Slide
1
of
52
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
Slide
2
of
52
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Assessment
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Assessment
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(
Multi­
pathway)

Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
3
of
52
Slide
4
of
52

Introduction
°
Steven
Bradbury,
Ph.
D.


Part
1:
Ground
Water
Model
Evaluation
°
Dirk
Young,
Ph.
D.


Part
2:
Drinking
Water
Exposure
Estimates
°
Nelson
Thurman

Questions
for
the
Panel
Session
2:

Drinking
Water
Exposure
Assessment
Slide
5
of
52
Issues
for
the
SAP

Ground
water
exposure
model
evaluation
1.
Revised
conceptual
model
for
ground
water
sources
of
drinking
water
2.
Comparisons
of
the
three
ground
water
models
3.
Drinking
water
exposure
estimates
4.
Regional
drinking
water
exposure
estimates
5.
Monitoring
comparisons
6.
Spatially­
explicit
assessments
Slide
6
of
52
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Ground
Water
Model
Evaluation
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Ground
Water
Model
Evaluation
Dirk
Young,
Ph.
D.

Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Dirk
Young,
Ph.
D.

Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Slide
7
of
52
Revised
GW
model

From
Feb
2005
SAP:

°
GW
concentrations
from
saturated
zone
rather
than
vadose
°
Consideration
of
Screen
Length

Temporal/
Spatial
averaging
°
More
systematic
setup
of
scenario

Additional
adjustments
°
Setback
distance
between
well
and
application
area
Slide
8
of
52
Revised
Conceptual
GW
Model
Water
Table
Lateral
Groundwater
Movement
100
cm
Set
back
distance
350
cm
Biodegradation
Zone
100
cm
Abiotic
Degradation
Pesticide
Application
Area
Potable
Well
Slide
9
of
52
Reminder
of
the
3
models

RZWQM
°
Root
Zone
Water
Quality
Model
°
Richard's
Equation
Model

LEACHP
°
Leaching
Estimation
and
Chemistry
 
Pesticides
°
Richard's
Equation
Model

PRZM
°
Pesticide
Root
Zone
Model
°
Fill­
to­
Capacity
Model
Slide
10
of
52
Simulation
of
Water
Tables
1­
m
well
screen:

Depth
to
water
table
(
3.5
m)

Well
outlet:
spatial
average
of
well
screen
Tile
drain
is
1m
below
head
gate
output
=
avg.

conc.
of
water
between
tile
drain
and
water
table
"
Field
capacity"

set
to
porosity
in
sat.
zone
PRZM
RZWQM
1­
m
screen
Head
Gate
Slide
11
of
52
Simulation
of
Water
Tables
1­
m
well
screen:
1­
m
Defined
section
for
output
concentration
Depth
to
water
table
=
3.5
m
LEACHP:
most
straight
forward
of
the
models
Slide
12
of
52
Degradation
with
depth
25
cm
75
cm
Aerobic
Degradation
in
Top
25
cm
Linearly
Decreasing
Rate
to
1
meter
Hydrolysis
below
1m
Water
Table
Slide
13
of
52
Well
Setbacks

Concentration
reductions
approximated
by
plug
flow:

where
C
=
concentration
at
well
C0=
concentration
at
point
of
application
L
=
well
setback
distance
v
=
lateral
groundwater
velocity
k
=
degradation
rate
in
aquifer






 

=
k
v
L
e
C
C
0
Slide
14
of
52
Well
Setback
Lateral
Velocity

0.15
m/
da
°
"
high­
end"
velocity
in
FL
central
ridge

Assumed
travel
time
due
to
natural
gradient
w/
negligible
velocity
effects
due
to
private
well
draw
Slide
15
of
52
Setback
Assumptions:

Example
of
300
ft
setback
660
days
travel
time,
natural
gradient
100
yr
travel
time,

well
induced
Well
300
ft
setback
Assumptions:

Lateral
flow:
0.15
m/
day
Well
flow:
0.28
m3/
day
Porosity
=
0.4
No
retardation
Slide
16
of
52
0
25
50
75
100
9/
19/
91
3/
11/
97
9/
1/
02
2/
22/
08
8/
14/
13
2/
4/
19
Date
Aldicarb
Residues
(

ppb)
No
Setback
300
ft
Setback
1000
ft
Setback
Setback
Effect
Slide
17
of
52
Relative
Performance
of
GW
models

On
average:
PRZM,
RZWQM,

LEACHP
provide
similar
estimates

Short­
term
differences
in
concentrations
varied
°
FL:
RZWQM
>>
PRZM
>
LEACHP
°
NC:
RZWQM
>>
PRZM
>>
LEACHP
°
GA:
LEACHP
>
PRZM
>
RZWQM
Slide
18
of
52
0
20
40
60
80
1/
31/
93
2/
9/
99
2/
17/
05
2/
26/
11
3/
6/
17
Date
Aldicarb
Residues
(

ppb)
RZWQM
LEACHP
PRZM
Comparisons
of
models:

Aldicarb
in
FL
central
ridge
FL
citrus,
w/
300
ft
buffer
Slide
19
of
52
0
40
80
120
160
4/
1/
1991
6/
18/
1999
9/
4/
2007
11/
21/
2015
days
ppb
RZWQM
PRZM
Comparisons
of
models:

Oxamyl
in
FL
central
ridge
Slide
20
of
52
0
20
40
60
80
0
2000
4000
6000
8000
10000
days
Total
Aldicarb(

ppb)
PRZM
RZWQM
LEACHP
Comparisons
of
models:

Aldicarb
in
GA
coastal
plain
Slide
21
of
52
Some
Modeling
Differences

Weather:

°
RZWQM
appears
to
show
more
concentrations
that
are
more
pattern
like
than
PRZM
or
LEACHP

Possibly
due
to
Cligen
weather
generation

Needs
further
investigation

Degradation
with
depth:

°
Implemented
nearly
equivalently
in
all
models

Degradation
with
soil
moisture:

°
Only
implemented
in
RZWQM
°
Not
possible
in
PRZM

Degradation
with
temperature:

°
Only
implemented
in
RZWQM
and
LEACHP
°
Problematic
in
PRZM
Slide
22
of
52
Dispersion
Differences
all
models
use
finite
differences

RZWQM
°
Numerical
dispersion
from
backward
differencing
of
velocity
only
°
Dispersivity
effectively
=
0.5
cm

PRZM
°
Backward
differencing
of
velocity
°
Can
also
enter
an
additional
dispersion
coefficient
°
Grid
spacing
of
1
cm

Dispersivity
effectively
=
0.5
cm

LEACHP
°
Central
differencing
of
velocity
°
Little
numerical
dispersion
°
Dispersivity
is
an
input,
but
limited
by
oscillation
behavior
°
Set
to
5
cm
(
Dispersivity
on
the
order
of
grid
size
for
stability)

Note:
relatively
large
spatial
average
output
concentration
should
damp
out
many
of
the
differences
due
to
dispersion
calculations
among
the
models
Slide
23
of
52
Summary

Revised
Conceptual
Model:

°
Saturated
zone
°
Vertical
spatial
averaging
°
Setbacks

Performance:

°
Similar
long­
term
behavior
°
Much
different
short­
term
behavior
Slide
24
of
52
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Estimates
Session
2
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Estimates
Nelson
Thurman
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Nelson
Thurman
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Slide
25
of
52
Cumulative
DW
Exposure

Preliminary
focus
on
potential
highexposure
areas
in
regions
across
the
country

Surface
water
exposure
based
on
same
methods
used
in
the
OP
CRA
°
Account
for:


Spatial

Temporal
co­
occurrence
°
Multiple
years
of
potential
exposure

Ground
water
exposure
new
to
NMC
CRA
Slide
26
of
52
Cumulative
regions
Southeast
Florida
Mid­
south
North
Central
/
Northeast
Northern
Great
Plains
Lower
Midwest
Southwest
Northwest
Slide
27
of
52
Brief
summary
of
DW
exposure

For
most
of
the
country,
NMC
residues
in
drinking
water
are
not
expected
to
contribute
substantially
to
cumulative
exposure

NMC
residues
estimated
for
shallow
private
wells
with
permeable
soils
are
major
contributors
in
localized
(
not
national)
areas

Further
modeling
in
other
areas
will
provide
spatially­
explicit
estimates
Slide
28
of
52
NMC
Cumulative
in
GW:

FL
central
ridge
NMC
Cumulative
(
oxamyl
equiv)

Aldicarb
(
unadjusted)

Estimated
aldicarb
peaks
(
20­
35
ppb)
comparable
to
monitoring
detections
in
private
wells
in
FL
Central
Ridge
Oxamyl
Slide
29
of
52
Comparison
of
regional
distributions
FL
central
ridge
groundwater
NC
coastal
plain
groundwater
GA
coastal
plain
groundwater
NC
coastal
plain
Surface
water
Slide
30
of
52
Evaluation
of
GW
exposure
estimates

Comparison
with
monitoring:

°
FL
central
ridge
°
Factors
related
to
detections
in
FL

Identifying
areas
with
similar
potential
for
high
NMC
exposures

Next
steps
°
Spatially­
explicit
exposure
assessments
Slide
31
of
52
Monitoring:
FL
DEP

Monitoring
data
for
aldicarb
in
private
wells
in
FL:

°
~
6,000
wells
and
12,975
samples
(
some
wells
sampled
multiple
times)

between
1990
and
2005
°
171
well
samples
with
detections
of
1
or
more
aldicarb
residues
(
1.3%

detections)

°
Total
aldicarb
residues
up
to
47
ug/
l
Slide
32
of
52
Spatial
pattern
of
aldicarb
detections
in
FL
private
wells
Slide
33
of
52
Aldicarb
detects
in
Central
Ridge
Total
aldicarb
residues
as
high
as
47
ug/
L
(
ppb).
Detections
(
red/
pink)
are
concentrated
in
citrus
area
(
orange)
on
the
central
ridge
Slide
34
of
52
Aldicarb
detects
in
Central
Ridge
Many
detections
(
red/
pink)
are
associated
with
soils
with
very
high
saturated
hydraulic
conductivity
(
blue)
Slide
35
of
52
Aldicarb
detects
in
Central
Ridge
Most
detections
(
red/
pink)
associated
with
well­
to
excessivelydrained
soils
(
blue/
dark
blue)
Slide
36
of
52
Extent
of
GW
exposure
real
but
limited
Overlaying
citrus
(
orange
color)

with
highly
permeable
soils
(
blue)
shows
the
potential
extent
of
the
high
GW
exposure
areas
(
dark
color).
Slide
37
of
52
Lake
Wales
Ridge
GW
Study
°
31
wells
along
central
ridge
°
Quarterly
sampling
from
1999­
present
°
Includes
well
characteristics,
water
depths,
precipitation,

water
quality
data
°
Provisional
data
°
http://
fisc.
er.
usgs.
gov/

Lake_
Wales_
Ridge
Slide
38
of
52
Lake
Wales
Ridge
Provisional
results

Wells
with
1
or
more
detections
of
aldicarb
residues:

°
32%
for
aldicarb
sulfone,
sulfoxide;
6%

for
aldicarb
parent
°
Aldicarb
residues
not
detected
in
NAWQA
GW
sampling
in
South
FL
Flatwoods
citrus
study

Concentration
of
total
aldicarb
residues
detected:

°
23.4
ug/
L
maximum;
9.9
ug/
L
95th
percentile
Slide
39
of
52
Provisional
Results:
total
aldicarb
residues
Aldicarb
+
Metabolites
(
LWRMN)

0
5
10
15
20
25
April
1999
Oct/
Nov
1999
Apr/
May
2000
Oct
2000
Apr
2001
Oct
2001
Apr
2002
Oct
2002
Apr
2003
Oct
2003
Apr
2004
Oct
2004
Sample
Date
Aldicarb+

Ald.

Sulfoxide+

Ald.

Sulfone
Concentration
(

ug/

L)
Well
screen
10­
21
ft
pH
4.4
Well
screen
25­
35
ft
pH
5.3
Well
screen
40­
50
ft
pH
6.7
Slide
40
of
52
Conditions
with
potential
for
high
NMC
exposure

Highly
permeable
soils
°
County­
level
soil
maps/
data

Shallow,
acidic
ground
water
°
Some
data
on
depth
to
ground
water,

chemistry
available
locally
°
Working
w/
USGS
for
national
coverages

Shallow
private
wells
°
Depths,
locations
generally
unknown
Slide
41
of
52
Monitoring
data
elsewhere

NAWQA
GW
monitoring
(
nontargeted)
found
few
detections
°
Aldicarb
residues:


Mostly
in
southeast

Up
to
1.8
ppb
sulfoxide
°
Carbofuran
residues:


Mostly
in
mid­
Atlantic

Up
to
1.3
ppb
°
Carbaryl
residues:


Mostly
in
mid­
Atlantic

<
0.6
ppb
°
Oxamyl
residues:


Mostly
in
midwest,
west

Up
to
2.6
ppb
°
Methomyl
residues:


Mid­
Atlantic,
MN

<
0.4
ppb
Slide
42
of
52
NC
Coastal
Plain:
Cropland
Land
use
coverages
are
less
detailed
than
in
FL.
This
is
based
on
1992
NLCD,

showing
all
cropland.
Slide
43
of
52
NC
Coastal
Plain:
High
permeable
soils
High
permeability
soils
are
not
as
common
as
in
FL.
Slide
44
of
52
NC
Coastal
Plain:
extent
of
potential
high
exposure
Overlaying
cropland
(
orange)
with
highly
permeable
soils
(
blue):

reflects
our
GW
modeling
area
(
dark)
Slide
45
of
52
Wrapping
up:

What
we
know,
where
we're
going

NMC
residues
in
drinking
water
are
not
likely
to
contribute
substantially
to
cumulative
exposure
in
most
of
the
country

Monitoring,
modeling,
and
mapping
allow
us
to
identify
those
local
conditions
where
the
potential
for
high
NMC
levels
on
ground
water
may
occur

The
models
and
mapping
approach
used
for
the
NMC
CRA
lend
themselves
to
use
for
GW
exposure
estimates
and
for
more
spatially­
explicit
DW
assessments
Slide
46
of
52
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Assessment
Questions
to
the
Panel
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Drinking
Water
Exposure
Assessment
Questions
to
the
Panel
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Ecological
Fate
&
Effects
Division
Office
of
Pesticide
Programs
Slide
47
of
52
Drinking
Water
Question
#
1:

Conceptual
Model
for
Ground
Water
Based
on
recommendations
of
SAP,

OPP
revised
its
ground
water
modeling
approach
to
estimate
pesticide
concentrations
in
the
upper
meter
of
a
fixed
saturated
zone
(
ground
water).
...

The
Agency
has
included
two
additional
adjustments
to
consider
variable
degradation
rates
with
depth
and
account
for
setback
distances
between
the
well
and
the
application
area
by
using
lateral
velocity
to
estimate
the
additional
travel
time
for
a
pesticide
to
reach
the
well.
Slide
48
of
52
Drinking
Water
Question
#
1
W1.
Please
comment
on
the
Agency's
revisions
to
the
ground
water
modeling
approach
to
account
for
variable
degradation
rates
with
depth
and
varying
setback
distances
between
the
well
and
treated
fields.
Slide
49
of
52
Drinking
Water
Question
#
2:

Comparisons
of
the
Three
Models
The
three
models
used
by
the
Agency
provided
predicted
concentrations
that
were
similar
on
average,
but
short­
term
concentration
differences
among
the
models
varied
considerably.
 
There
was
no
consistency
with
regard
to
which
model
gave
the
highest
or
lowest
predictions.
Some
of
these
differences
may
due
to
differences
in
the
way
the
models
handle
degradation­
temperature
relationships,
evapotranspiration,
and
weather
generation.
Slide
50
of
52
Drinking
Water
Question
#
2
W2.
Given
that
no
model
stands
out
as
superior
when
compared
to
the
monitoring
data
evaluated
so
far,
can
the
SAP
suggest
criteria
for
further
evaluation
of
the
models?
Slide
51
of
52
Drinking
Water
Question
#
3:

Evaluation
of
GW
Model
Estimates
The
Agency
compared
model
estimates
to
 
monitoring
studies .
Using
monitoring
data,
pesticide
properties,

and
soil
and
hydrologic
data,
the
Agency
identified
conditions
under
which
exposures
similar
to
that
estimated
in
the
NMC
CRA
may
occur:
private
wells
drawing
from
shallow,
acidic
GW
with
high
saturated
hydraulic
conductivities
in
the
soil/
vadose
zone.
This
has
allowed
the
Agency
to
move
toward
a
spatiallyexplicit
characterization
of
potential
high
exposure
areas.
Slide
52
of
52
Drinking
Water
Question
#
3
W3.
Please
comment
on
the
performance
of
the
models
against
the
available
monitoring
data.
What
additional
considerations
should
be
taken
in
when
applying
modeled
estimates
to
risk
assessments
for
areas
where
monitoring
data
are
not
available?
