Document ID: EPA-HQ-OPP-2004-0005-0004
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2004-03-08T05:00Z

Chapter
II,
1
of
7
Chapter
II.
Charge
to
the
Panel
A.
Purpose
of
This
Consultation
The
purpose
of
this
consultation
is
to
provide
the
SAP
with
an
overview
of
the
Agency's
updated
Level
II
Terrestrial
and
Aquatic
Models
(
Version
2.0),
which
were
developed
within
the
Environmental
Fate
and
Effects
Division
(
EFED),
OPP.
The
previous
version
of
these
models
was
reviewed
by
the
SAP
during
a
session
held
on
March
13
­
16,
2001
(
U.
S.
Environmental
Protection
Agency,
2001a
and
2001b).

Some
modifications
to
the
models
were
in
response
to
the
2001
SAP
comments
and
recommendations
(
FIFRA
Scientific
Advisory
Panel,
2001).
Other
modifications
were
based
on
recommendations
made
by
ECOFRAM
(
ECOFRAM,
Terrestrial
workgroup,
1999;
ECOFRAM,
Aquatic
Workgroup,
1999).
These
recommendations,
which
were
evaluated
within
the
context
of
the
2001
SAP
review,
were
discussed
within
the
Agency
and
in
national
and
international
professional
scientific
meetings.

The
Agency
is
interested
in
any
general
comments
and
recommendations
from
the
SAP
regarding
the
modifications
to
the
models
along
with
recommendations
on
meeting
the
objectives
identified
in
the
Agency's
risk
characterization
guidance,
namely
transparency,
clarity,
consistency,
and
reasonableness
(
U.
S.
Environmental
Protection
Agency,
2000).

In
addition,
the
Agency
requests
that
the
SAP
respond
to
specific
questions
regarding
the
Terrestrial
and
Aquatic
Level
II
Models
(
Version
2.0)
that
follow
in
the
next
two
sections
of
this
chapter.

B.
Questions
Regarding
the
Terrestrial
Level
II
Model
(
Version
2.0)

1.
Guild
Parameters
Used
for
Defining
Generic
Species.
The
process
for
defining
generic
species
described
in
this
document
separated
species
into
guilds
based
on
three
parameters:
feeding
substrate,
nesting
substrate,
and
food
type.

a.)
Please
comment
on
the
representative
guilds
used
to
define
the
generic
organisms.
b.)
Are
there
any
additional
parameters
that
need
to
be
considered
when
defining
the
guilds
and
associated
generic
representatives
for
a
Level
II
assessment?
If
so,
please
identify.
c.)
Please
provide
direction
on
the
appropriate
application
of
the
additional
parameter(
s)
in
defining
the
generic
species
and
provide
discussion
on
how
the
additional
parameters
will
improve
the
characterization
of
the
uncertainty
in
risk
estimate.

2.
Assigning
Values
to
Generic
Species
Variables.
Four
variables
were
used
to
define
a
generic
species:
body
weight,
food
type,
frequency
on
field,
and
persistence
factor.
Values
for
each
variable
were
established
as
follows:
Chapter
II,
2
of
7
Body
Weight:
Selected
as
the
smallest
species
within
each
guild
Frequency
on
Field:
Selected
as
the
95th
percentile
of
available
observations
for
species
within
the
guild
Food
Type:
Assumed
obligate
feeders
for
granivore,
insectivore,
and
herbivore
acknowledging
that
omnivore
exposures
would
be
bracketed
by
these
groups.
Persistence
Factor:
Values
assigned
to
reflect
past
SAP
comments
that
repetitive
behavior
patterns
be
included
in
the
assessment.

a.)
Please
comment
on
whether
the
methods
used
for
establishing
values
and
their
results
appear
to
be
appropriate
for
generic
species
for
a
Level
II
assessment.
b.)
Does
the
SAP
believe
that
more
rigorous
analysis
is
necessary
or
indeed
possible
for
generic
species?
Or,
should
such
an
in­
depth
analysis
be
more
appropriately
applied
at
the
species­
specific
level
of
assessment?
Please
explain.

3.
Bimodal
Feeding
Pattern
and
Serial
Correlation
of
Foraging
Events.
The
model
was
modified
to
incorporate
hourly
choices
for
foraging
areas,
a
bimodal
feeding
pattern,
and
to
account
for
serial
correlation
in
sequential
foraging
events.

a.)
Please
comment
on
the
strengths
and
weaknesses
of
the
modified
algorithm
in
representing
avian
feeding
behavior
for
the
more
vulnerable
species
in
agro­
ecosystems.
b.)
Please
provide
additional
suggestions
for
modifications
in
the
algorithm
to
more
closely
represent
avian
activity
patterns.
c.)
Please
provide
direction
on
the
appropriate
application
of
the
additional
modifications
and
provide
discussion
on
how
the
modifications
will
improve
the
characterization
of
the
uncertainty
in
risk
estimates.

4.
New
Puddle
Algorithm.
A
new
puddle
algorithm
was
developed
to
account
for
a
number
of
parameters
that
affect
puddling
after
a
rainfall
event
in
agro­
environments.
The
new
algorithm
addresses
rainfall
amount,
rainfall
duration,
soil
infiltration
rates,
evaporation,
degradation
and
the
stochastic
nature
of
field
topography
and
its
relation
to
puddle
formation
and
duration.

a.)
Please
comment
on
the
overall
model
structure
in
relation
to
mimicking
puddles
in
agroenvironments
including
any
suggestions
on
modifications
or
additional
parameters
to
considered
that
would
improve
pesticide
concentration
estimates
in
this
environmental
media.
b.)
Please
provide
suggestions
for
assigning
values
to
puddle
input
variables
and
for
locating
additional
sources
of
information
that
may
help
in
defining
these
values.

5.
Air
Concentration
Estimation.
.
The
model
currently
employs
an
equilibrium­
based
two
compartmental
model,
for
estimating
pesticide
air
concentration
in
the
plant
canopy.
Please
comment
on
the
merits
and
limitations
of
this
approaches.
Would
the
SAP
provide
suggestions
on
additional
alternatives
for
estimating
vapor
phase
concentrations
that
would
be
consistent
with
the
physical/
chemical
property
and
environmental
fate
data
available
to
the
Agency
as
guideline
Chapter
II,
3
of
7
information?
Please
comment
on
the
merits
and
limitations
of
these
additional
approaches.

6.
Relating
Inhalation
Exposure
to
Oral
Exposure
Toxicity
Endpoints:
The
absence
of
avian
inhalation
toxicity
data
and
the
need
to
track
all
exposure
routes
simultaneously
has
lead
to
the
development
of
a
method
to
relate
inhalation
exposures
to
oral­
dose
equivalents.
The
method
uses
the
relationship
between
mammalian
inhalation
and
oral
acute
toxicity
endpoints
along
with
an
adjustment
factor
to
account
for
some
basic
physiological
differences
between
the
mammalian
and
avian
lungs
assumed
important
to
inhaled
pesticide
bioavailability.

a.)
Please
comment
on
whether
OPP's
proposed
approach
for
relating
inhalation
exposure
to
oral­
dose
equivalents
addresses
SAP's
previous
comments
concerning
the
use
of
the
mammalian
inhalation/
oral
relationship
for
estimating
toxicity
in
birds.
b.)
Please
provide
suggestions
on
alternatives
for
estimating
avian
inhalation
toxicity
that
would
be
consistent
with
the
kinds
of
toxicity
data
generally
available
to
the
Agency.

7.
Estimating
Dermal
Exposure:
The
incidental
dermal
contact
model
relies
on
methods
currently
employed
by
the
OPP's
Health
Effects
Division
that
rely
on
estimates
of
foliar
contact
and
dislodgeable
foliar
residues
to
estimate
an
external
dermal
dose.

a.)
Please
comment
on
applying
this
general
approach
to
birds
and
whether
any
other
model
alternatives
have
been
used
for
wildlife
dermal
exposure.
b.)
If
alternative
models
for
estimating
dermal
exposure
for
birds
are
available,
please
discuss
their
advantages
and
limitations
in
comparison
to
the
proposed
model.
c.)
Please
comment
on
the
following:

1.)
The
reliance
on
the
lower
leg
and
foot
as
the
significant
contact
area
for
birds.
Are
other
portions
of
avian
anatomy
significant?
If
so,
which
other
areas
should
be
included?
2.)
Recognizing
that
the
use
of
human
foliar
contact
data
has
limitations,
can
the
SAP
share
any
insights
on
available
data
that
would
allow
for
a
more
specific
foliar
contact
rate
estimate
for
birds?
3.)
Is
the
SAP
aware
of
any
data
specific
to
pesticide
foliar
residue
transfer
coefficients
for
wildlife?
If
so,
please
identify.

8.
Relating
Dermal
Exposure
to
Oral
Exposure
Toxicity
Endpoints:
The
general
absence
of
avian
dermal
toxicity
data
and
the
need
to
track
all
exposure
routes
simultaneously
have
lead
to
the
development
of
a
method
to
relate
dermal
exposures
to
oral­
dose
equivalents.
The
method
uses
existing
avian
dermal
toxicity
for
a
subset
of
pesticides
to
establish
a
relationship
between
avian
dermal
and
oral
acute
toxicity
endpoints.
It
is
recognized
that
this
approach
is
statistically
limited
with
regards
to
the
strength
of
that
relationship,
and
that
this
method
is
constrained
by
the
limited
number
of
pesticide
modes
of
action
considered.
Please
provide
suggestions
regarding
other
route
normalization
techniques.

9.
Physiologically­
based
Toxicokinetic
Modeling.
The
methods
developed
to
estimate
risk
from
Chapter
II,
4
of
7
multimedia
and
different
routes
of
exposure
are
based
on
external
dose
estimates
that
do
not
directly
account
for
physiological,
morphological,
and
biochemical
processes
that
underlie
the
toxicokinetic
behavior
of
a
pesticide.
In
human
health
and
aquatic
life
risk
assessments
for
drugs,
and
in
some
cases
environmental
contaminants,
use
of
physiologically­
based
toxicokinetic
(
PBTK
models,
are
beginning
to
be
employed
to
derive
internal
dose
estimates
for
more
refined
dose­
response
analyses
and
to
support
route­
to­
route
and
interspecies
extrapolation.
In
this
regard,
PB­
TK
modeling
was
mentioned
by
the
SAP
during
the
2001
review
of
the
case
studies.

a.)
If
you
are
aware
of
any
developmental
work
on
avian
PB­
TK
models
since
2001,
please
discuss.
Is
the
SAP
aware
of
information
sources
that
have
compiled
measured
physiological,
morphological,
and/
or
biochemical
parameters
that
are
required
to
develop
avian
PB­
TK
models?
If
so,
please
comment.
b.)
Recognizing
that
research
to
support
PB­
TK
models
is
a
long­
term
and
collaborative
endeavor
across
the
Agency
and
the
scientific
community,
identifying
potential
applications
in
a
risk
assessment
context
can
provide
insights
for
prioritizing
developmental
efforts.
In
this
regard,
any
suggestions
by
the
SAP
in
terms
of
an
incremental
application
of
physiologically­
based
perspectives
in
problem
formulation,
analysis
and/
or
the
risk
characterization
phases
of
an
assessment
would
be
welcomed.
In
addition,
any
suggestions
that
may
be
helpful
to
the
broader
scientific
community
in
terms
of
research
priorities
to
develop
avian
PB­
TK
models
would
be
appreciated.

C.
Questions
Regarding
the
Aquatic
Level
II
Model
(
Version
2.0)

1.
Varying
Volume
Water
Model
(
VVWM).
For
aquatic
risk
assessments,
OPP
currently
uses
a
water
body
fate
model
that
has
a
fixed
volume
and
does
not
consider
hydrologic
inputs
and
outputs.
The
SAP
2001
suggested
that
adding
volume
variations
and
overflow
to
the
Level
II
fate
model
would
improve
the
characterization
of
the
water
body
and
improve
estimates
of
aquatic
pesticide
concentrations.

In
response,
a
new
model
has
been
developed
that
allows
volume
variations
and
overflow
in
the
water
body.
The
new
model
also
allows
for
meteorologically
dependent
parameters,
such
as
temperature
and
wind
speed,
to
vary
on
a
daily
basis,
rather
than
a
monthly
basis,
to
better
capture
temporal
variability.
In
addition,
the
model
was
constructed
to
improve
runtime
because
of
the
potential
use
in
Monte
Carlo
simulations.

a.)
Please
discuss
the
new
model's
capability
to
capture
the
most
salient
processes
influencing
the
variations
in
water
body
volume,
and
also
discuss
the
modification
allowing
daily
variations
in
meteorological
dependent
variables.
b.)
Inputs
of
mass
on
a
given
day
are
assumed
to
occur
instantaneously.
Please
discuss
the
advantages
and
disadvantages
of
this
assumption
with
specific
consideration
for
the
trade
off
between
runtime,
accuracy
and
the
consideration
that
input
data
are
given
as
daily
values.
What,
if
any,
additional
approaches
regarding
modeling
input
mass
would
the
SAP
recommend,
please
provide
a
discussion
of
the
pros
and
cons
as
compared
to
the
current
Chapter
II,
5
of
7
method?
c.)
What
additional
model
characterization
or
documentation
is
required
to
ensure
clarity
and
transparency?

2.
Exposure
Model
Testing.
The
QA/
QC
testing
of
the
aquatic
Level
II
Version
2.0
exposure
model
demonstrated
that
the
refined
risk
assessment
shell
is
consistent
with
the
Level
II
Version
1.0
shell
(
PE4)
for
launching
PRZM
and
is
compatible
with
all
crop
scenarios
and
meteorological
files.
The
testing
also
showed
that
the
dissipation
algorithms
in
the
VVWM
are
consistent
with
EXAMS
and
that
the
volume
and
overflow
algorithms
are
correct.
Evaluation
of
the
VVWM
showed
the
potential
effect
that
a
varying
volume
water
body,
using
current
standard
field
size
and
water
body
volume
and
surface
area,
can
have
on
estimated
environmental
concentrations
due
to
dilution,
evaporation,
and
overflow.

a.)
What
additional
testing,
evaluation
and/
or
sensitivity
analysis
can
the
SAP
recommend
to
ensure
that
the
aquatic
Level
II
exposure
model
meets
the
Agency
objectives
of
transparent
processes,
and
clear,
consistent
and
reasonable
products
suitable
for
risk
characterization?
b.)
Based
on
the
evaluation
performed
using
the
VVWM
under
standard
field
(
10
ha)
and
standard
surface
water
scenario
conditions
(
1
ha
surface
area,
20000
m3
volume),
please
discuss
the
advantages
or
disadvantages
to
characterizing
risk
by
replacing
a
single
standard
with
multiple,
crop
scenario­
specific
standards
at
Level
II.

3.
Field
Drainage
Area
and
Water
Body
Size
Selection.
At
Level
II,
the
risk
assessment
approach
is
aimed
at
addressing
the
risk
to
aquatic
species
in
high
exposure,
edge­
of­
field
situations.
The
surrogate
surface
water
used
for
Level
II
consists
of
a
small,
perennial
surface
water
body
at
the
edge
of
an
agricultural
field.
This
water
body
is
capable
of
being
supported
by
agricultural
field
runoff
alone,
and
of
supporting
an
aquatic
community.
Crop
scenario­
specific
input
values
for
field
size,
surface
water
volume,
surface
area,
and
depth
were
developed
and
systematically
explored
using
three
methods.
The
methods
used
readily
available
drainage
area
to
volume
capacity
(
DA/
VC)
ratios
and
associated
water
depth
guidance
for
construction
of
small
permanent
surface
waters
of
the
continental
U.
S.

a.)
The
U.
S.
Department
of
Agriculture's
(
1997)
DA/
VC
ratios
and
depth
guidelines
for
construction
of
small
permanent
water
supplies
(
e.
g.,
irrigation,
livestock,
fish
and
wildlife)
were
used
as
the
source
of
national
and
regional
DA/
VC
ratios
and
associated
water
depths.
What
additional
existing
sources
of
national
or
regional
DA/
VC
ratios
for
small,
permanent
surface
waters
(
e.
g.,
wetlands,
pools,
ponds)
should
be
considered?
b.)
Please
describe
the
merits
or
limitations
to
the
approaches
and
assumptions
evaluated
for
using
the
U.
S.
Department
of
Agriculture's
(
1997)
guidelines
to
derive
field
size,
surface
water
volume,
and
surface
area
input
values
for
specific
crop
scenarios?
What,
if
any,
additional
approaches
and
assumptions
should
be
considered?
c.)
A
default
minimum
depth
was
set
as
0.01
m.
What
minimum
depth
would
the
SAP
recommend
as
a
criterion
to
evaluate
the
biological
relevancy
of
the
scenario?
Chapter
II,
6
of
7
d.)
Simulations
with
the
PRZM/
VVWM
were
performed
using
both
the
crop­
specific
surface
water
area
and
volume
and
the
historic
standard
values
(
DA/
VC
=
1.5
acres/
acre­
ft)
to
characterize
effect
on
exposure
outputs
for
a
relatively
arid
growing
region
(
DA/
VC
=
50
acres/
acre­
ft)
and
a
wetter
climate
(
DA/
VC
=
1
acre/
acre­
ft)
for
both
a
short­
lived
and
long­
lived
pesticide.
In
addition,
the
effect
on
volume
in
the
surface
water
body
was
characterized
for
all
crop­
specific
scenarios.
Please
discuss
what,
if
any,
additional
crop
scenario/
pesticide
evaluations
should
be
performed
to
further
characterize
the
impact
to
exposure
outputs,
and/
or
to
volume.
e.)
What
are
the
advantages
or
disadvantages
to
characterizing
exposure
for
small,
perennial
surface
waters
at
the
edge
of
treated
fields
using
the
method
selected
for
setting
crop
scenario­
specific
DA/
VC
ratio,
depth,
surface
area
and
volume
input
values?
What
adjustments
or
changes
to
the
method
does
the
SAP
recommend,
and
what
are
their
advantages
and
disadvantages?
f.)
Please
describe
the
weaknesses
and
strengths
of
using
simulated
exposure
concentrations
from
these
crop
scenario­
specific
water
bodies
as
a
surrogate
for
a
low­
order
stream
at
the
edge
of
a
field,
for
a
temporary
pool
or
pond,
and
for
a
small
tidal
creek
or
estuary.
g.)
Simulations
with
PRZM/
EXAMS,
a
fixed
volume
surface
water
model,
will
be
performed
using
both
the
crop­
specific
DA/
VC
approach
and
the
historic
standard
values
to
characterize
effect
on
exposure
outputs
for
relatively
arid
growing
regions
(
DA/
VC
=
50
and
80)
and
a
wetter
climate
(
DA/
VC
=
1)
for
both
a
short­
lived
and
long­
lived
pesticide.
Please
discuss
what,
if
any,
additional
crop
scenario/
pesticide
evaluations
should
be
performed
to
further
characterize
the
impact
to
exposure
outputs
in
a
fixed
volume
situation.
h.)
Please
discuss
sources
or
approaches
for
national
or
regional
DA/
VC
ratios
and
associated
water
depth
and
size
information
for
temporary
pool
and
pond
aquatic­
life
resources.

4.
Curve
Number.
The
SAP
2001
recommended
that
additional
characterizations
of
variability
should
be
given
to
those
parameters
in
the
exposure
model
that
have
a
major
impact
on
exposure
concentrations.
The
curve
number
is
perhaps
the
most
influential
parameter
in
PRZM,
and
it
has
been
interpreted
in
recent
literature
as
a
random
variable.
PRZM
currently
treats
the
curve
number
as
a
function
of
soil
moisture,
although
recent
literature
suggests
that
the
curve
number
may
more
appropriately
be
interpreted
as
a
random
variable.

a.)
Please
discuss
the
pros
and
cons
of
assuming
strict
dependence
of
curve
number
on
calculated
soil
moisture
versus
treatment
as
a
random
variable
unrelated
to
soil
moisture
as
a
means
of
characterizing
runoff
variability?
Please
identify
and
discuss
alternative
methods.
b.)
Since
the
curve
number
was
not
designed
for
use
in
continuous
modeling,
what
problems
may
arise
when
the
curve
number
is
used
in
this
manner?
Could
a
probabilistic
interpretation
address
some
of
these
issues?
If
so,
how?
c.)
What
is
the
impact
on
interpretation
of
probabilistic­
simulated
exposure
values
when
the
curve
number
is
used
as
a
random
variable
and
autocorrelation
of
temporally
varying
physical
properties
that
may
impact
run
off
is
ignored?
Chapter
II,
7
of
7
d.)
A
lognormal
distribution
is
being
investigated
to
characterize
variability
in
certain
curve
number
parameters.
Is
it
reasonable
to
assume
such
a
distribution
has
stationary
properties
(
constant
mean
and
variance)
for
all
rain
events
(
e.
g.,
large
and
small)?
Please
provide
rationale.
e.)
Monte
Carlo
modeling
is
being
investigated
as
a
method
of
integrating
the
potential
variability
of
curve
numbers
into
exposure
modeling.
Can
the
SAP
recommend
other
methods
available
to
incorporate
variable
and
uncertain
curve
numbers
into
a
continuous
runoff
model.
Please
discuss
the
pros
and
cons
of
these
methods
versus
Monte
Carlo.