Document ID: EPA-HQ-OPP-2002-0079-0020
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2002-06-19T04:00Z

UNITED
STATES
ENVIRONMENTAL
PROTECTION
AGENCY
WASHINGTON,
D.
C.
20460
OFFICE
OF
PREVENTION,
PESTICIDES
AND
TOXIC
SUBSTANCES
PC
Code:
035506
DP
Barcode:
D275651
Date:
10/
14/
01
MEMORANDUM:
Drinking
Water
Assessment
for
Linuron
on
Carrots
in
California.

TO:
Carol
Christensen
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:
Jim
Carleton/
Chemist
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:

The
3­(
3­,
4­
dichlorophenyl)­
1­
methoxy­
1­
methylurea
(Linuron)
use
on
carrots
in
Griffin
Label
(EPA
Reg.
No.
1812­
320)
is
represented
by
this
memorandum.

Linuron
is
a
herbicide
used
to
control
germinating
and
newly
emerging
grasses
and
broad­
leafed
weeds.
It
is
applied
to
agricultural
crops,
ornamental
bulbs,
and
poplar
trees
for
use
in
shelterbelts
in
the
mid­
west.
2
Formulations
include
water
dispersable
granules,
wettable
powders,
flowable
concentrates,
and
emulsifiable
concentrates/
liquid
suspensions.

Linuron
usually
is
applied
after
a
crop
has
been
planted
but
before
weeds
emerge,
using
ground
or
aerial
equipment.
In
some
crops,
such
as
carrots
and
celery,
linuron
is
applied
to
newly
emerging
plants
as
an
over­
top
spray.
In
asparagus,
linuron
is
applied
between
cuttings
of
newly
emerging
spears
for
weed
control
during
harvest.

The
Tier
II
screening
models
PRZM
1
and
EXAMS
2
with
the
Index
Reservoir
and
Percent
Crop
Area
adjustment
(IR­
PCA
PRZM/
EXAMS)
were
used
to
determine
estimated
surface
water
concentrations
of
linuron.
The
Screening
Concentration
in
Groundwater
(SCI­
GROW
3
)
model
was
used
to
estimate
groundwater
concentrations
for
linuron.
Modeling
results
are
shown
in
Table
1.

Table
1.
Estimated
environmental
concentrations
in
surface
and
groundwater
for
linuron
use
on
carrots.

model
EECs
(µg/
L)
use(
s)
modeled
PCA
Surface
water/
peak
(90
th
percentile
annual
daily
max.)
31.3
two
applications
on
carrots
@
1.0
lb
ai/
acre,
ground
application
Defaul
t
PCA
(0.87)

Surface
water/
90
t
h
percentile
annual
mean)
12.5
Surface
water/
36­
year
overall
mean
7.31
Groundwater/
peak
and
long
term
average
0.54
The
IR­
PCA
PRZM/
EXAMS
modeling
results
indicate
that
linuron
has
the
potential
to
contaminate
surface
waters
by
spray
drift,
and
runoff
in
areas
with
large
amounts
of
annual
rainfall.
Modeling
results
are
higher
than
those
from
existing
surface
water
monitoring
data
for
linuron
targeted
to
the
pesticide
use
area.
3
The
recommended
groundwater
drinking
water
EECs
is
5.0
ppb
(from
the
USEPA
Pesticide
in
Groundwater
Database).
The
modeling
result
is
lower
than
historical
data
from
the
USEPA
(data
>
10
years
old).
The
maximum
observed
concentration
was
5.0
ppb.
Recent
NAWQA
data
which
includes
drinking
water
wells
show
no
concentration
>
0.029
ppb.
This
recommendation
is
based
on
the
fact
that
there
are
no
obvious
changes
in
the
use
pattern
presented
in
the
June
7,
2001
Linuron
SMART
meeting.

Usage
map
for
linuron
4
is
attached.

Environmental
Fate
and
Transport
Assessment
Although
the
environmental
fate
data
base
for
parent
linuron
is
essentially
complete,
two
environmental
fate
data
requirements
(leaching/
adsorption/
desorption
and
terrestrial
field
dissipation
studies)
are
not
fulfilled.
The
environmental
fate
assessment
for
linuron
is
incomplete
and
tentative
because
information
on
the
persistence,
mobility
and
dissipation
pathways
of
several
degradates
of
linuron
is
not
available.

Parent
linuron
appears
to
be
moderately
persistent
and
relatively
immobile.
Increased
mobility
may
occur
under
specific
environmental
conditions
such
as
in
coarse
textured
soils
and
soils
with
low
levels
of
organic
matter.
Linuron
dissipates
principally
by
biotic
processes
such
as
microbial
degradation.
In
surface
soils
with
adequate
organic
matter,
the
combined
processes
of
adsorption
and
microbial
degradation
would
limit
linuron's
potential
to
migrate
to
ground
water.
Linuron
could
runoff
to
surface
water
bodies.
In
that
case,
it
would
degrade
fairly
rapidly
to
three
primary
metabolites
(desmethoxy
linuron,
desmethyl
linuron,
norlinuron,
and
3,4­
DCA,
none
of
each
is
>10%
of
the
applied
radioactivity
in
the
aerobic
soil
metabolism
study).
However,
information
on
the
persistence
and
mobility
of
these
degradates
is
not
currently
available.

Linuron
exhibits
some
of
the
properties
and
characteristics
of
chemicals
that
have
been
detected
in
ground
water,
and
linuron
itself
has
been
detected
in
ground
water
in
four
states
(Georgia,
Missouri,
Virginia
and
Wisconsin).
Linuron
is
moderately
persistent
with
an
aerobic
soil
metabolism
half­
life
ranging
from
57
to
100
days.
Because
linuron
is
sufficiently
persistent
and
may
be
mobile
under
certain
environmental
conditions,
it
has
the
potential
to
impact
ground
water
quality.

Linuron
can
be
applied
aerially
or
by
ground
spray
and
therefore
could
contaminate
surface
waters
through
spray
drift.
It
has
the
potential
to
be
somewhat
persistent
in
surface
4
waters,
particularly
those
with
low
microbiological
activity
and
long
hydrological
residence
times.
Linuron
degraded
with
a
halflife
of
less
than
3
weeks
in
nonsterile
anaerobic
silt
loam
and
sand
soil:
water
(1:
1)
systems.
It
may
be
less
persistent
in
water
and
sediment
under
anaerobic
conditions
than
under
aerobic
conditions.
Its
bioconcentration
potential
is
relatively
low.

Linuron
is
not
currently
regulated
under
the
Safe
Drinking
Water
Act,
and
water
supply
systems
are
not
required
to
sample
and
analyze
for
it.
No
Maximum
Contaminant
Level
(MCL)
or
drinking
water
health
advisories
have
been
established
for
linuron.
The
primary
treatment
processes
employed
by
most
water
systems
may
not
always
be
completely
effective
in
removing
linuron.
As
a
result,
the
Agency
does
have
some
moderate
concerns
regarding
potential
risks
of
linuron
to
surface
water
source
supply
systems.

Surface
Water
Monitoring
The
EFED
has
limited
monitoring
data
on
the
concentrations
of
linuron
in
surface
water
at
the
present
time.

The
USGS­
National
Water
Quality
Assessment
Program,
San
Joaquin
­
Tulare
Basins
analyzed
surface
water
samples
from
a
fixed
site
on
the
San
Joaquin
River
near
Vernalis,
CA.
Grab
water
samples
were
collected
biweekly
for
one
year
(1993).
Maximum
linuron
concentration
was
0.29
ppb
5
,
even
though
the
San
Joaquin
Valley
is
a
major
production
region
for
carrots
in
California
6
.

In
another
study,
the
US
Geological
Survey
(USGS)
National
Water
Quality
Assessment
Program
(NAWQA)
collected
5196
surface
water
samples
from
40
agricultural
stream
sites
through
the
nation
during
the
period
from
1992­
1998.
One
to
two
samples
were
collected
at
each
site
each
month
during
periods
when
pesticide
transport
in
the
streams
was
expected
to
be
low.
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.
Linuron
was
detected
in
2.70%
of
the
samples
(detection
limit
=
0.01
ppb)
with
a
linuron
maximum
concentration
of
1.4
ppb
7
.

The
frequency
of
sampling
and
the
length
of
sampling
period
of
both
of
the
USGS
studies
were
not
sufficient
to
represent
the
temporal
and
spatial
requirements
for
use
in
making
regulatory
determinations
concerning
drinking
water.
5
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
potentially
vulnerable
drinking
water
source
based
on
the
geometry
of
an
actual
reservoir
and
its
watershed
in
a
specific
area
(Illinois),
using
regional
screening
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
scenario.

The
IR­
PCA
PRZM/
EXAMS
model
use
and
fate
input
parameters
for
linuron
in
surface
water
are
shown
in
Table
2.
The
IR­
PCA
PRZM/
EXAMS
model
input
and
output
files
for
linuron
are
shown
in
Appendix
I.

Table
2:
IR­
PC
PRZM/
EXAMS
input
parameters
for
linuron
use
on
carrots
in
California.

Input
variable
Input
value
&
calculations
Source/
Quality
of
data
Crop
name
carrots
label
EPA
Reg.
No.
1812­
320).

application
rate
(lb
ai/
acre)
2
label
EPA
Reg.
No.
1812­
320).

Interval
between
appl.
(d)
14
label
EPA
Reg.
No.
1812­
320).

Application
efficiency
0.99
IR­
PCA
Guidance
8
Spray
drift
fraction
0.064
IR­
PCA
Guidance
Application
method
ground
label
(EPA
Reg.
No.
1812­
362).

DWRATE
(day
­1
)
0.005
MRID#
41625401;
Input
parameters
guidance
9
;
single
value
X
3.

DSRATE
(day
­1
)
0.005
MRID#
41625401;
Input
parameters
guidance;
single
value
X
3
Kd
(mL/
g)
2.7
(sandy
loam)
MRID#
00148443;
Input
parameters
guidance.
Soil­
Kd
for
best
match
of
soil
in
model
was
used.

Henry
(atm.
m
3
/mole)
6.07X10
­8
(calculated)
RED,
1994.
6
KBACW
(h
­1
)
0.0003
No
aerobic
aquatic
data
is
available,
the
aerobic
soil
met.
degradation
rate
was
multiplied
by
0
.5.
MRID#
41625401.
Input
parameters
guidance.

KBACS
(h
­1
)
0.0002
Anaerobic
aquatic
half­
life
(21
days)
was
multiplied
by
3.
MRID#
40142501.
Input
parameters
guidance
.

KDP
(h
­1
)
0.0006
MRID#
40103601;
Input
parameters
guidance.

KBH,
KNH,
KAH
(h
­1
)
(stable)
MRID#
40916201;
Input
parameters
guidance.

KPS
(mL/
g)
2.7
MRID#
00148443;
Input
parameters
guidance.

MWT
(g/
mole)
249.1
RED,
1994.

Solubility
@
25
0
C
(ppm)
81
RED,
1994.

Vapor
pressure
(torr)
1.5X10
­5
The
MERCK
Index
10
.

Assumptions
and
Uncertainties
11,12
Index
Reservoir
The
results
from
the
index
reservoir
represent
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
reservoir
during
wet
periods
and
overestimates
removal
during
dry
periods.
This
assumption
can
underestimate
or
overestimate
the
concentration
in
the
pond
depending
upon
the
annual
precipitation
pattern
at
the
site.
7
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
over
or
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
field
scale
processes
simulated
by
the
coupled
PRZM
and
EXAMS
models
are
a
reasonable
approximation
of
pesticide
fate
and
transport
within
a
watershed
that
contains
a
drinking
water
reservoir.
If
the
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
8
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.

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.
9
Groundwater
Monitoring
EFED
has
limited
monitoring
data
on
the
concentrations
of
Linuron
in
groundwater.
Table
3
shows
validated
monitoring
data
for
linuron
that
are
available
for
the
states
of
Georgia
(GA),
Missouri
(MO),
Virginia
(VA),
and
Wisconsin
(WI).

Table
3.
Groundwater
monitoring
data
for
linuron.
Number
of
wells
sampled
(number
of
wells
with
residues)
13
.

State
well
results
range
of
conc.
(ppb)

GA
70
(67)
1.0
­
5.0
Mo
269
(38)
0.2
­
1.9
VA
12
(5)
0.042
­
3.79
WI
26
(1)
3.00
In
addition,
the
US
Geological
Survey
(USGS)
National
Water
Quality
Assessment
Program
(NAWQA)
analyzed
pesticide
occurrence
and
concentrations
in
shallow
ground
water
in
agricultural
areas
(detection
limit
=
0.01
ppb).
Analysis
of
924
samples
showed
linuron
in
0.11%
of
the
samples
analyzed
with
a
maximum
concentration
of
0.029
ppb
14
.

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
sites
in
the
NAWQA
study
were
sampled
for
pesticides
once
at
each
site.

Even
though
the
groundwater
monitoring
data
collected
by
USGS
NAWQA
are
from
sites
considered
to
represent
typical
use
areas,
the
frequency
and
duration
of
sampling
were
not
sufficient
to
represent
an
adequate
monitoring
data
set
for
exclusive
use
in
drinking
water
exposure
determination.

The
SCI­
GROW
model
was
used
to
estimate
potential
groundwater
concentrations
of
linuron.
10
Table
4
shows
the
input
parameter
values
used
in
SCI­
GROW
modeling.

Table
4.
Input
parameters
for
linuron
used
in
the
SCI­
GROW
model.

Input
variable
Input
value
&
calculation
s
Source/
Quality
of
data
1
Application
rate
(lb
ai/
acre)
1.0
(EPA
Reg.
No.
1812­
320).

Maximum
No.
of
Applications
2
(EPA
Reg.
No.
1812­
320).

Koc
(mL/
g)
208
MRID#
46007015
(median
value);
Input
parameters
guidance.

Aerobic
Soil
metabolism
t1/
2.
(day)
49
MRID#
41625401;
Input
parameters
guidance.

Groundwater
EECs
predicted
using
the
SCI­
GROW
screening
model
are
substantially
less
than
those
estimated
for
surface
water
using
PRZM
and
EXAMS.
SCI­
GROW
estimated
concentrations
of
linuron
are
also
much
less
than
those
from
monitoring
data
shown
in
Table
3.
Therefore,
for
drinking
water
concentrations
from
groundwater
sources
we
recommend
5.0
ppb
to
be
used
in
the
drinking
water
assessment.

REFERENCES
1.
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.
11
4.
U.
S
GS.
1992.
National
Water
Quality
Assessment
(NWQA),
Pesticides
National
Synthesis
Project,
Annual
Use:
Linuron.

5.
U.
S
GS.
1993.
National
Water
Quality
Assessment
Program
San
Joaquin
­
Tulare
Basins
Study
Unit,
[Online].
Available
at
http://
ca.
water.
usgs.
gov/
sanj_
nawqa/
data_
sw/
ifs.
1993.
herb2.

6.
The
United
State
Department
of
Agriculture,
Office
of
Pesticide
Management
Policy
&
Pesticide
Impact
Assessment
Program.
Crop
Profile
for
Carrots
in
California,
[Online].
A
v
a
i
l
a
b
l
e
a
t
http://
pestdata.
ncsu.
edu/
cropprofiles/
Detail.
CFM?
FactShee
ts_
RecordID=
285.

7.
USGS.
1998.
Pesticides
in
Surface
and
Ground
Water
of
the
United
States:
Summary
of
Results
of
the
National
Water
Quality
Assessment
Program,
[Online].
Available
at
http://(
NAWQA)=
http://
ca.
water.
usgs.
gov/
pnsp/
allsum/#
t1.

8.
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.

9.
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.

10.
The
Merck
Index.
1989.
An
encyclopedia
of
chemicals,
drugs,
and
biologicals.
11
th
ed.
Rahway,
N.
J.
p.
533.

11.
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.

12.
U.
S.
Environmental
Protection
Agency.
1984.
Chemical
Information
Fact
Sheet
Number
28:
Linuron.
Office
of
Pesticides
and
Toxic
Substances,
Washington,
DC,
9­
13.

13.
U.
S.
EPA.
1992.
Pesticides
in
Ground
Water
Database­
A
compilation
of
Monitoring
Studies:
1971
­
1991.
Office
of
12
Prevention,
Pesticides,
and
Toxic
Substances,
EPA
734­
12­
92­
001.

14.
U.
S
GS.
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
FILE
FOR
THE
USE
OF
LINURON
ON
CARROTS
IN
CALIFORNIA
LINURON
Lerdo
clay
loam,
MLRA
C­
17;
Central
Valley,
CA,
Carrots
0.700
0.500
0
17.00
1
1
4
0.21
1.00
1.000
172.8
3
1.00
600.00
1
1
0.20
60.00
80.00
3
91
85
88
0.00
100.00
1
3
0101
21
9
2209
0.10
0.10
0.10
.023
.023
.023
36
100948
231248
311248
1
100949
231249
311249
1
100950
231250
311250
1
100951
231251
311251
1
100952
231252
311252
1
100953
231253
311253
1
100954
231254
311254
1
100955
231255
311255
1
100956
231256
311256
1
100957
231257
311257
1
100958
231258
311258
1
100959
231259
311259
1
100960
231260
311260
1
100961
231261
311261
1
100962
231262
311262
1
100963
231263
311263
1
100964
231264
311264
1
100965
231265
311265
1
100966
231266
311266
1
100967
231267
311267
1
13
100968
231268
311268
1
100969
231269
311269
1
100970
231270
311270
1
100971
231271
311271
1
100972
231272
311272
1
100973
231273
311273
1
100974
231274
311274
1
100975
231275
311275
1
100976
231276
311276
1
100977
231277
311277
1
100978
231278
311278
1
100979
231279
311279
1
100980
231280
311280
1
100981
231281
311281
1
100982
231282
311282
1
100983
231283
311283
1
2
non­
incorporated
applications
of
2.0
lbs
A.
I./
acre
(2.24
Kg/
Ha),
spry
drift
0.99,
APPEFF.
0.064
72
1
0
Linuron
***
Kd:
2.7
AeSM:
T1/
2=
49
days
AnAQ
Met:
T1/
2=
21
days
***
101248
0
2
0.00
2.24
0.99
0.064
241248
0
2
0.00
2.24
0.99
0.064
101249
0
2
0.00
2.24
0.99
0.064
241249
0
2
0.00
2.24
0.99
0.064
101250
0
2
0.00
2.24
0.99
0.064
241250
0
2
0.00
2.24
0.99
0.064
101251
0
2
0.00
2.24
0.99
0.064
241251
0
2
0.00
2.24
0.99
0.064
101252
0
2
0.00
2.24
0.99
0.064
241252
0
2
0.00
2.24
0.99
0.064
101253
0
2
0.00
2.24
0.99
0.064
241253
0
2
0.00
2.24
0.99
0.064
101254
0
2
0.00
2.24
0.99
0.064
241254
0
2
0.00
2.24
0.99
0.064
101255
0
2
0.00
2.24
0.99
0.064
241255
0
2
0.00
2.24
0.99
0.064
101256
0
2
0.00
2.24
0.99
0.064
241256
0
2
0.00
2.24
0.99
0.064
101257
0
2
0.00
2.24
0.99
0.064
241257
0
2
0.00
2.24
0.99
0.064
101258
0
2
0.00
2.24
0.99
0.064
241258
0
2
0.00
2.24
0.99
0.064
101259
0
2
0.00
2.24
0.99
0.064
241259
0
2
0.00
2.24
0.99
0.064
101260
0
2
0.00
2.24
0.99
0.064
241260
0
2
0.00
2.24
0.99
0.064
101261
0
2
0.00
2.24
0.99
0.064
241261
0
2
0.00
2.24
0.99
0.064
101262
0
2
0.00
2.24
0.99
0.064
241262
0
2
0.00
2.24
0.99
0.064
101263
0
2
0.00
2.24
0.99
0.064
241263
0
2
0.00
2.24
0.99
0.064
14
101264
0
2
0.00
2.24
0.99
0.064
241264
0
2
0.00
2.24
0.99
0.064
101265
0
2
0.00
2.24
0.99
0.064
241265
0
2
0.00
2.24
0.99
0.064
101266
0
2
0.00
2.24
0.99
0.064
241266
0
2
0.00
2.24
0.99
0.064
101267
0
2
0.00
2.24
0.99
0.064
241267
0
2
0.00
2.24
0.99
0.064
101268
0
2
0.00
2.24
0.99
0.064
241268
0
2
0.00
2.24
0.99
0.064
101269
0
2
0.00
2.24
0.99
0.064
241269
0
2
0.00
2.24
0.99
0.064
101270
0
2
0.00
2.24
0.99
0.064
241270
0
2
0.00
2.24
0.99
0.064
101271
0
2
0.00
2.24
0.99
0.064
241271
0
2
0.00
2.24
0.99
0.064
101272
0
2
0.00
2.24
0.99
0.064
241272
0
2
0.00
2.24
0.99
0.064
101273
0
2
0.00
2.24
0.99
0.064
241273
0
2
0.00
2.24
0.99
0.064
101274
0
2
0.00
2.24
0.99
0.064
241274
0
2
0.00
2.24
0.99
0.064
101275
0
2
0.00
2.24
0.99
0.064
241275
0
2
0.00
2.24
0.99
0.064
101276
0
2
0.00
2.24
0.99
0.064
241276
0
2
0.00
2.24
0.99
0.064
101277
0
2
0.00
2.24
0.99
0.064
241277
0
2
0.00
2.24
0.99
0.064
101278
0
2
0.00
2.24
0.99
0.064
241278
0
2
0.00
2.24
0.99
0.064
101279
0
2
0.00
2.24
0.99
0.064
241279
0
2
0.00
2.24
0.99
0.064
101280
0
2
0.00
2.24
0.99
0.064
241280
0
2
0.00
2.24
0.99
0.064
101281
0
2
0.00
2.24
0.99
0.064
241281
0
2
0.00
2.24
0.99
0.064
101282
0
2
0.00
2.24
0.99
0.064
241282
0
2
0.00
2.24
0.99
0.064
101283
0
2
0.00
2.24
0.99
0.064
241283
0
2
0.00
2.24
0.99
0.064
0.
1
0.0
0.00
0.072
0.5
Lerdo
clay
loam;
Hydrologic
Group
C
100.00
0
0
0
0
0
0
0
0
0
0.0
0.000
0.00
2
1
18.00
1.600
0.325
0.000
0.000
0.000
0.005
0.005
0.000
1.00
0.325
0.175
0.017
2.700
2
82.00
1.500
0.249
0.000
0.000
0.000
0.005
0.005
0.000
1.0
0.249
0.129
0.002
2.700
0
WATR
YEAR
10
PEST
YEAR
10
CONC
YEAR
10
1
6
15
11
­­­­

5
DAY
RUNF
TSER
0
0
1.
E0
EFLX
TSER
0
0
1.
E0
ESLS
TSER
0
0
1.
E0
RUNF
TSER
0
0
1.
E0
PRCP
TSER
0
0
1.
E0
16
IR­
PCA
PRZM/
EXAMS
OUTPUT
FILE
FOR
THE
USE
OF
LINURON
ON
CARROTS
IN
CALIFORNIA
WATER
COLUMN
DISSOLVED
CONCENTRATION
(PPB)

YEAR
PEAK
96
HOUR
21
DAY
60
DAY
90
DAY
YEARLY
­­­­
­­­­
­­­­­­­
­­­­­­
­­­­­­
­­­­­­
­­­­­

1948
9.435
9.325
6.422
2.329
1.553
0.404
1949
9.955
9.840
8.497
7.644
7.053
3.680
1950
11.890
11.760
11.330
10.280
9.819
5.169
1951
10.600
10.480
9.986
9.396
8.974
4.647
1952
21.000
20.770
19.830
18.430
17.710
9.137
1953
12.020
11.890
11.640
10.740
9.992
5.283
1954
27.190
26.890
25.790
23.460
21.690
10.420
1955
15.580
15.410
14.710
13.740
12.790
6.510
1956
11.290
11.160
9.362
8.490
7.831
4.669
1957
12.600
12.460
11.880
10.920
10.360
5.678
1958
13.920
13.710
13.000
11.700
10.870
6.840
1959
10.270
10.160
9.697
8.797
8.148
4.086
1960
16.510
16.330
15.640
14.390
13.280
6.984
1961
11.670
11.550
11.020
9.910
9.124
4.476
1962
37.470
37.030
35.920
32.460
29.850
14.070
1963
28.360
28.030
26.690
24.540
22.870
11.350
1964
11.050
10.930
10.430
9.383
8.641
4.259
1965
24.260
20.610
10.820
10.240
9.405
5.963
1966
29.150
28.820
27.450
24.760
23.860
11.930
1967
17.300
17.110
16.320
14.610
13.830
7.722
1968
10.360
10.240
9.774
8.791
8.478
4.829
1969
12.320
12.180
11.610
10.550
10.350
5.865
1970
14.000
13.840
13.180
11.760
10.730
5.920
1971
10.990
10.870
10.400
9.372
8.630
5.431
1972
11.240
11.110
9.625
8.657
7.971
5.639
1973
25.240
24.960
23.810
21.460
20.490
10.300
1974
17.600
17.380
16.470
14.490
13.000
7.568
1975
15.110
14.940
14.230
12.690
11.890
6.320
1976
12.340
12.200
11.650
10.480
10.000
5.397
1977
22.180
21.930
11.240
9.721
9.147
6.433
1978
73.250
72.400
68.960
61.770
56.310
26.430
1979
16.730
16.550
15.790
14.530
13.770
6.910
1980
15.100
14.940
14.580
14.130
13.460
7.027
1981
13.080
12.930
12.390
11.380
11.000
6.157
1982
11.170
11.000
10.320
9.676
9.229
5.630
1983
36.360
35.960
34.270
31.170
28.770
13.920
17
SORTED
FOR
PLOTTING
­­­­­­
­­­
­­­­­­­

PROB
PEAK
96
HOUR
21
DAY
60
DAY
90
DAY
EARLY
­­­­
­­­­
­­­­­­­
­­­­­­
­­­­­­
­­­­­­
­­­­

0.027
73.250
72.400
68.960
61.770
56.310
26.430
0.054
37.470
37.030
35.920
32.460
29.850
14.070
0.081
36.360
35.960
34.270
31.170
28.770
13.920
0.108
29.150
28.820
27.450
24.760
23.860
11.930
0.135
28.360
28.030
26.690
24.540
22.870
11.350
0.162
27.190
26.890
25.790
23.460
21.690
10.420
0.189
25.240
24.960
23.810
21.460
20.490
10.300
0.216
24.260
21.930
19.830
18.430
17.710
9.137
0.243
22.180
20.770
16.470
14.610
13.830
7.722
0.270
21.000
20.610
16.320
14.530
13.770
7.568
0.297
17.600
17.380
15.790
14.490
13.460
7.027
0.324
17.300
17.110
15.640
14.390
13.280
6.984
0.351
16.730
16.550
14.710
14.130
13.000
6.910
0.378
16.510
16.330
14.580
13.740
12.790
6.840
0.405
15.580
15.410
14.230
12.690
11.890
6.510
0.432
15.110
14.940
13.180
11.760
11.000
6.433
0.459
15.100
14.940
13.000
11.700
10.870
6.320
0.486
14.000
13.840
12.390
11.380
10.730
6.157
0.514
13.920
13.710
11.880
10.920
10.360
5.963
0.541
13.080
12.930
11.650
10.740
10.350
5.920
0.568
12.600
12.460
11.640
10.550
10.000
5.865
0.595
12.340
12.200
11.610
10.480
9.992
5.678
0.622
12.320
12.180
11.330
10.280
9.819
5.639
0.649
12.020
11.890
11.240
10.240
9.405
5.630
0.676
11.890
11.760
11.020
9.910
9.229
5.431
0.703
11.670
11.550
10.820
9.721
9.147
5.397
0.730
11.290
11.160
10.430
9.676
9.124
5.283
0.757
11.240
11.110
10.400
9.396
8.974
5.169
0.784
11.170
11.000
10.320
9.383
8.641
4.829
0.811
11.050
10.930
9.986
9.372
8.630
4.669
0.838
10.990
10.870
9.774
8.797
8.478
4.647
0.865
10.600
10.480
9.697
8.791
8.148
4.476
0.892
10.360
10.240
9.625
8.657
7.971
4.259
0.919
10.270
10.160
9.362
8.490
7.831
4.086
0.946
9.955
9.840
8.497
7.644
7.053
3.680
0.973
9.435
9.325
6.422
2.329
1.553
0.404
1/
10
31.313
30.962
29.496
26.683
25.333
12.527
MEAN
OF
ANNUAL
VALUES
=
7.307
STANDARD
DEVIATION
OF
ANNUAL
VALUES
=
4.336
UPPER
90%
CONFIDENCE
LIMIT
ON
MEAN
=
8.377
18
SCI­
GROW
output
file
RUN
No.
1
FOR
linuron
INPUT
VALUES
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

­­

APPL
(#/
AC)
APPL.
URATE
SOIL
SOIL
AEROBIC
RATE
NO.
(#/
AC/
YR)
KOC
METABOLISM
(DAYS)

­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

­­­

1.000
2
2.000
208.0
49.0
GROUND­
WATER
SCREENING
CONCENTRATIONS
IN
PPB
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

.544
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

A=
44.000
B=
207.000
C=
1.643
D=
2.316
RILP=
2.768
F=
­.
553
G=
.280
URATE=
2.000
GWSC=
.560119