UNITED
STATES
ENVIRONMENTAL
PROTECTION
AGENCY
WASHINGTON,
D.
C.
20460
OFFICE
OF
PREVENTION,
PESTICIDES
AND
TOXIC
SUBSTANCES
PC
Code:
035505
DP
Barcode:
D281404
MEMORANDUM
March
11,
2002
SUBJECT:
Drinking
Water
Reassessment
for
diuron
and
its
degradates
TO:
Diana
Locke
Reregistration
Actions
Branch
II
Health
Effects
Division
(
7509C)

FROM:
Ibrahim
Abdel­
Saheb/
Agronomist
Environmental
Risk
Branch
II
Environmental
Fate
and
Effects
Division
(
7507C)

PEER
REVIEW:
Sid
Abel/
Environmental
Scientist
Environmental
Risk
Branch
II
Environmental
Fate
and
Effects
Division
(
7507C)

THRU:
Tom
Bailey,
Branch
Chief
Environmental
Risk
Branch
II
Environmental
Fate
and
Effects
Division
(
7507C)

CONCLUSIONS
This
memorandum
transmits
re­
calculated
estimated
drinking
water
concentrations
for
use
in
the
human
health
risk
assessment.
Griffin
Label
(
EPA
Reg.
No.
1812­
362)
was
used
to
determine
the
estimated
concentrations.

The
Tier
II
screening
models
PRZM1
and
EXAMS2
were
rerun
using
the
Index
Reservoir
and
Percent
Crop
Area
adjustment
to
2
determine
estimated
surface
water
concentrations
of
diuron
and
its
degradates
dichlorophenylmethylurea
(
DCPMU);
dichlorophenylurea
(
DCPU);
3,4­
dichloraniline
(
3,4­
DCA);
and
N'­(
3­
chlorophenyl)­
N­
Ndimethylurea
(
mCPDMU).
The
Screening
Concentration
in
Groundwater
(
SCI­
GROW3)
model
was
used
to
estimate
groundwater
concentrations
for
Diuron
and
its
degradates.
Modeling
results
are
shown
in
Table
1.

Table
1.
Estimated
environmental
concentrations
in
surface
and
groundwater
for
diuron
and
its
degradates
use
on
citrus.

Toxicity
end
point
model
EECs
(
F
g/
L)
use(
s)
modeled
PCA
Diuron
DCPMU
DCPU
3,4­
DCA
mCPDMU
one
application
of
diuron
on
citrus
@
9.6
lb
ai/
acre,
ground
application
Default
(
0.87)

Surface
water/
peak
613
130
5.80
0.08
136
Surface
water/
1­
10­
year
average)
128
27.0
1.20
0.02
36.4
Surface
water/
mean
of
annual
values)
85.0
18.0
0.80
0.01
25.5
Groundwater/
(
peak
and
long­
term
average)
6.5
2.50
0.1
2X10­
4
1.38
The
IR­
PCA
modeling
results
indicate
that
diuron
and
its
degradates
have
the
potential
to
contaminate
surface
waters
by
runoff
in
areas
with
large
amounts
of
annual
rainfall.
The
degradate
3,4­
DCA
is
commonly
seen
in
surface
water
in
areas
with
high
diuron
and
propanil
usage,
however,
EFED
has
received
no
guideline
studies
on
the
environmental
fate
and
transport
of
3,4­
DCA
or
other
degradate
of
diuron.
EFED
believes
that
additional
studies
are
needed
to
fully
understand
both
the
fate
and
transport
of
these
compounds
in
the
environment.

Modeling
results
were
higher
than
data
from
existing
diuron
surface
water
monitoring
studies
targeted
to
the
pesticide
use
area.
Modeling
values
where
several
orders
of
magnitude
(
ranging
from
9­
100
times)
higher
than
monitoring
data.

Major
degradates
that
were
determined
by
HED
to
be
of
toxicological
concern
include:
dichlorophenylmethylurea
(
DCPMU),
3
dichlorophenylurea
(
DCPU),
3,4­
dichloroaniline
(
3,4­
DCA),
and
N'­(
3­
chlorophenyl)­
N­
N­
dimethylurea
(
mCPDMU)].
Because
the
EFED
lacks
complete
environmental
fate
data
(
such
as
the
aerobic
aquatic
and
anaerobic
aquatic
studies)
on
these
degradates,
this
memorandum
addresses
the
estimated
environmental
concentrations
(
EEC's)
for
surface
and
groundwater
based
on
half­
lives
that
were
calculated
on
cumulative
residues.

Usage
map
for
diuron4
is
attached.

Surface
Water
Monitoring
The
EFED
has
targeted,
but,
limited
monitoring
data
on
the
concentrations
of
diuron
and
its
degradates
in
surface
water.

A
study
on
the
occurrence
of
cotton
herbicides
and
insecticides
in
Playa
lakes
of
the
high
plains
of
western
Texas
concluded
that
diuron
was
the
major
pesticide
detected
in
water
samples
collected
from
32
lakes
with
a
mean
concentration
of
2.7
ppb.
Diuron
metabolites
(
DCPMU,
DCPU,
and
3,4­
DCA)
were
found
in
71%
of
the
samples
analyzed.
The
mean
concentrations
of
these
metabolites
were
0.45
ppb
for
DCPMU,
0.31
ppb
for
3,4­
DCA,
and
0.2
ppb
for
DCPU5.
In
this
study,
water
samples
were
taken
within
two
days
after
diuron
application
to
cotton
in
the
region.
Diuron
usage
on
cotton
in
this
part
of
the
state
reached
an
average
of
$
1.379
lb
ai/
mile2/
yr.
Even
though,
the
monitoring
of
diuron
concentrations
from
use
on
Cotton
in
this
part
of
the
state
is
an
example
of
a
targeted
study,
the
frequency
of
surface
water
sampling
and
the
length
of
sampling
period
were
insufficient
to
satisfy
the
temporal
and
spatial
requirements
for
regulatory
purposes.
This
study
has
limited
use
in
a
national
assessment
because
we
do
not
expect
western
Texas
to
be
one
of
the
most
vulnerable
use
areas
for
runoff.
However,
because
the
samples
were
taken
within
two
days
after
application,
the
results
may
represent
a
lower
bound
of
possible
peak
concentrations
that
could
occur
in
drinking
water
in
that
area.

The
US
Geological
Survey
(
USGS)
National
Water
Quality
Assessment
Program
(
NAWQA)
collected
1420
surface
water
samples
4
from
62
agricultural
stream
sites
during
the
period
from
1992­
1998.
One
to
two
samples
was
collected
each
month
during
periods
when
pesticide
transport
in
the
streams
was
expected
to
be
low
throughout
the
year.
At
most
sites,
the
sampling
frequency
was
increased
to
1
to
3
samples
per
week
during
periods
when
elevated
levels
of
pesticides
were
expected
in
the
streams.
Diuron
was
detected
in
7.32%
of
the
samples
(
detection
limit
=
0.05
ppb)
with
concentration
of
0.13
ppb
in
95%
of
samples.
Diuron
maximum
concentration
was
13
ppb
(
estimated
concentration)
6.

Modeling
Tier
II
surface
water
modeling
was
done
using
the
Index
Reservoir
(
IR)
and
Percent
Crop
Area
(
PCA)
modifications
to
PRZM
and
EXAMS.

The
index
reservoir
represents
a
potential
vulnerable
drinking
water
source
from
a
specific
area
(
Illinois)
with
specific
cropping
patterns,
weather,
soils,
and
other
factors.

The
PCA
is
a
generic
watershed­
based
adjustment
factor
which
represent
the
portion
of
a
watershed
planted
to
a
crop
or
crops
and
will
be
applied
to
pesticide
concentrations
estimated
for
the
surface
water
component
of
the
drinking
water
exposure
assessment
using
PRZM/
EXAMS
with
the
index
reservoir
scenario7.

The
IR­
PCA
PRZM/
EXAMS
model
use
and
fate
input
parameters
for
diuron
and
its
degradates
in
surface
water
are
shown
in
Tables
2­
6.
The
IR­
PC
PRZM/
EXAMS
model
input
and
output
files
for
diuron
and
its
degradates
are
shown
in
Appendix
I.
5
Table
2:
IR­
PC
PRZM/
EXAMS
input
parameters
for
diuron.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
citrus
label
(
EPA
Reg.
No.
1812­
362).

application
rate
(
lb
ai/
acre)
9.6
label
(
EPA
Reg.
No.
1812­
362).

Application
efficiency
0.99
IR­
PC
Guidance8
Spray
drift
fraction
0.064
IR­
PC
Guidance
Application
method
ground
label
(
EPA
Reg.
No.
1812­
362).

DWRATE
(
day­
1)
0.0006
MRID#
41719303;
Input
parameters
guidance8
DSRATE
(
day­
1)
0.0006
MRID#
41719303;
Input
parameters
guidance
Kd
(
mL/
g)
14
MRID#
44490501;
Input
parameters
guidance
Henry
(
atm.
m3/
mole)
2.2X10­
10
(
calculated)
Product
Chemistry
chapter
for
HED
RED,
2001.

KBACW
(
h­
1)
0.0003
Aerobic
aquatic
met.
t
½
was
multiplied
by
3.
MRID#
42260501.
Input
parameters
guidance.

KBACS
(
h­
1)
0.002
aquatic
met.
t
½
was
multiplied
by
3.
MRID#
MRID#
42661901.
Input
parameters
guidance.

KDP
(
h­
1)
0.0007
MRID#
41418805;
Input
parameters
guidance.

KBH,
KNH,
KAH
(
h­
1)
0
(
stable)
MRID#
41418804.

KPS
(
mL/
g)
16.6
MRID#
44490501;
Input
parameters
guidance.
6
MWT
(
g/
mole)
233.1
The
MERCK
INDEX9
Solubility
@
25
0C
(
ppm)
420
Product
Chemistry
chapter
for
HED
RED,
2001;
Input
parameters
guidance.

Vapor
pressure
(
torr)
2.0X10­
7
Product
Chemistry
chapter
for
HED
RED,
2001.
7
Table
3:
IR­
PC
PRZM/
EXAMS
input
parameters
for
DCPMU.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
citrus
label
(
EPA
Reg.
No.
1812­
362).

application
rate
(
lb
ai/
acre)
2.03
label
(
EPA
Reg.
No.
1812­
362).
An
equivalent
value
based
on
maximum
conversion
of
diuron
to
degradates
and
the
molecular
weight
ratio
adjustment.

Application
efficiency
0.99
IR­
PC
Guidance
Spray
drift
fraction
0.064
IR­
PC
Guidance
Application
method
ground
label
(
EPA
Reg.
No.
1812­
362).

DWRATE
(
day­
1)
0.0003
MRID#
41719303;
Input
parameters
guidance8
DSRATE
(
day­
1)
0.0003
MRID#
41719303;
Input
parameters
guidance
Kd
(
mL/
g)
for
diuron:
14
MRID#
44490501;
Input
parameters
guidance
Henry
(
atm.
m3/
mole)
for
diuron:
2.2X10­
10
(
calculated)
Product
Chemistry
chapter
for
HED
RED,
2001.

KBACW
(
h­
1)
for
diuron:
0.0003
No
aerobic
aquatic
data
is
available,
diruon­
t
½
was
multiplied
by
3,
MRID#
41719303.
Input
parameters
guidance.

KBACS
(
h­
1)
for
diuron:
0.002
No
anaerobic
aquatic
data
is
available,
the
anaerobic
soil
met.
t
½
was
multiplied
by
0.5.
MRID#
41418806.
Input
parameters
guidance.

KDP
(
h­
1)
for
diuron:
0.0007
MRID#
41418805;
Input
parameters
guidance.

KBH,
KNH,
KAH
(
h­
1)
for
diuron:
0
(
stable)
MRID#
41418804.

KPS
(
mL/
g)
for
diuron:
16.6
MRID#
44490501;
Input
parameters
guidance.

MWT
(
g/
mole)
219.1
The
MERCK
INDEX
Solubility
@
25
0C
(
ppm)
for
diuron:
420
Product
Chemistry
chapter
for
HED
RED,
2001;
Input
parameters
guidance.
8
Vapor
pressure
(
torr)
for
diuron:
2.0X10­
7
Product
Chemistry
chapter
for
HED
RED,
2001.

Table
4:
IR­
PC
PRZM/
EXAMS
input
parameters
for
DCPU.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
citrus
label
(
EPA
Reg.
No.
1812­
362).

application
rate
(
lb
ai/
acre)
0.08
label
(
EPA
Reg.
No.
1812­
362).
An
equivalent
value
based
on
maximum
conversion
of
diuron
to
degradates
and
the
molecular
weight
ratio
adjustment.

Application
efficiency
0.99
IR­
PC
Guidance
Spray
drift
fraction
0.064
IR­
PC
Guidance
Application
method
ground
label
(
EPA
Reg.
No.
1812­
362).

DWRATE
(
day­
1)
0.0003
MRID#
41719303;
Input
parameters
guidance8
DSRATE
(
day­
1)
0.0003
MRID#
41719303;
Input
parameters
guidance
Kd
(
mL/
g)
for
diuron:
14
MRID#
44490501;
Input
parameters
guidance
Henry
(
atm.
m3/
mole)
for
diuron:
2.2X10­
10
(
calculated)
Product
Chemistry
chapter
for
HED
RED,
2001.

KBACW
(
h­
1)
for
diuron:
0.0003
No
aerobic
aquatic
data
is
available,
diruon­
t
½
was
multiplied
by
3,
MRID#
41719303.
Input
parameters
guidance.

KBACS
(
h­
1)
for
diuron:
0.002
No
anaerobic
aquatic
data
is
available,
the
anaerobic
soil
met.
t
½
was
multiplied
by
0.5.
MRID#
41418806.
Input
parameters
guidance.

KDP
(
h­
1)
for
diuron:
0.0007
MRID#
41418805;
Input
parameters
guidance.

KBH,
KNH,
KAH
(
h­
1)
for
diuron:
0
(
stable)
MRID#
41418804.
9
KPS
(
mL/
g)
for
diuron:
16.6
MRID#
44490501;
Input
parameters
guidance.

MWT
(
g/
mole)
205.1
The
MERCK
INDEX
Solubility
@
25
0C
(
ppm)
for
diuron:
420
Product
Chemistry
chapter
for
HED
RED,
2001;
Input
parameters
guidance.

Vapor
pressure
(
torr)
for
diuron:
2.0X10­
7
Product
Chemistry
chapter
for
HED
RED,
2001.

Table
5:
IR­
PC
PRZM/
EXAMS
input
parameters
for
3,4­
DCA.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
citrus
label
(
EPA
Reg.
No.
1812­
362).

application
rate
(
lb
ai/
acre)
0.0021
label
(
EPA
Reg.
No.
1812­
362).
An
equivalent
value
based
on
maximum
conversion
of
diuron
to
degradates
and
the
molecular
weight
ratio
adjustment.

Application
efficiency
0.99
IR­
PC
Guidance7
Spray
drift
fraction
0.064
IR­
PC
Guidance
Application
method
ground
label
(
EPA
Reg.
No.
1812­
362).

DWRATE
(
day­
1)
0.008
MRID#
41719303;
Input
parameters
guidance8
DSRATE
(
day­
1)
0.008
MRID#
41538701;
Input
parameters
guidance
Kd
(
mL/
g)
for
diuron:
14
MRID#
44490501;
Input
parameters
guidance
Henry
(
atm.
m3/
mole)
for
diuron:
2.2X10­
10
(
calculated)
Product
Chemistry
chapter
for
HED
RED,
2001.

KBACW
(
h­
1)
for
diuron:
0.0003
No
aerobic
a
q
u
a
t
i
c
d
a
t
a
i
s
available,
diruon­
t
½
was
multiplied
by
3,
MRID#
41719303.
Input
parameters
guidance.

KBACS
(
h­
1)
for
diuron:
0.002
No
anaerobic
aquatic
data
is
available,
the
anaerobic
soil
met.
t
½
was
multiplied
by
0.5.
MRID#
41418806.
Input
parameters
guidance.
10
KDP
(
h­
1)
for
diuron:
0.0007
MRID#
41418805;
Input
parameters
guidance.

KBH,
KNH,
KAH
(
h­
1)
for
diuron:
0
(
stable)
MRID#
41418804.

KPS
(
mL/
g)
for
diuron:
16.6
MRID#
44490501;
Input
parameters
guidance.

MWT
(
g/
mole)
162.1
The
MERCK
INDEX
Solubility
@
25
0C
(
ppm)
for
diuron:
420
Product
Chemistry
chapter
for
HED
RED,
2001;
Input
parameters
guidance.

Vapor
pressure
(
torr)
for
diuron:
2.0X10­
7
Product
Chemistry
chapter
for
HED
RED,
2001.

Table
6:
IR­
PC
PRZM/
EXAMS
input
parameters
for
mPDMU.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
citrus
label
(
EPA
Reg.
No.
1812­
362).

application
rate
(
lb
ai/
acre)
2.04
label
(
EPA
Reg.
No.
1812­
362).
An
equivalent
value
based
on
maximum
conversion
of
diuron
to
degradates
and
the
molecular
weight
ratio
adjustment.

Application
efficiency
0.99
IR­
PC
Guidance
Spray
drift
fraction
0.064
IR­
PC
Guidance
Application
method
ground
label
(
EPA
Reg.
No.
1812­
362).

DWRATE
(
day­
1)
for
diuron:
0.0006
MRID#
41719303;
Input
parameters
guidance8
DSRATE
(
day­
1)
for
diuron:
0.0006
MRID#
41719303;
Input
parameters
guidance
Kd
(
mL/
g)
for
diuron:
14
MRID#
44490501;
Input
parameters
guidance
Henry
(
atm.
m3/
mole)
for
diuron:
2.2X10­
10
(
calculated)
Product
Chemistry
chapter
for
HED
RED,
2001.

KBACW
(
h­
1)
0.00008
MRID#
42661901.
Input
parameters
guidance.
11
KBACS
(
h­
1)
0.00005
MRID#
42260501.
Input
parameters
guidance.

KDP
(
h­
1)
for
diuron:
0.0007
MRID#
41418805;
Input
parameters
guidance.

KBH,
KNH,
KAH
(
h­
1)
for
diuron:
0
(
stable)
MRID#
41418804.

KPS
(
mL/
g)
for
diuron:
16.6
MRID#
44490501;
Input
parameters
guidance.

MWT
(
g/
mole)
198.1
The
MERCK
INDEX
Solubility
@
25
0C
(
ppm)
for
diuron:
420
Product
Chemistry
chapter
for
HED
RED,
2001;
Input
parameters
guidance.

Vapor
pressure
(
torr)
for
diuron:
2.0X10­
7
Product
Chemistry
chapter
for
HED
RED,
2001.

Assumptions
and
Uncertainties7,10
Index
Reservoir
The
index
reservoir
represents
potential
drinking
water
exposure
from
a
specific
area
(
Illinois)
with
specific
cropping
patterns,
weather,
soils,
and
other
factors.
Use
of
the
index
reservoir
for
areas
with
different
climates,
crops,
pesticides
used,
sources
of
water
(
e.
g.
rivers
instead
of
reservoirs,
etc),
and
hydrogeology
creates
uncertainties.
In
general,
because
the
index
reservoir
represents
a
fairly
vulnerable
watershed,
the
exposure
estimated
with
the
index
reservoir
will
likely
be
higher
than
the
actual
exposure
for
most
drinking
water
sources.
However,
the
index
reservoir
is
not
a
worst
case
scenario,
communities
that
derive
their
drinking
water
from
smaller
bodies
of
water
with
minimal
outflow,
or
with
more
runoff
prone
soils
would
likely
get
higher
drinking
water
exposure
than
estimated
using
the
index
reservoir.
Areas
with
a
more
humid
climate
that
use
a
similar
reservoir
and
cropping
patterns
may
also
get
more
pesticides
in
their
drinking
water
than
predicted
using
this
scenario.

A
single
steady
flow
has
been
used
to
represent
the
flow
through
the
reservoir.
Discharge
from
the
reservoir
also
removes
chemical
so
this
assumption
will
underestimate
removal
from
the
12
reservoir
during
wet
periods
and
overestimates
removal
during
dry
periods.
This
assumption
can
both
underestimate
or
overestimate
the
concentration
in
the
pond
depending
upon
the
annual
precipitation
pattern
at
the
site.

The
index
reservoir
scenario
uses
the
characteristics
of
a
single
soil
to
represent
the
soil
in
the
basin.
In
fact,
soils
can
vary
substantially
across
even
small
areas,
and
this
variation
is
not
reflected
in
these
simulations.

The
index
reservoir
scenario
does
not
consider
tile
drainage.
Areas
that
are
prone
to
substantial
runoff
are
often
tile
drained.
Tile
drainage
contributes
additional
water
and
in
some
cases,
additional
pesticide
loading
to
the
reservoir.
This
may
cause
either
an
increase
or
decrease
in
the
pesticide
concentration
in
the
reservoir.
Tile
drainage
also
causes
the
surface
soil
to
dry
out
faster.
This
will
reduce
runoff
of
the
pesticide
into
the
reservoir.
The
watershed
used
as
the
model
for
the
index
reservoir
(
Shipman
City
Lake)
does
not
have
tile
drainage
in
the
cropped
areas.

EXAMS
is
unable
to
easily
model
spring
and
fall
turnover.
Turnover
occurs
when
the
temperature
drops
in
the
fall
and
the
thermal
stratification
of
the
reservoir
is
removed.
Turnover
occurs
again
in
the
spring
when
the
reservoir
warms
up.
This
results
in
complete
mixing
of
the
chemical
through
the
water
column
at
these
times.
Because
of
this
inability,
the
Index
Reservoir
has
been
simulated
without
stratification.
There
is
data
to
suggest
that
Shipman
City
Lake,
upon
which
the
Index
Reservoir
is
based,
does
indeed
stratify
in
the
deepest
parts
of
the
lake
at
least
in
some
years.
This
may
result
in
both
over
and
underestimation
of
the
concentration
in
drinking
water
depending
upon
the
time
of
the
year
and
the
depth
the
drinking
water
intake
is
drawing
from.

Percent
Crop
Area
Correction
Factor
The
PCA
is
a
watershed­
based
modification.
Implicit
in
its
application
is
the
assumption
that
currently­
used
field­
scale
models
reflect
basin­
scale
processes
consistently
for
all
pesticides
and
uses.
In
other
words,
we
assume
that
the
large
field
simulated
by
the
coupled
PRZM
and
EXAMS
models
is
a
reasonable
approximation
of
pesticide
fate
and
transport
within
a
watershed
that
contains
a
drinking
water
reservoir.
If
the
13
models
fail
to
capture
pertinent
basin­
scale
fate
and
transport
processes
consistently
for
all
pesticides
and
all
uses,
the
application
of
a
factor
that
reduces
the
estimated
concentrations
predicted
by
modeling
could,
in
some
instances,
result
in
inadvertently
passing
a
chemical
through
the
screen
that
may
actually
pose
a
risk.
Some
preliminary
assessments
made
in
the
development
of
the
PCA
suggest
that
PRZM/
EXAMS
may
not
be
realistically
capturing
basin­
scale
processes
for
all
pesticides
or
for
all
uses.
A
preliminary
survey
of
water
assessments
which
compared
screening
model
estimates
to
readily
available
monitoring
data
suggest
uneven
model
results.
In
some
instances,
the
screening
model
estimates
are
more
than
an
order
of
magnitude
greater
than
the
highest
concentrations
reported
in
available
monitoring
data;
in
other
instances,
the
model
estimates
are
less
than
monitoring
concentrations.
Because
of
these
concerns,
the
SAP
recommended
using
the
PCA
only
for
"
major"
crops
in
the
Midwest.
For
other
crops,
development
of
PCA's
will
depend
on
the
availability
of
relevant
monitoring
data
that
could
be
used
to
evaluate
the
result
of
the
PCA
adjustment.

The
spatial
data
used
for
the
PCA
came
from
readily­
available
sources
and
have
a
number
of
inherent
limitations:

°
The
size
of
the
8­
digit
HUC
[
mean
=
366,989
ha;
range
=
6.7­
2,282,081
ha;
n
=
2,111]
may
not
provide
reasonable
estimates
of
actual
PCA's
for
smaller
watersheds.
The
watersheds
that
drain
into
drinking
water
reservoirs
are
generally
smaller
than
the
8­
digit
HUC
and
may
be
better
represented
by
watersheds
defined
for
drinking
water
intakes.
°
The
conversion
of
the
county
level
data
to
watershed­
based
percent
crop
areas
assumes
the
distribution
of
the
crops
within
a
county
is
uniform
and
homogeneous
throughout
the
county
area.
Distance
between
the
treated
fields
and
the
water
body
is
not
addressed.
°
The
PCA's
were
generated
using
data
from
the
1992
Census
of
Agriculture.
However,
recent
changes
in
the
agriculture
sector
from
farm
bill
legislation
may
significantly
impact
the
distribution
of
crops
throughout
the
country.
The
methods
described
in
this
report
can
rapidly
be
updated
as
more
current
agricultural
crops
data
are
obtained.
The
assumption
that
yearly
changes
in
cropping
patterns
will
cause
minimal
impact
needs
to
be
evaluated.
14
The
PCA
adjustment
is
only
applicable
to
pesticides
applied
to
agricultural
crops.
Contributions
to
surface
waters
from
nonagricultural
uses
such
as
urban
environments
are
not
wellmodeled
Currently,
non­
agricultural
uses
are
not
included
in
the
screening
model
assessments
for
drinking
water.

The
PCA
does
not
consider
percent
crop
treated
because
detailed
pesticide
usage
data
are
extremely
limited
at
this
time.
Detailed
pesticide
usage
data
are
currently
available
for
only
a
few
states.

Groundwater
Monitoring
EFED
has
limited
targeted
monitoring
data
on
the
concentrations
of
diuron
and
its
degradates
in
groundwater.
Table
7
shows
validated
monitoring
data
for
diuron
that
are
available
for
the
states
of
California
(
CA),
Florida
(
FL),
Georgia
(
GA),
and
Texas
(
TX).

Table
7.
Groundwater
monitoring
data
for
diuron.
Number
of
wells
sampled
(
number
of
wells
with
residues)
11.

State
number
of
well
range
of
conc.
(
ppb)
15
CA
2010
(
82)
0.05
­
3.95
FL
15385
(
9)
1.18
­
5.37
GA
70
(
67)
1.00
­
5.00
TX
31
(
2)
0.01
­
0.02
According
to
the
Ground
Water
Protection
Section
of
the
Florida
Department
of
Environmental
Protection12,
ground
water
samples
from
wells
collected
between
May/
1990
and
November/
1997,
showed
diuron
detections
ranging
from
0.94
­
12
ppb
(
detection
limit
=
0.48
ppb).
The
arithmetic
mean
concentration
was
2.44
ppb.
Well
water
samples
were
collected
from
the
following
counties:
Highlands,
Jackson,
Lake,
Orange,
and
Polk.
With
the
exception
of
the
12
ppb
sample
in
Orange
County,
the
majority
of
the
detections
were
in
Highlands
County
where
citrus
is
grown.
Diuron
concentrations
in
Highlands
County
decreased
with
time
to
about
1
ppb
but
were
detected
every
year.
In
Polk
County,
diuron
concentrations
show
a
seasonal
pattern,
with
highest
concentrations
in
the
spring
and
lowest
concentrations
in
the
fall,
but
was
not
detected
in
all
years.

The
US
Geological
Survey
(
USGS)
National
Water
Quality
Assessment
Program
(
NAWQA)
13
analyzed
pesticide
occurrence
and
concentrations
for
major
aquifers
and
shallow
ground
water
in
agricultural
areas
(
detection
limit
=
0.05
ppb).
Analysis
of
2608
samples
(
major
aquifers
study)
showed
diuron
in
71%
of
the
samples
analyzed
with
a
maximum
concentration
of
0.34
ppb.
Maximum
diuron
concentration
in
897
samples
from
shallow
groundwater
sites
was
2.0
ppb,
with
diuron
detected
in
only
1.23%
of
samples
analyzed
(
USGS,
1998).
A
major
component
of
the
sampling
design
in
the
NAWQA
study
was
to
target
specific
watersheds
and
shallow
ground
water
areas
that
are
influenced
primarily
by
a
single
dominant
land
use(
agricultural
or
urban)
that
is
important
in
the
particular
area.
The
ground­
water
data
were
primarily
collected
from
a
combination
of
production
and
monitoring
wells.
Ground­
water
sampling
sites
were
sampled
for
pesticides
from
a
single
snap­
shot
in
time.

Even
though,
the
groundwater
monitoring
data
collected
by
NAWQA
are
from
sites
considered
typical
for
use
areas,
the
frequency
of
sampling
and
the
length
of
sampling
period
were
not
sufficient
to
represent
the
temporal
and
spatial
requirements
for
regulatory
purposes.
16
Major
component
of
the
sampling
design
in
the
NAWQA
study
was
to
target
specific
watersheds
and
shallow
ground
water
areas
that
are
influenced
primarily
by
a
single
dominant
land
use(
agricultural
or
urban)
that
is
important
in
the
particular
area.
The
ground­
water
data
were
primarily
collected
from
a
combination
of
production
and
monitoring
wells.
Ground­
water
sites
in
the
ground­
water
data
were
sampled
for
pesticides
from
a
single
snap­
shot
in
time.

Modeling
The
SCI­
GROW
model
was
used
to
estimate
potential
groundwater
concentrations
for
diuron
and
its
degradates.

Tables
8,
and
9
show
input
parameters
and
output
for
SCI­
GROW
modeling
of
diuron
and
its
degradates,
respectively.

Table
8.
Input
parameters
for
diuron
and
its
degradates
used
in
the
SCI­
GROW
model.

compound
appl.
rate
(
lb
ai/
acre)
No.
of
appl.
/
year
Aerobic
soil
t1/
2
(
d)
Koc
(
mL/
g)
Source/
Quality
of
data
Diuron
9.6
1
372
468
label
(
EPA
Reg.
No.
1812­
362);
MRID#
44490501;
MRID#
41719303;
Input
parameters
guideline
(
Aug.
2000).
Good
data.

DCPMU
2.03*
1
770
468
label
(
EPA
Reg.
No.
1812­
362);
MRID#
44490501;
MRID#
;
Input
parameters
guideline
(
Aug.
2000).
Good
data.

DCPU
0.08*
1
770
468
label
(
EPA
Reg.
No.
1812­
362);
MRID#
44490501;
MRID#
41719303;
Input
parameters
guideline
(
Aug.
2000).
Good
data.

3,4­
DCA
0.0021*
1
30
468
label
(
EPA
Reg.
No.
1812­
362);
MRID#
44490501;
MRID#
41719303;
MRID#
41538701;
Input
parameters
guideline
(
Aug.
2000).
Good
data.
17
mCPDMU
2.04*
1
372
468
label
(
EPA
Reg.
No.
1812­
362);
MRID#
44490501;
MRID#
41719303;
MRID#
42260501;
Input
parameters
guideline
(
Aug.
2000).
Good
data.

*:
An
equivalent
value
based
on
conversion
of
diuron
to
degradates.

Table
9.
SCI­
GROW
estimated
environmental
concentrations
for
diuron
and
its
degradates
in
groundwater.

Toxicity
end
point
model
EECs
(
F
g/
L)
use(
s)
modeled
Diuron
DCPMU
DCPU
3,4­
DCA
mCPDMU
one
application
of
diuron
on
citrus
@
9.6
lb
ai/
acre
acute
6.5
2.50
0.09
0.0002
1.38
Chronic
(
non
cancer)
6.5
2.50
0.09
0.0002
1.38
Chronic
(
cancer)
6.5
2.50
0.09
0.0002
1.38
The
SCI­
GROW
screening
model
developed
by
EFED
indicates
that
diuron
and
its
degradates
concentrations
are
much
less
than
those
estimated
for
surface
water.
SCI­
GROW
estimated
concentrations
of
diuron
do
fall
within
the
values
from
monitoring
data
shown
in
Table
8,
but
below
some
of
the
reported
monitoring
data.
This
means
that
SCI­
GROW
could
underestimate
chemical
concentrations
in
typical
use
areas
when
the
pesticide
is
used
at
the
maximum
allowed
label
rate
in
areas
with
ground
water
exceptionally
vulnerable
to
contamination
such
as
Florida.

Limitations
of
the
SCI­
GROW2
Analysis
The
SCI­
GROW
model
(
Screening
Concentrations
in
Ground
Water)
is
a
model
for
estimating
concentrations
of
pesticides
in
ground
water
under
"
maximum
loading"
conditions.
SCI­
GROW
provides
a
screening
concentration,
an
estimate
of
likely
ground
water
concentrations
if
the
pesticide
is
used
at
the
maximum
allowed
label
rate
in
areas
with
ground
water
that
is
vulnerable
to
contamination.
In
most
cases,
a
majority
of
the
use
area
will
have
ground
water
that
is
less
vulnerable
to
contamination
than
the
areas
used
to
derive
the
SCI­
GROW
estimate.
18
References:

4.
Carsel,
R.
F.,
J.
C.
Imhoff,
P.
R.
Hummel,
J.
M.
Cheplick
and
J.
S.
Donigian,
Jr.
1997.
PRZM­
3,
A
Model
for
Predicting
Pesticide
and
Nitrogen
Fate
in
Crop
Root
and
Unsaturated
Soil
Zones:
Users
Manual
for
Release
3.0;
Environmental
Research
Laboratory,
Office
of
Research
and
Development,
U.
S.
Environmental
Protection
Agency,
Athens,
GA.

2.
Burns,
L.
A.
March
1997.
Exposure
Analysis
Modeling
System
(
EXAMSII)
Users
Guide
for
Version
2.97.5,
Environmental
Research
Laboratory,
Office
of
Research
and
Development,
U.
S.
Environmental
Protection
Agency,
Athens,
GA.

3.
Barrett,
M.,
1997,
Proposal
For
a
Method
to
Determine
Screening
Concentration
Estimates
for
Drinking
Water
Derived
from
Groundwater
Studies,
EFED/
OPP.

4.
USGS.
1992.
National
Water
Quality
Assessment
(
NWQA),
Pesticides
National
Synthesis
Project,
Annual
Use:
Diuron.

5.
Thurman,
E.
M.,
K.
C.
Bastian,
and
T.
Mollhagen.
Occurrence
of
cotton
herbicides
and
insecticides
in
Playa
lakes
of
the
high
plains
of
western
Texas.
[
Online].
Available
at
http://
toxics.
usgs.
gov/
pubs/
wri99­
4018/
Volume2/
sectionC/
2
403Thurman/
pdf/
2403_
Thurman.
pdf,
May,
2001).

6.
U.
S
GS.
1998.
National
Water
Quality
Assessment
(
NWQA),
Pesticides
National
Synthesis
Project
[
Online]
at
(
http://
ca.
water.
usgs.
gov/
pnsp/
streamsum/
streamT1.
html).

7.
Effland,
W.,
N.
Thurman,
I.
Kennedy,
R.
D.
Jones,
J.
Breithaupt,
J.
Lin,
J.
Carleton,
L.
Libel.
R.
Parker,
and
R.
Matzner.
2000.
"
Guidance
for
use
of
the
index
Reservoir
and
Percent
Crop
Area
Factor
in
drinking
water
exposure
assessment
s.
Office
of
Pesticide
Programs.
8.
Guidance
for
Chemistry
and
Management
Practice
Input
Parameters
For
Use
in
Modeling
the
Environmental
Fate
and
Transport
of
Pesticides.
Version
2.
November
7,
2000.
U.
S.
EPA
Office
of
Pesticide
Programs,
Environmental
Fate
and
Effects
Division.

9.
The
Merck
Index.
1989.
An
encyclopedia
of
chemicals,
drugs,
and
biologicals.
11th
ed.
Rahway,
N.
J.
p.
533.
19
10.
Jones,
R.
D.,
S.
W.
Abel,
W.
Effland,
R.
Matzner,
and
R.
Parker.
1998.
"
An
Index
Reservoir
for
Use
in
Assessing
Drinking
Water
Exposures.
Chapter
IV
in
Proposed
Methods
for
Basin­
Scale
Estimation
of
Pesticide
Concentrations
in
Flowing
Water
and
Reservoirs
for
Tolerance
Reassessment.,
presented
to
the
FIFRA
Science
Advisory
Panel,
July
1998.
http://
www.
epa.
gov/
pesticides/
SAP/
1998/
index.
htm.

11.
U.
S.
EPA.
1992.
Pesticides
in
Ground
Water
Database­
A
compilation
of
Monitoring
Studies:
1971
­
1991.
Office
of
Prevention,
Pesticides,
and
Toxic
Substances,
EPA
734­
12­
92­
001.
12.
Florida
Department
of
Environmental
Protection.
2001.
Personal
communication
with
Bryan
Baker
@
the
Groundwater
Protection
Section
(
850/
921­
9435).

13.
USGS.
1998.
National
Water
Quality
Assessment
(
NWQA),
Pesticides
National
Synthesis
Project,
[
Online]
at
http://
ca.
water.
usgs.
gov/
pnsp/
allsum/#
over.

APPENDIX
I
IR­
PCA
PRZM/
EXAMS
INPUT
AND
OUT
PUT
FILES
FOR
MODELING
DIURON
AND
ITS
DEGRADATES
DIURON
Metfile:
met156a.
met
PRZM
scenario:
FLcitrusC.
txt
20
EXAMS
environment
file:
IRPRZM0.
EXV
Chemical
Name:
diuron
Description
Variable
Name
Value
Units
Comments
Molecular
weight
mwt
233.1
g/
mol
Henry's
Law
Const.
henry
2.2e­
10
atm­
m^
3/
mol
Vapor
Pressure
vapr
2e­
7
torr
Solubility
sol
420
mg/
L
Kd
Kd
16.6
mg/
L
Koc
Koc
mg/
L
Photolysis
half­
life
kdp
43
days
Half­
life
Aerobic
Aquatic
Metabolism
kbacw
99
days
Halfife
Anaerobic
Aquatic
Metabolism
kbacs
15
days
Halfife
Aerobic
Soil
Metabolism
asm
1116
days
Halfife
Hydrolysis:
pH
7
0
days
Half­
life
Method:
CAM
2
integer
See
PRZM
manual
Incorporation
Depth:
DEPI
0.1
cm
Application
Rate:
TAPP
10.76
kg/
ha
Application
Efficiency:
APPEFF
0.99
fraction
Spray
Drift
DRFT
0.064
fraction
of
application
rate
applied
to
pond
Application
Date
Date
1­
Jul
dd/
mm
or
dd/
mmm
or
dd­
mm
or
dd­
mmm
Record
17:
FILTRA
IPSCND
1
UPTKF
Record
18:
PLVKRT
PLDKRT
FEXTRC
0.5
Flag
for
Index
Res.
Run
IR
IR
Flag
for
runoff
calc.
RUNOFF
total
none
or
total(
average
of
entire
run)

OUTPUT
FILE
stored
as
diuron.
out
Chemical:
diuron
PRZM
environment:
FLcitrusC.
txt
EXAMS
environment:
IRPRZM0.
EXV
Metfile:
met156a.
met
Water
segment
concentrations
(
ppb)

Year
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
1948
458
439
399
333
289
123
21
1949
244
234
219
196
179
67.97
1950
325
311
281
260
237
100
1951
547
526
458
348
287
107
1952
912
873
735
540
448
187
1953
412
395
342
318
297
122
1954
528
506
472
352
316
117
1955
298
286
245
204
186
80.43
1956
373
358
318
255
215
78.67
1957
728
702
596
548
495
178
1958
249
242
219
187
182
74.4
1959
364
349
307
298
285
111
1960
721
691
641
512
422
148
1961
179
172
143
123
110
52.43
1962
315
302
270
223
194
82.85
1963
438
419
371
280
226
84.78
1964
698
669
561
433
385
146
1965
397
380
333
308
271
122
1966
248
238
205
172
154
73.71
1967
428
415
360
324
302
126
1968
328
315
286
227
213
91.01
1969
407
389
357
297
265
106
1970
284
271
232
166
135
55.88
1971
246
240
213
171
161
72.11
1972
372
360
318
277
249
89.16
1973
329
317
281
226
196
81.47
1974
321
308
265
207
175
64.67
1975
255
244
205
176
156
67.02
1976
421
408
351
274
244
101
1977
276
264
222
208
198
83.01
1978
80.59
78.02
71.26
63.29
53.82
29.89
1979
360
344
297
251
240
107
1980
407
394
359
329
292
113
1981
627
602
523
468
395
135
1982
159
154
140
116
99.89
51.04
1983
515
500
432
328
272
95.07
Sorted
results
Prob.
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
0.02702
7
912
873
735
548
495
187
0.05405
4
728
702
641
540
448
178
0.08108
1
721
691
596
512
422
148
0.10810
8
698
669
561
468
395
146
0.13513
5
627
602
523
433
385
135
0.16216
2
547
526
472
352
316
126
22
0.18918
9
528
506
458
348
302
123
0.21621
6
515
500
432
333
297
122
0.24324
3
458
439
399
329
292
122
0.27027
438
419
371
328
289
117
0.29729
7
428
415
360
324
287
113
0.32432
4
421
408
359
318
285
111
0.35135
1
412
395
357
308
272
107
0.37837
8
407
394
351
298
271
107
0.40540
5
407
389
342
297
265
106
0.43243
2
397
380
333
280
249
101
0.45945
9
373
360
318
277
244
100
0.48648
6
372
358
318
274
240
95.07
0.51351
4
364
349
307
260
237
91.01
0.54054
1
360
344
297
255
226
89.16
0.56756
8
329
317
286
251
215
84.78
0.59459
5
328
315
281
227
213
83.01
0.62162
2
325
311
281
226
198
82.85
0.64864
9
321
308
270
223
196
81.47
0.67567
6
315
302
265
208
194
80.43
0.70270
3
298
286
245
207
186
78.67
0.72973
284
271
232
204
182
74.4
0.75675
7
276
264
222
196
179
73.71
0.78378
4
255
244
219
187
175
72.11
0.81081
1
249
242
219
176
161
67.97
0.83783
8
248
240
213
172
156
67.02
0.86486
5
246
238
205
171
154
64.67
0.89189
244
234
205
166
135
55.88
23
2
0.91891
9
179
172
143
123
110
52.43
0.94594
6
159
154
140
116
99.89
51.04
0.97297
3
80.59
78.02
71.26
63.29
53.82
29.89
0.1
704.9
675.6
571.5
481.2
403.1
146.6
Average
of
yearly
average
s:
97.9047
2
Inputs
generaged
by
pe3.
pl
of
6­
March­
2002
DCPMU
Metfile:
met156a.
met
PRZM
scenario:
FLcitrusC.
txt
EXAMS
environment
file:
IRPRZM0.
EXV
Chemical
Name:
dcpmu
Description
Variable
Name
Value
Units
Comments
Molecular
weight
mwt
219.1
g/
mol
Henry's
Law
Const.
henry
2.2e­
10
atm­
m^
3/
mol
Vapor
Pressure
vapr
2e­
7
torr
Solubility
sol
420
mg/
L
Kd
Kd
16.6
mg/
L
Koc
Koc
mg/
L
Photolysis
half­
life
kdp
43
days
Half­
life
Aerobic
Aquatic
Metabolism
kbacw
99
days
Halfife
Anaerobic
Aquatic
Metabolism
kbacs
15
days
Halfife
Aerobic
Soil
Metabolism
asm
2310
days
Halfife
Hydrolysis:
pH
7
0
days
Half­
life
Method:
CAM
2
integer
See
PRZM
manual
Incorporation
Depth:
DEPI
0.1
cm
Application
Rate:
TAPP
2.27
kg/
ha
Application
Efficiency:
APPEFF
1.0
fraction
Spray
Drift
DRFT
fraction
of
application
rate
applied
to
pond
Application
Date
Date
1­
Jul
dd/
mm
or
dd/
mmm
or
dd­
mm
or
dd­
mmm
Record
17:
FILTRA
IPSCND
1
UPTKF
24
Record
18:
PLVKRT
PLDKRT
FEXTRC
0.5
Flag
for
Index
Res.
Run
IR
IR
Flag
for
runoff
calc.
RUNOFF
total
none
or
total(
average
of
entire
run)

OUTPUT
FILE
stored
as
dcpmu.
out
Chemical:
dcpmu
PRZM
environment:
FLcitrusC.
txt
EXAMS
environment:
IRPRZM0.
EXV
Metfile:
met156a.
met
Water
segment
concentrations
(
ppb)

Year
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
1948
98.66
94.47
86.14
71.43
62.07
25.85
1949
52.57
50.36
47.23
41.74
38.15
14.1
1950
65.48
62.73
56.33
53.1
48.39
20.87
1951
112
108
94.2
71.78
59.43
22.21
1952
190
182
153
113
93.65
39.57
1953
86.07
82.4
71.37
67.13
62.65
25.77
1954
109
104
97.29
72.81
65.75
24.6
1955
61.27
58.75
50.38
42.24
38.76
16.73
1956
79.68
76.47
68.08
54.8
45.89
16.47
1957
151
146
124
115
104
37.61
1958
47.89
46.62
42.55
37.05
36.71
15.3
1959
77.23
74.06
64.7
61.96
59.14
23.24
1960
156
150
138
111
91.33
31.36
1961
37.01
35.41
29.57
25.48
23.04
10.73
1962
66.38
63.52
56.79
47.19
40.25
17.36
1963
94.8
90.73
80.33
60.6
48.85
17.88
1964
150
143
120
93.04
83.1
31.06
1965
84.8
81.22
71.32
66.26
58.58
25.91
1966
50.82
48.7
41.83
34.92
31.53
15.2
1967
89.44
86.86
75.16
67.12
63.03
26.42
1968
68.38
65.51
59.28
47.49
44.72
18.93
1969
84.68
80.99
74.32
60.54
54.31
22.22
1970
60.77
58.12
49.66
35.48
28.64
11.44
1971
52.31
50.95
45.14
35.63
32.93
15.03
1972
75.94
73.54
65.2
57.42
51.97
18.52
1973
69.77
67.2
59.36
47.76
40.62
16.85
1974
67.55
64.68
55.56
43.6
36.69
13.28
1975
49.32
47.27
39.71
34.91
31.21
13.79
25
1976
88.99
86.16
74.33
58.16
50.53
21.14
1977
58.23
55.76
46.95
42.43
40.52
17.26
1978
17.29
16.59
15.17
13.58
11.52
5.866
1979
74.95
71.73
61.05
52.07
50.36
22.71
1980
85.67
82.81
75.83
68.59
61.02
23.67
1981
134
128
112
100
84.85
28.61
1982
34.17
32.94
29.9
25.07
21.51
10.49
1983
108
105
90.37
68.84
57.3
19.84
Sorted
results
Prob.
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
0.02702
7
190
182
153
115
104
39.57
0.05405
4
156
150
138
113
93.65
37.61
0.08108
1
151
146
124
111
91.33
31.36
0.10810
8
150
143
120
100
84.85
31.06
0.13513
5
134
128
112
93.04
83.1
28.61
0.16216
2
112
108
97.29
72.81
65.75
26.42
0.18918
9
109
105
94.2
71.78
63.03
25.91
0.21621
6
108
104
90.37
71.43
62.65
25.85
0.24324
3
98.66
94.47
86.14
68.84
62.07
25.77
0.27027
94.8
90.73
80.33
68.59
61.02
24.6
0.29729
7
89.44
86.86
75.83
67.13
59.43
23.67
0.32432
4
88.99
86.16
75.16
67.12
59.14
23.24
0.35135
1
86.07
82.81
74.33
66.26
58.58
22.71
0.37837
8
85.67
82.4
74.32
61.96
57.3
22.22
0.40540
5
84.8
81.22
71.37
60.6
54.31
22.21
0.43243
2
84.68
80.99
71.32
60.54
51.97
21.14
0.45945
9
79.68
76.47
68.08
58.16
50.53
20.87
0.48648
6
77.23
74.06
65.2
57.42
50.36
19.84
0.51351
4
75.94
73.54
64.7
54.8
48.85
18.93
0.54054
1
74.95
71.73
61.05
53.1
48.39
18.52
0.56756
69.77
67.2
59.36
52.07
45.89
17.88
26
8
0.59459
5
68.38
65.51
59.28
47.76
44.72
17.36
0.62162
2
67.55
64.68
56.79
47.49
40.62
17.26
0.64864
9
66.38
63.52
56.33
47.19
40.52
16.85
0.67567
6
65.48
62.73
55.56
43.6
40.25
16.73
0.70270
3
61.27
58.75
50.38
42.43
38.76
16.47
0.72973
60.77
58.12
49.66
42.24
38.15
15.3
0.75675
7
58.23
55.76
47.23
41.74
36.71
15.2
0.78378
4
52.57
50.95
46.95
37.05
36.69
15.03
0.81081
1
52.31
50.36
45.14
35.63
32.93
14.1
0.83783
8
50.82
48.7
42.55
35.48
31.53
13.79
0.86486
5
49.32
47.27
41.83
34.92
31.21
13.28
0.89189
2
47.89
46.62
39.71
34.91
28.64
11.44
0.91891
9
37.01
35.41
29.9
25.48
23.04
10.73
0.94594
6
34.17
32.94
29.57
25.07
21.51
10.49
0.97297
3
17.29
16.59
15.17
13.58
11.52
5.866
0.1
150.3
143.9
121.2
103.3
86.794
31.15
Average
of
yearly
average
s:
20.4968
3
Inputs
generaged
by
pe3.
pl
of
6­
March­
2002
DCPU
Metfile:
met156a.
met
PRZM
scenario:
FLcitrusC.
txt
EXAMS
environment
file:
IRPRZM0.
EXV
Chemical
Name:
dcpu
Description
Variable
Name
Value
Units
Comments
Molecular
weight
mwt
205.1
g/
mol
Henry's
Law
Const.
henry
2.2e­
10
atm­
m^
3/
mol
27
Vapor
Pressure
vapr
2e­
7
torr
Solubility
sol
420
mg/
L
Kd
Kd
16.6
mg/
L
Koc
Koc
mg/
L
Photolysis
half­
life
kdp
43
days
Half­
life
Aerobic
Aquatic
Metabolism
kbacw
99
days
Halfife
Anaerobic
Aquatic
Metabolism
kbacs
15
days
Halfife
Aerobic
Soil
Metabolism
asm
2310
days
Halfife
Hydrolysis:
pH
7
0
days
Half­
life
Method:
CAM
2
integer
See
PRZM
manual
Incorporation
Depth:
DEPI
0.1
cm
Application
Rate:
TAPP
0.1
kg/
ha
Application
Efficiency:
APPEFF
0.99
fraction
Spray
Drift
DRFT
0.064
fraction
of
application
rate
applied
to
pond
Application
Date
Date
1­
Jul
dd/
mm
or
dd/
mmm
or
dd­
mm
or
dd­
mmm
Record
17:
FILTRA
IPSCND
1
UPTKF
Record
18:
PLVKRT
PLDKRT
FEXTRC
0.5
Flag
for
Index
Res.
Run
IR
IR
Flag
for
runoff
calc.
RUNOFF
total
none
or
total(
average
of
entire
run)

OUTPUT
FILE
stored
as
dcpu.
out
Chemical:
dcpu
PRZM
environment:
FLcitrusC.
txt
EXAMS
environment:
IRPRZM0.
EXV
Metfile:
met156a.
met
Water
segment
concentrations
(
ppb)

Year
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
1948
4.341
4.156
3.788
3.152
2.737
1.159
1949
2.323
2.225
2.086
1.859
1.696
0.6452
1950
3.028
2.901
2.615
2.426
2.211
0.9436
1951
5.097
4.896
4.27
3.244
2.681
1.001
1952
8.496
8.137
6.848
5.031
4.181
1.757
1953
3.864
3.7
3.201
2.989
2.797
1.155
1954
4.917
4.712
4.398
3.283
2.952
1.103
1955
2.802
2.686
2.304
1.917
1.749
0.7618
28
1956
3.531
3.389
3.014
2.421
2.036
0.7488
1957
6.785
6.544
5.558
5.115
4.627
1.672
1958
2.314
2.252
2.039
1.744
1.701
0.6986
1959
3.428
3.287
2.879
2.786
2.672
1.045
1960
6.844
6.562
6.078
4.858
4.006
1.398
1961
1.691
1.618
1.351
1.158
1.041
0.4973
1962
2.978
2.85
2.554
2.112
1.837
0.7889
1963
4.169
3.99
3.535
2.664
2.148
0.8094
1964
6.601
6.319
5.301
4.094
3.653
1.385
1965
3.751
3.593
3.153
2.919
2.579
1.162
1966
2.325
2.228
1.919
1.609
1.443
0.6956
1967
4.002
3.888
3.369
3.025
2.826
1.184
1968
3.078
2.949
2.677
2.135
2.002
0.8574
1969
3.802
3.636
3.34
2.77
2.47
0.9989
1970
2.694
2.577
2.202
1.572
1.279
0.5299
1971
2.334
2.274
2.013
1.615
1.525
0.6873
1972
3.471
3.36
2.972
2.594
2.337
0.8386
1973
3.106
2.991
2.647
2.132
1.842
0.7682
1974
3.024
2.896
2.497
1.953
1.653
0.6105
1975
2.37
2.272
1.908
1.645
1.458
0.6315
1976
3.961
3.836
3.307
2.585
2.295
0.9525
1977
2.598
2.487
2.095
1.944
1.858
0.7839
1978
0.7702
0.7457
0.6815
0.6067
0.5158
0.2854
1979
3.38
3.235
2.787
2.357
2.264
1.022
1980
3.819
3.693
3.375
3.084
2.736
1.065
1981
5.908
5.677
4.929
4.421
3.736
1.277
1982
1.519
1.464
1.33
1.112
0.954
0.487
1983
4.822
4.683
4.045
3.074
2.555
0.895
Sorted
results
Prob.
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
0.02702
7
8.496
8.137
6.848
5.115
4.627
1.757
0.05405
4
6.844
6.562
6.078
5.031
4.181
1.672
0.08108
1
6.785
6.544
5.558
4.858
4.006
1.398
0.10810
8
6.601
6.319
5.301
4.421
3.736
1.385
0.13513
5
5.908
5.677
4.929
4.094
3.653
1.277
0.16216
2
5.097
4.896
4.398
3.283
2.952
1.184
0.18918
9
4.917
4.712
4.27
3.244
2.826
1.162
0.21621
6
4.822
4.683
4.045
3.152
2.797
1.159
0.24324
3
4.341
4.156
3.788
3.084
2.737
1.155
0.27027
4.169
3.99
3.535
3.074
2.736
1.103
0.29729
4.002
3.888
3.375
3.025
2.681
1.065
29
7
0.32432
4
3.961
3.836
3.369
2.989
2.672
1.045
0.35135
1
3.864
3.7
3.34
2.919
2.579
1.022
0.37837
8
3.819
3.693
3.307
2.786
2.555
1.001
0.40540
5
3.802
3.636
3.201
2.77
2.47
0.9989
0.43243
2
3.751
3.593
3.153
2.664
2.337
0.9525
0.45945
9
3.531
3.389
3.014
2.594
2.295
0.9436
0.48648
6
3.471
3.36
2.972
2.585
2.264
0.895
0.51351
4
3.428
3.287
2.879
2.426
2.211
0.8574
0.54054
1
3.38
3.235
2.787
2.421
2.148
0.8386
0.56756
8
3.106
2.991
2.677
2.357
2.036
0.8094
0.59459
5
3.078
2.949
2.647
2.135
2.002
0.7889
0.62162
2
3.028
2.901
2.615
2.132
1.858
0.7839
0.64864
9
3.024
2.896
2.554
2.112
1.842
0.7682
0.67567
6
2.978
2.85
2.497
1.953
1.837
0.7618
0.70270
3
2.802
2.686
2.304
1.944
1.749
0.7488
0.72973
2.694
2.577
2.202
1.917
1.701
0.6986
0.75675
7
2.598
2.487
2.095
1.859
1.696
0.6956
0.78378
4
2.37
2.274
2.086
1.744
1.653
0.6873
0.81081
1
2.334
2.272
2.039
1.645
1.525
0.6452
0.83783
8
2.325
2.252
2.013
1.615
1.458
0.6315
0.86486
5
2.323
2.228
1.919
1.609
1.443
0.6105
0.89189
2
2.314
2.225
1.908
1.572
1.279
0.5299
0.91891
9
1.691
1.618
1.351
1.158
1.041
0.4973
0.94594
6
1.519
1.464
1.33
1.112
0.954
0.487
0.97297
3
0.7702
0.7457
0.6815
0.6067
0.5158
0.2854
30
0.1
6.6562
6.3865
5.3781
4.5521
3.817
1.3889
Average
of
yearly
average
s:
0.92500
8
Inputs
generaged
by
pe3.
pl
of
6­
March­
2002
3,4­
DCA
Metfile:
met156a.
met
PRZM
scenario:
FLcitrusC.
txt
EXAMS
environment
file:
IRPRZM0.
EXV
Chemical
Name:
dca
Description
Variable
Name
Value
Units
Comments
Molecular
weight
mwt
162.1
g/
mol
Henry's
Law
Const.
henry
2.2e­
10
atm­
m^
3/
mol
Vapor
Pressure
vapr
2e­
7
torr
Solubility
sol
420
mg/
L
Kd
Kd
16.6
mg/
L
Koc
Koc
mg/
L
Photolysis
half­
life
kdp
43
days
Half­
life
Aerobic
Aquatic
Metabolism
kbacw
99
days
Halfife
Anaerobic
Aquatic
Metabolism
kbacs
15
days
Halfife
Aerobic
Soil
Metabolism
asm
90
days
Halfife
Hydrolysis:
pH
7
0
days
Half­
life
Method:
CAM
2
integer
See
PRZM
manual
Incorporation
Depth:
DEPI
0.1
cm
Application
Rate:
TAPP
0.002
kg/
ha
Application
Efficiency:
APPEFF
0.99
fraction
Spray
Drift
DRFT
0.064
fraction
of
application
rate
applied
to
pond
Application
Date
Date
1­
Jul
dd/
mm
or
dd/
mmm
or
dd­
mm
or
dd­
mmm
Record
17:
FILTRA
IPSCND
1
UPTKF
Record
18:
PLVKRT
PLDKRT
FEXTRC
0.5
Flag
for
Index
Res.
Run
IR
IR
Flag
for
runoff
calc.
RUNOFF
total
none
or
total(
average
of
entire
run)
31
OUTPUT
FILE
stored
as
dca.
out
Chemical:
dca
PRZM
environment:
FLcitrusC.
txt
EXAMS
environment:
IRPRZM0.
EXV
Metfile:
met156a.
met
Water
segment
concentrations
(
ppb)

Year
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
1948
0.05604
0.05366
0.04829
0.04227
0.03963
0.0168
1949
0.03089
0.02958
0.02592
0.02395
0.02129
0.00817
3
1950
0.05756
0.05515
0.04984
0.04374
0.03919
0.01502
1951
0.09672
0.0929
0.08095
0.06067
0.04932
0.017
1952
0.1644
0.1574
0.1324
0.09597
0.07833
0.02836
1953
0.06428
0.06155
0.05301
0.04696
0.04327
0.01666
1954
0.09236
0.08852
0.08241
0.06076
0.05219
0.01781
1955
0.04656
0.04464
0.03827
0.03056
0.02708
0.01081
1956
0.04566
0.04381
0.03866
0.03018
0.02559
0.00917
1
1957
0.1277
0.1231
0.1044
0.09292
0.08181
0.02749
1958
0.04567
0.04444
0.04004
0.0329
0.03033
0.0114
1959
0.057
0.05464
0.04989
0.0482
0.04557
0.01655
1960
0.08523
0.08155
0.07637
0.06018
0.04928
0.0182
1961
0.02439
0.02334
0.01953
0.01617
0.01399
0.00638
1
1962
0.0442
0.04229
0.03808
0.03048
0.02707
0.0104
1963
0.04797
0.04591
0.04079
0.03042
0.0245
0.00919
1
1964
0.09001
0.08616
0.07222
0.05433
0.04661
0.01731
1965
0.05059
0.04846
0.04221
0.03648
0.03361
0.01446
1966
0.03934
0.0377
0.03273
0.02745
0.0245
0.01027
1967
0.07425
0.07121
0.06034
0.05366
0.04746
0.0183
1968
0.05082
0.04869
0.04465
0.03509
0.03231
0.01294
1969
0.06664
0.06374
0.05879
0.0512
0.04456
0.01598
1970
0.03314
0.0317
0.02711
0.0193
0.01614
0.00688
2
1971
0.03197
0.03116
0.02745
0.02341
0.02274
0.00911
9
1972
0.06228
0.06027
0.05307
0.04418
0.03849
0.01308
1973
0.04528
0.04358
0.039
0.0319
0.02889
0.01146
1974
0.04502
0.0431
0.03754
0.02884
0.02441
0.00876
1975
0.04604
0.04412
0.03707
0.03059
0.02631
0.00974
32
1
1976
0.06054
0.05868
0.0504
0.03859
0.03733
0.01404
1977
0.04515
0.04326
0.03783
0.03344
0.03137
0.01177
1978
0.00829
9
0.00802
4
0.00722
1
0.00610
1
0.00561
1
0.00330
2
1979
0.05574
0.05335
0.04676
0.03877
0.03518
0.01331
1980
0.06207
0.06011
0.05451
0.05188
0.04513
0.01648
1981
0.08621
0.08298
0.07184
0.06153
0.05135
0.01771
1982
0.01739
0.01674
0.01527
0.01226
0.01118
0.00578
5
1983
0.08038
0.07804
0.06737
0.05031
0.04114
0.01369
Sorted
results
Prob.
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
0.02702
7
0.1644
0.1574
0.1324
0.09597
0.08181
0.02836
0.05405
4
0.1277
0.1231
0.1044
0.09292
0.07833
0.02749
0.08108
1
0.09672
0.0929
0.08241
0.06153
0.05219
0.0183
0.10810
8
0.09236
0.08852
0.08095
0.06076
0.05135
0.0182
0.13513
5
0.09001
0.08616
0.07637
0.06067
0.04932
0.01781
0.16216
2
0.08621
0.08298
0.07222
0.06018
0.04928
0.01771
0.18918
9
0.08523
0.08155
0.07184
0.05433
0.04746
0.01731
0.21621
6
0.08038
0.07804
0.06737
0.05366
0.04661
0.017
0.24324
3
0.07425
0.07121
0.06034
0.05188
0.04557
0.0168
0.27027
0.06664
0.06374
0.05879
0.0512
0.04513
0.01666
0.29729
7
0.06428
0.06155
0.05451
0.05031
0.04456
0.01655
0.32432
4
0.06228
0.06027
0.05307
0.0482
0.04327
0.01648
0.35135
1
0.06207
0.06011
0.05301
0.04696
0.04114
0.01598
0.37837
8
0.06054
0.05868
0.0504
0.04418
0.03963
0.01502
0.40540
5
0.05756
0.05515
0.04989
0.04374
0.03919
0.01446
0.43243
2
0.057
0.05464
0.04984
0.04227
0.03849
0.01404
0.45945
9
0.05604
0.05366
0.04829
0.03877
0.03733
0.01369
0.48648
6
0.05574
0.05335
0.04676
0.03859
0.03518
0.01331
0.51351
4
0.05082
0.04869
0.04465
0.03648
0.03361
0.01308
33
0.54054
1
0.05059
0.04846
0.04221
0.03509
0.03231
0.01294
0.56756
8
0.04797
0.04591
0.04079
0.03344
0.03137
0.01177
0.59459
5
0.04656
0.04464
0.04004
0.0329
0.03033
0.01146
0.62162
2
0.04604
0.04444
0.039
0.0319
0.02889
0.0114
0.64864
9
0.04567
0.04412
0.03866
0.03059
0.02708
0.01081
0.67567
6
0.04566
0.04381
0.03827
0.03056
0.02707
0.0104
0.70270
3
0.04528
0.04358
0.03808
0.03048
0.02631
0.01027
0.72973
0.04515
0.04326
0.03783
0.03042
0.02559
0.00974
1
0.75675
7
0.04502
0.0431
0.03754
0.03018
0.0245
0.00919
1
0.78378
4
0.0442
0.04229
0.03707
0.02884
0.0245
0.00917
1
0.81081
1
0.03934
0.0377
0.03273
0.02745
0.02441
0.00911
9
0.83783
8
0.03314
0.0317
0.02745
0.02395
0.02274
0.00876
0.86486
5
0.03197
0.03116
0.02711
0.02341
0.02129
0.00817
3
0.89189
2
0.03089
0.02958
0.02592
0.0193
0.01614
0.00688
2
0.91891
9
0.02439
0.02334
0.01953
0.01617
0.01399
0.00638
1
0.94594
6
0.01739
0.01674
0.01527
0.01226
0.01118
0.00578
5
0.97297
3
0.00829
9
0.00802
4
0.00722
1
0.00610
1
0.00561
1
0.00330
2
0.1
0.09366
8
0.08983
4
0.08138
8
0.06099
1
0.05160
2
0.01823
Average
of
yearly
average
s:
0.01343
9
Inputs
generaged
by
pe3.
pl
of
6­
March­
2002
mCPDMU
Metfile:
met156a.
met
34
PRZM
scenario:
FLcitrusC.
txt
EXAMS
environment
file:
IRPRZM0.
EXV
Chemical
Name:
mcpdmu
Description
Variable
Name
Value
Units
Comments
Molecular
weight
mwt
198.1
g/
mol
Henry's
Law
Const.
henry
2.2e­
10
atm­
m^
3/
mol
Vapor
Pressure
vapr
2e­
7
torr
Solubility
sol
420
mg/
L
Kd
Kd
16.6
mg/
L
Koc
Koc
mg/
L
Photolysis
half­
life
kdp
43
days
Half­
life
Aerobic
Aquatic
Metabolism
kbacw
345
days
Halfife
Anaerobic
Aquatic
Metabolism
kbacs
576
days
Halfife
Aerobic
Soil
Metabolism
asm
1116
days
Halfife
Hydrolysis:
pH
7
0
days
Half­
life
Method:
CAM
2
integer
See
PRZM
manual
Incorporation
Depth:
DEPI
0.1
cm
Application
Rate:
TAPP
2.28
kg/
ha
Application
Efficiency:
APPEFF
0.99
fraction
Spray
Drift
DRFT
0.064
fraction
of
application
rate
applied
to
pond
Application
Date
Date
1­
Jul
dd/
mm
or
dd/
mmm
or
dd­
mm
or
dd­
mmm
Record
17:
FILTRA
IPSCND
1
UPTKF
Record
18:
PLVKRT
PLDKRT
FEXTRC
0.5
Flag
for
Index
Res.
Run
IR
IR
Flag
for
runoff
calc.
RUNOFF
total
none
or
total(
average
of
entire
run)

OUTPUT
FILE
stored
as
mcpdmu.
out
Chemical:
mcpdmu
PRZM
environment:
FLcitrusC.
txt
EXAMS
environment:
IRPRZM0.
EXV
Metfile:
met156a.
met
Water
segment
concentrations
35
(
ppb)

Year
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
1948
111
108
102
86.4
78.22
33.14
1949
60.91
59.06
56.51
50.06
46.86
21.22
1950
76.32
74.87
66.68
65.08
61.07
29.53
1951
119
115
105
86.71
75.3
32.04
1952
195
190
168
135
118
54.72
1953
95.67
92.78
85.24
83.11
78.36
38.7
1954
118
114
109
88.57
82.88
35.84
1955
68.74
66.69
59.68
53.15
50.29
25.27
1956
83.6
81.27
75.69
65.67
57.19
23.27
1957
162
158
141
138
127
51.74
1958
59.05
57.28
52.13
48.45
48.39
24.67
1959
92.53
89.79
81.68
76.23
72.83
32.84
1960
171
166
153
132
113
42.44
1961
42.05
40.9
36.87
32.36
30.24
18.88
1962
74.52
72.24
65.92
58.63
51.84
24.17
1963
99.64
96.57
87.58
71.68
60.47
24.47
1964
154
150
132
111
102
41.51
1965
96.46
93.56
84.25
80.25
73.51
36.7
1966
59.13
57.35
51.49
44.58
41.52
23.24
1967
102
100
90.1
81.31
78.51
36.13
1968
78.5
76.13
70.05
60.24
58.09
28.19
1969
98.14
95.15
89.14
73.68
69.28
32.32
1970
65.69
63.67
56.86
44.59
37.25
18.31
1971
61.32
60.14
55.32
45.3
42.69
21.55
1972
82.3
79.84
73.47
68.58
64.48
26.28
1973
79.14
77.09
70.54
59.86
52.46
24.44
1974
73.61
71.37
63.83
53.37
46.59
19.75
1975
55.5
53.83
47.46
43.92
40.71
20.22
1976
98.21
95.81
86.64
72.98
64.27
29.55
1977
70.17
68.07
60.24
53.74
52.06
25.53
1978
21.38
20.81
18.84
17.6
15.52
10.4
1979
85.11
82.5
72.57
64.65
63.74
30.6
1980
99.32
96.8
91.78
82.6
76.45
33.65
1981
140
136
124
118
105
39.45
1982
39.57
38.59
35.96
31.95
28.43
17.34
1983
112
110
99.33
82.58
72.24
28.12
Sorted
results
Prob.
Peak
96
hr
21
Day
60
Day
90
Day
Yearly
0.02702
7
195
190
168
138
127
54.72
0.05405
4
171
166
153
135
118
51.74
0.08108
1
162
158
141
132
113
42.44
0.10810
8
154
150
132
118
105
41.51
36
0.13513
5
140
136
124
111
102
39.45
0.16216
2
119
115
109
88.57
82.88
38.7
0.18918
9
118
114
105
86.71
78.51
36.7
0.21621
6
112
110
102
86.4
78.36
36.13
0.24324
3
111
108
99.33
83.11
78.22
35.84
0.27027
102
100
91.78
82.6
76.45
33.65
0.29729
7
99.64
96.8
90.1
82.58
75.3
33.14
0.32432
4
99.32
96.57
89.14
81.31
73.51
32.84
0.35135
1
98.21
95.81
87.58
80.25
72.83
32.32
0.37837
8
98.14
95.15
86.64
76.23
72.24
32.04
0.40540
5
96.46
93.56
85.24
73.68
69.28
30.6
0.43243
2
95.67
92.78
84.25
72.98
64.48
29.55
0.45945
9
92.53
89.79
81.68
71.68
64.27
29.53
0.48648
6
85.11
82.5
75.69
68.58
63.74
28.19
0.51351
4
83.6
81.27
73.47
65.67
61.07
28.12
0.54054
1
82.3
79.84
72.57
65.08
60.47
26.28
0.56756
8
79.14
77.09
70.54
64.65
58.09
25.53
0.59459
5
78.5
76.13
70.05
60.24
57.19
25.27
0.62162
2
76.32
74.87
66.68
59.86
52.46
24.67
0.64864
9
74.52
72.24
65.92
58.63
52.06
24.47
0.67567
6
73.61
71.37
63.83
53.74
51.84
24.44
0.70270
3
70.17
68.07
60.24
53.37
50.29
24.17
0.72973
68.74
66.69
59.68
53.15
48.39
23.27
0.75675
7
65.69
63.67
56.86
50.06
46.86
23.24
0.78378
4
61.32
60.14
56.51
48.45
46.59
21.55
0.81081
1
60.91
59.06
55.32
45.3
42.69
21.22
0.83783
59.13
57.35
52.13
44.59
41.52
20.22
37
8
0.86486
5
59.05
57.28
51.49
44.58
40.71
19.75
0.89189
2
55.5
53.83
47.46
43.92
37.25
18.88
0.91891
9
42.05
40.9
36.87
32.36
30.24
18.31
0.94594
6
39.57
38.59
35.96
31.95
28.43
17.34
0.97297
3
21.38
20.81
18.84
17.6
15.52
10.4
0.1
156.4
152.4
134.7
122.2
107.4
41.789
Average
of
yearly
average
s:
29.3394
4
Inputs
generaged
by
pe3.
pl
of
6­
March­
2002
APPENDIX
II
SCI­
GROW
OUTPUT
FILES
FOE
MODELING
DIURON
AND
ITS
DEGRADATES
RUN
No.
1
FOR
diuron
INPUT
VALUES
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(
DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
38
9.600
1
9.600
468.0
372.0
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
6.521987
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
A=
367.000
B=
473.000
C=
2.565
D=
2.675
RILP=
3.399
F=
­.
168
G=
.679
URATE=
9.600
GWSC=
6.521987
RUN
No.
1
FOR
DCPMU
INPUT
VALUES
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(
DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
2.030
1
2.030
468.0
770.0
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
2.497237
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
A=
765.000
B=
473.000
C=
2.884
D=
2.675
RILP=
3.821
F=
.090
G=
1.230
URATE=
2.030
GWSC=
2.497237
RUN
No.
2
FOR
DCPU
INPUT
VALUES
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
39
APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(
DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
.080
1
.080
468.0
770.0
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
.098413
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
A=
765.000
B=
473.000
C=
2.884
D=
2.675
RILP=
3.821
F=
.090
G=
1.230
URATE=
.080
GWSC=
.098413
RUN
No.
3
FOR
3,4­
DCA
INPUT
VALUES
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(
DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
.002
1
.002
468.0
30.0
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
.000155
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
A=
25.000
B=
473.000
C=
1.398
D=
2.675
RILP=
1.852
F=
­
1.111
G=
.077
URATE=
.002
GWSC=
.000155
RUN
No.
4
FOR
mCPDMU
INPUT
VALUES
40
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(
DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
­­­
2.04
1
2.04
468.0
372
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
1.3827
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
A=
110.000
B=
473.000
C=
2.041
D=
2.675
RILP=
2.705
F=
­.
591
G=
.257
URATE=
1.120
GWSC=
.287307
