Slide
1
of
75
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(

Multipathway
Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
2
of
75
Slide
3
of
75
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Assessment
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Assessment
Health
Effects
Division
Office
of
Pesticide
Programs
Health
Effects
Division
Office
of
Pesticide
Programs

Residential
Exposure
Inputs
°
Jeff
Evans
&
Dana
Vogel

Distributional
Analysis
°
Philip
Villanueva

Residential
Pesticide
Use
Data
°
Steve
Nako
Session
3b
Roadmap
Slide
4
of
75
Slide
5
of
75
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Exposure
Inputs
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Exposure
Inputs
Jeff
Evans
&
Dana
Vogel
Health
Effects
Division
Office
of
Pesticide
Programs
Jeff
Evans
&
Dana
Vogel
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
6
of
75
Overview

Residential
Inputs
°
Discuss
overview
of
the
residential
assessment
and
a
discussion
of
the
model
inputs
for
the
hand
to
mouth
exposure
pathway
°
Jeff
Evans
&
Dana
Vogel

Distributional
Analysis
°
Philip
Villanueva

Pesticide
Use
Data
°
Residential
Exposure
Joint
Venture
(
REJV)

Survey
°
Steve
Nako
Slide
7
of
75
Introduction

Incorporation
of
SAP
Comments

Scenarios

Routes

Data
Categories
°
Non­
Dietary
Ingestion

Hand­
to­
mouth
behavior
Slide
8
of
75
Incorporation
of
SAP
Comments
from
NMC
CRA
Case
Study

Added
two
scenarios
°
Separated
fruit
trees
from
ornamentals
°
Golf
course
scenario
added

Reconsidered
overhead
and
below
waist
exposure
patterns
based
on
data
measuring
exposure
while
making
applications
to
vegetables,
ornamentals
and
fruit
trees
°
Data
evaluated
used
accounts
for
overhead
and
below
waist
applications
for
hose
end
sprayers
and
hand
wand
sprayers
°
Used
data
resulting
in
the
highest
unit
exposures
for
each
scenario
(
hand
wand)
Slide
9
of
75
Incorporation
of
SAP
Comments
from
NMC
CRA
Case
Study

Increased
Use
of
Distributional
Analysis
°
Appendix
II.
E.
2
contains
description
of
studies
used
and
explanation
of
distributional
analysis

Considered
soil
ingestion
contribution
to
overall
oral
nondietary
exposure
°
Provided
an
approach
for
inclusion
of
professional
applications
Slide
10
of
75
Future
Plans

Continue
to
conduct
sensitivity
analysis
for
selected
parameters
and
distributions

Investigate
additional
data
sources
to
better
assess
timing
of
exposure

Further
investigate
the
use
patterns
and
application
schedule
information
used
in
residential
assessment

Further
investigate
co­
occurrence
issues

Continued
development
of
nonstandard
residential
scenarios
such
as
track­
in

Continue
to
evaluate
alternative
methods
to
assess
oral
non­
dietary
exposures
(
Hand
to
Mouth)

°
Participate
in
September
ORD
workshop

Continue
to
develop
residential
assessments
using
CARES
 
,
LIFELINE
 
and
SHEDs
Slide
11
of
75
Stochastic
Human
Exposure
and
Dose
Simulation
Model
(
SHEDS)


Developed
by
ORD
and
was
used
by
the
Agency
to
assess
exposure
to
treated
wood.

°
Valerie
Zartarian,
Jianping
Xue,
Haluk
Ozkaynak

Originally
designed
to
utilize
micro
activity
data
such
as
hand
to
mouth
frequencies
°
Micro
activity
modeling
is
difficult
and
time
consuming

The
SHEDS
modelers
now
rely
on
the
macro
activity
approach

However,
they
were
able
to
address
some
of
the
hand­

tomouth
modeling
issues
we
have
encountered
°
Also
noted
the
problem
of
fixed
upper
percentile
values
for
long
exposure
durations
°
Enables
inter
and
intra
person
variability
for
hand
to
mouth
frequencies
°
You
can
change
the
hand
to
mouth
frequencies
for
each
hour
°
Hands
are
not
necessarily
replenished
with
residues
between
hand
to
mouth
contacts
Slide
12
of
75
Scope
of
Regional
Assessment
Slide
13
of
75
Residential
Scenarios

Lawn
Care
°
Carbaryl

Applicator
and
post
application

Vegetable
Gardens
°
Carbaryl

Applicator
and
post
application

Fruit
Trees
°
Carbaryl

Applicator
and
post
application

Ornamentals
°
Carbaryl
and
Methiocarb

Applicator

Post
application
for
carbaryl
uses

Indoor
Crack
and
Crevice
°
Propoxur

Applicator
and
post
application

Pet
Collars
°
Carbaryl
and
Propoxur

Post
application

Golf
Course
°
Carbaryl

Post
application
Slide
14
of
75
Exposure
Routes/
Scenarios

Non­
Dietary
Ingestion
(
Children:
1­
2,
3­
5)

°
Lawn
Care
°
Crack
and
Crevice
°
Pet
Collars

Inhalation
(
Adults,
Youths,
Children:
1­
2,
3­
5)

°
Lawn
Care
°
Vegetable
Gardens,
Fruit
Trees,
and
Ornamentals
°
Crack
and
Crevice
Slide
15
of
75
Exposure
Routes/
Scenarios

Dermal
(
Adults
and
Youths:
Applicator
and
Post
Application)

(
Children
1­
2,
3­
5:
Post
Application)

°
Lawn
Care
°
Vegetable
Gardens,
Fruit
Trees,
and
Ornamentals
°
Crack
and
Crevice
°
Pet
Collars
(
post
application
only)

°
Golf
Course
(
post
application
only)
Slide
16
of
75
Data
Categories

Pesticide
Use
Data

Unit
Exposure
Data

Residue
Concentration
Data

Exposure
Contact
Factors
Data
Slide
17
of
75
Pesticide
Use
Data

Percent
of
Household
Use
and
Timing
of
Application
°
Data
presented
in
the
Residential
Exposure
Joint
Venture
(
REJV)
survey
°
Label
Information
°
State
Extension
Service
Recommendations
on
timing
of
pesticide
applications
based
on
the
appearance
of
pests
by
region

Co­
occurrence
°
Assumed
independence
°
Sensitivity
analysis
performed
for
alternative
approach
Slide
18
of
75
Applicator
Exposure
Data

Unit
Exposure
°
ORETF
and
chemical­
specific
studies
used
°
Surrogate
data
used
for
ornamental
snail
bait
scenario
Slide
19
of
75
Residue
Concentration
Data

Relied
on
chemical
specific
residue
dissipation
data

Residue
data
are
based
on
the
maximum
rate
Slide
20
of
75
Residue
Concentration
Data

Leaf
foliage
residue
data
°
Liquid
TTR
used
in
absence
of
granular
data
for
lawn
care
scenarios
°
Deposition
data
following
crack
and
crevice
treatments
°
Air
concentrations
following
crack
and
crevice
treatments
°
Pet
fur
residue
data
Slide
21
of
75
Exposure
Contact
Factors

Duration
of
Exposure

Area
Treated
°
e.
g.,
lawn
sizes,
garden
sizes

Breathing
Rates
Slide
22
of
75
Exposure
Contact
Factors

Post
Application
Contact
°
Dermal
Exposure

Transfer
factors
°
Hand­
to­
Mouth
Exposure

Frequency
of
contacts

Surface
area
mouthed

Saliva
extraction
factor

Non­
dietary
ingestion
via
hand­

tomouth
behavior
has
been
discussed
at
several
SAPs
Slide
23
of
75
Hand­
to­
Mouth
Contact
Factors­
Previous
Assumptions

Based
on
discussions
at
August
1999
SAP
°
20
cm2
surface
area
of
hand
mouthed

Palmer
surface
area
of
3
fingers
(
4
year
old)

°
20
hand­
to­
mouth
events
per
hour

90th
percentile
from
videography
data
collected
and
analyzed
by
Reed
et.
al.,
1999

Considered
removal
by
saliva

Assumed
complete
replenishment
of
hands
between
events
Slide
24
of
75
What
We
Used
in
the
Case
Study

Hand­
to­
mouth
Frequency
°
Triangular
Distribution
(
Reed,
1999)


Range
from
0
to
26
events
per
hour

Mean
9.5

Surface
area
of
the
hand
(
fingers)

°
Uniform
Distribution

0
 
20
cm2
per
event
Slide
25
of
75
Considered
Additional
Data
to
Address
this
Pathway

Available
Micro­
Activity
Data
and
their
Applicability
to
Aggregate
Exposure
Modeling
°
Valerie
Zartarian's
SRA
Presentation,
Dec.
2003
°
Analyzed
hand­
to­
mouth
data
based
on
six
studies

Including
Reed
°
Relied
on
observational
data
using
video
tape
analysis,
trained
observers,
or
parental
observers
°
All
hand
contacts
were
recorded
as
hand­
to­
mouth
events,
regardless
of
the
fraction
of
hand
mouthed
°
One
study
(
Letkie)
was
used
to
estimate
the
percent
area
of
hand
mouthed
Available
Micro­
Activity
Data
and
their
Applicability
to
Aggregate
Exposure
Modeling
(
Zartarian
et
al.
,
December
2003)
These
data
were
used
to
enumerate
area
of
hand
mouthed;

Median:
2.3
hand
to
mouth
contacts/
hour;
mean:
7.3
contacts/
hour
33­
34
hours
of
children
"
in
view"

Video
observations
of
suburban
children
ages
1
to
6
years
of
age.

Letkie
et
al.,
2000
Mean
=
9.5
events/
hr.

Single
day
videos
(
6,
7,
8,
10
waking
hours)

4
Latino
children
in
agricultural
area
in
California
Zartarian
et
al.,
1998
Mean
hand
to
mouth
frequency
9.5
contacts
per
hour
A
total
of
20
day
care
(
3­
6
yrs)

and
10
at
home
(
2­
5
years)

30
urban
children
living
in
Newark,
NJ
Reed
et
al.,
1999
Median
3.5
hand
to
mouth
contacts/
hour,
3­
4
years
Median
2.5
contacts/
hour,
5­
6
Four
hour
videotaped
observations
ages
3
to
6
years
living
in
Minnesota
19
children
Freeman
et
al.,
2001
Mean
 
16
hand
to
mouth
contacts
per
hour
No
video­
tape
(
parental
and
trained
observers).

Observations
for
more
than
one
day
were
available
for
78%

of
the
children".

ages
11
to
60
months
72
urban
Washington
State
children
Tulve
et
al.,
2002
indoors
13.5/
hour
and
4.9/
hour
outdoors
Four
hour
videotaped
observations
13
infants;
12,
one
year
olds;

18
two
year
olds;
9
preschool)

living
in
the
vicinity
of
Rio
Bravo,

Texas
52
children
in
Laredo,
TX
Black
et
al.,
2003
Median
frequency/
Other
Information
Videotape
time
Description
No.
Subjects
Author
Slide
26
of
75
Slide
27
of
75
What
We
Used
In
The
Preliminary
Assessment

Increased
the
potential
area
of
the
hand
that
can
be
mouthed
(
Letkie)

°
Begin
with
surface
area
of
200
cm2
°
Multiply
times
percent
area
mouthed

Beta
distribution

Mean:
0.13
(
std
0.06)


99th
%­
ile:
0.3

Increased
hand
to
mouth
frequency
°
Weibull:


Mean:
12.5
events/
hr
(
std
16.4)


90th
%­
ile:
27.8

99th
%­
ile:
84.8
Slide
28
of
75
Uncertainties
in
Modeling
Hand­
to­
Mouth
Exposure

Assumes
residue
replenishment
between
each
hand
to
mouth
event

Fixed
values
for
exposure
duration
°
Up
to
8
hours
indoors
°
Surface
area
°
Frequency

Fixed
upper
percentiles
for
long
durations
has
the
potential
to
create
unrealistic
exposure
estimates
Slide
29
of
75
Conclusions

Added
two
scenarios

Used
REJV
Survey
information

Performed
distributional
analysis

Updated
our
hand
to
mouth
exposure
algorithm
to
include
all
of
the
currently
available
data
°
Zartarian,
2003

Continuing
our
sensitivity
analysis
and
other
items
discussed
in
our
future
plans
°
e.
g.,
SHEDs,
co­
occurrence

Residential
Exposure
Inputs
°
Jeff
Evans
&
Dana
Vogel

Distributional
Analysis
°
Philip
Villanueva

Residential
Pesticide
Use
Data
°
Steve
Nako
Session
3b
Roadmap
Slide
30
of
75
Slide
31
of
75
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Distributional
Analysis
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Distributional
Analysis
Philip
Villanueva
Health
Effects
Division
Office
of
Pesticide
Programs
Philip
Villanueva
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
32
of
75
Outline

Previous
SAP
Recommendations

Types
of
Data
Sets

Lognormal
Distributions
and
Assessing
Fit

Censored
Data
Sets

Uniform
Distributions/
Sensitivity
Analyses

Truncation/
Sensitivity
Analyses
Slide
33
of
75
SAP
Recommendations

The
previous
SAP
recommended
that
distributions
other
than
uniform
and
triangular
be
explored
for
residential
input
variables,
and
that
distribution
selection
procedures
be
more
fully
explained
°
OPP
fitted
lognormal
distributions
to
several
input
datasets
and
assessed
the
appropriateness
of
the
selected
distributions
°
OPP
continues
to
use
uniform
distributions
for
certain
input
data
Slide
34
of
75
Appendix
II.
E.
2
Objectives

Residential
Exposure
Scenarios
Appendix
°
Provide
study
summaries
°
Describe
estimation
of
residue
decline
data
characteristics

Initial
concentration
and
half­
life
°
Describe
lognormal
distributions
fitted
to
residential
input
datasets

Parametric
estimates
of
mean
and
SD
°
Assess
the
goodness­
of­
fit
of
the
distributions

Probability
plots
and
Shapiro­
Wilk
test
statistic
Slide
35
of
75
Types
of
Datasets

Robust
datasets
°
Unit
exposures
and
transfer
coefficients
for
specific
activities
°
More
amenable
to
statistically
rigorous
approach
°
Potential
for
refining
distribution
selection

Sparse
datasets
°
Lawn
sizes,
area
treated,
duration
of
exposure,

and
saliva
extraction
factors
°
Difficult
to
characterize/
imperfect
knowledge
°
Important
to
perform
sensitivity
analyses
Slide
36
of
75
Lognormal
Distributions

Many
types
of
environmental
data
are
reasonably
approximated
by
lognormal
distributions
°
Lognormal
distribution
selected
as
default
for
this
assessment

Probability
plots
and
test
statistics
were
used
to
assess
whether
or
not
lognormality
assumptions
were
reasonable
°
Normal
probability
plots
of
log­
transformed
data
°
Shapiro­
Wilk
normality
test
statistics
of
log­
transformed
data
Slide
37
of
75
Probability
Plots

Probability
plots
provide
°
Qualitative
approach
to
assessing
the
goodness­
of­
fit
of
a
particular
distribution
°
Comparison
of
empirical
and
parametrically
estimated
percentiles

If
the
probability
plot
is
linear,
this
suggests
that
the
data
is
reasonably
approximated
by
the
specified
distribution
Probability
Plots

Example:
dermal
unit
exposure
(
mg/
lb
ai
handled)
values
for
hose
end
sprayer
on
turf

Linear
relationship
indicates
the
data
is
reasonably
approximated
by
a
lognormal
distribution
ln_

OMA004_

SSSP
4
5
6
7
8
9
10
11
12
13
.01
.05
.10
.25
.50
.75
.90
.95
.99
­
3
­
2
­
1
0
1
2
3
Normal
Quantile
Slide
38
of
75
Slide
39
of
75
Normality
Test
Statistic

Shapiro­
Wilk
statistic
(
W)
tests
if
an
assumption
of
(
log)
normality
is
reasonable
given
the
observed
data
°
Used
to
assess
the
goodness­
of­
fit
of
normal
(
or
lognormal)
distribution
to
sample

Values
close
to
one
indicate
that
there
is
no
evidence
to
reject
the
assumption
of
normality
(
or
lognormality)
Shapiro­
Wilk
and
Probability
Plots

Example:
dermal
unit
exposure
values
for
hose
end
sprayer
on
turf
°
W
=
0.96,
p­
value
=
0.36

P­
value
>
0.05
indicates
that
lognormal
assumption
should
not
be
rejected

Assessment
of
probability
plot
agrees
with
Shapiro­
Wilk
ln_

OMA004_

SSSP
4
5
6
7
8
9
10
11
12
13
.01
.05
.10
.25
.50
.75
.90
.95
.99
­
3
­
2
­
1
0
1
2
3
Normal
Quantile
Slide
40
of
75
CDF
Comparison

Dermal
unit
exposure
values
for
hose
end
sprayer
on
turf
0%

10%
20%
30%
40%
50%
60%
70%
80%
90%

100%
0.0
0.1
1.0
10.0
100.0
Dermal
Unit
Exposure
(
mg/
lb
ai)

Fitted
Lognormal
CDF
Empirical
CDF
Slide
41
of
75
Slide
42
of
75
Censored
Datasets

Some
exposure
values
were
below
the
limit
of
quantitation
(
LOQ)
resulting
in
censored
datasets

Censored
data
are
generally
reported
as
½
LOQ
Censored
Datasets

Probability
plots
are
useful
for
identifying
significant
censoring
of
data

Example:
inhalation
unit
exposure
(
mg/
lb
ai
handled)
values
for
trigger
pump
sprayer
ln_

RTU_

gard_

inh
3
4
5
6
.01
.05
.10
.25
.50
.75
.90
.95
.99
­
3
­
2
­
1
0
1
2
3
Normal
Quantile
Slide
43
of
75
Slide
44
of
75
Maximum
Likelihood
Estimation

Imputing
½
LOD
for
censored
data
can
bias
parameter
estimates
of
the
distribution

Maximum
likelihood
estimation
(
MLE)

was
used
to
estimate
the
lognormal
parameters
for
censored
datasets

Example:
inhalation
unit
exposure
values
for
trigger
pump
sprayer
°
MLE:
mean
=
0.10,
std
=
0.14
°
½
LOD:
mean
=
0.10,
std
=
0.10
Probability
Plot
Comparison

Example:
inhalation
unit
exposure
values
for
trigger
pump
sprayer
ln_

RTU_

gard_

inh_

MLE
2.5
3
3.5
4
4.5
5
5.5
6
.01
.05.10
.25
.50
.75
.90
.95
.99
­
3
­
2
­
1
0
1
2
3
Normal
Quantile
ln_

RTU_

gard_

inh
3
4
5
6
.01
.05.10
.25
.50
.75
.90.95
.99
­
3
­
2
­
1
0
1
2
3
Normal
Quantile
Slide
45
of
75
Slide
46
of
75
Uniform
Distributions

Uniform
distributions
continue
to
be
used
when
°
General
idea
of
expected
range
of
data,
but
unable
to
determine
which
values
within
the
range
are
more
likely
to
occur
than
others
°
Data
is
combined
from
multiple
activities
Slide
47
of
75
Uniform
Distributions

Higher
percentiles
of
exposure
have
been
more
sensitive
to
range
of
the
input
data
rather
than
central
estimates

OPP
will
continue
to
perform
sensitivity
analyses
to
determine
the
extent
to
which
the
selected
ranges
affect
exposure
estimates
Slide
48
of
75
Truncation
of
Datasets

For
distributions
other
than
uniform
°
Professional
judgment
was
used
to
determine
the
percentile
at
which
to
truncate
°
Generally
the
99th
percentile
of
the
fitted
distribution
was
used
as
the
truncation
point

Sensitivity
analyses
will
continue
to
determine
the
extent
to
which
truncation
affects
exposure
estimates
Slide
49
of
75
Future
Activities

Explore
the
use
of
alternative
distribution
for
residential
input
data
as
recommended
by
the
SAP

Perform
sensitivity
analyses
to
determine
the
extent
to
which
ranges
of
input
data
and
truncation
affect
estimates
of
exposure

Residential
Exposure
Inputs
°
Jeff
Evans
&
Dana
Vogel

Distributional
Analysis
°
Philip
Villanueva

Residential
Pesticide
Use
Data
°
Steve
Nako
Session
3b
Roadmap
Slide
50
of
75
Slide
51
of
75
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Pesticide
Use
Data
Session
3b
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Residential
Pesticide
Use
Data
Steve
Nako,
Ph.
D.

Health
Effects
Division
Office
of
Pesticide
Programs
Steve
Nako,
Ph.
D.

Health
Effects
Division
Office
of
Pesticide
Programs
Slide
52
of
75
Background
Information
on
Residential
Use
Inputs

Derived
from
Residential
Exposure
Joint
Venture
(
REJV)

Survey
Data
1.
Background
of
REJV
Survey
2.
Approach
for
Compiling
REJV
Residential
Use
Inputs
3.
Notes
Regarding
Using
REJV
Data
Slide
53
of
75
1.
Background
of
the
REJV
Survey

NFO
Panel
Demographics
°
Volunteer
panel
members
°
Black
and
Hispanic
populations
are
underrepresented

Screening
Survey
°
Approx.
76%
of
Respondents
Apply
Pesticides
°
Recruited
only
`
Willing
Users'
to
Maintain
Diaries

Inventory
&
Application
Diaries
°
Study
period,
May
2001
 
April
2002
°
1,217
households
recorded
12
month
diaries
`
Valid'
Applications
defined
as
applications
using
products
that
were
listed
in
that
household's
Inventory
and/
or
having
reported
label
names
and
sites
consistent
with
residential
uses.

Participation
is
defined
as
the
latest
month
in
which
a
valid
application
was
reported.

Therefore,
all
1217
households
reported
at
least
one
valid
application
in
April
2002.
Slide
54
of
75
Avg
­
All
Households
Avg
­
12
Month
Households
Slide
55
of
75
Slide
56
of
75
1.
Background
of
REJV
Survey

REJV
Data
`
Standardization'

°
Logical
inconsistencies,
Missing
Values,
Completeness
(
no
reported
use,
Professional
Applicators)

°
All
Data
Provided
 
Transparency
REJV
Residential
Use
Profile
Trees
Garden
Ornamentals
Slide
57
of
75
Slide
58
of
75
2.
Approach
for
Compiling
REJV
Residential
Use
Inputs

Appendix
II.
E.
1

Current
versions
of
probabilistic
models
require
pesticide
use
inputs
to
generate
residential
use
patterns

General
approach:

°
Develop
residential
use
inputs
so
models
mimic
12
month
diaries
°
Sensitivity
analysis
to
address
data
uncertainties
Slide
59
of
75
3.
Notes
Regarding
Use
of
REJV
Data

Models
require
different
inputs
°
Table
II.
E.
1­
3,
Appendix
II.
E.
1,
p.
9

Inputs
depend
upon
modeling
options
used

Difficult
to
evaluate
modeled
use
patterns

Counts
(
N)
provided
for
all
tabulations

Residential
Use
Inputs
based
only
on
REJV
data
°
Appendix
II.
E.
1
°
Some
estimates
modified
to
reflect
other
information
regarding
homeowner
use
of
NMC
products
Slide
60
of
75
Summary

The
REJV
data
contains
comprehensive
information
on
the
timing
of
application
for
many
residential
products/
uses

NMC
Cumulative
Risk
Assessment
used
REJV­
based
regional
pesticide
use
inputs
to
model
residential
use
in
Calendex
 

Sensitivity
analyses
to
see
how
changes
in
use
inputs
affect
estimated
exposure

Future
work:

°
Comparing
REJV
with
other
use
data
to
evaluate
representativeness
and
data
uncertainties
°
Developing
CARES
 
and
LifeLine
 
inputs
based
on
recent
REJV
data*

*
US
EPA
Pesticide
Use
Data
Available

NHGPUS,
C\
CPAS
Slide
61
of
75
Session
3b
Preliminary
Cumulative
Residential
Assessment:

Questions
to
the
Panel
Session
3b
Preliminary
Cumulative
Residential
Assessment:

Questions
to
the
Panel
Health
Effects
Division
Office
of
Pesticide
Programs
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
62
of
75
Residential
Question
#
1

Use
of
REJV
Data
and
Professional
Judgment
°
To
generate
estimates
of
exposure
from
residential
use
of
NMC
pesticides,
the
probabilistic
models
use
a
variety
of
inputs
to
address
potential
exposure
from
multiple
use
scenarios.
Critical
inputs
include
the
percent
of
households
applying
the
various
pesticide
products,
and
the
timing
of
those
applications.
These
two
inputs,

coupled
with
potential
exposure
from
pesticide
residues
in
drinking
water
and
the
diet,
directly
impact
per
capita
estimates
of
cumulative
exposure.
In
its
February
Case
Study,
the
Agency
presented
background
information
on
the
Residential
Exposure
Joint
Venture
(
REJV)
survey.

The
Agency
used
this
database
as
the
primary
source
for
data
on
the
inputs
relating
to
timing
of
applications
and
percent
of
households
using
NMC
products.
Details
regarding
the
empirical
details
of
the
REJV
survey
are
presented
in
Appendix
II.
E.
1.
Slide
63
of
75
Residential
Question
#
1

Use
of
REJV
Data
and
Professional
Judgment
°
As
previously
mentioned,
the
REJV
survey
can
be
used
to
generate
empirically­
based
estimates
of
percent
of
household
use
and
the
frequency
of
product
specific
applications.
But,
because
the
REJV
did
not
collect
information
regarding
the
reason
for
the
reported
pesticide
use
(
pest
treated)
or
how
much
of
the
product
was
used,
the
empirical
timing
and
frequency
information
(
based
on
a
national
survey)
may
not
provide
a
clear
picture
of
regional
use.
Therefore,
to
establish
the
timing
of
pesticide
applications
for
the
scenarios
likely
to
result
in
the
highest
exposure,
OPP
made
these
estimates
based
on
a
combination
of
REJV
data,
product
label
information,
professional
judgment,
and
pest
pressure
information
available
from
the
Cooperative
State
Extension
Services.
Specific
examples
of
how
these
sources
were
used
to
determine
timing
and
frequency
of
pesticide
use
for
PNMC
residential
assessment
are
presented
in
Section
E
of
the
preliminary
NMC
CRA
document.
Slide
64
of
75
Residential
Question
#
1
R1.
Please
comment
on
the
use
of
information
sources
other
than
REJV
to
establish
periods
of
pesticide
use
and
other
use/
usage
information.
Does
the
Panel
suggest
an
alternative
method
to
improve
the
use
of
REJV
in
the
NMC
assessment?
Does
the
Panel
know
of
other
data
sources
that
may
be
available?
Slide
65
of
75
Residential
Question
#
2

Uncertainties
Associated
with
the
Hand­
To­
Mouth
Assessment
°
To
assess
non­
dietary
ingestion
(
mg/
day),
the
following
four
key
factors
are
used
in
the
models:


Residue
Concentration
(
turf
residues,
pet
fur
residues,
and
residues
from
hard
indoor
surfaces)


Hand
to
mouth
frequency
(
number
of
events
per
hour)


Surface
area
of
the
inserted
hand
parts
(
cm2)


Exposure
time
(
hours/
day)

°
Other
factors
include
both
saliva
extraction
efficiency
and
wet
hand
adjustment
factor.
This
exposure
estimate
is
then
used
along
with
the
Relative
Potency
Factor
(
RPF)
and
Benchmark
Dose
to
estimate
risk.
In
the
Preliminary
N­
methyl
carbamate
assessment,

risk
estimates
for
non­
dietary
oral
exposure
result
in
the
lowest
Margins
of
Exposure
(
MOEs),
and
would
therefore
be
of
greatest
concern
to
the
Agency;
however,
these
low
MOEs
appear
to
be
due
in
part
to
the
incorporation
of
micro­
activity
data
into
our
macro
activity
models.
As
a
result,
the
non­
dietary
ingestion
scenarios
in
the
Preliminary
N­
methyl
carbamate
cumulative
risk
assessment
are
the
least
refined.
Slide
66
of
75
Residential
Question
#
2

Uncertainties
Associated
with
the
Hand­
To­
Mouth
Assessment
°
The
residue
concentration
values
are
derived
from
individual
residue
dissipation
or
deposition
studies
which
are
discussed
in
the
Residential
Chapter
(
Section
E)
of
the
Cumulative
Risk
Assessment
document.
The
exposure
durations
are
taken
from
the
Agency's
Exposure
Factors
Handbook.
The
hand
to
mouth
frequencies
and
hand
surface
areas
come
from
behavior
studies
relying
either
on
observational
data
of
young
children
using
video
tape
analysis,
trained
observers,
or
parental
observers.
However,
study
data
that
evaluated
hand­
to­
mouth
frequency
and
surface
area
mouthed
is
difficult
to
interpret.
Specifically,

comparison
of
study
results
can
be
difficult
due
to
differences
in
study
practices
and
methodologies.
For
example,
there
are
no
standard
definitions
of
mouthing
(
superficial
contact,
licking,
biting,
fraction
of
hand
inserted)
and
thus
the
data
for
these
behaviors
likely
differs
among
studies
as
a
result
of
the
investigators
definitions.
In
addition,
the
degree
to
which
ancillary
data
(
such
as
surface
area
of
hand
contacted
or
inserted,
the
duration
of
contact,
and
the
length
of
videotaping)
are
collected
and
reported
differ
among
studies.
This
makes
broad­
based
and
generally­
applicable
interpretation
difficult.
Nevertheless,
Drs.

Zartarian
and
Xue
allowed
us
to
use
their
preliminary
distributional
analyses
of
these
children's
video
data
in
this
assessment.
The
studies
used
in
the
hand
to
mouth
frequency
analysis
performed
by
Zartarian
and
Xue
are
briefly
summarized
in
a
table
provided
in
a
memorandum
dated
August
8,
2005
and
provided
to
the
Panel
under
separate
cover.
Slide
67
of
75
Residential
Question
#
2

Uncertainties
Associated
with
the
Hand­
To­
Mouth
Assessment
°
The
distributions
of
hand­
to­
mouth
frequencies
and
surface
area
mouthed
used
in
the
Preliminary
NMC
CRA
were
based
on
the
analysis
performed
by
Zartarian
and
Xue
(
as
detailed
above).
In
the
aggregate
models
used
in
the
NMC
cumulative
assessment,
each
separate
iteration
selects
a
single
value
for
the
hand
to
mouth
events
variable
from
a
distribution
of
hand
to
mouth
frequency
values.
Also,
each
separate
iteration
of
the
model
selects
a
single
surface
area
from
a
distribution
of
the
fraction
of
hand
mouthed.
These
values
are
multiplied
by
the
residues
and
exposure
durations
which
are
similarly
selected
from
a
distribution
of
residue
and
exposure
durations
as
described
above.
This
relatively
simple
selection
process,
however,

ignores
the
numerous
complexities
and
interrelationships
involved
in
this
critical
behavior
pattern.
(
For
example,
the
fraction
of
a
hand
which
is
mouthed
during
each
mouthing
event
may
be
inversely
correlated
with
the
frequency
with
which
the
hand
is
mouthed.
Specifically,
a
high
frequency
of
hand­
to­
mouth
events
may
be
associated
with
a
smaller
fraction
of
the
hand
which
is
mouthed.
The
algorithms
used
in
the
NMC
CRA
however,
(
as
established
by
the
OPP
Residential
Standard
Operating
Procedures
(
SOP's))
assume
independence
between
these
two
parameters.
This
assumption
likely
leads
to
overestimates
of
exposures
when
upper
percentiles
of
the
hand­
to­
mouth
frequency
and
area
of
hand
mouthed
distributions
are
combined.
In
addition,
the
macroactivity
approach
used
in
the
NMC
CRA
aggregate
models
is
based
on
the
following
assumptions:


The
mouthing
frequency
(
events
per
hour),
as
recorded
during
the
course
of
observational
studies,
continues
at
the
same
rate
for
the
entire
exposure
duration
selected;
in
reality,
a
highend
mouthing
frequency
recorded
over
a
short
time
interval
(
e.
g.,
one
hour)
may
be
unlikely
to
continue
at
the
same
intensity
over
a
longer
time
period
(
e.
g.,
6
or
8
hours)


The
hand
is
fully
replenished
with
residues
from
a
contaminated
surface
(
e.
g.,
the
lawn,
pet
or
hard
flooring)
between
each
hand
to
mouth
event

The
contact
frequency
and
surface
area
data
used
in
this
assessment
are
taken
from
observational
studies
in
which
all
hand
contacts
were
recorded
as
hand­
to­
mouth
events,

regardless
of
the
fraction
of
hand
mouthed.
Additionally,
no
adjustment
was
made
for
the
duration
of
time
the
hand
remained
in
the
mouth.
Slide
68
of
75
Residential
Question
#
2
R2a.
The
methodology
used
in
the
NMC
CRA
in
which
micro­
activity
data
are
used
in
macro­
activity
approach
likely
leads
to
systematic
overestimates
of
exposure
when
upper
percentiles
of
mouthing
frequency
and
surface
area
of
hand
mouthed
are
combined.
Does
the
Panel
agree
that
this
methodology
does
indeed
overestimate
exposure?
Can
the
Panel
suggest
improvements
to
this
methodology
to
further
refine
exposure
estimates?
Slide
69
of
75
Residential
Question
#
2
R2b.
Does
the
Panel
have
suggestions
for
an
alternative
approach
to
the
one
used
to
estimate
the
nondietary
oral
exposure
pathway
in
the
Preliminary
NMC
CRA?
For
example,
would
the
use
of
a
time
weighted
frequency
value
based
on
random
hourly
draws
of
hand
frequency
distributions
more
accurately
estimate
hand­
to­
mouth
exposures?
Slide
70
of
75
Residential
Question
#
3

Distributional
Analysis
°
Assessing
residential
exposure
to
pesticides
is
a
complex
process
that
must
consider
exposure
from
a
variety
of
sources
via
multiple
routes.
To
account
for
exposure
from
different
sources,
the
PNMC
residential
exposure
assessment
identifies
scenarios
where
significant
exposure
may
occur.
Each
of
these
scenarios
is
defined
by
a
specific
type
of
activity
or
set
of
activities
that
may
result
in
exposure.
Generally
the
relationships
between
these
activities
and
the
resulting
exposures
are
well­
defined
in
that
algorithms,
equations,
and
standard
operating
procedures
exist
for
calculating
exposure
based
on
the
activity
being
performed.
However
the
supporting
data
sets
used
to
estimate
exposure
for
various
residential
scenarios
range
from
robust
(
e.
g.
unit
exposure
values)
to
limited
or
sparse
(
e.
g.
lawn
sizes,
area
treated,
duration
of
exposure,
and
saliva
extraction
factors).
Additionally,

information
characterizing
the
extent
to
which
each
activity
contributes
to
exposure
for
a
particular
scenario
does
not
always
exist
(
e.
g.
the
amount
of
time
spent
in
home
gardens
performing
activities
such
as
hand
weeding
versus
staking
tomatoes
or
harvesting
sweet
corn).
Slide
71
of
75
Residential
Question
#
3

Distributional
Analysis
°
In
general,
the
Agency
has
attempted
to
fit
distributions
(
as
described
in
Appendix
II.
E.
2
of
the
NMC
CRA)
to
the
exposure
measurements
for
residential
activities
when
supporting
information
exists
to
characterize
the
extent
to
which
the
activity
contributes
to
exposure
for
the
residential
scenario
of
interest.
However,
the
Agency
has
employed
uniform
distributions
to
the
data
sets
for
which
such
supporting
information
does
not
exist,
(
e.
g.

lawn
sizes,
area
treated,
duration
of
exposure,
and
saliva
extraction
factors).
The
Agency
has
elected
to
create
such
distributions
when
the
available
data
are
limited
to
such
an
extent
that
it
is
uncertain
how
well
they
represent
national
variability.
The
Agency
believes
use
of
uniform
distributions
to
be
conservative
in
estimating
potential
exposure
since
uniform
distributions
tend
to
overestimate
exposure.
Slide
72
of
75
Residential
Question
#
3
R3a.
Please
comment
specifically
on
the
Agency's
use
of
lognormal
distributions
to
estimate
residential
exposure
and
the
statistical
methods
and
procedures
by
which
the
Agency
has
selected
particular
distributions
(
e.
g.
probability
plots
and
goodness­
of­
fit
statistics).
Slide
73
of
75
Residential
Question
#
3
R3b.
Does
the
Panel
agree
that
the
Agency's
approach
to
creating
and
using
uniform
distributions
(
i.
e.
ranges
of
values)
for
residential
scenarios
lacking
adequate
supporting
information
tends
to
overestimate
exposure?
Is
the
Panel
aware
of
other
data
sources
that
may
be
better
suited
for
assessing
residential
exposure
scenarios
of
interest?
Does
the
Panel
have
any
suggestions
regarding
alternative
distributions
to
use
for
scenarios
where
supporting
exposure
information
is
inadequate?
To
what
extent
should
sensitivity
analyses
be
used
to
assess
the
appropriateness
of
alternative
distributions?
Slide
74
of
75
Residential
Question
#
3
R3c.
When
the
Agency
fits
distributions
to
various
exposure
values,
the
maximum
value
entered
into
the
probabilistic
models
for
a
particular
distribution
is
usually
defined
to
be
an
upper
percentile
value
such
as
the
99th
percentile
in
order
to
ensure
realistic
input
parameters.

Recognizing
that
the
Agency
intends
to
perform
sensitivity
analyses
to
evaluate
the
effects
of
this
truncation,
please
comment
on
the
Agency's
approach
of
truncating
distributions
that
are
input
to
the
probabilistic
models.
Please
comment
on
any
other
approaches
that
the
Agency
might
use
to
evaluate
uncertainties
associated
with
choices
about
whether
and
where
to
truncate
distributions.
Slide
75
of
75
End
of
Residential
