer
toxicity
risk
assessment.
Because
of
the
threshold
approach
that
is
being
used
for
risk
assessment,
the
uncertainty
factors
(
UFs)
of
10
(
interspecies)
and
10
(
intraspecies)
are
used.
In
the
absence
of
sensitivity
issues
the
FQPA
factor
is
1.
Thus,
MOEs
above
100
are
considered
to
be
outside
of
the
Agency's
level
of
concern
(
LOC).

i.
Toxicological
Considerations
The
CAG
members
in
this
document
were
evaluated
on
their
common
mode
of
action
for
the
production
of
tumors
of
the
nasal
olfactory
epithelium
in
rats.
Although
this
endpoint
is
observed
in
at
least
two
strains
of
rats,
it
has
not
been
observed
in
mice.
Experiments
conducted
in
vitro
with
primate
tissues
and
other
evidence,
did
not
rule
out
that
these
tumors
could
also
occur
in
humans
(
USEPA,
2004).
No
epidemiological
cancer
data
are
available.

The
existing
evidence
is
clearly
supportive
of
the
non­
linear,
non­
genotoxic
mode
of
action
in
the
causation
of
tumors
of
the
nasal
olfactory
epithelium
in
rats
(
USEPA
2004).
Thus,
in
accordance
with
The
Agency's
revised
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
2005b),
an
approach
akin
to
the
oral
reference
dose
approach,
MOE
calculations,
has
being
followed
in
this
document
to
assess
risk.

Under
FQPA,
the
potential
for
increased
sensitivity
to
adverse
effects
from
a
pesticide
to
children
during
gestation
and
postnatal
development
must
be
considered.
As
discussed
in
the
following
lines,
no
evidence
has
been
found
that
the
developing
fetus
or
young
animal
has
increased
sensitivity,
compared
to
the
adult,
to
chloroacetanilide
­
induced
nasal
olfactory
epithelial
tumors.

A
rat
multigeneration
reproductive
toxicity
study
on
acetochlor
(
MRID
45357503),
in
which
nasal
tissues
were
examined
microscopically
in
F0
and
F1
parental
animals,
provides
an
opportunity
to
compare
nasal
olfactory
epithelial
tumor
incidence
from
exposure
during
development
to
incidence
in
adult
rats
exposed
in
carcinogenicity
studies
on
acetochlor,
as
shown
below
in
Table
9.

The
Table
shows
that
a
similar
dose
threshold
for
nasal
epithelial
hyperplasia
and
neoplasia
was
observed
in
all
of
the
studies.
No
nasal
tumors
were
observed
in
the
reproductive
study
at
19­
22
mg/
kg/
day.
A
single
nasal
tumor
was
seen
in
a
male
at
38
mg/
kg/
day
in
a
carcinogenicity
study.
At
$
57
mg/
kg/
day,
a
positive
dose­
response
for
nasal
tumor
incidence
was
observed.
A
single
finding
of
papillary
hyperplasia
was
also
seen
in
a
carcinogenicity
study
at
20
mg/
kg/
day
in
males,
but
not
in
the
reproductive
study.
The
higher
tumor
incidence
in
F1
animals
compared
to
F0
at
mid
and
high
dose
probably
reflected
both
higher
F1
test
material
intake
between
postnatal
Day
29
and
Week
6
(
during
which
food
consumption
was
not
recorded)
and
additional
exposure
Page
31
of
74
time
during
gestation
and
lactation.
However,
the
tumor
incidence
in
the
reproductive
study
was
comparable
to
the
carcinogenicity
studies
at
similar
dose
levels.
Tumor
latency
also
was
not
affected
by
early
exposure.
In
the
reproductive
study,
tumors
were
observed
in
parental
F0
and
F1
rats
at
130­
154
days.

Table
9.
:
Comparison
of
nasal
epithelial
tumor
incidence
in
the
reproductive
toxicity
and
carcinogenicity
studies
in
the
rat1.

MALES
FEMALES
Study
Type/
MRID
Dose
in
ppm
Dietary
Intake
(
mg/
kg/
day)
Incidence
of
Nasal
Tumors
(%)
Dietary
Intake
(
mg/
kg/
day)
Incidence
of
Nasal
Tumors
(%)

Reproductive
toxicity2
F0
200
ppm
F1
200
ppm
F0
600
ppm
F1
600
ppm
F0
1750
ppm
F1
1750
ppm
21
19
57
66
166
196
000
12
15
31
22
22
65
71
198
216
0004
23
65
Chronic
toxicity/
carcinogenicit
y3
500
ppm
1000
ppm
1750
ppm
38
64
131
1
17
53
45
76
150
0
27
57
1
Table
adapted
from
Table
5
of
MRID
46081801.
Intake
values
represent
the
average
daily
intake
of
acetochlor
during
the
first
ten
weeks
of
the
chronic
toxicity/
carcinogenicity
studies
and
during
the
initial
ten­
week
premating
periods
from
the
reproductive
toxicity
study.
2
MRID
45357503
3
Dose
levels
are
taken
from
three
different
studies:
MRIDs
00131088/
40484801,
40077601
and
41592004.

The
carcinogenicity
studies
on
acetochlor
show
tumors
in
the
interim
(
12­
month)
sacrifice
animals,
but
no
data
are
available
at
earlier
times.
However,
a
nasal
epithelial
cell
proliferation
study
on
acetochlor
showed
proliferation
by
160
days
(
MRID
44496207).
In
published
studies
on
alachlor
in
rats,
nasal
tumors
were
reported
by
5­
6
months
of
exposure,
with
increased
cellular
proliferation
at
3­
4
months
(
Gentner
et
al.,
2002).
From
these
data,
it
is
concluded
that
the
POD
of
10
mg/
kg/
day
is
adequately
protective
during
development.

ii.
Exposure
Considerations
Evaluation
of
dietary
exposure
has
been
done
with
limited
refinement
and
thus
it
considered
to
an
overestimation
of
exposure
overall.
The
calculated
cumulative
MOEs
were
greater
than
13,000
for
all
population
sub­
groups
and
40,119
for
the
Total
U.
S.
Population.
Page
32
of
74
To
assess
the
significance
of
these
MOEs,
it
is
noted
that
compared
to
the
MOE
of
100,
defined
as
the
level
of
concern
(
LOC)
for
this
cumulative
risk
assessment,
the
cumulated
MOE
values
(
greater
than
13,000)
reported
in
this
document
for
the
subject
CAG,
are
well
outside
the
Agency's
LOC.

Table
10
shows
how
the
MOE
increases
as
smaller
percentiles
of
the
distribution
of
alachlor
equivalents
in
water
(
See
Table
8,
alachlor
+
acetochlor)
are
utilized
in
cumulative
MOE
calculations.
At
the
99.5
percentile,
all
MOE
values
exceed
15,000.

Table
10.
Cumulative
MOEs
for
Various
Populations
at
various
percentiles
of
alachlor
equivalents
in
water1.

Population
Group
MOE
at
Maximum
Multi­
year
TWAM
(
ppb)
MOE
at
the
following
percentiles
99.5
99
95
U.
S.
Population
40,119
50,334
52,218
61,891
All
Infants
(
less
than1
year
old)
16,464
22,649
23,921
31,259
Children
(
1­
2)
13,595
15,142
15,390
16,519
Children
(
3­
5)
17,815
20,336
20,788
22,757
Children
(
6­
12)
27,
875
32,234
32,964
36,408
Youth
(
13­
19)
47,799
57,923
59,714
68,573
Adults
(
20­
49)
52,303
69,463
72,849
91,470
Females
(
13­
49)
52,171
69,136
72,474
90,785
Adults
(
50+
years
)
54,027
73,854
77,904
101,049
1
The
DEEM­
FCIDTM
runs
used
the
same
food
values
used
in
Table
7.
The
maximum
Multi­
year
TWAM
concentrations
in
alachlor
equivalents
(
0.286
ppb)
and
the
percentiles
shown
in
Table
6
(
99.5,
99,
and
95
percentiles)
corresponding
to
multi­
year
TWAM
concentrations
of
0.166,
0.149
and
0.078
ppb,
respectively,
were
used
in
the
DEEM­
FCIDTM
runs,

IV.
Conclusions
A
risk
assessment
of
a
Cumulative
Assessment
Group
(
CAG)
consisting
of
the
Chloroacetanilide
pesticides
acetochlor
and
alachlor
has
been
conducted.
MOE
calculations
have
been
made
based
on
the
endpoint
of
nasal
olfactory
epithelium
tumors
in
rats,
and
using
slightly
refined
values
for
food
and
drinking
water,
Page
33
of
74
Compared
to
a
MOE
of
100,
defined
as
level
of
concern
(
LOC)
for
this
risk
assessment,
the
cumulated
MOE
values,
greater
than
13,000
for
the
subject
CAG
for
all
populations,
are
outside
the
Agency's
level
of
concern.

Because
these
cumulative
MOE
values
were
obtained
using
high­
end
exposures,
they
are
considered
to
be
conservative.
Additional
MOE
calculations
in
Appendixes
1
and
2,
using
more
conservative
approaches
to
estimation
of
drinking­
water
exposure,
support
the
conclusions
of
this
analysis
by
producing
MOE
values
that
exceed
the
LOC
of
100
by
nearly
an
order
of
magnitude
or
more.
Page
34
of
74
V.
References
Published
Studies
and
Agency
Reports.

Genter,
M.
B.,
Burman,
D.
W.
and
Bolon,
B.
(
2002)
Progression
of
Alachlor­
Induced
Olfactory
Mucosal
Tumours.
Int.
J.
Exp.
Path
83:
303­
307.

Konishi
Y.,
Kawabata
A.,
Denda
A.
et
al.
(
1980).
Fore
Stomach
Tumors
Induced
by
orally
Administered
Epichlorohydrin
in
Male
Wistar
Rats.
Gann
71(
6):
922­
923.

Laskin
S.,
Sellakumar
A.
R.,
Kushner
M.
et
al.
(
1980).
Inhalation
Carcinogenicity
of
Epichlorohydrin
in
Noninbred
Sprague­
Dawley
Rats.
J.
Natl.
Cancer.
Inst.
65(
4):
751­
757.

USEPA
(
1997)
SAP
REPORT,
April
28,
1997.
Report
of
the
FIFRA
Scientific
Advisory
Panel
Meeting,
March
19­
20,
1997,
held
at
the
Crystal
Gateway
Marriott,
1700
Jefferson
Davis
Highway,
Arlington,
VA
22202.
Available
at:
http://
www.
epa.
gov/
oppfod01/
cb/
csb_
page/
updates/
commechs.
htm
USEPA
(
1998).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Prvention,
Pesticides
and
Toxic
Substances.
Alachlor
Reregistration
Elegibility
Decision
(
RED).
December
1998.
Available
at:
http://
www.
epa.
gov/
REDs/
0063.
pdf
USEPA
(
2001)
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Health
Effects
Division.
The
Grouping
of
a
Series
of
Chloroacetanilide
Pesticides
Based
on
a
Common
Mechanism
of
Toxicity.
Draft
Document.
June
7,
2001.
Available
at:
http://
www.
epa.
gov/
oppfod01/
cb/
csb_
page/
updates/
commechs.
htm
USEPA
(
2002a).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Guidance
on
Cumulative
Risk
Assessment
of
Pesticide
Chemicals
That
Have
a
Common
Mechanism
of
Toxicity.
January
14,
2002.
Available
at:
http://
www.
epa.
gov/
pesticides/
trac/
science/
cumulative_
guidance.
pdf,

USEPA
(
2002b).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Organophosphate
Pesticides:
Revised
Cumulative
Risk
Assessment.
June
2002.
Available
at:
http://
www.
epa.
gov/
pesticides/
cumulative/
rra­
op/

USEPA
(
2004).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Health
Effects
Division.
Cancer
Assessment
Review
Committee.
Final
Report
for
Acetochlor.
Part
2:
Mode
of
Action
Assessment
Document:
Page
35
of
74
Evaluation
of
the
Mode
of
Action
of
Acetochlor
for
Nasal
Olfactory
Epithelium
Tumors
in
Rats
and
its
Relevance
to
Human
Cancer
Risk
Assessment.
August
31,
2004.
TXR
No.
0052727.

USEPA
(
2004b).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Ad
Hoc
MARC
memorandum
USEPA
(
2005a).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment.
August
2005.
Available
at
the
SAP
web
site:
http://
www.
epa.
gov/
oscpmont/
sap/

USEPA
(
2005b).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Risk
Assessment
Forum.
Guidelines
for
Carcinogen
Risk
Assessment.
March
2005.
Available
at:
http://
www.
epa.
gov/
iriswebp/
iris/
cancer032505.
pdf
USEPA
(
2005c).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
Acetochlor
TRED.
(
Reports
on
FQPA
Tolerance
Reassessment
Progress
and
[
Interim]
Risk
Management
Decisions).
Document
in
preparation.
September
2005.

USEPA
(
2006).
U.
S.
Environmental
Protection
Agency.
Washington
D.
C.
Office
of
Pesticide
Programs.
OPP/
EFED
Memorandum:
Cumulative
Drinking
Water
Exposure
Assessment
for
Chloroacetanilides
(
Acetochlor
and
Alachlor)
from
M.
R.
Barrett,
M.
Echeverria,
and
R.
Parker,
dated
February
6,
2006.
DP
292340
and
DP292353.

Submitted
Studies
00075709
Daly,
I.
W.;
Hogan,
G.
K.;
Plutnick,
R.;
et
al.
(
1981)
An
Eighteen
Month
Chronic
Feeding
Study
of
Alachlor
in
Mice:
Project
No.
77­
2064.
Final
rept.
(
Unpublished
study
received
Jul
1,
1981
under
524­
285;
prepared
by
Bio/
dynamics,
Inc.,
submitted
by
Monsanto
Co.,
Washington,
D.
C.;
CDL:
070168­
A;
070169)

00091050
Daly,
I.
W.;
McCandless,
J.
B.;
Jonassen,
H.;
et
al.
(
1981)
A
Chronic
Feeding
Study
of
Alachlor
in
Rats:
Project
No.
77­
2065.
Final
rept.
(
Unpublished
study
received
Jan
5,
1982
under
524­
285;
prepared
by
Bio/
dynamics,
Inc.,
submitted
by
Monsanto
Co.,
Washington,
D.
C.;
CDL:
070586­
A;
070587;
070588;
070589;
070590)

00131088
Ahmed
F.
E.
and
Seely,
J.
C.
(
1983)
Acetochlor:
Chronic
Feeding
Toxicity
and
Oncogenicity
Study
in
the
Rat.
Pharmacopathics
Research
Laboratories,
Inc.,
Laurel,
MD.
Study
No.
PR­
80­
006.
May
20,
1983.
Page
36
of
74
Unpublished
report.

40077601
Naylor,
M.
W.
and
Ribelin,
W.
E.
(
1986)
Chronic
Feeding
Study
of
MON
097
in
Albino
Rats.
Study
No.
ML­
83­
200,
Report
No.
MSL­
6119;
93­
190,
Laboratory
Project
ID
EHL­
83107.
September
25,
1986.
Unpublished
report.

40484801
Ribelin,
W.
E.
(
1987)
Histopathology
Findings
in
Noses
of
Rats
Administered
MON
097
in
a
Lifetime
Feeding
Study.
Tegeris
Laboratories,
Laurel,
MD
and
Monsanto
Environmental
Health
Laboratory,
St.
Louis,
MO.
Laboratory
Project
No.
ML­
86­
44/
EHL
86027.
November
4,
1987.
Unpublished
report
(
supplement
to
original
study
report).

41592004
Virgo,
D.
M.
and
Broadmeadow,
A.
(
1988)
SC­
5676:
Combined
Oncogenicity
and
Toxicity
Study
in
Dietary
Administration
to
CD
Rats
for
104
Weeks.
Life
Science
Research
Ltd.,
Suffolk,
England.
Study
No.
88/
SUC017/
0348.
March
18,
1988.
Unpublished
report.

42852102
Brewster,
D.;
Hotz,
K.
(
1991)
A
Study
to
the
Effect
of
Alachlor
on
Cell
Proliferation
in
Specific
Tissues
of
the
Rat
and
Mouse:
Lab
Project
Number:
HL
87112,90059,90114:
R.
D.
1156:
90114.
Unpublished
study
prepared
by
Monsanto
Co.
95
p.

44496209
Hotz,
K.
J.
and
Wilson,
A.
G.
E.
(
1998)
A
Study
on
the
Effects
of
Acetochlor
on
Mouse
Nasal
Cell
Proliferation.
Ceregen
(
EHL),
St.
Louis,
MO.
Report
No.
MSL­
14914.
December
12,
1996.
Unpublished
report.
Page
37
of
74
VII.
Appendices
To
bracket
the
maximum
potential
risk
associated
with
uncertainties
in
the
cumulative
exposure
to
acetochlor
and
alachlor
in
drinking
water,
two
additional
risk
assessments
have
been
performed
using
more
conservative
assumptions
for
the
determination
of
exposure
to
chloroacetanilides
in
water.

The
cumulative
risk
assessment
done
in
the
main
text
used
Monitored
Multi­
Year
TWAM
concentrations
of
chloroacetanilides
in
drinking
water.
In
contrast,
the
cumulative
risk
assessments
in
Appendices
1
and
2
used
the
following
more
conservative
approaches
for
determination
of
exposure
to
chloroacetanilides
in
drinking
water:

!
The
risk
assessment
in
Appendix
1,
uses
Monitored
Single­
Year
TWAM
concentrations
of
chloroacetanilides
in
drinking
water.
Single­
year
TWAMs
will
contain
still
the
higher
values
of
water
concentrations
that
get
averaged
out
in
obtaining
the
multi­
year
TWAMs.

!
The
risk
assessment
in
Appendix
2
uses
PRZM­
EXAMS
modeled
estimates
of
environmental
concentrations
of
alachlor
and
acetochlor
in
drinking
water
to
address
potential
limitations
in
the
monitored
data.

Outside
of
inputs
for
drinking
water,
all
other
inputs
to
DEEM­
FCIDTM
are
the
same
as
those
for
the
cumulative
risk
assessment
in
the
main
body
of
the
this
document:
Both
risks
assessments
in
the
Appendices
use:

!
The
same
POD
values
for
nasal
tumors
in
rats
summarized
in
Table
2
of
the
main
body
of
this
document:
Alachlor
is
the
index
chemical
with
a
POD
of
0.5
mg/
kg
bw/
day
and
acetochlor
has
a
POD
of
10
mg/
kg
bw/
day.
The
RPF
to
convert
acetochlor
exposure
to
alachlor
equivalents
is
thus
0.05.

!
The
same
DEEM­
FCIDTM
inputs
for
exposure
to
foods,
described
in
Section
D.
i.
a.
(
Input
from
Foods)
of
the
main
body
of
this
document
for
alachlor
and
acetochlor.
Page
38
of
74
A.
Appendix
1.

Cumulative
Risk
Assessment:
Use
of
Monitored
Single­
Year
TWAM
Concentrations
of
Alachlor
and
Acetochlor
in
Water.

i.
Introduction.

The
multi
year
monitored
annual
means
for
drinking
water
used
in
the
main
part
of
this
document
are
generally
most
appropriate
for
evaluation
of
risk
relating
to
chronic
endpoints
such
as
the
nasal
olfactory
epithelium
tumors
identified
as
the
common
mode
of
action
for
chloroacetanilides.
However,
to
allow
for
the
potential
of
higher
exposure
at
unmonitored
sites
or
with
change
use
patterns
or
weather
conditions,
we
use
in
this
Appendix
the
single­
year
annual
means
from
modeling
to
estimate
high­
end
lifetime
exposure
levels.

In
general,
the
highest
single­
year
exposure
levels
for
acetochlor
plus
alachlor
(
in
alachlor
equivalents
(
0.6
ppb
Tables
A1­
1
and
A1­
2
of
this
Appendix)
were
a
little
more
than
double
the
respective
highest
multi­
year
exposure
levels
(
0.286
ppb,
Tables
5
and
6
of
the
main
document).
Noteworthy
is
that
most
of
the
highest
annual
mean
concentrations
were
observed
from
sets
of
finished
water
samples
and
all
of
the
top
ten
exposure
sites
expressed
as
alachlor
toxic
equivalents
were
from
finished
water.
Data
on
treatment
effects
on
alachlor
or
acetochlor
concentrations
were
available
from
some
sites
showing
that
treatment
at
these
sites
typically
removed
from
30
to
90%
of
the
alachlor
equivalent
residues.

ii.
Combined
Co­
occurring
Acetochlor
and
Alachlor
Concentrations
The
risk
assessment
conducted
in
this
Appendix
uses
the
same
POD
values
and
DEEM­
FCIDTM
inputs
for
food
as
the
risk
assessment
in
the
main
body
of
the
document
Thus,
this
section
focuses
only
on
the
specification
of
the
DEEM­
FCIDTM
inputs
for
drinking
water
concentrations
of
the
chloroacetanilides.

To
conduct
the
risk
assessment
for
this
Appendix,
the
single­
year,
co­
occurring,
acetochlor
and
alachlor
TWAM
water
concentrations
in
surface
waters
in
the
ARP
SDWS
study,
were
combined
using
Relative
Potency
Factors
(
RPF).
The
concentrations
were
combined
using
the
RPF
factor
of
0.05
(
in
Table
2,
of
the
main
document)
for
acetochlor
with
alachlor
as
the
index
chemical.
The
concentrations,
expressed
as
"
alachlor
equivalents"
,
were
then
ranked
in
decreasing
order
(
Table
A1­
1,
below),
and
the
maximum
value
(
0.600
ppb)
corresponding
to
the
site
518­
US­
OH
for
1997
was
used
for
the
risk
assessment
in
this
Appendix.
Page
39
of
74
Table
A1­
1.
Top
six
co­
occurring
single­
year
Time­
Weighed
Annual
Mean
concentrations
(
TWAM)
of
alachlor
and
acetochlor
in
the
ARP
SDWS
study
expressed
as
Alachlor
equivalents,
1
(
No
raw
water
samples
were
in
the
top
six).

Site
ID
Year
Water
Type
Acetochlor
TWAM
(
ppb)
Alachlor
TWAM
(
ppb)
TWAM
in
Alachlor
Equivalents
(
ppb)

518­
US­
OH
1997
Finished
0.202
0.590
0.600
23­
WE­
KS
2001
Finished
0.004
0.406
0.406
340­
NV­
IN
1996
Finished
0.372
0.357
0.376
114­
RI­
KS
1997
Finished
0.002
0.345
0.345
125­
TO­
KS
1996
Finished
0.089
0.269
0.273
125­
TO­
KS
1999
Finished
0.115
0.234
0.247
1
Co­
occurring
acetochlor/
alachlor
concentrations
(
TWAMs)
were
converted
to
alachlor
equivalents
using
an
RPF
(
0.05)
and
ranked
in
decreasing
values
for
alachlor
for
each
year.
The
highest
value
for
alachlor
(
0.600
ppb)
is
in
bold
and
was
used
in
risk
assessment.

Table
A1­
2
summarizes
the
surface
water
single­
year
TWAM
concentrations
(
ppb)
from
the
ARP
SDWS
study.
The
table
shows
the
maxima
for
alachlor
and
acetochlor
alone
and
for
combined
concentrations
of
alachlor
plus
acetochlor
(
in
alachlor
equivalents)
plus
their
percentiles.
It
is
apparent
that
the
concentrations
of
combined
alachlor
plus
acetochlor
decline
very
rapidly,
so
that
the
99.5th
percentile
(
0.240
ppb)
is
quite
comparable
to
the
maximum
value
for
the
multi­
year
TWAM
concentration
(
0.286
ppb)
used
for
the
risk
assessment
in
the
main
body
of
this
document.
Page
40
of
74
Table
A1­
2.
Summary
of
Surface
Water
Exposure
Values
used
for
Risk
Assessment1.

Chemical
Maximum
single­
year
TWAM
(
ppb)
Percentiles3
(
ppb)
Median
(
ppb)
99.5th
99th
95th
Acetochlor
(
alone)
1.4282
0.458
0.363
0.143
0.008
Alachlor
(
alone)
0.590
0.232
0.187
0.055
0.007
Acetochlor
+
Alachlor
(
in
Alachlor
equivalents)
4
0.600
0.240
0.191
0.061
0.08
1
Single­
year
Time­
Weighed­
Annualized­
Means
(
TWAM)
in
surface
water
from
the
ARP
monitoring
program
for
Chloroacetanilides
(
SDWS
study).
Values
are
maximum
TWAM
values
(
in
ppb),
95
th
percentiles
(
in
ppb)
and
medians
(
in
ppb)
observed
for
all
sites.
Represents
predominantly
TWAMs
calculated
from
a
series
of
finished
water
samples,
although
for
a
minority
of
sampled
systems
the
ARP
also
regularly
monitored
raw
(
pre­
treatment)
water.

2
Data
from
EFED's
Drinking
Water
Exposure
Assessment
for
Acetochlor
(
USEPA,
2006).
3
Water
data
furnished
by
M.
Barrett
(
EFED)
on
July
21,
2005.

4
Acetochlor
concentration
(
in
alachlor
equivalents)
=
Acetochlor
concentration
x
RPF.
Where
RPF
=
NOAEL
Alachlor
/
NOAEL
Acetochlor
=
(
0.5
mg/
kg/
day
)
/
(
10
mg/
kg/
day)
=
0.05.
NOAEL
(
i.
e.
POD)
values
were
obtained
from
Table
2.
Each
acetochlor
concentration
was
converted
to
alachlor
equivalents
and
then
added
to
its
respective
co­
occurring
alachlor
concentration.
Then,
the
sums
were
ranked
in
descending
order
and
the
maximum
TWAM
was
selected
for
risk
assessment.

iii.
DEEM­
FCIDTM
Analysis
of
the
Data.

As
shown
in
Table
A1­
3
the
lowest
MOE
(
non
nursing
infants)
is
7,713
and
the
MOE
for
the
U.
S.
Population
(
Total)
is
26,
204.
Results
for
additional
populations
appear
in
Attachment
10.
Page
41
of
74
TableA1­
3
Cumulative
MOE
for
Alachlor
plus
Acetochlor
using
the
RPF
method
with
monitored
single­
year
TWAM
water
concentrations:
Highest
and
Lowest
chronic
MOE
values
obtained
using
DEEM­
FCID
for
various
population
subgroups
exposed
to
Acetochlor
or
Alachlor1,
2,
3.

Population
subgroup
Exposure
(
mg/
kg/
day)
Cumulated
MOE
(
MOE
T
)

U.
S.
Population
(
Total)
0.000019
26,204
All
Infants
(
Less
than
1
year
old)
0.000052
9,603
Non­
nursing
infants
0.000065
7,713
(
lowest)

Females
(
13­
19)
not
preg.
or
nursing
0.000014
35,590
(
highest)

Children
1­
2
years
0.000047
10,728
Children
3­
5
years
0.000037
13,417
Children
6­
12
years
0.000024
20,590
Youth
13­
19
years
old
0.000015
32,799
Adults
20­
49
0.000016
31,768
Adults
50+
years
old
0.000016
31,734
1
Acetochlor
and
Alachlor
were
refined
as
described
in
the
text.
2
Acetochlor
was
converted
to
alachlor
equivalents
using
the
RPF
method.
Acetochlor
concentration
(
in
alachlor
equivalents)
=
Acetochlor
concentration
x
RPF.
.
Where
RPF
=
NOAEL
Alachlor
/
NOAEL
Acetochlor
=
(
0.5
mg/
kg/
day
)
/
(
10
mg/
kg/
day),
NOAEL
(
i.
e.
POD)
values
from
Table
2,
in
the
main
body
of
this
document).
For
water,
each
acetochlor
concentration
was
converted
to
alachlor
equivalents
and
then
added
to
its
respective
co­
occurring
alachlor
concentration.
Then,
the
sums
were
ranked
in
descending
order
and
the
maximum
single­
year
TWAM
was
selected
for
risk
assessment.
For
agricultural
commodities,
each
value
was
multiplied
by
the
RPF
of
0.05
(
as
described
above
and
added
to
the
respective
value
for
alachlor).

3
Parameters
used
for
the
chronic
DEEM­
FCID
runs
for
alachlor
as
the
Index
Chemical
were:
(
a)
Water
concentration:
Max.
TWAM,
from
Table
A1­
1
for
alachlor
=
0.600
ppb.
(
b)
POD
(
i.
e
NOAEL)
for
Alachlor
=
0.5
mg/
kg/
day
(
From
Table
2,
in
the
main
body
of
this
document).
(
c)
Anticipated
residues
for
alachlor
as
summarized
in
USEPA
(
1998)
and
also
in
Attachment
1
and
correction
for
percent
crop
treated
from
Attachment
2.

iv.
Conclusions.
Page
42
of
74
A
cumulative
risk
assessment
has
been
done
using
Monitored
Single­
Year
TWAM
Concentrations
of
Alachlor
and
Acetochlor
in
drinking
water.
All
other
inputs
to
DEEM­
FCIDTM
analysis
of
the
data
are
the
same
as
those
used
cumulative
risk
assessment
in
the
main
body
of
this
document.

Compared
to
an
MOE
of
100,
defined
as
the
level
of
concern
(
LOC)
for
this
cumulative
risk
assessment,
the
cumulated
MOE
values,
greater
than
7,
700
for
the
subject
CAG
for
all
populations,
are
outside
the
Agency's
level
of
concern.
Page
43
of
74
B.
Appendix
2.

Cumulative
Risk
Assessment:
Use
of
Modeled
(
PRZM/
EXAMS)
Concentrations
of
Alachlor
and
Acetochlor
in
Drinking
Water.

i.
Introduction.

The
main
body
of
this
document
covers
a
cumulative
risk
assessment
of
chloroacetanilides
using
the
maximum
monitored
multi
year
TWAM
concentration
of
alachlor
and
acetochlor
in
drinking
water
(
0.286
ppb
in
alachlor
equivalents).
Appendix
1
of
this
document
adds
conservatism
to
that
assessment
by
using
the
maximum
singleyear
TWAM
concentration
of
alachlor
and
acetochlor
in
drinking
water
(
0.600
ppb
in
alachlor
equivalents).
The
present
appendix
adds
further
conservatism
to
the
previous
cumulative
risk
assessments
by
utilizing
PRZM/
EXAMS­
modeled
concentrations
for
the
chloroacetanilides
in
drinking
water
to
address
potential
limitations
in
the
monitoring
data.
The
PRZM/
EXAMS
modeling
assumes
high­
use
levels
and
conservative
modeling
inputs
in
vulnerable
watersheds.

ii.
Modeling
Based
Exposure
Estimation.

Crop
scenarios
only
for
corn,
sorghum,
soybeans,
sweet
corn
and
dry
beans
are
considered
in
this
assessment
since
these
uses
accounted
for
approximately
99%
of
all
national
alachlor
usage
for
the
years
2001­
2003
according
to
OPP's
BEAD
(
sweet
corn
and
dry
bean
use
are
reflected
in
the
monitoring­
based
exposure
only
to
the
extent
that
their
relatively
modest
usages
intersect
with
the
areas
monitored).
For
acetochlor,
only
the
corn
use
is
registered
currently,
although
applications
for
registrations
on
sorghum
for
grain
and
sweet
corn
have
been
submitted
to
and
are
currently
being
reviewed
by
EPA.
PRZM
scenarios
were
chosen
to
represent
each
of
these
uses
by
considering
state­
level
use
intensity
(
lbs
ai/
A
treated)
averaged
over
the
three
years
reported
by
BEAD
in
relation
to
the
existing
standard
PRZM
scenarios.
Final
cumulative
modeling
exposure
was
based
on
alachlor
use
on
corn,
sorghum
and
soybeans
and
acetochlor
use
on
corn.

Before
determining
a
combined
exposure
to
alachlor
and
acetochlor
(
as
alachlor
equivalents)
exposure
numbers
were
obtained
for
each
herbicide
from
separate
modeling
runs.
PRZM/
EXAMS
modeling
used
current
maximum
label
rate,
maximum
number
of
applications
per
year
and
the
minimum
application
interval.
Additional
model
inputs
are
detailed
in
USEPA(
2006).

Modeled
cumulative
exposure
estimates
are
expressed
as
alachlor
equivalents,
the
sum
of
alachlor
use
on
corn,
sorghum
and
soybeans
and
acetochlor
use
on
corn
adjusted
by
the
relative
potency
factor
(
0.05).
Separate
estimates
for
expected
environmental
concentrations
(
EEC)
of
chloroacetanilide
(
in
alachlor
equivalents)
were
calculated
for
Page
44
of
74
differing
ratios
of
alachlor
to
acetochlor
usage
on
corn.
All
cumulative
estimates
include
correction
for
Percent
Crop
Area
(
PCA)
and
assume
100%
of
the
crop
area
was
treated
with
the
assessed
chemical
(
i.
e.
there
was
no
correction
for
percent
crop
treated,
PCT).

iii.
PRZM/
EXAMS
Modeling
Results
Cumulative
multi
year
mean
estimated
environmental
concentrations
(
EEC)
of
the
subject
chemicals
(
as
alachlor
equivalents)
appear
in
Table
A2­
1.
The
three
columns
of
EECs
represent
the
assumptions
of
1:
0,
1:
1,
and
0:
1
alachlor
to
acetochlor
ratios
of
use
on
corn,
respectively;
assuming
exclusivity
of
use
(
i.
e.
either
alachlor
or
acetochlor,
but
not
both,
may
be
used
on
a
given
corn
field).

The
EEC
value
of
8.94
ppb
(
alachlor
equivalents)
for
the
50%/
50%
alachlor
to
acetochlor
scenario
was
used
as
drinking
water
input
for
DEEM­
FCIDTM
analysis
for
risk
assessment.
The
value
of
12.81
(
for
100%
alachlor)
was
not
used
as
it
pertained
only
to
alachlor.
As
noted
in
USEPA(
2006),
the
trend
has
been
for
the
overall
alachlor
to
acetochlor
ratio
of
usage
to
continue
to
decline.
Thus,
the
value
of
8.94
ppb
alachlor
equivalents
is
likely
to
be
more
conservative
than
a
value
closer
to
the
5.07
ppb
estimated
for
the
100%
acetochlor
use.

iv.
DEEM­
FCIDTM
Analysis
of
the
Data.

As
summarized
above,
the
risk
assessment
in
this
appendix
employs
the
same
POD
values
and
DEEM­
FCIDTM
inputs
for
food
as
the
risk
assessment
in
the
main
body
of
the
text.
The
cumulative
MOE
for
alachlor
plus
acetochlor,
using
the
modeled
EEC
of
8.94
ppb
alachlor
equivalents
as
DEEM­
FCIDTM
inputs
for
water
concentrations
of
the
chloroacetanilides,
is
shown
in
Table
A2­
3.

The
MOE
value
(
not
corrected
for
PCT)
for
the
U.
S.
population
is
2,556;
the
lowest
MOE
is
642
for
non­
nursing
infants
and
the
highest
is
3,513
for
youths
13­
19
years
old.

Because
all
EEC
estimates
assume
100%
of
the
crop
area
for
the
three
crops
was
treated
with
the
assessed
chemicals,
exposure
will
be
overestimated
to
the
extent
the
actual
PCT
is
less
than
100%.
For
example,
screening
levels
of
PCT
for
alachlor
for
2004
(
Attachment
3)
and
for
acetochlor
for
2003
(
USEPA
2005c)
were;

!
Alachlor:
Corn
5%,
Sorghum
15%,
soybeans
<
2.5%.

!
Acetochlor:
Corn
25%

Thus,
the
actual
MOEs
are
likely
to
be
much
larger
than
those
depicted
in
Table
A2­
3.
Page
45
of
74
Table
A2­
1.
Cumulative
multi
year
mean
estimated
environmental
concentrations
(
EEC)
of
the
subject
chemicals
(
as
alachlor
equivalents).

Watershed
Type
Pesticide
EEC
(
100%
alachlor
on
corn)
1,2
ppb
EEC
(
50%
alachlor,
50%
acetochlor
on
corn)
ppb
EEC
(
100%
acetochlor
on
corn)

ppb
High
Corn3
Both
12.81
8.94
5.07
Alachlor
12.81
8.89
4.97
Acetochlor
0.00
0.05
0.10
High
Sorghum
Both
5.67
5.31
4.95
Alachlor
5.67
5.30
4.94
Acetochlor
0.00
0.00
0.01
1
All
EEC
values
are
presented
as
ppb
in
water.
Data
from
USEPA
(
2006).
2
The
three
EEC
columns
represent
assumptions
of
1:
0,
1:
1,
and
0:
1
alachlor:
acetochlor
ratios
of
use
on
corn,
respectively;
assuming
exclusivity
of
use
(
i.
e.,
either
alachlor
or
acetochlor
but
not
both
may
be
used
on
a
given
corn
field.)
3
EEC
sources
used
:
IL
Corn
scenario
PRZM­
EXAMS
multi­
year
mean
(
High
Corn
EEC).
MS
Corn
scenario
PRZM­
EXAMS
multi­
year
mean
(
High
Sorghum
EEC).
MS
Soybean
scenario
PRZM­
EXAMS
multi­
year
mean
(
both
EEC
calculation
sets).
KS
Sorghum
scenario
PRZM­
EXAMS
multi­
year
mean
(
both
EEC
calculation
sets).

v.
Discussion
of
Monitoring­
Based
and
Modeling
Based
Cumulative
Exposure
Estimates
The
PRZM/
EXAMS
modeling
in
this
cumulative
assessment
is
based
on
estimating
exposure
concentrations
in
watersheds
in
two
counties
which
have
the
potential
to
be
among
the
highest
exposure
sites
in
the
United
States.
Major
reasons
for
higher
(
up
to
20x)
estimates
being
derived
from
the
modeling
are
likely
due
to
be
the
use
of
assumptions
in
the
modeling
input
which
may
lead
to
overestimation,
e.
g.;
assuming
higher
pesticide
persistence
and/
or
mobility
than
may
actually
occur
or
assuming
pesticide
usage
levels
(
100%
crop
land
treated
with
maximum
allowable
rates)
that
may
not
actually
occur
(
and
therefore
are
not
reflected
in
the
monitoring
data).

The
monitoring
data
automatically
reflects
actual
rates
and
amounts
of
use
of
the
pesticide.
To
the
extent
that
usage
of
chloroacetanilide
herbicides
remains
level
or
declines,
the
highest
one­
year
exposure
level
observed
should
rarely
if
ever
be
exceeded
for
a
lifetime
exposure
endpoint
(
as
is
being
considered
in
this
cumulative
risk
assessment).
Should
usage
rates
increase
in
the
future,
the
monitoring
estimates
may
Page
46
of
74
no
longer
being
reliable,
but
the
modeling
estimates
should
remain
conservative.
Future
changes
in
weather
or
crop
production
regions
resulting
in
scenarios
which
produce
greater
runoff
of
the
pesticide
are
an
unknown
that
could
adversely
affect
the
reliability
of
both
monitoring­
based
and
modeling­
based
exposure
estimates.

vi.
Summary
of
Exposure
Considerations:
Monitoring
vs
PRZM/
EXAMS
modeling
The
highest
alachlor
equivalent
single­
year
mean
concentration
observed
in
the
ARP
SDWS
monitoring
program
is
0.6
ppb,
The
highest
multi­
year
mean
concentration
is
0.286
ppb
alachlor
equivalents,
occurring
(
at
a
site
with
only
two
years
of
data,
Table
;
the
highest
7­
year
mean
concentration
was
0.16
ppb)
(
Table
5).
Evaluation
of
the
USGS
NAWQA
monitoring
dataset
indicates
concentrations
that
are
roughly
equivalent
for
about
the
same
monitoring
period.
Maximum
cumulative
exposure
values
(
assuming
maximum
possible
usage
levels)
estimated
by
computer
simulation
are
5
to
12
ppb
alachlor
equivalents.
The
latter
value
corresponds
to
an
alachlor:
acetochlor
usage
ratio
of
1:
0;
the
intermediate
value
of
8.94
was
used
for
risk
assessment,
corresponding
to
an
alachlor:
acetochlor
usage
ratio
of
1:
1.

The
modeled
values
exceed
those
developed
from
monitoring
data
by
a
factor
of
10
to
20,
and
are
likely
to
represent
upper
bound
exposures
to
combined
residues
of
alachlor
and
acetochlor.
Given
the
number
of
maximum
and
high­
end
exposure
assumptions
discussed
in
the
modeling
exposure
assessment
sections,
it
is
very
likely
that
exposures
in
CWS
across
the
country
will
not
exceed
predicted
modeling
levels.
In
addition,
given
the
decline
in
alachlor
use
across
the
US
and
the
lower
toxicity
of
acetochlor,
it
is
likely
that
the
current
annual
cumulative
alachlor
equivalents
exposure
levels
in
the
most
vulnerable
CWS
watersheds
may
fall
below
the
0.6
to
12
ppb
range
estimated
from
monitoring
data
and
computer
simulation
models.
In
the
event
there
would
be
changes
in
the
future
to
a
higher
level
of
usage
of
alachlor
or,
to
a
lesser
extent,
of
acetochlor
(
e.
g.,
from
increased
market
share
on
currently
registered
crops
or
additions
of
new
uses),
exposure
levels
could
increase,
but
would
not
be
expected
to
exceed
the
levels
estimated
by
modeling.
Should
a
higher
level
of
refinement
be
needed
for
this
exposure
assessment,
more
spatially
explicit
modeling
or
evaluation
of
monitoring
data
can
be
performed.
Page
47
of
74
Table
A2­
3
Cumulative
MOE
for
Alachlor
and
Acetochlor
using
the
RPF
method
with
modeled
PRZM­
EXAMS
TWAM
water
concentrations:
Highest
and
Lowest
chronic
MOE
values
obtained
using
DEEM­
FCID
for
various
population
subgroups
exposed
to
Acetochlor
or
Alachlor
1,
2,
3.
Data
corrected
for
PCA
but
not
PCT.

Population
subgroup
Exposure
(
mg/
kg/
day)
Cumulated
MOE
(
MOE
T
)

U.
S.
Population
(
Total)
0.000195
2,566
All
Infants
(
Less
than
1
year
old)
0.000628
796
Non­
nursing
infants
0.000779
642
(
lowest)

Children
1­
2
years
0.000138
1,625
Children
3­
5
years
0.000282
1,775
Children
6­
12
years
0.000193
2,593
Youth
13­
19
years
0.000142
3,513
(
highest)

Adults
20­
49
0.000179
2,790
Adults
50+
years
old
0.000188
2,653
1
Acetochlor
and
Alachlor
were
refined
as
described
in
the
text.

2
Acetochlor
was
converted
to
alachlor
equivalents
using
the
RPF
method.
Acetochlor
concentration
(
in
alachlor
equivalents)
=
Acetochlor
concentration
x
RPF.
.
Where
RPF
=
NOAEL
Alachlor
/
NOAEL
Acetochlor
=
(
0.5
mg/
kg/
day
)
/
(
10
mg/
kg/
day),
NOAEL
(
i.
e.
POD)
values
from
Table
2,
in
the
main
body
of
this
document).
PRISM­
EXAMS
modeled
values
were
used
for
water
concentrations.
50/
50
proportions
of
acetochlor/
alachlor
use
were
assumed.
There
was
correction
for
PCA
but
not
for
PCT.
For
agricultural
commodities,
each
value
was
multiplied
by
the
RPF
of
0.05
(
as
described
above
and
added
to
the
respective
value
for
alachlor.

3
Parameters
used
for
the
chronic
DEEM­
FCID
runs
for
alachlor
as
the
Index
Chemical
were:
(
a)
Water
concentration:
Max.
TWAM,
from
Table
A1­
1
for
alachlor
=
0.600
ppb.
(
b)
POD
(
i.
e
NOAEL)
for
Alachlor
=
0.5
mg/
kg/
day
(
From
Table
2,
in
the
main
body
of
this
document).
(
c)
Anticipated
residues
for
alachlor
as
summarized
in
USEPA
(
1998)
and
also
in
Attachment
2
and
correction
for
percent
crop
treated
from
Attachment
3.
Page
48
of
74
vii.
Conclusion
A
cumulative
risk
assessment
has
been
done
using
PRZM/
EXAMS­
modeled
EECs
of
Alachlor
and
Acetochlor
in
drinking
water.
All
other
inputs
to
DEEM­
FCIDTM
analysis
of
the
data
are
the
same
as
those
used
cumulative
risk
assessment
in
the
main
body
of
this
document.

The
cumulated
MOE
values
observed
using
the
PRZM/
EXAMS­
modeled
EECs
are
greater
than
640
for
the
subject
CAG
for
all
populations.
Compared
to
an
MOE
of
100,
defined
as
the
level
of
concern
(
LOC)
for
this
cumulative
risk
assessment
in
the
main
part
of
this
document,
these
values
are
outside
the
Agency's
level
of
concern.
Because
PCT
was
not
incorporated
in
the
modeling,
the
reported
MOEs
are
expected
to
be
underestimates
of
the
actual
MOEs.
Page
49
of
74
VII.
Attachments
Attachment
1.
Anticipated
Residues
in
Plant
and
Livestock
Commodities
for
Alachlor.

Attachment
2.
Screening
Level
Usage
analysis
(
SLUA)
for
Alachlor.

Attachment
3.
DEEM
CRA
(
Multi­
year)
Food
and
Water
Residue
Input
File.

Attachment
4.
DEEM
CRA
(
Multi
year)
Food
and
Water
Results
File.

Attachment
5.
DEEM
Acetochlor
Alone
(
Multi
year)
Food
and
Water
Residue
Input
File
Attachment
6.
DEEM
Acetochlor
Alone
(
Multi
year)
Food
and
Water
Results
File.

Attachment
7.
DEEM
Alachlor
Alone
(
Multi­
year)
Food
and
Water
Residue
Input
File
Attachment
8.
DEEM
Alachlor
Alone
(
Multi
year)
Food
and
Water
Results
File.

Attachment
9.
DEEM
CRA
(
Single­
Year)
Food
and
Water
Residue
Input
File.

Attachment
10.
DEEM
CRA
(
Single­
Year)
Food
and
Water
Results
File.

Attachment
11.
DEEM
CRA
(
PRZM­
EXAMS)
Food
and
Water
Residue
Input
File.

Attachment
12.
DEEM
CRA
(
PRZM­
EXAMS)
Food
and
Water
Results
File.
Page
50
of
74
Attachment
1
(
page
1
of
3):
Anticipated
Residues
in
Plant
and
Livestock
Commodities
for
Alachlor.
From:
Reregistration
ELEGIBILITY
Decision
(
RED)
for
Alachlor.
U.
S.
EPA.
Office
of
Prevention,
Pesticides
and
Toxic
Substances.
EPA
738­
R­
020.
December
1998,
pages
81­
83.
Page
51
of
74
Attachment
1
(
continued,
page
2
of
3):
Page
52
of
74
Att
ach
m
ent
1
(
pag
e
3
of
3):
Page
53
of
74
Attachment
2
(
Page
1
of
3).
Usage
Report
in
Support
of
Reregistration
for
Acetochlor.
Screening
Level
Usage
Analysis
(
SLUA)
for
(
Alachlor)/(
01/
31/
05)

What
is
a
Screening
Level
Usage
Analysis
(
SLUA)?
It
is
a
summary
report
of
the
available
usage
information
for
a
particular
pesticide
active
ingredient
being
used
on
agricultural
crops
at
a
national
level
for
the
United
States.

What
does
it
contain?

!
Estimates
of
pesticide
usage
for
a
single
active
ingredient
only.

!
Estimates
of
pesticide
usage
for
agricultural
use
sitescrops)
only.

!
Estimates
of
national
level
pesticide
usage
for
the
United
States.

!
Estimates
of
usage
for
use
sites
with
reported
pesticide
usage
only.

!
Estimates
of
the
average
&
maximum
annual
percent
of
crop
treated
with
the
pesticide
for
each
agricultural
use
site.

!
Estimates
of
the
average
annual
pounds
of
the
pesticide
applied
for
each
agricultural
use
site.

What
assumptions
can
I
make
about
the
data
reported?

!
Average
pounds
of
active
ingredient
applied
­
Values
are
calculated
by
merging
pesticide
usage
data
sources
together;
averaging
by
year,
averaging
across
all
years,
&
then
rounding.
(
If
the
estimated
value
is
less
than
500,
then
that
value
is
labeled
<
500.
Estimated
values
between
500
&
<
1,000,000
are
rounded
to
1
significant
digit.
Estimated
values
of
1,000,000
or
greater
are
rounded
to
2
significant
digits.)

!
Average
percent
of
crop
treated
­
Values
are
calculated
by
merging
data
sources
together;
averaging
by
year,
averaging
across
all
years,
&
rounding
to
the
nearest
multiple
of
5.
(
If
the
estimated
value
is
less
than
1,
then
the
value
is
labeled
<
1.)

!
Maximum
percent
of
crop
treated
­
Value
is
the
single
maximum
value
reported
across
all
data
sources,
across
all
years,
&
rounded
up.
(
If
the
estimated
value
is
less
than
2.5,
then
the
value
is
labeled
<
2.5.)

What
are
the
data
sources
used?

!
United
States
Department
of
Agriculture's
National
Agricultural
Statistics
Service
(
USDA­
NASS)
­
pesticide
usage
data
from
1998
to
2003.

!
National
Center
on
Food
and
Agriculture
Policy
(
NCFAP)
­
pesticide
usage
data
from
1997
&
is
only
used
if
data
is
not
available
from
the
other
sources.

!
Private
pesticide
market
research
­
pesticide
usage
data
from
1998
to
2003.

What
are
the
limitations
to
the
data?
Page
54
of
74
!
There
may
be
instances
where
registered/
labeled
uses
exist
but
are
not
surveyed
by
the
available
data
sources.

!
Lack
of
reported
usage
data
for
the
pesticide
on
a
crop
does
not
imply
zero
usage.

!
Cases
may
occur
where
usage
on
a
particular
use
site
is
noted
in
the
pesticide
usage
data,
but
not
quantified.
In
these
instances,
no
usage
would
be
reported
in
the
SLUA
for
that
use
site.
Page
55
of
74
Attachment
3
(
Page
2
of
3)

!
The
SLUA
does
not
report
estimates
of
pesticide
usage
for
non­
agricultural
use
sites
(
e.
g.,
turf,
post­
harvest,
mosquito
control,
etc.).
A
separate
request
must
be
made
to
receive
estimates
of
pesticide
usage
for
non­
agricultural
use
sites.

Who
do
I
contact
for
further
information
and/
or
questions
on
this
SLUA?

!
(
Jihad
Alsadek,
Economist,
EAB)

!
(
Jihad
Alsadek
û
703­
308­
8140
&
alsadek.
jihad@
epa.
gov
)

SAS
Monday,
January
31,
2005
10:
45
1
Screening
Level
Estimates
of
Agricultural
Uses
of
alachlor
Sorted
Alphabetically
OBS
Crop
Lbs.
A.
I.
Percent
Crop
Treated
Avg.
Max.
1
Apples
<
500
<
1
<
2.5
2
Beans,
Dry
(
NCFAP
'
97)
300,000
10
3
Beans,
Green
6,000
5
15
4
Cabbage
<
500
<
1
<
2.5
5
Corn
4,200,000
5
5
6
Cotton
20,000
<
1
<
2.5
7
Dry
Beans/
Peas
200,000
5
5
8
Grapefruit
7,000
5
5
9
Peanuts
30,000
<
1
<
2.5
10
Peas,
Dry
(
NCFAP
'
97)
4,000
20
11
Peas,
Green
<
500
<
1
<
2.5
12
Potatoes
2,000
<
1
<
2.5
13
Pumpkin
<
500
<
1
<
2.5
14
Sorghum
1,500,000
10
15
15
Soybeans
1,300,000
<
1
<
2.5
16
Spinach
1,000
<
1
<
2.5
17
Sunflowers
30,000
<
1
<
2.5
18
Sweet
Corn
200,000
15
20
19
Watermelons
2,000
<
1
<
2.5
________________________________
Page
56
of
74
Attachment
3
(
Page
3
of
3)

All
numbers
rounded.
'<
500'
indicates
less
than
500
pounds
of
active
ingredient.
'<
2.5'
indicates
less
than
2.5
percent
of
crop
is
treated.
Use
of
alachlor
on
this
crop
may
also
have
occurred
in
other
states.

(
slua003k.
sas
a005a8n.
sas
alachlor
)
Page
57
of
74
Attachment
3.
DEEM
CRA
(
Multi­
year)
Food
and
Water
Residue
Input
File.
U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
1994­
98
data
Residue
file:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
CRA_
Multiyear_
Res_
File.
R98
Adjust.
#
2
used
Analysis
Date
02­
24­
2006
Residue
file
dated:
02­
24­
2006/
18:
15:
30/
8
Reference
dose
(
NOEL)
=
0.5
mg/
kg
bw/
day
Comment:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Food
Crop
Residue
Adj.
Factors
Comment
EPA
Code
Grp
Food
Name
(
ppm)
#
1
#
2
­­­­­­­­
­­­­
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­
­­­­­­
­­­­­­
­­­­­­­
06030300
6C
Bean,
black,
seed
0.010000
0.050
1.000
06030320
6C
Bean,
broad,
seed
0.010000
0.050
1.000
06030340
6C
Bean,
cowpea,
seed
0.010000
0.050
1.000
06030350
6C
Bean,
great
northern,
seed
0.010000
0.050
1.000
06030360
6C
Bean,
kidney,
seed
0.010000
0.050
1.000
06030380
6C
Bean,
lima,
seed
0.008000
0.050
1.000
06030390
6C
Bean,
mung,
seed
0.010000
0.050
1.000
06030400
6C
Bean,
navy,
seed
0.010000
0.050
1.000
06030410
6C
Bean,
pink,
seed
0.010000
0.050
1.000
06030420
6C
Bean,
pinto,
seed
0.010000
0.050
1.000
21000440
M
Beef,
meat
0.000400
1.000
1.000
21000441
M
Beef,
meat­
babyfood
0.000400
1.000
1.000
21000450
M
Beef,
meat,
dried
0.000400
1.000
1.000
21000460
M
Beef,
meat
byproducts
0.000400
1.000
1.000
21000461
M
Beef,
meat
byproducts­
babyfood
0.000400
1.000
1.000
21000470
M
Beef,
fat
0.000340
1.000
1.000
21000471
M
Beef,
fat­
babyfood
0.000340
1.000
1.000
21000480
M
Beef,
kidney
0.001610
1.000
1.000
21000490
M
Beef,
liver
0.001700
1.000
1.000
21000491
M
Beef,
liver­
babyfood
0.001700
1.000
1.000
40000930
P
Chicken,
meat
0.000020
1.000
1.000
40000931
P
Chicken,
meat­
babyfood
0.000020
1.000
1.000
40000940
P
Chicken,
liver
0.000090
1.000
1.000
40000950
P
Chicken,
meat
byproducts
0.000020
1.000
1.000
40000951
P
Chicken,
meat
byproducts­
babyfoo
0.000020
1.000
1.000
40000960
P
Chicken,
fat
0.000010
1.000
1.000
40000961
P
Chicken,
fat­
babyfood
0.000010
1.000
1.000
40000970
P
Chicken,
skin
0.000020
1.000
1.000
40000971
P
Chicken,
skin­
babyfood
0.000020
1.000
1.000
06030980
6C
Chickpea,
seed
0.010000
1.000
1.000
06030981
6C
Chickpea,
seed­
babyfood
0.010000
1.000
1.000
06030990
6C
Chickpea,
flour
0.010000
1.000
1.000
15001200
15
Corn,
field,
flour
0.000925
1.000
1.000
s
15001201
15
Corn,
field,
flour­
babyfood
0.000925
1.000
1.000
s
15001210
15
Corn,
field,
meal
0.000875
1.000
1.000
s
15001211
15
Corn,
field,
meal­
babyfood
0.000875
1.000
1.000
s
15001220
15
Corn,
field,
bran
0.001125
1.000
1.000
s
15001230
15
Corn,
field,
starch
0.000485
1.000
1.000
s
15001231
15
Corn,
field,
starch­
babyfood
0.000485
1.000
1.000
s
15001240
15
Corn,
field,
syrup
0.000735
1.000
1.000
s
15001241
15
Corn,
field,
syrup­
babyfood
0.000735
1.000
1.000
s
15001250
15
Corn,
field,
oil
0.000445
1.000
1.000
s
15001251
15
Corn,
field,
oil­
babyfood
0.000445
1.000
1.000
s
15001270
15
Corn,
sweet
0.007000
0.150
1.000
15001271
15
Corn,
sweet­
babyfood
0.007000
0.150
1.000
70001450
P
Egg,
whole
0.000260
1.000
1.000
70001451
P
Egg,
whole­
babyfood
0.000260
1.000
1.000
70001460
P
Egg,
white
0.000260
1.000
1.000
70001461
P
Egg,
white
(
solids)­
babyfood
0.000260
1.000
1.000
70001470
P
Egg,
yolk
0.000260
1.000
1.000
Page
58
of
74
70001471
P
Egg,
yolk­
babyfood
0.000260
1.000
1.000
23001690
M
Goat,
meat
0.000400
1.000
1.000
23001700
M
Goat,
meat
byproducts
0.000400
1.000
1.000
23001710
M
Goat,
fat
0.000340
1.000
1.000
23001720
M
Goat,
kidney
0.001610
1.000
1.000
23001730
M
Goat,
liver
0.001700
1.000
1.000
06031820
6C
Guar,
seed
0.010000
1.000
1.000
06031821
6C
Guar,
seed­
babyfood
0.010000
1.000
1.000
24001890
M
Horse,
meat
0.000400
1.000
1.000
06032030
6C
Lentil,
seed
0.010000
1.000
1.000
27002220
D
Milk,
fat
0.000620
1.000
1.000
27002221
D
Milk,
fat
­
baby
food/
infant
for
0.000620
1.000
1.000
27012230
D
Milk,
nonfat
solids
0.000620
1.000
1.000
27012231
D
Milk,
nonfat
solids­
baby
food/
in
0.000620
1.000
1.000
27022240
D
Milk,
water
0.000620
1.000
1.000
27022241
D
Milk,
water­
babyfood/
infant
form
0.000620
1.000
1.000
27032251
D
Milk,
sugar
(
lactose)­
baby
food/
0.000620
1.000
1.000
06032580
6C
Pea,
pigeon,
seed
0.010000
1.000
1.000
95002630
O
Peanut
0.150000
0.010
1.000
95002640
O
Peanut,
butter
0.110000
0.010
1.000
95002650
O
Peanut,
oil
0.009000
0.010
1.000
25002900
M
Pork,
meat
0.000160
1.000
1.000
25002901
M
Pork,
meat­
babyfood
0.000160
1.000
1.000
25002910
M
Pork,
skin
0.000160
1.000
1.000
25002920
M
Pork,
meat
byproducts
0.000160
1.000
1.000
25002921
M
Pork,
meat
byproducts­
babyfood
0.000160
1.000
1.000
25002930
M
Pork,
fat
0.000180
1.000
1.000
25002931
M
Pork,
fat­
babyfood
0.000180
1.000
1.000
25002940
M
Pork,
kidney
0.000170
1.000
1.000
25002950
M
Pork,
liver
0.000340
1.000
1.000
60003010
P
Poultry,
other,
meat
0.000020
1.000
1.000
60003020
P
Poultry,
other,
liver
0.000090
1.000
1.000
60003030
P
Poultry,
other,
meat
byproducts
0.000020
1.000
1.000
60003040
P
Poultry,
other,
fat
0.000010
1.000
1.000
60003050
P
Poultry,
other,
skin
0.000020
1.000
1.000
26003390
M
Sheep,
meat
0.000400
1.000
1.000
26003391
M
Sheep,
meat­
babyfood
0.000400
1.000
1.000
26003400
M
Sheep,
meat
byproducts
0.000400
1.000
1.000
26003410
M
Sheep,
fat
0.000340
1.000
1.000
26003411
M
Sheep,
fat­
babyfood
0.000340
1.000
1.000
26003420
M
Sheep,
kidney
0.001610
1.000
1.000
26003430
M
Sheep,
liver
0.001700
1.000
1.000
15003440
15
Sorghum,
grain
0.002070
1.000
1.000
s
15003450
15
Sorghum,
syrup
0.000070
1.000
1.000
aceto
06003470
6
Soybean,
seed
0.001950
1.000
1.000
s
06003480
6
Soybean,
flour
0.001738
1.000
1.000
s
06003481
6
Soybean,
flour­
babyfood
0.001738
1.000
1.000
s
06003490
6
Soybean,
soy
milk
0.001950
1.000
1.000
s
06003491
6
Soybean,
soy
milk­
babyfood
or
in
0.001950
1.000
1.000
s
06003500
6
Soybean,
oil
0.000350
1.000
1.000
s
06003501
6
Soybean,
oil­
babyfood
0.000350
1.000
1.000
s
50003820
P
Turkey,
meat
0.000020
1.000
1.000
50003821
P
Turkey,
meat­
babyfood
0.000020
1.000
1.000
50003830
P
Turkey,
liver
0.000090
1.000
1.000
50003831
P
Turkey,
liver­
babyfood
0.000090
1.000
1.000
50003840
P
Turkey,
meat
byproducts
0.000020
1.000
1.000
50003841
P
Turkey,
meat
byproducts­
babyfood
0.000020
1.000
1.000
50003850
P
Turkey,
fat
0.000010
1.000
1.000
50003851
P
Turkey,
fat­
babyfood
0.000010
1.000
1.000
50003860
P
Turkey,
skin
0.000020
1.000
1.000
50003861
P
Turkey,
skin­
babyfood
0.000020
1.000
1.000
86010000
O
Water,
direct,
all
sources
0.000286
1.000
1.000
s
86020000
O
Water,
indirect,
all
sources
0.000286
1.000
1.000
s
15004010
15
Wheat,
grain
0.000060
1.000
1.000
aceto
15004011
15
Wheat,
grain­
babyfood
0.000060
1.000
1.000
aceto
15004020
15
Wheat,
flour
0.000060
1.000
1.000
aceto
Page
59
of
74
15004021
15
Wheat,
flour­
babyfood
0.000060
1.000
1.000
aceto
15004030
15
Wheat,
germ
0.000060
1.000
1.000
aceto
15004040
15
Wheat,
bran
0.000060
1.000
1.000
aceto
Attachment
4.
DEEM
CRA
(
Multi
year)
Food
and
Water
Results
File.

U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
(
1994­
98
data)
Residue
file
name:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
CRA_
Multiyear_
Res_
File.
R
98
Adjustment
factor
#
2
used.
Analysis
Date
02­
24­
2006/
18:
41:
35
Residue
file
dated:
02­
24­
2006/
18:
15:
30/
8
NOEL
(
Chronic)
=
.5
mg/
kg
bw/
day
COMMENT
1:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
===============================================================================
Total
exposure
by
population
subgroup
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Total
Exposure
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Population
mg/
kg
Percent
Margin
of
Subgroup
body
wt/
day
of
NOEL
Exposr
1/
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­­­­
­­­­­­­­­
­­­­­­­­­
U.
S.
Population
(
total)
0.000012
0.00%
40,119
U.
S.
Population
(
spring
season)
0.000012
0.00%
40,540
U.
S.
Population
(
summer
season)
0.000013
0.00%
39,041
U.
S.
Population
(
autumn
season)
0.000012
0.00%
40,206
U.
S.
Population
(
winter
season)
0.000012
0.00%
40,792
Northeast
region
0.000012
0.00%
42,504
Midwest
region
0.000013
0.00%
38,934
Southern
region
0.000012
0.00%
42,855
Western
region
0.000014
0.00%
35,850
Hispanics
0.000015
0.00%
34,027
Non­
hispanic
whites
0.000012
0.00%
41,259
Non­
hispanic
blacks
0.000012
0.00%
42,740
Non­
hisp/
non­
white/
non­
black
0.000015
0.00%
33,389
All
infants
(<
1
year)
0.000030
0.01%
16,464
Nursing
infants
0.000010
0.00%
48,127
Non­
nursing
infants
0.000038
0.01%
13,175
Children
1­
6
yrs
0.000030
0.01%
16,508
Children
7­
12
yrs
0.000017
0.00%
29,674
Females
13­
19
(
not
preg
or
nursing)
0.000009
0.00%
53,237
Females
20+
(
not
preg
or
nursing)
0.000009
0.00%
52,829
Females
13­
50
yrs
0.000010
0.00%
47,736
Females
13+
(
preg/
not
nursing)
0.000012
0.00%
41,915
Females
13+
(
nursing)
0.000014
0.00%
35,668
Males
13­
19
yrs
0.000011
0.00%
43,704
Males
20+
yrs
0.000009
0.00%
53,580
Seniors
55+
0.000009
0.00%
54,056
Children
1­
2
yrs
0.000037
0.01%
13,595
Children
3­
5
yrs
0.000028
0.01%
17,815
Children
6­
12
yrs
0.000018
0.00%
27,875
Youth
13­
19
yrs
0.000010
0.00%
47,799
Adults
20­
49
yrs
0.000010
0.00%
52,303
Page
60
of
74
Adults
50+
yrs
0.000009
0.00%
54,027
Females
13­
49
yrs
0.000010
0.00%
52,171
Attachment
5.
DEEM
Acetochlor
Alone
(
Multi
year)
Food
and
Water
Residue
Input
File
U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
ACETOCHLOR
1994­
98
data
Residue
file:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
acetochlor_
tolerance_
plus_
water_
PF_
PCT.
R98
Adjust.
#
2
used
Analysis
Date
02­
24­
2006
Residue
file
dated:
02­
10­
2006/
18:
53:
10/
8
Reference
dose
(
NOEL)
=
10
mg/
kg
bw/
day
Comment:
DEEM
analysis
with
foods
&
water
(
max
TWAM)
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Food
Crop
Residue
Adj.
Factors
Comment
EPA
Code
Grp
Food
Name
(
ppm)
#
1
#
2
­­­­­­­­
­­­­
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­
­­­­­­
­­­­­­
­­­­­­­
15001200
15
Corn,
field,
flour
0.050000
0.600
0.250
PF
&
%
Full
comment:
PF
&
%
CT,
resp.
15001201
15
Corn,
field,
flour­
babyfood
0.050000
0.600
0.250
15001210
15
Corn,
field,
meal
0.050000
0.600
0.250
15001211
15
Corn,
field,
meal­
babyfood
0.050000
0.600
0.250
15001220
15
Corn,
field,
bran
0.050000
1.000
0.250
15001230
15
Corn,
field,
starch
0.050000
0.600
0.250
15001231
15
Corn,
field,
starch­
babyfood
0.050000
0.600
0.250
15001240
15
Corn,
field,
syrup
0.050000
1.000
0.250
15001241
15
Corn,
field,
syrup­
babyfood
0.050000
1.000
0.250
15001250
15
Corn,
field,
oil
0.050000
0.600
0.250
15001251
15
Corn,
field,
oil­
babyfood
0.050000
0.600
0.250
15003440
15
Sorghum,
grain
0.020000
1.000
0.070
15003450
15
Sorghum,
syrup
0.020000
1.000
0.070
06003470
6
Soybean,
seed
0.100000
1.000
0.170
06003480
6
Soybean,
flour
0.100000
0.750
0.170
06003481
6
Soybean,
flour­
babyfood
0.100000
0.750
0.170
06003490
6
Soybean,
soy
milk
0.100000
1.000
0.170
06003491
6
Soybean,
soy
milk­
babyfood
or
in
0.100000
1.000
0.170
06003500
6
Soybean,
oil
0.100000
0.200
0.170
06003501
6
Soybean,
oil­
babyfood
0.100000
0.200
0.170
86010000
O
Water,
direct,
all
sources
0.000282
1.000
1.000
Modele
Full
comment:
Modeled
data
86020000
O
Water,
indirect,
all
sources
0.000282
1.000
1.000
modele
Full
comment:
modeled
data
15004010
15
Wheat,
grain
0.020000
1.000
0.060
15004011
15
Wheat,
grain­
babyfood
0.020000
1.000
0.060
15004020
15
Wheat,
flour
0.020000
1.000
0.060
15004021
15
Wheat,
flour­
babyfood
0.020000
1.000
0.060
15004030
15
Wheat,
germ
0.020000
1.000
0.060
15004040
15
Wheat,
bran
0.020000
1.000
0.060
Page
61
of
74
Attachment
6.
DEEM
Acetochlor
Alone
(
Multi
year)
Food
and
Water
Results
File.

U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
ACETOCHLOR
(
1994­
98
data)
Residue
file
name:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
acetochlor_
tolerance_
plus_
water_
PF_
PCT.
R98
Adjustment
factor
#
2
used.
Analysis
Date
02­
24­
2006/
19:
01:
10
Residue
file
dated:
02­
10­
2006/
18:
53:
10/
8
NOEL
(
Chronic)
=
10
mg/
kg
bw/
day
COMMENT
1:
DEEM
analysis
with
foods
&
water
(
max
TWAM)
===============================================================================
Total
exposure
by
population
subgroup
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Total
Exposure
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Population
mg/
kg
Percent
Margin
of
Subgroup
body
wt/
day
of
NOEL
Exposr
1/
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­­­­
­­­­­­­­­
­­­­­­­­­
U.
S.
Population
(
total)
0.000025
0.00%
392,207
U.
S.
Population
(
spring
season)
0.000026
0.00%
389,031
U.
S.
Population
(
summer
season)
0.000027
0.00%
373,685
U.
S.
Population
(
autumn
season)
0.000025
0.00%
402,977
U.
S.
Population
(
winter
season)
0.000025
0.00%
405,066
Northeast
region
0.000023
0.00%
441,111
Midwest
region
0.000027
0.00%
371,253
Southern
region
0.000025
0.00%
396,985
Western
region
0.000027
0.00%
370,738
Hispanics
0.000029
0.00%
349,586
Non­
hispanic
whites
0.000025
0.00%
404,820
Non­
hispanic
blacks
0.000027
0.00%
370,887
Non­
hisp/
non­
white/
non­
black
0.000027
0.00%
371,089
All
infants
(<
1
year)
0.000049
0.00%
202,383
Nursing
infants
0.000016
0.00%
630,390
Non­
nursing
infants
0.000062
0.00%
160,914
Children
1­
6
yrs
0.000051
0.00%
195,033
Children
7­
12
yrs
0.000038
0.00%
259,840
Females
13­
19
(
not
preg
or
nursing)
0.000026
0.00%
377,562
Females
20+
(
not
preg
or
nursing)
0.000018
0.00%
553,328
Females
13­
50
yrs
0.000023
0.00%
442,705
Females
13+
(
preg/
not
nursing)
0.000022
0.00%
459,752
Females
13+
(
nursing)
0.000024
0.00%
419,157
Males
13­
19
yrs
0.000034
0.00%
295,184
Males
20+
yrs
0.000020
0.00%
489,163
Seniors
55+
0.000015
0.00%
676,613
Children
1­
2
yrs
0.000050
0.00%
200,888
Children
3­
5
yrs
0.000054
0.00%
186,331
Children
6­
12
yrs
0.000040
0.00%
251,336
Youth
13­
19
yrs
0.000030
0.00%
331,081
Adults
20­
49
yrs
0.000022
0.00%
462,509
Adults
50+
yrs
0.000015
0.00%
660,144
Females
13­
49
yrs
0.000021
0.00%
468,331
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Page
62
of
74
Attachment
7.
DEEM
Alachlor
Alone
(
Multi­
year)
Food
and
Water
Residue
Input
File
U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
ALACHLOR
1994­
98
data
Residue
file:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
alachlor_
Avg_
Res_
SLUA_
PCT_
Water.
R98
Adjust.
#
2
used
Analysis
Date
02­
24­
2006
Residue
file
dated:
02­
10­
2006/
19:
05:
02/
8
Reference
dose
(
NOEL)
=
0.5
mg/
kg
bw/
day
Comment:
Risk
Assessment
using
Average
residues
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Food
Crop
Residue
Adj.
Factors
Comment
EPA
Code
Grp
Food
Name
(
ppm)
#
1
#
2
­­­­­­­­
­­­­
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­
­­­­­­
­­­­­­
­­­­­­­
06030300
6C
Bean,
black,
seed
0.010000
0.050
1.000
06030320
6C
Bean,
broad,
seed
0.010000
0.050
1.000
06030340
6C
Bean,
cowpea,
seed
0.010000
0.050
1.000
06030350
6C
Bean,
great
northern,
seed
0.010000
0.050
1.000
06030360
6C
Bean,
kidney,
seed
0.010000
0.050
1.000
06030380
6C
Bean,
lima,
seed
0.008000
0.050
1.000
06030390
6C
Bean,
mung,
seed
0.010000
0.050
1.000
06030400
6C
Bean,
navy,
seed
0.010000
0.050
1.000
06030410
6C
Bean,
pink,
seed
0.010000
0.050
1.000
06030420
6C
Bean,
pinto,
seed
0.010000
0.050
1.000
21000440
M
Beef,
meat
0.000400
1.000
1.000
21000441
M
Beef,
meat­
babyfood
0.000400
1.000
1.000
21000450
M
Beef,
meat,
dried
0.000400
1.000
1.000
21000460
M
Beef,
meat
byproducts
0.000400
1.000
1.000
21000461
M
Beef,
meat
byproducts­
babyfood
0.000400
1.000
1.000
21000470
M
Beef,
fat
0.000340
1.000
1.000
21000471
M
Beef,
fat­
babyfood
0.000340
1.000
1.000
21000480
M
Beef,
kidney
0.001610
1.000
1.000
21000490
M
Beef,
liver
0.001700
1.000
1.000
21000491
M
Beef,
liver­
babyfood
0.001700
1.000
1.000
40000930
P
Chicken,
meat
0.000020
1.000
1.000
40000931
P
Chicken,
meat­
babyfood
0.000020
1.000
1.000
40000940
P
Chicken,
liver
0.000090
1.000
1.000
40000950
P
Chicken,
meat
byproducts
0.000020
1.000
1.000
40000951
P
Chicken,
meat
byproducts­
babyfoo
0.000020
1.000
1.000
40000960
P
Chicken,
fat
0.000010
1.000
1.000
40000961
P
Chicken,
fat­
babyfood
0.000010
1.000
1.000
40000970
P
Chicken,
skin
0.000020
1.000
1.000
40000971
P
Chicken,
skin­
babyfood
0.000020
1.000
1.000
06030980
6C
Chickpea,
seed
0.010000
1.000
1.000
06030981
6C
Chickpea,
seed­
babyfood
0.010000
1.000
1.000
06030990
6C
Chickpea,
flour
0.010000
1.000
1.000
15001200
15
Corn,
field,
flour
0.011000
1.000
1.000
15001201
15
Corn,
field,
flour­
babyfood
0.011000
0.050
1.000
15001210
15
Corn,
field,
meal
0.010000
0.050
1.000
15001211
15
Corn,
field,
meal­
babyfood
0.010000
0.050
1.000
15001220
15
Corn,
field,
bran
0.010000
0.050
1.000
15001230
15
Corn,
field,
starch
0.002200
0.050
1.000
15001231
15
Corn,
field,
starch­
babyfood
0.002200
0.050
1.000
15001240
15
Corn,
field,
syrup
0.002200
0.050
1.000
15001241
15
Corn,
field,
syrup­
babyfood
0.002200
0.050
1.000
15001250
15
Corn,
field,
oil
0.001400
0.050
1.000
15001251
15
Corn,
field,
oil­
babyfood
0.001400
0.050
1.000
15001270
15
Corn,
sweet
0.007000
0.150
1.000
15001271
15
Corn,
sweet­
babyfood
0.007000
0.150
1.000
70001450
P
Egg,
whole
0.000260
1.000
1.000
70001451
P
Egg,
whole­
babyfood
0.000260
1.000
1.000
70001460
P
Egg,
white
0.000260
1.000
1.000
70001461
P
Egg,
white
(
solids)­
babyfood
0.000260
1.000
1.000
Page
63
of
74
70001470
P
Egg,
yolk
0.000260
1.000
1.000
70001471
P
Egg,
yolk­
babyfood
0.000260
1.000
1.000
23001690
M
Goat,
meat
0.000400
1.000
1.000
23001700
M
Goat,
meat
byproducts
0.000400
1.000
1.000
23001710
M
Goat,
fat
0.000340
1.000
1.000
23001720
M
Goat,
kidney
0.001610
1.000
1.000
23001730
M
Goat,
liver
0.001700
1.000
1.000
06031820
6C
Guar,
seed
0.010000
1.000
1.000
06031821
6C
Guar,
seed­
babyfood
0.010000
1.000
1.000
24001890
M
Horse,
meat
0.000400
1.000
1.000
06032030
6C
Lentil,
seed
0.010000
1.000
1.000
27002220
D
Milk,
fat
0.000620
1.000
1.000
27002221
D
Milk,
fat
­
baby
food/
infant
for
0.000620
1.000
1.000
27012230
D
Milk,
nonfat
solids
0.000620
1.000
1.000
27012231
D
Milk,
nonfat
solids­
baby
food/
in
0.000620
1.000
1.000
27022240
D
Milk,
water
0.000620
1.000
1.000
27022241
D
Milk,
water­
babyfood/
infant
form
0.000620
1.000
1.000
27032251
D
Milk,
sugar
(
lactose)­
baby
food/
0.000620
1.000
1.000
06032580
6C
Pea,
pigeon,
seed
0.010000
1.000
1.000
95002630
O
Peanut
0.150000
0.010
1.000
95002640
O
Peanut,
butter
0.110000
0.010
1.000
95002650
O
Peanut,
oil
0.009000
0.010
1.000
25002900
M
Pork,
meat
0.000160
1.000
1.000
25002901
M
Pork,
meat­
babyfood
0.000160
1.000
1.000
25002910
M
Pork,
skin
0.000160
1.000
1.000
25002920
M
Pork,
meat
byproducts
0.000160
1.000
1.000
25002921
M
Pork,
meat
byproducts­
babyfood
0.000160
1.000
1.000
25002930
M
Pork,
fat
0.000180
1.000
1.000
25002931
M
Pork,
fat­
babyfood
0.000180
1.000
1.000
25002940
M
Pork,
kidney
0.000170
1.000
1.000
25002950
M
Pork,
liver
0.000340
1.000
1.000
60003010
P
Poultry,
other,
meat
0.000020
1.000
1.000
60003020
P
Poultry,
other,
liver
0.000090
1.000
1.000
60003030
P
Poultry,
other,
meat
byproducts
0.000020
1.000
1.000
60003040
P
Poultry,
other,
fat
0.000010
1.000
1.000
60003050
P
Poultry,
other,
skin
0.000020
1.000
1.000
26003390
M
Sheep,
meat
0.000400
1.000
1.000
26003391
M
Sheep,
meat­
babyfood
0.000400
1.000
1.000
26003400
M
Sheep,
meat
byproducts
0.000400
1.000
1.000
26003410
M
Sheep,
fat
0.000340
1.000
1.000
26003411
M
Sheep,
fat­
babyfood
0.000340
1.000
1.000
26003420
M
Sheep,
kidney
0.001610
1.000
1.000
26003430
M
Sheep,
liver
0.001700
1.000
1.000
15003440
15
Sorghum,
grain
0.020000
0.100
1.000
06003470
6
Soybean,
seed
0.110000
0.010
1.000
06003480
6
Soybean,
flour
0.110000
0.010
1.000
06003481
6
Soybean,
flour­
babyfood
0.110000
0.010
1.000
06003490
6
Soybean,
soy
milk
0.110000
0.010
1.000
06003491
6
Soybean,
soy
milk­
babyfood
or
in
0.110000
0.010
1.000
06003500
6
Soybean,
oil
0.018000
0.010
1.000
06003501
6
Soybean,
oil­
babyfood
0.018000
0.010
1.000
50003820
P
Turkey,
meat
0.000020
1.000
1.000
50003821
P
Turkey,
meat­
babyfood
0.000020
1.000
1.000
50003830
P
Turkey,
liver
0.000090
1.000
1.000
50003831
P
Turkey,
liver­
babyfood
0.000090
1.000
1.000
50003840
P
Turkey,
meat
byproducts
0.000020
1.000
1.000
50003841
P
Turkey,
meat
byproducts­
babyfood
0.000020
1.000
1.000
50003850
P
Turkey,
fat
0.000010
1.000
1.000
50003851
P
Turkey,
fat­
babyfood
0.000010
1.000
1.000
50003860
P
Turkey,
skin
0.000020
1.000
1.000
50003861
P
Turkey,
skin­
babyfood
0.000020
1.000
1.000
86010000
O
Water,
direct,
all
sources
0.000276
1.000
1.000
Multiy
Full
comment:
Multi
year
Ave
TWAM
86020000
O
Water,
indirect,
all
sources
0.000276
1.000
1.000
Multiy
Full
comment:
Multi
year
Ave
TWAM
Page
64
of
74
Page
65
of
74
Attachment
8.
DEEM
Alachlor
Alone
(
Multi
year)
Food
and
Water
Results
File.

U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
ALACHLOR
(
1994­
98
data)
Residue
file
name:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
alachlor_
Avg_
Res_
SLUA_
PCT_
Water.
R98
Adjustment
factor
#
2
used.
Analysis
Date
02­
24­
2006/
18:
57:
00
Residue
file
dated:
02­
10­
2006/
19:
05:
02/
8
NOEL
(
Chronic)
=
.5
mg/
kg
bw/
day
COMMENT
1:
Risk
Assessment
using
Average
residues
===============================================================================
Total
exposure
by
population
subgroup
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Total
Exposure
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Population
mg/
kg
Percent
Margin
of
Subgroup
body
wt/
day
of
NOEL
Exposr
1/
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­­­­
­­­­­­­­­
­­­­­­­­­
U.
S.
Population
(
total)
0.000012
0.00%
40,813
U.
S.
Population
(
spring
season)
0.000012
0.00%
41,596
U.
S.
Population
(
summer
season)
0.000013
0.00%
39,792
U.
S.
Population
(
autumn
season)
0.000012
0.00%
40,708
U.
S.
Population
(
winter
season)
0.000012
0.00%
41,265
Northeast
region
0.000012
0.00%
43,434
Midwest
region
0.000012
0.00%
40,349
Southern
region
0.000011
0.00%
44,113
Western
region
0.000014
0.00%
35,173
Hispanics
0.000016
0.00%
30,682
Non­
hispanic
whites
0.000012
0.00%
42,911
Non­
hispanic
blacks
0.000011
0.00%
43,585
Non­
hisp/
non­
white/
non­
black
0.000015
0.00%
34,155
All
infants
(<
1
year)
0.000028
0.01%
17,621
Nursing
infants
0.000010
0.00%
51,227
Non­
nursing
infants
0.000035
0.01%
14,109
Children
1­
6
yrs
0.000031
0.01%
16,357
Children
7­
12
yrs
0.000017
0.00%
29,199
Females
13­
19
(
not
preg
or
nursing)
0.000009
0.00%
56,016
Females
20+
(
not
preg
or
nursing)
0.000009
0.00%
54,593
Females
13­
50
yrs
0.000011
0.00%
47,417
Females
13+
(
preg/
not
nursing)
0.000012
0.00%
41,713
Females
13+
(
nursing)
0.000015
0.00%
33,824
Males
13­
19
yrs
0.000011
0.00%
45,092
Males
20+
yrs
0.000009
0.00%
55,118
Seniors
55+
0.000009
0.00%
55,311
Children
1­
2
yrs
0.000037
0.01%
13,636
Children
3­
5
yrs
0.000029
0.01%
17,467
Children
6­
12
yrs
0.000018
0.00%
27,470
Youth
13­
19
yrs
0.000010
0.00%
49,690
Adults
20­
49
yrs
0.000009
0.00%
53,970
Adults
50+
yrs
0.000009
0.00%
55,460
Females
13­
49
yrs
0.000009
0.00%
54,053
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Page
66
of
74
Attachment
9.
CRA
(
Single­
Year
TWAM)­
DEEM
Food
and
Water
Residue
Input
File
(
Page
1
of
3).

U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
1994­
98
data
Residue
file:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
Cum_
acet_
ala_
Avg_
Res_
SLU
A_
PCT_
Water(
equiv).
R98
Adjust.
#
2
used
Analysis
Date
09­
16­
2005
Residue
file
dated:
09­
16­
2005/
16:
31:
17/
8
Reference
dose
(
NOEL)
=
0.5
mg/
kg
bw/
day
Comment:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Food
Crop
Residue
Adj.
Factors
Comment
EPA
Code
Grp
Food
Name
(
ppm)
#
1
#
2
­­­­­­­­
­­­­
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­
­­­­­­
­­­­­­
­­­­­­
­
06030300
6C
Bean,
black,
seed
0.010000
0.050
1.000
06030320
6C
Bean,
broad,
seed
0.010000
0.050
1.000
06030340
6C
Bean,
cowpea,
seed
0.010000
0.050
1.000
06030350
6C
Bean,
great
northern,
seed
0.010000
0.050
1.000
06030360
6C
Bean,
kidney,
seed
0.010000
0.050
1.000
06030380
6C
Bean,
lima,
seed
0.008000
0.050
1.000
06030390
6C
Bean,
mung,
seed
0.010000
0.050
1.000
06030400
6C
Bean,
navy,
seed
0.010000
0.050
1.000
06030410
6C
Bean,
pink,
seed
0.010000
0.050
1.000
06030420
6C
Bean,
pinto,
seed
0.010000
0.050
1.000
21000440
M
Beef,
meat
0.000400
1.000
1.000
21000441
M
Beef,
meat­
babyfood
0.000400
1.000
1.000
21000450
M
Beef,
meat,
dried
0.000400
1.000
1.000
21000460
M
Beef,
meat
byproducts
0.000400
1.000
1.000
21000461
M
Beef,
meat
byproducts­
babyfood
0.000400
1.000
1.000
21000470
M
Beef,
fat
0.000340
1.000
1.000
21000471
M
Beef,
fat­
babyfood
0.000340
1.000
1.000
21000480
M
Beef,
kidney
0.001610
1.000
1.000
21000490
M
Beef,
liver
0.001700
1.000
1.000
21000491
M
Beef,
liver­
babyfood
0.001700
1.000
1.000
40000930
P
Chicken,
meat
0.000020
1.000
1.000
40000931
P
Chicken,
meat­
babyfood
0.000020
1.000
1.000
40000940
P
Chicken,
liver
0.000090
1.000
1.000
40000950
P
Chicken,
meat
byproducts
0.000020
1.000
1.000
40000951
P
Chicken,
meat
byproducts­
babyfoo
0.000020
1.000
1.000
40000960
P
Chicken,
fat
0.000010
1.000
1.000
40000961
P
Chicken,
fat­
babyfood
0.000010
1.000
1.000
40000970
P
Chicken,
skin
0.000020
1.000
1.000
40000971
P
Chicken,
skin­
babyfood
0.000020
1.000
1.000
06030980
6C
Chickpea,
seed
0.010000
1.000
1.000
06030981
6C
Chickpea,
seed­
babyfood
0.010000
1.000
1.000
06030990
6C
Chickpea,
flour
0.010000
1.000
1.000
15001200
15
Corn,
field,
flour
0.000925
1.000
1.000
s
15001201
15
Corn,
field,
flour­
babyfood
0.000925
1.000
1.000
s
15001210
15
Corn,
field,
meal
0.000875
1.000
1.000
s
15001211
15
Corn,
field,
meal­
babyfood
0.000875
1.000
1.000
s
15001220
15
Corn,
field,
bran
0.001125
1.000
1.000
s
15001230
15
Corn,
field,
starch
0.000485
1.000
1.000
s
15001231
15
Corn,
field,
starch­
babyfood
0.000485
1.000
1.000
s
15001240
15
Corn,
field,
syrup
0.000735
1.000
1.000
s
15001241
15
Corn,
field,
syrup­
babyfood
0.000735
1.000
1.000
s
15001250
15
Corn,
field,
oil
0.000445
1.000
1.000
s
15001251
15
Corn,
field,
oil­
babyfood
0.000445
1.000
1.000
s
Page
67
of
74
15001270
15
Corn,
sweet
0.007000
0.150
1.000
15001271
15
Corn,
sweet­
babyfood
0.007000
0.150
1.000
70001450
P
Egg,
whole
0.000260
1.000
1.000
70001451
P
Egg,
whole­
babyfood
0.000260
1.000
1.000
70001460
P
Egg,
white
0.000260
1.000
1.000
70001461
P
Egg,
white
(
solids)­
babyfood
0.000260
1.000
1.000
70001470
P
Egg,
yolk
0.000260
1.000
1.000
70001471
P
Egg,
yolk­
babyfood
0.000260
1.000
1.000
23001690
M
Goat,
meat
0.000400
1.000
1.000
23001700
M
Goat,
meat
byproducts
0.000400
1.000
1.000
23001710
M
Goat,
fat
0.000340
1.000
1.000
23001720
M
Goat,
kidney
0.001610
1.000
1.000
23001730
M
Goat,
liver
0.001700
1.000
1.000
06031820
6C
Guar,
seed
0.010000
1.000
1.000
06031821
6C
Guar,
seed­
babyfood
0.010000
1.000
1.000
24001890
M
Horse,
meat
0.000400
1.000
1.000
06032030
6C
Lentil,
seed
0.010000
1.000
1.000
27002220
D
Milk,
fat
0.000620
1.000
1.000
27002221
D
Milk,
fat
­
baby
food/
infant
for
0.000620
1.000
1.000
27012230
D
Milk,
nonfat
solids
0.000620
1.000
1.000
27012231
D
Milk,
nonfat
solids­
baby
food/
in
0.000620
1.000
1.000
27022240
D
Milk,
water
0.000620
1.000
1.000
27022241
D
Milk,
water­
babyfood/
infant
form
0.000620
1.000
1.000
27032251
D
Milk,
sugar
(
lactose)­
baby
food/
0.000620
1.000
1.000
06032580
6C
Pea,
pigeon,
seed
0.010000
1.000
1.000
95002630
O
Peanut
0.150000
0.010
1.000
95002640
O
Peanut,
butter
0.110000
0.010
1.000
95002650
O
Peanut,
oil
0.009000
0.010
1.000
25002900
M
Pork,
meat
0.000160
1.000
1.000
25002901
M
Pork,
meat­
babyfood
0.000160
1.000
1.000
25002910
M
Pork,
skin
0.000160
1.000
1.000
25002920
M
Pork,
meat
byproducts
0.000160
1.000
1.000
25002921
M
Pork,
meat
byproducts­
babyfood
0.000160
1.000
1.000
25002930
M
Pork,
fat
0.000180
1.000
1.000
25002931
M
Pork,
fat­
babyfood
0.000180
1.000
1.000
25002940
M
Pork,
kidney
0.000170
1.000
1.000
25002950
M
Pork,
liver
0.000340
1.000
1.000
60003010
P
Poultry,
other,
meat
0.000020
1.000
1.000
60003020
P
Poultry,
other,
liver
0.000090
1.000
1.000
60003030
P
Poultry,
other,
meat
byproducts
0.000020
1.000
1.000
60003040
P
Poultry,
other,
fat
0.000010
1.000
1.000
60003050
P
Poultry,
other,
skin
0.000020
1.000
1.000
26003390
M
Sheep,
meat
0.000400
1.000
1.000
26003391
M
Sheep,
meat­
babyfood
0.000400
1.000
1.000
26003400
M
Sheep,
meat
byproducts
0.000400
1.000
1.000
26003410
M
Sheep,
fat
0.000340
1.000
1.000
26003411
M
Sheep,
fat­
babyfood
0.000340
1.000
1.000
26003420
M
Sheep,
kidney
0.001610
1.000
1.000
26003430
M
Sheep,
liver
0.001700
1.000
1.000
15003440
15
Sorghum,
grain
0.002070
1.000
1.000
s
15003450
15
Sorghum,
syrup
0.000070
1.000
1.000
aceto
06003470
6
Soybean,
seed
0.001950
1.000
1.000
s
06003480
6
Soybean,
flour
0.001738
1.000
1.000
s
06003481
6
Soybean,
flour­
babyfood
0.001738
1.000
1.000
s
06003490
6
Soybean,
soy
milk
0.001950
1.000
1.000
s
06003491
6
Soybean,
soy
milk­
babyfood
or
in
0.001950
1.000
1.000
s
06003500
6
Soybean,
oil
0.000350
1.000
1.000
s
06003501
6
Soybean,
oil­
babyfood
0.000350
1.000
1.000
s
50003820
P
Turkey,
meat
0.000020
1.000
1.000
50003821
P
Turkey,
meat­
babyfood
0.000020
1.000
1.000
50003830
P
Turkey,
liver
0.000090
1.000
1.000
50003831
P
Turkey,
liver­
babyfood
0.000090
1.000
1.000
50003840
P
Turkey,
meat
byproducts
0.000020
1.000
1.000
50003841
P
Turkey,
meat
byproducts­
babyfood
0.000020
1.000
1.000
Page
68
of
74
50003850
P
Turkey,
fat
0.000010
1.000
1.000
50003851
P
Turkey,
fat­
babyfood
0.000010
1.000
1.000
50003860
P
Turkey,
skin
0.000020
1.000
1.000
50003861
P
Turkey,
skin­
babyfood
0.000020
1.000
1.000
86010000
O
Water,
direct,
all
sources
0.000600
1.000
1.000
s
86020000
O
Water,
indirect,
all
sources
0.000600
1.000
1.000
s
15004010
15
Wheat,
grain
0.000060
1.000
1.000
aceto
15004011
15
Wheat,
grain­
babyfood
0.000060
1.000
1.000
aceto
15004020
15
Wheat,
flour
0.000060
1.000
1.000
aceto
15004021
15
Wheat,
flour­
babyfood
0.000060
1.000
1.000
aceto
15004030
15
Wheat,
germ
0.000060
1.000
1.000
aceto
15004040
15
Wheat,
bran
0.000060
1.000
1.000
aceto
Page
69
of
74
Attachment
10.
CRA
(
Single­
Year
TWAM)
­
DEEM
Food
and
Water
Results
File
(
Page
1
of
2)

U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
(
1994­
98
data)
Residue
file
name:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
Cum_
acet_
ala_
Avg_
Res_
SLU
A_
PCT_
Water(
equiv).
R98
Adjustment
factor
#
2
used.
Analysis
Date
09­
16­
2005/
16:
38:
22
Residue
file
dated:
09­
16­
2005/
16:
31:
17/
8
NOEL
(
Chronic)
=
.5
mg/
kg
bw/
day
COMMENT
1:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
===============================================================================
Total
exposure
by
population
subgroup
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Total
Exposure
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Population
mg/
kg
Percent
Margin
of
Subgroup
body
wt/
day
of
NOEL
Exposr
1/
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­­­­
­­­­­­­­­
­­­­­­­­­
U.
S.
Population
(
total)
0.000019
0.00%
26,204
U.
S.
Population
(
spring
season)
0.000019
0.00%
26,464
U.
S.
Population
(
summer
season)
0.000020
0.00%
25,105
U.
S.
Population
(
autumn
season)
0.000019
0.00%
26,549
U.
S.
Population
(
winter
season)
0.000019
0.00%
26,801
Northeast
region
0.000018
0.00%
28,088
Midwest
region
0.000020
0.00%
25,597
Southern
region
0.000018
0.00%
27,843
Western
region
0.000022
0.00%
23,224
Hispanics
0.000022
0.00%
22,516
Non­
hispanic
whites
0.000019
0.00%
26,917
Non­
hispanic
blacks
0.000018
0.00%
27,807
Non­
hisp/
non­
white/
non­
black
0.000023
0.00%
21,656
All
infants
(<
1
year)
0.000052
0.01%
9,603
Nursing
infants
0.000018
0.00%
27,120
Non­
nursing
infants
0.000065
0.01%
7,713
Children
1­
6
yrs
0.000040
0.01%
12,647
Children
7­
12
yrs
0.000023
0.00%
21,871
Females
13­
19
(
not
preg
or
nursing)
0.000014
0.00%
35,590
Females
20+
(
not
preg
or
nursing)
0.000016
0.00%
31,112
Females
13­
50
yrs
0.000017
0.00%
29,626
Females
13+
(
preg/
not
nursing)
0.000018
0.00%
27,226
Females
13+
(
nursing)
0.000023
0.00%
21,564
Males
13­
19
yrs
0.000016
0.00%
30,655
Males
20+
yrs
0.000015
0.00%
32,761
Seniors
55+
0.000016
0.00%
31,752
Children
1­
2
yrs
0.000047
0.01%
10,728
Children
3­
5
yrs
0.000037
0.01%
13,417
Children
6­
12
yrs
0.000024
0.00%
20,590
Youth
13­
19
yrs
0.000015
0.00%
32,799
Adults
20­
49
yrs
0.000016
0.00%
31,768
Adults
50+
yrs
0.000016
0.00%
31,734
Females
13­
49
yrs
0.000016
0.00%
31,771
Page
70
of
74
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Page
71
of
74
Attachment
11.
DEEM
CRA
(
PRZM­
EXAMS)
Food
and
Water
Residue
Input
File.
U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
1994­
98
data
Residue
file:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
Cum_
acet_
ala_
999_
Avg_
Res_
SLUA_
PC
T_
Water(
equiv).
R98
Adjust.
#
2
used
Analysis
Date
02­
24­
2006
Residue
file
dated:
02­
24­
2006/
19:
11:
27/
8
Reference
dose
(
NOEL)
=
0.5
mg/
kg
bw/
day
Comment:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Food
Crop
Residue
Adj.
Factors
Comment
EPA
Code
Grp
Food
Name
(
ppm)
#
1
#
2
­­­­­­­­
­­­­
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­
­­­­­­
­­­­­­
­­­­­­­
06030300
6C
Bean,
black,
seed
0.010000
0.050
1.000
06030320
6C
Bean,
broad,
seed
0.010000
0.050
1.000
06030340
6C
Bean,
cowpea,
seed
0.010000
0.050
1.000
06030350
6C
Bean,
great
northern,
seed
0.010000
0.050
1.000
06030360
6C
Bean,
kidney,
seed
0.010000
0.050
1.000
06030380
6C
Bean,
lima,
seed
0.008000
0.050
1.000
06030390
6C
Bean,
mung,
seed
0.010000
0.050
1.000
06030400
6C
Bean,
navy,
seed
0.010000
0.050
1.000
06030410
6C
Bean,
pink,
seed
0.010000
0.050
1.000
06030420
6C
Bean,
pinto,
seed
0.010000
0.050
1.000
21000440
M
Beef,
meat
0.000400
1.000
1.000
21000441
M
Beef,
meat­
babyfood
0.000400
1.000
1.000
21000450
M
Beef,
meat,
dried
0.000400
1.000
1.000
21000460
M
Beef,
meat
byproducts
0.000400
1.000
1.000
21000461
M
Beef,
meat
byproducts­
babyfood
0.000400
1.000
1.000
21000470
M
Beef,
fat
0.000340
1.000
1.000
21000471
M
Beef,
fat­
babyfood
0.000340
1.000
1.000
21000480
M
Beef,
kidney
0.001610
1.000
1.000
21000490
M
Beef,
liver
0.001700
1.000
1.000
21000491
M
Beef,
liver­
babyfood
0.001700
1.000
1.000
40000930
P
Chicken,
meat
0.000020
1.000
1.000
40000931
P
Chicken,
meat­
babyfood
0.000020
1.000
1.000
40000940
P
Chicken,
liver
0.000090
1.000
1.000
40000950
P
Chicken,
meat
byproducts
0.000020
1.000
1.000
40000951
P
Chicken,
meat
byproducts­
babyfoo
0.000020
1.000
1.000
40000960
P
Chicken,
fat
0.000010
1.000
1.000
40000961
P
Chicken,
fat­
babyfood
0.000010
1.000
1.000
40000970
P
Chicken,
skin
0.000020
1.000
1.000
40000971
P
Chicken,
skin­
babyfood
0.000020
1.000
1.000
06030980
6C
Chickpea,
seed
0.010000
1.000
1.000
06030981
6C
Chickpea,
seed­
babyfood
0.010000
1.000
1.000
06030990
6C
Chickpea,
flour
0.010000
1.000
1.000
15001200
15
Corn,
field,
flour
0.000925
1.000
1.000
s
15001201
15
Corn,
field,
flour­
babyfood
0.000925
1.000
1.000
s
15001210
15
Corn,
field,
meal
0.000875
1.000
1.000
s
15001211
15
Corn,
field,
meal­
babyfood
0.000875
1.000
1.000
s
15001220
15
Corn,
field,
bran
0.001125
1.000
1.000
s
15001230
15
Corn,
field,
starch
0.000485
1.000
1.000
s
15001231
15
Corn,
field,
starch­
babyfood
0.000485
1.000
1.000
s
15001240
15
Corn,
field,
syrup
0.000735
1.000
1.000
s
15001241
15
Corn,
field,
syrup­
babyfood
0.000735
1.000
1.000
s
15001250
15
Corn,
field,
oil
0.000445
1.000
1.000
s
15001251
15
Corn,
field,
oil­
babyfood
0.000445
1.000
1.000
s
15001270
15
Corn,
sweet
0.007000
0.150
1.000
15001271
15
Corn,
sweet­
babyfood
0.007000
0.150
1.000
70001450
P
Egg,
whole
0.000260
1.000
1.000
70001451
P
Egg,
whole­
babyfood
0.000260
1.000
1.000
70001460
P
Egg,
white
0.000260
1.000
1.000
70001461
P
Egg,
white
(
solids)­
babyfood
0.000260
1.000
1.000
Page
72
of
74
70001470
P
Egg,
yolk
0.000260
1.000
1.000
70001471
P
Egg,
yolk­
babyfood
0.000260
1.000
1.000
23001690
M
Goat,
meat
0.000400
1.000
1.000
23001700
M
Goat,
meat
byproducts
0.000400
1.000
1.000
23001710
M
Goat,
fat
0.000340
1.000
1.000
23001720
M
Goat,
kidney
0.001610
1.000
1.000
23001730
M
Goat,
liver
0.001700
1.000
1.000
06031820
6C
Guar,
seed
0.010000
1.000
1.000
06031821
6C
Guar,
seed­
babyfood
0.010000
1.000
1.000
24001890
M
Horse,
meat
0.000400
1.000
1.000
06032030
6C
Lentil,
seed
0.010000
1.000
1.000
27002220
D
Milk,
fat
0.000620
1.000
1.000
27002221
D
Milk,
fat
­
baby
food/
infant
for
0.000620
1.000
1.000
27012230
D
Milk,
nonfat
solids
0.000620
1.000
1.000
27012231
D
Milk,
nonfat
solids­
baby
food/
in
0.000620
1.000
1.000
27022240
D
Milk,
water
0.000620
1.000
1.000
27022241
D
Milk,
water­
babyfood/
infant
form
0.000620
1.000
1.000
27032251
D
Milk,
sugar
(
lactose)­
baby
food/
0.000620
1.000
1.000
06032580
6C
Pea,
pigeon,
seed
0.010000
1.000
1.000
95002630
O
Peanut
0.150000
0.010
1.000
95002640
O
Peanut,
butter
0.110000
0.010
1.000
95002650
O
Peanut,
oil
0.009000
0.010
1.000
25002900
M
Pork,
meat
0.000160
1.000
1.000
25002901
M
Pork,
meat­
babyfood
0.000160
1.000
1.000
25002910
M
Pork,
skin
0.000160
1.000
1.000
25002920
M
Pork,
meat
byproducts
0.000160
1.000
1.000
25002921
M
Pork,
meat
byproducts­
babyfood
0.000160
1.000
1.000
25002930
M
Pork,
fat
0.000180
1.000
1.000
25002931
M
Pork,
fat­
babyfood
0.000180
1.000
1.000
25002940
M
Pork,
kidney
0.000170
1.000
1.000
25002950
M
Pork,
liver
0.000340
1.000
1.000
60003010
P
Poultry,
other,
meat
0.000020
1.000
1.000
60003020
P
Poultry,
other,
liver
0.000090
1.000
1.000
60003030
P
Poultry,
other,
meat
byproducts
0.000020
1.000
1.000
60003040
P
Poultry,
other,
fat
0.000010
1.000
1.000
60003050
P
Poultry,
other,
skin
0.000020
1.000
1.000
26003390
M
Sheep,
meat
0.000400
1.000
1.000
26003391
M
Sheep,
meat­
babyfood
0.000400
1.000
1.000
26003400
M
Sheep,
meat
byproducts
0.000400
1.000
1.000
26003410
M
Sheep,
fat
0.000340
1.000
1.000
26003411
M
Sheep,
fat­
babyfood
0.000340
1.000
1.000
26003420
M
Sheep,
kidney
0.001610
1.000
1.000
26003430
M
Sheep,
liver
0.001700
1.000
1.000
15003440
15
Sorghum,
grain
0.002070
1.000
1.000
s
15003450
15
Sorghum,
syrup
0.000070
1.000
1.000
aceto
06003470
6
Soybean,
seed
0.001950
1.000
1.000
s
06003480
6
Soybean,
flour
0.001738
1.000
1.000
s
06003481
6
Soybean,
flour­
babyfood
0.001738
1.000
1.000
s
06003490
6
Soybean,
soy
milk
0.001950
1.000
1.000
s
06003491
6
Soybean,
soy
milk­
babyfood
or
in
0.001950
1.000
1.000
s
06003500
6
Soybean,
oil
0.000350
1.000
1.000
s
06003501
6
Soybean,
oil­
babyfood
0.000350
1.000
1.000
s
50003820
P
Turkey,
meat
0.000020
1.000
1.000
50003821
P
Turkey,
meat­
babyfood
0.000020
1.000
1.000
50003830
P
Turkey,
liver
0.000090
1.000
1.000
50003831
P
Turkey,
liver­
babyfood
0.000090
1.000
1.000
50003840
P
Turkey,
meat
byproducts
0.000020
1.000
1.000
50003841
P
Turkey,
meat
byproducts­
babyfood
0.000020
1.000
1.000
50003850
P
Turkey,
fat
0.000010
1.000
1.000
50003851
P
Turkey,
fat­
babyfood
0.000010
1.000
1.000
50003860
P
Turkey,
skin
0.000020
1.000
1.000
50003861
P
Turkey,
skin­
babyfood
0.000020
1.000
1.000
86010000
O
Water,
direct,
all
sources
0.008940
1.000
1.000
s
86020000
O
Water,
indirect,
all
sources
0.008940
1.000
1.000
s
15004010
15
Wheat,
grain
0.000060
1.000
1.000
aceto
15004011
15
Wheat,
grain­
babyfood
0.000060
1.000
1.000
aceto
Page
73
of
74
15004020
15
Wheat,
flour
0.000060
1.000
1.000
aceto
15004021
15
Wheat,
flour­
babyfood
0.000060
1.000
1.000
aceto
15004030
15
Wheat,
germ
0.000060
1.000
1.000
aceto
15004040
15
Wheat,
bran
0.000060
1.000
1.000
aceto
Attachment
12.
DEEM
CRA
(
PRZM­
EXAMS)
Food
and
Water
Results
File
U.
S.
Environmental
Protection
Agency
Ver.
2.00
DEEM­
FCID
Chronic
analysis
for
CUMULATIVE
ALA
+
ACETO
(
ALA
EQUIVS)
(
1994­
98
data)
Residue
file
name:
C:\
AProtzel\
ALBERTO\
Cumulative\
Chloroacetanilides\
DEEM_
Files\
Cum_
acet_
ala_
999_
Avg_
Res_
SLUA_
PC
T_
Water(
equiv).
R98
Adjustment
factor
#
2
used.
Analysis
Date
02­
24­
2006/
19:
13:
40
Residue
file
dated:
02­
24­
2006/
19:
11:
27/
8
NOEL
(
Chronic)
=
.5
mg/
kg
bw/
day
COMMENT
1:
Cumulative
(
Aceto)
+
Ala
(
Avg.
res+
SLUA
PCt)
+
Water
in
ala
equiv
===============================================================================
Total
exposure
by
population
subgroup
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Total
Exposure
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
Population
mg/
kg
Percent
Margin
of
Subgroup
body
wt/
day
of
NOEL
Exposr
1/
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­­­­­­­­­­­
­­­­­­­­­
­­­­­­­­­
U.
S.
Population
(
total)
0.000195
0.04%
2,566
U.
S.
Population
(
spring
season)
0.000193
0.04%
2,589
U.
S.
Population
(
summer
season)
0.000209
0.04%
2,395
U.
S.
Population
(
autumn
season)
0.000189
0.04%
2,649
U.
S.
Population
(
winter
season)
0.000189
0.04%
2,651
Northeast
region
0.000178
0.04%
2,806
Midwest
region
0.000197
0.04%
2,535
Southern
region
0.000185
0.04%
2,702
Western
region
0.000223
0.04%
2,243
Hispanics
0.000222
0.04%
2,255
Non­
hispanic
whites
0.000190
0.04%
2,630
Non­
hispanic
blacks
0.000185
0.04%
2,705
Non­
hisp/
non­
white/
non­
black
0.000239
0.05%
2,096
All
infants
(<
1
year)
0.000628
0.13%
796
Nursing
infants
0.000232
0.05%
2,153
Non­
nursing
infants
0.000779
0.16%
642
Children
1­
6
yrs
0.000285
0.06%
1,754
Children
7­
12
yrs
0.000183
0.04%
2,739
Females
13­
19
(
not
preg
or
nursing)
0.000138
0.03%
3,630
Females
20+
(
not
preg
or
nursing)
0.000192
0.04%
2,610
Females
13­
50
yrs
0.000187
0.04%
2,675
Females
13+
(
preg/
not
nursing)
0.000189
0.04%
2,641
Females
13+
(
nursing)
0.000267
0.05%
1,875
Males
13­
19
yrs
0.000146
0.03%
3,433
Males
20+
yrs
0.000173
0.03%
2,894
Seniors
55+
0.000188
0.04%
2,655
Children
1­
2
yrs
0.000308
0.06%
1,625
Children
3­
5
yrs
0.000282
0.06%
1,775
Children
6­
12
yrs
0.000193
0.04%
2,593
Youth
13­
19
yrs
0.000142
0.03%
3,513
Adults
20­
49
yrs
0.000180
0.04%
2,780
Adults
50+
yrs
0.000188
0.04%
2,653
Page
74
of
74
Females
13­
49
yrs
0.000179
0.04%
2,790
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­