Document ID: EPA-HQ-OW-2002-0033-0233
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2003-05-13T04:00Z

MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
INTRODUCTION
EPA
estimates
both
cost
and
benefits
of
environmental
regulations
based
on
a
random
stratified
sample
of
MP&
M
facilities.
1
EPA
then
estimates
national
level
costs
and
benefits
by
extrapolating
analytic
results
from
sample
facilities
to
the
national
level
using
statistically
determined
sample
facility
weights.

Sample
facility
weights
used
in
the
benefit
cost
analysis
of
environmental
regulations
are
based
on
detailed
questionnaire
stratification.
Stratification
means
dividing
the
population
of
facilities
into
a
number
of
non­
overlapping
sub­
populations
(
strata).
These
strata
consist
of
facilities
that
are
homogeneous
with
respect
to
facility
size
(
i.
e.,
number
of
employees
or
revenue)
or
engineering
characteristics
such
as
wastewater
flow
because
this
information
was
not
available
at
the
time
the
sample
frame
was
developed.
The
sample
weights
for
facilities
in
the
sample
are
based
on
the
total
population
in
each
category
and
probabilities
of
selection
in
each
stratum.
Appendix
G:
Extrapolation
Methods
APPENDIX
CONTENTS
G.
1
Raking
to
Adjust
MP&
M
Facility
Sample
Weights
.................
.............
G­
2
G.
1.1
.................
.......
G­
2
G.
1.2
ing
Adjustment
.................
..
G­
3
G.
2
y
for
Developing
Sample­
Weighted
Estimates
for
Sites
with
More
Than
One
MP&
M
Facility
.................
......
G­
7
G.
3
Study
Results
to
the
National
Level
.......
G­
13
G.
3.1
e
in
Pollutant
Loads
.............
G­
14
G.
3.2
ctivities
on
Reaches
Affected
by
MP&
M
Discharges
..........
G­
14
G.
3.3
Differences
in
Household
Income
.......
G­
14
G.
4
ults
.................
................
G­
15
Glossary
.................
.................
..
G­
17
Using
Data
Sources
Rak
Methodolog
Methodology
for
Extrapolation
of
Ohio
Case
Chang
Level
of
Recreational
A
Res
EPA
traditionally
uses
a
standard
linear
weighting
technique
(
hereafter,
traditional
extrapolation)
to
estimate
national
compliance
costs,
changes
in
pollutant
removals,
and
national­
level
benefits
of
environmental
regulations.
However,
using
sample
weights
that
are
based
only
on
facility­
specific
(
e.
g.,
engineering)
characteristics
and
various
non­
facility
factors
can
lead
to
a
conditional
bias
in
the
estimation
of
national­
level
benefits.
In
particular,
this
approach
omits
consideration
of
important
non­
facility
factors
that
influence
the
occurrence
and
size
of
benefits.

Non­
facility
factors
that
are
likely
to
affect
the
occurrence
and
size
of
benefits
from
reduced
sample
facility
discharges
and
that
are
not
reflected
in
the
standard
stratification
and
sample­
weighting
approach
include
the
receiving
water
body
type
and
size
and
the
size
of
the
population
residing
in
the
vicinity
of
a
sample
facility.
Furthermore,
co­
occurrences
of
facilities
discharging
to
the
same
reach
may
also
affect
the
occurrence
of
benefits.
Many
of
the
environmental
assessment
and
benefits
analyses
include
comparisons
of
the
estimated
baseline
and
post­
compliance
pollutant
concentrations
(
e.
g.,
sludge
concentrations
or
in­
waterway
concentrations)
with
the
relevant
threshold
values.
Because
the
effect
of
aggregate
discharges
from
several
facilities
is
likely
to
be
different
from
the
sum
of
effects
from
these
facilities
considered
independently,
it
is
also
important
to
account
for
the
likelihood
of
joint
discharges
of
MP&
M
facilities
to
the
same
reach.

The
Agency
used
two
approaches
to
address
omission
of
these
important
non­
facility
factors
(
i.
e.,
water
body
type
and
size,

affected
population,
and
co­
occurrence
of
MP&
M
discharges)
in
designing
the
MP&
M
facilities
sample.
First,
EPA
adjusted
sampling
weights
through
post­
stratification
using
two
variables
receiving
water
body
type
and
size
and
the
size
of
the
population
residing
in
the
vicinity
of
the
sample
facility.
Section
G.
1
presents
the
method
of
doing
this
adjustment.
Second,

EPA
used
a
differential
sample
weighting
technique
in
developing
national
estimates
of
environmental
effects
and
benefits.
This
method
accounts
for
the
presence
of
more
than
one
facility
with
different
sample
weights
discharging
directly
or
indirectly
(
through
a
POTW)
to
reaches
affected
by
multiple
MP&
M
dischargers.
Section
G.
2
of
this
appendix
describes
the
differential
sample
weighting
technique.

EPA
used
both
the
traditional
extrapolation­
based
weights
and
the
sample
weights
adjusted
through
post­
stratification
(
hereafter,
post­
stratification
extrapolation)
to
analyze
the
final
MP&
M
rule s
benefits.
The
benefit
estimates
based
on
the
post­
stratification
extrapolation
weights
are
used
to
validate
general
conclusions
that
EPA
draws
from
its
main
analysis
based
on
the
traditional
extrapolation
method.
In
addition
to
developing
national
benefit
estimates
based
on
both
traditional
and
1
A
census
of
all
MP&
M
facilities
was
not
performed
due
to
the
large
size
of
the
MP&
M
industry.

G­
1
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
post­
stratification
extrapolation
weights,
EPA
developed
a
third
estimate
of
national
benefits
based
on
the
Ohio
case
study
results.
2
Section
G.
3
of
this
appendix
discusses
this
method
in
detail.
The
Agency
recognizes
that
the
extrapolation
method
used
for
the
Ohio
case
study
results
is
not
rigorous.
Therefore,
this
method
is
used
to
supplement
the
main
results.

G.
1
USING
RAKING
TO
ADJUST
MP&
M
FACILITY
SAMPLE
WEIGHTS
Omitting
information
that
affects
the
occurrence
and
size
of
benefits
from
the
original
sample
frame s
design
may
lead
to
conditional
bias
in
MP&
M
rule
benefit
estimates.
To
address
this
problem,
EPA
used
a
post­
stratification
weight­
adjustment
method
called
raking
to
account
for
two
additional
variables
that
were
not
accounted
for
in
the
original
sample
design
and
that
may
affect
benefit
occurrence:

 
physical
characteristics
of
the
receiving
water
body
(
including
type
and
size);
and
 
size
of
the
population
residing
in
the
vicinity
of
the
sample
facility.

G.
1.1
Data
Sources
EPA
first
classified
the
universe
of
MP&
M
facilities
into
different
poststrata.
The
Agency
relied
on
three
data
sources
to
identify
discharge
reach
characteristics
and
the
population
size
in
the
vicinity
of
the
discharge
reach:

1.
EPA's
Permit
Compliance
System
database
(
PCS)
indicated
water
bodies
to
which
MP&
M
facilities
discharge;

2.
EPA's
Reach
File
1
(
RF1)
provided
additional
information
on
the
receiving
water
bodies,
including
water
body
type,

flow
characteristics,
and
counties
abutting
these
water
bodies;
and
3.
Census
data
provided
information
on
county
populations.

The
PCS
database
provides
information
on
facilities
covered
by
NPDES
permits.
The
database
covers
only
those
facilities
that
discharge
directly
to
surface
or
ground
water.
No
information
is
available
on
the
location
of
MP&
M
facilities
that
discharge
to
surface
water
indirectly
or
via
POTWs.
EPA
therefore
limited
post­
stratification
to
direct
discharging
facilities.

The
Agency
used
the
resulting
adjusted
sample
weights
to
estimate
national­
level
benefits
for
only
the
final
regulatory
option,

which
covers
only
direct
discharging
facilities.
Chapters
13
through
19
of
this
report
present
benefit
estimates
in
various
benefit
categories
considered
in
this
analysis.

The
extent
of
improvement
in
estimation
accuracy
depends
on
the
reliability
of
the
information
used
for
post­
stratification.

Accordingly,
it
was
necessary
to
understand
and
account
for
PCS
database
limitations
in
implementing
a
post­
stratification
approach.
The
PCS
database
is
designed
to
provide
information
on
a
facility's
SIC
codes,
facility
flow,
and
receiving
reach
characteristics.
These
characteristics
include
water
body
name
and
type,
stream
ID,
and
stream
flow.
Although
these
data
can
be
used
to
classify
facilities
in
the
identified
poststrata,
these
fields
are
not
always
populated
in
the
database.
To
fill
missing
data,
EPA
combined
data
from
PCS
with
supplementary
analyses
and
information
from
RF1,
using
the
following
framework:

 
PCS
provided
a
stream
ID
and
information
on
the
water
body
type
and
flow
characteristics.
EPA
obtained
stream
characteristics
from
PCS
and
used
the
stream
ID
to
obtain
information
on
counties
abutting
the
reach
from
RF1;

 
PCS
provided
a
stream
ID,
but
not
the
water
body
type
and
flow
characteristics.
EPA
used
the
stream
ID
to
obtain
information
on
water
body
type,
flow
characteristics,
and
counties
abutting
the
reach
from
RF1;

 
PCS
provided
water
body
name
and
type,
but
not
stream
ID
and
flow
characteristics.
EPA
first
used
facility
lat/
long
data
to
assign
the
PCS
facility
to
the
nearest
reach
that
matches
the
water
body
name
provided
in
PCS.
The
Agency
then
used
the
identified
stream
ID
from
RF1
to
obtain
information
on
water
body
type,
flow
characteristics,
and
counties
abutting
the
reach
from
RF1;

2
See
Chapter
21
for
a
detailed
discussion
the
Ohio
case
study.

G­
2
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
 
PCS
provided
no
receiving
stream
information
on
the,
but
facility
lat/
long
data
were
available.
EPA
first
used
these
data
to
assign
the
PCS
facility
to
the
nearest
reach.
The
Agency
then
used
the
identified
stream
ID
to
obtain
information
on
water
body
type,
flow
characteristics,
and
counties
abutting
the
reach;

 
PCS
provided
neither
information
on
the
receiving
stream
nor
facility
lat/
long
data.
EPA
assumed
the
distribution
of
the
receiving
water
body
characteristics,
including
the
size
of
the
population
residing
in
the
counties
abutting
the
receiving
reaches,
to
be
similar
to
the
distribution
of
these
characteristics
across
facilities
with
known
characteristics.

PCS
identifies
4,290
direct
discharging
facilities
with
MP&
M
SIC
codes
that
had
active
NPDES
permits
1997.
Of
these,

EPA
classified
3,242
facilities
into
the
poststrata
considered
in
this
analysis.
Because
the
total
number
of
PCS
facilities
with
MP
&
M
SIC
codes
differs
from
the
sum
of
sampling
weights
of
direct
dischargers
considered
in
the
final
regulation,
the
Agency
assumed
that
the
sum
of
the
sampling
weights
provides
the
correct
estimate
of
the
MP&
M
facility
universe.
Thus,
the
count
of
facilities
in
the
benefits
analysis
matches
the
number
of
MP&
M
facilities.
This
analysis
yielded
an
adjustment
factor
of
2,832/
3,242=
0.87
Table
G.
1
lists
facility
counts
from
PCS
data,
adjusted
to
equal
the
sample
frame
total.

Table
G.
1:
Facility
Counts
from
PCS
Data
(
Adjusted
to
Equal
the
Sample
Frame
Total)

First
Variable:
Water
Body
Type
and
Size
Second
Variable:
Population
Size
Variable
Category
PCS
Facilities
Count
Variable
Category
PCS
Facilities
Count
Bay­
Lakes
Combined
288
Pop
 
100,000
934
Small
Streams
543
100,000<
Pop
 
500,000
1155
Medium
Streams
1514
500,000<
Pop
 
1,000,000
403
Large
Streams
487
1,000,000<
Pop
 
2,000,000
276
2,000,000<
Pop
 
4,000,000
47
Pop<
4,000,000
17
Total
2,832
2,832
Source:
PCS
data.

G.
1.2
Raking
Adjustment
Raking
is
a
post­
stratification
method
that
can
be
used
when
multiple
variables
form
the
poststrata.
If
the
original
sampling
weights
need
to
be
adjusted
using
post­
stratification
with
two
variables,
then
the
analysis
must
create
a
set
of
poststrata
resulting
from
the
cross­
classification
of
the
two
post­
stratification
variables.
EPA s
analysis
used
the
following
steps:

1.
Combine
the
variables
"
water
body
type"
(
four
categories),
with
"
population
size
residing
in
the
vicinity
of
the
sampled
facility"
(
six
categories)
to
yield
24
poststrata.

2.
Classify
each
sampled
facility
into
one
of
the
24
poststrata.

3.
Determine
how
many
facilities
fall
into
each
poststratum.

4.
Multiply
the
sampling
weight
of
a
facility
in
a
poststratum
by
the
ratio
of
the
number
of
facilities
in
the
population
in
the
poststratum
to
the
sum
of
the
sampling
weights
of
all
facilities
in
that
stratum.
If
the
number
of
facilities
in
the
population
are
known
only
by
each
category
of
the
two
variables,
then
the
weights
can
be
adjusted
through
raking.

This
section
briefly
describes
the
raking
procedure.

G­
3
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Water
body
type
was
one
of
the
two
post­
stratification
variables
used
for
raking.
EPA
originally
used
six
categories
of
this
variable:
Bay/
Ocean,
Great
Lakes,
Lakes,
Lakes,
Small
Streams,
Medium
Streams,
and
Large
Streams.
However,
the
number
of
MP&
M
sample
facilities
in
Bay/
Ocean,
Great
Lake,
and
Lake
categories
was
too
small
for
some
categories
to
implement
raking.
Therefore,
EPA
combined
categories
in
which
the
number
of
facilities
in
the
sample
was
either
zero
or
too
small
to
create
four
categories:

 
Bay­
Lakes
Combined
(
includes,
Bays,
Oceans,
Great
Lakes
and
Lakes);

 
Small
Streams;

 
Medium
Streams;
and
 
Large
Streams.

Table
G.
2
shows
the
number
of
sampled
facilities
in
each
category
of
water
body
type,
the
sum
of
the
sampling
weights
of
the
sampled
facilities,
and
the
known
number
of
facilities
in
the
population
in
that
category.
Comparing
the
sum
of
the
MP&
M
facilities
sampling
weights
and
the
PCS­
based
count
of
facilities
for
each
category
of
water
body
type
shows
that
Bay­
Lake
Combined
and
Small
Streams
are
under­
represented
in
the
MP&
M
sample
frame
while
Medium
and
Large
Streams
are
over­

represented.

Table
G.
2:
Facility
Distribution
by
Water
Body
Type
Number
of
Facilities
in
the
MP&
M
Sample
Frame
MP&
M
Sample
Frame
PCS
Facilities
Number
of
Facilities
in
the
Sample
Sum
of
the
Sampling
Weights
Number
of
Facilities
in
the
Population
Ratio
of
Number
PCS
to
Sample­

Weighted
Facilities
Bay­
Combined
7
38.7
288
7.44
Small
Streams
7
231.3
543
2.35
Medium
Streams
43
1,439.4
1514
1.05
Large
Streams
25
1,122.6
487
0.43
Total
82
2,832.0
2,832.0
1.00
Source:
PCS
data.

Table
G.
3
shows
the
six
population
categories
created
in
the
EPA
analysis.
Comparing
the
sum
of
the
MP&
M
facilities 

sampling
weights
and
the
PCS­
based
count
of
facilities
corresponding
to
each
category
of
water
body
type
shows
that
facilities
from
the
population
size
category
of
less
than
100,000,
greater
than
4,000,000,
and
greater
than
2,000,000
but
less
than
4,000,000
are
over­
represented
in
the
sample.
Conversely,
facilities
in
the
population
categories
from
100,000
to
500,000
and
from
500,000
to
1,000,000
are
under­
represented.

G­
4
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Table
G.
3:
Distribution
of
Facilities
by
Population
Size
Population
MP&
M
Sample
Frame
PCS
Facilities
Number
of
Facilities
in
the
Sample
Sum
of
the
Sampling
Weights
Number
of
Facilities
in
the
Population
Ratio
of
Sample­

Weighted
to
PCS
Facilities
Pop
 
100,000
18
1,30
3.0
934
1.40
100,000<
Pop
 
500,000
35
1,17
1.8
1,155
1.01
500,000<
Pop
 
1,000,000
12
136.3
403
0.34
1,000,000<
Pop
 
2,000,000
12
121.6
276
0.44
2,000,000<
Pop
 
4,000,000
3
61.8
47
1.31
>
4,000,000
2
37.6
17
2.21
Total
82
2,832.0
2,832.0
1.00
Source:
PCS
data.

Raking
is
an
iterative
process
in
which
adjusted
sample
weights
are
synthetically
generated
to
match
known
characteristics
of
the
population
along
single
stratification
dimensions
and,
as
a
result,
should
reflect
the
population
characteristics
within
multi­

dimensional
stratification
cells.
The
iterative
process
works
as
follows.
First,
EPA
multiplied
the
sampling
weight
of
each
facility
in
each
category
of
water
body
type
by
the
ratio
of
the
total
number
of
facilities
in
the
population
to
the
sum
of
the
sampling
weights
in
that
category.
For
example,
using
the
numbers
in
Table
G.
2,
EPA
multiplied
the
sampling
weights
of
all
sampled
facilities
in
the
Bay­
Combined
category
by
the
ratio
288/
38.7
=
7.44.
The
sum
of
the
adjusted
weights,
38.72x
7.44=
288.08,
is
the
known
population
total.
Similarly,
EPA
multiplied
all
the
sampling
weights
of
facilities
in
the
Large
Streams
category
by
the
ratio
487/
1122.6
=
0.43,
to
yield
1,122.6x0.43=
482.7
as
the
sum
of
the
adjusted
weights.
EPA
performed
the
same
calculations
for
the
other
categories
of
water
body
type.

These
calculations
match
the
sum
of
the
sampling
weights
to
the
known
control
totals
for
the
single
stratification
dimension
of
water
body
type.
At
this
first
step,
however,
it
is
very
unlikely
that
the
resulting
sums
will
agree
with
the
known
number
of
facilities
within
categories
of
the
second
stratification
dimension,
population
size
category.
Table
G.
4
shows
the
sum
of
the
adjusted
sampling
weights
and
the
PCS
population
totals
by
population
sizes
after
Iteration
1.

Table
G.
4:
Sum
of
the
Sampling
Weight
by
Population
Category
after
Iteration
1
Population
Sum
of
the
Adjusted
Sampling
Weights
Number
of
Facilities
in
the
Population
(
PCS
Based)

 
100,000
1,542.49
934
100,000<
Pop
 
500,000
728.62
1,155
500,000<
Pop
 
1,000,000
133.42
403
1,000,000<
Pop
 
2,000,000
294.18
276
2,000,000<
Pop
 
4,000,000
58.31
47
>
4,000,000
74.99
17
Total
2,832.01
2,832
Source:
U.
S.
EPA
analysis.

To
correct
for
this
inconsistency,
EPA
multiplied
each
weight
by
the
ratio
of
the
known
total
to
the
sum
of
the
adjusted
G­
5
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
weights
for
each
facility
in
each
population
size
category.
For
example,
the
Agency
multiplied
each
facility
in
the
first
population
category
by
the
ratio
934/
1542.49.
Now,
the
resulting
sum
of
the
adjusted
weights
agrees
with
the
category
totals
for
the
population
category,
but
differs
from
the
category
totals
for
water
body
type.

EPA
therefore
repeated
this
process
of
sequentially
adjusting
sample
weights
one
dimension
at
a
time
until
the
sum
of
the
adjusted
sampling
weights
simultaneously
agreed
with
the
total
population
counts
of
facilities
for
both
water
body
type
and
population
size
categories.
After
seven
iterations,
the
sum
of
the
sampling
weights
agreed
with
PCS­
based
counts
for
both
variables
except
for
a
difference
of
less
than
one.

Tables
G.
5
and
G.
6
show
the
sum
of
the
sampling
weights
before
and
after
this
iterative
process
in
each
cell.
Obtaining
the
estimated
numbers
in
each
cell
of
Table
G.
6
by
aggregating
the
final
raked
sampling
weights
may
yield
better
estimates
of
the
cell
populations
than
summing
the
original
sampling
weights
in
Table
G.
5.

G­
6
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Table
G.
5:
Estimated
Number
of
MP&
M
Facilities
in
each
Poststratum
before
Raking
Population
Size
Water
Body
Type
Bay­

Combination
Small
Streams
Medium
Streams
Large
Streams
Total
Pop
 
100,000
0
151
1,114
38
1,303
100,000<
Pop500,000
11
9
208
944
1,172
500,000<
Pop
 
1,000,000
1
25
31
79
136
1,000,000<
Pop
 
2,000,000
27
19
25
51
122
2,000,000<
Pop
 
4,000,000
0
0
51
11
62
>
4,000,000
0
27
10
0
37
Total
39
232
1,439
1,122
2,832
Source:
PCS
data.

Table
G.
6:
Estimated
Number
of
MP&
M
Facilities
in
Each
Poststratum
after
Raking
Population
Water
Body
Type
TotalBay­

Combination
Small
Streams
Medium
Streams
Large
Streams
Pop
 
100,0000
0
204
726
4
934
100,000<
Pop
 
500,000
112
50
575
418
1155
500,000<
Pop
 
1,000,000
16
210
126
51
403
1,000,000<
Pop
 
2,000,000
161
64
39
13
277
2,000,000<
Pop
 
4,000,0000
0
45
2
47
>
4,000,0000
0
4
3
0
17
Total
289
542
1,514
488
2,833
0
1
Source:
U.
S.
EPA
analysis
Tables
G.
5
and
G.
6
show
that
sampling
weights
increase
for
small
stream
facilities
in
the
population
 
100,000
category,
while
sampling
weights
decrease
for
medium
and
large
stream
facilities
in
the
same
population
category,
due
to
their
over­

representation
in
the
sample.

G.
2
METHODOLOGY
FOR
DEVELOPING
SAMPLE­
WEIGHTED
ESTIMATES
FOR
SITES
WITH
MORE
THAN
ONE
MP&
M
FACILITY
The
MP&
M
analysis
is
based
on
a
random
stratified
sample
of
MP&
M
facilities
intended
to
provide
detailed
information
about
specific
facility
characteristics
and
to
provide
national
estimates
with
these
characteristics.
They
are
not
reach­
specific
sample
weights
designed
to
estimate
the
national
occurrence
of
reaches
associated
with
a
specific
characteristic
of
MP&
M
discharges.
For
example,
the
sum
of
MP
&
M
sample
facility
weights
discharging
to
one
reach
is
an
accurate
estimate
of
the
number
of
national
facilities
similar
to
the
sample
facilities,
but
is
not
a
valid
national
estimate
of
all
potential
MP&
M
discharges
to
that
reach
or
the
number
of
reaches
similar
to
that
reach.
Accordingly,
to
use
the
sample
weights
to
estimate
G­
7
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
the
number
of
similar
facilities
on
similar
reaches
nationwide
requires
some
adjustments
to
the
standard
sample­
weight
based
extrapolation
process.

It
may
not
be
valid
to
assume
that
the
co­
location
of
sample
facilities
is
similar
to
the
co­
location
characteristics
of
all
MP&
M
facilities.
This
point
is
illustrated
by
the
case
in
which
two
sample
facilities
with
different
weights
discharge
to
the
same
reach.
Assume
that
one
of
these
two
sample
facilities
has
a
sample
weight
of
five
and
the
other
has
a
sample
weight
of
200.

The
sample
weights
indicate
that
there
are
four
additional
facilities
in
the
U.
S.
that
are
economically
and
technically
similar
to
the
facility
with
the
weight
of
five.
It
is
also
correct
to
estimate
that
the
other
four
facilities
will
discharge
the
same
volume
of
the
same
pollutants
as
the
other
four
facilities.
Now
let
us
assume
that
there
are
199
other
facilities
nationwide
similar
to
the
facility
with
the
weight
of
200.
The
more
numerous
facilities
represented
by
the
facility
with
a
weight
of
200
could
only
rarely
be
co­
located
with
one
of
the
four
facilities
represented
by
the
sample
facility
with
a
weight
of
five.

EPA
developed
a
method
that
accounts
for
joint
occurrence
on
reaches
of
facilities
with
different
statistical
weights
to
estimate
the
number
of
reaches
affected
by
MP&
M
facilities
nationwide.
EPA
created
a
series
of
new
discharge
variables
(
a
discharge
event)
for
each
reach
affected
by
MP&
M
sample
facilities,
and
assigned
weights
for
the
discharge
events
that
provide
a
national
estimate
of
pollutant
discharges
across
all
reaches.
The
sample
discharge
events
(
flows
and
pollutant
loadings)
are
calculated
based
on
the
sum
of
the
flows
and
pollutant
loadings
for
subsets
of
the
MP&
M
sample
facilities
that
discharge
to
that
reach.
The
weights
for
the
discharge
events
are
developed
from
the
facility
weights
for
those
subsets
of
facilities.
The
calculation
includes
direct
MP&
M
facility
discharges
and
indirect
discharges
from
POTW
s
(
for
options
that
include
them)
after
considering
pollutant
removals
from
POTW
treatment.

The
number
of
discharge
events
on
a
sample
reach
equals
the
number
of
unique
sample
weights
for
the
facilities
on
the
reach.

EPA
calculated
a
sample
weight
for
each
discharge
event
based
on
the
sample
weights
of
the
facilities
contributing
loadings
and
flows
to
the
event.
Table
G.
7
illustrates
discharge
event
calculations
and
corresponding
sample
weights.
Steps
for
calculating
the
relevant
parameters
for
discharge
events
on
reaches
affected
by
multiple
discharges
are
as
follows:

 
Rank
pollutant
loadings
(
or
discharge
flows)
in
ascending
order
of
facility
sample
weight
for
each
pollutant
of
concern
discharged
by
one
or
more
of
those
facilities.

 
Generate
the
first
discharge
event
loadings
(
or
flows)
as
the
total
loadings
(
or
flows)
from
all
sample
facilities
on
the
reach.
Assign
the
smallest
sample
weight
to
the
first
discharge
event
(
Wt1
in
Table
G.
7)
among
the
facilities
discharging
to
the
reach.
A
smaller
sample
weight
relative
to
the
others
means
that
this
facility
represents
no
other
population
facilities
that
could
occur
jointly
with
the
other
facilities.
The
weight
of
the
first
facility
is
therefore
considered
as
 
used
up, 
and
that
facility s
loadings
(
or
flows)
are
not
included
in
subsequent
discharge
events
defined
for
the
reach.

 
Generate
subsequent
discharge
events
by
removing
the
loadings
(
or
flows)
of
facilities
with
the
smallest
sample
weight
from
a
running
sum
of
loadings
(
or
flows)
of
all
facilities
in
the
ranking.
The
weight
assigned
to
each
subsequent
event
is
the
remaining
unused
weight
of
the
facility
with
the
smallest
weight
among
the
facilities
remaining
in
the
particular
discharge
event.
Calculate
this
weight
as
the
difference
between
the
weight
of
the
next
facility
in
the
ranking
and
the
weight
of
the
previous
facility
(
Wt2
­
Wt1
).

EPA
avoids
double
counting
indirect
dischargers
by
including
the
discharge
flow
of
any
given
POTW
into
a
reach
only
once
in
any
given
discharge
event,
even
when
multiple
sample
facilities
discharge
indirectly
into
one
POTW.

This
methodology
generates
a
set
of
discharge
events
(
loadings
or
flows)
for
each
pollutant
discharged
to
the
reach.
The
following
steps
illustrate
application
of
the
differential
weighting
technique
to
estimating
the
national
number
of
reaches
on
which
ambient
water
quality
criteria
(
AWQC)
are
exceeded:

 
assign
a
weight
to
each
discharge
event
based
on
the
weights
of
the
facilities
discharging
to
the
reach;

 
combine
the
effluent
flow
with
the
stream
flow
of
the
reach;

 
divide
the
pollutant
loading
into
the
stream
flow
to
determine
the
pollutant
concentration
caused
by
the
event;

 
compare
pollutant
concentration
to
AWQC
values
to
determine
whether
the
concentration
exceeds
those
values;

 
identify
an
estimated
AWQC
 
exceedance 
if
the
concentration
is
greater
than
a
criterion;
and
G­
8
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
 
give
the
AWQC
exceedance
event
the
weight
of
the
discharge
event,
to
establish
national
estimates
of
the
number
of
reaches
on
which
an
AWQC
is
exceeded.

Table
G.
7:
Construction
of
Discharge
Events
for
Any
Pollutant
Discharged
to
Any
Reach
Event
Number
Loadings
and
Flows
Assigned
to
Event
Weight
Assigned
to
Event
One
Wt1
Two
Wt2
­
Wt1
 
 
N
­
2
LoadN­
2
+
LoadN­
1
+
LoadN
FlowN­
2
+
FlowN­
1
+
FlowN
WtN­
2
­
WtN­
3
N
­
1
LoadN­
1
+
LoadN
FlowN­
1
+
FlowN
WtN­
1
­
WtN­
2
N
LoadN
+
FlowN
WtN
­
WtN­
1
 
Notes:
N
sample
facilities
discharge
to
the
reach
and
are
ranked
in
ascending
order
of
sample
weight
and
indexed
by
i
(
1
=

facility
with
smallest
weight,
N
=
facility
with
largest
weight);
Loadi
=
Loading
from
facility
i;
Flowi
=
Flow
from
facility
i
or
the
POTW
associated
with
facility
i;
Wti
=
Sample
weight
of
facility
i;
and
a
POTW s
flow
is
included
only
once
per
event,

even
if
multiple
facilities
in
that
event
discharged
through
that
POTW,
to
avoid
over­
counting
the
POTW s
flow.

Source:
U.
S.
EPA
analysis.

This
weighting
method
is
a
relatively
simplistic
approach
to
a
complex
analytic
issue,
and
does
not
provide
a
precise
estimate
of
the
national
distribution
of
in­
stream
MP&
M
pollutant
concentrations
that
reflects
the
true
co­
location
characteristics
of
MP&
M
facilities.
A
statistically­
valid
estimate
of
that
distribution
is
not
possible
given
the
design
of
the
Section
308
surveys.

However,
the
differential
weighting
technique
does
correct
for
the
significant
overstatement
of
benefits
that
would
result
from
using
a
simple
weighting
approach
to
estimate
national
reach
characteristics.
The
Agency
believes
that
this
method
is
a
reasonable
approach
to
addressing
this
issue,
given
time
and
resource
constraints.
Approaches
that
are
both
more
sophisticated
and
more
expensive
might
not
yield
significantly
different
aggregate
findings.

Figure
G.
1
provides
a
graphical
example
of
a
hypothetical
reach
to
which
three
known
sample
facilities
discharge.
Table
G.
8
provides
a
numeric
example
of
this
calculation
for
a
hypothetical
reach
to
which
three
known
sample
facilities
discharge.

G­
9
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Figure
G.
1a:
Estimating
MP&
M
Pollutant
Loadings
to
Receiving
Streams
When
Using
a
Random
Sample
of
MP&
M
Facilities
Source:
U.
S.
EPA
analysis.

G­
10
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Figure
G.
1b:
Estimating
MP&
M
Pollutant
Loadings
to
Receiving
Streams
When
Using
a
Random
Sample
of
MP&
M
Facilities
Note:
The
situation
may
be
further
complicated
by
actually
having
a
non­
sampled
MP&
M
facility
on
the
same
reach.
The
differential
weighting
method
does
not
address
this
issue.

Source:
U.
S.
EPA
analysis.

G­
11
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Figure
G.
1c:
Estimating
MP&
M
Pollutant
Loadings
to
Receiving
Streams
When
Excluding
Background
Concentrations
Source:
U.
S.
EPA
analysis.

G­
12
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Table
G.
8:
Example
of
Differential
Sample
Weighting
Technique
Facility
Weight
Pollutant
A
lbs/
yr
Flow
gal/
year
Raw
data:

1
10
5
2,000,000
2
3
2
4,000,000
3
1
12
10,000,000
Total
14
19
16,000,000
Reach
flow
(
gal/
year):
100,000,000
Calculating
flow
and
pollutant
loadings
for
the
reach:

1.
facilities
in
ascending
order
of
weights
3
1
12
10,000,000
2
3
2
4,000,000
1
10
5
2,000,000
2.
late
flow
and
pollutant
loadings
for
discharge
event
1
with
weight
=
1
Facility
Pollutant
A
lbs/
yr
Flow
gal/
year
Remaining
Weight
3
12
10,000,000
0
2
2
4,000,000
2
1
5
2,000,000
9
Event
1
19
16,000,000
3.
ity
with
the
lowest
weight
and
calculate
flow
and
pollutant
loadings
for
discharge
event
2
with
weight
=
2
(
3­
1)

2
2
4,000,000
0
1
5
2,000,000
7
Event
2
7
6,000,000
4.
inate
the
facility
with
the
next
lowest
weight
and
calculate
and
pollutant
loadings
for
discharge
event
3
with
weight
=
7
(
10­
3)

1
5
2,000,000
0
Event
3
5
2,000,000
5.
tream
concentrations
based
on
the
flows,
loadings,
and
weights
for
each
discharge
event
and
the
reach
flow
Discharge
Event
Pollutant
A
Loading
lbs/
yr
Facility
Flow
gal/
year
Stream
Flow
gal/
year
Total
Flow
gal/
year
In­
stream
Concentration
ppb
Weight
1
19
16,000,000
100,000,000
116,000,000
0.0955
1
2
7
6,000,000
100,000,000
106,000,000
0.0385
2
2,000,000
100,000,000
102,000,000
0.0286
7
Total
Affected
Reaches:
10
Rank
Calcu
Eliminate
the
facil
Elim
Estimate
national
in­
s
Source:
U.
S.
EPA
analysis.

G.
3
METHODOLOGY
FOR
EXTRAPOLATION
OF
OHIO
CASE
STUDY
RESULTS
TO
THE
NATIONAL
LEVEL
EPA
extrapolated
the
Ohio
case
study
results
to
the
national
level
based
on
three
key
factors
that
affect
the
occurrence
and
magnitude
of
benefits:

 
the
estimated
change
in
MP&
M
pollutant
loadings,
which
reflects
the
potential
for
improvements
in
surface
water
quality;

G­
13
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
 
the
level
of
recreational
activities
on
the
reaches
affected
by
MP&
M
discharges.
Recreational
level
reflects
the
degree
to
which
potentially
affected
water
resources
are
likely
to
be
in
demand
by
local
residents;
and
 
the
average
household
income
level,
which
affects
the
willingness­
to­
pay
(
WTP)
for
water
quality
improvements.

G.
3.1
Change
in
Pollutant
Loads
The
first
step
in
applying
this
alternative
extrapolation
method
was
to
develop
a
measure
of
benefits
per
pound
of
pollutant
removed
for
each
category
of
benefits.
EPA
developed
this
measure
by
simply
dividing
the
state­
level
benefit
estimates
by
the
total
number
of
pounds
of
pollutant
removed
by
the
regulation
in
the
state
of
Ohio
($
per
pound
of
pollutant
removed).

EPA
developed
three
different
measures
to
better
represent
the
relationship
between
pollutants
and
benefit
categories:

 
Cancer
health
benefits:
EPA
divided
cancer
benefits
from
the
Ohio
case
study
by
total
carcinogen
pounds
removed
in
Ohio
to
estimate
cancer
health
benefit
per
pound
of
carcinogen
load
removed;

 
Lead
health
benefits:
EPA
divided
lead
health
benefits
from
the
Ohio
case
study
by
total
lead
pounds
removed
in
Ohio
to
estimate
lead
health
benefit
per
pound
of
lead
load
removed;
and
 
Recreational
benefits:
EPA
divided
recreational
benefits
from
the
Ohio
case
study
by
total
pounds
of
pollutants
removed
(
i.
e.,
all
pollutants
except
for
total
dissolved
solids
and
biological
oxygen
demand)
in
Ohio
to
estimate
recreational
benefit
per
pound
of
pollutant
load
removed.

All
of
these
values
are
readily
available
from
the
Ohio
case
study.
EPA
extrapolated
the
state­
level
benefits
for
each
of
these
benefit
categories
to
the
national
level.
First,
the
Agency
multiplied
the
three
estimated
benefit
per
pound
of
pollutant
values
for
Ohio
by
the
total
number
of
pounds
of
pollutant
removed
in
each
of
the
three
pollutant
categories
at
the
national
level.

Then,
EPA
summed
across
the
three
benefit
categories
to
obtain
an
initial
estimate
for
total
benefits
at
the
national
level.

G.
3.2
Level
of
Recreational
Activities
on
Reaches
Affected
by
MP&
M
Discharges
The
second
step
was
to
adjust
for
differences
between
Ohio
and
the
nation
in
the
level
of
recreational
activity
on
reaches
affected
by
MP&
M
discharges.
The
level
of
recreational
activity
reflects
the
degree
to
which
water
resources
likely
to
be
affected
by
MP&
M
discharges
are
in
demand
by
local
residents.
EPA
accounted
for
differences
between
Ohio
and
the
nation
in
recreational
intensity
because
the
total
user
value
of
water
quality
improvements
is
a
function
of
the
number
of
users
associated
with
a
particular
reach.
For
this
adjustment
factor,
EPA
used
the
ratio
of
the
number
of
recreational
user
days
per
reach
mile
at
the
national
level
to
the
number
of
recreational
user
days
per
reach
mile
in
Ohio.
Due
to
data
limitations
preventing
identification
of
all
reaches
affected
by
MP&
M
discharges,
this
analysis
used
total
recreational
user
days
and
reach
miles
nationally
and
in
Ohio,
rather
than
only
for
those
reaches
affected
by
MP&
M
discharges.
EPA
used
the
National
Demand
Study
(
NDS)
to
estimate
the
number
of
user
days
for
each
recreation
activity.
Appendix
N
of
this
report
provides
the
relevant
data
by
state
and
recreation
activity.
To
estimate
the
number
of
recreational
user
days,
EPA
summed
the
activity­

specific
values
over
the
four
activities
considered
in
this
analysis
(
i.
e.,
recreational
fishing,
boating,
swimming,
and
wildlife
viewing).
EPA s
Reach
File
1
provided
information
on
the
total
number
of
reach
miles
in
Ohio
and
in
the
48
contiguous
states.
The
Agency
then
calculated
the
number
of
user
days
per
reach
mile
in
the
state
of
Ohio
and
in
the
nation
by
simply
dividing
the
total
number
of
user
days
by
the
total
number
of
reach
miles
in
the
corresponding
region.
EPA
then
calculated
the
adjustment
factor
as
follows:

(
G.
1)

G.
3.3
Differences
in
Household
Income
In
the
third
step,
EPA
adjusted
the
extrapolated
benefits
based
on
the
expectation
that
the
WTP
for
water
quality
improvements
will
vary
with
household
income
level
for
different
parts
of
the
country.
The
adjustment
factor
used
is
the
ratio
G­
14
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
of
the
average
household
income
of
the
nation
to
the
average
household
income
of
Ohio.
This
adjustment
factor
assumes
that
households
around
the
country
are
willing
to
pay
the
same
proportion
of
their
incomes
for
water
quality
improvements,

although
the
absolute
value
of
this
dollar
amount
will
vary
due
to
regional
differences
in
average
household
income.
The
average
household
income
of
the
nation
is
estimated
as
a
weighted
average,
with
the
median
household
income
for
each
state
weighted
by
the
proportion
of
MP&
M
facilities
located
in
that
state.
The
U.
S.
Census
Bureau s
Current
Population
Surveys
(
March
1999,
2000,
and
2001)
provide
the
basis
for
data
on
the
median
household
income
by
state
for
the
year
2000.3
The
4
1992
Economic
Census
provides
information
on
total
MP&
M
facilities
by
state.

(
G.
2)

G.
4
RESULTS
Table
G
.9
presents
national
benefits
based
on
the
extrapolation
of
Ohio
case
study
results.

monetary
value
of
benefits
from
reduced
MP&
M
discharges
is
$
2.5
million
(
2001$)
for
the
final
option.
This
estimate
is
60%

higher
compared
to
the
benefit
estimate
based
on
the
traditional
extrapolation
methodology
(
i.
e.,
$
1.5
million
(
2001$)).
As
noted
in
the
prior
disc
ussion,
this
difference
is
likely
to
be
due
to
the
mor
e
rigor
ous
appr
oac
h
used
for
the
O
hio
ca
se
stud
y.

The
national­
level
analysis
of
human
health
benefits
finds
negligible
health
benefits
from
the
final
rule.
In
contrast,
the
Ohio­

based
extrapolation
of
human
health
benefits
yields
$
10,860
and
$
295,202
(
2001$)
in
human
health
benefits
at
the
national
level
fro
m
red
uced
incidences
o
f
cance
r
cases
and
adve
rse
hea
lth
impa
cts
from
lead
e
xpo
sure,
respec
tively.
wn
in
Ta
ble
G
.9,
the
estimated
huma
n
health
bene
fits
to
Oh
io
resid
ents
exc
eed
the
natio
nal­
leve
l
bene
fits
based
on
this
extrap
olation
metho
d.
T
his
finding
is
due
to
the
fact
tha
t
the
estima
ted
p
ollutant
re
mov
als
for
lea
d
and
carcin
ogens
in
O
hio
exceed
those
at
the
national
level.
As
discussed
in
Appendix
H,
EP
A
administered
1,600
screener
questionnaires
to
augment
inform
ation
o
n
Oh
io s
M
P&
M
facilities.
T
he
Agenc
y
used
inform
ation
fro
m
the
sa
mple
d
M
P&
M
facilities
to
estim
ate
discharge
c
harac
teristics
of
non­
sam
pled
M
P&
M
chara
cteristics
(
se
e
Ap
pendix
H
for
detail
on
estimating
sa
mple
facility
discharges
in
Ohio).
As
a
result,
the
MP
&
M
facilities
included
in
the
case
study
analysis
represent
a
significant
portion
of
the
MP
&
M
facility
universe
in
Ohio.
,
the
sample
facilities
used
at
the
national­
level
analysis
represent
only
2
percent
of
the
MP
&
M
facility
universe.
Thus,
analytic
findings
from
the
national­
level
analysis
may
have
a
larger
than
desired
degree
of
uncertainty
due
to
a
very
small
sample
size.
Based
on
this
approach,
the
As
sho
In
contrast
3
Source:
http://
www.
census.
gov/
hhes/
income/
income00/
statemhi.
html
4
Appendix
J
presents
information
on
distribution
of
MP&
M
facilities
by
state.

G­
15
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
Table
G.
9:
Extrapolation
of
Ohio
Case
Study
Results
to
the
National
Level
(
2001$)

Category
Ohio
Nation
Pounds
removal
of
carcinogens
52.45
17.86
Total
cancer
benefits
$
31,895.42
$
10,860.86
Total
cancer
benefits
per
pound
removal
of
carcinogens
$
608.11
Pounds
removal
of
Lead
217.06
118.54
Total
lead
benefits
$
540,549.14
$
295,202.69
Total
lead
benefits
per
pound
removal
of
lead
$
2,490.32
Pounds
removal
of
total
pollutants
483,258.02
5,412,810.88
Total
recreational
benefits
$
250,932.62
$
2,810,612.05
Total
recreational
benefits
per
pound
removal
of
total
pollutants
$
0.52
Nonuse
benefits
(
½
of
total
recreational
benefits)
$
125,466.31
$
1,405,306.03
Total
benefits
prior
to
application
of
adjustment
factors
$
948,843.49
$
4,521,981.63
Reach
miles
11,927
713,702
Annual
recreation
days
(
millions)
49
1,646
Annual
recreation
days
per
reach
mile
4,148
2,306
Recreational
activity
adjustment
factor
0.5559
Total
benefits
prior
to
application
of
income
adjustment
factor
$
2,513,907.82
Average
household
income
$
43,894
$
42,909
Income
Adjustment
factor
0.9776
Total
benefits
$
2,457,494.66
Source:
U.
S.
EPA
analysis.

G­
16
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
GLOSSARY
ambient
water
quality
criteria
(
AWQC):
levels
of
water
quality
expected
to
render
a
body
of
water
suitable
for
its
designated
use.
Criteria
are
based
on
specific
levels
of
pollutants
that
would
make
the
water
harmful
if
used
for
drinking,

swimming,
farming,
fish
production,
or
industrial
processes.
(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

differential
sample
weighting
technique:
weighting
method
for
all
threshold
value­
based
analyses,
such
as
the
lead­

related
benefits
analysis.

reach:
a
specific
length
of
river,
lake,
or
marine
shoreline
standard
linear
weighting
technique:
weighting
method
used
where
the
effects
being
considered
(
e.
g.,
compliance
costs)
are
linearly
additive
over
facilities.

G­
17
MP&
M
EEBA:
Appendices
Appendix
G:
Extrapolation
Methods
THIS
PAGE
INTENTIONALLY
LEFT
BLANK
G­
18
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
INTRODUCTION
For
the
drinking
water
(
DW)
and
Ohio
analyses,
EPA
used
a
simplified
fate
and
transport
model
to
quantify
the
fate
and
transport
of
MP&
M
pollutant
releases
to
surface
waters.
This
model
estimates
pollutant
concentrations
at
the
initial
point
of
discharge
and
below
the
initial
discharge
reach.

The
national
MP&
M
analysis
considered
pollutant
concentrations
only
at
the
point
of
discharge
(
see
Appendix
I.
2.2).
The
drinking
water
and
Ohio
analyses
account
for
the
in­
stream
concentrations
of
pollutants
at
the
initial
point
of
discharge
and
in
reaches
downstream
from
the
initial
discharge
reach.

This
appendix
describes
the
equations
characterizing
the
model,
its
underlying
assumptions,
and
the
data
sources
used
in
model
estimation.
EPA
combined
the
equations
defining
the
model
with
geographic
information
(
reach
flow,
velocity,
length,
etc.)
to
estimate
pollutant
concentrations
at
the
initial
point
of
discharge
and
below
the
initial
discharge
reach.
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
APPENDIX
CONTENTS
H.
1
ions
.................
...........
H­
1
H.
2
ssumptions
.................
.........
H­
3
H.
2.1
Stream
or
River
Reach
.................
...
H­
3
H.
2.2
tudinal
Dispersion
of
the
Pollutant
is
Negligible
.................
.............
H­
3
H.
2.3
eometry,
Suspension
of
Solids,
and
Reaction
Rates
Are
Constant
within
a
River
Reach
.................
...........
H­
4
H.
3
.................
........
H­
4
H.
4
ociating
Risk
with
Exposed
Populations
.......
H­
4
H.
5
.................
...............
H­
4
H.
5.1
g
Data
Used
in
the
Drinking
Water
Risk
Analysis
..............
H­
4
H.
5.2
g
Data
Used
in
the
Ohio
Case
Study
Analysis
.................
.........
H­
4
Glossary
.................
.................
.....
H­
8
Acronyms
.................
.................
....
H­
9
References
.................
.................
..
H­
10
Model
Equat
Model
A
Steady
Flow
Conditions
Exist
within
the
Longi
Flow
G
Hydrologic
Linkages
Ass
Data
Sources
Pollutant
Loadin
Pollutant
Loadin
The
estimation
of
pollutant
concentrations
below
the
initial
discharge
reach
includes
several
factors
that
reduce
the
in­
stream
pollutant
concentrations
with
the
passage
of
time.
These
factors
include:
volatilization,
sedimentation,
and
chemical
decay
from
hydrolysis
and
microbial
degradation.
EPA
adjusted
concentrations
for
changes
in
stream
flow
volume
in
downstream
reaches.
The
discussion
below
outlines
the
main
assumptions
of
this
analysis.
Although
more
advanced
models
are
available
that
account
for
time­
variable
flow,
sediment
transport,
channel
geometry
changes
within
a
reach,
and
detailed
simulation
of
all
in­
stream
processes,
these
models
will
not
necessarily
produce
more
accurate
results
without
sufficient
data
to
support
the
input
parameters.
Estimates
of
the
additional
input
parameters
required
by
these
models
are
subject
to
a
high
degree
of
uncertainty
when
applied
on
a
national
scale,
and
gathering
such
data
is
beyond
the
scope
of
this
study.

EPA
has
previously
applied
the
approach
used
in
this
analysis.
For
example,
the
first­
order
contaminant
degradation
relationship
described
below
in
Equation
H.
1
is
currently
being
used
by
the
Office
of
Pollution
Prevention
and
Toxics
for
exposure
analysis
in
the
Risk
Screening
Environmental
Indicator
(
RSEI)
model
(
U.
S.
EPA,
1999).

H.
1
MODEL
EQUATIONS
The
total
pollutant
concentration
in
the
water
columns
for
each
reach
included
in
the
analysis
is
calculated
by
the
following
equation
expressed
in
generic
terms
of
mass
(
M),
length
(
L),
and
time
(
T):

(
H.
1)

H­
1
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
where:

CT
=
total
tox
icant
co
ncentration
in
th
e
water
colum
n
(
M
/
L3),

W
T
=
mass
input
rate
of
toxicant
(
M/
T),

Q
=
river
flow
(
L3/
T),

VT
=
overall
net
loss
rate
of
chemical
(
L/
T),

H
=
flow
depth
(
L),

x
=
distance
downstream
from
the
point
of
release
(
L),
and
U
=
flow
velocity
(
L/
T).

In
reaches
where
more
than
one
facility
discharges
or
where
pollutant
loadings
occur
from
upstream
reaches,
the
mass
input
rate
(
WT)
represents
a
combined
input
rate
from
all
relevant
industrial
facilities
affecting
the
reach.
The
relevant
industrial
facilities
in
the
drinking
water
risk
analysis
are
all
MP
&
M
sample
facilities
(
see
Chapter
13).
The
relevant
industrial
facilities
in
the
Ohio
case
study
analysis
include:
1
 
all
sample
M
P&
M
facilities,

 
non­
sample
MP&
M
facilities,
and
 
non­
M
P&
M
facilities.

Th
e
ove
rall
net
loss
rate
of
chem
ical
(
VT)
is
given
b
y:

(
H.
2)

where:

VT
=
overall
net
loss
rate
of
chemical
(
L/
T),

VTd
=
dissolved
chemical
loss
rate
(
L/
T),

VTs
=
loss
of
chemical
due
to
sediment
interaction
(
L/
T),

kl
=
volatilization
transfer
coefficient
(
L/
T),

Kd
=
dissolved
chemical
decay
rate
(
hydrolysis
and
microbial
degradation)
(
1/
T),

H
=
flow
depth
(
L),

fd
=
dissolved
fraction
of
toxicant
(
unitless),

v
n
=
net
loss
of
solids
(
L/
T),
and
f
p
=
particulate
fraction
of
toxicant
(
unitless).

The
disso
lved
and
particulate
fractions
of
the
p
ollutant,
fd,
and
f
p
,
respectively,
are
estimated
by:

(
H.
3)

and
(
H.
4)

1
See
Chapter
22
for
detail.

H­
2
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
where
:

K
p
=
partition
coefficient
[
L3/
M],
and
S
=
suspended
solid
s
[
M
/
L3].

The
disso
lved
concentration
of
metals
and
m
ost
other
po
llutants
in
the
w
ater
co
lumn
is
genera
lly
consid
ered
a
mo
re
acc
urate
expression
than
the
total
concentrations
o
f
the
toxic
or
bio
availab
le
fractio
n.
this
reaso
n,
EP
A
modified
Eq
uation
(
H.
1
)
to
express
the
p
ollutant
concentration
s
in
terms
of
dissolved
c
oncentration.
he
dissolved
fraction
of
a
pollutant
is
estimated
as:

(
H.
5)

Substituting
Equation
(
H.
1)
for
CT
yields
the
dissolved
pollutant
concentration
for
downstream
distance
x
from
the
discharge
reach:

(
H.
6)
For
T
H.
2
MODEL
ASSUMPTIONS
The
following
three
principal
assumptions
underlie
Equation
H.
5:

H.
2.1
Steady
Flow
Conditions
Exist
within
the
Stream
or
River
Reach
This
assumption
is
necessary
due
to
this
study s
broad
geographical
coverage.
This
assumption
significantly
reduces
the
computational
effort
and
input
parameter
requirements
and
still
produces
a
good
first­
order
fate
and
transport
model
of
pollutants
in
surface
waters.

The
pollutant
concentration
is
completely
mixed,
both
laterally
(
across
the
stream)
and
vertically
(
with
depth)
within
each
reach.
The
approach
involves
a
two­
dimensional
model
in
which
the
concentration
is
uniform
over
the
entire
cross­
section
of
the
stream
reach
but
varies
with
the
distance
of
the
reach.
EPA
assumed
that
the
contaminant
completely
mixes
at
the
point
of
release.
This
assumption
will
likely
underestimate
the
concentration
of
a
contaminant
release
in
areas
where
mixing
is
incomplete
(
e.
g.,
shore­
hugging
plume)
and
overestimate
concentrations
in
areas
beyond
the
point
showing
incomplete
mixing
(
e.
g.,
in
areas
beyond
a
shore­
hugging
plume).

H.
2.2
Longitudinal
Dispersion
of
the
Pollutant
is
Negligible
The
model
does
not
account
for
mixing
outside
the
plane
of
discharge
along
the
river
reach,
although
it
predicts
variation
in
pollutant
concentrations
over
distance
due
to
both
pollutant
fate
and
decay
and
the
differing
hydrology
of
downstream
reaches.
In
natural
streams,
longitudinal
velocity
gradients
due
to
channel
irregularities
can
cause
mixing,
thereby
decreasing
the
peak
concentrations
as
the
contaminant
moves
downstream
from
the
point
of
release.
Under
steady­
state
situations,

however,
the
longitudinal
dispersion
of
the
pollutant
is
assumed
to
be
negligible.

The
solution
of
the
dispersion
equation
approximates
a
first­
order
decay
function
such
as
the
one
shown
in
Equations
H.
1
and
H.
5
under
steady
flow
conditions
and
complete
lateral
and
vertical
mixing.

H­
3
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
H.
2.3
Flow
Geometry,
Suspension
of
Solids,
and
Reaction
Rates
Are
Constant
within
a
River
Reach
EPA
assumes
the
data
that
describe
a
river
reach
and
that
are
calculated
for
a
reach
to
be
constant
for
the
full
extent
of
the
reach.

H.
3
HYDROLOGIC
LINKAGES
EPA
modeled
pollutant
concentrations
for
a
distance
of
500
km
downstream
from
the
discharge
point
in
the
drinking
water
risk
analysis.
In
the
Ohio
case
study
analysis,
EPA
used
the
lesser
of
500
km
or
the
distance
to
the
Ohio
border
from
the
initial
discharge
point
to
identify
reaches
potentially
affected
by
pollutant
discharges
from
the
discharge
point.
The
Agency
obtained
information
on
the
hydrologic
linkages
between
reaches
from
the
RSEI
Model
(
U.
S.
EPA,
1999).
The
data
file
in
RSEI
provided
flow
(
mean
flow,
7Q10)
and
velocity
(
mean,
low)
data
for
each
reach.

EPA
used
the
process
equations
listed
above
to
estimate
both
the
initial
pollutant
concentrations
at
the
beginning
of
each
reach
and
the
changes
in
concentrations
as
pollutants
traveled
to
the
end
of
the
reach.
The
concentration
at
the
end
of
each
reach
served
as
the
value
for
the
beginning
of
the
next
reach.

H.
4
ASSOCIATING
RISK
WITH
EXPOSED
POPULATIONS
The
number
of
individuals
served
by
each
drinking
water
intake
is
an
output
of
the
fate
and
transport
model
described
in
this
appendix.
If
a
drinking
water
intake
exists
on
the
initial
reach
or
any
downstream
reach,
then
the
model
calculates
the
in­

stream
pollutant
concentration
at
that
intake.
Data
on
the
population
served
by
the
intake
is
saved
with
the
concentration
for
further
analysis
(
see
Chapter
13
for
a
discussion
of
the
cancer
risk
assessment).

H.
5
DATA
SOURCES
Data
sources
used
for
the
fate
and
transport
model
are
discussed
briefly
in
the
section
below,
by
categories
of
information.

H.
5.1
Pollutant
Loading
Data
Used
in
the
Drinking
Water
Risk
Analysis
EPA
estimated
annual
pollutant
loadings
(
kg/
yr)
for
the
direct
and
indirect
sample
MP&
M
facilities
analyzed
under
the
various
regulatory
options.
2
The
Agency
first
adjusted
pollutant
loadings
for
indirect
dischargers
to
reflect
POTW
treatment,

and
then
divided
annual
pollutant
loadings
by
the
number
of
days
in
one
year
(
365)
to
establish
daily
pollutant
loadings.

H.
5.2
Pollutant
Loading
Data
Used
in
the
Ohio
Case
Study
Analysis
EPA
estimated
pollutant
discharges
from
both
MP&
M
and
significant
non­
MP&
M
sources
at
the
reaches
included
in
the
Ohio
case
study
analysis.
Consumer
perception
and
valuation
of
enhanced
water­
based
recreational
opportunities
depend
on
the
absolute
level
of
pollutant
contamination
at
recreation
sites,
and
on
the
change
in
contamination
from
the
baseline
to
the
post­
compliance
cases.
For
this
reason,
capturing
the
effect
of
concurrent
discharges
from
all
MP&
M
and
other
pollutant
sources
is
particularly
important
for
the
recreational
benefits
analysis.

EPA
used
the
Office
of
Water's
BASINS
software
package
to
identify
all
possible
point
source
dischargers
contributing
to
ambient
pollutant
concentrations
at
a
given
reach.
BASIN
S
is
a
GIS­
based
system
that
serves
as
a
database
management
system
for
water
quality
monitoring,
point­
source
pollutant
discharge,
and
various
geo­
technical
data.
Several
sources
provide
information
on
point
source
discharges
to
BASINS,
including
the
Permit
Compliance
System
(
PCS)
and
Toxic
Release
Inventory
(
TRI)
databases.
Version
2.0
includes
data
reported
through
1996.
Preprogrammed
queries
in
BASINS
2
EPA
is
not
establishing
pretreatment
standards
for
indirect
dischargers
under
the
final
rule.

H­
4
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
generate
information
on
various
point
source
discharge
variables
at
either
the
state
or
watershed
level.
BASINS
data
on
point
source
dischargers
include:

 
location
information
on
major
industrial
dischargers,
including
PCS
facilities
and
facilities
reporting
under
TRI;

 
SIC
codes;

 
flow
volume;
and
 
discharge
characteristics
for
up
to
50
pollutants
or
parameters
for
PCS
facilities.

The
following
sections
describe
steps
used
to
characterize
both
MP&
M
and
non­
MP&
M
discharges
in
Ohio.

a.
Characterize
MP&
M
facility
discharges
EPA
used
different
approaches
to
assign
discharge
characteristics
to
MP&
M
facilities
in
Ohio,
based
on
the
level
of
information
available
for
each
facility.
The
Agency
divided
all
MP&
M
facilities
into
three
groups,
based
on
the
level
of
information
provided
by
different
sources:

 
Facilities
covered
by
the
detailed
Phase
1
and
2
questionnaire
(
hereafter,
sampled
MP&
M
facilities)

The
detailed
surveys
contain
data
on:

 
discharge
status;

 
discharge
volume;

 
industrial
processes
used;

 
pollution
prevention
activities;

 
employment,
revenue,
and
costs.

EPA
engineers
estimated
loadings
of
126
MP&
M
pollutants
using
information
on
facilities'
processes
and
pollution
prevention
activities.
3
All
MP&
M
facilities
in
this
group
therefore
have
extensive
data
on
their
location,
size,
and
discharge
characteristics.

 
Facilities
covered
by
the
detailed
Iron
and
Steel
questionnaire
(
hereafter
sampled
I&
S
facilities)

The
detailed
I&
S
survey
contained
data
similar
to
the
detailed
MP&
M
survey.
EPA
engineers
used
data
on
I&
S
facilities'

processes
and
pollution
prevention
activities
to
estimate
pollutant
loadings
from
these
facilities.

 
Facilities
covered
by
the
Phase
2
screener
questionnaire
or
that
were
covered
by
the
Phase
1
mini­
DCP
(
hereafter,

MP&
M
screener
facilities).

The
screener
surveys
contain
significantly
fewer
data
on
MP&
M
facilities.
The
data
collected
from
the
screener
survey
recipients
include:

 
facility
location,
which
can
be
used
to
assign
the
facilities
to
receiving
waterways
or
receiving
POTWs;

 
SIC
codes;

 
discharge
status
(
i.
e.,
whether
the
facility
discharges
process
wastewater
and
the
approximate
amount);

 
employment
and
revenue
data;

 
whether
the
facility
is
engaged
in
manufacturing,
maintenance
or
repairing
activities;
and
3
There
are
132
pollutants
of
concern.
EPA
engineers
estimated
pollutant
loadings
for
only
the
pollutants
for
which
EPA
is
considering
calculating
pollutant
removals
at
each
option.
For
example,
pollutant
loadings
are
not
provided
for
sodium,
calcium,
and
TDS.

H­
5
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
 
data
on
MP&
M
unit
operations
(
including
type
of
MP&
M
unit
operations
performed
at
the
site,
and
whether
process
wastewater
is
discharged
as
a
result
of
each
operation).

The
project
engineers
used
these
data
to
estimate
pollutant
loadings
for
these
facilities.
Loading
estimates
for
the
screener
facilities,
which
are
based
on
less
comprehensive
information,
involve
greater
uncertainty.

 
Facilities
that
respond
to
neither
the
screener
nor
detailed
questionnaires
(
hereafter
referred
to
as
non­
sampled
MP&
M
facilities)

To
address
the
problem
of
omitted
discharge
information
on
non­
sampled
MP&
M
facilities,
EPA
used
information
from
the
1600
screener
MP&
M
facilities
and
a
random
draw
approach
to
assign
the
relevant
characteristics
for
non­
sampled
MP&
M
facilities.
Each
screener
facility
represents
n
non­
sampled
facilities,
where
n
is
determined
by
the
screener
facility
sample
weight.
All
non­
sampled
facilities
are
smaller
indirect
dischargers
because
all
direct
MP&
M
facility
dischargers
and
large
indirect
discharging
facilities
in
Ohio
are
covered
by
the
long,
short,
or
screener
questionnaire.

The
exact
location
of
non­
sampled
facilities
is
unknown.
All
non­
sampled
facilities
discharge
to
one
of
the
Ohio
POTWs
because
they
are
indirect
dischargers.
The
Agency
assigned
n
facilities
represented
by
each
screener
facility
to
the
receiving
POTW
s
by
drawing
a
random
sample
of
n
POTW
s
from
the
universe
of
POTWs
in
Ohio.
4
The
Agency
assigned
screener
facility
characteristics
(
i.
e.,
pollutant
loadings)
to
all
n
facilities
represented
by
the
screener
facility.

EPA
used
a
random
draw
procedure
for
all
observations
from
the
screener
survey
that
have
a
sample
weight
greater
than
one.

b.
Characterize
non­
MP&
M
point
source
discharges
EPA
used
preprogrammed
queries
in
BASINS
to
obtain
information
on
all
non­
MP&
M
point
source
discharges
in
Ohio.

BASINS
data
on
non­
MP&
M
point
source
dischargers
include:

 
location,

 
SIC
codes,

 
flow
volume,
and
 
discharge
characteristics
for
up
to
50
pollutants
or
parameters
for
PCS
facilities.

The
Agency
assigned
discharge
characteristics
to
all
non­
MP&
M
industrial
direct
discharges
based
on
the
information
provided
in
BASINS.
POTW
effluent
may
contain
pollutants
from
both
MP&
M
and
non­
MP&
M
discharges.
The
Agency
combined
information
from
BASINS
with
loading
estimates
provided
by
the
project
engineers
to
estimate
total
pollutant
loadings
from
a
given
POTW
.
This
analysis
used
the
following
assumptions
to
estimate
total
POTW
pollutant
loadings
under
the
baseline
discharge
levels:

 
If
a
POTW
was
not
estimated
to
receive
discharges
from
the
MP&
M
facilities,
then
the
analysis
used
POTW
loadings
reported
in
BASINS.

 
If
a
pollutant
or
a
parameter
was
not
reported
in
BASINS,
then
the
analysis
used
aggregate
loadings
from
all
MP&
M
facilities
discharging
to
a
given
POTW
to
calculate
total
POTW
loadings
of
a
given
pollutant.

 
If
a
POTW
was
estimated
to
receives
discharges
from
MP&
M
facilities
and
a
given
pollutant
was
reported
in
BASINS,
then
the
analysis
used
the
greater
of
the
aggregate
loadings
from
all
MP&
M
facilities
or
POTW
loadings
reported.

EPA
estimated
post­
compliance
pollutant
loadings
from
each
POTW
by
subtracting
the
estimated
reduction
in
the
MP&
M
facility
loadings
for
a
given
pollutant
from
its
total
baseline
loadings
for
a
given
POTW.

c.
Characterize
non­
point
source
discharges
The
water
quality
analysis
in
Ohio
used
empirical
data
on
Total
Kjeldahl
Nitrogen
(
TKN)
concentrations
to
characterize
the
baseline
water
quality
conditions.
Empirical
data
on
in­
stream
concentrations
captured
TKN
contribution
from
both
point
4
The
Agency
was
unable
to
validate
random
assignments
because
POTWs
do
not
know
all
of
their
MP&
M
dischargers.

H­
6
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
and
non­
point
sources
under
baseline
conditions.
EPA
estimated
changes
in
TKN
concentrations
resulting
from
the
final
rule
by
using
the
estimated
pollutant
loading
reductions
from
MP&
M
sources
and
the
water
quality
model
described
above.
The
Agency
assumed
that
the
non­
point
source
contribution
of
toxic
pollutants
found
in
MP&
M
effluent
to
ambient
concentrations
of
these
pollutants
in
Ohio s
streams
and
lakes
is
negligible.

H­
7
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
GLOSSARY
BASINS:
a
software
package
that
serves
as
a
database
management
system
for
water
quality
monitoring,
point
source
pollutant
discharge,
and
various
geo­
technical
data,
and
also
provides
an
analytic
platform
for
modeling
in­
stream
pollutant
concentrations
over
an
entire
watershed
based
on
multiple
sources
of
pollutants
within
the
watershed.

(
http://
www.
epa.
gov.
OST/
BASINS)

hydrolysis:
the
decomposition
of
organic
compounds
by
interaction
with
water.
(
http://
www.
epa.
gov/
OCEPAterms)

metals:
inorganic
compounds,
generally
nonvolatile,
and
which
cannot
be
broken
down
by
biodegradation
processes.
They
are
a
particular
concern
because
of
their
prevalence
in
MP&
M
effluents.
Metals
can
accumulate
in
biological
tissues,

sequester
into
sewage
sludge
in
POTWs,
and
contaminate
soils
and
sediments
when
released
to
the
environment.
Some
metals
are
quite
toxic
even
when
present
at
relatively
low
levels.

microbial
degradation:
a
process
whereby
organic
molecules
are
broken
down
by
microbial
metabolism.

Permit
Compliance
System
(
PCS):
a
computerized
database
of
information
on
water
discharge
permits,
designed
to
support
the
National
Pollutant
Discharge
Elimination
System
(
NPDES).

(
http://
www.
epa.
gov/
ceisweb1/
ceishome/
ceisdocs/
pcs/
pcs­
exec.
htm)

MP&
M
reach:
a
reach
to
which
an
MP&
M
facility
discharges.

sedimentation:
letting
solids
settle
out
of
wastewater
by
gravity.
(
http://
www.
epa.
gov/
OCEPAterms)

Total
Kjeldahl
Nitrogen
(
TKN):
the
total
of
organic
and
ammonia
nitrogen.
TKN
is
determined
in
the
same
manner
as
organic
nitrogen,
except
that
the
ammonia
is
not
driven
off
before
the
digestion
step.

Toxic
Release
Inventory
(
TRI):
database
of
toxic
releases
in
the
United
States
compiled
from
SARA
Title
III
Section
313
reports.
(
http://
www.
epa.
gov/
OCEPAterms)

volatilization:
a
process
whereby
chemicals
dissolved
in
water
escape
into
the
air.

(
http://
www.
epa.
gov/
OCEPAterms)

H­
8
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
ACRONYMS
PCS:
Permit
Compliance
System
RSEI:
Risk
Screening
Environmental
Indicator
model
TKN:
Total
Kjeldahl
Nitrogen
TRI:
Toxic
Release
Inventory
H­
9
MP&
M
EEBA:
Appendices
Appendix
H:
Fate
and
Transport
Model
for
DW
and
Ohio
Analyses
REFERENCES
U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1999.
Risk­
Screening
Environmental
Indicators
Model:
Version
1.0,

July
6,
Washington,
DC:
Office
of
Pollution
Prevention
and
Toxics.
http://
www.
epa.
gov/
opptintr/
env_
ind/
index.
html.

H­
10
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
INTRODUCTION
This
Environmental
Assessment
estimates
the
environmental
impact
of
MP&
M
discharges
on
water
bodies
and
POTWs
under
both
current
conditions
and
those
corresponding
to
four
regulatory
options:
the
Final
Option,
Proposed/
NODA
Option,
Directs
+
413
to
433
Upgrade
Option,
and
Directs
+

All
to
433
Upgrade
Option.
1
EPA
estimates
four
types
of
environmental
impacts:

 
the
occurrence
of
pollutant
concentrations
in
excess
of
EPA
ambient
water
quality
criteria
(
AWQC)
for
protection
of
human
health
in
waterways
(
e.
g.,
streams,
lakes,
bays,
and
estuaries)

receiving
discharges
from
MP&
M
facilities;

 
the
occurrence
of
pollutant
concentrations
in
excess
of
AWQC
for
protection
of
aquatic
species
in
waterways
receiving
discharges
from
MP&
M
facilities;

 
the
occurrence
of
POTW
inhibition
problems
resulting
from
MP&
M
facilities'
discharges;
and
 
barriers
to
POTW
s 
use
of
preferred
sewage
sludge
management
or
disposal
methods
(
i.
e.,
beneficial
land
application
or
surface
disposal),
due
to
metals
discharges
from
MP&
M
facilities.
Appendix
I:
Environmental
Assessment
APPENDIX
CONTENTS
I.
1
racterization
.................
.
I­
4
I.
1.1
ying
MP&
M
Pollutants
.................
I­
4
I.
1.2
hysical­
Chemical
Characteristics
and
Toxicity
Data
of
MP&
M
Pollutants
.................
.....
I­
9
I.
1.3
MP&
M
Pollutants
Based
on
Risk
to
Aquatic
Receptors
.................
..........
I­
21
I.
1.4
umptions
and
Limitations
.................
I­
23
I.
2.
hodology
.................
.................
I­
23
I.
2.1
ple
Set
Data
Analysis
and
National
Extrapolation
.................
..............
I­
23
I.
2.2
r
Quality
Modeling
.................
....
I­
23
I.
2.3
ct
of
Indirect
Discharging
Facilities
on
POTW
Operations
.................
..........
I­
25
I.
2.4
umptions
and
Limitations
.................
I­
27
I.
3
.................
.................
I­
28
I.
3.1
ty­
Specific
Data
.................
......
I­
28
I.
3.2
r
Body­
Specific
Data
.................
..
I­
28
I.
3.3
ation
Used
to
Evaluate
POTW
Operations
.
.
I­
29
I.
4
.................
.................
.....
I­
33
I.
4.1
n
Health
Impacts
.................
.....
I­
34
I.
4.2
ic
Life
Effects
.................
.......
I­
37
I.
4.3
Effects
.................
............
I­
41
Glossary
.................
.................
.......
I­
44
Acronyms
.................
.................
......
I­
48
References
.................
.................
.....
I­
49
MP&
M
Pollutant
Cha
Identif
P
Grouping
Ass
Met
Sam
Wate
Impa
Ass
Data
Sources
Facili
Wate
Inform
Results
Huma
Aquat
POTW
EPA
also
estimated
changes
in
human
health
risk
from
reduced
exposure
to
MP&
M
pollutants
via
consumption
of
contaminated
fish
and
drinking
water.
Chapters
13
and
14
of
this
EEBA
present
both
the
methodology
used
to
estimate
human
health
impacts
from
exposure
to
MP&
M
pollutants
and
the
results
of
this
analysis.

EPA
assessed
potential
environmental
impacts
of
MP&
M
discharges
on
the
receiving
water
bodies
and
POTWs
by
using
pollutant
fate
and
toxicity
data
in
conjunction
with
various
modeling
techniques.
EPA
quantified
the
releases
of
132
pollutants
of
concern
under
the
final
and
alternative
regulatory
options.
2
EPA
then
evaluated
potential
site­
specific
aquatic
life
and
human
health
impacts
resulting
from
the
baseline
and
post­
regulation
pollutant
releases.
EPA
compared
projected
water
concentrations
for
each
pollutant
to
either
(
a)
EPA
water
quality
criteria,
or
(
b)
toxic
effect
levels
(
i.
e.,
lowest
reported
1
The
results
of
the
Proposed/
NODA
Option
are
not
directly
comparable
to
the
final
option
alternatives.
The
total
number
of
facilities
reported
for
the
Proposed/
NODA
Option
analysis
differs
from
the
facility
count
reported
for
the
final
rule
and
the
two
upgrade
options.

After
deciding
in
July
2002
not
to
consider
the
NODA
option
as
the
basis
for
the
final
rule,
EPA
performed
no
more
analysis
on
the
NODA
option,
including
not
updating
facility
counts
and
related
analyses
for
the
change
in
subcategory
and
discharge
status
classifications.

2
EPA
originally
identified
150
MP&
M
POCs.
Of
these
150
POCs,
the
Agency
estimated
loadings
for
132
pollutants
for
the
phase
2
proposal
and
NODA.
The
benefits
analysis
presented
in
earlier
chapters
is
based
on
132
pollutants
for
which
loadings
are
available.
The
final
regulation
covers
only
the
Oily
Wastes
subcategory
and
benefit
reductions
were
estimated
for
122
pollutants.

I­
1
MP&
M
EEBA:
Appendices
or
estimated
toxic
concentration
that
causes
a
problem)
in
the
absence
of
water
quality
criteria
for
a
pollutant.
Figure
I.
1
depicts
steps
used
in
the
environmental
assessment.
The
following
sections
detail
these
analytic
steps.
Appendix
I:
Environmental
Assessment
Figure
I.
1a:
MP&
M
Environmental
Impact
Assessment
Source:
U.
S.
EPA
analysis.

I­
2
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Figure
I.
1b:
MP&
M
Environmental
Impact
Assessment
(
Continued)

Source:
U.
S.
EPA
analysis.

I­
3
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
The
remainder
of
this
appendix
is
organized
as
follows.
Section
I.
1
provides
information
on
the
pollutants
found
in
MP&
M
discharges.
Section
I.
2
describes
the
methodology
used
to
estimate
environmental
impacts,
including
extrapolation
of
sample
sets
to
the
national
level
and
estimates
of
water
quality
impacts.
Section
I.
3
describes
data
sources
for
both
MP&
M
facilities
and
POTWs.
Section
I.
4
presents
the
environmental
assessment
results.

I.
1
MP&
M
POLLUTANT
CHARACTERIZATION
The
extent
of
human
and
ecological
exposure
and
risk
from
environmental
releases
of
toxic
chemicals
depends
on
chemical­
specific
properties,
the
mechanism
and
media
of
release,
and
site­
specific
environmental
conditions.

Chemical­
specific
properties
include
toxic
effects
on
living
organisms,
and
the
fate
of
chemicals
in
the
environment.
EPA
estimated
the
fate
of
MP&
M
pollutants
based
on
their
propensity
to
volatilize,
adsorb
onto
sediments,
bioconcentrate,
and
biodegrade.
EPA
characterized
the
fate
and
toxicity
of
MP&
M
pollutants
in
three
steps:

 
 
identifying
pollutants
of
concern
(
POCs)
in
MP&
M
discharges,

 
 
compiling
physical­
chemical
and
toxicity
data
for
those
pollutants,
and
 
 
grouping
pollutants
based
on
their
characteristics.

The
pollutant­
specific
fate
and
toxicity
data
were
used
in
various
portions
of
the
quantitative
benefits
assessment.
In
addition,

EPA
summarized
the
distribution
of
MP&
M
pollutants
based
on
their
fate
and
toxicity
properties
using
the
groupings
developed
in
the
third
step.
This
summary
is
presented
in
Chapter
12.

I.
1.1
Identifying
MP&
M
Pollutants
EPA
sampled
MP&
M
facilities
nationwide
to
assess
the
concentrations
of
pollutants
in
MP&
M
effluents.
The
Agency
collected
samples
of
raw
wastewater
from
MP&
M
facilities
and
applied
standard
water
analysis
protocols
to
identify
and
quantify
the
pollutant
levels
in
each
sample.
EPA
used
these
analytical
data,
along
with
selection
criteria,
to
identify
132
contaminants
of
potential
concern.
MP&
M
POCs
include
43
priority
pollutants
(
PP),
3
conventional
pollutants,
and
86
nonconventional
pollutants.

EPA
then
evaluated
the
potential
environmental
fate
of
these
pollutants
and
their
toxicity
to
humans
and
aquatic
receptors.

EPA
was
able
to
assess
the
potential
fate
and
toxicity
of
118
of
these
pollutants,
including
43
priority
pollutants
(
33
priority
organics,
nine
priority
metals
and
one
inorganic)
and
75
nonconventional
pollutants
(
50
nonconventional
organics,
18
nonconventional
metals,
and
seven
nonconventional
inorganics).
Table
I.
1
presents
the
potential
fate
and
toxicity,
based
on
known
characteristics
of
each
chemical,
of
132
pollutants
of
concern.
Potential
fate
and
toxicity
data
are
not
available
for
four
conventional,
2
nonconventional,
and
eight
bulk
nonconventional
pollutants
(
also
listed
in
Table
I.
1)
associated
with
adverse
water
quality
impacts,
as
described
in
Section
12.1.3
of
this
report.

I­
4
MP&
M
EEBA:
AppendicesAppendix
I:
Environmental
Assessment
I­
5
Table
I.
1:
Potential
Fate
and
Toxicity
of
Pollutants
of
Concern
TypeaPollutantCAS
Toxicity
to
Aquatic
Life
(
Freshwater)
Toxicity
to
Aquatic
Life
(
Saltwater)
VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/
gHAPhPPi
AcuteChronicAcuteChronic
OAcenaphthene83329ModerateLowModerateLowModerateModerateModerateLow
 
OAcetone67641LowLowLowLowModerateLowInsignificantModerate
 
OAcetophenone98862LowLowUnknownUnknownLowLowLowModerate
 
OAcrolein107028HighHighHighHighModerateNonadsorptiveModerateLow
 
 
OAniline62533ModerateHighLowLowLowLowLowModerate
 
OAnthracene120127HighHighHighModerateModerateHighModerateResistant
 
OBenzoic
acid65850LowLowUnknownUnknownLowLowLowModerate
 
OBenzyl
alcohol100516LowLowLowLowLowNonadsorptiveInsignificantModerate
 
OBiphenyl92524ModerateLowLowLowModerateModerateModerateModerate
 
OBis(
2­
ethylhexyl)
phthalate117817UnknownUnknownUnknownUnknownNonvolatileHighModerateModerate
 
M
 
OBromo­
2­
chlorobenzene,
1­
694804LowLowUnknownUnknownModerateModerateModerateLow
OBromo­
3­
chlorobenzene,
1­
108372LowLowUnknownUnknownModerateModerateModerateLow
OButyl
benzyl
phthalate85687ModerateLowModerateLowLowHighModerateModerate
 
OCarbon
disulfide75150LowHighUnknownHighHighLowLowUnknown
 
OChlorobenzene108907LowLowLowLowHighLowLowLow
 
M
 
OChloroethane75003LowLowUnknownUnknownHighLowLowLow
 
 
 
OCresol,
o­
95487LowLowLowLowLowLowLowModerate
 
OCresol,
p­
106445LowLowUnknownUnknownLowLowLowHigh
 
OCyanide57125HighHighHighHighUnknownLowInsignificantModerate
 
M
 
OCymene,
p­
99876LowLowLowLowHighModerateHighLow
ODecane,
n­
124185LowLowLowLowUnknownHighHighModerate
ODibenzothiophene132650ModerateLowUnknownUnknownModerateHighHighUnknown
ODichloroethene,
1,1­
75354LowLowLowLowHighLowLowResistant
 
M
 
ODichloromethane75092LowLowLowLowHighLowInsignificantLow
 
M
 
ODimethyl
phthalate131113LowLowLowLowNonvolatileLowLowModerate
 
ODimethylformamide,
N,
N­
68122LowLowUnknownUnknownNonvolatileNonadsorptiveInsignificantModerate
 
ODimethylphenanthrene,
3,6­
1576676ModerateModerateUnknownUnknownLowHighHighModerate
ODimethylphenol,
2,4­
105679LowLowUnknownUnknownLowLowModerateModerate
 
ODi­
n­
butyl
phthalate84742ModerateLowModerateHighLowModerateModerateModerate
 
 
ODinitrophenol,
2,4­
51285LowLowLowLowLowModerateInsignificantResistant
 
 
ODinitrotoluene,
2,6­
606202LowModerateUnknownUnknownLowLowLowResistant
 
ODi­
n­
octyl
phthalate117840ModerateModerateUnknownUnknownLowModerateHighLow
 
ODioxane,
1,4­
123911LowLowUnknownUnknownLowLowInsignificantResistant
 
ODiphenylamine122394LowLowUnknownUnknownLowModerateModerateModerate
 
ODiphenyl
ether101848ModerateLowLowUnknownModerateModerateModerateModerate
ODocosane,
n­
629970LowLowLowLowUnknownHighHighModerate
ODodecane,
n­
112403LowLowLowLowUnknownHighHighModerate
OEicosane,
n­
112958LowLowLowLowUnknownHighHighModerate
OEthylbenzene100414LowLowModerateModerateHighLowLowModerate
 
M
 
OFluoranthene206440HighHighHighModerateModerateHighHighResistant
 
OFluorene86737ModerateHighModerateModerateModerateModerateLowLow
 
OHexacosane,
n­
630013LowLowLowLowUnknownUnknownUnknownModerate
OHexadecane,
n­
544763LowLowLowLowUnknownHighHighModerate
OHexanoic
acid142621LowLowUnknownUnknownModerateLowLowModerate
OHexanone,
2­
591786LowLowUnknownUnknownModerateLowLowModerate
 
OIsobutyl
alcohol78831LowLowLowLowModerateLowInsignificantModerate
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MP&
M
EEBA:
AppendicesAppendix
I:
Environmental
Assessment
Table
I.
1:
Potential
Fate
and
Toxicity
of
Pollutants
of
Concern
TypeaPollutantCAS
Toxicity
to
Aquatic
Life
(
Freshwater)
Toxicity
to
Aquatic
Life
(
Saltwater)
VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/
gHAPhPPi
AcuteChronicAcuteChronic
I­
6
OIsophorone78591LowLowLowLowLowLowInsignificantLow
 
 
 
OIsopropylnaphthalene,
2­
2027170ModerateModerateUnknownUnknownModerateHighHighUnknown
OMethyl
ethyl
ketone78933LowLowLowLowModerateNonadsorptiveInsignificantModerate
 
OMethyl
isobutyl
ketone108101LowLowLowLowModerateLowInsignificantModerate
 
OMethyl
methacrylate80626LowLowUnknownUnknownModerateLowLowLow
 
OMethylfluorene,
1­
1730376ModerateLowUnknownUnknownModerateHighHighUnknown
OMethylnaphthalene,
2­
91576LowLowModerateModerateModerateModerateHighModerate
 
OMethylphenanthrene,
1­
832699ModerateModerateUnknownUnknownLowHighHighUnknown
ONaphthalene91203LowLowLowLowModerateLowLowModerate
 
 
ONitrophenol,
2­
88755LowLowLowLowLowLowLowLow
 
ONitrophenol,
4­
100027LowLowLowLowNonvolatileLowModerateModerate
 
 
ONitrosodimethylamine,
N­
62759LowLowLowLowNonvolatileLowInsignificantResistant
 
 
ONitrosodiphenylamine,
N­
86306LowLowLowLowLowModerateModerateLow
 
ONitrosopiperidine,
N­
100754LowLowUnknownUnknownNonvolatileNonadsorptiveInsignificantResistant
OOctacosane,
n­
630024LowLowLowLowUnknownUnknownUnknownModerate
OOctadecane,
n­
593453LowLowLowLowUnknownHighHighModerate
OParachlorometacresol59507LowLowUnknownUnknownLowLowModerateLow
 
OPhenanthrene85018ModerateModerateModerateModerateModerateHighModerateResistant
 
OPhenol108952LowLowLowLowLowLowInsignificantHigh
 
 
OPyrene129000ModerateModerateUnknownUnknownModerateHighHighResistant
 
OPyridine110861LowLowUnknownUnknownLowNonadsorptiveInsignificantModerate
 
OStyrene100425LowLowLowLowHighLowLowLow
 
M
 
OTerpineol,
alpha­
98555LowLowUnknownUnknownModerateLowLowModerate
OTetrachloroethene127184LowLowLowLowHighLowLowResistant
 
M
 
OTetracosane,
n­
646311LowLowLowLowUnknownHighHighModerate
OTetradecane,
n­
629594LowLowLowLowUnknownHighHighModerate
OToluene108883LowLowLowLowHighLowLowModerate
 
M
 
OTriacontane,
n­
638686LowLowLowLowUnknownUnknownUnknownModerate
OTrichloroethene79016LowLowLowLowHighLowLowResistant
 
M
 
OTrichlorofluoromethane75694LowLowUnknownUnknownHighLowLowResistant
 
OTrichloromethane67663LowLowLowLowHighLowInsignificantResistant
 
THM
 
OTripropyleneglycolmethylether20324338LowLowUnknownUnknownNonvolatileLowInsignificantModerate
OXylene,
m­
108383LowLowLowLowHighLowModerateLow
 
M
 
OXylene,
m­
&
p­*
179601231LowLowLowLowHighLowModerateLow
 
M
 
OXylene,
o­
95476LowLowLowLowHighLowModerateLow
 
M
 
OXylene,
o­
&
p­*
136777612LowLowLowLowHighLowModerateLow
 
M
 
OZiram
\
Cymate137304HighHighLowLowNonvolatileNonadsorptiveInsignificantResistant
 
MAluminum7429905ModerateModerateUnknownUnknownNonvolatileHighModerateResistant
 
SM
MAntimony7440360LowLowLowLowNonvolatileHighInsignificantResistant
 
M
 
MBarium7440393LowLowUnknownUnknownNonvolatileHighUnknownResistant
 
M
MBeryllium7440417ModerateHighUnknownUnknownNonvolatileHighLowResistant
 
M
 
MCadmium7440439HighHighHighHighNonvolatileHighModerateResistant
 
M
 
MCalcium7440702UnknownLowUnknownUnknownNonvolatileHighUnknownResistant
MChromium7440473ModerateModerateLowModerateNonvolatileHighLowResistant
 
M
MChromium
hexavalent18540299HighModerateLowModerateNonvolatileHighLowResistant
 
M
MCobalt7440484LowModerateUnknownModerateNonvolatileHighUnknownResistant
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MP&
M
EEBA:
AppendicesAppendix
I:
Environmental
Assessment
Table
I.
1:
Potential
Fate
and
Toxicity
of
Pollutants
of
Concern
TypeaPollutantCAS
Toxicity
to
Aquatic
Life
(
Freshwater)
Toxicity
to
Aquatic
Life
(
Saltwater)
VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/
gHAPhPPi
AcuteChronicAcuteChronic
I­
7
MCopper7440508HighHighHighHighNonvolatileHighModerateResistant
 
TT
 
MGold7440575UnknownUnknownUnknownUnknownNonvolatileHighUnknownResistant
MIron7439896UnknownLowLowLowNonvolatileHighUnknownResistant
 
SM
MLead7439921HighHighModerateHighNonvolatileHighLowResistantTT
 
MMagnesium7439954LowLowUnknownUnknownNonvolatileHighHighResistant
MManganese7439965UnknownLowUnknownModerateNonvolatileHighUnknownResistant
 
SM
MMercury7439976HighHighHighHighHighHighHighResistantM
 
MMolybdenum7439987UnknownModerateUnknownUnknownNonvolatileHighUnknownResistant
 
MNickel7440020ModerateModerateHighHighNonvolatileLowLowResistant
 
M
 
MSelenium7782492HighHighModerateModerateNonvolatileHighInsignificantResistant
 
M
MSilver7440224HighHighHighHighNonvolatileHighInsignificantResistant
 
SM
 
MSodium7440235LowLowUnknownUnknownNonvolatileHighUnknownResistant
MThallium7440280LowModerateLowLowNonvolatileHighModerateResistant
 
M
 
MTin7440315UnknownModerateUnknownUnknownNonvolatileHighUnknownResistant
 
MTitanium7440326UnknownLowUnknownUnknownNonvolatileHighUnknownResistant
 
MVanadium7440622LowHighUnknownUnknownNonvolatileHighUnknownResistant
 
MYttrium7440655UnknownUnknownUnknownUnknownNonvolatileHighUnknownResistant
MZinc7440666ModerateLowHighModerateNonvolatileHighLowResistant
 
SM
OIAmmonia
as
N7664417LowLowLowLowModerateNonadsorptiveUnknownModerate
OIArsenic7440382ModerateLowHighModerateUnknownUnknownLowUnknown
 
M
 
OIBoron7440428UnknownModerateUnknownUnknownUnknownUnknownUnknownUnknown
 
OIChloride16887006LowLowUnknownUnknownUnknownUnknownUnknownUnknownSM
OIFluoride16984488LowLowUnknownUnknownUnknownUnknownUnknownUnknown
 
M
OIPhosphate14265442UnknownUnknownUnknownUnknownUnknownUnknownUnknownUnknown
OISulfate14808798UnknownLowUnknownUnknownUnknownUnknownUnknownUnknownSM
OISulfide18496258UnknownHighUnknownHighUnknownUnknownUnknownUnknown
OIPhosphorus
(
as
PO4)
UnknownUnknownUnknownUnknownUnknownUnknownUnknownUnknown
CPBOD
5­
day
(
carbonaceous)
C­
003
CPOil
and
Grease
CPOil
and
Grease
(
as
Hem)
C­
036
CPTotal
Suspended
Solids
(
TSS)
C­
009
BNCPAmenable
CyanideC­
025
BNCPChemical
Oxygen
Demand
(
COD)
C­
004
BNCPTotal
Dissolved
Solids
(
TDS)
C­
010
BNCPTotal
Kjeldahl
NitrogenC­
021
BNCPTotal
Organic
Carbon
(
TOC)
C­
012
 
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
1:
Potential
Fate
and
Toxicity
of
Pollutants
of
Concern
Typea
Pollutant
CAS
Toxicity
to
Aquatic
Life
(
Freshwater)
Toxicity
to
Aquatic
Life
(
Saltwater)
Volatility
Adsorption
BCFb
Biodegc
RfDd
SFe
DWC
f/
g
HAPh
PPi
Acute
Chronic
Acute
Chronic
BNCP
Total
Petroleum
Hydrocarbons
(
as
Sgt
­
hem)
C­
037
BNCP
Total
Recoverable
Phenolics
C­
020
BNCP
Weak­
acid
Dissociable
Cyanide
C­
042
Table
Notes:

Unless
indicated
otherwise,
all
metals
are
assumed
to
be
nonvolatile,
to
have
high
adsorption,

and
to
be
resistant
to
biodegradation.

a
Type
O
=
Organic
M
=
Metal
OI
=
Other
Inorganic
CP
=
Conventional
Pollutant
BNCP
=
Bulk
Nonconventional
Pollutant
b
BCF
=
Bioconcentration
Factor
Biodeg
=
Biodegradation
Potential
d
RfD
=
Reference
Dose
Source:
U.
S.
EPA
analysis.
e
SF
=
Slope
Factor
f
DWC
=
Drinking
Water
Criteria
g
Drinking
Water
Criteria
Codes
M
=
Maximum
Contaminant
Level
(
MCL)
established
for
health­
based
effect
SM
=
Secondary
Maximum
Contaminant
Level
(
SMCL)
established
for
taste
or
aesthetic
effect
THM
=
MCL
established
for
trihalomethanes
TT
=
Treatment
technology
action
level
established
h
HAP
=
Hazardous
Air
Pollutant
i
PP
=
Priority
Pollutant
I­
8
c
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
I.
1.2
PhysicalChemical
Characteristics
and
Toxicity
Data
of
MP&
M
Pollutants
Pollutants
present
in
MP&
M
effluents
can
have
significant
effects
on
human
health
and
aquatic
receptors.
EPA
used
various
data
sources
to
evaluate
both
pollutant­
specific
fate
and
toxicity
and
potential
human
health
effects,
including:

 
reference
doses
(
RfDs
),

 
cancer
potency
slope
factors
(
SFs),

 
human
health­
based
water
quality
criteria
(
WQC
),

 
maximum
contaminant
levels
(
MCLs)
for
drinking
water
protection
and
other
drinking
water
related
criteria,

and
 
hazardous
air
pollutant
(
HAP)
and
priority
pollutant
(
PP)
lists.

To
evaluate
potential
fate
and
effects
in
aquatic
environments,
the
Agency
relied
on:

 
measures
of
acute
and
chronic
toxicity
to
aquatic
species,

 
bioconcentration
factors
for
aquatic
species,

 
Henry's
Law
(
H)
constants
(
to
estimate
volatility),

 
adsorption
coefficients
(
to
estimate
association
with
bottom
sediments),
and
 
biodegradation
half­
lives
(
to
estimate
the
removal
of
chemicals
via
microbial
metabolism).

The
data
sources
used
in
the
assessment
include:

 
EPA
ambient
WQC
documents
and
updates;

 
EPA's
ASsessment
Tools
for
the
Evaluation
of
Risk
(
ASTER);

 
the
AQUatic
Information
REtrieval
System
(
AQUIRE)
and
the
Environmental
Research
Laboratory­
Duluth
fathead
minnow
database;

 
EPA's
Integrated
Risk
Information
System
(
IRIS);

 
EPA's
Health
Effects
Assessment
Summary
Tables
(
HEAST);

 
EPA's
1991
and
1993
Superfund
Chemical
Data
Matrix
(
SCDM)
;

 
Syracuse
Research
Corporation's
CHEMFATE
and
BIODEG
databases;
and
 
EPA
and
other
government
reports,
scientific
literature,
and
other
primary
and
secondary
data
sources.

EPA
also
obtained
information
on
chemicals
for
which
the
sources
listed
above
did
not
provide
physical­
chemical
properties
and/
or
toxicity
data,
to
ensure
that
the
assessment
be
as
comprehensive
as
possible.
To
the
extent
possible,
EPA
estimated
values
for
the
chemicals
using
the
quantitative
structure­
activity
relationship
(
QSAR)
model
incorporated
in
ASTER.

The
Agency
also
used
published
linear
regression
correlation
equations
to
determine
some
physical­
chemical
properties.

a.
Human
health
effects
EPA
used
various
data
sources
to
determine
pollutant­
specific
toxicity
to
human
health.
EPA
obtained
RfDs
and
SFs
from
IRIS,
HEAST,
and
EPA s
Region
II
Risk­
Based
Concentration
(
RBC)
table.
EPA
developed
drinking
water
criteria
and
human
health­
based
AWQC
values
for
two
exposure
routes:
(
1)
ingesting
the
pollutant
via
contaminated
aquatic
organisms
only
(
carcinogens
and
non­
carcinogens),
and
(
2)
ingesting
the
pollutant
via
both
water
and
contaminated
aquatic
organisms
I­
9
MP&
M
EEBA:
Appendices
(
non­
carcinogens
only).
Table
I.
2
summarizes
pollutant
toxicity
data
pertaining
to
human
health.
In
addition
to
fate
and
toxicity
data,
Table
I.
1
also
includes
HAP
and
PP
lists.
Short
descriptions
and
definitions
for
each
of
the
measures
of
human
health
effects
are
provided
below.
Appendix
I:
Environmental
Assessment
Table
I.
2:

CAS
Number
Pollutant
Name
Human
Health
AWQC
Values
Ingesting
Water
and
Organisms
Ingesting
Organisms
Only
Slope
Factor
Reference
Dose
Drinking
Water
Criteria
(
 
g/
l)
(
 
g/
l)
(
mg/
kg/
day)
(
mg/
kg/
day)
(
 
g/
l)

51285
Dinitrophenol,
2,4­
70
14000
0.002
57125
Cyanide
700
220000
0.02
200
59507
Parachlorometacresol
56000
270000
2
62533
Aniline
5.8
95
0.0057
62759
Nitrosodimethylamine,
N­
0.00069
8.1
51
65850
Benzoic
acid
130000
2900000
4
67641
Acetone
3500
2800000
0.1
67663
Trichloromethane
5.7
470
0.0061
0.01
100
68122
Dimethylformamide,
N,
N­
3500
220000000
0.1
75003
Chloroethane
12
520
0.0029
0.4
75092
Dichloromethane
4.7
1600
0.0075
0.06
5
75150
Carbon
disulfide
3400
94000
0.1
75354
Dichloroethene,
1,1­
0.057
3.2
0.6
0.009
7
75694
Trichlorofluoromethane
9100
66000
0.3
78591
Isophorone
36
2600
0.00095
0.2
78831
Isobutyl
alcohol
10000
1500000
0.3
78933
Methyl
ethyl
ketone
21000
6500000
0.6
79016
Trichloroethene
3.1
92
0.011
0.006
5
80626
Methyl
methacrylate
48000
2300000
1.4
83329
Acenaphthene
1200
2700
0.06
84742
Di­
n­
butyl
phthalate
2700
12000
0.1
85018
Phenanthrene
85687
Butyl
benzyl
phthalate
3000
5200
0.2
86306
Nitrosodiphenylamine,
N­
5
16
0.0049
86737
Fluorene
720
1500
0.04
88755
Nitrophenol,
2­

91203
Naphthalene
680
21000
0.02
91576
Methylnaphthalene,
2­
75
84
0.02
92524
Biphenyl
720
1200
0.05
95476
Xylene,
o­
42000
100000
2
10000
95487
Cresol,
o­
1700
30000
0.05
98555
Terpineol,
alpha­

98862
Acetophenone
3400
98000
0.1
99876
Cymene,
p­

100027
Nitrophenol,
4­
220
1100
0.008
100414
Ethylbenzene
3100
29000
0.1
700
100425
Styrene
6700
160000
0.2
100
100516
Benzyl
alcohol
10000
810000
0.3
100754
Nitrosopiperidine,
N­

101848
Diphenyl
Ether
105679
Dimethylphenol,
2,4­
540
2300
0.02
106445
Cresol,
p­
170
3100
0.005
107028
Acrolein
410
1000
0.02
Human
Health
Data
for
132
MP&
M
Pollutants
of
Concern
I­
10
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
2:

CAS
Number
Pollutant
Name
Human
Health
AWQC
Values
Ingesting
Water
and
Organisms
Ingesting
Organisms
Only
Slope
Factor
Reference
Dose
Drinking
Water
Criteria
(
 
g/
l)
(
 
g/
l)
(
mg/
kg/
day)
(
mg/
kg/
day)
(
 
g/
l)

108101
Methyl
isobutyl
ketone
2800
360000
0.08
108372
Bromo­
3­
chlorobenzene,
1­

108383
Xylene,
m­
42000
100000
2
10000
108883
Toluene
6800
200000
0.2
1000
108907
Chlorobenzene
680
21000
0.02
100
108952
Phenol
21000
4600000
0.6
110861
Pyridine
35
5400
0.001
112403
Dodecane,
n­
(
a)

112958
Eicosane,
n­
(
a)

117817
Bis(
2­
ethylhexyl)
phthalate
1.8
5.9
0.014
0.02
6
117840
Di­
n­
octyl
phthalate
37
39
0.02
120127
Anthracene
4100
6800
0.3
122394
Diphenylamine
470
1000
0.025
123911
Dioxane,
1,4­
3.2
2400
0.011
124185
Decane,
n­

127184
Tetrachloroethene
320
3500
0.052
0.01
5
129000
Pyrene
230
290
0.03
131113
Dimethyl
phthalate
310000
2900000
132650
Dibenzothiophene
137304
Ziram
\
Cymate
700
220000000
0.02
142621
Hexanoic
acid
206440
Fluoranthene
300
370
0.04
544763
Hexadecane,
n­
(
a)

591786
Hexanone,
2­
1400
65000
0.04
593453
Octadecane,
n­
(
a)

606202
Dinitrotoluene,
2,6­
34
900
0.001
629594
Tetradecane,
n­
(
a)

629970
Docosane,
n­

630013
Hexacosane,
n­
(
b)

630024
Octacosane,
n­
(
b)

638686
Triacontane,
n­
(
b)

646311
Tetracosane,
n­
(
b)

694804
Bromo­
2­
chlorobenzene,
1­

832699
Methylphenanthrene,
1­

1576676
Dimethylphenanthrene,
3,6­

1730376
Methylfluorene,
1­

2027170
Isopropylnaphthalene,
2­

7429905
Aluminum
20000
47000
1
50
7439896
Iron
300
0.3
300
7439921
Lead
15
7439954
Magnesium
7439965
Manganese
50
100
0.14
50
7439976
Mercury
0.05
0.051
2
7439987
Molybdenum
0.005
7440020
Nickel
610
4600
0.02
7440224
Silver
170
110000
0.005
100
7440235
Sodium
7440280
Thallium
1.8
6.5
0.00007
2
Human
Health
Data
for
132
MP&
M
Pollutants
of
Concern
I­
11
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
2:

CAS
Number
Pollutant
Name
Human
Health
AWQC
Values
Ingesting
Water
and
Organisms
Ingesting
Organisms
Only
Slope
Factor
Reference
Dose
Drinking
Water
Criteria
(
 
g/
l)
(
 
g/
l)
(
mg/
kg/
day)
(
mg/
kg/
day)
(
 
g/
l)

7440315
Tin
0.6
7440326
Titanium
4
7440360
Antimony
14
4300
0.0004
6
7440382
Arsenic
0.02
0.16
1.5
0.0003
50
7440393
Barium
1000
0.07
2000
7440417
Beryllium
66
1100
0.002
4
7440428
Boron
0.09
7440439
Cadmium
14
84
0.0005
5
7440473
Chromium
50000
1000000
1.5
100
7440484
Cobalt
0.06
7440508
Copper
650
1200
0.04
1300
7440575
Gold
7440622
Vanadium
0.007
7440655
Yttrium
7440666
Zinc
9100
69000
0.3
5000
7440702
Calcium
7664417
Ammonia
as
N
7782492
Selenium
170
11000
0.005
50
14265442
Phosphate
14808798
Sulfate
250000
16887006
Chloride
250000
16984488
Fluoride
0.06
4000
18496258
Sulfide
100
10000
18540299
Chromium
hexavalent
100
2000
0.003
100
20324338
Tripropyleneglycolmethyl
ether
136777612
Xylene,
o­
&
p­
(
c)
42000
100000
2
10000
179601231
Xylene,
m­
&
p­
(
c)
42000
100000
2
10000
C003
BOD
5­
day
(
carbonaceous)

C004
Chemical
Oxygen
Demand
(
COD)

C009
Total
Suspended
Solids
(
TSS)

C010
Total
Dissolved
Solids
(
TDS)

C012
Total
Organic
Carbon
(
TOC)

C020
Total
Recoverable
Phenolics
C021
Total
Kjeldahl
Nitrogen
C025
Amenable
Cyanide
C036
Oil
And
Grease
(
as
Hem)

C037
Total
Petroleum
Hydrocarbons
(
as
Sgt­
hem)

C042
Weak­
acid
Dissociable
Cyanide
Phosphorus
(
as
PO4)

Oil
and
Grease
Human
Health
Data
for
132
MP&
M
Pollutants
of
Concern
Sources:
U.
S.
EPA
(
1980),
U.
S.
EPA
(
1984),
U.
S.
EPA
(
1997),
U.
S.
EPA
(
1998),
U.
S.
EPA
(
1998/
99),
Worthing
(
1987).

I­
12
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
 
 
Systemic
toxicants
System
ic
toxicants
are
chemicals
that
EPA
believes
can
cause
significant
non­
carcinogenic
health
effects
when
present
in
the
human
body
above
chemical­
specific
toxicity
thresholds.
These
effects
may
result
from
acute
or
chronic
chemical
exposures,
and
include:

 
systemic
health
effects
(
i.
e.,
loss
of
one
or
more
neurological,
respiratory,
reproductive,
immunological,
or
circulatory
functions);

 
organ­
specific
toxicity
(
e.
g.,
liver
and
kidney
effects);

 
developmental
toxicity
(
e.
g.,
reduced
weight
in
newborns
or
loss
of
IQ);
and
 
lethality.

EPA
typically
relies
on
animal
toxicity
data
to
develop
RfDs
for
systemic
toxicants
that
can
enter
the
human
body
via
ingestion.
These
values
represent
chemical
concentrations
expressed
in
mg
of
pollutant/
kg
body
weight/
day.
Certain
exposed
populations
are
considered
to
be
protected
if
these
chemical
concentrations
are
not
exceeded.
These
populations
include
sensitive
groups,
such
as
young
children
or
pregnant
women.
EPA
included
all
available
RfD
data
for
the
MP&
M
pollutants
of
concern
(
POCs)
in
the
analysis.

 
 
Carcinogens
Carcinogens
are
chemicals
that
EPA
believes
can
cause
or
have
the
potential
to
cause
cellular
damage,
which
can
lead
to
tumors
or
cancers
in
humans,
either
directly
or
indirectly.
Unlike
systemic
toxicants,
most
carcinogens
are
not
believed
to
have
a
toxicity
threshold.
Any
amount
of
a
carcinogen
therefore
has
the
potential
to
result
in
a
cancer
event,
even
though
such
a
probability
can
be
very
small
at
low
concentrations.
The
Agency
has
developed
SFs,
using
animal
or
epidemiological
data,

that
express
the
probability
that
a
chemical
will
induce
tumor
or
cancer
development.
EPA
included
all
available
SF
data
for
the
MP&
M
POCs
in
the
analysis.

 
 
Drinking
water
criteria
EPA
developed
human
health­
based
drinking
water
criteria
to
assess
the
health
hazards
associated
with
the
presence
of
certain
toxic
chemicals
in
drinking
water.
The
criteria
are
usually
presented
as
MCLs.
MC
Ls
for
non­
carcinogens
represent
chemical­
specific
concentrations
(
expressed
in
 
g/
l)
that
are
not
expected
to
result
in
adverse
health
effects
in
exposed
populations
if
not
exceeded
in
drinking
water.
MCLs
for
carcinogens
represent
chemical­
specific
concentrations
(
expressed
in
 
g/
l)
that
are
expected
to
result
in
less
than
one
additional
cancer
case
per
million
lifetime
exposures
if
not
exceeded
in
drinking
water.
The
Agency
also
investigated
additional
drinking
water
criteria,
including:

 
Secondary
Maximum
Contaminant
Levels
(
SMCLs)
established
for
taste
or
aesthetic
effects,

 
MCLs
established
specifically
for
trihalomethanes,
and
 
action
levels
developed
on
the
basis
of
treatment
technology.

EPA
included
all
the
available
primary
and
secondary
drinking
water
criteria
for
the
MP&
M
POCs
in
the
analysis.

 
 
Pollutant
uptake
via
water
and/
or
organisms
EPA
has
developed
WQC
for
numerous
priority
toxic
pollutants
to
protect
the
health
of
humans
who
consume
water
and
organisms
or
only
organisms
obtained
from
aquatic
habitats
contaminated
by
those
PPs.
The
criteria,
expressed
in
 
g/
l,

represent
concentrations
in
surface
waters
that
will
cause
adverse
health
effects
in
humans
when
exceeded.
EPA
obtained
all
available
human
health
WQC
for
the
MP&
M
POCs
and
included
them
in
the
analysis.

 
 
Priority
pollutants
(
PPs)

Priority
pollutants
are
126
individual
chemicals,
defined
by
the
Agency
as
toxic,
that
EPA
routinely
analyzes
when
assessing
contaminated
surface
water,
sediment,
groundwater,
or
soil
samples.
These
chemicals
are
of
particular
concern
to
the
Agency
because
of
their
high
toxicity
or
persistence
in
the
environment.
EPA
identified
all
MP&
M
PPs
and
included
them
in
the
analysis.

I­
13
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
 
 
Hazardous
air
pollutants
(
HAPs)

HAPs
are
compounds
that
EPA
believes
may
represent
an
unacceptable
risk
to
human
health
if
present
in
the
air.
HAPs,

expressed
in
 
g/
m3
,
can
be
of
particular
concern
to
POTW
workers
if
released
into
the
air
at
high
enough
concentrations
during
the
wastewater
treatment
cycle.
EPA
identified
all
HAPs
among
the
MP&
M
POCs
analyzed.

b.
Aquatic
receptor
effects
The
potential
impact
of
chemicals
on
aquatic
receptors
can
be
assessed
qualitatively
based
on
five
effect
and
fate
parameters:

 
aquatic
toxicity
(
acute
and
chronic),

 
bioconcentration,

 
volatilization,

 
adsorption,
and
 
biodegradation.

Site­
specific
risks
require
a
measure
of
exposure
and
cannot
be
quantified
using
this
approach.
Chemicals
can
be
classified
and
ranked
in
terms
of
their
impacts
on
aquatic
receptors,
however,
by
using
the
five
parameters
discussed
below.
Table
I.
3
summarizes
the
measured
or
estimated
values
of
these
parameters
for
the
MP&
M
POCs.
Each
effect
and
fate
parameter
is
described
below.

Biological
oxygen
demand
(
BOD),
oil
and
grease
(
O&
G),
pH,
and
total
suspended
solids
(
TSS):
These
fate/
effect
parameters
are
relevant
only
for
specific
chemicals.
These
parameters
are
not
available
for
the
conventional
pollutants
or
bulk
nonconventional
pollutants,
such
as
total
petroleum
hydrocarbons
(
TPH),
alkalinity,
total
organic
carbon
(
TOC),
or
total
Kjeldahl
nitrogen
(
TKN).
Most
of
these
pollutants
are
responsible
for
significant
environmental
impacts,
however.
Section
12.2.4
outlines
these
impacts
in
greater
detail.

 
 
Aquatic
toxicity
data
The
Agency
addressed
two
general
classes
of
aquatic
toxicity:

 
Acute
toxicity
(
AT)
assesses
the
impacts
of
a
pollutant
after
a
relatively
short
exposure
duration,
typically
48
and
96
hours
for
invertebrates
and
fish,
respectively.
The
endpoint
of
concern
is
mortality,
reported
as
the
LC50.
This
value
represents
the
concentration
lethal
to
50
percent
of
the
test
organisms
for
the
duration
of
the
exposure.

 
Chronic
toxicity
(
CT)
assesses
the
impact
of
a
pollutant
after
a
longer
exposure
duration,
typically
from
one
week
to
several
months.
The
endpoints
of
concern
are
one
or
more
sub­
lethal
responses,
such
as
changes
in
reproduction
or
growth
in
the
affected
organisms.
The
results
are
reported
in
various
ways,
including
EC1
or
EC5
(
i.
e.,
the
concentration
at
which
one
percent
or
five
percent
of
the
test
organisms
show
a
significant
sub­
lethal
response),

NOEC
(
No
Observed
Effect
Concentration),
LOEC
(
Lowest
Observed
Effect
Concentration),
or
MATC
(
Maximum
Allowable
Toxicant
Concentration).

 
 
Bioconcentration
factor
(
BCF)
data
The
bioconcentration
factor
(
BCF
,
measured
in
l/
kg)
is
a
good
indicator
of
the
potential
for
a
chemical
dissolved
in
the
water
column
to
be
taken
up
by
aquatic
biota
across
external
surface
membranes,
usually
fish
gills.
The
BCF
is
defined
as
follows:

BCF
=
equilibrium
chemical
concentration
in
target
organism
(
mg/
kg,
wet
weight)

mean
chemical
concentration
in
surrounding
water
(
 
g/
L)
(
I.
1)

EPA
analyzes
POCs
with
elevated
BCF
values
because
these
pollutants
can
bioconcentrate
in
aquatic
organisms
and
transfer
up
the
food
chain
if
they
are
not
metabolized
and
excreted.
This
transfer
can
result
in
significant
exposures
to
predators
(
including
humans)
consuming
contaminated
fish
or
shellfish.

I­
14
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Although
the
bioaccumulation
factor
(
BAF)
is
a
better
measure
of
the
potential
for
a
chemical
dissolved
in
the
water
column
to
be
taken
up
by
aquatic
biota,
field
measured
BAFs
are
not
yet
available.
EPA
recognizes
that
using
bioconcentration
factors
will
underestimate
the
risk
to
aquatic
organisms.

 
 
Volatilization
data
Volatilization
is
a
process
whereby
chemicals
dissolved
in
water
escape
into
the
air.
Chemicals
with
higher
volatilization
potential
are
typically
of
less
concern
to
aquatic
receptors
because
they
tend
to
be
removed
quickly
from
the
water
column.

These
volatile
pollutants
are
a
concern
to
human
health
when
inhaled.
For
aquatic
receptors,
however,
POCs
with
higher
volatilization
potential
present
lower
hazards.

EPA
used
the
air/
water
partitioning
coefficient
H
to
estimate
a
chemical s
volatilization
potential.
H
represents
the
ratio
of
a
chemical s
aqueous
phase
concentration
to
its
equilibrium
partial
pressure
in
the
gas
phase
(
at
25
°
C);
units
are
typically
expressed
as
atm.
m3
/
mole.
Metals
do
not
have
measurable
partial
pressures
(
with
some
notable
exceptions,
including
several
organic
mercury
compounds),
and
are
therefore
considered
to
be
nonvolatile
unless
otherwise
indicated.

 
 
Adsorption
data
Adsorption
is
a
process
whereby
chemicals
associate
preferentially
with
the
organic
carbon
(
OC)
found
in
soils
and
sediments.
Highly
adsorptive
compounds
tend
to
accumulate
in
sludge
or
sediments.
Such
chemicals
are
also
more
likely
to
be
taken
up
by
benthic
invertebrates
and
to
affect
local
food
chains.
Both
accumulation
in
sediment
and
the
effect
on
local
food
chains
make
these
chemicals
more
likely
to
impact
higher
predators,
including
humans.

EPA
used
the
adsorption
coefficient
(
Koc)
to
assess
the
potential
of
organic
MP&
M
POCs
to
associate
with
organic
carbon.
Koc
represents
the
ratio
of
the
target
chemical
adsorbed
per
unit
weight
of
organic
carbon
in
the
soil
or
sediment
to
the
concentration
of
that
same
chemical
in
solution
at
equilibrium.
Metals
in
the
aquatic
environment
typically
end
up
in
the
sediment
phase
but
do
not
bind
to
the
organic
carbon
(
except
for
nickel).
The
Agency
assumed
that
all
metals
show
a
high
affinity
for
sludge
and
sediments
independent
of
their
negligible
Koc
values.

 
 
Biodegradation
data
Biodegradation
is
a
process
whereby
organic
molecules
are
broken
down
by
microbial
metabolism.
Biodegradation
represents
an
important
removal
process:
compounds
that
are
readily
biodegraded
generally
represent
lower
intrinsic
hazards
because
they
can
be
eliminated
rapidly.
These
compounds
are
therefore
less
likely
to
create
long­
term
toxicity
problems
or
to
accumulate
in
sludge
or
sediments
and
organisms.
Chemicals
that
biodegrade
slowly
or
not
at
all
can
accumulate
and
linger
for
longer
periods
of
time
in
sludge
or
sediments,
and
represent
a
higher
hazard
to
aquatic
receptors.

EPA
used
biodegradation
half­
life
to
estimate
the
potential
for
an
organic
chemical
to
biodegrade
in
the
aquatic
environment.
Biodegradation
half­
life
represents
the
number
of
days
a
compound
takes
to
be
degraded
to
half
of
its
starting
concentration
under
prescribed
laboratory
conditions.
Metals
do
not
biodegrade.

Table
I.
3
summarizes
pollutant
toxicity
data
pertaining
to
aquatic
life.

I­
15
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
3:

CAS
Number
Pollutant
Name
Freshwater
Aquatic
Life
Saltwater
Aquatic
Life
Bio

concentration
Factor
Henry's
Law
Constant
Adsorption
Coefficient
(
Koc)
Bio

degradation
Half­
Life
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Value
(
l/
kg)
Value
(
atm/

m
3­
mole)
Value
Value
(
days)

51285
Dinitrophenol,
2,4­
1160
790
1500
940
1.51
0.000000443
2386
263
57125
Cyanide
22
5.2
1
1
1
45
16
59507
Parachlorometacresol
4050
1300
79
0.0000025
604
100
62533
Aniline
250
4
29400
2940
19.9
0.0000019
54
26
62759
Nitrosodimethylamine,
N­
280000
4000
4300000
430000
0.026
0.000000263
12
180
65850
Benzoic
acid
180000
17178
15
0.00000154
182
16
67641
Acetone
6210000
1866000
5640000
10000
0.39
0.00004
18
7
67663
Trichloromethane
13300
6300
19610
1961
3.75
0.00367
40
180
68122
Dimethylformamide,
N,
N­
7100000
710000
0.005
0.000000018
6.1
16
75003
Chloroethane
65614
21069
7.2
0.00882
37.6
28
75092
Dichloromethane
330000
82500
256000
2560
0.91
0.00219
28
28
75150
Carbon
disulfide
2100
2
2
11.5
0.0303
89
75354
Dichloroethene,
1,1­
11600
5114
224000
22400
5.6
0.0261
343
180
75694
Trichlorofluoromethane
17387
6412
49
0.097
93
360
78591
Isophorone
120000
11000
12900
1290
4.38
0.00000576
25
28
78831
Isobutyl
alcohol
949000
4000
600000
60000
2.2
0.0000118
61.7
7.2
78933
Methyl
ethyl
ketone
3220000
233550
1287000
128700
1
0.00006
5.2
7
79016
Trichloroethene
40700
14850
14000
2000
10.6
0.0103
104
360
80626
Methyl
methacrylate
191000
19100
6.6
0.00034
22
28
83329
Acenaphthene
580
208
970
710
242
0.00009
3890
102
84742
Di­
n­
butyl
phthalate
850
500
450
3.4
89
0.00000181
6310
23
85018
Phenanthrene
180
19
110
11
486
0.00002
18800
200
85687
Butyl
benzyl
phthalate
820
260
510
400
414
0.00000126
17000
7
86306
Nitrosodiphenylamine,
N­
5800
1000
3300000
33000
136
0.000005
1200
34
86737
Fluorene
212
8
1000
100
30
0.00006
2830
60
88755
Nitrophenol,
2­
160000
3451
32000
16000
13.5
0.00000947
114
28
91203
Naphthalene
1600
370
1200
120
10.5
0.00048
871
20
91576
Methylnaphthalene,
2­
1133
417
600
60
2566
0.00052
8500
20
92524
Biphenyl
360
230
4600
460
436
0.0003
1400
7
95476
Xylene,
o­
3820
1332
6000
600
208
0.00519
129
28
95487
Cresol,
o­
14000
2251
10200
1020
18
0.0000012
103
7
98555
Terpineol,
alpha­
12742
4879
48
0.0000544
589
15
Aquatic
Life
Toxicity
Data
for
132
MP&
M
Pollutants
of
Concern
I­
16
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
3:

CAS
Number
Pollutant
Name
Freshwater
Aquatic
Life
Saltwater
Aquatic
Life
Bio

concentration
Factor
Henry's
Law
Constant
Adsorption
Coefficient
(
Koc)
Bio

degradation
Half­
Life
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Value
(
l/
kg)
Value
(
atm/

m
3­
mole)
Value
Value
(
days)

98862
Acetophenone
162000
31094
11
0.00001
45
16
99876
Cymene,
p­
6500
237
4400
440
770
0.011
4000
100
100027
Nitrophenol,
4­
7680
1300
7170
1900
79
0.000000000415
236
7
100414
Ethylbenzene
9090
4600
430
43
37.5
0.00788
250
10
100425
Styrene
4020
402
9100
910
13.5
0.00283
920
28
100516
Benzyl
alcohol
10000
1000
15000
1500
4
0.000000743
6.1
16
100754
Nitrosopiperidine,
N­
1019538
282592
0.000000275
9
180
101848
Diphenyl
Ether
4000
240
930
0.000448
7800
15
105679
Dimethylphenol,
2,4­
2120
1970
94
0.000000951
18
7
106445
Cresol,
p­
7500
2570
17.6
0.000001
49
0.667
107028
Acrolein
14
5.8
55
5.5
215
0.00012
5
28
108101
Methyl
isobutyl
ketone
505000
50445
812000
81200
2.4
0.00014
19
7
108372
Bromo­
3­
chlorobenzene,
1­
1784
682
190
0.00078
1500
100
108383
Xylene,
m­
16000
3900
12000
1200
208
0.00718
190
28
108883
Toluene
5500
1000
6300
5000
10.7
0.00664
95
22
108907
Chlorobenzene
2370
2100
10500
1050
10.3
0.00377
275
150
108952
Phenol
4200
200
5800
2410
1.4
0.000000333
30.2
3.5
110861
Pyridine
93800
25000
2
0.00000888
5
7
112403
Dodecane,
n­
(
a)
18000
1300
500000
50000
14500
95000
17
112958
Eicosane,
n­
(
a)
18000
1300
500000
50000
100000
30000000
17
117817
Bis(
2­
ethylhexyl)
phthalate
130
0.0000001
87420
23
117840
Di­
n­
octyl
phthalate
690
69
5460
0.000000445
2390
28
120127
Anthracene
2.78
2.2
40
16
478
0.00007
16000
460
122394
Diphenylamine
3790
734
269
0.000000496
1910
20
123911
Dioxane,
1,4­
9850000
1457300
0.4
0.0000048
17
180
124185
Decane,
n­
a
18000
1300
500000
50000
8800
58200
17
127184
Tetrachloroethene
4990
510
10200
450
30.6
0.0184
363
360
129000
Pyrene
591
61
1110
0.000011
62700
1900
131113
Dimethyl
phthalate
33000
1700
58000
5800
36
0.000000105
40
7
132650
Dibenzothiophene
420
122
1100
0.00002
11000
137304
Ziram
\
Cymate
8
1.8
5200
520
0.001
0.4
142621
Hexanoic
acid
320000
15170
16
0.0000225
38
12
Aquatic
Life
Toxicity
Data
for
132
MP&
M
Pollutants
of
Concern
I­
17
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
3:

CAS
Number
Pollutant
Name
Freshwater
Aquatic
Life
Saltwater
Aquatic
Life
Bio

concentration
Factor
Henry's
Law
Constant
Adsorption
Coefficient
(
Koc)
Bio

degradation
Half­
Life
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Value
(
l/
kg)
Value
(
atm/

m
3­
mole)
Value
Value
(
days)

206440
Fluoranthene
45
7.1
40
16
1150
0.0000161
41700
440
544763
Hexadecane,
n­
(
a)
18000
1300
500000
50000
32300
207000
17
591786
Hexanone,
2­
428000
38868
6.6
0.000113
12
16
593453
Octadecane,
n­
(
a)
18000
1300
500000
50000
10100
66900
17
606202
Dinitrotoluene,
2,6­
18500
60
12
0.000000747
100
180
629594
Tetradecane,
n­
(
a)
18000
1300
500000
50000
19500
126000
17
629970
Docosane,
n­
b
530000
68000
500000
50000
100000
110000000
17
630013
Hexacosane,
n­
(
b)
530000
68000
500000
50000
17
630024
Octacosane,
n­
(
b)
530000
68000
500000
50000
17
638686
Triacontane,
n­
(
b)
530000
68000
500000
50000
17
646311
Tetracosane,
n­
(
b)
530000
68000
500000
50000
100000
420000000
17
694804
Bromo­
2­
chlorobenzene,
1­
2942
1196
240
0.0006
1500
100
832699
Methylphenanthrene,
1­
555
54
4790
0.0000078
36000
1576676
Dimethylphenanthrene,
3,6­
543
21
33000
0.0000053
330000
20
1730376
Methylfluorene,
1­
627
115
3300
0.00008
33000
2027170
Isopropylnaphthalene,
2­
540
78
3200
0.00063
33000
7429905
Aluminum
750
87
231
7439896
Iron
1000
33000
3300
7439921
Lead
65
2.5
210
8.1
49
7439954
Magnesium
64700
6470
85215
7439965
Manganese
388
10
7439976
Mercury
1.4
0.77
1.8
0.94
5500
0.018
30000
7439987
Molybdenum
27.8
7440020
Nickel
470
52
74
8.2
47
300
7440224
Silver
3.4
0.34
1.9
0.19
0.5
7440235
Sodium
1640000
1020000
7440280
Thallium
1400
40
2130
213
116
7440315
Tin
18.6
7440326
Titanium
191
7440360
Antimony
3500
1600
4800
2900
1
7440382
Arsenic
340
150
69
36
44
7440393
Barium
410000
2813
Aquatic
Life
Toxicity
Data
for
132
MP&
M
Pollutants
of
Concern
I­
18
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
3:

CAS
Number
Pollutant
Name
Freshwater
Aquatic
Life
Saltwater
Aquatic
Life
Bio

concentration
Factor
Henry's
Law
Constant
Adsorption
Coefficient
(
Koc)
Bio

degradation
Half­
Life
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Value
(
l/
kg)
Value
(
atm/

m
3­
mole)
Value
Value
(
days)

7440417
Beryllium
130
5.3
19
7440428
Boron
31.6
7440439
Cadmium
4.3
2.2
42
9.3
64
7440473
Chromium
570
74
1100
50
16
7440484
Cobalt
1620
49
10
7440508
Copper
13
9
4.8
3.1
360
7440575
Gold
7440622
Vanadium
11200
9
7440655
Yttrium
7440666
Zinc
120
120
90
81
47
7440702
Calcium
200000
7664417
Ammonia
as
N
13300
3060
3800
570
0.0000161
3.1
16
7782492
Selenium
12.83
5
290
71
4.8
14265442
Phosphate
14808798
Sulfate
1000000
16887006
Chloride
860000
230000
16984488
Fluoride
1600
160
18496258
Sulfide
2
2
18540299
Chromium
hexavalent
16
11
1100
50
16
20324338
Tripropyleneglycolmethylether
2484600
683870
0.2
0.0000000001
46
16
136777612
Xylene,
o­&
p­
c
2600
1205
6000
600
208
0.0076
260
28
179601231
Xylene,
m­&
p­
c
2600
1205
6000
600
208
0.0076
260
28
C003
BOD
5­
day
(
carbonaceous)

C004
Chemical
Oxygen
Demand
(
COD)

C009
Total
Suspended
Solids
(
TSS)

C010
Total
Dissolved
Solids
(
TDS)

C012
Total
Organic
Carbon
(
TOC)

C020
Total
Recoverable
Phenolics
C021
Total
Kjeldahl
Nitrogen
C025
Amenable
Cyanide
C036
Oil
and
Grease
(
as
Hem)
Aquatic
Life
Toxicity
Data
for
132
MP&
M
Pollutants
of
Concern
I­
19
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
3:

CAS
Number
Pollutant
Name
Freshwater
Aquatic
Life
Saltwater
Aquatic
Life
Bio

concentration
Factor
Henry's
Law
Constant
Adsorption
Coefficient
(
Koc)
Bio

degradation
Half­
Life
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Acute
Value
(
 
g/
l)
Chronic
Value
(
 
g/
l)
Value
(
l/
kg)
Value
(
atm/

m
3­
mole)
Value
Value
(
days)

C037
Total
Petroleum
Hydrocarbons
(
as
Sgt­
hem)

C042
Weak­
acid
Dissociable
Cyanide
Phosphorus
(
as
PO4)

Oil
and
Grease
Aquatic
Life
Toxicity
Data
for
132
MP&
M
Pollutants
of
Concern
a
Aquatic
toxicity
data
for
n­
decane
are
reported
based
on
structural
similarity
b
Aquatic
toxicity
data
for
n­
docosane
are
reported
based
on
structural
similarity
Values
for
the
most
stringent
isomer
(
p­
Xylene)
are
assumed
Sources:
Arthur
D.
Little
(
1983),
Arthur
D.
Little
(
1986),
Birge
et
al.
(
1979),
Clay
(
1986),
Holdway
and
Spraque
(
1979),
ICF,
Inc.
(
1985),
Leblanc
(
1980),
Lyman
et
al.
(
1981),
U.
S.

Atomic
Energy
Commission
(
1973),
U.
S.
EPA
(
1972),
U.
S.
EPA
(
1976),
U.
S.
EPA
(
1980),
U.
S.
EPA
(
1993),
U.
S.
EPA
(
1998/
99a),
U.
S.
EPA
(
1998/
99b),
Zhang
and
Zhang
(
1982).

I­
20
c
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
I.
1.3
Grouping
MP&
M
Pollutants
Based
on
Risk
to
Aquatic
Receptors
The
impact
assessment
for
aquatic
receptors
looks
at
the
six
individual
fate
and
effects
parameters
for
each
MP&
M
POC,

including
acute
and
chronic
aquatic
toxicities,
bioconcentration
factors,
Henry s
Law
constants,
adsorption
coefficients,
and
biodegradation
half­
lives.
EPA
grouped
POCs
with
similar
attributes,
and
assigned
qualitative
descriptors
of
potential
environmental
behavior
and
impact
to
each
group.
This
grouping
was
used
to
describe
the
range
of
MP&
M
pollutant
characteristics
in
Chapter
12.
The
grouping
described
below
focuses
specifically
on
aquatic
environments
and
their
biological
receptors;
it
does
not
cover
the
human
health
toxicity
data
discussed
in
the
previous
section.

Table
I.
4
provides
a
summary
of
the
categorization
scheme
for
the
six
fate
and
effects
parameters.

Table
I.
4:
Summary
of
Categorization
Scheme
For
Six
Fate
and
Effects
Parameters
Parameter
High
Hazard
Moderate
Hazard
Low
Hazard
Insignificant
Hazard
Acute
Toxicity
(
AT)
AT
<
100
 
g/
l
100
 
AT
 
1,000
 
g/
l
AT
>
1,000
 
g/
l
Chronic
Toxicity
(
CT)
CT
<
10
 
g/
l
10
 
CT
 
100
 
g/
l
CT
>
100
 
g/
l
Bioconcentration
Factor
(
BCF)
BCF
>
500
50
 
BCF
 
500
5
 
BCF
<
50
BCF
<
5
Henry s
Law
Constant
(
H)
H
>
10­
3
10­
5
 
H
 
10­
3
3.0x10­
7
 
H
<
10­
5
H
<
3.0x10­
7
Adsorption
Coefficient
(
KOC)
oc
>
10,000
1,000
 
K
oc
 
10,000
10
 
Koc
<
1,000
K
oc
<
10
Biodegradation
Half­
Life
(
t1/
2)
1/
2
<
7
d
7
d
 
t1/
2
<
28
d
28
d
 
t1/
2
<
180
d
t1/
2
 
180
d
K
t
Source:
U.
S.
EPA
analysis.

a.
Acute
and
chronic
aquatic
toxicity
EPA
used
the
available
AT
data
to
group
chemicals
according
to
their
relative
short­
term
effects
on
aquatic
organisms,
using
the
following
categories:

 
AT
<
100
 
g/
l
High
acute
toxicity
 
100
 
g/
l
 
AT
 
1,000
 
g/
l
Moderate
acute
toxicity
 
AT
>
1,000
 
g/
l
Low
acute
toxicity
These
categories
reflect
the
fact
that
acute
toxicity
decreases
when
higher
concentrations
of
a
pollutant
are
required
to
induce
short­
term
mortality
in
the
test
organisms.
EPA s
Office
of
Pollution
Prevention
and
Toxics
(
OPPT)
uses
this
categorization
as
guidance
to
assess
data
submitted
in
Premanufacture
Notices
(
PMN)
(
EPA,
1996).

EPA
used
the
available
CT
data
to
group
chemicals
according
to
their
relative
long­
term
effects
on
aquatic
organisms,
based
on
the
following
categories:

 
CT
<
10
 
g/
l
High
chronic
toxicity
 
10
 
g/
l
 
CT
 
100
 
g/
l
Moderate
chronic
toxicity
 
CT
>
100
 
g/
l
Low
chronic
toxicity
These
categories
assume
that
CT
occurs
at
a
concentration
averaging
one
tenth
of
that
responsible
for
acute
toxicity.
They
also
reflect
the
fact
that
chronic
toxicity
decreases
when
higher
concentrations
of
a
pollutant
are
required
to
induce
longer­

term
lethal
or
sub­
lethal
responses
in
the
test
organisms.

I­
21
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
b.
Bioconcentration
factor
(
BCF)

EPA
used
the
available
BCF
data
to
group
chemicals
according
to
their
potential
to
bioconcentrate
in
aquatic
organisms,

based
on
the
following
categories:

 
BCF
>
500
High
potential
to
bioconcentrate
 
50
 
BCF
 
500
Moderate
potential
to
bioconcentrate
 
5
 
BCF<
50
Low
potential
to
bioconcentrate
 
BCF<
5
No
significant
potential
to
bioconcentrate
These
categories
reflect
the
fact
that
decreased
BCF
reduces
the
intrinsic
hazard
of
a
chemical
to
aquatic
receptors,
because
the
chemical
is
less
likely
to
accumulate
in
biological
tissues.

c.
Volatilization
potential
EPA
used
available
H
data
to
group
organic
chemicals
according
to
their
potential
to
volatilize
from
water
into
air,
based
on
the
following
categories:

 
H
>
10­
3
High
potential
to
volatilize
 
10­
5
 
H
 
10­
3
Moderate
potential
to
volatilize
 
3.0
×
10­
7
 
H
<
10­
5
Low
potential
to
volatilize
 
H
<
3.0
×
10­
7
No
potential
to
volatilize
Increased
volatility
decreases
a
chemical s
hazard
to
aquatic
receptors
because
the
chemical
is
more
likely
to
quickly
move
from
the
receiving
water
into
the
atmosphere.
(
The
opposite
is
true
for
human
health;
hazard
to
human
health
increases
with
increased
volatility
because
a
volatile
chemical
is
more
available
for
intake
by
inhalation.)

d.
Adsorption
potential
EPA
used
the
available
Koc
to
group
the
organic
POCs
according
to
their
potential
to
adsorb
to
sediments,
based
on
the
following
categories:

 
K
oc
>
10,000
High
potential
for
adsorption
 
1,000
 
Koc
 
10,000
Moderate
potential
for
adsorption
 
10
 
K
oc
<
1,000
Low
potential
for
adsorption
 
Koc
<
10
No
significant
adsorption
A
lower
adsorption
potential
indicates
a
lower
potential
for
a
chemical
to
be
a
hazard
to
aquatic
receptors.
The
lower
the
adsorption
potential
the
less
likely
a
chemical
is
to
accumulate
in
sediments
or
to
affect
benthic
invertebrates
and
to
be
taken
up
into
local
food
chains.

e.
Biodegradation
potential
EPA
used
biodegradation
half­
lives
to
group
organic
POCs
according
to
their
potential
to
biodegrade,
based
on
the
following
categories:

 
t1/
2
<
7
d
Rapid
rate
of
biodegradation
 
7
d
 
t1/
2
<
28
d
Moderate
rate
of
biodegradation
 
28
d
 
t1/
2
<
180
d
Slow
rate
of
biodegradation
 
t1/
2
 
180
d
Resistant
to
biodegradation
A
faster
rate
of
biodegradation
by
microbial
metabolism
decreases
an
organic
chemical s
hazard
to
aquatic
receptors.
The
more
rapid
the
rate
of
biodegradation,
the
more
quickly
a
chemical
will
be
removed
from
the
aquatic
environment.
Most
metals
occur
as
inorganic
compounds
(
notable
exceptions
include
organic
forms
of
certain
metals,
such
as
mercury,
lead,
or
selenium),
and
are
not
removed
by
biodegradation.
EPA
assumes
that
all
metals
are
resistant
to
biodegradation
for
the
purposes
of
this
assessment.

I­
22
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
I.
1.4
Assumptions
and
Limitations
The
following
are
the
major
assumptions
and
limitations
associated
with
the
data
compilation
and
categorization
used
in
the
MP&
M
analysis:

 
Some
data
are
estimated,
and
subject
to
uncertainty;

 
Data
are
unavailable
for
some
chemicals
and
parameters;

 
The
POCs
considered
in
this
study
do
not
include
all
the
constituents
that
may
be
present
in
MP&
M
pollutants;

 
Data
derived
from
laboratory
tests
may
not
accurately
reflect
conditions
in
the
field;
and
 
Available
aquatic
toxicity
and
bioconcentration
test
data
may
not
represent
the
most
sensitive
species.

I.
2
METHODOLOGY
I.
2.1
Sample
Set
Data
Analysis
and
National
Extrapolation
This
analysis
uses
discharge
information
from
862
sample
MP&
M
facilities
(
excluding
two
sample
facilities
in
Puerto
Rico)

that
discharge
directly
or
indirectly
to
607
receiving
waterways
(
521
rivers/
streams,
62
bays/
estuaries,
and
24
lakes).
The
in­
stream
water
quality
analysis
excluded
eight
of
the
62
marine
reaches
due
to
data
limitations.
EPA
performed
environmental
assessment
on
a
basis
of
the
sample
facility
data.
The
Agency
then
extrapolated
findings
from
the
sample
facility
analyses
to
the
national
level
using
two
alternative
extrapolation
methods:
(
1)
traditional
extrapolation
and
(
2)
post­

stratification
extrapolation.
EPA
also
used
the
differential
extrapolation
technique
in
addition
to
both
traditional
and
post­

stratification
approaches
when
a
sample
reach
was
estimated
to
receive
discharges
from
multiple
facilities.
Appendix
G
provides
detailed
information
on
the
extrapolation
approaches
used
in
this
analysis.
Based
on
the
extrapolation
methods
used
in
this
analysis,
EPA
estimates
that
approximately
43,901
MP&
M
facilities
discharge
to
between
29,500
and
40,000
water
bodies
nationwide.
3
EPA
evaluated
the
national­
level
environmental
impacts
of
reducing
pollutant
discharges
from
MP&
M
facilities
to
the
nation's
water
bodies
for
the
final
rule.
EPA
considered
only
pollutant
loadings
from
MP&
M
facilities
to
particular
water
bodies
in
the
national
analysis.
With
one
exception,
EPA
did
not
take
background
loadings
from
other
sources
into
account.

For
the
analysis
of
sewage
sludge
quantity,
EPA
was
able
to
use
information
from
the
Phase
2
Section
308
survey
of
POTWs
to
estimate
total
metal
loadings
from
all
sources
to
a
POTW
of
a
given
size
(
i.
e.,
small,
medium,
and
large).
The
Agency
based
this
estimate
on
survey
estimates
of
the
average
number
of
small,
medium,
and
large
MP&
M
facilities
discharging
to
a
POTW
in
each
size
category
and
the
percent
contribution
of
total
metal
loadings
discharged
from
MP&
M
facilities.

I.
2.2
Water
Quality
Modeling
EPA
used
four
different
equations
to
model
the
impacts
of
MP&
M
discharges
on
receiving
waterways.
EPA
used
a
simple
stream
dilution
model
for
MP&
M
facilities
that
discharge
into
streams
or
rivers.
This
model
does
not
account
for
fate
processes
other
than
complete
immediate
mixing.
4
EPA
derived
the
facility­
specific
data
(
i.
e.,
pollutant
loading
and
facility
flow)
used
in
this
equation
from
sources
described
in
Sections
3.1
and
5.2
of
this
report.

The
Agency
used
one
of
three
receiving
stream
flow
conditions
(
the
lowest
one­
day
average
flow
with
a
recurrence
interval
of
10
years
(
1Q10),
the
lowest
consecutive
seven­
day
average
flow
with
a
recurrence
interval
of
10
years
(
7Q10),
and
the
harmonic
mean
flow),
depending
on
the
criterion
or
toxic
effect
level
being
considered.

3
These
estimates
include
facilities
that
were
assessed
to
be
baseline
closures
by
the
MP&
M
economic
analysis.

4
EPA
used
an
exponential
decay
model
to
estimate
pollutant
concentrations
for
the
analysis
of
cancer
risk
from
drinking
water
consumption
for
streams.
This
model
is
discussed
in
detail
in
Appendix
G.

I­
23
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
The
1Q10
and
7Q10
flows
are
used
in
comparisons
of
in­
stream
concentrations
with
acute
and
chronic
aquatic
life
criteria
or
toxic
effect
levels,
respectively,
as
recommended
in
the
Technical
Support
Document
for
Water
Quality­
based
Toxics
Control
(
U.
S.
EPA
,
1991).

The
harmo
nic
mean
flow,
defined
as
the
inverse
mean
of
reciprocal
daily
arithmetic
mean
flow
values,
is
used
in
comparisons
of
in­
stream
concentrations
with
human
health
criteria
or
toxic
effect
levels
based
on
lifetime
exposure.
mmends
the
long­
term
harmonic
mean
flow
as
the
design
flow
for
assessing
potential
long­
term
human
health
impacts.

flow
is
preferab
le
to
arithm
etic
me
an
flow
beca
use
in­
strea
m
po
llutant
co
ncentration
is
a
function
of,
and
inverse
ly
proportional
to,
the
stream
flow
downstream
of
the
discharge.

The
event
frequency
represents
the
number
of
times
an
exposure
event
occurs
during
a
specified
time
period.

event
frequency
equal
to
the
facility
operating
days
to
assess
impacts
on
aquatic
life.
The
calculated
in­
stream
concentration
is
thus
the
averag
e
con
centra
tion
on
days
the
facility
is
discha
rging
w
astewa
ter.
A
set
the
event
frequency
at
365
d
ays
to
assess
lo
ng­
term
huma
n
health
impacts.
e
calculated
in­
stream
conc
entration
is
thus
the
a
verag
e
con
centra
tion
on
all
days
of
the
year.
This
frequency
leads
to
a
lower
calculated
concentration
because
of
the
additional
dilution
from
days
when
the
facility
is
not
operating,
but
it
is
consistent
with
the
conservative
assumption
that
the
target
population
is
present
to
consume
drinking
water
every
day
and
contaminated
fish
throughout
an
entire
lifetime.
owing
equation
calculates
in­
stream
concen
tration
for
streams
and
rivers:

(
I.
2)

where:

C
is
=
in­
stream
pollutant
concentration
(
 
g/
L);

L
=
facility
pollutant
loading
(
 
g/
yr);
for
indirect
dischargers,
L
=
L
indirect
facility
*
(
1­
TMT),
where
TMT
is
POTW
treatment
removal
efficiency
(
unitless);

OD
=
facility
or
POT
W
operating
days
(
days/
yr);

FF
=
MP
&
M
facility
flow
(
L/
day);
for
indirect
dischargers,
FF
=
PO
TW
flow
(
L/
day);

EF
=
event
frequency
(
days/
yr);
and
SF
=
receiving
stream
flow
(
L/
day).

EP
A
use
d
the
fo
llowing
simple
steady­
sta
te
mo
del
for
facilities
that
d
ischarge
into
lakes
othe
r
than
the
Gre
at
lakes.
his
model
takes
into
account
pollutant
degradation
and
the
hydraulic
residence
time
of
the
lake:

(
I.
3)

where:

Clake
=
stead
y­
state
lake
conc
entratio
n
of
po
llutant
(
 
g/
L),

Ci
=
stead
y­
state
inflow
conc
entratio
n
of
po
llutant
(
 
g/
L),

T
w
=
mean
hydraulic
residence
time
(
yr),

k
=
first­
order
pollutant
decay
rate
(
yr­
1),
and
(
I.
4)

where:

V
=
lake
volume
(
m3),
and
Q
=
mean
total
inflow
rate
(
m3/
yr).
EPA
reco
Harmonic
mean
EPA
set
the
EP
Th
The
foll
T
I­
24
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
EPA
used
alternative
means
to
predict
pollutant
concentrations
suitable
for
comparison
with
ambient
criteria
or
toxic
effect
levels
for
facilities
discharging
to
hydrologically
complex
waters,
such
as
bays
and
estuaries.
e,
EPA
employed
site­
specific
critical
dilutio
n
fac
tors
(
CDFs)
to
predict
the
concentration
at
the
edge
of
a
mixing
zone.
re
CDFs
were
not
available,
EPA
used
available
estuarine
diss
olve
d
co
nc
en
tratio
n
p
ote
ntia
ls
(
DCPs).

EPA
obtained
site­
specific
CDFs
from
a
survey
of
states
and
regions
conducted
by
EPA's
Office
of
Pollution
Prevention
and
To
xics
(
Mixing
Z
one
Dilution
F
actors
for
New
C
hem
ical
E
xpo
sure
A
ssessm
ents,
U.
S.
EPA,
1992a).
The
dilution
model
for
estimating
estuary
concentrations
by
using
a
CDF
is
presented
below:

(
I.
5)

where:

C
es
=
estuary
pollutant
concentration
(
 
g/
L);

L
=
facility
pollutant
loading
(
 
g/
yr);
for
indirect
dischargers,
L
=
L
indirect
facility
*
(
1­
TMT),
where
TMT
is
POTW
treatment
removal
efficiency
(
unitless);

EF
=
event
frequency
(
days/
yr);

FF
=
facility
flow
(
L/
day);
for
indirect
dischargers,
FF
=
PO
TW
flow
(
L/
day);
and
CDF
=
critical
dilution
factor
(
unitless).

EP
A
used
a
cute
CD
Fs
to
evaluate
ac
ute
aquatic
life
effects
and
chron
ic
CD
Fs
to
evaluate
ch
ronic
aqu
atic
life
or
adverse
human
health
effects.
ng
water
intake
and
fishing
location
are
at
the
edge
of
the
chronic
mixing
zone.
he
event
frequency
equal
to
the
facility
operating
days
for
comparison
with
aquatic
life
criteria
or
toxic
effect
levels,
and
equ
al
to
365
days
for
com
parison
with
hum
an
health
criteria
or
toxic
effect
levels.

The
National
Oceanic
and
Atmospheric
Administration
(
NOAA)
has
developed
D
CPs
to
predict
pollutant
concen
trations
in
various
salinity
zones
for
each
e
stuary
in
NO
AA's
Nat
iona
l
Estu
arine
In
ven
tory
(
NE
I).

represents
the
concentration
of
a
nonreactive
dissolved
substance
under
well­
mixed,
steady­
state
conditions
given
an
annual
load
of
10,000
tons.
DCPs
account
for
the
effects
of
flushing
by
considering
the
freshwater
inflow
rate,
and
dilution
by
considering
the
total
estuarine
volume.
s
reflect
the
predicted
estuary­
wide
response,
and
may
therefore
not
be
indicative
of
concentrations
at
the
edge
of
much
smaller
mixing
zones.
mating
pollutant
concentrations
using
DCPs
is
presented
below:

(
I.
6)

where:

C
es
=
estuary
pollutant
concentration
(
 
g/
L);

L
=
facility
pollutant
loa
ding
(
k
g/
yr);
for
ind
irect
disc
harge
rs,

L
=
L
indirect
facility
*(
1­
TM
T),
where
TM
T
is
PO
TW
treatment
removal
efficiency
(
unitless);

DCP
=
dissolved
c
oncentratio
n
po
tential
(
 
g/
L);

BL
=
benchmark
load
(
10,00
0
tons/
yr);
and
CF
=
conversion
factor
(
907.2
kg/
ton).

EPA
determined
potential
water
quality
impacts
by
comparing
projected
waterway
pollutant
concentrations
to
EPA
water
quality
criteria
or
toxic
effect
levels
fo
r
the
pr
otectio
n
of
aq
uatic
life
an
d
hum
an
hea
lth.
A
de
termin
ed
w
ater
q
uality
exceedances
by
dividing
the
projected
waterway
pollutant
concentration
by
the
EPA
water
quality
criteria
or
toxic
effect
levels
for
the
protection
of
aquatic
life
and
human
health.
A
value
greater
than
one
indicates
an
exceedance.

I.
2.3
a.
ition
Inhibition
of
PO
TW
operations
occurs
when
high
levels
of
toxics,
such
as
metals
or
cyanide,
kill
the
bacteria
required
for
the
wastewater
treatment
process.
on
of
PO
TW
operations
by
comparing
calculated
POT
W
influent
Where
possibl
Whe
EPA
assumed
that
the
drinki
EPA
set
t
A
DCP
DCP
The
dilution
model
used
for
esti
EP
Impact
of
Indirect
Discharging
Facilities
on
POTW
Operations
Analysis
of
biological
inhib
EPA
analyzed
inhibiti
I­
25
MP&
M
EEBA:
Appendices
concentrations
with
available
inhibition
levels.
Exceedances
are
indicated
by
a
value
greater
than
one.
POTW
influent
concentrations
are
estimated
as:
Appendix
I:
Environmental
Assessment
(
I.
7)

where:

Cpi
=
POT
W
influent
concentration
(
 
g/
L),

L
=
facility
pollutant
loading
(
 
g/
yr),

O
D
=
facility
operating
days
(
days/
yr),
and
PF
=
POT
W
flow
(
L/
day).

b.
udge
disposal
practices
EPA
also
analyzed
the
effects
of
MP&
M
discharges
on
POTW
operations
by
comparing
the
estimated
concentrations
of
metals
in
sewage
slud
ge
with
the
pub
lished
m
etals
co
ncen
tration
lim
its
for
pre
ferab
le
sewa
ge
slud
ge
disp
osal
o
r
use
p
ractice
s.

In
particular,
E
PA
exam
ined:

 
whether
M
P&
M
base
line
discharge
s
wou
ld
pre
vent
P
OT
W
s
from
being
able
to
meet
the
me
tals
con
centra
tion
limits
required
for
more
favorable
and
lower­
cost
sewage
sludge
use/
disposal
practices
(
i.
e.,
beneficial
land
application
and
surface
disposal);
and
 
whether
limitations
on
the
selection
of
management
practices
would
be
removed
under
the
final
rule.

EPA
estimated
the
sewage
sludge
concentrations
of
eight
metals
for
sample
facilities
under
baseline
and
post­
regulatory
option
discharge
levels.
EPA
compared
these
concentrations
with
the
relevant
metals
concentration
limits
for
three
sewage
sludge
management
options:
Land
Application­
High
(
Concentration
Limits),
Land
Application­
Low
(
Ceiling
Limits),
and
Surface
D
isposal.
etal
concentration
s
in
sewage
sludge
are
estimated
as:

(
I.
8)

where:

C
sp
=
sewage
sludge
pollutant
concentration
(
mg/
kg),

L
=
facility
pollutant
loading
(
 
g/
yr),

T
M
T
=
POT
W
treatment
removal
efficiency
(
unitless),

PART
=
pollutant­
specific
sludge
partition
factor
(
unitless),

S
G
F
=
sludge
generation
factor
(
mg/
kg
per
 
g/
L),

OD
=
POT
W
operating
days
(
days/
yr),
and
PF
=
POT
W
flow
(
L/
day).

EPA
derived
the
facility­
specific
data
to
evaluate
POTW
operations
from
the
sources
described
in
Sections
3.1
and
5.2.
EPA
examined
multiple
MP&
M
facilities
discharging
to
the
same
POT
W
by
summing
the
individual
loadings
before
calculating
the
PO
TW
influent
and
sewage
slud
ge
conc
entrations.

The
partition
factor
is
a
chemical­
specific
value
representing
the
fraction
of
the
load
expected
to
partition
to
sewage
sludge
during
wastewater
treatment.
s
analysis,
EPA
used
a
sludge
generation
factor
of
5.96
mg/
kg
per
 
g/
L.
This
factor
indicated
that
the
resulting
concentration
in
sewage
sludge
is
5.96
mg/
kg
dry
weight
for
every
1
 
g/
L
of
pollutant
removed
from
wastewater
and
partitioned
to
sewage
sludge.
Analysis
of
sl
M
For
thi
I­
26
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
I.
2.4
Assumptions
and
Limitations
The
following
discussion
focuses
on
major
assumptions
and
limitations
associated
with
these
in­
stream
water
quality
analyses.

a.
Other
source
contributions
EPA
did
not
account
for
"
other
source
contributions"
of
MP&
M
pollutants
to
estimate
in­
stream
concentrations
of
these
pollutants.
Accounting
for
the
discharges
from
other
sources
is
important
because
assessing
benefits
from
reduced
exceedance
of
AWQC
limits
depends
on
comparing
concentrations
of
pollutants
from
all
sources
with
applicable
thresholds.

Analyses
must
also
identify
situations
in
which
threshold
criteria
are
exceeded
in
the
baseline
case
but
met
under
a
regulatory
option.
Failing
to
account
for
other
source
contributions
has
an
uncertain
effect
on
estimated
benefits.
For
example,
if
non­
sample
MP&
M
facilities
are
major
contributors
to
aggregate
pollutant
discharges
to
a
receiving
stream,
then
the
analysis
will
likely
understate
the
extent
of
aquatic
habitat
improvements
that
may
be
accomplished
by
reduced
MP&
M
pollutant
discharges.
Conversely,
if
the
total
MP&
M
contribution
to
the
aggregate
pollutant
discharges
to
a
receiving
stream
is
not
significant,
then
reducing
MP&
M
discharges
may
reduce
but
not
eliminate
AWQC
exceedances,
and
the
benefits
of
the
MP
&
M
regulation
can
be
overstated.
The
net
effect
of
the
following
are
unknown:

 
excluding
other
sources
understates
the
number
and
extent
of
baseline
exceedances;

 
excluding
non­
sample
MP&
M
facilities
understates
the
reduction
in
MP&
M
pollutant
discharges
due
to
the
rule;
and
 
the
number
of
cases
in
which
estimated
baseline
exceedances
are
eliminated
may
be
either
over­
or
understated,

depending
on
the
contribution
of
pollutants
from
non­
MP&
M
sources.

b.
Water
body
modeling
EPA
made
four
major
assumptions
concerning
all
water
body
modeling,
and
two
major
assumptions
specific
to
stream
modeling.
These
assumptions
are
summarized
below:

 
Complete
mixing
of
POTW
discharge
flow
occurs
immediately.
This
mixing
results
in
the
calculation
of
an
"
average"
concentration,
even
though
the
actual
concentration
may
vary
across
the
width
and
depth
of
the
water
body.

 
Pollutant
loads
to
the
receiving
water
body
are
continuous
and
representative
of
long­
term
facility
operations.
This
assumption
may
overestimate
long­
term
risks
to
human
health
and
aquatic
life,
but
may
underestimate
potential
short­
term
effects.

 
In
the
absence
of
data
from
EPA's
Permit
Compliance
System
(
PCS)
on
specific
individual
POTW
flow,

POTW
daily
flow
rates
were
set
equal
to
the
simple
arithmetic
mean
flow
among
minor
POTWs
reporting
flows
in
PCS.
The
arithmetic
mean
for
minor
POTWs
was
used
because
all
POTWs
receiving
discharges
from
the
sample
MP&
M
facilities
for
which
flow
data
are
not
available
in
the
PCS
database
are
classified
as
minor
dischargers
in
the
PCS
database.

 
EPA
used
1Q10
and
7Q10
receiving
stream
flow
rates
to
estimate
aquatic
life
impacts,
and
harmonic
mean
flow
rates
to
estimate
human
health
impacts,
when
modeling
stream
reaches.
EPA
estimated
1Q10
low
flows
by
using
the
results
of
a
regression
analysis
conducted
for
OPPT
of
1Q10
and
7Q10
flows
from
representative
U.
S.
rivers
and
streams
(
Versar,
1992).
EPA
estimated
harmonic
mean
flows
from
the
mean
and
7Q10
flows
as
recommended
in
the
Technical
Support
Document
for
Water
Quality­
based
Toxics
Control
(
U.
S.
EPA,
1991).
These
flows
may
not
be
the
same
as
those
used
by
specific
states
to
assess
impacts.

 
Where
data
on
stream
flow
parameters
were
not
available,
EPA
set
mean
and
7Q10
flow
values
equal
to
the
corresponding
mean
values
associated
with
reaches
located
upstream
and
downstream
of
the
sample
reach.

I­
27
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
c.
Exposure
analyses
MP&
M
exposure
assessment
in
freshwater
locations
uses
two
sets
of
human
health­
based
AWQC:

 
AW
QC
for
the
protection
of
human
health
from
the
consumption
of
organisms
and
drinking
water,
and
 
AWQC
for
the
protection
of
human
health
from
consumption
of
organisms
only.

MP&
M
exposure
assessments
in
marine
locations
use
AWQC
for
the
protection
of
human
health
from
the
consumption
of
organisms
only,
because
saltwater
is
not
used
for
drinking
water
supply.

d.
Extrapolation
from
sample
set
to
national
level
Although
the
sample
set
should
represent
a
national
group
of
facilities
discharging
to
waterways
and
POTWs,
effluent
from
an
individual
sample
facility
may
have
a
different
potential
environmental
impact
than
effluent
from
the
facilities
it
is
assumed
to
represent.
For
example,
a
facility
that
discharges
to
a
stream
with
a
very
low
flow
may
be
similar
to
the
facilities
it
represents
in
all
aspects
except
available
dilution
in
the
receiving
stream.
The
sample
frame
used
in
the
MP&
M
analysis
was
not
designed
to
take
receiving
water
body
characteristics
into
account.
Using
sample
weights
to
extrapolate
environmental
impacts
may
either
under­
or
overstate
estimated
impacts.

I.
3
DATA
SOURCES
The
following
three
sections
describe
the
various
data
sources
used
to
evaluate
water
quality
and
POTW
impacts.

I.
3.1
FacilitySpecific
Data
Section
I.
2.1
provides
detailed
information
on
sample
size
and
distribution,
and
on
receiving
waterways.
The
names,

locations,
and
the
flow
data
for
the
POTWs
to
which
the
MP&
M
facilities
discharge
were
obtained
from
the
MP&
M
facility
surveys
and
EPA's
PCS
database.
EPA
took
alternative
measures
to
obtain
a
complete
set
of
receiving
POTWs
if
these
sources
did
not
yield
information
for
a
given
facility.
EPA
used
latitude/
longitude
coordinates
(
if
available)
to
locate
those
POTW
s
that
have
not
been
assigned
a
reach
number
in
PCS.
EPA
identified
the
nearest
POTW
in
the
case
of
facilities
for
which
the
POTW
receiving
the
plant
discharge
could
not
be
positively
identified.
EPA
based
its
identification
of
the
closest
linear
distance
on
the
latitude/
longitude
coordinates
of
the
MP&
M
facility
or
the
city
in
which
it
was
located.
EPA
then
identified
the
corresponding
reach
in
PCS,
and
obtained
POTW
flow
from
the
Needs
Survey
or
PCS.

EPA
identified
reaches
to
which
direct
MP&
M
facilities
discharge
by
identifying
the
receiving
reach
in
PCS
or
by
identifying
the
nearest
reach.
EPA
based
its
identification
of
the
closest
linear
distance
on
the
MP&
M
facility s
latitude/
longitude
coordinates.

I.
3.2
Water
bodySpecific
Data
a.
Streams
and
rivers
EPA
used
1Q10,
7Q10,
and
mean
flow
data
for
the
521
streams
and
rivers.
EPA
obtained
7Q10
and
mean
flow
data
from
the
W.
E.
Gates
study
data
or
from
measured
stream
flow
data,
both
of
which
are
contained
in
EPA's
GAGE
file.
The
W.
E.
Gates
study
contains
calculated
average
and
low
flow
statistics
based
on
the
best
available
flow
data
and
on
drainage
areas
for
reaches
throughout
the
United
States.
The
GAGE
file
also
includes
average
and
low
flow
statistics
based
on
measured
data
from
United
States
Geological
Survey
(
USGS)
gaging
stations.
In
the
absence
of
data
on
stream
flow
parameters,
EPA
set
7Q10
and
mean
flow
values
equal
to
the
corresponding
median
values
associated
with
the
sample
reaches.
EPA
used
the
results
of
a
regression
analysis
conducted
for
OPPT
of
1Q10
and
7Q10
flows
from
representative
U.
S.
rivers
and
streams
(
Versar,
1992)
to
estimate
1Q10
flows.
EPA
estimated
harmonic
mean
flows
from
the
mean
and
7Q10
flows
as
recommended
in
the
Technical
Support
Document
for
Water
Quality­
based
Toxics
Control
(
U.
S.
EPA,
1991).

I­
28
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
b.
Lakes
EPA
used
data
on
hydraulic
residence
time
(
i.
e.,
the
amount
of
time
water
remains
in
a
lake)
to
analyze
small
lakes,
and
CDFs
(
which
describe
dilution
in
a
portion
of
a
lake)
to
analyze
the
Great
lakes.
5
The
sample
MP&
M
facilities
discharged
directly
to
one
lake
reach
and
indirectly
to
23
lake
reaches:
15
to
small
lakes,
3
to
sections
of
Lake
Erie,
5
to
sections
of
Lake
Michigan,
and
1
to
a
section
of
Lake
Ontario.
EPA
calculated
the
average
hydraulic
residence
time
for
small
lakes
based
on
lake
surface
and
drainage
areas.
EPA
obtained
data
on
lake
surface
and
drainage
area
from
the
U.
S.
Army
Corps
of
Engineers,
Major
Dams:
Map
Layer
Description
File
(
USCE,
1999).
CDFs
were
readily
available
for
Lake
Michigan,
but
not
for
the
three
sample
reaches
on
Lake
Erie.
EPA
arithmetically
averaged
the
seven
chronic
CDFs
available
for
reaches
discharging
to
Lake
Erie
(
1,
1,
4,
4,
10,
10,
4)
(
U.
S.
EPA,
1992a,
p.
A­
4)
for
the
three
reaches
being
modeled.

c.
Estuaries
and
bays
Sixty­
two
bays
and
estuaries
receive
discharges
from
sample
MP&
M
facilities.
Data
necessary
to
support
water
quality
modeling
were
not
available
for
eight
of
the
62
bays/
estuaries.
A
dilution
model
predicted
pollutant
concentrations
in
the
chronic
and
acute
mixing
zones,
based
on
site­
specific
CDFs
(
U.
S.
EPA,
1992a
and
Versar,
1994),
to
estimate
the
pollutant
concentrations
in
28
of
these
complex
water
bodies.

Both
acute
and
chronic
CDFs
were
available
for
20
of
the
62
bays/
estuaries.
EPA
estimated
acute
and
chronic
CDFs
for
New
York
bays/
estuaries
by
arithmetically
averaging
available
values
for
nearby
New
Jersey
sites
discharging
to
the
Arthur
Kill
(
acute:
1.5,
4.0,
5.0;
chronic:
5;
20;
10)
and
Upper
New
York
Bay
(
acute:
8.0;
chronic:
22.9).
Acute
and
chronic
CDFs
for
Buzzards
Bay
in
Massachusetts
were
estimated
by
arithmetically
averaging
values
for
nearby
Massachusetts
and
Rhode
Island
sites
discharging
to
the
Atlantic
Ocean.

EPA
could
not
identify
or
approximate
chronic
CDFs
for
the
remaining
13
sample
reaches.
Acute
CDFs
are
available
for
46
of
the
62
bays/
estuaries.
EPA
extrapolated
acute
CDFs
for
two
bays/
estuaries
in
Florida
by
using
CDFs
for
another
Florida
bay.
Likewise,
EPA
extrapolated
acute
CDFs
for
four
bays/
estuaries
in
California
by
using
CDFs
for
another
California
bay.

EPA
obtained
DCP
values
for
five
of
the
13
sample
bays/
estuaries
for
which
CDFs
were
not
available
from
the
Development
of
Mixing
Zone
Dilution
Factors
report
(
Versar,
1994).
EPA
then
used
a
dilution
model
that
predicts
pollutant
concentrations
in
the
estuarine
environment
using
a
site­
specific
DCP
value.

I.
3.3
Information
Used
to
Evaluate
POTW
Operations
Since
many
MP&
M
facilities
considered
in
the
alternative
options
are
indirect
dischargers,
the
Agency
consulted
with
POTW
s
as
they
would
have
had
to
implement
the
rule.
EPA
consulted
with
POTWs
individually
and
through
the
Association
of
Municipal
Sewerage
Agencies
(
AMSA).
In
addition,
EPA
consulted
with
pretreatment
coordinators
and
State
and
local
regulators.

EPA
used
removal
efficiency
rates,
inhibition
values,
and
sewage
sludge
regulatory
levels
to
evaluate
POTW
operations.

EPA
obtained
POTW
removal
efficiency
rates
from
several
sources.
The
Agency
developed
rates
from
POTW
removal
data
and
pilot­
plant
studies
or
used
removals
for
a
similar
pollutant
when
data
were
not
available.
Use
of
the
selected
removal
rates
assumes
that
the
evaluated
POTWs
are
well­
operated
and
have
at
least
secondary
treatment
in
place
(
U.
S.
EPA,
2000).

EPA
obtained
inhibition
values
from
the
Guidance
Manual
for
Preventing
Interference
at
POTWs
(
U.
S.
EPA,
1987a)
and
from
CERCLA
Site
Discharges
to
POTWs:
Guidance
Manual
(
U.
S.
EPA,
1990).
EPA
used
the
most
conservative
values
for
activated
sludge
(
i.
e.,
the
lowest
influent
concentrations
that
would
cause
inhibition).
The
Agency
used
a
value
based
on
compound
type
(
e.
g.,
aromatics)
for
pollutants
with
no
specific
inhibition
value.

EPA
obtained
sewage
sludge
regulatory
levels
from
the
Federal
Register
40
CFR
Part
257
et
al.,
Standards
for
the
Use
or
Disposal
of
Sewage
Sludge;
Final
Rules
(
February
19,
1993)
and
from
the
Federal
Register
59(
38):
9095­
9099
(
February
25,

1994)
and
60(
206):
54,764­
54,770
(
October
25,
1995)
for
eight
metals
regulated
in
sewage
sludge.
EPA
used
pollutant
limits
established
for
the
final
use
or
disposal
of
sewage
sludge
when
the
sewage
sludge
is
applied
to
agricultural
and
non­
agricultural
land
or
is
applied
to
a
dedicated
surface
disposal
site.

5
Small
lakes
are
defined
as
any
non­
Great
lakes,
including
reservoirs.

I­
29
MP&
M
EEBA:
Appendices
Finally,
EPA
obtained
sludge
partition
factors
from
the
Report
to
Congress
on
the
Discharge
of
Hazardous
Wastes
to
Publicly­
Owned
Treatment
Works
(
Domestic
Sewage
Study)
(
U.
S.
EPA,
1986).

Table
I.
5
lists
POTW
treatment
removal
efficiency
rates,
inhibition
values,
sewage
sludge
partition
factors,
and
sewage
sludge
regulatory
levels
used
in
the
evaluation
of
POTW
operations.
Appendix
I:
Environmental
Assessment
Table
I.
5:

CAS
Number
Pollutant
Name
POTW
Inhibition
Level
Value
(
 
g/
l)
POTW
Sludge
Partition
Factor
Sludge
Criteria
Value
(
mg/
kg)
POTW
Removal
Efficiency
Rate
(
Percentage)

51285
Dinitrophenol,
2,4­
1000
0.10000000149
77.51
57125
Cyanide
5000
1
70.44
59507
Parachlorometacresol
5000
0.07900000364
63
62533
Aniline
1000
0.1
93.41
62759
Nitrosodimethylamine,
N­
0.1
77.51
65850
Benzoic
acid
10000
0.1
80.5
67641
Acetone
120000
0.1
83.75
67663
Trichloromethane
500000
0.015
68122
Dimethylformamide,
N,
N­
1000
0.1
87
75003
Chloroethane
0.0075
77.51
75092
Dichloromethane
150000
0.1395
54.28
75150
Carbon
disulfide
50000
0.0075
84
75354
Dichloroethene,
1,1­
150000
77.51
75694
Trichlorofluoromethane
700
77.32
78591
Isophorone
120000
0.079
77.51
78831
Isobutyl
alcohol
1000000
0.1
28
78933
Methyl
ethyl
ketone
120000
0.1
96.6
79016
Trichloroethene
20000
0.0578
77.51
80626
Methyl
methacrylate
120000
99.96
83329
Acenaphthene
500000
0.366
98.29
84742
Di­
n­
butyl
phthalate
10000
0.216
84.66
85018
Phenanthrene
500000
0.366
94.89
85687
Butyl
benzyl
phthalate
10000
0.452
81.65
86306
Nitrosodiphenylamine,
N­
90.11
86737
Fluorene
500000
0.366
69.85
88755
Nitrophenol,
2­
50000
26.83
91203
Naphthalene
500000
0.275
94.69
91576
Methylnaphthalene,
2­
5000
0.079
28
92524
Biphenyl
5000
0.366
96.28
95476
Xylene,
o­
5000
0.149
77.32
95487
Cresol,
o­
90000
0.079
52.5
98555
Terpineol,
alpha­
1000000
0.1
94.4
98862
Acetophenone
120000
0.1
95.34
99876
Cymene,
p­
5000
0.0075
99.79
100027
Nitrophenol,
4­
50000
0.1
77.51
100414
Ethylbenzene
200000
0.06
93.79
100425
Styrene
500000
0.149
93.65
100516
Benzyl
alcohol
1000000
0.1
78
100754
Nitrosopiperidine,
N­
1000
77.32
101848
Diphenyl
Ether
1000
77.32
105679
Dimethylphenol,
2,4­
40000
0.079
77.51
POTWRelated
Data
for
132
MP&
M
Pollutants
I­
30
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
5:

CAS
Number
Pollutant
Name
POTW
Inhibition
Level
Value
(
 
g/
l)
POTW
Sludge
Partition
Factor
Sludge
Criteria
Value
(
mg/
kg)
POTW
Removal
Efficiency
Rate
(
Percentage)

106445
Cresol,
p­
90000
0.079
71.67
107028
Acrolein
50
0.10000000149
77.51
108101
Methyl
isobutyl
ketone
120000
0.1
87.87
108372
Bromo­
3­
chlorobenzene,
1­
100
77.32
108383
Xylene,
m­
5000
0.149
95.07
108883
Toluene
200000
0.278
96.18
108907
Chlorobenzene
140000
0.154
96.37
108952
Phenol
90000
0.146
95.25
110861
Pyridine
1000
0.1
95.4
112403
Dodecane,
n­
(
a)

112958
Eicosane,
n­
(
a)

117817
Bis(
2­
ethylhexyl)
phthalate
10000
0.728
59.78
117840
Di­
n­
octyl
phthalate
10000
0.075
68.43
120127
Anthracene
500000
0.55
77.51
122394
Diphenylamine
1000
0.08
77.32
123911
Dioxane,
1,4­
120000
0.1
45.8
124185
Decane,
n­
9
127184
Tetrachloroethene
20000
0.034
84.61
129000
Pyrene
500000
0.366
83.9
131113
Dimethyl
phthalate
0.1
77.51
132650
Dibenzothiophene
5000
0.366
84.68
137304
Ziram
\
Cymate
50
142621
Hexanoic
acid
10000
84
206440
Fluoranthene
5000
0.366
42.46
544763
Hexadecane,
n­
(
a)

591786
Hexanone,
2­
120000
77.32
593453
Octadecane,
n­
(
a)

606202
Dinitrotoluene,
2,6­
5000
0.1
77.51
629594
Tetradecane,
n­
(
a)

629970
Docosane,
n­
88
630013
Hexacosane,
n­
(
b)

630024
Octacosane,
n­
(
b)

638686
Triacontane,
n­
(
b)

646311
Tetracosane,
n­
(
b)

694804
Bromo­
2­
chlorobenzene,
1­
100
77.32
832699
Methylphenanthrene,
1­
5000
0.366
84.55
1576676
Dimethylphenanthrene,
3,6­
500000
0.366
84.55
1730376
Methylfluorene,
1­
500000
0.366
84.55
2027170
Isopropylnaphthalene,
2­
500000
0.1
77.32
7429905
Aluminum
1
91.36
7439896
Iron
5000
1
81.99
7439921
Lead
100
1
300
77.45
7439954
Magnesium
1000000
1
14.14
7439965
Manganese
10000
1
35.51
7439976
Mercury
100
1
17
71.66
7439987
Molybdenum
1
18.93
7440020
Nickel
5000
1
420
51.44
POTWRelated
Data
for
132
MP&
M
Pollutants
I­
31
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
5:

CAS
Number
Pollutant
Name
POTW
Inhibition
Level
Value
(
 
g/
l)
POTW
Sludge
Partition
Factor
Sludge
Criteria
Value
(
mg/
kg)
POTW
Removal
Efficiency
Rate
(
Percentage)

7440224
Silver
30
1
88.28
7440235
Sodium
3500000
1
2.69
7440280
Thallium
1
71.66
7440315
Tin
9000
1
42
7440326
Titanium
1
91.82
7440360
Antimony
1
66.78
7440382
Arsenic
40
1
41
65.77
7440393
Barium
1
15.98
7440417
Beryllium
1
71.66
7440428
Boron
1000
1
30.42
7440439
Cadmium
500
1
39
90.05
7440473
Chromium
1000
1
80.33
7440484
Cobalt
1
6.11
7440508
Copper
1000
1
1500
84.2
7440575
Gold
1
32.52
7440622
Vanadium
20000
1
9.51
7440655
Yttrium
1
32.52
7440666
Zinc
5000
1
2800
79.14
7440702
Calcium
2500000
1
8.54
7664417
Ammonia
as
N
480000
38.94
7782492
Selenium
1
100
34.33
14265442
Phosphate
57.41
14808798
Sulfate
84.61
16887006
Chloride
57.41
16984488
Fluoride
61.35
18496258
Sulfide
25000
57.41
18540299
Chromium
hexavalent
1000
1
57.41
20324338
Tripropyleneglycolmethylether
120000
52.4
136777612
Xylene,
o­
&
p­(
c)
5000
0.149
36832
179601231
Xylene,
m­
&
p­
(
c)

C003
BOD
5­
day
(
carbonaceous)
89.12
C004
Chemical
Oxygen
Demand
(
COD)
81.3
C009
Total
Suspended
Solids
(
TSS)

C010
Total
Dissolved
Solids
(
TDS)

C012
Total
Organic
Carbon
(
TOC)
70.28
C020
Total
Recoverable
Phenolics
57.41
C021
Total
Kjeldahl
Nitrogen
57.41
C025
Amenable
Cyanide
57.41
C036
Oil
and
Grease
(
as
Hem)
86.08
C037
Total
Petroleum
Hydrocarbons
(
as
Sgt­
hem)

C042
Weak­
acid
Dissociable
Cyanide
Phosphorus
(
as
PO4)

Oil
and
Grease
POTWRelated
Data
for
132
MP&
M
Pollutants
Sources:
U.
S.
EPA
(
1985),
U.
S.
EPA
(
1987),
U.
S.
EPA
(
1990).

I­
32
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
In
the
absence
of
data
on
POTW
flow
rates,
EPA
set
the
POTW
flow
rate
equal
to
the
arithmetic
mean
flow
among
minor
POTWs
in
the
PCS
database,
using
the
following
steps:

1.
Calculate
arithmetic
mean
flow
among
minor
POTWs
in
the
PCS
database.
The
estimated
arithmetic
mean
flow
for
minor
POTWs
in
the
PCS
database
is
one
million
gallons
per
day
(
MGD).

2.
Set
POTW
flow
rate
equal
to
the
relevant
arithmetic
mean
flow.
For
all
POTWS
with
missing
flow
data,
EPA
set
their
flow
rates
equal
to
the
arithmetic
mean
flow
rate
for
minor
POTWs
in
the
PCS
database,
one
MGD.

I.
4
RESULTS
EPA
assessed
the
environmental
impacts
of
MP&
M
dischargers
on
water
bodies
and
POTWs
under
the
baseline
conditions
and
those
corresponding
to
four
regulatory
options:
the
Final
Option,
Proposed/
NODA
Option,
and
two
433
Upgrade
Options
on
the
basis
of
sample
facility
data.
The
Agency
extrapolated
the
findings
from
the
sample
facility
analyses
to
the
national
level
using
facility
sample
weights,
as
described
in
Appendix
G.

MP&
M
facilities
nationwide
currently
discharge
an
estimated
53
million
pounds
of
pollutants
per
year
to
publicly­
owned
treatment
works
(
POTWs)
and
approximately
6.2
million
pounds
of
pollutants
directly
to
surface
waters.
MP&
M
facility
effluents
contain
42
priority
or
toxic
pollutants,
81
nonconventional
pollutants,
and
three
conventional
pollutants
(
BOD,
TSS,

and
O&
G).

EPA
estimates
that
the
final
rule
will
lead
to
a
modest
reduction
in
pollutant
discharges
to
the
waters
of
the
U.
S.
As
shown
by
Table
I.
6,
the
regulation
will
reduce
discharges
of
pollutants
with
acute
and
chronic
effects
on
aquatic
life
by
8,959
and
12,270
pounds
per
year,
respectively.
The
final
rule
does
not
regulate
indirect
dischargers
and
thus
will
not
reduce
pollutant
loads
received
by
POTWs.

EPA
estimates
that
the
Proposed/
NODA
Option,
Directs
+
413
to
433
Upgrade
Option,
and
Directs
+
All
to
433
Upgrade
Option
would
remove
3,299,
91,
and
110
thousand
pounds
per
year
of
eight
sewage
sludge
contaminants,
respectively.
In
addition,
the
Proposed/
NODA
Option,
Directs
+
413
to
433
Upgrade
Option,
and
Directs
+
All
to
433
Upgrade
Option
would
result
in
30,226,
133,
and
551
thousand
pounds
per
year
reduction
in
86
pollutants
causing
inhibition
of
POTW
operations.

The
Proposed/
NODA
Option
would
reduce
discharges
of
pollutants
with
acute
and
chronic
effects
on
aquatic
life
by
97
and
117
million
pounds
per
year,
respectively.
The
Directs
+
413
to
433
Upgrade
Option
and
the
Directs
+
All
to
433
Upgrade
Option
would
reduce
discharges
of
132
and
353
thousand
pounds
of
pollutants
with
acute
effects
on
aquatic
life,
and
136
and
576
thousand
pounds
of
pollutants
with
chronic
effects
on
aquatic
life,
respectively.

I­
33
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
6:
&
M
Facility
Discharges
(
National
Basis)
a
Category
POTW
Impacts
Receiving
Stream
Impacts:
Aquatic
Life
Toxicity
Activated
Sludge
Inhibition
Biosolids
Contaminants
HAP
Acute
Chronic
Selected
Option
#
of
Pollutants
N/
A
N/
A
N/
A
106
113
Baseline
(
1,000
lbs/
yr)
N/
A
N/
A
N/
A
868
1,154
Post­
Compliance
(
1,000
lbs/
yr)
N/
A
N/
A
N/
A
859
1,142
Proposed/
NODA
Option
#
of
Pollutants
85
8
35
105
112
Baseline
(
1,000
lbs/
yr)
39,594
3,589
408
141,522
187,742
Post­
Compliance
(
1,000
lbs/
yr)
9,369
290
189
44,827
70,428
Directs
+
413
to
433
Upgrade
#
of
Pollutants
86
8
35
106
113
Baseline
(
1,000
lbs/
yr)
1,085
253
3
868
1,154
Post­
Compliance
(
1,000
lbs/
yr)
952
161
3
935
1,018
Directs
+
All
to
433
Upgrade
#
of
Pollutants
86
8
35
106
113
Baseline
(
1,000
lbs/
yr)
1,085
253
3
868
1,154
Post­
Compliance
(
1,000
lbs/
yr)
534
143
3
514
578
MP
a
Excludes
loadings
from
facilities
projected
to
close
in
the
baseline.
See
Chapter
5.

Source:
U.
S.
EPA
analysis.

I.
4.1
Human
Health
Impacts
Under
this
human
health
benefit
category
EPA
assessed
the
reduced
occurrence
of
pollutant
concentrations
that
are
estimated
to
exceed
human
health­
based
AWQC.
This
analysis
provides
an
alternative
measure
of
the
expected
reduction
in
risk
to
human
health.
Table
I.
7
presents
information
on
baseline
and
post­
compliance
exceedances
of
human
health
AWQC
criteria
for
all
the
regulatory
options.

EPA
estimates
that
in­
stream
concentrations
of
four
pollutants
(
i.
e.,
arsenic,
iron,
manganese,
and
n­
nitrosodimethylamine)

will
exceed
human
health
criteria
for
consumption
of
water
and
organisms
in
78
receiving
reaches
nationwide
as
the
result
of
baseline
MP&
M
pollutant
discharges.
EPA
estimates
that
there
are
human
health
AWQC
exceedances
caused
by
n­
nitrosodimethylamine
(
NDM
A).
EPA
did
not
consider
NDM
A
pollutant
reductions
in
its
benefits
analyses
because
of
the
low
number
of
detected
values
for
that
pollutant.
EPA
estimates
that
the
final
rule
will
not
eliminate
the
occurrence
of
concentrations
in
excess
of
human
health
criteria
for
consumption
of
water
and
organisms
and
for
consumption
of
organisms
on
any
of
the
reaches
on
which
baseline
discharges
are
estimated
to
cause
concentrations
in
excess
of
AWQC
values.

The
Proposed/
NODA
Option
would
eliminate
instances
of
in­
stream
pollutant
concentrations
exceeding
AWQC
limits
for
consumption
of
water
and
organisms
and
consumption
of
organisms
only
in
63
and
68
reaches,
respectively,
nationwide.
The
Directs
+
413
to
433
Upgrade
O
ption
would
not
eliminate
any
instances
of
in­
stream
pollutant
concentrations
exceeding
AWQ
C
limits
for
consumption
of
water
and
organisms
and
consumption
of
organisms
only.
The
Directs
+
All
to
433
Upgrade
Option
would
not
eliminate
any
occurrences
of
pollutant
concentrations
in
excess
of
AWQC
values
for
consumption
of
water
and
organisms,
but
would
eliminate
instances
of
pollutant
concentrations
in
excess
of
AWQC
values
for
consumption
of
organisms
only
in
21
reaches
nationwide.
As
noted
above
the
Agency
did
not
estimate
reductions
in
NDM
A
loadings
under
the
post­
compliance
scenario
due
to
data
limitations.

I­
34
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
7:
mmary
of
Estimated
AWQC
Exceedances
for
Protection
of
Human
Health
(
National
Basis)

Category
Human
Health
Water
and
Organisms
Human
Health
Organisms
Only
Streams
(
No.)
Pollutants
(
No.)
Total
Exceedances
Streams
(
No.)
Pollutants
(
No.)
Total
Exceedances
Selected
Option:
Traditional
Extrapolation
Baseline
78
4
121
21
1
21
Post­
Compliance
78
4
121
21
1
21
Selected
Option:
Post­
Stratification
Extrapolation
Baseline
112
4
154
21
1
21
Post­
Compliance
112
4
154
21
1
21
Proposed/
NODA
Option
Baseline
5,852
26
7,085
197
12
335
Post­
Compliance
5,789
21
6,667
128
9
212
Directs
+
413
to
433
Upgrade
Baseline
78
4
121
21
1
21
Post­
Compliance
78
4
121
21
1
21
Directs
+
All
to
433
Upgrade
Baseline
78
4
121
21
1
21
Post­
Compliance
78
2
78
0
Su
0
0
Source:
U.
S.
EPA
analysis.

I­
35
MP&
M
EEBA:
Appendices
Table
I.
8
summarizes
pollutants
estimated
to
exceed
human
health­
based
AWQC
criteria
for
consumption
of
water
and
organisms
under
the
baseline
and
post­
compliance
conditions.
Appendix
I:
Environmental
Assessment
Table
I.
8:
Summary
of
Pollutants
Estimated
to
Exceed
Human
HealthBased
AWQC
Criteria
for
Consumption
of
Water
and
Organisms
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Aniline
0
0
0
0
20
17
0
0
0
0
Antimony
0
0
0
0
0
0
0
0
0
0
Arsenic
45
45
45
45
772
557
45
45
45
45
Bis(
2­
ethylhexyl)
phthalate
0
0
0
0
85
43
0
0
0
0
Cadmium
0
0
0
0
0
0
0
0
0
0
Chloroethane
0
0
0
0
17
14
0
0
0
0
Copper
0
0
0
0
16
0
0
0
0
0
Cresol,
p­
0
0
0
0
9
9
0
0
0
0
Dibenzofuran
0
0
0
0
12
9
0
0
0
0
Dichloroethene,
1,1­
0
0
0
0
97
81
0
0
0
0
Dichloromethane
0
0
0
0
17
17
0
0
0
0
Dinitrophenol,
2,4­
0
0
0
0
9
9
0
0
0
0
Dinitrotoluene,
2,6­
0
0
0
0
9
9
0
0
0
0
Dioxane,
1,4­
0
0
0
0
17
17
0
0
0
0
Fluoranthene
0
0
0
0
9
9
0
0
0
0
Iron
21
21
21
21
28
0
21
21
21
0
Isophorone
0
0
0
0
9
9
0
0
0
0
Manganese
21
21
21
21
54
0
21
21
21
0
Mercury
0
0
0
0
0
0
0
0
0
0
Naphthalene
0
0
0
0
9
9
0
0
0
0
Nickel
0
0
0
0
16
0
0
0
0
0
Nitrophenol,
4­
0
0
0
0
9
9
0
0
0
0
Nitrosodimethylamine,
N­
32
32
67
67
5,789
5,789
32
32
32
32
Nitrosodiphenylamine,
N­
0
0
0
0
17
17
0
0
0
0
Pyrene
0
0
0
0
9
9
0
0
0
0
Pyridine
0
0
0
0
12
9
0
0
0
0
Thallium
0
0
0
0
16
0
0
0
0
0
Trichloroethene
0
0
0
0
21
17
0
0
0
0
Trichloromethane
0
0
0
0
12
12
0
0
0
0
Total
Exceedances
121
121
154
154
7,085
6,667
121
121
121
77
a
Base
=
Baseline
discharge
level
b
PC
=
Post­
Compliance
discharge
level
Source:
U.
S.
EPA
analysis.

I­
36
MP&
M
EEBA:
Appendices
Table
I.
9
summarizes
pollutants
estimated
to
exceed
human
health­
based
AWQC
criteria
for
consumption
of
organisms
only
under
the
baseline
and
post­
compliance
conditions.
Appendix
I:
Environmental
Assessment
Table
I.
9:
Summary
of
Pollutants
Estimated
to
Exceed
Human
HealthBased
AWQC
Criteria
for
Consumption
of
Organisms
Only
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Aniline
0
0
0
0
12
9
0
0
0
0
Antimony
0
0
0
0
0
0
0
0
0
0
Arsenic
0
0
154
111
0
0
Bis(
2­
ethylhexyl)
phthalate
0
0
0
0
24
12
0
0
0
0
Cadmium
0
0
0
0
0
0
0
0
0
0
Chloroethane
0
0
0
0
0
0
0
0
0
0
Copper
0
0
0
0
16
0
0
0
0
0
Cresol,
p­
0
0
0
0
0
0
0
0
0
0
Dibenzofuran
0
0
0
0
12
9
0
0
0
0
Dichloroethene,
1,1­
0
0
0
0
17
17
0
0
0
0
Dichloromethane
0
0
0
0
0
0
0
0
0
0
Dinitrophenol,
2,4­
0
0
0
0
0
0
0
0
0
0
Dinitrotoluene,
2,6­
0
0
0
0
0
0
0
0
0
0
Dioxane,
1,4­
0
0
0
0
0
0
0
0
0
0
Fluoranthene
0
0
0
0
9
9
0
0
0
0
Iron
0
0
0
0
0
0
0
0
0
0
Isophorone
0
0
0
0
0
0
0
0
0
0
Manganese
21
21
21
21
32
0
21
21
21
0
Mercury
0
0
0
0
0
0
Naphthalene
0
0
0
0
0
0
0
0
0
0
Nickel
0
0
0
0
16
0
0
0
0
0
Nitrophenol,
4­
0
0
0
0
0
0
0
0
0
0
Nitrosodimethylamine,
N­
0
0
0
0
27
27
0
0
0
0
Nitrosodiphenylamine,
N­
0
0
0
0
9
9
0
0
0
0
Pyrene
0
0
0
0
9
9
0
0
0
0
Pyridine
0
0
0
0
0
0
0
0
0
0
Thallium
0
0
0
0
0
0
0
0
0
0
Trichloroethene
0
0
0
0
0
0
0
0
0
0
Trichloromethane
0
0
0
0
0
0
0
0
0
0
Total
Exceedances
21
21
21
21
335
212
21
21
21
0
0
0
0
0
0
0
0
a
Base
=
Baseline
discharge
level
b
PC
=
Post­
Compliance
discharge
level
Source:
U.
S.
EPA
analysis.

I.
4.2
Aquatic
Life
Effects
EPA
evaluated
the
effects
of
MP&
M
facility
discharges
on
aquatic
habitats
and
ecosystem
functioning
under
the
baseline
conditions
and
the
post­
compliance
scenarios
corresponding
to
the
four
regulatory
alternatives
considered
for
the
MP&
M
regulation.
This
analysis
compared
the
estimated
baseline
and
post­
compliance
in­
stream
concentrations
of
MP&
M
pollutants
with
AWQC
for
aquatic
species.
As
noted
in
the
preceding
sections,
aquatic
life
AWQCs
addressed
in
this
analysis
set
the
upper
limit
on
pollutant
concentrations
assumed
to
be
protective
of
aquatic
life.

I­
37
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
10
presents
the
number
of
MP&
M
discharge
reaches
on
which
pollutant
concentrations
are
estimated
to
exceed
chronic
and
acute
exposure
criteria
for
protection
of
aquatic
life.
EPA
estimated
that,
as
the
result
of
baseline
MP&
M
pollutant
discharges,
in­
stream
concentrations
exceed
acute
exposure
criteria
for
aquatic
species
in
18
and
15
receiving
reaches
nationwide
based
on
the
traditional
extrapolation
and
post­
stratification
extrapolation,
respectively.
In
addition,

baseline
in­
stream
concentrations
in
353
and
350
receiving
reaches
exceed
chronic
AWQC
for
protection
of
aquatic
life
based
on
the
traditional
extrapolation
and
post­
stratification
extrapolation,
respectively.

Table
I.
10:
mmary
of
Estimated
AWQC
Exceedances
for
Protection
of
Aquatic
Life
(
National
Basis)

Category
Acute
Aquatic
Life
Chronic
Aquatic
Life
Streams
(
No.)
Pollutants
(
No.)
Total
Exceedances
Streams
(
No.)
Pollutants
(
No.)
Total
Exceedances
Selected
Option:
Traditional
Extrapolation
Baseline
18
4
35
353
9
423
Post­
Compliance
9
1
9
344
5
362
Selected
Option:
Post­
Stratification
Extrapolation
Baseline
15
4
26
350
9
402
Post­
Compliance
9
1
9
344
5
362
Proposed/
NODA
Option
Baseline
330
17
631
928
47
2,582
Post­
Compliance
86
12
254
539
39
1,369
Directs
+
413
to
433
Upgrade
Baseline
18
4
35
353
9
423
Post­
Compliance
0
0
0
53
3
53
Directs
+
All
to
433
Upgrade
Baseline
18
4
35
353
9
423
Post­
Compliance
0
0
0
32
2
32
Su
Source:
U.
S.
EPA
analysis.

Based
on
the
traditional
extrapolation,
EPA
estimates
that
the
final
option
will
eliminate
concentrations
in
excess
of
acute
and
chronic
criteria
in
nine
reaches.
Likewise,
EPA
estimates
that
the
final
option
will
eliminate
concentrations
in
excess
of
acute
and
chronic
criteria
in
six
reaches
based
on
the
post­
stratification
extrapolation.

The
Proposed/
NODA
Option,
Directs
+
413
to
433
Upgrade
Option,
and
Directs
+
All
to
433
Upgrade
Option
would
eliminate
exceedances
of
chronic
AWQC
values
on
389,
300,
and
321
reaches,
respectively.
These
options
would
also
eliminate
in­
stream
pollutant
concentrations
in
excess
of
acute
AWQC
value
on
244,
18,
and
18
reaches
under
the
Proposed/
NODA
Option,
Directs
+
413
to
433
Upgrade
Option,
and
Directs
+
All
to
433
Upgrade
Option,
respectively.

Table
I.
11
presents
the
number
MP&
M
reaches
on
which
pollutant
concentrations
are
estimated
to
exceed
chronic
AWQC
for
protection
of
aquatic
life
by
pollutant.

I­
38
MP&
M
EEBA:
AppendicesAppendix
I:
Environmental
Assessment
I­
39
Table
I.
11:
Aquatic
Life
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
BaseaPCbBasePCBasePCBasePCBasePC
Acenaphthene00
Acrolein0443300
Aluminum0321200
Ammonia
as
N051000
Aniline0454200
Anthracene0642900
Biphenyl0900
Butyl
benzyl
phthalate0900
Cadmium970210
Carbon
disulfide038340
Chromium046120
Cobalt012120
Copper93446990
Cyanide00
Di­
n­
butyl
phthalate00
Di­
n­
octyl
phthalate012120
Dibenzofuran000021120
Dibenzothiophene015120
Dimethylphenanthrene,
3,6­
024210
Dinitrophenol,
2,4­
00
Dinitrotoluene,
2,6­
021210
Diphenyl
Ether021210
Fluoranthene030240
Fluorene027210
Fluoride0541300
Iron012000
Isopropylnaphthalene,
2­
0151200
Lead02448300
Magnesium0121200
Manganese032000
Methylfluorene,
1­
0151200
Methylnaphthalene,
2­
0121200
Methylphenanthrene,
1­
0151200
Molybdenum01033900
Naphthalene0900
Nickel01631600
Phenanthrene0242100
Phenol0900
Pyrene0212100
Selenium0785000
Silver91661310
Styrene00
Sulfide029328300
Summary
of
Pollutants
Estimated
to
Exceed
Chronic
AWQC
for
Protection
of
00099000
00000
00000
00000
00000
00000
000900
000000
060909
000000
000000
000000
99990
00003000
00099000
000000
000
000000
000000
00099000
000000
000000
000000
000000
00000
00000
00000
00000
00000
00000
00000
00000
00000
00000
000900
00000
00000
000000
00000
00000
060909
00009000
00000
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
11:
Aquatic
Life
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Tin
0
83
21
0
0
Titanium
0
6
0
0
Vanadium
0
157
142
0
0
Zinc
9
85
33
0
Total
Exceedances
35
9
26
9
2,582
1,369
35
0
35
0
Summary
of
Pollutants
Estimated
to
Exceed
Chronic
AWQC
for
Protection
of
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
9
0
9
a
Base
=
Baseline
discharge
level
b
PC
=
Post­
Compliance
discharge
level
Source:
U.
S.
EPA
analysis.

Table
I.
12
presents
the
number
MP&
M
reaches
on
which
pollutant
concentrations
are
estimated
to
exceed
acute
AWQC
for
protection
of
aquatic
life
by
pollutant.

Table
I.
12:
mmary
of
Pollutants
Estimated
to
Exceed
Aquatic
Life
Based
Acute
AWQC
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Acenaphthene
0
0
Acrolein
0
33
26
0
Aluminum
9
10
0
Ammonia
as
N
0
0
0
0
Aniline
0
9
0
0
Anthracene
0
64
29
0
0
Biphenyl
0
9
0
0
Butyl
benzyl
phthalate
0
0
0
0
Cadmium
9
0
Carbon
disulfide
0
0
Chromium
0
0
Cobalt
0
0
Copper
276
267
273
267
241
69
276
9
276
9
Cyanide
0
0
Di­
n­
butyl
phthalate
0
0
Di­
n­
octyl
phthalate
0
0
Dibenzofuran
0
0
Dibenzothiophene
0
0
Dimethylphenanthrene,
3,6­
0
0
Dinitrophenol,
2,4­
0
0
Dinitrotoluene,
2,6­
0
0
Diphenyl
Ether
0
0
Fluoranthene
0
21
21
0
Fluorene
0
21
21
0
Fluoride
0
0
Iron
0
0
Isopropylnaphthalene,
2­
0
0
Su
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
9
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
9
6
9
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
I­
40
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
12:
mmary
of
Pollutants
Estimated
to
Exceed
Aquatic
Life
Based
Acute
AWQC
(
National
Basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Lead
18
9
15
18
0
18
0
Magnesium
21
21
21
21
0
0
21
21
21
0
Manganese
9
0
Methylfluorene,
1­
0
0
Methylnaphthalene,
2­
0
0
Methylphenanthrene,
1­
0
0
0
Molybdenum
0
0
Naphthalene
0
0
Nickel
9
23
0
Phenanthrene
0
0
Phenol
0
0
Pyrene
0
0
Selenium
0
12
12
0
Silver
64
56
61
56
60
12
64
23
64
23
Styrene
0
0
Sulfide
0
0
Tin
0
0
Titanium
0
0
Vanadium
0
0
Zinc
9
85
33
0
Total
Exceedances
423
362
402
362
631
254
423
53
423
32
Su
6
9
9
9
0
9
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
9
9
9
0
9
0
0
0
0
9
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
9
0
9
a
Base
=
Baseline
discharge
level
b
PC
=
Post­
Compliance
discharge
level.

Source:
U.
S.
EPA
analysis.

I.
4.3
POTW
Effects
EPA
evaluated
the
effects
of
indirect
MP&
M
dischargers
on
POTW
operations
for
the
final
and
alternative
options.
788
sample
MP&
M
facilities
discharge
132
pollutants
to
572
POTWs.
Of
these,
EPA
evaluated
89
pollutants
for
potential
inhibition
of
POTW
operations
and
eight
pollutants
for
potential
sludge
contamination.
The
788
indirect
sample
MP&
M
facilities
discharge
52.8
million
pounds
per
year
of
priority
and
nonconventional
pollutants
to
the
receiving
POTW
s.
The
final
MP&
M
rule
does
not
regulate
indirect
dischargers
and
thus
will
not
reduce
the
baseline
MP&
M
loadings
to
receiving
POTWs.

a.
Biological
inhibition
EPA
estimated
inhibition
of
POTW
operations
by
comparing
predicted
POTW
influent
concentrations
to
available
inhibition
levels
for
89
pollutants.
EPA s
analysis
shows
that
51
POTW
s
had
influent
concentrations
that
exceed
the
biological
inhibition
values
for
one
of
the
four
following
pollutants
silver,
cadmium,
chromium
and
copper
under
the
baseline
conditions
corresponding
to
the
Final
Option
and
the
433
Upgrade
Options
(
see
Table
I.
13).
Both
of
the
433
Upgrade
Options
would
eliminate
influent
concentrations
in
excess
of
POTW
inhibition
criteria
at
21
POTWs.
Under
the
baseline
conditions
corresponding
to
the
Proposed/
NODA
Option,
293
POTWs
had
influent
concentrations
in
excess
of
the
biological
inhibition
criteria.
The
Proposed/
NODA
Option
would
eliminate
influent
concentrations
in
excess
of
the
biological
inhibition
criteria
at
156
POTWs.

I­
41
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
Table
I.
13:
National
Summary
of
Projected
Inhibition
and
Sludge
Contamination
Problems
Category
Biolo
gical
Inh
ibition
(#
of
P
OT
W
s)
Sludg
e
Co
ntam
ination
(#
of
PO
TW
s)

POTWs
(
No.)
Pollutants
(
No.)
Total
Exceedances
POTWs
(
No.)
Pollutants
(
No.)
Total
Exceedances
Selected
Option:
Traditional
Extrapolation
Baseline
51
4
139
1,020
7
2,702
Post­
Compliance
51
4
139
1,020
7
2,702
Selected
Option:
Post­
Stratification
Extrapolation
Baseline
51
4
139
1,020
7
2,702
Post­
Compliance
51
4
139
1,020
7
2,702
Proposed/
NODA
Option
Baseline
293
12
885
5,328
8
14,493
Post­
Compliance
137
8
410
5,259
8
14,321
Directs
+
413
to
433
Upgrade
Baseline
51
4
139
1,020
7
2,702
Post­
Compliance
30
4
115
1,005
7
2,626
Directs
+
All
to
433
Upgrade
Baseline
5
4
139
1,020
7
2,702
Post­
Compliance
30
4
115
1,005
7
2,562
Source:
U.
S.
EPA
analysis.

I­
42
MP&
M
EEBA:
Appendices
Table
I.
14
presents
MP&
M
pollutants
that
are
estimated
to
upset
POTW
operations
and
contaminate
sewage
sludge.
Appendix
I:
Environmental
Assessment
Table
I.
14:
Summary
of
Pollutants
Estimated
to
Impact
POTW
Operations
(
National
basis)

Pollutant
Selected
Option:

Traditional
Extrapolation
Selected
Option:

Post­
Stratification
Extrapolation
Proposed/
NODA
Option
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Basea
PCb
Base
PC
Base
PC
Base
PC
Base
PC
Biological
Inhibition
(#
of
POTWs)

Acrolein
0
0
0
0
77
65
0
0
0
0
Arsenic
0
0
75
65
0
0
Benzoic
acid
0
0
0
0
68
0
0
0
0
0
Bromo­
2­
chlorobenzene,
1­
0
0
0
0
48
48
0
0
0
0
Bromo­
3­
chlorobenzene,
1­
0
0
0
0
48
48
0
0
0
0
Chromium
30
30
30
30
81
7
30
27
30
27
Copper
27
27
27
27
142
0
27
27
27
27
Iron
0
0
0
0
65
32
0
0
0
0
Lead
39
39
39
39
150
81
39
30
39
30
Nickel
0
0
0
0
50
0
0
0
0
0
Silver
42
42
42
42
65
65
42
30
42
30
Zinc
0
0
0
0
16
0
0
0
0
0
Total
Exceedances
139
139
139
139
885
410
139
115
139
115
Sludge
Contamination
(#
of
POTWs)

Lead
234
234
234
234
2,829
2,790
234
234
234
234
Mercury
0
0
0
0
118
118
0
0
0
0
Nickel
763
763
763
763
2,371
2,325
763
751
763
687
Arsenic
84
84
84
84
1,686
1,683
84
84
84
84
Cadmium
754
754
754
754
1,877
1,871
754
739
754
739
Copper
534
534
534
534
1,874
1,835
534
500
534
500
Zinc
224
224
224
224
2,132
2,132
224
209
224
209
Selenium
109
109
109
109
1,567
1,567
109
109
109
109
0
0
0
0
a
Base
=
Baseline
discharge
level
b
PC
=
Post­
Compliance
discharge
level.

Source:
U.
S.
EPA
analysis.

b.
Sewage
sludge
EPA
estimated
that
baseline
concentrations
of
seven
metals
at
the
national
level
fail
to
meet
Land
Application­
High
limits
for
sludge
disposal
at
1,020
POTWs
under
the
final
regulatory
alternatives.
These
concentrations
were
compared
with
the
relevant
metals
concentration
limits
for
the
following
sewage
sludge
management
options:
Land
Application­
High
(
Concentration
Limits),
Land
Application­
Low
(
Ceiling
Limits),
and
Surface
Disposal.

The
Agency
estimates
that
the
final
regulation
will
not
eliminate
metal
concentrations
in
excess
of
sludge
contamination
criteria
at
any
of
these
1,020
POTW
s,
since
indirect
dischargers
are
exempted
from
the
final
rule.
EPA
estimated
that
15
POTWs
would
be
able
to
upgrade
their
sewage
sludge
disposal
practices
by
meeting
Land
Application­
High
sludge
concentration
limits
under
the
433
Upgrade
Options.
Under
the
Proposed/
NODA
Option,
69
POTWs
would
be
able
to
upgrade
their
sewage
sludge
disposal
practices
to
Land
Application­
High.

I­
43
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
GLOSSARY
action
levels:
the
existence
of
a
contaminant
concentration
in
the
environment
high
enough
to
warrant
implementation
of
drinking
water
treatment
technology.

acute
toxicity
(
AT):
the
ability
of
a
substance
to
cause
severe
biological
harm
or
death
soon
after
a
single
exposure
or
dose.
Also,
any
poisonous
effect
resulting
from
a
single
short­
term
exposure
to
a
toxic
substance
(
See
also:
chronic
toxicity).

(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

adsorption:
removal
of
a
pollutant
from
air
or
water
by
collecting
the
pollutant
on
the
surface
of
a
solid
material;
an
advanced
method
of
treating
waste
in
which
activated
carbon
removes
organic
matter
from
wastewater.

(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

adsorption
coefficient
(
Koc):
represents
the
ratio
of
the
target
chemical
adsorbed
per
unit
weight
of
organic
carbon
in
the
soil
or
sediment
to
the
concentration
of
that
same
chemical
in
solution
at
equilibrium.

alkalinity:
the
capacity
of
bases
to
neutralize
acids
(
e.
g.,
adding
lime
to
lakes
to
decrease
acidity).

(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

ambient
water
quality
criteria
(
AWQC):
levels
of
water
quality
expected
to
render
a
body
of
water
suitable
for
its
designated
use.
Criteria
are
based
on
specific
levels
of
pollutants
that
would
make
the
water
harmful
if
used
for
drinking,

swimming,
farming,
fish
production,
or
industrial
processes.
(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

atm/
m3­
mole:
atmosphere
per
cubic
meter
mole
(
see
also:
mole).

benthic:
relating
to
the
bottom
of
a
body
of
water;
living
on,
or
near,
the
bottom
of
a
water
body.

(
http://
www.
ucmp.
berkeley.
edu/
glossary/
gloss5ecol.
html)

bioconcentration
factor
(
BCF):
indicator
of
the
potential
for
a
chemical
dissolved
in
the
water
column
to
be
taken
up
by
aquatic
biota
across
external
surface
membranes,
usually
gills.

BIODEG:
a
web­
based
biodegradation
database
developed
by
Syracuse
Research
Corporation.

(
http://
esc.
syrres.
com/
efdb/
biodgsum.
htm)

biodegradation:
a
process
whereby
organic
molecules
are
broken
down
by
microbial
metabolism.

biodegradation
half­
life:
represents
the
number
of
days
a
compound
takes
to
be
degraded
to
half
of
its
starting
concentration
under
prescribed
laboratory
conditions.

biological
oxygen
demand
(
BOD):
the
amount
of
dissolved
oxygen
consumed
by
microorganisms
as
they
decompose
organic
material
in
an
aquatic
environment.

cancer
potency
slope
factors
(
SFs):
a
plausible
upper­
bound
estimate
of
the
probability
of
a
response
per
unit
intake
of
a
chemical
over
a
lifetime.
The
slope
factor
is
used
to
estimate
an
upper­
bound
probability
of
an
individual
developing
cancer
as
a
result
of
a
lifetime
of
exposure
to
a
particular
level
of
a
potential
carcinogen.

carcinogens:
chemicals
that
EPA
believes
can
cause
or
have
the
potential
to
cause
tumors
or
cancers
in
humans,
either
directly
or
indirectly.

CHEMFATE:
a
web­
based
chemical
fate
database
developed
by
Syracuse
Research
Corporation.

(
http://
esc.
syrres.
com/
efdb/
Chemfate.
htm)

chronic
toxicity
(
CT):
the
capacity
of
a
substance
to
cause
long­
term
toxic
or
poisonous
health
effects
in
humans,
animals,

fish,
and
other
organisms
(
see
also:
acute
toxicity).
(
http://
www.
epa.
gov/
OCEPAterms/
cterms.
html)

critical
dilution
factors
(
CDFs):
express
the
relationship
between
a
point
source
loading
and
the
resulting
concentration
at
the
edge
of
the
mixing
zone.
Typically,
this
is
expressed
as
a
ratio
of
parts
receiving
water
to
one
part
effluent.

I­
44
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
dissolved
concentration
potentials
(
DCPs):
represents
the
concentration
of
a
nonreactive
dissolved
substance
under
well­
mixed,
steady­
state
conditions
given
an
annual
load
of
10,000
tons.

dry
metric
tons
(
DMT):
dry
measure
is
a
system
of
units
for
measuring
dry
commodities.
1
DMT=
1,000
kilogram.

EC1:
the
concentration
at
which
one
percent
of
the
test
organisms
show
a
significant
sub­
lethal
response.

EC5:
the
concentration
at
which
five
percent
of
the
test
organisms
show
a
significant
sub­
lethal
response.

Environmental
Research
Laboratory­
Duluth
fathead
minnow
database:
a
database
developed
by
EPA's
Mid­
Continent
Ecology
Division
(
MED)
which
provides
data
on
the
acute
toxicity
of
hundreds
of
industrial
organic
compounds
to
the
fathead
minnow.
(
http://
www.
eoa.
gov/
med/
databases/
fathead_
minnow.
html)

GAGE:
a
U.
S.
Geological
Survey
stream
flow
database.
The
database
contains
stream
flow
data
and
drainage
area
measurement
from
all
U.
S.
Geological
Survey
flow
gages.

hazardous
air
pollutant
(
HAP):
air
pollutants
that
are
not
covered
by
ambient
air
quality
standards
but
which,
as
defined
in
the
Clean
Air
Act,
may
present
a
threat
of
adverse
human
health
effects
or
adverse
environmental
effects
(
e.
g.,
beryllium,

mercury,
ethylbenzene,
chloroethane,
and
doxane).
(
http://
www.
epa.
gov/
OCEPAterms/
hterms.
html)

Health
Effects
Assessment
Summary
Tables
(
HEAST):
a
comprehensive
listing
of
provisional
human
health
risk
assessment
data
relative
to
oral
and
inhalation
routes
for
chemicals
of
interest
to
EPA.
Unlike
data
in
IRIS,
HEAST
entries
have
received
insufficient
review
to
be
recognized
as
high
quality,
Agency­
wide
consensus
information
(
U.
S.
EPA.
1997.

Health
Effects
Assessment
Table;
FY
1997
Update.
EPA­
540­
R­
97­
036).

Henry's
Law
(
H):
chemical
law
stating
that
the
amount
of
a
gas
that
dissolves
in
a
liquid
is
proportional
to
the
partial
pressure
of
the
gas
over
the
liquid,
provided
no
chemical
reaction
takes
place
between
the
liquid
and
the
gas.
The
law
is
named
after
William
Henry
(
1774
1836),
the
English
chemist
who
first
reported
the
relationship.
(
www.
infoplease.
com)

human
health­
based
water
quality
criteria
(
WQC):
levels
of
water
quality
expected
to
render
a
body
of
water
suitable
for
its
designated
use.
Criteria
are
based
on
specific
levels
of
pollutants
that
would
make
the
water
harmful
if
used
for
drinking,
swimming,
farming,
fish
production,
or
industrial
processes.
(
http://
www.
epa.
gov/
OCEPAterms/
wterms.
html)

Integrated
Risk
Information
System
(
IRIS):
IRIS
is
an
electronic
database
with
information
on
human
health
effects
of
various
chemicals.
IRIS
provides
consistent
information
on
chemical
substances
for
use
in
risk
assessments,
decision­
making,

and
regulatory
activities.

LC50
(
Lethal
Concentration):
a
standard
measure
of
toxicity
that
tells
how
much
of
a
substance
is
needed
to
kill
half
of
a
group
of
experimental
organisms
in
a
given
time
(
see
also:
LD
50).
(
http://
www.
epa.
gov/
OCEPAterms/
lterms.
html)

LD50
(
Lethal
Dose):
the
dose
of
a
toxicant
or
microbe
that
will
kill
50
percent
of
the
test
organisms
within
a
designated
period.
The
lower
the
LD
50,
the
more
toxic
the
compound.

l/
kg:
liter
per
kilogram
Lowest
Observed
Effect
Concentration
(
LOEC):
the
lowest
level
of
pollutant
concentration
that
causes
statistically
and
biologically
significant
differences
in
test
samples
as
compared
to
other
samples
subjected
to
no
stressor.

(
http://
www.
epa.
gov/
OCEPAterms)

Maximum
Allowable
Toxicant
Concentration
(
MATC):
for
a
given
ecological
effects
test,
the
range
(
or
geometric
mean)
between
the
No
Observable
Adverse
Effect
Level
and
the
Lowest
Observable
Adverse
Effects
Level.

(
http://
www.
epa.
gov/
OCEPAterms/
mterms.
html)

maximum
contaminant
levels
(
MCLs):
the
maximum
permissible
level
of
a
contaminant
in
water
delivered
to
any
user
of
a
public
system.
MCLs
are
enforceable
standards.
(
http://
www.
epa.
gov/
OCEPAterms/
mterms.
html)

mg/
kg:
milligram
per
kilogram
I­
45
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
 
g/
l:
microgram
per
liter
mole:
the
amount
of
substance
that
contains
Avogardo s
number
of
atoms,
molecules
or
other
elementary
units.

National
Estuarine
Inventory
(
NEI):
The
National
Estuarine
Inventory
is
a
series
of
inter­
related
activities
that
define,

characterize,
and
assess
the
nation's
estuarine
systems.
NEI
data
are
compiled
in
a
systematic
and
consistent
manner
that
enables
the
nation's
estuaries
to
be
compared
and
assessed
according
to
their
environmental
quality,
economic
values,
and
resource
uses.
A
principal
feature
of
the
NEI
is
the
determination
of
the
physical
dimensions
and
hydrologic
features
of
estuarine
systems
of
the
United
States
which
are
primary
determinants
of
estuarine
processes
and
ultimately
affect
the
ecology
of
a
system.

National
Oceanic
and
Atmospheric
Administration
(
NOAA):
organization
within
the
Bureau
of
Commerce
that
conducts
research
and
gathers
data
about
the
global
oceans,
atmosphere,
space,
and
sun.

No
Observed
Effect
Concentration
(
NOEC):
exposure
level
at
which
there
are
no
statistically
or
biologically
significant
differences
in
the
frequency
or
severity
of
any
effect
in
the
exposed
or
control
populations.

(
http://
www.
epa.
gov/
OCEPAterms/
nterms.
html)

oil
and
grease
(
O&
G):
organic
substances
that
may
include
hydrocarbons,
fats,
oils,
waxes,
and
high­
molecular
fatty
acids.
Oil
and
grease
may
produce
sludge
solids
that
are
difficult
to
process.
(
http://
www.
epa.
gov/
owmitnet/
reg.
htm)

organic
carbon
(
OC):
carbon
in
compounds
derived
from
living
organisms.

partition
factor:
a
chemical­
specific
value
representing
the
fraction
of
the
load
expected
to
partition
to
sewage
sludge
during
wastewater
treatment.

Permit
Compliance
System
(
PCS):
a
computerized
database
of
information
on
water
discharge
permits,
designed
to
support
the
National
Pollutant
Discharge
Elimination
System
(
NPDES).

(
http://
www.
epa.
gov/
ceisweb1/
ceishome/
ceisdocs/
pcs/
pcs­
exec.
htm)

pH:
an
expression
of
the
intensity
of
the
basic
or
acid
condition
of
a
liquid;
natural
waters
usually
have
a
pH
between
6.5
and
8.5.
(
http://
www.
epa.
gov/
OCEPAterms/
pterms.
html)

pollutants
of
concern
(
POCs):
the
150
contaminants
identified
by
EPA
as
being
of
potential
concern
for
this
rule
and
which
are
currently
being
discharged
by
MP&
M
facilities.

Premanufacture
Notices
(
PMN):
a
notice,
required
by
Section
5
of
TSCA,
that
must
be
submitted
to
EPA
by
anyone
who
plans
to
manufacture
or
import
a
new
chemical
substance
for
a
non­
exempt
commercial
distribution.
The
notice
must
be
submitted
at
least
90
days
prior
to
the
manufacture
or
import
of
the
chemical.

(
http://
www.
epa.
gov/
oppt/
newchems/
index.
htm)

priority
pollutant
(
PP):
126
individual
chemicals
that
EPA
routinely
analyzes
when
assessing
contaminated
surface
water,

sediment,
groundwater,
or
soil
samples.
These
chemicals
are
also
known
as
toxic
pollutants.

quantitative
structure­
activity
relationship
(
QSAR):
an
expert
system
that
uses
a
large
database
of
measured
physicochemical
properties,
such
as
melting
point,
vapor
pressure,
and
water
solubility,
to
estimate
the
fate
and
effect
of
a
specific
chemical
based
on
its
molecular
structure.
(
http://
www.
epa.
gov/
med/
databases/
aster.
html)

reference
doses
(
RfDs):
RfDs
represent
chemical
concentrations
­
expressed
in
mg
of
pollutant/
kg
body
weight/
day
­

which,
if
not
exceeded,
are
expected
to
protect
an
exposed
population,
including
sensitive
groups
such
as
young
children
or
pregnant
women.

Secondary
Maximum
Contaminant
Levels
(
SMCLs):
non­
enforceable
water
treatment
levels
applying
to
public
water
systems
and
specifying
the
maximum
contamination
levels
that,
in
the
judgment
of
EPA,
are
required
to
protect
the
public
welfare.
These
treatment
levels
apply
to
any
contaminants
that
may
adversely
affect
the
odor
or
appearance
of
such
water
and
consequently
may
cause
people
served
by
the
system
to
discontinue
its
use.

I­
46
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
suspended
solids:
small
particles
of
solid
pollutants
that
float
on
the
surface
of,
or
are
suspended
in,
sewage
or
other
liquids.
They
resist
removal
by
conventional
means.

Superfund
Chemical
Data
Matrix
(
SCDM):
a
source
for
factor
values
and
benchmark
values
applied
when
evaluating
potential
National
Priorities
List
(
NPL)
sites
using
the
Hazard
Ranking
System
(
HRS).

(
http://
www.
epa.
gov/
superfund/
resources/
scdm/
index.
htm)

systemic
toxicants:
chemicals
that
EPA
believes
can
cause
significant
non­
carcinogenic
health
effects
when
present
in
the
human
body
above
chemical­
specific
toxicity
thresholds.

total
Kjeldahl
nitrogen
(
TKN):
TKN
is
defined
as
the
total
of
organic
and
ammonia
nitrate.
It
is
determined
in
the
same
manner
as
organic
nitrogen,
except
that
the
ammonia
is
not
driven
off
before
the
digestion
step.

total
organic
carbon
(
TOC):
a
measure
of
the
suspended
solids
in
wastewater,
effluent,
or
water
bodies,
determined
by
tests
for
"
total
suspended
non­
filterable
solids"
(
see
also:
suspended
solids).

total
petroleum
hydrocarbons
(
TPH):
a
general
measure
of
the
amount
of
crude
oil
or
petroleum
product
present
in
an
environmental
media
(
e.
g.
soil,
water,
or
sediments).
While
it
provides
a
measure
of
the
overall
concentration
of
petroleum
hydrocarbons
present,
TPH
does
not
distinguish
between
different
types
of
petroleum
hydrocarbons.

total
suspended
particles
(
TSP):
method
of
monitoring
airborne
particulate
matter
by
total
weight.

(
http://
www.
epa.
gov/
OCEPAterms/
tterms.
html)

total
suspended
solids
(
TSS):
a
measure
of
the
suspended
solids
in
wastewater,
effluent,
or
water
bodies,
determined
by
tests
for
"
total
suspended
non­
filterable
solids"
(
see
also:
suspended
solids).

United
States
Geological
Survey
(
USGS):
a
governmental
organization
that
provides
reliable
scientific
information
to:

describe
and
understand
the
Earth;
minimize
loss
of
life
and
property
from
natural
disasters;
manage
water,
biological,

energy,
and
mineral
resources;
and
enhance
and
protect
our
quality
of
life.
(
www.
noaa.
gov)

volatilization:
a
process
whereby
chemicals
dissolved
in
water
escape
into
the
air.

I­
47
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
ACRONYMS
AQUIRE:
AQUatic
Information
REtrieval
System
ASTER:
ASsessment
Tools
for
the
Evaluation
of
Risk
AT:
acute
toxicity
AWQC:
ambient
water
quality
criteria
BCF:
bioconcentration
factor
BOD:
biological
oxygen
demand
CDF:
critical
dilution
factor
CT:
chronic
toxicity
DCP:
dissolved
concentration
potential
DMT:
dry
metric
tons
H:
Henry's
Law
HAP:
hazardous
air
pollutant
HEAST:
Health
Effects
Assessment
Summary
Tables
IRIS:
Integrated
Risk
Information
System
Koc:
adsorption
coefficient
LOEC:
Lowest
Observed
Effect
Concentration
MATC:
Maximum
Allowable
Toxicant
Concentration
MCL:
maximum
contaminant
level
NEI:
National
Estuarine
Inventory
NOAA:
National
Oceanic
and
Atmospheric
Administration
NOEC:
No
Observed
Effect
Concentration
O&
G:
oil
and
grease
OC:
organic
carbon
PCS:
Permit
Compliance
System
PMN:
Premanufacture
Notices
POC:
pollutant
of
concern
PP:
priority
pollutant
QSAR:
quantitative
structure­
activity
relationship
RBC:
EPA s
Region
III
Risk­
Based
Concentration
Table
RfD:
reference
dose
SCDM:
Superfund
Chemical
Data
Matrix
SF:
cancer
potency
slope
factor
SMCL:
Secondary
Maximum
Contaminant
Level
TKN:
total
Kjeldahl
nitrogen
TOC:
total
organic
carbon
TPH:
total
petroleum
hydrocarbons
TSP:
total
suspended
particulates
TSS:
total
suspended
solids
USGS:
United
States
Geological
Survey
WQC:
water
quality
criteria
I­
48
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
REFERENCES
Arthur
D.
Little.
1983.
Evaluation
of
waterborne
exposure
pathways
to
paragraph
4(
c)
pollutants.
Draft
Report,
April
28.

Also:
Paragraph
4(
c)
list
of
detected
chemicals.

Arthur
D.
Little.
1986.
Bioaccumulation
Study.

Birge,
W.
J.
et
al.
1979.
Aquatic
toxicity
tests
on
inorganic
elements
occurring
in
oil
shale.
Oil
Shale
Symposium
­

Sampling,
Analysis
and
Quality
Assurance,
March.
EPA­
600/
9­
80­
022.

Clay,
D.
R.
1986.
Office
of
Toxic
Substances,
U.
S.
Environmental
Protection
Agency.
Memorandum
to
J.
M.
Conlon,

OWRS,
U.
S.
Environmental
Protection
Agency.

Holdway,
D.
A.
and
J.
B.
Spraque.
1979.
 
Chronic
toxicity
of
vanadium
to
flagfish. 
Water
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13:
905­
910.

Howard,
P.
H.,
ed.
1991.
Handbook
of
Environmental
Degradation
Rates.
Chelsea.
MI:
Lewis
Publishers,
Inc.

ICF,
Inc.
1985.
Superfund
Public
Health
Evaluation
Manual
­
Draft.

Leblanc,
G.
A.
1980.
 
Acute
toxicity
of
priority
pollutants
to
water
flea
(
Daphnia
magna). 
Bull
Environmental
Contamination
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24:
684­
691.

Lyman,
W.
J.;
W.
F.
Reehl,
and
D.
H.
Rosenblatt.
1981.
Handbook
of
Chemical
Property
Estimation
Methods
­

Environmental
Behavior
of
Organic
Compounds.
New
York,
NY:
McGraw­
Hill,
Inc.
(
reported
or
estimated
using
methods
outlined).

Syracuse
Research
Corporation.
1997.
CHEMFATE
Datafile
within
the
Environmental
Fate
Database.
Syracuse,
NY:

Syracuse
Research
Corporation.

U.
S.
Atomic
Energy
Commission.
1973.
Toxicity
of
power
plant
chemicals
to
aquatic
life.
Washington,
DC:
U.
S.
Atomic
Energy
Commission.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1972.
"
Blue
Book ,
NAS­
NAE
(
Water
Quality
Criteria
­
1972).

Washington,
DC:
U.
S.
EPA.
EPA­
R3­
73­
033.
If
the
code
@
AA
is
provided
with
this
reference,
the
value
is
an
estimate
based
on
application
factors
described
in
the
"
Blue
Book."

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1976.
 
Red
Book 
(
Quality
Criteria
For
Water).
Washington,
DC:
U.
S.

EPA.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1980.
Ambient
water
quality
criteria
documents.
Washington,
DC:

Office
of
Water,
U.
S.
EPA.
EPA
440/
5­
80
Series.
Also
refers
to
any
update
of
criteria
documents
(
EPA
440/
5­
85
and
EPA
440/
5­
87
Series)
or
any
Federal
Register
notices
of
proposed
criteria
or
criteria
corrections.
The
most
recent
National
Recommended
Water
Quality
Criteria
used
in
this
report
were
published
in
the
Federal
Register
on
December
10,
1998.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1984.
Summary
of
current
oral
Acceptable
Daily
Intakes
(
ADIs)
for
systemic
toxicants.
Cincinnati,
Ohio:
Environmental
Criteria
and
Assessment
Office,
U.
S.
EPA,
May.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1985.
Report
to
Congress
on
the
Discharge
of
Hazardous
Wastes
to
Publicly­
Owned
Treatment
Works
(
Domestic
Sewage
Study).
Office
of
Water
Regulations
and
Standards.
Washington,
DC:

U.
S.
EPA.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1987.
Guidance
Manual
for
Preventing
Interference
at
POTWs.

Washington,
DC:
U.
S.
EPA
U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1990.
CERCLA
Site
Discharges
to
POTWs:
Guidance
Manual.

Washington,
DC:
Office
of
Emergency
and
Remedial
Response
EPA/
540/
G­
90/
005.

I­
49
MP&
M
EEBA:
Appendices
Appendix
I:
Environmental
Assessment
U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1997.
Health
Effects
Assessment
Summary
Tables
(
HEAST).

Washington,
DC:
Office
of
Research
and
Development
and
Office
of
Emergency
and
Remedial
Response.
U.
S.
EPA.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998.
Risk­
Based
Concentration
Table,
October.
Philadelphia,
PA:

Region
III,
U.
S.
EPA.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998/
99.
QSAR.
Duluth,
MN:
Environmental
Research
Laboratory,

U.
S.
EPA.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998/
99a.
Aquatic
Toxicity
Information
Retrieval
(
AQUIRE)
Data
Base.
Duluth,
MN:
Environmental
Research
Laboratory,
U.
S.
Environmental
Protection
Agency.
1998
Database
retrieval.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998/
99b.
Assessment
Tolls
for
Evaluation
of
Risk
(
ASTER)
Data
Base.
Duluth,
MN:
Environmental
Research
Laboratory,
U.
S.
Environmental
Protection
Agency.
1998
Database
retrieval.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998/
99c.
Integrated
Risk
Information
System
(
IRIS)
Retrieval.

Washington,
DC:
U.
S.
EPA.
1998
Database
retrieval.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
2000.
Development
Document
for
the
Proposed
Effluent
Limitations
Guidelines
and
Standards
for
the
Metal
Products
and
Machinery
Point
Source
Category.
Washington,
DC:
U.
S.
EPA.

Versar.
1992.
Upgrade
of
Flow
Statistics
Used
to
Estimate
Surface
Water
Chemical
Concentrations
for
Aquatic
and
Human
Exposure
Assessment.
Prepared
for
the
Office
of
Pollution
Prevention
and
Toxics,
U.
S.
EPA.

Versar.
1994.
Development
of
Mixing
Zone
Dilution
Factors.
Preliminary
Draft,
Progress
Report.
Prepared
for
U.
S.
EPA,

Office
of
Pollution
Prevention
and
Toxics,
Economics,
Exposure,
and
Technology
Division.
October
27.
EPA
Contract
No.

68­
D3­
0013.

Worthing,
C.
R.
1987.
The
Pesticide
Manual
­
a
world
compendium,
Eighth
Edition.
Surry,
UK:
The
British
Crop
Protection
Council.
ISBN
0­
948404­
42­
6.

Zhang,
R.
and
Zhang,
S.
1982.
 
Toxicity
of
Fluorides
to
Fishes. 
C.
A.
Selects
­
Environm
Pollut
24,
97:
176354k.

I­
50
MP&
M
EEBA:
Appendices
Appendix
J:
Spacial
Distribution
of
MP&
M
Facilities
and
Recreational
User
Populations
Appendix
J:
Spacial
Distribution
of
MP&
M
Facilities
and
Recreational
User
Populations
INTRODUCTION
This
appendix
compares
the
national
distribution
of
all
MP&
M
facilities
by
state
and
the
national
distribution
of
recreational
participants
by
state
(
see
Table
J.
1
and
Figure
J.
1).

EPA
based
the
distribution
of
MP&
M
facilities
by
state
on
Census
data
on
total
numbers
of
facilities
in
the
SICs
that
make
up
the
MP&
M
industries,
not
just
water
dischargers.
This
comparison
assumes
that
the
state
distribution
of
water­
discharging
MP&
M
facilities
is
the
same
as
the
overall
distribution
of
MP&
M
facilities.
APPENDIX
CONTENTS
Table
J.
1
Distribution
of
MP&
M
Facilities
and
Participants
of
Water­
Based
Recreation
by
State
.....
J­
2
Figure
J.
1
Cumulative
Distribution
of
Facilities
and
Participants
.................
...
J­
4
EPA
based
the
distribution
of
recreational
participants
by
state
and
by
type
of
recreation
activity
on
information
provided
by
the
National
Demand
Study
data.
This
comparison
suggests
that
the
reaches
that
benefit
from
the
final
rule
are
also
those
where
a
very
large
percentage
of
all
recreational
participants
reside
and
recreate.

J­
1
MP&
M
EEBA:
AppendicesAppendix
J:
Spacial
Distribution
of
MP&
M
Facilities
and
Recreational
User
Populations
J­
2
Table
J.
1:
Distribution
of
MP&
M
Facilities
and
Participants
of
Water­
based
Recreation
by
State
State
Percent
of
State
Population
Participating
by
Activity
Average
#
of
Per­
Person
Trips
per
Season
by
Activity
Total
State
Pop.
(
1990)

(
Millions)
Potential
(
Extrapolated)
#
Participants
Based
on
State
Population
Nat. 
l
#
of
MP&
M
Facilities
State
%
of
National
Facilities
Cum.
ST
%
of
Facilities
Cumulative
Percent
Distribution
of
Participants
by
State
BoatViewFishSwimBoatViewFishSwimBoatViewFishSwimBoatViewFishSwim
CA11.7%
36.9%
13.6%
20.1%
5.4147.111.729.83,490,51310,992,8494,057,1545,983,73668,35911.9%
11.9%
10.3%
19.5%
9.4%
13.8%

TX12.8%
16.4%
18.9%
14.5%
7.2510.66.517.02,171,7912,792,3033,205,9782,456,19238,1766.6%
18.5%
16.7%
24.5%
16.8%
19.4%

NY12.4%
25.6%
11.2%
20.5%
7.95.79.28.718.02,231,3744,602,2092,022,1833,695,71436,3296.3%
24.8%
23.3%
32.6%
21.5%
27.9%

FL18.7%
32.6%
20.5%
24.2%
10.117.917.115.412.92,423,4184,221,4382,657,9433,126,99130,1985.2%
30.0%
30.5%
40.1%
27.7%
35.1%

IL11.8%
17.2%
14.6%
9.4%
9.6913.75.711.41,349,1051,962,3351,667,9851,079,28428,3434.9%
34.9%
34.5%
43.6%
31.5%
37.6%

OH11.5%
15.8%
14.2%
14.0%
88.213.18.810.81,251,5901,718,8511,535,2841,518,59626,4604.6%
39.5%
38.2%
46.6%
35.1%
41.1%

PA10.5%
14.4%
15.2%
13.7%
9.47.410.9811.91,249,0141,713,3911,809,4691,633,32626,2374.6%
44.1%
41.9%
49.7%
39.3%
44.8%

MI16.0%
24.8%
18.4%
20.8%
8.69.4128.59.31,484,6652,307,6871,710,5931,936,52023,6624.1%
48.2%
46.3%
53.8%
43.2%
49.3%

NJ15.9%
32.3%
15.9%
23.9%
10.96.46.37.37.71,225,2462,495,0461,225,2461,849,00819,8053.4%
51.6%
49.9%
58.2%
46.1%
53.5%

NC8.8%
17.9%
16.5%
13.5%
7.75.213.67.46.6586,3171,188,9201,091,201895,76215,1582.6%
54.3%
51.6%
60.3%
48.6%
55.6%

IN14.3%
15.0%
20.3%
16.3%
7.7911.85.55.5794,663831,6241,127,312905,54614,6562.5%
56.8%
54.0%
61.8%
51.2%
57.7%

MA15.7%
30.9%
15.7%
28.9%
8.711.614.39.56.0942,3321,860,501942,3321,739,68913,9152.4%
59.2%
56.8%
65.1%
53.4%
61.7%

WI15.7%
22.1%
18.1%
19.7%
106.111.56.24.9768,9401,079,788883,463965,26613,8452.4%
61.6%
59.0%
67.0%
55.4%
63.9%

GA11.5%
13.9%
16.6%
11.5%
11.44.110.37.46.5746,819903,1291,076,808746,81913,7472.4%
64.0%
61.2%
68.6%
57.9%
65.6%

MO13.0%
12.6%
18.8%
15.2%
5.24585.1665,035646,562960,606775,87413,3952.3%
66.3%
63.2%
69.8%
60.1%
67.4%

VA13.4%
17.0%
16.2%
13.4%
94.28.46.16.2827,1021,049,7831,002,066827,10212,8292.2%
68.6%
65.7%
71.6%
62.5%
69.3%

WA25.0%
39.2%
18.8%
25.9%
5.811.718.25.84.91,216,6731,907,623916,2601,261,73511,9912.1%
70.6%
69.3%
75.0%
64.6%
72.2%

MN17.6%
19.6%
19.6%
17.6%
5.416.511.56.84.4767,875857,162857,162767,87511,2722.0%
72.6%
71.5%
76.5%
66.6%
73.9%

TN17.9%
13.5%
22.6%
14.5%
7.53.715.16.74.9873,280659,0791,103,957708,51010,8081.9%
74.5%
74.1%
77.7%
69.1%
75.6%

MD14.8%
18.7%
17.1%
12.1%
8.812.113.28.44.8706,988893,037818,617576,7538,9931.6%
76.0%
76.2%
79.3%
71.0%
76.9%

AL14.7%
11.9%
20.6%
13.8%
7.59.218.610.64.0593,114481,905834,066556,0448,8251.5%
77.6%
77.9%
80.1%
72.9%
78.2%

CT16.4%
37.1%
14.5%
27.0%
7.76.87.712.33.3537,5161,219,747475,495888,9698,5931.5%
79.1%
79.5%
82.3%
74.0%
80.2%

LA16.4%
15.3%
27.0%
13.8%
43.413.44.44.2692,165647,5091,138,723580,5258,5001.5%
80.5%
81.6%
83.4%
76.7%
81.5%

CO6.6%
13.2%
25.9%
11.3%
17.214.813.15.23.3217,554435,109854,678372,9508,2311.4%
82.0%
82.2%
84.2%
78.7%
82.4%

OR20.3%
37.8%
24.9%
23.0%
8.87.213.27.42.8576,3231,074,057707,306654,9137,9781.4%
83.3%
83.9%
86.1%
80.3%
83.9%

KY11.9%
12.3%
22.4%
10.0%
6.539.417.53.7437,524454,352824,564370,2127,8221.4%
84.7%
85.2%
86.9%
82.2%
84.8%

AZ7.3%
11.2%
11.8%
10.7%
7.288.35.73.7267,685411,823432,415391,2327,7991.4%
86.1%
86.0%
87.7%
83.2%
85.7%

IA13.5%
16.4%
18.7%
13.5%
54.413.82.72.8373,482454,673519,627373,4827,6611.3%
87.4%
87.1%
88.5%
84.4%
86.5%

OK11.2%
12.6%
25.2%
14.0%
4.93.414.64.23.1351,954395,948791,896439,9426,9721.2%
88.6%
88.2%
89.2%
86.2%
87.5%
MP&
M
EEBA:
AppendicesAppendix
J:
Spacial
Distribution
of
MP&
M
Facilities
and
Recreational
User
Populations
Table
J.
1:
Distribution
of
MP&
M
Facilities
and
Participants
of
Water­
based
Recreation
by
State
State
Percent
of
State
Population
Participating
by
Activity
Average
#
of
Per­
Person
Trips
per
Season
by
Activity
Total
State
Pop.
(
1990)

(
Millions)
Potential
(
Extrapolated)
#
Participants
Based
on
State
Population
Nat. 
l
#
of
MP&
M
Facilities
State
%
of
National
Facilities
Cum.
ST
%
of
Facilities
Cumulative
Percent
Distribution
of
Participants
by
State
BoatViewFishSwimBoatViewFishSwimBoatViewFishSwimBoatViewFishSwim
J­
3
SC13.8%
19.9%
26.0%
15.5%
9.88.516.27.53.5481,589693,488905,387539,3806,9071.2%
89.8%
89.6%
90.4%
88.3%
88.8%

KS6.7%
17.0%
18.5%
13.3%
17.6912.96.22.5165,172422,105458,810330,3436,3701.1%
90.9%
90.1%
91.1%
89.4%
89.5%

AR14.1%
12.5%
28.1%
18.0%
4.610.213.37.32.4330,571293,841661,141422,3965,8251.0%
91.9%
91.1%
91.7%
90.9%
90.5%

MS13.6%
12.1%
23.6%
15.7%
6.324.217.412.92.6349,222312,462606,544404,3635,1650.9%
92.8%
92.1%
92.2%
92.3%
91.4%

NE10.7%
15.5%
10.7%
15.5%
3.92.113.93.91.6169,113244,274169,113244,2744,4240.8%
93.6%
92.6%
92.7%
92.7%
92.0%

UT8.1%
17.1%
13.5%
12.6%
6.63.53.66.81.7139,691294,902232,818217,2963,6330.6%
94.2%
93.0%
93.2%
93.3%
92.5%

WV9.5%
10.3%
18.3%
15.9%
6.64.617.26.71.8170,807185,041327,381284,6793,4420.6%
94.8%
93.5%
93.5%
94.0%
93.1%

RI15.8%
40.4%
19.3%
36.8%
6.94.68.371.0158,442404,907193,651369,6973,1060.5%
95.3%
94.0%
94.2%
94.5%
94.0%

ME22.2%
44.4%
27.8%
37.5%
7.65.710.510.31.2272,873545,746341,091460,4732,9800.5%
95.9%
94.8%
95.2%
95.3%
95.1%

NH18.8%
31.2%
14.1%
34.4%
3.314.913.215.71.1207,985346,641155,989381,3052,9600.5%
96.4%
95.4%
95.8%
95.6%
95.9%

NM6.7%
8.6%
12.4%
9.5%
3.75.69.83.81.5101,005129,863187,580144,2922,9270.5%
96.9%
95.7%
96.0%
96.1%
96.3%

ID24.1%
25.3%
20.5%
20.5%
5.84.313.49.51.0242,590254,720206,202206,2022,5720.4%
97.3%
96.4%
96.5%
96.5%
96.7%

NV17.3%
21.3%
13.3%
12.0%
4.87.315.46.31.2208,318256,391160,244144,2202,4060.4%
97.7%
97.0%
96.9%
96.9%
97.1%

MT14.5%
20.0%
34.5%
29.1%
7.815.620.78.30.8116,228159,813276,041232,4552,2040.4%
98.1%
97.4%
97.2%
97.5%
97.6%

SD16.7%
21.4%
16.7%
21.4%
2.31.8670.7116,001149,144116,001149,1442,0490.4%
98.5%
97.7%
97.5%
97.8%
97.9%

ND15.0%
15.0%
25.0%
15.0%
3.734.511.50.695,82095,820159,70095,8201,7490.3%
98.8%
98.0%
97.7%
98.2%
98.2%

HI16.4%
58.2%
18.2%
47.3%
6.733.96.615.51.1181,347644,788201,496523,8901,6770.3%
99.1%
98.5%
98.8%
98.7%
99.4%

VT20.6%
17.6%
8.8%
20.6%
7.15.58.710.40.6115,86299,31049,655115,8621,4880.3%
99.3%
98.9%
99.0%
98.8%
99.6%

DE15.7%
41.2%
15.7%
13.7%
6.41111.56.90.7104,497274,305104,49791,4351,3790.2%
99.6%
99.2%
99.5%
99.0%
99.8%

WY19.4%
16.1%
48.4%
6.5%
6.34.68.180.587,79173,159219,47829,2641,3090.2%
99.8%
99.4%
99.6%
99.5%
99.9%

AK34.5%
41.4%
37.9%
6.9%
5.47.117.420.6189,670227,604208,63737,9341,1560.2%
100.0%
100.0%
100.0%
100.0%
100.0%

Source:
mation
on
total
MP&
M
facilitiesby
state
is
from
Census
data;
information
on
where
recreating
people
live
is
from
NDS
data.

Infor
MP&
M
EEBA:
Appendices
Appendix
J:
Spacial
Distribution
of
MP&
M
Facilities
and
Recreational
User
Populations
Figure
J.
1:
Cumulative
Distribution
of
Facilities
and
Participants
a
The
numbers
refer
to
states
in
the
order
they
appear
in
the
above
table.
Therefore,
1
is
California,
2
is
Texas,
3
is
New
York,
etc.

Sources:
Information
on
total
MP&
M
facilities
by
state
is
from
Census
data;
information
on
where
recreating
people
live
is
from
NDS
data.

J­
4
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
INTRODUCTION
EPA
identified
eight
surface
water
evaluation
studies
that
quantified
the
effects
of
water
quality
improvements
on
various
water­
based
recreational
activities.
As
noted
in
Chapter
15
of
this
report,
the
Agency
selected
these
studies
based
on
technical
criteria
for
evaluating
study
transferability
(
Desvousges
et
al.,
1987;
Desvousges
et
al.,

1992;
and
B
oyle
and
Bergstrom,
1992),
including
the
following:

 
The
environmental
change
valued
at
the
study
site
must
be
the
same
as
the
environmental
quality
change
caused
by
the
rule
(
e.
g.,
changes
in
toxic
contamination
vs.
changes
in
nutrient
concentrations);

 
The
populations
affected
at
the
study
site
and
at
the
policy
site
must
be
the
same
(
e.
g.,

recreational
users
vs
nonusers);

 
The
assignment
of
property
rights
at
both
the
study
and
policy
sites
must
lead
to
the
same
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
APPENDIX
CONTENTS
K.
1
Desvousges
et
al.,
1987.

for
Water
Quality
Improvements:
A
Contingent
Valuation
Study
for
the
Monongahela
River
......
K­
2
K.
2
Farber
and
Griner,
2000.
ng
Watershed
Quality
Improvements
Using
Conjoint
Analysis
...........
K­
3
K.
3
Jakus
et
al.,
1997.
portfish
Consumption
Advisories
Affect
Reservoir
Anglers 
Site
Choice?
.
.
K­
5
K.
4
Lant
and
Roberts,
1990.
reenbelts
in
the
Cornbelt:
Riparian
Wetlands,
Intrinsic
Values,
and
Market
Failure
.................
.............
K­
6
K.
5
Audrey
Lyke,
1993.
rete
Choice
Models
to
Value
Changes
in
Environmental
Quality:
A
Great
Lakes
Case
Study
.................
................
K­
7
K.
6
Montgomery
and
Needelman,
1997.
re
Effects
of
Toxic
Contamination
in
Freshwater
Fish
.
.
K­
8
K.
7
Phaneuf
et
al.,
1998.
Water
Quality
Improvements
Using
Revealed
Preference
Methods
When
Corner
Solutions
are
Present 
.............
K­
8
Glossary
.................
.................
....
K­
10
Acronyms
.................
.................
...
K­
11
References
.................
.................
..
K­
12
Option
Price
Estimates
Valui
Do
S
G
Disc
The
Welfa
 
Valuing
theoretically­
appropriate
welfare
measure
(
e.
g.,
willingness­
to­
pay
(
WTP)
vs.
willingness­
to­
accept
compensation);
and
 
The
candidate
studies
should
be
based
on
defensible
research
methods.
Six
of
the
eight
studies
are
published
in
peer
reviewed
journals.
One
study,
Tudor
et
al.
(
2002),
was
presented
at
the
annual
American
Agricultural
Economic
Association
and
the
Northeastern
Resource
and
Environmental
Economic
meetings.
1
The
eighth
study,
Lyke
(
1993),

is
an
unpublished
Ph.
D.
dissertation.

In
addition
to
the
above
criteria,
the
Agency
considered
authors'
recommendations
regarding
the
robustness
and
theoretical
soundness
of
various
estimates
in
selecting
point
estimates
for
benefits
transfer.

The
rest
of
this
appendix
presents
welfare
estimates
from
seven
studies
used
in
estimating
recreational
benefits
from
the
final
regulation
and
provides
EPA s
reasons
for
selecting
specific
values
from
each
study.
The
study
by
Tudor
et
al.
(
2002)
is
discussed
in
detail
in
Chapter
21.
All
welfare
estimates
from
that
study
are
eligible
for
use
in
benefits
transfer,
because
the
study
is
based
on
the
policy
scenarios
specific
to
the
MP&
M
regulation.

1
Preliminary
results
of
this
study
were
presented
at
the
annual
American
Agricultural
Economic
Association
meeting
(
L.
Tudor
et
al.,
1999a)
and
at
the
annual
Northeastern
Agricultural
and
Resource
Economic
Association
meeting
(
L.
Tudor
et
al.,
1999b).
EPA
subjected
this
study
to
a
formal
peer
review
by
experts
in
the
natural
resource
valuation
field.
The
peer
review
concluded
that
EPA
had
done
a
competent
job,
especially
given
the
available
data.
This
study
can
be
found
in
Chapter
21.
The
peer
review
report
is
in
the
docket
for
the
rule.

K­
1
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
K.
1
DESVOUSGES
ET
AL.,
1987.
OPTION
PRICE
ESTIMATES
FOR
WATER
QUALITY
IMPROVEMENTS:
A
CONTINGENT
VALUATION
STUDY
FOR
THE
MONONGAHELA
RIVER
This
study
used
findings
from
a
contingent
valuation
(
CV)
survey
to
estimate
WTP
for
improved
recreational
fishing
from
enhanced
water
quality
in
the
Pennsylvania
portion
of
the
Monongahela
River.
In
a
hypothetical
market,
each
survey
respondent
was
asked
to
provide
an
option
price
for
different
water
quality
changes,
such
as
"
raising
the
water
quality
from
suitable
for
boating
(
hereafter,
 
boatable 
water)
to
a
level
where
gamefish
would
survive
(
hereafter,
 
fishable 
water)."
Table
K.
1
lists
water
quality
changes
evaluated
in
the
study
and
the
corresponding
WTP
estimates.
The
following
discussion
provides
justification
for
selecting
the
point
estimates
EPA
used
in
the
benefits
transfer
analysis
in
Chapter
15.

Table
K.
1:
Changes
in
the
Resource
Value
from
a
Specified
Water
Quality
Improvement
from
Desvousges
et
al.

Water
Quality
Change
Valued
Adjusted
to
2001$
b
Original
Estimates
(
1981$)

User
Nonuser
Combined
User
Nonuser
Combined
Iterative
Bidding:
$
25
starting
point
Unsuitable
to
Boatable
$
53.4
$
57.8
$
56.4
$
27.4
$
29.7
$
29.0
Boatable
to
Fishablea
$
36.8
$
28.3
$
30.9
$
18.9
$
14.5
$
15.9
Fishable
to
Swimmable
$
23.0
$
14.0
$
16.9
$
11.8
$
7.2
$
8.7
Boatable
to
Swimmable
$
62.5
$
42.2
$
48.9
$
32.1
$
21.7
$
25.1
Unsuitable
to
Swimmable
$
115.9
$
100.0
$
105.3
$
59.5
$
51.4
$
54.1
Iterative
Bidding:
$
125
starting
point
Unsuitable
to
Boatable
$
184.4
$
75.6
$
111.7
$
94.7
$
38.8
$
57.4
Boatable
to
Fishable
$
113.1
$
51.2
$
71.9
$
58.1
$
26.3
$
36.9
Fishable
to
Swimmable
$
64.4
$
22.5
$
36.6
$
33.1
$
11.6
$
18.8
Boatable
to
Swimmable
$
194.1
$
78.9
$
117.3
$
99.7
$
40.5
$
60.2
Unsuitable
to
Swimmable
$
378.5
$
154.2
$
229.0
$
194.4
$
79.2
$
117.6
Direct
Question:
no
payment
card
Boatable
to
Unsuitable
$
88.2
$
27.6
$
47.7
$
45.3
$
14.2
$
24.5
Boatable
to
Fishable
$
60.9
$
21.0
$
34.2
$
31.3
$
10.8
$
17.6
Fishable
to
Swimmable
$
39.3
$
16.6
$
24.1
$
20.2
$
8.5
$
12.4
Boatable
to
Swimmable
$
103.0
$
39.5
$
60.7
$
52.9
$
20.3
$
31.2
Unsuitable
to
Swimmable
$
191.2
$
67.2
$
108.4
$
98.2
$
34.5
$
55.7
Direct
Question:
payment
card
Boatable
to
Unsuitable
$
91.1
$
103.2
$
99.3
$
46.8
$
53.0
$
51.0
Boatable
to
Fishable
$
88.2
$
42.6
$
57.1
$
45.3
$
21.9
$
29.3
Fishable
to
Swimmable
$
44.5
$
15.0
$
24.3
$
22.9
$
7.7
$
12.5
Boatable
to
Swimmable
$
138.6
$
58.3
$
83.6
$
71.2
$
29.9
$
42.9
Unsuitable
to
Swimmable
$
229.6
$
161.3
$
182.8
$
117.9
$
82.8
$
93.9
(
1987)

Location:
Pennsylvania
portion
of
the
Monongahela
River
Estimating
Approach:
CV
Survey
Population
:
Recreational
Users
and
Nonusers
a
The
value
selected
for
benefits
transfer
is
given
in
bold.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
the
Consumer
Price
Index
(
CPI).

Source:
Desvousges
et
al.,
1987.

EPA
judged
that
only
one
value
from
this
study
met
the
requirements
for
the
quality
of
research
methods
and
was
compatible
with
the
environmental
changes
and
population
characteristics
considered
in
the
analysis
of
recreational
benefits
from
the
MP&
M
rule.
EPA
selected
this
value
for
the
following
reasons:

K­
2
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
 
Environmental
quality
change.
The
Desvousges
et
al.
(
1987)
study
derived
WTP
values
for
five
different
changes
in
water
quality,
as
shown
in
Table
K.
1
above.
EPA
judged
that
only
one
of
these
improvements,
from
 
boatable 
to
 
fishable, 
is
compatible
with
the
changes
in
water
quality
expected
under
the
MP&
M
rule.
Streams
unsuitable
for
recreational
activities
such
as
boating
are
likely
to
be
affected
by
multiple
environmental
stressors
from
many
sources,
including
many
that
are
not
related
to
MP&
M
discharges
(
e.
g.,
severe
oxygen
depletion.)
In
these
cases,
it
is
reasonable
to
assume
that
changes
in
concentrations
of
MP&
M
pollutants
would
reduce
or
eliminate
one
of
the
stressors
on
the
reach,
but
would
be
unlikely
to
change
the
designation
of
the
reach.

The
analysis
in
Chapter
15
assumes
that
reaches
with
ambient
water
quality
criteria
(
AWQC)
exceedances
under
the
baseline
conditions
are
boatable
and
likely
to
support
rough
fishing,
but
may
not
be
clean
enough
to
support
gamefishing.
AWQC
are
set
at
a
level
below
which
pollutant
concentrations
are
not
expected
to
cause
significant
harm
to
human
health
or
aquatic
life.
Exposure
to
pollutant
concentrations
above
the
AW
QC
levels
are
expected
to
have
a
harmful
effect.
Therefore,
by
definition,
water
with
pollutant
levels
that
exceed
criteria
set
to
protect
human
health
or
aquatic
life
are
not
suitable
waters
for
sensitive
aquatic
species
or
ideal
as
a
sources
of
fish
for
consumption.

Removing
AWQC
exceedances
is
therefore
comparable
to
shifting
water
quality
from
"
boatable"
to
"
fishable."
The
Agency
did
not
use
the
boatable
to
swimmable
designation
because
a
more
limited
number
of
reaches
are
suitable
for
swimming
nationally
due
to
reasons
not
related
to
MP&
M
discharges
(
e.
g.,
amenities,
pathogens).
Determining
national
level
locations
affected
by
MP&
M
pollutants
that
are
suitable
for
swimming
required
more
resources
than
were
available
for
the
national
analysis.

 
Research
methods.
The
authors
used
four
different
payment
vehicles
in
their
CV
study.
For
the
recreational
benefits
analysis,
EPA
decided
to
use
the
WTP
estimates
derived
from
the
 
iterative
bidding 
(
IB)
payment
vehicle,
because
it
is
universally
preferred
to
the
 
direct
question/
open­
ended 
format
for
eliciting
option
price
bids.

Survey
respondents
in
the
direct
question
format
are
asked
to
state
the
most
that
they
would
be
willing
to
pay
for
the
program
or
policy.
This
format
confronts
respondents
with
an
unfamiliar
choice.
Studies
that
use
this
approach
usually
have
high
non­
response
rates.

Respondents
in
the
IB
format
are
asked
whether
they
would
be
willing
to
pay
a
given
amount.
If
the
answer
is
yes,

then
this
amount
is
raised
in
pre­
set
increments
until
the
respondent
says
that
he
or
she
will
not
pay
the
last
amount
given.
If
the
answer
is
no,
then
the
amount
is
decreased
until
the
respondent
indicates
WTP
the
stated
amount.

Some
studies
found
that
the
respondent s
final
WTP
amount
depends
on
the
initial
amount
offered.
This
problem
is
referred
to
in
economic
literature
as
starting
point
bias.
The
Agency
selected
the
WTP
estimates
derived
using
the
$
25
starting
point
IB
process
to
avoid
upward
starting
point
bias.
Table
K.
1
shows
that
the
selected
estimates
are
the
most
conservative
among
all
the
payment
vehicles
used.

 
Population
characteristics.
The
user
population
considered
in
this
study
matches
the
user
population
characteristics
considered
in
EPA s
analysis
(
i.
e.,
recreational
anglers,
boaters,
and
wildlife
viewers).

K.
2
FARBER
AND
GRINER,
2000.
VALUING
WATERSHED
QUALITY
IMPROVEMENTS
USING
CONJOINT
ANALYSIS
Farber
and
Griner
(
2000)
used
a
CV
study
to
estimate
changes
in
water
resource
values
to
users
from
various
improvements
in
Pennsylvania s
water
quality.
The
study
defines
water
quality
as
 
polluted, 
 
moderately
polluted, 
and
 
unpolluted 

based
on
a
water
quality
scale
developed
by
EPA
Region
III.
 
Polluted 
streams
are
unable
to
support
aquatic
life,

 
moderately
polluted 
streams
are
somewhat
unable
to
support
aquatic
life,
and
 
unpolluted 
streams
adequately
support
aquatic
life.
Farber
and
Griner
developed
WTP
estimates
for
water
quality
improvements
for
the
following
three
water
quality
changes:

 
from
 
moderately
polluted 
to
 
unpolluted, 

 
from
 
severely
polluted 
to
 
moderately
polluted, 
and
K­
3
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
 
from
 
severely
polluted 
to
 
unpolluted. 

The
authors
used
six
different
model
variations
to
estimate
the
WTP
for
the
three
improvements
scenarios
for
various
population
groups
(
e.
g.,
users,
nonusers,
and
a
mix
of
users
and
nonusers).
Table
K.
2
presents
the
estimated
WTP
values.

The
following
discussion
provides
EPA s
reasons
for
selecting
point
estimates
for
the
use
in
benefits
transfer.

Table
K.
2:
Estimate
WTP
for
Specified
Water
Quality
Improvements
from
Farber
and
Griner
(
2001$)

Water
Quality
Change
Valued
Binary
Choice
Model
Intensity
of
Preference
Model
User
Nonuser
Combine
User
Nonuser
Combine
Basic
Moderately
Polluted
to
Unpolluted
$
49
.7
$
6.3
$
40
.4
$
56
.2
$
14
.0
$
54
.2
Severely
Polluted
to
Moderately
Polluted
$
66
.9
$
5.8
$
55
.6
$
73
.8
$
51
.4
$
70
.9
Severely
Polluted
to
Unpolluted
$
11
7.3
$
44
.9
$
95
.7
$
12
9.6
$
57
.7
$
11
6.8
Interactive
Moderately
Polluted
to
Unpolluted
$
48
.2
$
3.2
$
38
.0
$
56
.9
$
13
.3
$
54
.6
Severely
Polluted
to
Moderately
Polluted
$
65
.2
$
1.5
$
52
.7
$
75
.1
$
50
.6
$
71
.9
Severely
Polluted
to
Unpolluted
$
11
5.5
$
41
.3
$
92
.9
$
13
3.1
$
57
.6
$
11
9.5
Fixed
Effects
Moderately
Polluted
to
Unpolluted
a
$
24
.5
$
16
.4
$
28
.3
$
41
.8
$
5.5
$
41
.0
Severely
Polluted
to
Moderately
Polluted
$
42
.4
$
10
.6
$
38
.2
$
63
.4
$
30
.3
$
59
.0
Severely
Polluted
to
Unpolluted
$
86
.6
$
48
.4
$
80
.4
$
11
0.5
$
31
.0
$
98
.6
Location:
Lower
Allegheny
Watershed
in
Western
Pennsylvania
Estimating
Approach:
Conjoint
Analysis
Survey
Population:
Recreational
users
and
nonusers
a
Values
selected
for
the
use
in
benefits
transfer
are
given
in
bold.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Farber
and
Griner,
2000.

The
Agency
selected
only
two
values
from
this
study
based
on
their
compatibility
with
the
environmental
changes
and
population
characteristics
considered
in
both
the
original
study
and
the
analysis
of
recreational
benefits
from
the
MP&
M
rule.

The
following
discussion
summarizes
EPA s
reasons
used
in
the
selection
process:

 
Environmental
quality
change.
EPA
judged
that
only
one
water
quality
improvement
scenario
change
from
 
moderately
polluted 
to
 
unpolluted 
is
compatible
with
the
environmental
quality
change
expected
from
the
final
regulation
AWQC
are
set
at
a
level
below
which
pollutant
concentrations
have
not
been
demonstrated
to
cause
significant
harm
to
human
health
or
aquatic
life.
Exposure
to
pollutant
concentrations
above
the
AWQ
C
levels
are
expected
to
have
a
harmful
effect.
Therefore,
by
definition,
water
with
pollutant
levels
that
exceed
criteria
set
to
protect
human
health
or
aquatic
life
are
polluted
waters.

EPA
chose
the
case
where
the
policy
variable
changed
from
moderately
polluted
to
unpolluted
because
this
is
likely
to
be
the
most
frequently
occurring
scenario
for
reaches
with
MP&
M
discharges.
Streams
unable
to
support
any
aquatic
life
(
i.
e.,
 
severely
polluted )
are
likely
to
be
affected
by
numerous
environmental
stressors,
in
addition
to
MP&
M
discharges.
Eliminating
MP&
M­
related
AWQC
exceedences
would
eliminate
or
reduce
one
of
the
stressors,

but
is
unlikely
to
change
the
quality
of
the
water
from
severely
polluted
to
unpolluted.
It
is
more
realistic
to
assume
that
most
streams
affected
by
MP&
M
facility
discharges
are
moderately
polluted,
i.
e.,
these
streams
support
some
aquatic
life;
but
sensitive
species
are
adversely
affected
by
MP&
M
pollutants
exceeding
AWQC
values
protective
of
aquatic
life.
Removing
all
AWQC
exceedances
would
make
such
streams
unpolluted.

 
Research
methods.
EPA
considered
only
two
of
the
six
versions
of
the
benefits
transfer
model
based
on
the
authors 
recommendations.
The
authors
appear
to
prefer
the
 
fixed
effects 
versions
of
both
the
binary
choice
K­
4
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
(
BC)
and
intensity
of
preference
(
IP)
models.
Specifically,
they
note
that
"
A
likelihood
ratio
test,
with
degrees
of
freedom
being
the
number
of
individuals
in
the
estimating
sample,
can
be
used
to
test
the
superiority
of
the
fixed
effects
model.
Such
a
test
shows
the
fixed
effects
model
to
be
a
statistical
improvement
over
either
the
basic
or
interactive
models"
(
see
Table
K.
2).
In
addition,
they
state
that,
"
the
purpose
of
estimating
a
fixed
effects
model
was
to
account
for
the
possibility
that
some
respondents
may
approve
of
all
changes,
regardless
of
price
and
quality.
If
this
behavior
existed
in
the
sample,
not
controlling
for
it
would
result
in
overestimates
of
marginal
valuations
for
each
type
of
quality
change.
This
expectation
is
supported
by
the
fact
that
the
fixed
effects
valuation
estimates
are
lower
than
the
others."

 
Population
characteristics.
The
user
population
considered
in
this
study
matches
the
user
population
characteristics
considered
in
EPA s
analysis
(
i.
e.,
recreational
anglers,
boaters,
and
wildlife
viewers).

K.
3
JAKUS
ET
AL.,
1997.
DO
SPORTFISH
CONSUMPTION
ADVISORIES
AFFECT
RESERVOIR
ANGLERS SITE
CHOICE?

Jakus
et
al.
(
1997)
used
a
repeated
discrete
choice
travel
cost
(
TC)
model
to
examine
the
impacts
of
fish
consumption
advisories
(
FCA)
in
eastern
and
middle
Tennessee.
The
estimated
consumer
surplus
from
recreational
fishing
in
middle
and
east
Tennessee
is
$
26.02
and
$
52.57
per
angler
per
day,
respectively,
under
the
baseline
water
quality
conditions.
The
estimated
welfare
gain
from
removing
FCAs
is
$
2.04
and
$
3.16
per
angler
per
day
,
respectively.
Table
K.
3
summarizes
the
study s
estimates.

Table
K.
3:
Consumer
Surplus
from
Recreational
Fishing
from
Jakus
et
al.
(
1997)
a
Water
Quality
Change
Valued
Consumer
Surplus
Adjusted
to
2001$
Consumer
Surplus
($
1997)

Site
Choice
Model
­­
multinomial
logit
Average
surplus
per
trip
in
middle
TN
(
baseline
water
quality
$
26.02
$
23.60
Benefit
per
trip
from
removing
all
advisories
in
middle
TN
$
2.04
$
1.85
Average
surplus
per
trip
in
East
TN
(
baseline
water
quality
conditions)
$
52.57
$
47.67
Benefit
per
trip
from
removing
all
advisories
in
east
TN
$
3.16
$
2.86
Benefit
per
trip
from
removing
Watts
Bar
advisory
$
1.75
$
1.59
Repeated
Discrete
Choice
Model
­­
repeated
nested
logit
model
Seasonal
benefit
from
removing
all
advisories
in
middle
TN
$
24.22
$
21.96
Seasonal
benefit
from
removing
all
advisories
in
east
TN
$
52.27
$
47.40
Seasonal
benefit
from
removing
Watts
Bar
advisory
$
30.43
$
27.60
Location:
Tennessee
Estimating
Approach:
TC
Survey
Population:
Tennessee
residents;
anglers
and
non­
anglers
a
Values
selected
for
the
use
in
benefits
transfer
are
given
in
bold.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Jakus
et
al,
1997.

EPA
selected
two
values
from
this
study
for
use
in
benefits
transfer,
based
on
their
compatibility
with
the
environmental
quality
change
and
population
characteristics
at
both
the
original
study
and
policy
sites,
for
the
following
reason:

 
Environmental
quality
change.
FCAs
are
usually
triggered
by
the
presence
of
toxic
pollutants
in
fish
tissue.
EPA
expects
the
final
regulation
to
reduce
discharges
of
toxic
pollutants,
including
those
linked
to
FCAs
(
e.
g.,
mercury
and
lead).
The
Agency
therefore
assumed
that
the
removal
of
FCAs
is
compatible
with
water
quality
improvements
expected
from
the
final
regulation.

K­
5
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
The
recreational
benefits
analysis
uses
consumer
surplus
estimates
for
both
regions
studied
by
the
authors,
because
MP&
M
facilities
are
located
in
these
regions
as
well
as
throughout
heavily
populated
regions
of
the
U.
S.
EPA
did
not
include
the
value
corresponding
to
the
Watts
Bar
lake
in
the
benefits
transfer
analysis
because
this
lake
is
included
in
the
set
of
fishing
areas
for
east
Tennessee.

K.
4
LANT
AND
ROBERTS,
1990.
GREENBELTS
IN
THE
CORNBELT:
RIPARIAN
WETLANDS,
INTRINSIC
VALUES,
AND
MARKET
FAILURE
Lant
and
Roberts
(
1990)
used
a
CV
study
to
estimate
the
recreational
and
nonuse
benefits
of
improved
water
quality
in
selected
Iowa
and
Illinois
river
basins.
River
quality
was
defined
by
means
of
an
interval
scale
of
 
poor, 
 
fair, 
 
good, 
and
 
excellent. 
The
authors
defined
the
four
water
quality
intervals
as
follows:

 
 
poor 
water
quality
is
inadequate
to
support
any
recreation
activity,

 
 
fair 
water
quality
is
adequate
for
boating
and
rough
fishing,

 
 
good 
water
quality
is
adequate
for
gamefishing,
and
 
 
excellent 
is
adequate
to
support
swimming
and
exceptional
fishing.

Table
K.
4
summarizes
WTP
values
for
specified
water
quality
improvements
from
this
study.

Table
K.
4:
WTP
Values
for
a
Specified
Water
Quality
Improvement
from
Lant
and
Roberts
(
1990)

Water
Quality
Change
Valued
Adjusted
to
2001$
Original
Study
Values
1987$

Use
Value
Nonuse
Value
Use
Value
Nonuse
Value
Poor
to
fair
$
47.5
$
58.6
$
30.50
$
37.61
Fair
to
good
a
$
57.8
$
73.5
$
37.10
$
47.16
Good
to
excellent
$
64.7
$
67.3
$
41.51
$
43.22
Location:
Selected
Iowa
and
Illinois
river
basins
Estimating
Approach:
CV
Survey
Population:
Recreational
users
and
nonusers
a
The
values
given
in
bold
were
selected
for
the
use
in
benefits
transfer.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Lant
and
Roberts,
1990.

The
Agency
judged
that
only
one
value
from
this
study
is
compatible
with
the
environmental
changes
and
population
characteristics
considered
in
the
analysis
of
recreational
benefits
from
the
MP&
M
rule,
for
the
following
reasons:

 
Environmental
quality
change.
The
Agency
judged
that
only
one
of
the
three
possible
water
quality
changes
considered
in
this
study
 
fair 
to
 
good 
was
compatible
with
the
water
quality
change
expected
under
the
MP&
M
rule.
EPA
assumed
in
its
analysis
of
recreational
benefits
expected
from
the
MP&
M
rule
that
reaches
with
AWQC
exceedances
under
the
baseline
conditions
may
support
rough
fishing,
but
may
not
be
clean
enough
to
support
more
sensitive
species
such
as
those
desired
for
game
fishing.
Removing
AWQC
exceedances
will
shift
water
quality
from
 
fair 
to
 
good. 

 
Population
characteristics.
The
user
population
considered
in
this
study
matches
the
population
characteristics
considered
in
EPA s
analysis
(
i.
e.,
recreational
anglers,
boaters,
and
wildlife
viewers).

K­
6
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
K.
5
AUDREY
LYKE,
1993.
DISCRETE
CHOICE
MODELS
TO
VALUE
CHANGES
IN
ENVIRONMENTAL
QUALITY:
A
GREAT
LAKES
CASE
STUDY
Lyke s
(
1993)
study
of
the
Wisconsin
Great
Lakes
open
water
sport
fishery
showed
that
anglers
may
place
a
significantly
higher
value
on
a
contaminant­
free
fishery
than
on
one
with
some
level
of
contamination.
Lyke
estimated
the
value
of
the
fishery
to
Great
Lakes
trout
and
salmon
anglers
if
it
was
improved
enough
to
be
"
completely
free
of
contaminants
that
may
threaten
human
health. 
The
author
also
estimated
various
policy
scenarios
that
affect
the
value
of
recreational
fishing
in
the
Wisconsin
Great
Lakes,
including
reducing
the
daily
bag
limit
for
lake
trout
and
restoring
naturally
reproducing
populations
of
lake
trout.
Table
K.
5
presents
welfare
estimates
from
this
study.

Table
K.
5:
WTP
Estimates
for
Specified
Water
Quality
Improvements
from
Lyke
(
1993)
a
Water
Quality
Change
Valued
Adjusted
to
2001$
b
Original
Study
Value
Value
of
WI
Fishing
Change
in
Value
Value
of
WI
Fishing
Change
in
Value
CV
­­
linear
logit
model
1990
fishing
conditions
remain
the
same
as
1989
$
95,062,744
$
66,600,000
WI
daily
bag
limit
for
lake
trout
reduced
to
one
a
day
$
43,962,951
($
51,099,793
$
30,800,000
($
35,800,000
Great
Lakes
fish
are
free
of
pollutants
affecting
human
health
$
105,625,27
$
10,562,527
$
74,000,000
$
7,400,000
Restoring
naturally
reproducing
populations
of
lake
trout
$
17,271,159
$
17,271,159
$
12,100,000
$
12,100,000
WI
inland
fishing
conditions
remain
the
same
as
1989
$
964,330,17
$
675,600,00
Restoring
naturally
reproducing
populations
of
lake
trout
in
WI
waters
of
Great
Lakes
(
inland
anglers
only)
$
0
$
0
$
0
$
0
CV
­­
constant
elasticity
of
substitution
model
(
mean)

1990
fishing
conditions
remain
the
same
as
1989
$
118,899,79
$
83,300,000
Great
Lakes
fish
are
free
of
pollutants
affecting
human
health
$
156,011,38
$
37,111,581
$
109,300,00
$
26,000,000
CV
­­
constant
elasticity
of
substitution
model
(
median)

1990
fishing
conditions
remain
the
same
as
1989
$
26,834,528
$
18,800,000
Great
Lakes
fish
are
free
of
pollutants
that
affect
human
health
$
40,537,266
$
13,702,738
$
28,400,000
$
9,600,000
Location:
Wisconsin
Estimating
Approach:
TC
and
CV
Survey
Population:
Wisconsin
Great
Lakes
and
inland
anglers
a
The
values
selected
for
the
use
in
benefits
transfer
are
given
in
bold.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Lyke,
1993.

EPA
selected
two
WTP
values
from
this
study
for
use
in
benefits
transfer
for
the
following
reasons:

 
Environmental
quality
change.
EPA
judged
that
only
one
policy
scenario
Great
Lakes
fish
that
are
free
from
contaminants
harmful
to
human
health
is
compatible
with
water
quality
improvements
associated
with
removal
of
all
AWQ
C
exceedances.
Other
scenarios,
such
as
reducing
daily
bag
limit
for
lake
trout
to
one
per
day
and
restoring
naturally
reproducing
populations
of
lake
trout,
are
irrelevant
to
the
MP&
M
regulation.
The
Agency
used
estimates
from
the
 
1990
fishing
conditions
remain
the
same
as
1989
conditions"
scenario
as
an
estimate
of
the
baseline
value
of
recreational
fishing
in
Wisconsin.

 
Research
methods.
The
Agency
did
not
consider
estimates
from
the
TC
model
because
the
author
noted
that
 
the
nested
logit
travel
cost
model
results
seem
too
high. 

K­
7
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
K.
6
MONTGOMERY
AND
NEEDELMAN,
1997.
THE
WELFARE
EFFECTS
OF
TOXIC
CONTAMINATION
IN
FRESHWATER
FISH
Montgomery
and
Needelman
(
1997)
estimated
benefits
from
removing
 
toxic 
contamination
from
lakes
and
ponds
in
New
York
State.
They
used
a
binary
variable
as
their
primary
water
quality
measure,
which
indicates
whether
the
New
York
Department
of
Environmental
Conservation
considers
water
quality
in
a
given
lake
to
be
impaired
by
toxic
pollutants.
Their
model
controls
for
major
causes
of
impairments
other
than
 
toxic 
pollutants,
to
separate
the
effects
of
various
pollution
problems
that
affect
the
fishing
experience.
Table
K.
6
lists
environmental
quality
changes
considered
in
the
study
and
the
WTP
values
corresponding
to
a
specified
water
quality
change.

Table
K.
6:
Welfare
Estimates
from
Montgomery
and
Needelman
(
1997)

Water
Quality
Change
Valued
Compensating
Variation
per
Capita
per
Season
(
2001$)
b
Compensating
Variation
per
Capita
per
Season
(
1989$)

Eliminate
toxic
contamination
in
all
lakes
a
$
90.28
$
63.25
All
toxic
lakes
are
closed
to
fishing
$
124.31
$
87.09
Raise
pH
in
acidic
lakes
(
none
are
threatened
or
impaired)
$
19.73
$
13.82
Close
all
acidic
lakes
to
fishing
$
21.20
$
14.85
Eliminate
toxic
contamination
and
raise
pH
in
acidic
lakes
$
113.39
$
79.44
Location:
New
York
State
Estimating
Approach:
TC
­­
Repeated
discrete
choice
model
Survey
Population:
New
York
State
residents;
anglers
and
non­
anglers
a
The
values
selected
for
the
use
in
benefits
transfer
are
given
in
bold.

b
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Montgomery
and
Needelman,
1997.

The
Agency
selected
only
one
value
from
this
study
for
use
in
the
benefits
transfer
based
on
its
compatibility
with
environmental
quality
changes
at
both
the
original
study
and
the
MP&
M
sites,
for
the
following
reason:

 
Environmental
quality
change.
Only
one
of
the
five
policy
scenarios
considered
eliminate
toxic
contamination
in
all
lakes
is
directly
compatible
with
the
potential
changes
brought
about
by
the
MP&
M
rule.
The
MP&
M
rule
is
unlikely
to
significantly
affect
the
acidity
in
lakes
and
streams
affected
by
MP&
M
discharges.
The
last
three
policy
scenarios
in
Table
K.
6
involve
changes
in
pH
levels,
and
are
therefore
not
included
in
the
benefits
transfer.

The
Agency
also
did
not
consider
the
estimate
from
the
second
scenario
in
Table
K.
6
closing
all
toxic
lakes
to
fishing
in
benefits
transfer,
because
it
does
not
consider
water
quality
improvement
per
se.

K.
7
PHANEUF
ET
AL.,
1998.
VALUING
WATER
QUALITY
IMPROVEMENTS
USING
REVEALED
PREFERENCE
METHODS
WHEN
CORNER
SOLUTIONS
ARE
PRESENT
Phaneuf
et
al.
(
1998)
studied
angling
in
Wisconsin
Great
Lakes.
They
estimated
changes
in
recreational
fishing
values
resulting
from
a
20
percent
reduction
of
toxin
levels
in
lake
trout
flesh.
The
study
uses
a
TC
model
to
value
water
quality
improvements
when
corner
solutions
are
present
in
the
data.
Corner
solutions
arise
when
consumers
visit
only
a
subset
of
the
available
recreation
sites,
setting
their
demand
to
zero
for
the
remaining
sites.
Phaneuf
et
al.
found
that
improved
industrial
and
municipal
waste
management
results
in
general
water
quality
improvement.
Table
K.
7
presents
findings
from
this
study
based
on
two
policy
scenarios
and
four
different
model
specifications.

K­
8
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
Table
K.
7:
Welfare
Estimates
from
Phaneuf
et
al.
(
1998)

Water
Quality
Change
Valued
Adjusted
to
2001$
a
Study
Values
(
1989$)

RNL
RPRN
KT
System
RNL
RPRN
KT
System
20%
reduction
in
toxins
$
41.62
$
12.53
$
166.21
$
15.69
$
29.16
$
8.78
$
116.45
$
10.99
Loss
of
South
Lake
Michigan
$
232.19
$
140.37
$
12,119
$
441.36
$
162.67
$
98.34
$
849.09
$
309.21
Location:
Wisconsin
Great
Lakes
Estimating
Approach:
TC
models,
including:
RNL:
Repeated
Nested
Logit
model;

RPRNL:
Random
Parameters
Repeated
Nested
Logit
model;
KT:
Kuhn­
Tucker
model;
and
System:
Systems
of
Demands
model
Survey
Population:
Wisconsin
anglers;
Great
Lakes
and
inland
anglers
a
WTP
values
from
the
original
study
are
adjusted
to
2001$
based
on
CPI.

Source:
Phaneuf
et
al,
1998.

The
Agency
selected
only
one
value
for
use
in
benefits
transfer
for
the
following
reasons:

 
Environmental
quality
change.
Only
one
policy
scenario
evaluated
in
this
study
a
20
percent
reduction
in
the
toxin
levels
in
fish
tissue
is
compatible
with
the
water
quality
changes
expected
from
the
MP&
M
regulation
(
i.
e.,

removal
of
aquatic
life­
based
AWQC
exceedances.
The
second
scenario
loss
of
South
Lake
Michigan
fishing
sites
is
irrelevant
to
the
final
regulation.

 
Research
methods.
Phaneuf
et
al.
estimated
four
different
models
and
provided
WTP
estimates
based
on
each
of
them.
The
authors
indicated,
however,
that
"
the
KT
model
comes
closest
to
matching
the
ideal
theoretical
model"

(
see
authors
conclusions,
page
1030).
Other
models
either
rely
on
more
restrictive
assumptions
or
require
additional
research.
The
Agency
chose
the
value
from
the
KT
model
based
on
the
authors 
recommendation,
which
is
one
of
the
selection
criteria
for
values
used
in
benefits
transfer.

K­
9
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
GLOSSARY
ambient
water
quality
criteria
(
AWQC):
Levels
of
water
quality
expected
to
render
a
body
of
water
suitable
for
its
designated
use.
Criteria
are
based
on
specific
levels
of
pollutants
that
would
make
the
water
harmful
if
used
for
drinking,

swimming,
farming,
fish
production,
or
industrial
processes.
(
http://
www.
epa.
gov/
OCEPAterms/
aterms.
html)

binary
choice
(
BC):
offers
respondents
to
a
contingent
valuation
survey
specific
dollars
and
cents
choices,
for
example,

 
Would
you
be
willing
to
pay
between
$
10
and
$
20
per
year
to
improve
visibility
at
the
Grand
Canyon? 

conjoint
analysis:
"
any
decompositional
method
that
estimates
the
structure
of
consumer's
preferences
given
his
or
her
overall
evaluations
of
a
set
of
alternatives
that
are
prespecified
in
terms
of
levels
of
different
attributes.
Price
typically
is
included
as
an
attribute."
(
Green
and
Srinivasan,
1990).

contingent
valuation
(
CV):
a
method
used
to
determine
a
value
for
a
particular
event,
where
people
are
asked
what
they
are
willing
to
pay
for
a
benefit
and/
or
are
willing
to
receive
in
compensation
for
tolerating
a
cost.
Personal
valuations
for
increases
or
decreases
in
the
quantity
of
some
good
are
obtained
contingent
upon
a
hypothetical
market.
The
aim
is
to
elicit
valuations
or
bids
that
are
close
to
what
would
be
revealed
if
an
actual
market
existed.

(
http://
www.
damagevaluation.
com/
glossary.
htm)

corner
solutions:
a
corner
solution
arises
when
a
consumer
who
has
a
choice
of
two
goods,
x1
and
x2,
chooses
to
consume
no
x1
at
the
utility
maximum.

direct
question/
open­
ended
(
OE):
in
the
OE
approach,
respondents
are
asked
the
most
they
would
be
willing
to
pay
for
the
program
or
policy.
This
approach
has
a
virtue
of
not
providing
any
hints
about
what
might
be
a
reasonable
value.
This
approach,
however,
confronts
respondents
with
an
unfamiliar
choice
(
i.
e.,
placing
a
price
on
environmental
commodities).

Studies
that
use
the
OE
approach
have
high
item
non­
response
rates.

fish
consumption
advisory
(
FCA):
an
official
notification
to
the
public
about
specific
areas
where
fish
tissue
samples
have
been
found
to
be
contaminated
by
toxic
chemicals
which
exceed
FDA
action
limits
or
other
accepted
guidelines.

Advisories
may
be
species
specific
or
community
wide.

intensity
of
preference
(
IP):
an
experimental
design
that
allows
individuals
to
state
an
intensity
of
preferences
for
or
against
the
alternative
to
the
status
quo.
For
example,
the
individual
designates
they
would
"
probably
yes"
or
"
definitely
yes"

prefer
the
alternative
to
the
status
quo.

iterative
bidding
(
IB):
with
IB,
respondents
are
asked
whether
they
would
be
WTP
a
given
amount.
If
the
answer
is
yes,

this
amount
is
raised
in
pre­
set
increments
until
the
respondent
says
that
he
or
she
will
not
pay
the
last
amount
given.
If
the
answer
is
no,
then
the
amount
is
decreased
until
the
respondent
indicates
a
willingness­
to­
pay
the
stated
amount.

starting
point
bias:
when
survey
interviewers
suggest
a
first
bid
this
can
influence
the
respondent s
answer
and
cause
the
respondent
to
agree
too
readily
with
bids
in
the
vicinity
of
the
initial
bid.
(
http://
www.
damagevaluation.
com/
glossary.
htm)

travel
cost
(
TC):
method
to
determine
the
value
of
an
event
by
evaluating
expenditures
of
participants.
Travel
costs
are
used
as
a
proxy
for
price
in
deriving
demand
curves
for
a
recreation
site.
(
http://
www.
damagevaluation.
com/
glossary.
htm)

willingness­
to­
pay
(
WTP):
maximum
amount
of
money
one
would
be
willing
to
pay
or
give
up
to
buy
some
good.

(
http://
www.
damagevaluation.
com/
glossary.
htm)

K­
10
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
ACRONYMS
AWQC:
ambient
water
quality
criteria
BC:
binary
choice
CV:
contingent
valuation
FCA:
fish
consumption
advisory
IB:
iterative
bidding 

IP:
intensity
of
preference
TC:
travel
cost
WTP:
willingness­
to­
pay
K­
11
MP&
M
EEBA:
Appendices
Appendix
K:
Selecting
WTP
Values
for
Benefits
Transfer
REFERENCES
Boyle,
K.
J.
and
J.
C
Bergstrom.
1992.
 
Benefit
Transfer
Studies:
Myths,
Pragmatism
and
Idealism. 
Water
Resources
Research,
Vol.
28,
No.
3,
March:
657­
663.

Desvousges,
W.
H.
et
al.
1987.
 
Option
Price
Estimates
for
Water
Quality
Improvements:
A
Contingent
Valuation
Study
for
the
Monongahela
River. 
Journal
of
Environmental
Economics
and
Management,
14:
248­
267.

Desvousges,
W.
H.
et
al.
1992.
 
Benefit
Transfer:
Conceptual
Problems
in
Estimating
Water
Quality
Benefits
Using
Existing
Studies. 
Water
Resources
Research,
Vol.
28,
No.
3,
March:
675­
683.

Farber,
S.
and
B.
Griner.
2000.
Valuing
Watershed
Quality
Improvements
Using
Conjoint
Analysis.
University
of
Pittsburgh,
PA.

Jakus,
P.
M.,
M.
Downing,
M.
S.
Bevelhimer,
and
J.
M.
Fly.
1997.
 
Do
Sportfish
Consumption
Advisories
Affect
Reservoir
Anglers 
Site
Choice? 
Agricultural
and
Resource
Economic
Review,
26(
2).

Lant,
C.
L.
and
R.
S.
Roberts.
1990.
 
Greenbelts
in
the
Cornbelt:
Riparian
Wetlands,
Intrinsic
Values,
and
Market
Failure. 

Environment
and
Planning
A,
Vol.
22:
1375­
1388.

Lyke,
A.
J.
1993.
Discrete
Choice
Models
to
Value
Changes
in
Environmental
Quality:
A
Great
Lakes
Case
Study.
PhD
dissertation,
Madison,
WI:
University
of
Wisconsin,
Department
of
Agricultural
Economics.

Montgomery,
M.
and
M.
Needelman.
1997.
 
The
Welfare
Effects
of
Toxic
Contamination
in
Freshwater
Fish. 
Land
Economics
73(
2):
211­
223.

Phaneuf,
D.
J.,
C.
L.
Kling,
and
J.
A.
Herriges.
1998.
 
Valuing
Water
Quality
Improvements
Using
Revealed
Preference
Methods
When
Corner
Solutions
Are
Present. 
American
Journal
of
Agricultural
Economics,
80:
1025­
1031.

Tudor,
L.,
E.
Besedin,
M.
Fisher,
S.
Smith,
and
L.
Snyder.
1999.
What
Pollutants
Matter
for
Consumers
of
Water­
Based
Recreation?
Presented
at
the
annual
meeting
of
the
Northeastern
Agricultural
and
Resource
Economics
Association.

Morgantown,
WV.
June
1999.

Tudor,
L.,
E.
Besedin,
M.
Fisher,
and
S.
Smith.
1999.
Economic
Analysis
of
Environmental
Regulations:
Application
of
the
RUM
model
to
Ecological
Benefits
Assessment
for
MP&
M
Effluent
Guideline
Limitations.
Presented
at
the
annual
meeting
of
American
Agricultural
Economics
Association.
Nashville,
TN.
August
1999.

K­
12
MP&
M
EEBA:
Appendices
Appendix
L:
Parameters
Used
in
the
IEUBK
Model
Appendix
L:
Parameters
Used
in
the
IEUBK
Model
INTRODUCTION
APPENDIX
CONTENTS
Table
B.
1:
Description
of
Parameters
Used
In
the
IEUBK
Lead
Model
This
appendix
contains
a
comprehensive
list
of
model
parameters
that
are
used
in
the
IEUBK
model
for
lead
in
children.

The
remainder
of
this
appendix
is
a
reproduction
of
Appendix
B:
Description
of
Parameters
In
the
IEUBK
Lead
Model,
taken
from
the
Technical
Support
Document
for
the
Integrated
Exposure
Uptake
Biokinetic
Model
for
Lead
in
Children
(
v0.99d)

(
December
1994).

L­
1
APPENDIX
B:
DESCRIPTION
OF
PARAMETERS
IN
THE
IEUBK
LEAD
MODEL
TABLE
B­
1.
DESCRIPTION
OF
PARAMETERS
IN
THE
IEUBK
LEAD
MODEL
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
ABSD
Total
absorption
for
dust
at
low
saturation
0.3
0­
84
E
Based
on
US
EPA
(
1989a).
unitless
U­
1c,

U­
2
ABSF
Total
absorption
for
food
at
low
saturation
0.5
0­
84
E
Based
on
US
EPA
(
1989a).
unitless
U­
1a,
U­
2
ABSO
Total
absorption
for
other
ingested
lead
at
low
saturation
0.0
0­
84
E
Based
on
the
default
condition
that
there
is
no
other
source
of
lead
ingestion
in
the
household.
unitless
U­
1d,
U­
2
ABSS
Total
absorption
for
soil
at
low
saturation
0.3
0­
84
E
Based
on
US
EPA
(
1989a).
unitless
U­
1e,
U­
2
ABSW
Total
absorption
for
water
at
low
saturation
0.5
0­
84
E
Based
on
US
EPA
(
1989a).
unitless
U­
1b,
U­
2
air_
absorb(
t)
Net
percentage
absorption
of
air
lead
32
32
32
32
32
32
32
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Deposition
efficiencies
of
airborne
lead
particles
were
estimated
by
U
S
EPA
(
1989a).
A
respiratory
deposition/
absorption
rate
of
25%
to
45%
is
reported
for
young
children
living
in
non­
point
source
areas
while
a
rate
of
42%
is
calculated
for
those
living
near
point
sources.
An
intermediate
value
of
32%
was
chosen.
%
U­
4
air_
concentration(
t)
Outdoor
air
lead
concentration
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Based
on
the
lower
end
of
the
range
0.1
­
0.3
µ
g
Pb/
m3
that
is
reported
for
outdoor
air
lead
concentration
in
U.
S.
cities
without
lead
point
sources
(
US
EPA
1989)
µ
g/
m3
E­
1,2,11
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
2 
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
AVF,
AVW,
AVD,
AVO,

AVS
Bioavailability
1
0­
84
I
Parameter
added
for
later
flexibility
in
describing
the
absorption
process;

has
no
effect
in
current
algorithm.
unitless
U­
1a­
U­
1e
AVINTAKE
Available
intake
U­
2
0­
84
I
The
amount
of
Pb
that
is
available
for
intake
µ
g
U­
1a,
b,
c,
d,
e
can_
fruit(
t)
Lead
intake
from
canned
fruit
when
fruit
is
consumed
only
in
canned
form
1.811
1.063
1.058
0.999
0.940
0.969
1.027
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
5d
can_
veg(
t)
Lead
intake
from
canned
vegetables
when
vegetable
is
consumed
only
in
canned
form
0.074
0.252
0.284
0.295
0.307
0.291
0.261
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
5b
contrib_
percent
Ratio
of
indoor
dust
lead
concentration
to
soil
lead
concentration
0.70
0­
84
E
Analysis
of
soil
and
dust
data
from
1983
East
Helena
study
(
US
EPA,
1989)
g/
g
per
g/
g
E­
11
CONRBC
Maximum
lead
concentration
capacity
of
red
blood
cells
1200
0­
84
I
Based
on
Marcus
(
1983)
reanalysis
of
infant
baboon
data
from
Mallon
(
1983).
See
Marcus
(
1985a)
for
assessment
of
form
of
relationship
and
estimates
from
data
on
human
adults
[
data
from
deSilva
(
1981a,
b),
Manton
and
Malloy
(
1983),
and
Manton
and
Cook
(
1984)]
and
infant
and
juvenile
baboons
(
Mallon,
1983).
µ
g/
dL
B­
2.5
constant_
soil_
conc(
t)
Soil
lead
concentration
200
200
200
200
200
200
200
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Air
Quality
Criteria
Document
for
Lead.
(
US
EPA,
1986)
µ
g/
g
E­
8
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
3
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
constant_
water_
conc
Water
lead
concentration
4.0
0­
84
E
Based
on
analysis
of
data
from
the
American
Water
Works
Service
Co.

(
Marcus,
1989)
µ
g/
L
E­
6a
CRBONEBL(
t)
Ratio
of
lead
concentration
(
µ
g/
kg)
in
bone
to
blood
lead
concentration
(
µ
g/
L)
B­
4c
0­
84
I
Data
in
Barry
(
1981)
were
used.

Bone
lead
concentration
was
calculated
as
an
arithmetic
average
of
the
concentrations
in
the
rib,
tibia,
and
calvaria.
The
blood
lead
concentrations
were
taken
directly
from
the
study.

Concentrations
in
each
of
the
following
eight
age
groups
were
considered:

stillbirths,
0­
12
days,
1­
11
mos,
1­
5
yrs,
6­
9
yrs,
11­
16
yrs,
adult
(
men),
and
adult
(
women).
Ages
0
and
40
yrs
were
assumed
for
stillbirths
and
adults,

respectively.
L/
kg
B­
1h
CRKIDBL(
t)
Ratio
of
lead
concentration
(
µ
g/
kg)
in
kidney
to
blood
lead
concentration
(
µ
gL)
B­
4a
0­
84
I
Data
in
Barry
(
1981)
were
used.

Lead
concentrations
in
kidney
(
combined
values
for
cortex
and
medulla)

and
blood
were
taken
directly
from
the
study.

Concentrations
in
each
of
the
following
eight
age
groups
were
considered:

stillbirths,
0­
12
days,
1­
11
mos,
1­
5
yrs,
6­
9
yrs,
11­
16
yrs,
adult
(
men),
and
adult
(
women).
Ages
0
and
40
yrs
were
assumed
for
stillbirths
and
adults,

respectively.
L/
kg
B­
2h
CRLIVBL(
t)
Ratio
of
lead
concentration
(
µ
g/
kg)
in
liver
to
blood
lead
concentration
(
µ
g/
l)
B­
4b
0­
84
I
Data
in
Barry
(
1981)
were
used.

Lead
concentrations
in
liver
and
blood
were
taken
directly
from
the
study.

Concentrations
in
each
of
the
following
eight
age
groups
were
considered:

stillbirths,
0­
12
days,
1­
11
mos,
1­
5
yrs,
6­
9
yrs,
11­
16
yrs,
adult
(
men),
and
adult
(
women).
Ages
0
and
40
yrs
were
assumed
for
stillbirths
and
adults,

respectively.
L/
kg
B­
2e,
2f
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
4
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
CROTHBL(
t)
Ratio
of
lead
concentration
(
µ
g/
kg)
in
other
soft
tissue
to
blood
lead
concentration
(
µ
g/
L)
B­
4d
0­
84
I
Data
in
Barry
(
1981)
were
used.

Lead
concentration
ratio
for
soft
tissues
was
calculated
as
a
weighted
arithmetic
average
of
concentration
ratios
for
muscle
(
53.8%),
fat
(
24.0%),

skin
(
9.4%),
dense
connective
tissue
(
4.4%),
brain
(
2.7%),
GI
tract
(
2.3%),

lung
(
1.9%),
heart
(
0.7%),
spleen
(
0.3%),
pancreas
(
0.2%),
and
aorta
(
0.2%),
where
the
weights
applied
are
given
in
parentheses.
The
weight
associated
with
each
soft
tissue
component
was
equal
to
the
weight
of
the
component
(
kg)
divided
by
weight
of
all
soft
tissues
(
kg).
These
weights
were
estimated
from
Schroeder
and
Tipton
(
1968)
and
are
assumed
to
apply
in
the
range
0­
84
months
of
age.

Concentrations
in
each
of
the
following
eight
age
groups
were
considered:

stillbirths,
0­
12
days,
1­
11
mos,
1­
5
yrs,
6­
9
yrs,
11­
16
yrs,
adult
(
men),
and
adult
(
women).
Ages
0
and
40
yrs
were
assumed
for
stillbirths
and
adults,

respectively.
L/
kg
B­
2n,
2o
DAYCARE(
t)
Dust
lead
intake
at
daycare
E­
12c
0­
84
I
Simple
combination
of
the
total
amount
of
dust
ingested
daily,
fraction
of
total
dust
ingested
as
daycare
dust,
and
dust
lead
concentration
at
daycare.
µ
g/
day
E­
9d
DaycareConc
Dust
lead
concentration
at
daycare
200
0­
84
E
Based
on
the
assumption
that
default
daycare
dust
concentrations
are
the
same
as
default
residence
dust
concentrations.
µ
g/
g
E­
12c
DaycareFraction
Fraction
of
total
dust
ingested
daily
as
daycare
dust
0
0­
84
E
Based
on
the
default
assumption
that
the
child
does
not
attend
daycare.
unitless
E­
9.5,12c
diet_
intake(
t)
User­
specified
diet
lead
intake
5.53
5.78
6.49
6.24
6.01
6.34
7.00
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
4a
DietTotal(
t)
Total
Dietary
Intake
E­
4b
0.84
I
Summation
of
all
dietary
sources;
same
as
INDIET(
t)
µ
g/
day
E­
4b
DustTotal(
t)
Daily
amount
of
dust
ingested
E­
10
0­
84
I
Simple
combination
of
total
amount
soil
and
dust
ingested
daily
and
fraction
of
this
combined
ingestion
that
is
dust
alone.
g/
day
E­
9c,
12a­

12e
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
5
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
EXAIR(
t)
Air
lead
intake
E­
3
0­
84
I
Simple
combination
of
average
air
lead
concentration
and
ventilation
rate.
µ
g/
day
U­
4
f_
fruit(
t)
Lead
intake
from
fresh
fruit
if
no
home­
grown
fruit
is
consumed
0.039
0.196
0.175
0.175
0.179
0.203
0.251
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
5e
f_
veg(
t)
Lead
intake
from
fresh
vegetables
if
no
home­
grown
vegetables
are
consumed
0.148
0.269
0.475
0.466
0.456
0.492
0.563
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
5c
FirstDrawConc
First
Draw
water
lead
concentration
4.0
0­
84
E
Based
on
analysis
of
data
from
the
American
Water
Works
Service
Co.

(
Marcus,
1989)
µ
g/
L
E­
6b
FirstDrawFraction
Fraction
of
total
water
consumed
daily
as
first
draw
0.5
0­
84
E
In
the
absence
of
appropriate
data,
a
conservative
value
corresponding
to
consumption
largely
after
four
fours
stagnation
time
was
used,
e.
g.
early
morning
or
late
afternoon.
unitless
E­
6b,
7
FountainConc
Fountain
water
lead
concentration
10
0­
84
E
Default
assumption
is
that
the
drinking
fountain
has
a
lead­
lined
reservoir,

but
that
consumption
is
not
always
first
draw.
Therefore,
a
value
was
selected
from
the
range
of
5­
25
g/
L.
µ
g/
L
E­
6b
FountainFraction
Fraction
of
total
water
consumed
daily
from
fountains
0.15
0­
84
E
A
default
value
was
based
on
4­
6
trips
to
the
water
fountain
at
40­
50
ml
per
trip.
none
E­
6b,
7
fruit_
all(
t)
Daily
amount
of
all
frults
consumed
38.481
169.000
63.166
61.672
61.848
67.907
80.024
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
g/
day
E­
5f
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
6
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
HomeFlushedConc
Home
flushed
water
lead
concentration
1.0
0­
84
E
Based
on
analysis
of
data
from
the
American
Water
Works
Service
Co.

(
Marcus,
1989)
µ
g/
L
E­
6b
HCT0
Hematocrit
at
birth
0.45
0
I
Data
from
Silve
et
al.
(
1987);
also
Spector
(
1956)
and
Altman
and
Ditmer
(
1973)
decimal
percent
B­
7b,
d
InCanFruit(
t)
Lead
intake
from
canned
fruit
E­
5d
0­
84
I
Simple
combination
of
the
fraction
of
non­
home
grown
fruits
consumed
daily,
and
lead
intake
from
canned
fruits
when
fruits
are
consumed
only
in
canned
form.
µ
g/
day
E­
4b
InCanVeg(
t)
Lead
intake
from
canned
vegetables
E­
5b
0­
84
I
Simple
combination
of
the
fraction
of
vegetables
consumed
daily
as
non­

home
grown,
and
lead
intake
from
canned
vegetables
when
vegetables
are
consumed
only
in
canned
form.
µ
g/
day
E­
4b
INDIET(
t)
Diet
lead
intake
E­
4a
or
E­
4b
0­
84
I
Two
options
are
provided.

Default
option
­
Considers
composite
diet
lead
intake.

Alternate
option
­
Combines
lead
intake
from
several
individual
components
of
diet.
µ
g/
day
U­
1a,
U­
2
IndoorConc(
t)
Indoor
air
lead
concentration
E­
1
0­
84
I
Algebraic
expression
of
relationship
µ
g/
m3
E­
2
indoorpercent
Ratio
of
indoor
dust
lead
concentration
to
corresponding
outdoor
concentration
30
0­
84
E
Based
on
homes
near
lead
point
sources.
The
default
value
is
reported
in
OAQPS
(
USEPA
1989,
pp
A­
1)
and
is
estimated
by
Cohen
and
Cohen
(
1980).
%
E­
1
INDUST(
t)
Household
dust
lead
intake
E­
9a
or
E­
9c
0­
84
I
Two
options
are
provided.

Default
option
­
Assumes
that
all
dust
lead
exposure
is
from
the
household.

Alternate
option
­
Considers
dust
lead
exposure
from
several
alternative
sources
as
well.
µ
g/
day
U­
1­
c,
U­
2
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
7
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
INDUSTA(
t)
Lead
intake
from
alternate
dust
sources
E­
9b
or
E­
9d
0­
84
I
Two
options
are
provided.

Default
option
­
Assumes
that
lead
intake
from
alternate
sources
is
zero.

Alternate
option
­
Combines
lead
intake
from
several
alternate
sources.
µ
g/
day
U­
1.5c,
U­
2
InFish(
t)
Lead
intake
from
fish
E­
5h
0­
84
I
Simple
combination
of
total
meat
consumed
daily,
fraction
of
meat
consumed
as
fish,
and
lead
concentration
in
fish.
µ
g/
day
E­
4b
InFrFruit(
t)
Lead
intake
from
non­
home
grown
fresh
fruits
E­
5e
0­
84
I
Simple
combination
of
the
fraction
of
fruits
consumed
daily
as
non­
home
grown
and
lead
intake
from
fresh
fruits.
µ
g/
day
E­
4b
InFrVeg(
t)
Lead
intake
from
non­
home
grown
fresh
vegetables
E­
5c
0­
84
I
Simple
combination
of
the
fraction
of
vegetables
consumed
daily
as
non­

home
grown
and
lead
intake
from
fresh
vegetables.
µ
g/
day
E­
4b
InGame(
t)
Lead
intake
from
game
animal
meat
E­
5i
0­
84
I
Simple
combination
of
total
meat
consumed
daily,
fraction
of
meat
consumed
as
game
animal
meat,
and
lead
concentration
in
game
animal
meat.
µ
g/
day
E­
4b
InHomeFruit(
t)
Lead
intake
from
home
grown
fruits
E­
5f
0­
84
I
Simple
combination
of
total
amount
of
fruit
consumed
daily,
fraction
of
fruit
consumed
as
home
grown,
and
lead
concentration
in
home
grown
fruit.
µ
g/
day
E­
4b
InHomeVeg(
t)
Lead
intake
from
home
grown
vegetables
E­
5g
0­
84
I
Simple
combination
of
total
amount
of
vegetable
consumed
daily,
fraction
of
vegetables
consumed
as
home
grown,
and
lead
concentration
in
home
grown
vegetables.
µ
g/
day
E­
4b
InMeat(
t)
Lead
intake
from
non­
game
and
non­
fish
meat
E­
5a
0­
84
I
Simple
combination
of
total
amount
of
meat
consumed
daily,
fraction
of
meat
consumed
as
non­
game
and
non­
fish
meat,
and
lead
concentration
in
non­
game
and
non­
fish
meat.
µ
g/
day
E­
4b
InOtherDiet(
t)
Combined
lead
intake
from
dairy
food,
juice,
nuts,

beverage,
pasta,

bread,
sauce,

candy,
infant
and
formula
food
3.578
3.506
3.990
3.765
3.545
3.784
4.215
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Sum
of
the
amounts
of
lead
ingested
in
food
items
not
substituted
by
the
calculation
of
exposure
to
lead
in
home
grown
fruits
and
vegetables,
wild
game
or
fish.
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).
Quantity
consumed
from
Pennington
(
1983).
µ
g/
day
E­
4b,
E­
4c
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
8
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
INOTHER(
t)
Combined
other
sources
of
ingested
lead,

such
as
paint
chips,
ethnic
medicines,
etc.
0
0­
84
E
Assumes
no
other
sources
of
ingested
lead
g/
day
U­
1d,
U­
2
INSOIL(
t)
Soil
lead
intake
E­
8
0­
84
I
Simple
combination
of
total
amount
of
soil
and
dust
ingested
daily,
fraction
of
this
combined
ingestion
that
is
soil
alone,
and
lead
concentration
in
soil.
µ
g/
day
U­
1e,
U­
2
INWATER(
t)
Water
lead
intake
E­
6a
or
E­
6b
0­
84
I
Two
options
are
provided.

Default
option
­
Simple
combination
of
water
consumed
daily
and
a
constant
water
lead
concentration.

Alternate
option
­
Water
lead
concentration
depends
on
contribution
from
several
individual
sources
of
water.
µ
g/
day
U­
1b,
U­
2
MCORT(
t)
Mass
of
lead
in
cortical
bone
B­
7e
and
B­
9f
0
and
0­
84
I
0
months
­
Simple
combination
of
an
assumed
bone
to
blood
lead
concentration
ratio,
blood
lead
concentration,
and
weight
of
cortical
bone.

Basis
for
value
of
bone
to
blood
lead
concentration
ratio
was
human
autopsy
data
(
Barry,
1981).

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).

Both
cases
above
assume
that
the
cortical
bone
to
blood
lead
concentration
ratio
is
equal
to
the
bone
(
composite)
to
blood
lead
concentration
ratio.
µ
g
B­
6b,
6i,
6.5b,

6.5i,
8a,
9f
meat_
all(
t)
Daily
amount
of
meat
(
including
fish
and
game)

consumed
29.551
87.477
95.700
101.570
107.441
111.948
120.961
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
g/
day
E­
5h
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
9
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
meat(
t)
Lead
intake
from
meat
if
no
game
meat
or
fish
is
consumed
0.226
0.630
0.811
0.871
0.931
1.008
1.161
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
µ
g
/
day
E­
5a
MKIDNEY(
t)
Mass
of
lead
in
kidney
B­
7f
and
B­
9c
0
and
0­
84
I
0
months
­
Simple
combination
of
an
assumed
kidney
to
blood
lead
concentration
ratio,
blood
lead
concentration,
and
weight
of
kidney.
Basis
for
the
value
of
the
kidney
to
blood
lead
concentration
ratio
was
human
autopsy
data
(
Barry,
1981).

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).
µ
g
B­

6b,
6f,
6.5b,
6.

5f,
8d,
9c
MLIVER(
t)
Mass
of
lead
in
liver
B­
7g
and
B­
9b
0
and
0­
84
I
0
months
­
Simple
combination
of
an
assumed
liver
to
blood
lead
concentration
ratio,
blood
lead
concentration,
and
weight
of
the
liver.
Basis
for
the
value
of
the
liver
to
blood
lead
concentration
ratio
was
human
autopsy
data
(
Barry,
1981).

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).
µ
g
B­

6b,
6e,
6.5b,
6.

5e,
8d,
9b
MOTHER(
t)
Mass
of
lead
in
soft
tissues
B­
7h
and
B­
9d
0
and
0­
84
I
0
months
­
Simple
combination
of
an
assumed
soft
tissue
to
blood
lead
concentration
ratio,
blood
lead
concentration,
and
weight
of
the
soft
tissues
at
birth.
Basis
for
the
value
of
soft
tissue
to
blood
lead
concentration
ratio
was
human
autopsy
data
(
Barry
et
al.,
1981),
using
total
lead
and
total
weight
of
other
tissue.

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).
µ
g
B­

6b,
6g,
6.5b,
6.

5g,
8d,
9d
MPLASM(
t)
Mass
of
lead
in
plasma
pool
B­
7d
and
B­
9g
0
and
0­
84
I
0
months
­
Simple
combination
of
the
mass
of
lead
in
blood
and
red
blood
cells.

0­
84
months
­
Based
on
the
assumption
that
the
lead
concentration
in
plasma­
ECF
is
equal
to
the
lead
concentration
in
the
plasma.
µ
g
B­
10a
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
10
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
MPLECF(
t)
Mass
of
lead
in
plasma­
extra­

cellular
fluid
(
plasma­
ECF)
B­
7b
and
B­
8a
0
and
0­
84
I
0
months
­
Based
on
two
assumptions.

(
1)
masses
of
lead
in
plasma­
ECF
and
red
blood
cells
are
in
kinetic
quasi­

equilibrium,
and
(
2)
lead
concentration
in
the
plasma­
ECF
is
equal
to
lead
concentration
in
the
plasma.

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).
µ
g
B­
6a,
6c­

6i,
6.5a,

6.5c­

6.5i,
8a,
9a­
9g
MRBC(
t)
Mass
of
lead
in
red
blood
cells
B­
7c
and
B­
9a
0
and
0­
84
I
0
months
­
Based
on
the
assumption
that
the
masses
of
lead
in
plasma­

ECF
and
red
blood
cells
are
in
kinetic
quasi­
equilibrium.

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).
µ
g
B­

6a,
6d,
6.5a,
6.

5d,
8d,
9a,
10a
MTRAB(
t)
Mass
of
lead
in
trabecular
bone
B­
7i
and
B­
9e
0
and
0­
84
I
0
months
­
Simple
combination
of
an
assumed
bone
to
blood
lead
concentration
ratio,
blood
lead
concentration,
and
weight
of
trabecular
bone.
Basis
for
the
value
of
bone
to
blood
lead
concentration
ratio
was
human
autopsy
data
(
Barry,
1981).

0­
84
months
­
Application
of
the
Backward
Euler
solution
algorithm
to
the
system
of
differential
equations
(
B­
6a­
B­
6i
in
Table
A­
3).

Both
cases
above
assume
that
trabecular
bone
to
blood
lead
concentration
ratio
is
equal
to
bone
(
composite)
to
blood
lead
concentration
ratio.
µ
g
B­

6b,
6h,
6.5b,
6.

5h,
8d,
9e
multiply_
factor
Ratio
of
indoor
dust
lead
concentration
to
air
lead
concentration
100
0­
84
E
Analyses
of
the
1983
East
Helena
study
in
(
USEPA
1989,
Appendix
B­
8)

suggest
about
267
µ
g/
g
increment
of
lead
in
dust
for
each
µ
g
/
m
³
.
lead
in
air.
A
much
smaller
factor
of
100
µ
g/
g
PbD
per
µ
g/
m
³
is
assumed
for
non­

smelter
community
exposure.
µ
g
/
g
per
µ
g/
m3
E­
11
OCCUP(
t)
Dust
lead
intake
from
secondary
occupation
E­
12a
0­
84
I
Simple
combination
of
amount
of
dust
ingested,
fraction
of
the
total
dust
ingested
as
secondary
occupational
dust,
and
lead
concentration
in
secondary
occupational
dust
µ
g/
day
E­
9d
OccupConc
Secondary
occupational
dust
lead
concentration
1200
0­
84
E
Air
Quality
Criteria
Document
for
Lead.
(
US
EPA,
1986)
µ
g/
g
E­
12a
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
11
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
OccupFraction
Fraction
of
total
dust
ingested
as
secondary
occupation
dust
0
0­
84
E
The
default
condition
is
that
there
is
no
adult
in
the
residence
who
works
at
a
lead­
related
job.
unitless
E­
9.5,12a
PAINT(
t)
Dust
lead
intake
from
lead
based
home
paint
E­
12e
0­
84
I
Simple
combination
of
amount
of
dust
ingested
daily,
fraction
of
the
total
dust
ingested
as
lead­
based
home
paint,
and
lead
concentration
in
lead­

based
home
paint.
µ
g/
day
E­
9d
PaintConc
Leadconcentration
in
housedust
containing
lead
based
paint
1200
0­
84
E
Air
Quality
Criteria
Document
for
Lead.
(
US
EPA,
1986)
µ
g/
g
E­
12e
PAF
Fraction
of
total
absorption
as
passive
absorption
at
low
dose
0.20
0­
84
E
Based
on
in
vitro
everted
rat
intestine
data
(
Aungst
and
Fung,
1981),

reanalyses
(
Marcus,
1994)
of
infant
baboon
data
(
Mallon,
1983)
and
infant
duplicate
diet
study
(
Sherlock
and
Quinn,
1986)
unitless
U­
1a
thru
U­

1f
PaintFraction
Fraction
of
total
dust
ingested
that
results
from
lead
based
home
paint
0
0­
84
E
The
default
is
that
there
is
no
lead­
based
paint
in
the
home.
unitless
E­
12e
PBBLDMAT
Maternal
blood
lead
concentration
2.5
adult
E
Based
in
part
on
Midvale
1989
study.
The
default
value
of
2.5
g/
dL
has
little
influence
of
the
early
post
natal
exposure
of
the
child.
µ
g/
dL
B­
7a
PBBLD0
Lead
concen

tration
in
blood
B­
7a
0
I
Based
on
85%
of
maternal
blood
lead
concentration
(
US
EPA
1989)
µ
g/
dL
B­
7b,
7c,
7e­

7i
PBBLOODEND(
t)
Lead
concen

tration
in
blood
B­
10a
0­
84
I
Simple
combination
of
the
blood
lead
concentrations
determined
in
each
iteration
in
the
solution
algorithm
between
the
previous
month
and
that
month.
µ
g/
dL
B­
10c
RATBLPL
Ratio
of
lead
mass
in
blood
to
lead
mass
in
plasma­

ECF
100
0­
84
I
Based
on
the
lower
end
of
the
50­
500
range
for
the
red
cell/
plasma
lead
concentration
ratio
recommended
in
Diamond
and
O'Flaherty
(
1992a).
unitless
B­
2b­

2d,
2g,
2i,
2k,
2
m
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
12
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
RATFECUR
Ratio
of
endogenous
fecal
lead
elimination
rate
to
urinary
lead
elimination
rate
0.75
0­
84
I
Assume
child
ratio
is
larger
than
the
adult
ratio;
values
derived
from
a
reanalysis
of
data
from
Ziegler
et
al.
(
1978)
and
Rabinowitz
and
Wetherill
(
1973).
unitless
B­
1f
RATOUTFEC
Ratio
of
elimination
rate
via
soft
tissues
to
endogenous
fecal
lead
elimination
rate
0.75
0­
84
I
Within
the
range
of
values
derived
from
a
reanalysis
of
data
from
Ziegler
et
al.
(
1978)
and
Rabinowitz
and
Wetherill
(
1973).
unitless
B­
1g
SATINTAKE(
t)
Half
saturation
absorbable
lead
intake
U­
3
0­
84
I
Assumed
proportional
to
the
weight
of
body
.
The
coefficient
of
proportionality
is
assumed
to
depend
on
the
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
weight.
µ
g/
day
U­
1a
thru
U­

1e
SATINTAKE24
Half
saturation
absorbable
lead
intake
for
a
24
month
old
100
0­
84
E
Extrapolated
from
reanalysis
of
human
infant
data
(
Sherlock
and
Quinn,

1986)
and
infant
baboon
data
(
Mallon,
1983)
µ
g/
day
U­
3
SCHOOL(
t)
Dust
lead
intake
from
school
E­
12b
0­
84
I
Simple
combination
of
amount
of
dust
ingested
daily,
the
fraction
of
total
dust
ingested
daily
as
school
dust,
and
lead
concentration
in
dust
at
school
µ
g/
day
E­
9d
SchoolConc
Dust
lead
concentration
at
school
200
0­
84
E
By
default,
this
dust
lead
concentration
is
set
to
the
same
as
the
residential
dust
lead
concentration.
µ
g/
g
E­
12b
SchoolFraction
Fraction
of
total
dust
ingested
daily
as
school
dust
0
0­
84
E
Based
on
the
default
assumption
that
children
are
not
in
school.
unitless
E­
9c,
E­

9.5,12b
SECHOME(
t)
Dust
lead
intake
at
secondary
home
E­
12d
0­
84
I
Simple
combination
of
amount
of
dust
ingested
daily,
fraction
of
dust
ingested
daily
as
secondary
home
dust,
and
lead
concentration
in
dust
at
the
secondary
home.
µ
g/
day
E­
9d
SecHomeConc
Secondary
home
dust
lead
concentration
200
0­
84
E
Based
on
the
assumption
that
dust
lead
concentration
in
a
secondary
home
is
the
same
as
the
default
dust
lead
concentration
in
the
primary
home.
µ
g/
g
E­
12d
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
13
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
SecHomeFraction
Fraction
of
total
dust
ingested
daily
as
secondary
home
dust
0
0­
84
E
Based
on
the
default
assumption
that
the
child
does
not
spend
a
significant
amount
of
time
in
a
secondary
home.
unitless
E­
9b,
12d
soil_
indoor(
t)
Indoor
household
dust
lead
concentration
E­
11
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Under
alternate
dust
sources
model,
based
on
assumption
that
both
soil
and
outdoor
air
contribute
to
indoor
dust
lead.
µ
g/
g
E­
9c
soil_
ingested(
t)
Soil
and
dust
(
combined)
consumption
0.085
0.135
0.135
0.135
0.100
0.090
0.085
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Based
on
values
reported
in
OAQPS
report
(
USEPA
1989,
pp.
A­
16).
The
values
reported
were
estimated
for
children,
ages
12­
48
mos,
by
several
authors
such
as
Binder
et
al.
(
1986)
and
Clausing
et
al.
(
1987).
Sedman
(
1987)
extrapolated
these
estimates
to
those
for
children,
ages
0­
84
mos.
g/
day
E­
8­
9a,
10
TBLBONE(
t)
Lead
transfer
time
from
blood
to
bone
1
and
B­
1e
24
and
0­
84
I
24
months
­
Initialization
is
keyed
to
the
two
year
old
child,
based
in
part
on
information
from
Heard
and
Chamberlain,
(
1982)
for
adults,
and
O'Flaherty
(
1992).
Once
the
concentration
ratios
are
fixed,
the
exact
value
of
this
parameter,
within
a
wide
range
of
possible
values,
has
little
effect
on
the
blood
lead
value.

0­
84
months
­
Assumed
proportional
body
surface
area.
The
coefficient
of
proportionality
is
assumed
to
depend
on
an
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
surface
area.
Also,
it
is
assumed
that
body
surface
area
varies
as
1/
3
power
of
the
weight
of
body
based
on
Mordenti
(
1986).
days
B­
1h,
2i,
2k
TBLFEC(
t)
Lead
transfer
time
from
blood
to
feces
B­
1f
0­
84
I
Simple
combination
of
an
assumed
ratio
of
urinary
lead
elimination
rate
to
endogenous
fecal
lead
elimination
rate,
and
lead
transfer
time
from
blood
to
urine
(
See
RATFECUR).

The
ratio
of
of
elimination
rates
was
estimated
for
adults
using
Chamberlain
et
al.
(
1978),
and
Chamberlain
(
1985)
and
is
assumed
to
apply
to
ages
0­
84
months.
days
B­
1g,
2e,
2f
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
14
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
TBLKID(
t)
Lead
transfer
time
from
blood
to
kidney
10
and
B­
1d
24
and
0­
84
I
24
months
­

on
information
from
Heard
and
Chamberlain,
(
1982)
for
adults,
and
O'Flaherty
(
1992).

of
this
parameter,
within
a
wide
range
of
possible
values,
has
little
effect
on
the
blood
lead
value.

0­
84
months
­
Assumed
proportional
body
surface
area.

proportionality
is
assumed
to
depend
on
an
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
surface
area.

assumed
that
body
surface
area
varies
as
1/
3
power
of
the
weight
of
body
based
on
(
Mordenti,
1986).
days
B­
2g,
2h
TBLLIV(
t)
Lead
transfer
time
from
blood
to
liver
10
and
B­
1b
24
and
0­
84
I
24
months
­

information
from
Heard
and
Chamberlain,
(
1982)
for
adults,
and
O'Flaherty
(
1992).
parameter,
within
a
wide
range
of
possible
values,
has
little
effect
on
the
blood
lead
value.

0­
84
months
­
Assumed
proportional
body
surface
area.

proportionality
is
assumed
to
depend
on
an
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
surface
area.

assumed
that
body
surface
area
varies
as
1/
3
power
of
the
weight
of
body
based
on
(
Mordenti,
1986).
days
B­
2d,
2e
TBLOTH(
t)
Lead
transfer
time
from
blood
to
other
soft
tissue
10
and
B­
1c
24
and
0­
84
I
24
months
­

on
information
from
Heard
and
Chamberlain,
(
1982)
for
adults,
and
O'Flaherty
(
1992).

of
this
parameter,
within
a
wide
range
of
possible
values,
has
little
effect
on
the
blood
lead
value.

0­
84
months
­
Assumed
proportional
body
surface
area.

proportionality
is
assumed
to
depend
on
an
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
surface
area.

that
body
surface
area
varies
as
1/
3
power
of
the
weight
of
body
based
on
(
Mordenti,
1986).
days
B­
2m,
2n
TBLOUT(
t)
Lead
transfer
time
from
blood
to
elimination
pool
via
soft
tissue
B­
1g
0­
84
I
Simple
combination
of
an
assumed
ratio
of
to
endogenous
fecal
lead
elimination
rate,
times
the
lead
transfer
time
from
blood
to
feces
(
See
RATOUTFEC).
days
B­
2n,
2o
Initialization
is
keyed
to
the
two
year
old
child,
based
in
part
Once
the
concentration
ratios
are
fixed,
the
exact
value
The
coefficient
of
Also,
it
is
Initialization
is
keyed
to
the
two
year
old
child,
based
in
part
on
Once
the
concentration
ratios
are
fixed,
the
exact
value
of
this
The
coefficient
of
Also,
it
is
Initialization
is
keyed
to
the
two
year
old
child,
based
in
part
Once
the
concentration
ratios
are
fixed,
the
exact
value
The
coefficient
of
Also,
it
is
assumed
elimintion
rate
via
soft
tissues
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
15
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
TBLUR(
t)
Lead
transfer
time
from
blood
to
urine
20
and
B­
1a
24
and
0­
84
I
24
months
­
Assumed
proportional
to
body
surface
area.
The
coefficient
of
proportionality
is
assumed
to
depend
on
an
adult
estimate
for
the
parameter
and
the
corresponding
body
surface
area.
The
adult
estimate
of
39
days
was
obtained
using
Araki
et
al
(
1986a,
1986b,
1987),
Assenato
et
al
(
1986),
Campbell
et
al
(
1981),
Carton
et
al
(
1987),
Chamberlain
et
al.

(
1978),
Folashade
et
al
(
1991),
Heard
and
Chamberlain
(
1981),
He
et
al
(
1988),
Kawaii
et
al
(
1983),
Kehoe
(
1961),
Koster
et
al
(
1989),
Manton
and
Malloy
(
1983),
Rabinowitz
and
Wetherill
(
1973),
Rabinowitz
et
al
(
1976),

and
Yokoyama
et
al
(
1985).

0­
84
months
­
Assumed
proportional
body
surface
area.
The
coefficient
of
proportionality
is
assumed
to
depend
on
an
estimate
of
the
parameter
for
a
24
month
old
and
the
corresponding
body
surface
area.

Both
cases
above
assume
that
(
a)
body
surface
area
varies
as
1/
3
power
of
weight
of
body
based
on
(
Mordenti,
1986)
and
(
b)
respectively,
70
kg
and
12.3
kg
are
standard
adult
and
2
year
old
body
weights
based
on
Spector
(
1956).

Since
glomerular
filtration
rate
(
GFR)
is
proportional
to
body
surface
area
for
ages
24
months
based
on
(
Weil,
1955),
surface
area
scaling
is
equivalent
to
scaling
by
GFR
for
ages
24
months.
days
B­
1f,
2c
TBONEBL(
t)
Lead
transfer
time
from
bone
to
blood
B­
1h
0­
84
I
Based
on
the
assumption
that
masses
of
lead
in
bone
and
blood
are
in
kinetic
quasi­
equilibrium.
days
B­
2j,
2l
TCORTPL(
t)
Lead
transfer
time
from
cortical
bone
to
plasma­
ECF
B­
2l
0­
84
I
Based
on
the
assumption
that
the
cortical
and
trabecular
bone
pools
have
similar
lead
kineticsfor
children
younger
than
84
months.
days
B­
6b,
6i,
6.5b,

6.5i,
8d,
9f
time_
out(
t)
Time
spent
outdoors
1
2
3
4
4
4
4
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Values
are
reported
in
the
OAQPS
staff
report
(
USEPA
1989,
pp.
A­
2)
and
the
TSD
(
USEPA
1990a).
The
values
have
been
derived
from
a
literature
review
(
Pope,
1985).
hrs/
day
E­
2
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
16
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
TimeStep
Length
of
time­

step
in
solution
algorithm
1/
6
0­
84
E
This
user­
selectable
parameter
is
available
mainly
for
adjusting
the
model
run
time
to
the
speed
of
the
computer.
Newer,
faster
computers
can
run
the
model
at
the
shortest
TimeStep
(
15
min)
in
less
than
one
minute.
The
default
value,
4
hours,
is
based
on
a
tradeoff
between
numerical
accuracy
of
results
and
computer
run­
time.
Except
in
the
case
of
extreme
exposure
scenarios,
there
is
no
difference
in
the
numerical
accuracy
at
any
user
selectable
value
for
TimeStep.
day
B­
6.5a,
6.5d­

6.5i,
7b,
7c,

8a,
d,
9a­

9f,
10a­
10b
TKIDPL(
t)
Lead
transfer
time
from
kidney
to
plasma­
ECF
B­
2h
0­
84
I
Based
on
the
assumption
that
the
lead
transfer
time
from
kidney
to
blood
is
equal
to
the
lead
transfer
time
from
kidney
to
plasma­
ECF.
days
B­

6b,
6f,
6.5b,
6.

5f,
8d,
9c
TLIVFEC(
t)
Lead
transfer
time
from
liver
to
feces
B­
2f
0­
84
I
Based
on
the
assumption
that
the
masses
of
lead
in
liver
and
blood
are
in
kinetic
quasi­
equilibrium.
days
B­
6e,
6.5e,

8c,
d,
9b
TLIVPL(
t)
Lead
transfer
time
from
liver
to
plasma­
ECF
B­
2e
0­
84
I
Based
on
the
assumption
that
the
lead
transfer
time
from
liver
to
blood
is
equal
to
the
lead
transfer
time
from
liver
to
plasma­
ECF.
days
B­

6b,
6e,
6.5b,
6.

5e,
8c,
d,

9b
TOTHOUT(
t)
Lead
transfer
time
from
soft
tissues
to
elimination
pool
B­
2o
0­
84
I
Based
on
the
assumption
that
the
masses
of
lead
in
soft
tissues
and
blood
are
in
kinetic
quasi­
equilibrium.
days
B­
6g,
6.5g,

8c,
d,
9h
TOTHPL(
t)
Lead
transfer
time
from
soft
tissues
to
plasma­
ECF
B­
2n
0­
84
I
Based
on
the
assumption
that
the
lead
transfer
time
from
soft
tissues
to
blood
is
equal
to
the
lead
transfer
time
from
soft
tissues
to
plasma­
ECF.
days
B­

6c,
6g,
6.5c,
6.

5g,
8c,
d,

9h
TPLCORT(
t)
Lead
transfer
time
from
plasma­
ECF
to
cortical
bone
B­
2k
0­
84
I
Based
on
the
following
assumptions:

The
rate
at
which
lead
leaves
the
plasma­
ECF
to
reach
the
bone
is
proportional
to
the
rate
which
lead
leaves
the
blood
to
reach
the
same
pool.

The
cortical
and
trabecular
bone
pools
have
similar
lead
kinetics
for
children
younger
than
84
months.

The
cortical
bone
is
80%
of
the
weight
of
bone
based
on
Leggett
et
al.

(
1982).
days
B­
6c,
6i,
6.5c,

6.5i,
8b,
c,
9f
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
17
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
TPLKID(
t)
Lead
transfer
time
from
plasma­
ECF
to
kidney
B­
2g
0­
84
I
Based
on
the
assumption
that
the
rate
at
which
lead
leaves
the
plasma­

ECF
to
reach
the
kidney
is
proportional
to
the
rate
at
which
lead
leaves
the
blood
to
reach
the
same
pool.
days
B­

6c,
6f,
6.5c,
6.

5f,
8b,
c,
9c
TPLLIV(
t)
Lead
transfer
time
from
plasma­
ECF
to
liver
B­
2d
0­
84
I
Based
on
the
assumption
that
the
rate
at
which
lead
leaves
the
plasma­

ECF
to
reach
the
liver
is
proportional
to
the
rate
at
which
lead
leaves
the
blood
to
reach
the
same
pool.
days
B­

6c,
6e,
6.5c,
6.

5e,
8b,
c,

9b
TPLOTH(
t)
Lead
transfer
time
from
plasma­
ECF
to
soft
tissues
B­
2m
0­
84
I
Based
on
the
assumption
that
the
rate
at
which
lead
leaves
the
plasma­

ECF
to
reach
the
soft
tissues
is
proportional
to
the
rate
which
lead
leaves
the
blood
to
reach
the
same
pool.
days
B­

6c,
6g,
6.5c,
6.

5g,
8b,
c,

9d
TPLRBC
Lead
transfer
time
from
plasma­

ECF
to
red
blood
cells
0.1
0­
84
I
Initialization
value
of
0.1
was
assigned
as
plausible
nominal
value
reflecting
best
professional
judgement
on
appropriate
time
scale
for
composite
process
of
transfer
of
lead
through
the
red
blood
cell
membrane
to
lead
binding
components.
days
B­
2b,
2.5,7b,

7c
TPLRBC2(
t)
Lead
transfer
time
from
plasma­

ECF
to
red
blood
cells
constrained
by
the
maximum
capacity
of
red
blood
cell
lead
concentration
B­
2.5
0­
84
I
Simple
combination
of
the
lead
transfer
time
from
plasma­
ECF
to
red
blood
cells,
and
the
ratio
of
red
blood
cell
lead
concentration
to
the
corresponding
maximum
concentration.
Based
on
Marcus
(
1985a)
and
reanalysis
of
infant
baboon
data.
days
B­

6a,
6d,
6.5a,
6.

5d,
8b,
9a
TPLTRAB(
t)
Lead
transfer
time
from
plasma­
ECF
to
trabecular
bone
B­
2i
0­
84
I
Based
on
the
following
assumptions:

The
rate
at
which
lead
leaves
the
plasma­
ECF
to
reach
the
bone
is
proportional
to
the
rate
which
lead
leaves
the
blood
to
reach
the
same
pool.

The
cortical
and
trabecular
bone
pools
have
similar
lead
kinetics.

The
trabecular
bone
is
20%
of
the
weight
of
bone
based
on
Leggett
et
al.

(
1982).
days
B­

6c,
6h,
6.5c,
6.

5h,
8b,
c,

9e
TPLUR(
t)
Lead
transfer
time
from
plasma­
ECF
to
urine
B­
2c
0­
84
I
Based
on
the
assumption
that
the
rate
at
which
lead
leaves
the
plasma­

extra­
cellular
fluid
to
reach
the
urine
pool
is
proportional
to
the
rate
at
which
lead
leaves
the
blood
to
reach
the
same
pool.
days
B­
6c,
6.5c,
8a
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
18
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
TRBCPL
Lead
transfer
time
from
red
blood
cells
to
plasma­

ECF
B­
2b
0­
84
I
Based
on
the
assumption
that
the
transfer
time
out
of
RBC
is
similar
at
all
ages,
since
mean
red
cell
value
is
similar.
days
B­

6b,
6d,
6.5b,
6.

5d,
7b,
7c,

8c,
d,
9a
TTRABPL(
t)
Lead
transfer
time
from
trabecular
bone
to
plasma­

extra­
cellular
fluid
B­
2j
0­
84
I
Based
on
the
assumption
that
the
cortical
and
trabecular
bone
pools
have
similar
lead
kinetics
for
children
younger
than
84
months.
days
B­

6b,
6h,
6.5b,
6.

5h,
8c,
d,

9e
TWA(
t)
Time
weighted
average
air
lead
concentration
E­
2
0­
84
I
Simple
combination
of
outdoor
and
indoor
air
lead
concentrations
and
the
number
of
hours
spent
outdoors.
µ
g/
m3
E­
3
UPAIR(
t)
Air
lead
uptake
U­
4
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
5
UPDIET(
t)
Diet
lead
uptake
U­
1a
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
1f
UPDUST(
t)
Dust
lead
uptake
U­
1c
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
1f
UPDUSTA(
t)
Dust
lead
uptake
rate
from
alternate
sources
U­
1.5c
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
1f
UPGUT(
t)
Total
gut
uptake
U­
1f
0­
84
I
Sum
of
all
gastrointestinal
uptake.
µ
g/
day
U­
5
UPOTHER(
t)
Uptake
of
other
ingested
lead
U­
1d
0­
84
I
Assumes
no
other
gut
lead
intake
µ
g/
day
U­
1f
UPSOIL(
t)
Soil
lead
uptake
U­
1e
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
1f
UPTAKE(
t)
Total
lead
uptake
U­
5
0­
84
I
Simple
combination
of
the
media­
specific
daily
lead
uptake
rates,

translated
to
a
monthly
rate.
µ
g/
mo
B­
6a,
6.5a,
8a
UPWATER(
t)
Water
lead
uptake
U­
1b
0­
84
I
Simple
combination
of
media­
specific
lead
intake
and
the
corresponding
net
absorption
coefficient.
µ
g/
day
U­
1f
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
19
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
UserFishConc
Lead
concentration
in
fish
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
fish
are
consumed.
µ
g/
g
E­
5h
userFishFraction
Fraction
of
total
meat
consumed
as
fish
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
fish
are
consumed.
unitless
E­
5a,
5h
UserFruitConc
Lead
concentration
in
home
grown
fruits
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
fruits
are
consumed.
µ
g/
g
E­
5f
userFruitFraction
Fraction
of
total
fruits
consumed
as
home
grown
fruits
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
fruits
are
consumed.
unitless
E­
5d,
5e,
5f
UserGameConc
Lead
concentration
in
game
animal
meat
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
meat
is
consumed.
µ
g/
g
E­
5i
userGameFraction
Fraction
of
total
meat
consumed
as
game
animal
meat
excluding
fish
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
meat
is
consumed.
unitless
E­
5a,
5i
UserVegConc
Lead
concentration
in
home
grown
vegetables
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
vegetables
are
consumed.
µ
g/
g
E­
5g
userVegFraction
Fraction
of
total
vegetables
consumed
as
home
grown
vegetables
0
0­
84
E
Based
on
the
assumption
that
only
commercially
available
vegetables
are
consumed.
unitless
E­
5b,
5c,
5g
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
20
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
veg_
all(
t)
Daily
amount
of
all
vegetables
consumed
56.84
106.50
155.75
157.34
158.93
172.50
199.65
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
I
Pb
concentration
from
data
provided
to
EPA
by
FDA
(
US
EPA
(
1986).

Quantity
consumed
from
Pennington
(
1983).
g/
day
E­
5g
vent_
rate(
t)
Ventilation
rate
2
3
5
5
5
7
7
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Values
are
reported
in
the
OAQPS
report
(
USEPA
1989,
pp.
A­
3)
and
the
TSD
(
USEPA
1990a).
These
estimates
are
based
on
body
size
in
combination
with
smoothed
data
from
Phalen
et
al.,
(
1985).
m
3/
day
E­
3
VOLBLOOD(
t)
Volume
of
blood
B­
5a
0­
84
I
Statistical
fitting
of
data
from
Silve
et
al
(
1987);
also
Spector
(
1956)
and
Altman
and
Ditmer
(
1973)
µ
g/
dL
B­

1h,
2e,
2f,
2h,
2
n,
2o,
5d,

5e,
5m,
10a
VOLECF(
t)
Volume
of
extra­

cellular
fluid
(
ECF)
B­
5d
0­
84
I
The
volume
of
extracellular
fluid
that
exchanges
rapidly
with
plasma
is
estimated
73%
of
the
blood
volume
based
on
Rabinowitz
(
1976).
This
additional
volume
of
distribution
is
assumed
to
be
the
volume
the
extra­

cellular
fluid
pool,
which
is
the
difference
between
the
volume
of
the
distribution
and
the
blood
volume.
dL
B­
9g
VOLPLASM(
t)
Volume
of
plasma
B­
5c
0­
84
I
Statistical
fit
to
VOLBLOOD(
t)
­
VOLRBC(
t)
dL
B­
7b,
7c,
9g
VOLRBC(
t)
Volume
of
red
blood
cells
B­
5b
0­
84
I
Statistical
fit
to
hematocrit
×
blood
volume
dL
B­
2.5
water_
consumption(
t)
Daily
amount
of
water
consumed
0.20
0.50
0.52
0.53
0.55
0.58
0.59
0­
11
12­
23
24­
35
36­
47
48­
59
60­
71
72­
84
E
Exposure
Factors
Handbook
(
US
EPA,
1989b)
L/
day
E­
6a,
6b
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
21
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
weight_
soil
Percentage
of
total
soil
and
dust
ingestion
that
is
soil
45
0­
84
E
Guidance
Manual,
Section
2.3
(
US
EPA,
1994)
%
E­
8,10
WTBLOOD(
t)
Weight
of
blood
B­
5m
0­
84
I
Based
on
an
blood
density
of
1.056
kg/
l
(
Spector
1956).
kg
B­
5l
WTBODY(
t)
Weight
of
body
B­
5f
0­
84
I
Statistical
fitting
of
data
from
Silve
et
al.
(
1987);
also
Spector
(
1956)
and
Altman
and
Ditmer
(
1973).
Also,
body
weight
of
24
month
old
is
assumed
to
be
12.3
kg
(
Spector
1956).
kg
B­
1a­

1e,
5f,
5g,
5l
WTBONE(
t)
Weight
of
bone
B­
5g
0­
84
I
12­
84
months
­
Based
on
child
skeletal
ash
data
in
Harley
and
Kneip
(
1984)
and
the
following
assumptions.

WTBONE
=
(
WTBONEADULT
/
WTSKEL_
ASHADULT)
*
WTSKEL_
ASH
where
WTBONEADULT
=
10
kg
WTSKEL_
ASHADULT
=
2.91
kg
0­
12
months
­
Assumed
to
be
11%
of
the
weight
of
the
body.
The
ratio
of
weight
of
bone
to
weight
of
body
(
11%)
is
based
on
the
12­
month
estimate
for
WTBONE
from
the
above
equation,
and
an
estimate
for
WTBODY
at
the
same
age.
kg
B­
5h,
5i
WTCORT(
t)
Weight
of
cortical
bone
B­
5i
0­
84
I
Assumed
to
be
80%
of
the
weight
of
the
bone
based
on
Leggett
et
al.

(
1982).
kg
B­
1h,
5l,
7e
WTECF(
t)
Weight
of
extra­

cellular
fluid
(
ECF)
B­
5e
0­
84
I
Based
on
an
assumed
ECF
density
approximately
the
same
as
water,
of
1.0
kg/
L.
kg
B­
5l
WTKIDNEY(
t)
Weight
of
kidney
B­
5j
0­
84
I
Statistical
fitting
of
data
from
Silve
et
al.
(
1987);
also
Spector
(
1956)
and
Altman
and
Ditmer
(
1973).
Also,
body
weight
of
24
month
old
is
assumed
to
be
12.3
kg
(
Spector
1956).
kg
B­
5j,
5l,
7f
WTLIVER(
t)
Weight
of
liver
B­
5k
0­
84
I
Statistical
fitting
of
data
from
Silve
et
al.
(
1987);
also
Spector
(
1956)
and
Altman
and
Ditmer
(
1973).
Also,
body
weight
of
24
month
old
is
assumed
to
be
12.3
kg
(
Spector
1956).
kg
B­
2e,
2f,
5l,
7g
WTOTHER(
t)
Weight
of
soft
tissues
B­
5l
0­
84
I
Simple
combination
of
the
weight
of
body
and
the
weights
of
kidney,
liver,

bone,
blood
and
extra­
cellular
fluid.
kg
B­
2n,
2o,
7h
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
22
PARAMETER
NAME
DESCRIPTION
DEFAUL
T
VALUE
OR
EQN.

NO.
AGE
RANGE
(
mo)
I
or
E
BASIS
FOR
VALUES/
EQUATIONS
UNITS
EQUATION
WHERE
USED
WTTRAB(
t)
Weight
of
trabecular
bone
B­
5h
0­
84
I
Assumed
to
be
20%
of
the
weight
of
the
bone
based
on
Leggett
et
al.

(
1982).
kg
B­
1h,
5l,
7i
NOTE:
I
=
interior
parameter,
E
=
Exterior,
user
selectable
parameter
B­
23
MP&
M
EEBA:
Appendices
Appendix
M:
Sensitivity
Analysis
of
Lead­
Related
Benefits
INTRODUCTION
The
methodology
for
estimating
lead­
related
benefits
for
the
MP&
M
regulation
is
discussed
in
Chapter
14.
In
its
main
analysis,
EPA
uses
a
three
percent
discount
rate
to
value
benefits
associated
with
reductions
in
exposure
to
lead.
OMB,
however,
frequently
recommends
the
use
of
a
seven
percent
discount
rate
in
benefit­
cost
analyses
for
government
regulations.
This
appendix
therefore
presents
a
sensitivity
analysis
of
the
results
for
lead­
related
benefits
estimated
using
a
seven
percent
discount
rate
and
compares
them
with
estimated
lead­
related
benefits
in
the
main
(
three
percent)
analysis.
Because
EPA
found
that
the
final
rule
will
not
yield
any
lead­
related
health
benefits
to
either
children
or
adults,
the
analysis
in
this
appendix
is
limited
only
to
the
two
Upgrade
Options
considered
as
alternatives
to
the
final
rule,
and
the
Proposed/
NODA
Option.
Appendix
M:
Sensitivity
Analysis
of
Lead­
Related
Benefits
APPENDIX
CONTENTS
M.
1
or
Quantified
Lead­
Related
Health
Effects
.....
M­
1
M.
2
Related
Benefit
Results
.................
.....
M­
2
M.
2.1
chool
Age
Children
Lead­
Related
Benefits
.................
.................
.
M­
2
M.
2.2
dult
Lead­
Related
Benefits
.................
M­
3
Values
f
Lead­

Pres
A
M.
1
VALUES
FOR
QUANTIFIED
LEAD­
RELATED
HEALTH
EFFECTS
Table
M.
1
below
compares
per­
case
values
for
lead­
related
health
effects
estimated
using
a
three
percent
discount
rate
and
a
seven
percent
discount
rate.
Values
for
some
health
effect
categories
do
not
change
for
the
following
two
reasons:

 
Discounting
is
not
used
in
estimating
a
specific
value.
For
example,
the
cost
of
treating
hypertension
used
in
this
analysis
is
the
estimate
of
annual
medical
costs
and
lost
work
time
associated
with
this
condition.

 
The
original
study
did
not
provide
sufficient
information
for
estimating
the
cost
of
illness
value
based
on
a
seven
percent
discount
rate.
Taylor
et
al.
(
1996)
used
a
five
percent
discount
rate
to
estimate
the
expected
lifetime
cost
of
a
stroke.
The
authors
do
not
provide
sufficient
information
to
recalculate
the
value
based
on
a
different
discount
rate.
Therefore,
EPA
did
not
revise
this
value
in
the
main
analysis
to
reflect
discounting
at
a
three
percent
rate.

M­
1
MP&
M
EEBA:
Appendices
Appendix
M:
Sensitivity
Analysis
of
Lead­
Related
Benefits
Table
M.
1:
Comparison
of
Per­
Case
Values
for
Lead­
Related
Health
Effects
(
2001
$)

Health
Category
Value/
Cost
@
3%

Discount
Rate
Value/
Cost
@
7%

Discount
Rate
Lead­
Related
Health
Effects
for
Children
Value
of
an
IQ
point
[
A­(
B+
C)]
$
9,419
$
1,817
(
A)
Wage
loss
per
IQ
point
$
10,675
$
2,427
(
B)
Cost
of
additional
education
per
IQ
point
$
511
$
247
(
C)
Opportunity
cost
of
lost
income
while
in
school
$
746
$
363
Additional
education
cost
for
children
with
IQ
<
70
$
58,012
$
36,831
Additional
education
cost
for
children
with
PbB
>
20
µ
g/
dL
$
16,485
$
12,169
Value
of
preventing
neonatal
mortalitya
$
6,500,000
$
6,500,000
Lead­
Related
Health
Effects
for
Adults
Hypertension
(
male
&
female)
b
$
1,141
$
1,141
CHD
(
male
&
female)
$
76,347
$
74,115
Stroke
(
male)
c
$
335,135
$
335,135
Stroke
(
female)
c
$
251,351
$
251,351
Mortality
(
male
&
female)
a
$
6,500,000
$
6,500,000
a
Value
of
a
Statistical
Life
(
VSL)
is
taken
from
U.
S.
EPA s
Guidelines
for
Preparing
Economic
Analyses.
The
recommended
value
was
not
adjusted
in
the
main
analysis.

b
Annual
cost
of
treatment.
No
discounting
is
required.

C
Values
based
on
Taylor
et
al.
(
1996)
which
uses
a
five
percent
discount
rate
to
estimate
the
expected
lifetime
cost
of
a
stroke.
EPA
used
this
value
in
the
main
analysis
presented
in
Chapter
14
of
this
report.

Source:
U.
S.
EPA
analysis.

M.
2
LEAD­
RELATED
BENEFIT
RESULTS
This
section
presents
lead­
related
benefits
of
the
alternative
regulatory
options
 
the
433
Upgrade
Options
and
the
Proposed/
NODA
Option
 
based
on
a
seven
percent
discount
rate.

M.
2.1
Preschool
Age
Children
Lead­
Related
Benefits
Table
M.
2
summarizes
lead­
related
benefits
for
children
estimated
for
the
433
Upgrade
Options
based
on
a
three
percent
and
a
seven
percent
discount
rate.
As
shown
in
Table
M.
2,
using
a
seven
percent
discount
rate
results
in
a
19
percent
reduction
in
the
total
monetary
value
of
lead­
related
benefits
for
preschool
children
compared
to
the
value
of
benefits
estimated
based
on
a
three
percent
discount
rate.
Changes
in
the
monetary
values
associated
with
individual
benefit
categories
range
from
zero
percent
(
neonatal
mortality)
to
81
percent
(
avoided
IQ
loss).

M­
2
MP&
M
EEBA:
Appendices
Appendix
M:
Sensitivity
Analysis
of
Lead­
Related
Benefits
Table
M.
2:
Comparison
of
the
Monetary
Value
of
Lead­
Related
Benefits
to
Children
(
2001$)
Based
on
Alterantive
Discount
Rates
 
433
Upgrade
Options
Category
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Reduced
Cases
or
IQ
Points
Mean
Benefit
Value
(
2001$)
Reduced
Cases
or
IQ
Points
Mean
Benefit
Value
(
2001$)

3%
DR
7%
DR
%

Change
3%
DR
7%
DR
%

Change
Neonatal
mortality
0.15
$
995,630
$
995,630
0%
0.17
$
1,109,294
$
1,109,294
0%

Avoided
IQ
Loss
31.99
$
301,323
$
58,128
81%
36.19
$
340,845
$
65,752
81%

Reduced
IQ
<
70
0.11
$
6,637
$
4,213
37%
0.13
$
7,501
$
4,762
37%

Reduced
PbB
>
20
 
g/
L
0.00
$
0
$
0
0%
0.00
$
0
$
0
0%

Total
Benefits
$
1,305,590
$
1,057,970
19%
$
1,457,640
$
1,179,808
19%

Source:
U.
S.
EPA
analysis.

Table
M.
3
summarizes
lead­
related
benefits
for
children
estimated
for
the
Proposed/
NODA
Option
based
on
a
three
percent
and
a
seven
percent
discount
rate.
As
shown
in
Table
M
.3,
using
a
seven
percent
discount
rate
results
in
a
40
percent
reduction
in
the
total
monetary
value
of
lead­
related
benefits
for
preschool
children
compared
to
the
value
of
benefits
estimated
based
on
a
three
percent
discount
rate.
Changes
in
the
monetary
values
associated
with
individual
benefit
categories
range
from
zero
percent
(
neonatal
mortality)
to
81
percent
(
avoided
IQ
loss).

Table
M.
3:
Comparison
of
the
Monetary
Value
of
Lead­
Related
Benefits
to
Children
(
2001$)
Based
on
Alterantive
Discount
Rates
 
Proposed/
NODA
Option
Category
Reduced
Cases
or
IQ
Points
Benefit
Value
(
2001$)

Category
3%
DR
7%
DR
%
C
hange
Neonatal
Mortality
1.60
$
10,417,781
$
10,417,781
0%

Avoided
IQ
Loss
1,078.38
$
10,157,286
$
1,959,421
81%

Reduced
IQ
<
70
3.72
$
216,007
$
137,140
37%

Reduced
PbB
>
20
 
g/
L
0.00
$
0
$
0
0%

Total
Benefits
$
20,791,073
$
12,514,342
40%

Source:
U.
S.
EPA
analysis.

M.
2.2
Adult
Lead­
Related
Benefits
Table
M.
4
presents
lead­
related
benefits
for
adults
for
the
433
Upgrade
Options
based
on
a
three
percent
and
a
seven
percent
discount
rate.
Under
both
433
Upgrade
Options
the
difference
between
the
total
monetary
value
of
benefits
to
adults
estimated
based
on
a
three
percent
and
a
seven
percent
discount
rate
is
negligible
(
less
than
0.1
percent).
The
reduction
in
total
benefits
is
marginal
between
the
two
discount
rate
scenarios
because
the
monetary
value
of
only
one
lead­
related
benefit
category
for
adults
(
i.
e.,
CHD)
is
affected
by
the
discount
rate.

M­
3
MP&
M
EEBA:
Appendices
Appendix
M:
Sensitivity
Analysis
of
Lead­
Related
Benefits
Table
M.
4:
Alterantive
Discount
Rates
 
433
Upgrade
Options
Category
Directs
+
413
to
433
Upgrade
Directs
+
All
to
433
Upgrade
Reduced
Cases
Mean
Value
of
Benefits
Reduced
Cases
Mean
Value
of
Benefits
3%
DR
7%
DR
3%
DR
7%
DR
Men
Hypertension
53.47
$
61,004
$
61,004
59.58
$
67,982
$
67,982
CHD
0.05
$
4,155
$
4,033
0.06
$
4,631
$
4,495
CBA
0.02
$
5,698
$
5,698
0.02
$
6,350
$
6,350
BI
0.01
$
3,226
$
3,226
0.01
$
3,596
$
3,596
Mortality
0.07
$
474,735
$
474,735
0.08
$
529,125
$
529,125
Women
CHD
0.02
$
1,662
$
1,614
0.02
$
1,853
$
1,799
CBA
0.01
$
2,417
$
2,417
0.01
$
2,694
$
2,694
BI
0.01
$
1,487
$
1,487
0.01
$
1,658
$
1,658
Mortality
0.02
$
150,190
$
150,190
0.03
$
167,417
$
167,417
Total
Benefits
$
704,574
$
704,404
$
785,304
$
785,115
Comparison
of
the
Monetary
Value
of
Lead­
Related
Benefits
to
Adults
(
2001$)
Based
on
Table
M.
5
summarizes
lead­
related
benefits
for
adults
for
the
Proposed/
NODA
Option
based
on
a
three
percent
and
a
seven
percent
discount
rate.
For
this
option,
the
estimated
total
monetary
values
of
benefits
drop
from
$
7,048,025
under
the
three
percent
discount
rate
to
$
7,046,328
under
the
seven
percent
discount
rate
(
i.
e.,
a
decrease
of
less
than
0.1
percent).
This
marginal
difference
in
the
total
value
of
benefits
based
the
three
percent
and
the
seven
percent
discount
rate
is
due
to
the
fact
that
only
one
benefit
category
(
i.
e.,
CHD)
is
affected
by
the
discount
rate.
Source:
U.
S.
EPA
analysis.

Table
M.
5:
Comparison
of
the
Monetary
Value
of
Lead­
Related
Benefits
to
Adults
(
2001$)
Based
on
Alterantive
Discount
Rates
 
Proposed/
NODA
Option
Category
Reduced
Cases
Mean
Value
of
Benefits
3%
DR
7%
DR
Men
Hypertension
545.25
$
622,126
$
622,126
CHD
0.54
$
41,564
$
40,349
CBA
0.17
$
56,907
$
56,907
BI
0.10
$
32,197
$
32,197
Mortality
0.73
$
4,750,132
$
4,750,132
Women
CHD
0.22
$
16,472
$
15,991
CBA
0.10
$
23,928
$
23,928
BI
0.06
$
14,714
$
14,714
Mortality
0.23
$
1,489,984
$
1,489,984
Total
Benefits
$
7,048,025
$
7,046,328
Source:
U.
S.
EPA
analysis.

M­
4
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
Based
Recreation
Survey
INTRODUCTION
This
appendix
presents
EPA s
analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
(
NDS).
The
objective
of
this
analysis
is
to
determine
the
number
of
people
who
participate
in
water­
based
recreation
and
their
total
number
of
recreation
trips,
characterize
participation
and
number
of
trips
taken
by
water
body
type,
and
provide
more
detailed
information
on
specific
recreation
activities
(
e.
g.,
fish
species
targeted
on
fishing
trips)
and
expenditures
associated
with
various
activities.

N.
1
BACKGROUND
INFORMATION
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
Based
Recreation
Survey
APPENDIX
CONTENTS
N.
1
ground
Information
.................
.........
N­
1
N.
2
Analysis
.................
.................
N­
2
N.
3
tion
in
Water­
Based
Recreation
by
Activity
Type
N­
2
N.
4
ation
of
Trips
by
Water
Body
Type
............
N­
11
N.
5
el
Distance
.................
......
N­
16
N.
6
l
Expenditures
per
Trip
.................
.
N­
19
N.
7
bution
of
Direct
Costs
for
Single­
day
Trips
......
N­
22
N.
8
of
Boating
Trips
.................
........
N­
27
N.
9
of
Fishing
Trips
.................
........
N­
30
Back
Data
Participa
Alloc
One­
Way
Trav
Individua
Distri
Profile
Profile
U.
S.
EPA
cooperated
with
the
National
Forest
Service
and
several
other
federal
agencies
and
interested
groups
to
collect
data
on
the
outdoor
recreation
activities
of
Americans.
The
1993
NDS
collected
data
on
demographic
characteristics
and
water­

based
recreation
behavior
using
a
nationwide
stratified
random
sample
of
13,059
individuals
aged
16
and
over.
Respondents
reported
on
water­
based
recreation
trips
taken
within
the
past
12
months,
including
the
primary
purpose
of
their
trips
(
i.
e.,

fishing,
boating,
swimming,
and
viewing),
and
number
of
trips,
trip
length,
distance
to
the
recreation
site(
s),
number
of
participants,
their
trip
expenditures,
and
detailed
trip
allocation
information
on
the
last
trip
taken
for
each
recreation
type.
For
example,
respondents
reported:

 
where
fishing
was
the
primary
purpose
of
a
trip,
the
number
of
fish
caught
and
the
species
targeted
(
i.
e.,
coldwater,

warmwater,
anadromous,
or
marine);

 
the
type
of
water
body
(
e.
g.,
lake,
river,
ocean,
wetland);
and
 
where
boating
was
the
primary
purpose
of
trip,
the
type
of
boating
(
i.
e.,
motorboating,
sailing,
canoeing,
rowing,

rafting,
and
other
floating).

EPA
used
NDS
data
to
characterize
water­
based
recreation
activities
nationwide,
including:

 
percent
of
state
population
participating
in
water­
based
recreation
by
recreation
activity
and
trip
length
(
i.
e.,
single­

day
vs.
multiple­
day
trips);

 
average
number
of
water­
based
recreation
trips
per
person
by
recreation
activity
and
trip
length;

 
allocation
of
single­
or
multiple­
day
trips
among
different
water
body
types
by
recreation
type;

 
mean
one­
way
distance
traveled
to
the
site
visited
on
last
trip;

 
total
expenditures
per
person
for
last
single­
day
or
multiple­
day
trip;

 
distribution
of
total
expenditures
among
various
expenditure
categories
for
single­
and
multiple­
day
trips
(
e.
g.,

lodging,
boat
rental,
and
entrance
fee);

 
allocation
of
fishing
trips
by
target
species;
and
 
allocation
of
boating
trips
by
boating
type.

N­
1
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
Based
Recreation
Survey
N.
2
DATA
ANALYSIS
The
NDS
used
a
random
digit
dialed
population­
based
sample
(
aged
16
and
over)
of
the
nation.
For
simple
random
sampling,

estimates
of
the
sample
mean
and
total
are
consistent
estimates
of
the
population
mean
and
total.
EPA
therefore
treats
sample­
based
estimates
as
being
representative
of
the
population­
based
estimates.
For
example,
the
percent
of
survey
respondents
participating
in
a
given
water­
based
recreation
activity
is
theoretically
consistent
with
the
percent
of
the
state
population
(
aged
16
and
over)
that
participates
in
that
activity.
The
estimated
percentages
can
be
applied
to
the
state
population
(
aged
16
and
over)
to
derive
the
number
of
participants
in
various
water­
based
recreation
activities
in
each
state.

The
survey
database
cannot
be
used
to
characterize
subsistence
fishing
because
subsistence
fishermen s
behavior
differs
significantly
from
recreational
fishermen s
behavior.
In
addition,
this
population
subgroup
is
likely
to
be
under­
represented
in
the
survey
database
due
to
various
factors.
First,
subsistence
fishermen
constitute
a
relatively
small
portion
of
the
total
fisherman
population.
They
also
tend
to
have
a
lower
education
level.
Some
of
them
may
lack
long­
distance
telephone
services
and/
or
have
language
barriers.
These
factors
are
likely
to
result
in
inadequate
representation
of
this
subgroup
in
the
survey
data.

N.
3
PARTICIPATION
IN
WATER­
BASED
RECREATION
BY
ACTIVITY
TYPE
This
analysis
estimates
the
percent
and
the
number
of
state
residents
who
participated
in
water­
based
recreation
by
activity
type
and
trip
length
(
i.
e.,
single­
day
vs.
multiple­
day).
Participants
in
each
activity
in
a
given
state
include
state
residents
who
took
at
least
one
single­
day
and/
or
multiple­
day
trip
for
each
respective
activity
during
the
previous
12
months.
Because
some
participants
took
both
single­
day
and
multiple­
day
trips,
the
percent
and
the
number
of
state
residents
participating
in
all
trips
does
not
equal
the
sum
of
the
single­
day
plus
multiple­
day
percentages
or
number
of
participants.
The
analysis
also
estimates
the
average
number
of
recreation
trips
per
person
per
year,
by
recreational
activity,
trip
length
(
single­
day
vs.

multiple­
day
trips),
and
state
of
residence.
Tables
N.
1,
N.
2,
N.
3,
and
N
.4
characterize
participation
in
boating,
fishing,

swimming,
and
viewing,
respectively.

1.
Estimating
the
percent
of
state
population
participating
in
each
of
the
four
water­
based
recreation
activities.
The
total
percent
of
state
residents
participating
in
each
activity
equals
the
total
number
of
respondents
who
took
at
least
one
single­
day
and/
or
multiple­
day
trip
divided
by
the
state s
sample
size.
Similarly,
the
percent
participating
in
single­
day
or
multiple­
day
trips
for
each
respective
activity
equals
the
respective
number
of
sample
respondents
who
took
either
single­
day
or
multiple­
day
trips,
respectively,
divided
by
the
state s
sample
size.

2.
Estimating
the
number
of
state
residents
participating
in
each
of
the
four
water­
based
recreation
activities.
EPA
calculated
the
total
number
of
participants
in
each
state
by
multiplying
the
percent
of
sample
respondents
who
took
at
least
one
single­
day
and/
or
multiple­
day
trip
by
each
state's
actual
population
16
years
of
age
and
older.
Similarly,

the
total
number
of
participants
in
single­
day
or
multiple­
day
trips
for
each
respective
activity
equals
the
respective
percent
of
sample
respondents
who
took
either
single­
day
or
multiple­
day
trips,
respectively,
times
the
state's
population
16
years
of
age
and
older.

3.
Estimating
the
average
number
of
trips
per
person
per
year.
EPA
estimated
the
average
number
of
recreation
trips
per
person
per
year
by
dividing
the
total
number
of
trips
taken
for
each
activity
by
state
residents
by
the
total
number
of
participants
in
this
activity.
Similarly,
dividing
the
number
of
single­
day
trips
or
multiple­
day
trips
by
the
respective
number
of
participants
provided
the
average
number
of
single­
day
and
multiple­
day
trips
per
person,

respectively.
Tables
N.
1­
N.
4
also
show
the
mean
trip
length
for
the
last
multiple­
day
trip.

For
comparison
purposes,
Tables
N.
2
and
N.
4
also
present
estimates
of
the
total
percent
and
the
number
of
state
residents
participating
in
recreational
fishing
and
wildlife
viewing
based
on
the
U.
S.
Fish
and
Wildlife
Service s
(
USFWS)
1996
National
Survey
of
Fishing
Hunting
and
Wildlife
Associated
Recreation.
The
table
shows
that
the
two
surveys
yield
similar
results.
NDS
estimates,
however,
are
slightly
higher
than
USFWS
estimates
for
some
states.
NDS
fishing
and
viewing
participation
estimates
are
higher
for
47
and
30
states,
respectively.
This
discrepancy
may
be
due
to
the
difference
in
the
year
when
the
respective
surveys
were
conducted.

N­
2
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
3
Table
N.
1:
Participation
in
Boating
State
State
Pop.

16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
BoatingParticipation
in
Single­
Day
TripsParticipation
in
Multiple­
Day
Trips
Percent
Population
#
People
Avg
#

Trips
per
Person
per
Year
Days
per
Year
Percent
Population
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Population
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
Trip
Length
(
days)

AK457,7282915,78448%
219,7095.15.745%
205,9785.114%
64,0821.22.5
AL3,451,58621815,83318%
621,2857.910.616%
552,2547.16%
207,0955.52.3
AR2,072,62212816,19220%
414,5246.424.116%
331,6205.07%
145,0846.98.3
AZ3,907,52617821,95212%
468,9037.310.89%
351,6777.36%
234,4523.33.2
CA25,599,2751,31319,49720%
5,119,8555.311.314%
3,583,8985.111%
2,815,9203.34.2
CO3,322,45521215,67213%
431,91910.614.08%
265,79614.67%
232,5722.63.5
CT2,651,45215916,67620%
530,2908.718.318%
477,2617.97%
185,6025.26.2
DC468,5753513,38811%
51,5432.02.79%
42,1722.33%
14,0571.03.0
DE610,2695111,96620%
122,05410.613.318%
109,84810.88%
48,8222.24.0
FL12,741,82166219,24723%
2,930,61910.316.620%
2,548,3649.85%
637,0917.05.3
GA6,250,70837316,75818%
1,125,12710.919.015%
937,60610.49%
562,5645.24.0
HI949,1845517,25820%
189,8377.69.518%
170,8536.62%
18,98418.02.0
IA2,281,00217113,33919%
433,3906.613.317%
387,7704.75%
114,0508.34.1
ID969,1668311,67730%
290,7505.88.125%
242,2925.68%
77,5334.03.2
IL9,530,32746620,45118%
1,715,4598.315.913%
1,238,9439.08%
762,4264.94.3
IN4,682,39230015,60821%
983,3029.318.315%
702,3597.58%
374,5919.33.6
KS2,058,48913515,24813%
267,60413.927.19%
185,26414.29%
185,2646.63.8
KY3,161,28321914,43516%
505,8056.29.013%
410,9676.45%
158,0642.64.7
LA3,394,85418917,96220%
678,9714.26.218%
611,0744.15%
169,7432.05.0
MA5,008,00724920,11223%
1,151,84211.811.718%
901,4418.18%
400,6414.23.7
MD4,085,34225715,89619%
776,2159.118.217%
694,5088.96%
245,1214.17.9
ME1,010,2737214,03233%
333,3907.118.226%
262,6716.713%
131,3354.77.0
MI7,628,17057613,24324%
1,830,7619.317.419%
1,449,3529.011%
839,0995.04.5
MN3,782,81724515,44024%
907,8765.98.820%
756,5635.97%
264,7974.13.3
MO4,331,93727715,63922%
953,0266.011.416%
693,1105.312%
519,8323.53.9
MS2,160,16514015,43018%
388,83010.014.916%
345,6267.16%
129,61010.32.5
MT701,4235512,75322%
154,3135.87.915%
105,2137.87%
49,1001.84.7
NC6,291,18240715,45714%
880,7657.512.112%
754,9427.26%
377,4714.03.5
ND502,1764012,55428%
140,6094.213.818%
90,3923.913%
65,2833.86.4
NE1,314,9748415,65420%
262,9955.213.313%
170,9473.810%
131,4975.83.8
NH960,5936415,00923%
220,9363.78.422%
211,3303.25%
48,0303.37.3
NJ6,545,47134718,86318%
1,178,18510.112.217%
1,112,73010.53%
196,3642.75.1
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
1:
Participation
in
Boating
State
State
Pop.

16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Boating
Participation
in
Single­
Day
Trips
Participation
in
Multiple­
Day
Trips
Percent
Population
#
People
Avg
#

Trips
per
Person
per
Year
Days
per
Year
Percent
Population
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Population
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
Trip
Length
(
days)

NM
1,370,134
105
13,049
19%
260,325
3.8
8.9
10%
137,013
3.5
11%
150,715
3.4
3.6
NV
1,537,896
75
20,505
23%
353,716
10.1
18.4
21%
322,958
6.8
9%
138,411
8.9
3.5
NY
14,797,284
774
19,118
18%
2,663,511
6.5
9.2
13%
1,923,647
7.5
5%
739,864
3.0
4.5
OH
8,789,530
650
13,522
17%
1,494,220
7.0
10.6
13%
1,142,639
7.5
7%
615,267
3.3
3.6
OK
2,665,966
143
18,643
20%
533,193
4.5
8.3
13%
346,576
4.9
8%
213,277
3.2
3.9
OR
2,673,283
217
12,319
26%
695,054
8.4
12.2
22%
588,122
8.9
9%
240,595
2.7
4.9
PA
9,693,987
742
13,065
15%
1,454,098
9.2
16.1
12%
1,163,278
9.1
5%
484,699
5.6
4.7
RI
827,474
57
14,517
16%
132,396
8.0
N/
A
16%
132,396
6.9
2%
16,549
10.0
N/
A
SC
3,115,130
181
17,211
19%
591,875
9.0
16.5
15%
467,270
8.9
7%
218,059
4.5
5.8
SD
577,391
42
13,747
26%
150,122
6.5
24.1
21%
121,252
4.8
10%
57,739
7.0
7.5
TN
4,445,987
296
15,020
23%
1,022,577
7.9
10.3
20%
889,197
7.7
6%
266,759
4.5
3.1
TX
15,618,097
657
23,772
18%
2,811,257
8.2
14.7
15%
2,342,715
7.4
7%
1,093,267
5.7
3.9
UT
1,598,531
111
14,401
20%
319,706
4.5
9.6
11%
175,838
5.6
13%
207,809
2.3
4.4
VA
5,529,436
389
14,214
19%
1,050,593
9.9
17.4
15%
829,415
8.6
6%
331,766
8.1
4.2
VT
479,265
34
14,096
24%
115,024
7.1
12.4
21%
100,646
7.1
9%
43,134
2.3
7.0
WA
4,552,631
324
14,051
35%
1,593,421
6.0
10.4
29%
1,320,263
5.7
14%
637,368
3.4
4.2
WI
4,156,609
299
13,902
22%
914,454
10.3
14.7
19%
789,756
10.0
7%
290,963
4.5
4.2
WV
1,455,370
126
11,551
13%
189,198
5.1
8.3
10%
145,537
6.6
2%
29,107
2.3
9.0
WY
381,882
31
12,319
26%
99,289
5.5
6.2
23%
87,833
5.7
6%
22,913
2.0
2.5
N/
A
­
Not
Available
Source:
NDS.

N­
4
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
5
Table
N.
2:
Participation
in
Recreational
Fishing
State
Pop.
16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
FishingSingle­
Day
TripsMultiple­
Day
Trips
Percent
Pop.

(
NDS­
based)
Percent
Pop.

(
USFW
S­
based)
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Days
per
Year
%
Pop.#
People
Avg
#
of
Trips
per
Person
per
Year
%
Pop.#
People
Avg
#
of
Trips
per
Person
per
Year
Mean
trip
length
(
days)

AK457,7282915,78459%
41%
270,06013.818.355%
251,75012.824%
109,8554.33.7
AL3,451,58621815,83325%
21%
862,89616.819.521%
724,83318.56%
207,0955.03.2
AR2,072,62212816,19237%
26%
766,87011.916.231%
642,51312.512%
248,7154.14.3
AZ3,907,52617821,95220%
14%
781,5058.116.515%
586,1297.011%
429,8285.43.8
CA25,599,2751,31319,49722%
12%
5,631,8406.513.116%
4,095,8846.610%
2,559,9283.84.8
CO3,322,45521215,67238%
23%
1,262,53312.019.530%
996,73612.217%
564,8175.14.3
CT2,651,45215916,67618%
14%
477,2616.98.016%
424,2327.14%
106,0582.13.6
DC468,5753513,388N/
AN/
AN/
AN/
AN/
AN/
AN/
AN/
AN/
AN/
AN/
AN/
A
DE610,2695111,96620%
19%
122,05410.316.018%
109,84810.84%
24,4113.010.5
FL12,741,82166219,24725%
18%
3,185,45515.317.421%
2,675,78216.65%
637,0914.73.7
GA6,250,70837316,75823%
18%
1,437,6638.812.619%
1,187,6359.88%
500,0572.94.6
HI949,1845517,25820%
14%
189,8376.16.218%
170,8536.62%
18,9841.03.0
IA2,281,00217113,33924%
23%
547,44011.916.719%
433,39013.47%
159,6703.85.4
ID969,1668311,67740%
32%
387,66612.319.433%
319,82510.820%
193,8336.03.5
IL9,530,32746620,45120%
18%
1,906,06513.727.416%
1,524,85214.27%
667,1237.85.9
IN4,682,39230015,60826%
19%
1,217,42211.014.623%
1,076,95011.18%
374,5913.84.0
KS2,058,48913515,24828%
19%
576,37711.318.821%
432,28312.213%
267,6044.84.3
KY3,161,28321914,43530%
23%
948,3859.013.925%
790,3219.212%
379,3543.94.0
LA3,394,85418917,96234%
26%
1,154,25015.018.929%
984,50815.38%
271,5887.83.2
MA5,008,00724920,11222%
12%
1,101,76215.324.018%
901,44113.36%
300,48014.13.4
MD4,085,34225715,89621%
15%
857,92212.215.718%
735,36212.96%
245,1212.95.7
ME1,010,2737214,03231%
21%
313,1859.911.228%
282,87610.56%
60,6162.04.5
MI7,628,17057613,24327%
20%
2,059,60610.516.420%
1,525,63411.310%
762,8175.04.3
MN3,782,81724515,44034%
31%
1,286,15810.318.724%
907,87610.919%
718,7354.54.4
MO4,331,93727715,63926%
23%
1,126,3045.810.520%
866,3875.59%
389,8744.34.2
MS2,160,16514015,43027%
21%
583,24517.019.325%
540,04116.87%
151,2125.82.5
MT701,4235512,75342%
24%
294,59819.526.236%
252,51219.720%
140,2854.94.0
NC6,291,18240715,45725%
20%
1,572,79611.215.120%
1,258,23612.512%
754,9423.23.4
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
2:
Participation
in
Recreational
Fishing
State
Pop.
16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Fishing
Single­
Day
Trips
Multiple­
Day
Trips
Percent
Pop.

(
NDSbased
Percent
Pop.

(
USFW
S­
based)
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Days
per
Year
%
Pop.
#
People
Avg
#
of
Trips
per
Person
per
Year
%
Pop.
#
People
Avg
#
of
Trips
per
Person
per
Year
Mean
trip
length
(
days)

ND
502,176
40
12,554
33%
24%
165,718
4.5
6.3
30%
150,653
3.9
10%
50,218
3.0
3.0
NE
1,314,974
84
15,654
20%
19%
262,995
11.9
24.8
14%
184,096
13.2
12%
157,797
4.3
6.0
NH
960,593
64
15,009
16%
18%
153,695
14.9
23.6
14%
134,483
13.2
8%
76,847
6.0
4.0
NJ
6,545,471
347
18,863
19%
13%
1,243,639
5.9
7.0
16%
1,047,275
6.1
4%
261,819
3.1
2.8
NM
1,370,134
105
13,049
22%
19%
301,429
8.7
12.3
16%
219,221
8.5
12%
164,416
4.2
2.7
NV
1,537,896
75
20,505
21%
17%
322,958
11.9
15.2
17%
261,442
13.5
5%
76,895
3.8
4.8
NY
14,797,284
774
19,118
15%
11%
2,219,593
8.8
16.2
12%
1,775,674
9.1
5%
739,864
4.4
6.0
OH
8,789,530
650
13,522
19%
13%
1,670,011
14.6
23.2
16%
1,406,325
14.0
7%
615,267
7.6
4.1
OK
2,665,966
143
18,643
32%
31%
853,109
13.0
12.9
28%
746,470
13.4
1%
26,660
4.1
9.0
OR
2,673,283
217
12,319
36%
21%
962,382
11.4
15.2
29%
775,252
11.8
13%
347,527
4.6
3.5
PA
9,693,987
742
13,065
21%
15%
2,035,737
10.8
16.1
17%
1,647,978
11.1
8%
775,519
5.0
3.7
RI
827,474
57
14,517
21%
14%
173,770
7.8
7.5
19%
157,220
8.3
2%
16,549
2.0
N/
A
SC
3,115,130
181
17,211
29%
24%
903,388
16.1
20.4
27%
841,085
15.6
7%
218,059
6.8
3.6
SD
577,391
42
13,747
26%
31%
150,122
8.2
13.7
24%
138,574
7.7
12%
69,287
2.6
5.5
TN
4,445,987
296
15,020
26%
17%
1,155,957
15.1
19.0
24%
1,067,037
14.8
6%
266,759
5.2
4.4
TX
15,618,097
657
23,772
29%
18%
4,529,248
10.2
16.6
23%
3,592,162
10.1
13%
2,030,353
5.3
3.6
UT
1,598,531
111
14,401
23%
21%
367,662
5.6
17.9
15%
239,780
4.1
11%
175,838
5.9
5.4
VA
5,529,436
389
14,214
26%
18%
1,437,653
8.2
13.0
19%
1,050,593
8.7
10%
552,944
4.3
4.0
VT
479,265
34
14,096
9%
19%
43,134
12.0
8.7
9%
43,134
8.7
3%
14,378
10.0
N/
A
WA
4,552,631
324
14,051
27%
22%
1,229,210
14.9
21.3
22%
1,001,579
16.1
12%
546,316
4.6
4.0
WI
4,156,609
299
13,902
29%
25%
1,205,417
10.4
18.4
22%
914,454
11.1
14%
581,925
4.5
4.6
WV
1,455,370
126
11,551
25%
18%
363,842
17.0
22.4
22%
320,181
16.3
8%
116,430
6.9
3.7
WY
381,882
31
12,319
58%
31%
221,492
12.9
46.0
52%
198,579
8.2
32%
122,202
10.0
7.0
N/
A
­
Not
Available
Source:
U.
S.
Fish
and
Wildlife
Service s
(
USFWS)
1996
National
Survey
of
Fishing
Hunting
and
Wildlife
Associated
Recreation.

N­
6
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
7
Table
N.
3:
Participation
in
Recreational
Swimming
State
State
Pop.

16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Swimming
Participation
in
Single­
Day
Trips
Participation
in
Multiple­
Day
Trips
Percent
Pop.
Number
of
People
Avg
#

Trips
per
Person
per
Year
Days
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
Trip
length
(
days)

AK457,7282915,7847%
32,0412.0N/
A7%
32,0412.03%
13,7321.0N/
A
AL3,451,58621815,83323%
793,8657.711.517%
586,7709.37%
241,6113.34.6
AR2,072,62212816,19223%
476,7038.520.121%
435,2516.89%
186,5365.96.0
AZ3,907,52617821,95219%
742,4304.57.613%
507,9785.37%
273,5271.95.6
CA25,599,2751,31319,49729%
7,423,7909.513.022%
5,631,84011.09%
2,303,9353.14.9
CO3,322,45521215,67217%
564,8174.06.412%
398,6955.06%
199,3471.74.8
CT2,651,45215916,67641%
1,087,09511.019.833%
874,97911.318%
477,2614.45.5
DC468,5753513,38817%
79,6582.56.49%
42,1722.09%
42,1723.33.0
DE610,2695111,96622%
134,2595.38.514%
85,4386.96%
36,6162.75.8
FL12,741,82166219,24733%
4,204,80113.317.926%
3,312,87314.98%
1,019,3465.24.9
GA6,250,70837316,75829%
1,812,7055.311.015%
937,6066.613%
812,5923.64.8
HI949,1845517,25858%
550,52719.227.656%
531,54316.524%
227,8048.33.4
IA2,281,00217113,33918%
410,5802.44.013%
296,5302.74%
91,2401.36.8
ID969,1668311,67725%
242,2928.911.123%
222,9088.88%
77,5333.13.0
IL9,530,32746620,45121%
2,001,3694.08.012%
1,143,6395.48%
762,4262.25.9
IN4,682,39230015,60822%
1,030,1265.08.917%
796,0075.48%
374,5912.35.5
KS2,058,48913515,24819%
391,1135.59.314%
288,1886.07%
144,0943.04.4
KY3,161,28321914,43517%
537,41811.013.211%
347,74115.75%
158,0641.85.5
LA3,394,85418917,96224%
814,7654.39.316%
543,1774.711%
373,4342.84.9
MA5,008,00724920,11241%
2,053,2839.417.834%
1,702,7228.914%
701,1216.15.0
MD4,085,34225715,89627%
1,103,0425.611.414%
571,9487.812%
490,2413.25.1
ME1,010,2737214,03246%
464,72614.529.740%
404,10912.815%
151,5419.95.8
MI7,628,17057613,24330%
2,288,4518.516.324%
1,830,7618.610%
762,8174.76.0
MN3,782,81724515,44024%
907,8765.46.518%
680,9076.65%
189,1412.23.4
MO4,331,93727715,63922%
953,0266.410.916%
693,1107.79%
389,8742.55.1
MS2,160,16514015,43021%
453,63510.512.817%
367,22811.96%
129,6102.54.4
MT701,4235512,75340%
280,5696.910.133%
231,4707.616%
112,2282.04.8
NC6,291,18240715,45723%
1,446,9725.710.815%
943,6777.010%
629,1182.46.0
ND502,1764012,55425%
125,5448.0N/
A15%
75,32611.513%
65,2832.4N/
A
NE1,314,9748415,65419%
249,8453.510.715%
197,2463.96%
78,8981.615.0
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
3:
Participation
in
Recreational
Swimming
State
State
Pop.

16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Swimming
Participation
in
Single­
Day
Trips
Participation
in
Multiple­
Day
Trips
Percent
Pop.
Number
of
People
Avg
#

Trips
per
Person
per
Year
Days
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
Trip
length
(
days)

NH
960,593
64
15,009
42%
403,449
15.8
56.5
38%
365,025
14.5
16%
153,695
7.2
15.8
NJ
6,545,471
347
18,863
39%
2,552,734
6.2
12.7
28%
1,832,732
6.9
16%
1,047,275
3.1
6.1
NM
1,370,134
105
13,049
15%
205,520
2.7
4.2
10%
137,013
3.8
5%
68,507
1.4
3.7
NV
1,537,896
75
20,505
19%
292,200
6.3
13.2
12%
184,548
6.3
9%
138,411
4.6
4.2
NY
14,797,284
774
19,118
33%
4,883,104
7.6
15.0
25%
3,699,321
8.1
11%
1,627,701
4.5
6.0
OH
8,789,530
650
13,522
23%
2,021,592
7.3
15.6
15%
1,318,430
8.7
6%
527,372
4.7
8.1
OK
2,665,966
143
18,643
28%
746,470
3.4
5.7
16%
426,555
4.1
8%
213,277
2.9
4.1
OR
2,673,283
217
12,319
34%
908,916
6.7
12.7
27%
721,786
7.1
12%
320,794
3.2
6.4
PA
9,693,987
742
13,065
28%
2,714,316
5.7
10.4
17%
1,647,978
7.5
12%
1,163,278
2.7
5.1
RI
827,474
57
14,517
40%
330,990
6.9
N/
A
37%
306,165
7.0
11%
91,022
2.5
N/
A
SC
3,115,130
181
17,211
22%
685,329
6.0
9.4
17%
529,572
6.9
5%
155,756
3.0
6.0
SD
577,391
42
13,747
24%
138,574
7.3
9.2
24%
138,574
7.2
7%
40,417
1.0
7.0
TN
4,445,987
296
15,020
23%
1,022,577
5.8
9.7
17%
755,818
6.7
8%
355,679
2.5
5.4
TX
15,618,097
657
23,772
24%
3,748,343
5.1
7.7
16%
2,498,896
6.0
9%
1,405,629
2.3
4.3
UT
1,598,531
111
14,401
20%
319,706
5.9
10.4
15%
239,780
6.2
10%
159,853
2.5
4.5
VA
5,529,436
389
14,214
28%
1,548,242
4.9
11.1
17%
940,004
5.5
13%
718,827
3.2
5.2
VT
479,265
34
14,096
26%
124,609
12.3
19.6
24%
115,024
11.6
6%
28,756
8.5
4.5
WA
4,552,631
324
14,051
35%
1,593,421
5.4
10.7
28%
1,274,737
5.7
14%
637,368
2.4
6.3
WI
4,156,609
299
13,902
27%
1,122,284
5.5
8.9
22%
914,454
6.1
7%
290,963
2.1
7.0
WV
1,455,370
126
11,551
25%
363,842
6.5
12.7
18%
261,967
6.4
9%
130,983
5.1
4.4
WY
381,882
31
12,319
6%
22,913
8.0
N/
A
6%
22,913
8.0
3%
11,456
1.0
N/
A
N/
A
­
Not
Available
Source:
NDS.

N­
8
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
9
Table
N.
4:
Participation
in
Wildlife
Viewing
(
Near­
Water
Recreation)

State
Pop.
16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Near­
Water
RecreationSingle­
Day
TripsMultiple­
Day
Trips
Percent
Pop.

(
NDS­
based)
Percent
Pop.

(
USFWS­

based)
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Days
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
trip
length
AK457,7282915,78448%
50%
219,7097.28.441%
187,6687.17%
32,0418.02.0
AL3,451,58621815,83336%
30%
1,242,5714.48.212%
414,1909.224%
828,3811.94.0
AR2,072,62212816,19228%
34%
580,3346.415.013%
269,44110.216%
331,6203.35.5
AZ3,907,52617821,95225%
31%
976,8824.78.711%
429,8288.013%
507,9782.14.7
CA25,599,2751,31319,49751%
25%
13,055,63011.214.337%
9,471,73214.016%
4,095,8843.24.2
CO3,322,45521215,67225%
42%
830,6148.611.713%
431,91914.810%
332,2461.95.4
CT2,651,45215916,67660%
31%
1,590,8715.38.838%
1,007,5526.720%
530,2903.14.4
DC468,5753513,38851%
N/
A238,9733.930.723%
107,7722.431%
145,2584.610.5
DE610,2695111,96657%
34%
347,8539.916.641%
250,21011.024%
146,4654.74.5
FL12,741,82166219,24744%
25%
5,606,40114.218.232%
4,077,38317.913%
1,656,4373.74.7
GA6,250,70837316,75836%
29%
2,250,2553.19.414%
875,0994.121%
1,312,6492.65.2
HI949,1845517,25864%
14%
607,47830.330.756%
531,54333.99%
85,4271.84.0
IA2,281,00217113,33932%
38%
729,9212.97.116%
364,9604.415%
342,1501.29.0
ID969,1668311,67743%
40%
416,7413.27.024%
232,6004.223%
222,9081.55.6
IL9,530,32746620,45131%
35%
2,954,4015.910.617%
1,620,1569.013%
1,238,9432.26.1
IN4,682,39230015,60831%
35%
1,451,5425.411.215%
702,3599.014%
655,5352.46.3
KS2,058,48913515,24833%
32%
679,3015.812.817%
349,9439.014%
288,1882.57.7
KY3,161,28321914,43528%
32%
885,1592.49.112%
379,3543.015%
474,1922.07.3
LA3,394,85418917,96234%
27%
1,154,2503.28.515%
509,2283.419%
645,0223.14.0
MA5,008,00724920,11250%
35%
2,504,0049.821.031%
1,552,48211.522%
1,101,7625.95.3
MD4,085,34225715,89646%
34%
1,879,2576.312.718%
735,36212.129%
1,184,7492.45.2
ME1,010,2737214,03254%
46%
545,5475.46.644%
444,5205.711%
111,1303.52.8
MI7,628,17057613,24344%
36%
3,356,3956.310.324%
1,830,7619.416%
1,220,5072.75.2
MN3,782,81724515,44033%
38%
1,248,33010.515.219%
718,73516.514%
529,5942.45.5
MO4,331,93727715,63932%
40%
1,386,2202.78.113%
563,1524.017%
736,4292.15.8
MS2,160,16514015,43029%
23%
626,44811.315.212%
259,22024.214%
302,4231.85.7
MT701,4235512,75333%
47%
231,47010.112.920%
140,28515.615%
105,2131.26.0
NC6,291,18240715,45745%
35%
2,831,0324.111.518%
1,132,4135.229%
1,824,4433.24.5
ND502,1764012,55425%
23%
125,5442.63.415%
75,3263.05%
25,1094.02.0
NE1,314,9748415,65425%
35%
328,7441.85.914%
184,0962.18%
105,1981.78.6
NH960,5936415,00942%
44%
403,44912.221.531%
297,78414.99%
86,4535.29.5
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
4:
Participation
in
Wildlife
Viewing
(
Near­
Water
Recreation)

State
Pop.
16
and
Up
NDS
Sample
Size
Sample
Weight
Total
Participation
in
Near­
Water
Recreation
Single­
Day
Trips
Multiple­
Day
Trips
Percent
Pop.

(
NDSbased
Percent
Pop.

(
USFWSbased
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Days
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Percent
Pop.
Number
of
People
Avg
#
of
Trips
per
Person
per
Year
Mean
trip
length
NJ
6,545,471
347
18,863
54%
26%
3,534,554
5.5
11.8
32%
2,094,551
6.3
23%
1,505,458
3.7
5.0
NM
1,370,134
105
13,049
29%
29%
397,339
2.6
7.8
9%
123,312
5.6
18%
246,624
1.4
6.9
NV
1,537,896
75
20,505
35%
21%
538,264
6.2
11.0
21%
322,958
7.2
23%
353,716
2.6
3.8
NY
14,797,284
774
19,118
45%
23%
6,658,778
4.3
9.9
25%
3,699,321
5.7
18%
2,663,511
2.2
7.5
OH
8,789,530
650
13,522
36%
33%
3,164,231
4.7
11.1
16%
1,406,325
8.2
19%
1,670,011
1.9
7.3
OK
2,665,966
143
18,643
34%
35%
906,428
1.9
5.3
12%
319,916
3.4
17%
453,214
1.5
5.4
OR
2,673,283
217
12,319
59%
42%
1,577,237
6.4
12.4
38%
1,015,848
7.2
33%
882,183
3.3
4.3
PA
9,693,987
742
13,065
39%
37%
3,780,655
3.9
9.4
14%
1,357,158
7.4
24%
2,326,557
1.9
5.7
RI
827,474
57
14,517
56%
32%
463,385
4.0
9.2
40%
330,990
4.6
9%
74,473
4.6
8.0
SC
3,115,130
181
17,211
45%
29%
1,401,808
5.3
10.9
20%
623,026
8.3
25%
778,782
2.8
4.7
SD
577,391
42
13,747
29%
30%
167,443
2.1
7.9
21%
121,252
1.8
5%
28,870
4.5
8.5
TN
4,445,987
296
15,020
41%
37%
1,822,855
2.1
6.1
13%
577,978
3.7
25%
1,111,497
1.4
5.7
TX
15,618,097
657
23,772
33%
25%
5,153,972
3.6
7.6
16%
2,498,896
5.0
16%
2,498,896
2.2
4.8
UT
1,598,531
111
14,401
31%
30%
495,545
2.4
4.6
17%
271,750
3.5
11%
175,838
1.2
5.9
VA
5,529,436
389
14,214
41%
37%
2,267,069
3.4
11.4
17%
940,004
4.2
25%
1,382,359
2.7
5.7
VT
479,265
34
14,096
47%
48%
225,255
5.6
9.6
18%
86,268
5.5
32%
153,365
2.5
4.3
WA
4,552,631
324
14,051
58%
39%
2,640,526
9.2
13.4
40%
1,821,052
11.6
29%
1,320,263
2.6
4.1
WI
4,156,609
299
13,902
38%
42%
1,579,511
4.6
8.8
22%
914,454
6.1
16%
665,057
2.3
5.4
WV
1,455,370
126
11,551
27%
31%
392,950
4.3
16.1
10%
145,537
4.6
17%
247,413
3.9
5.8
WY
381,882
31
12,319
29%
39%
110,746
3.1
4.5
16%
61,101
4.6
13%
49,645
1.2
3.5
N/
A
­
Not
Available
Source:
U.
S.
Fish
and
Wildlife
Service s
(
USFWS)
1996
National
Survey
of
Fishing
Hunting
and
Wildlife
Associated
Recreation.

N­
10
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
4
ALLOCATION
OF
TRIPS
BY
WATER
BODY
TYPE
This
analysis
assesses
the
allocation
of
trips
by
water
body
type,
recreation
activity,
and
state
of
residence.
EPA
determined
the
number
of
trips
taken
to
each
water
body
type
based
on
the
water
body
type
visited
on
the
last
single­
or
multiple­
day
trip
for
each
recreation
activity.
Dividing
the
total
number
of
trips
taken
in
a
state
to
a
given
water
body
type
for
a
given
activity
by
the
total
number
of
trips
taken
for
that
activity
in
the
state
provided
estimates
of
the
percent
taken
to
the
various
water
body
types.
The
NDS
distinguishes
four
general
water
body
types:

 
Lakes:

 
lakes,

 
ponds,
and
 
reservoirs;

 
Streams:

 
rivers,

 
streams,
and
 
canals;

 
Oceans:

 
oceans,

 
bays,
and
 
sounds;
and
 
Other:

 
wetlands,
and
 
unknown
water
body
types.

Note
that
respondents
in
several
states
apparently
provided
inaccurate
information.
For
example,
Montana
residents
are
unlikely
to
take
single­
day
trips
to
the
ocean.
The
data
indicate,
however,
that
five,
six,
and
eleven
percent
of
participants
reported
that
they
took
single­
day
fishing,
swimming,
and
viewing
trips
to
the
ocean,
respectively.
This
inconsistency
may
arise
due
to
the
following
two
factors:

 
respondents
traveled
to
other
states
for
multi­
purpose
multiple­
day
trips
and
participated
in
the
given
activity
on
only
one
day
per
trip;
and
 
response
errors
(
e.
g.,
some
respondents
identified
water
body
types
incorrectly).

Tables
N.
5
and
N.
6
show
allocation
of
single­
and
multiple­
day
trips
by
water
body
type
for
boating,
fishing,
swimming,
and
viewing.

N­
11
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
12
Table
N.
5:
Allocation
of
Single­
Day
Trips
by
Water
Body
Type
State
Boating
(%)
Fishing
(%)
Swimming
(%)
Viewing
(%)
LakeStreamOceanaOtherbLakeStreamOceanOtherLakeStreamOceanOtherLakeStreamOceanOther
AK30%
20%
50%
0%
9%
45%
45%
0%
100%
0%
0%
0%
18%
18%
64%
0%

AL50%
44%
6%
0%
56%
29%
16%
0%
57%
13%
30%
0%
25%
15%
55%
5%

AR78%
22%
0%
0%
78%
22%
0%
0%
78%
17%
0%
4%
38%
38%
23%
0%

AZ100%
0%
0%
0%
76%
19%
5%
0%
63%
32%
5%
0%
50%
11%
33%
6%

CA38%
8%
51%
2%
43%
16%
40%
1%
28%
9%
61%
2%
11%
2%
86%
1%

CO79%
21%
0%
0%
65%
33%
2%
0%
83%
4%
8%
4%
65%
15%
19%
0%

CT38%
27%
35%
0%
35%
22%
43%
0%
33%
5%
60%
2%
20%
9%
69%
2%

DC0%
33%
67%
0%
N/
AN/
AN/
AN/
A67%
0%
33%
0%
0%
50%
50%
0%

DE25%
0%
75%
0%
25%
38%
38%
0%
0%
0%
100%
0%
18%
6%
76%
0%

FL15%
27%
56%
1%
24%
24%
52%
1%
13%
9%
79%
0%
4%
5%
90%
1%

GA79%
12%
9%
0%
60%
21%
18%
2%
67%
7%
23%
2%
53%
2%
44%
0%

HI22%
0%
78%
0%
10%
0%
90%
0%
0%
0%
100%
0%
0%
0%
93%
7%

IA43%
52%
0%
4%
59%
38%
0%
3%
70%
17%
9%
4%
60%
28%
8%
4%

ID65%
35%
0%
0%
47%
53%
0%
0%
59%
35%
6%
0%
44%
44%
6%
6%

IL55%
40%
5%
0%
74%
22%
4%
0%
93%
7%
0%
0%
76%
11%
9%
4%

IN88%
12%
0%
0%
82%
13%
5%
0%
92%
4%
2%
2%
78%
15%
5%
2%

KS100%
0%
0%
0%
96%
4%
0%
0%
94%
6%
0%
0%
71%
10%
14%
5%

KY77%
23%
0%
0%
73%
24%
2%
0%
64%
18%
18%
0%
58%
25%
13%
4%

LA45%
45%
10%
0%
39%
43%
14%
4%
38%
27%
27%
8%
32%
16%
48%
4%

MA21%
36%
44%
0%
49%
21%
31%
0%
33%
4%
63%
0%
15%
8%
76%
2%

MD13%
34%
53%
0%
30%
32%
39%
0%
23%
19%
58%
0%
10%
18%
70%
3%

ME63%
19%
19%
0%
65%
15%
20%
0%
67%
4%
30%
0%
19%
6%
74%
0%

MI80%
14%
5%
0%
73%
20%
8%
0%
92%
3%
5%
0%
81%
8%
8%
3%

MN77%
23%
0%
0%
90%
10%
0%
0%
84%
7%
7%
2%
95%
5%
0%
0%

MO53%
42%
3%
3%
73%
21%
4%
2%
52%
36%
7%
5%
60%
30%
7%
3%

MS47%
42%
11%
0%
76%
15%
9%
0%
50%
36%
14%
0%
38%
13%
50%
0%

MT75%
25%
0%
0%
42%
53%
5%
0%
63%
31%
6%
0%
78%
11%
11%
0%

NC61%
19%
19%
0%
52%
24%
24%
0%
36%
15%
42%
7%
22%
14%
63%
2%

ND100%
0%
0%
0%
80%
20%
0%
0%
83%
17%
0%
0%
100%
0%
0%
0%

NE89%
11%
0%
0%
100%
0%
0%
0%
77%
23%
0%
0%
64%
27%
9%
0%
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
5:
Allocation
of
Single­
Day
Trips
by
Water
Body
Type
State
Boating
(%)
Fishing
(%)
Swimming
(%)
Viewing
(%)

Lake
Stream
Oceana
Otherb
Lake
Stream
Ocean
Other
Lake
Stream
Ocean
Other
Lake
Stream
Ocean
Other
NH
58%
25%
17%
0%
44%
44%
11%
0%
55%
0%
41%
5%
25%
0%
75%
0%

NJ
24%
11%
65%
0%
31%
13%
55%
2%
20%
2%
77%
0%
9%
4%
86%
1%

NM
43%
43%
14%
0%
38%
62%
0%
0%
50%
30%
20%
0%
25%
38%
38%
0%

NV
92%
0%
8%
0%
60%
40%
0%
0%
100%
0%
0%
0%
85%
0%
8%
8%

NY
47%
18%
35%
0%
53%
21%
26%
0%
43%
7%
49%
1%
40%
9%
50%
1%

OH
83%
11%
5%
1%
84%
13%
2%
1%
86%
4%
7%
3%
71%
9%
18%
2%

OK
88%
13%
0%
0%
94%
3%
3%
0%
80%
15%
0%
5%
87%
7%
7%
0%

OR
41%
36%
23%
0%
31%
56%
13%
0%
50%
26%
22%
2%
11%
13%
77%
0%

PA
46%
32%
19%
3%
54%
27%
18%
2%
53%
19%
26%
2%
37%
10%
51%
2%

RI
11%
22%
67%
0%
36%
18%
45%
0%
29%
5%
67%
0%
4%
0%
96%
0%

SC
64%
20%
12%
4%
66%
13%
19%
2%
68%
4%
29%
0%
31%
7%
62%
0%

SD
100%
0%
0%
0%
57%
43%
0%
0%
89%
11%
0%
0%
75%
13%
13%
0%

TN
75%
17%
8%
0%
63%
34%
3%
0%
72%
23%
5%
0%
48%
18%
33%
0%

TX
74%
8%
18%
0%
64%
13%
23%
0%
62%
16%
20%
2%
41%
10%
48%
1%

UT
78%
0%
22%
0%
87%
13%
0%
0%
64%
36%
0%
0%
89%
6%
6%
0%

VA
31%
35%
35%
0%
27%
38%
35%
0%
23%
17%
58%
2%
16%
13%
70%
2%

VT
100%
0%
0%
0%
67%
33%
0%
0%
71%
14%
14%
0%
80%
0%
20%
0%

WA
38%
27%
33%
1%
36%
30%
34%
0%
63%
25%
12%
0%
21%
11%
67%
1%

WI
66%
30%
4%
0%
78%
20%
2%
0%
80%
10%
5%
5%
73%
13%
7%
7%

WV
83%
8%
8%
0%
43%
57%
0%
0%
55%
35%
10%
0%
55%
18%
27%
0%

WY
83%
0%
17%
0%
73%
27%
0%
0%
100%
0%
0%
0%
100%
0%
0%
0%

a
Note
that
respondents
in
several
states
apparently
provided
inaccurate
information
because
some
states
at
great
distances
from
the
ocean
report
individuals
taking
single­

day
trips
to
the
ocean.

b
Other
includes
wetlands
and
unknown
water
body
types.

N/
A
­
Not
Available
Source:
NDS.
N­
13
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
14
Table
N.
6:
Allocation
of
Multiple­
Day
Trips
by
Water
Body
Type
State
Boating
(%)
Fishing
(%)
Swimming
(%)
Viewing
(%)
LakeStreamOceanOtheraLakeStreamOceanOtherLakeStreamOceanOtherLakeStreamOceanOther
AK75%
0%
25%
0%
33%
0%
67%
0%
0%
0%
0%
0%
33%
0%
33%
33%

AL43%
0%
43%
14%
40%
0%
40%
20%
19%
0%
62%
19%
7%
0%
72%
21%

AR71%
14%
0%
14%
45%
36%
0%
18%
0%
17%
67%
17%
30%
0%
43%
26%

AZ63%
13%
25%
0%
73%
13%
7%
7%
7%
7%
50%
36%
7%
11%
52%
30%

CA49%
23%
25%
3%
58%
17%
11%
13%
19%
12%
41%
29%
13%
6%
52%
29%

CO85%
8%
0%
8%
48%
36%
12%
4%
33%
0%
25%
42%
30%
4%
41%
26%

CT33%
33%
33%
0%
60%
0%
40%
0%
14%
5%
73%
9%
10%
0%
60%
30%

DC0%
0%
100%
0%
N/
AN/
AN/
AN/
A0%
0%
67%
33%
8%
0%
75%
17%

DE100%
0%
0%
0%
50%
0%
50%
0%
0%
0%
100%
0%
0%
8%
83%
8%

FL11%
25%
43%
21%
21%
11%
36%
32%
9%
2%
52%
38%
7%
3%
53%
36%

GA46%
19%
27%
8%
24%
12%
40%
24%
15%
5%
62%
18%
8%
2%
70%
20%

HI0%
0%
50%
50%
0%
0%
100%
0%
0%
0%
83%
17%
0%
0%
40%
60%

IA78%
0%
11%
11%
78%
22%
0%
0%
14%
0%
57%
29%
21%
7%
52%
21%

ID80%
20%
0%
0%
56%
38%
0%
6%
75%
25%
0%
0%
50%
11%
28%
11%

IL58%
23%
10%
10%
70%
7%
11%
11%
33%
8%
25%
35%
23%
7%
36%
34%

IN68%
16%
5%
11%
69%
6%
13%
13%
35%
0%
24%
41%
17%
4%
57%
23%

KS100%
0%
0%
0%
100%
0%
0%
0%
63%
0%
25%
13%
30%
9%
39%
22%

KY78%
22%
0%
0%
71%
18%
0%
12%
25%
19%
38%
19%
5%
0%
78%
16%

LA57%
0%
14%
29%
31%
15%
31%
23%
11%
0%
79%
11%
8%
5%
67%
21%

MA42%
11%
32%
16%
53%
0%
20%
27%
7%
0%
63%
30%
16%
3%
52%
29%

MD18%
9%
55%
18%
44%
33%
11%
11%
15%
5%
69%
10%
9%
5%
71%
15%

ME63%
13%
13%
13%
50%
50%
0%
0%
50%
0%
17%
33%
25%
0%
25%
50%

MI76%
10%
10%
4%
65%
8%
6%
20%
50%
6%
22%
22%
40%
2%
22%
36%

MN63%
6%
6%
25%
83%
9%
0%
9%
50%
0%
6%
44%
44%
2%
30%
23%

MO75%
17%
4%
4%
52%
29%
5%
14%
30%
10%
25%
35%
20%
5%
42%
32%

MS50%
33%
17%
0%
60%
0%
20%
20%
14%
0%
57%
29%
8%
4%
67%
21%

MT25%
25%
25%
25%
50%
50%
0%
0%
50%
0%
17%
33%
33%
11%
33%
22%

NC52%
19%
19%
10%
11%
8%
72%
8%
3%
3%
79%
16%
2%
2%
82%
13%
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
6:
Allocation
of
Multiple­
Day
Trips
by
Water
Body
Type
State
Boating
(%)
Fishing
(%)
Swimming
(%)
Viewing
(%)

Lake
Stream
Ocean
Othera
Lake
Stream
Ocean
Other
Lake
Stream
Ocean
Other
Lake
Stream
Ocean
Other
ND
100%
0%
0%
0%
100%
0%
0%
0%
25%
0%
0%
75%
25%
0%
0%
75%

NE
75%
25%
0%
0%
75%
13%
13%
0%
33%
0%
33%
33%
40%
10%
30%
20%

NH
0%
33%
67%
0%
0%
0%
100%
0%
40%
0%
60%
0%
14%
0%
71%
14%

NJ
0%
0%
67%
33%
18%
18%
18%
45%
4%
0%
83%
13%
9%
3%
58%
30%

NM
85%
8%
0%
8%
70%
20%
10%
0%
33%
17%
0%
50%
27%
0%
50%
23%

NV
75%
0%
25%
0%
33%
17%
17%
33%
20%
20%
40%
20%
31%
0%
62%
8%

NY
41%
17%
22%
20%
50%
17%
20%
13%
22%
0%
62%
16%
12%
5%
55%
28%

OH
68%
18%
9%
6%
70%
13%
7%
10%
14%
5%
59%
21%
20%
2%
59%
19%

OK
62%
8%
8%
23%
50%
10%
10%
30%
30%
10%
25%
35%
21%
6%
38%
35%

OR
38%
31%
15%
15%
42%
33%
8%
17%
9%
17%
30%
43%
8%
5%
72%
16%

PA
42%
9%
36%
12%
53%
16%
22%
9%
17%
3%
64%
16%
12%
4%
65%
20%

RI
0%
0%
0%
0%
0%
0%
0%
100%
0%
0%
0%
100%
11%
11%
33%
44%

SC
56%
0%
33%
11%
17%
17%
50%
17%
0%
8%
67%
25%
2%
2%
81%
15%

SD
75%
0%
0%
25%
25%
75%
0%
0%
100%
0%
0%
0%
25%
0%
25%
50%

TN
50%
14%
21%
14%
33%
33%
11%
22%
15%
4%
65%
15%
11%
1%
71%
16%

TX
53%
6%
26%
15%
53%
10%
25%
12%
16%
16%
42%
26%
17%
2%
57%
24%

UT
85%
0%
0%
15%
70%
10%
0%
20%
50%
25%
0%
25%
25%
0%
38%
38%

VA
35%
15%
45%
5%
24%
19%
43%
14%
12%
2%
71%
16%
4%
3%
78%
15%

VT
100%
0%
0%
0%
0%
0%
0%
0%
50%
0%
50%
0%
55%
0%
45%
0%

WA
27%
27%
36%
9%
29%
32%
25%
14%
29%
19%
39%
13%
12%
9%
68%
11%

WI
78%
11%
11%
0%
73%
15%
3%
9%
43%
9%
30%
17%
34%
4%
38%
25%

WV
20%
20%
0%
60%
25%
50%
0%
25%
9%
9%
55%
27%
9%
9%
70%
13%

WY
100%
0%
0%
0%
100%
0%
0%
0%
0%
0%
0%
0%
50%
0%
0%
50%

a
Other
includes
wetlands
and
unknown
water
body
types.

N/
A
­
Not
Available
Source:
NDS.
N­
15
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
5
ONE­
WAY
TRAVEL
DISTANCE
This
analysis
estimates
the
average
one­
way
distance
to
sites
by
trip
duration
(
i.
e.,
single
day
versus
multi­
day
trips),
trip
length,
recreation
activity,
and
state
of
residence.
EPA
estimated
the
mean
one­
way
distance
traveled
based
on
the
distance
reported
for
the
last
single­
or
multiple­
day
trip
for
each
activity.
As
shown
in
Table
N.
7,
some
respondents
indicated
traveling
to
the
ocean
across
long
distances
on
single­
day
trips.
These
values
are
likely
to
be
due
to
the
following
two
factors:

 
respondents
traveled
long
distances
for
multi­
purpose
multiple­
day
trips
and
participated
in
the
given
activity
on
only
one
day
on
the
trip;
and
 
response
errors.

EPA
estimated
the
average
travel
distance
traveled
after
dropping
outliers
because
these
outliers
may
provide
undue
influence
on
sample
means.

N­
16
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
7:
Average
One­
Way
Distance
State
Miles
to
Single­
Day
Site
Miles
to
Multiple­
Day
Site
Boating
Fishing
Swimming
Viewing
Boating
Fishing
Swimming
Viewing
AK
41
47
32
39
76
193
N/
A
43
AL
31
29
35
53
93
218
230
214
AR
52
38
19
222
215
246
282
394
AZ
54
45
44
117
205
323
413
383
CA
32
40
26
31
233
316
272
226
CO
41
56
15
69
372
260
548
894
CT
30
41
36
49
168
161
194
330
DC
46
N/
A
417
85
1000a
N/
A
165
688
DE
36
32
50
189
1,625
1,700
85
248
FL
21
23
20
24
317
381
154
237
GA
34
52
42
46
199
283
261
336
HI
37
13
14
13
3
80
32
45
IA
60
25
26
49
314
321
228
1,354
ID
35
48
101
54
228
146
100
507
IL
52
34
30
29
255
368
213
707
IN
47
29
50
64
295
378
368
813
KS
42
22
52
68
151
177
272
861
KY
55
32
46
106
151
143
391
697
LA
30
27
39
53
132
76
244
245
MA
22
25
30
29
136
154
144
398
MD
36
40
56
38
581
199
200
263
ME
44
24
30
23
436
148
152
31
MI
31
33
25
32
192
249
234
387
MN
45
55
16
16
354
185
132
552
MO
42
40
39
71
195
265
200
628
MS
11
27
36
39
72
122
203
483
MT
43
172
31
102
588
154
352
500
NC
42
49
45
67
153
182
264
262
ND
69
55
35
75
154
130
45
120
NE
70
29
71
56
125
603
400
152
NH
27
24
28
34
186
248
108
712
NJ
49
31
41
41
483
179
227
476
NM
76
50
71
252
161
191
207
1,315
NV
108
46
43
53
48
401
254
565
N­
17
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
7:
Average
One­
Way
Distance
State
Miles
to
Single­
Day
Site
Miles
to
Multiple­
Day
Site
Boating
Fishing
Swimming
Viewing
Boating
Fishing
Swimming
Viewing
NY
32
26
25
37
202
195
194
692
OH
63
38
30
45
265
262
498
778
OK
62
50
46
47
189
244
232
542
OR
31
36
33
51
398
200
97
143
PA
40
38
36
57
296
228
210
391
RI
10
26
18
26
433
SC
15
33
40
60
713
132
200
250
SD
43
35
19
46
352
143
400
740
TN
29
27
24
84
61
888
493
481
TX
40
38
38
65
190
187
261
442
UT
43
66
44
68
235
122
207
598
VA
37
30
39
69
407
159
256
303
VT
41
20
33
50
70
78
334
WA
23
28
20
41
154
198
205
277
WI
34
30
30
33
289
303
104
545
WV
86
30
95
158
338
278
328
429
WY
69
46
32
47
73
56
230
a
Based
on
one
observation
only.

N/
A
­
Not
Available
Source:
NDS.

N­
18
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
6
INDIVIDUAL
EXPENDITURES
PER
TRIP
This
analysis
estimates
the
mean
total
expenditures
per
person
by
trip
length,
recreation
activity,
and
state
of
residence.
Total
expenditures
for
single­
day
boating,
fishing,
and
viewing
trips
consist
of
transportation,
entrance
fee,
and
boat
rental.
Total
expenditures
for
multiple­
day
boating,
fishing,
and
viewing
trips
include
expenses
for
transportation,
entrance
fees,
boat
rental,
and
lodging.
Transportation
includes
expenses
for
plane,
train,
bus,
or
ship
only,
and
do
not
reflect
costs
associated
with
operating
a
car.
Expenditures
on
single­
day
and
multiple­
day
swimming
trips
do
not
include
boat
rental.
Expenditures
on
single­
day
and
multiple­
day
trips
for
all
activities
do
not
include
bait,
tackle,
recreational
clothing
and
equipment
(
e.
g.,

photographic
supply
and
binoculars),
boat
ownership,
or
food.
Results
of
the
analysis
are
presented
below
in
Table
N.
8.

N­
19
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
8:
Individual
Expenditures
per
Trip
State
Average
Expenditures
per
Person
on
Single­
day
Trips
(
1993$
per
trip)
Average
Expenditures
per
Person
on
Multiple­
day
Trips
(
1993$
per
trip)

Boating
Fishing
Swimming
Viewing
Boating
Fishing
Swimming
Viewing
AK
$
16
$
10
$
0
$
12
$
66
$
98
$
0
$
70
AL
$
14
$
15
$
2
$
5
$
23
$
421
$
153
$
261
AR
$
18
$
24
$
1
$
1
$
59
$
48
$
361
$
399
AZ
$
16
$
5
$
7
$
3
$
41
$
84
$
184
$
126
CA
$
53
$
22
$
5
$
3
$
454
$
220
$
455
$
328
CO
$
49
$
11
$
3
$
18
$
320
$
235
$
248
$
325
CT
$
19
$
12
$
35
$
7
$
387
$
114
$
330
$
505
DCa
$
17
N/
A
$
2
$
3
$
2,000
N/
A
$
200
$
354
DE
$
6
$
18
$
2
$
2
$
43
$
63
$
325
$
120
FL
$
22
$
22
$
2
$
4
$
376
$
852
$
234
$
375
GA
$
19
$
17
$
8
$
28
$
147
$
275
$
279
$
249
HI
$
22
$
7
$
0
$
0
$
110
$
0
$
118
$
75
IA
$
8
$
2
$
1
$
2
$
119
$
662
$
340
$
488
ID
$
21
$
0
$
1
$
1
$
54
$
29
$
63
$
118
IL
$
37
$
9
$
2
$
1
$
342
$
333
$
241
$
495
IN
$
18
$
10
$
3
$
14
$
299
$
321
$
175
$
661
KS
$
19
$
3
$
2
$
2
$
89
$
175
$
178
$
518
KY
$
18
$
2
$
35
$
18
$
340
$
180
$
117
$
298
LA
$
50
$
14
$
1
$
1
$
186
$
58
$
251
$
245
MA
$
19
$
13
$
18
$
3
$
89
$
197
$
309
$
274
MD
$
49
$
51
$
2
$
36
$
116
$
178
$
300
$
288
ME
$
2
$
2
$
1
$
2
$
329
$
44
$
143
$
22
MI
$
26
$
7
$
2
$
4
$
227
$
125
$
379
$
255
MN
$
10
$
6
$
2
$
1
$
198
$
160
$
99
$
261
MO
$
26
$
11
$
2
$
9
$
164
$
122
$
278
$
352
MS
$
14
$
26
$
1
$
1
$
52
$
169
$
181
$
329
N­
20
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
8:
Individual
Expenditures
per
Trip
State
Average
Expenditures
per
Person
on
Single­
day
Trips
(
1993$
per
trip)
Average
Expenditures
per
Person
on
Multiple­
day
Trips
(
1993$
per
trip)

Boating
Fishing
Swimming
Viewing
Boating
Fishing
Swimming
Viewing
MT
$
8
$
23
$
1
$
0
$
25
$
95
$
542
$
86
NC
$
13
$
26
$
24
$
3
$
165
$
132
$
393
$
227
ND
$
14
$
2
$
0
$
0
$
53
$
3
$
0
$
0
NE
$
3
$
4
$
85
$
2
$
24
$
310
$
150
$
237
NH
$
16
$
7
$
3
$
0
$
127
$
0
$
955
$
342
NJ
$
32
$
44
$
13
$
7
$
360
$
168
$
631
$
414
NM
$
15
$
3
$
22
$
32
$
73
$
78
$
41
$
218
NV
$
25
$
1
$
1
$
4
$
104
$
25
$
554
$
116
NY
$
25
$
29
$
5
$
8
$
242
$
76
$
298
$
459
OH
$
26
$
15
$
8
$
22
$
403
$
239
$
560
$
465
OK
$
11
$
22
$
2
$
3
$
173
$
314
$
137
$
268
OR
$
15
$
5
$
22
$
1
$
429
$
51
$
543
$
248
PA
$
23
$
21
$
19
$
9
$
275
$
310
$
275
$
399
RI
$
23
$
16
$
3
$
2
$
0
$
0
$
0
$
240
SC
$
27
$
12
$
2
$
7
$
576
$
201
$
731
$
265
SD
$
5
$
6
$
0
$
2
$
248
$
54
$
10
$
715
TN
$
26
$
4
$
0
$
14
$
458
$
49
$
329
$
315
TX
$
152
$
12
$
2
$
3
$
151
$
138
$
324
$
349
UT
$
25
$
2
$
4
$
13
$
164
$
10
$
117
$
419
VA
$
10
$
23
$
1
$
243
$
175
$
116
$
317
$
319
VT
$
6
$
1
$
6
$
2
$
100
$
0
$
22
$
372
WA
$
11
$
19
$
13
$
1
$
266
$
170
$
217
$
165
WI
$
10
$
5
$
3
$
8
$
425
$
135
$
468
$
308
WV
$
46
$
2
$
14
$
3
$
250
$
275
$
209
$
356
WY
$
5
$
5
$
0
$
22
$
85
$
17
$
0
$
114
a
Average
boating
expenditures
in
Washington,
D.
C.
are
based
on
a
single
observation.

N/
A
­
Not
Available
Source:
NDS.
N­
21
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
7
DISTRIBUTION
OF
DIRECT
COSTS
FOR
SINGLE­
DAY
TRIPS
This
analysis
estimates
the
percent
of
total
expenditures
for
single­
day
and
multiple­
day
trips
spent
on
each
component
of
total
expenditures.
Total
expenditures
for
single­
day
boating,
fishing,
and
viewing
trips
consist
of:

 
transportation,

 
entrance
fee,
and
 
boat
rental.

Lodging
is
not
included
in
single­
day
expenditures.
Swimming
trip
expenditures
do
not
include
boat
rental.
Transportation
includes
expenses
for:

 
plane,

 
train,

 
bus,
or
 
ship
only
and
do
not
include
automobile
travel
costs.

EPA
determined
the
percent
of
total
expenditures
for
each
category
by
dividing
the
total
amount
spent
on
each
category
by
the
total
expenditures
in
a
state
for
a
given
activity.

Tables
N.
9
and
N.
10
present
results
for
single­
and
multiple­
day
trips,
respectively.

N­
22
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
9:
Distribution
of
Direct
Costs
for
Single­
Day
Trips
State
Boating
(%
of
total
expenditures)
Fishing
(%
of
total
expenditures)
Swimminga
(%
of
total
expenditures)
Viewing
(%
of
total
expenditures)

Transb
Enter
Fee
Boat
Rental
Trans
Enter
Fee
Boat
Rental
Trans
Enter
Fee
Trans
Enter
Fee
Boat
Rental
AK
0%
3%
97%
0%
5%
95%
N/
A
N/
A
0%
27%
73%

AL
0%
12%
88%
4%
28%
68%
0%
100%
0%
61%
39%

AR
0%
8%
92%
36%
41%
23%
0%
100%
0%
100%
0%

AZ
0%
6%
94%
0%
25%
75%
0%
100%
83%
17%
0%

CA
45%
13%
42%
15%
21%
65%
37%
63%
50%
34%
16%

CO
0%
9%
91%
57%
17%
25%
0%
100%
84%
16%
0%

CT
0%
9%
91%
0%
3%
97%
0%
100%
0%
100%
0%

DC
0%
35%
65%
N/
A
N/
A
N/
A
0%
100%
0%
73%
27%

DE
0%
30%
70%
0%
52%
48%
0%
100%
0%
17%
83%

FL
0%
10%
90%
1%
12%
87%
0%
100%
0%
26%
74%

GA
0%
7%
93%
0%
29%
71%
66%
34%
83%
11%
5%

HI
62%
0%
38%
0%
18%
82%
N/
A
N/
A
N/
A
N/
A
N/
A
IA
0%
3%
97%
0%
0%
100%
0%
100%
0%
63%
37%

ID
0%
5%
95%
0%
0%
100%
0%
100%
0%
100%
0%

IL
4%
13%
82%
0%
13%
87%
0%
100%
6%
80%
13%

IN
0%
2%
98%
0%
24%
76%
0%
100%
53%
41%
6%

KS
0%
24%
76%
0%
51%
49%
0%
100%
0%
55%
45%

KY
0%
2%
98%
0%
27%
73%
96%
4%
82%
9%
9%

LA
0%
68%
32%
0%
46%
54%
0%
100%
0%
100%
0%

MA
0%
43%
57%
4%
28%
68%
88%
12%
0%
78%
22%

MD
31%
17%
52%
0%
2%
98%
0%
100%
17%
82%
1%

ME
0%
23%
77%
0%
0%
100%
0%
100%
0%
94%
6%

MI
0%
8%
92%
0%
10%
90%
0%
100%
0%
65%
35%

MN
0%
17%
83%
0%
65%
35%
0%
100%
0%
20%
80%

MO
0%
25%
75%
0%
20%
80%
0%
100%
1%
92%
7%

MS
0%
36%
64%
0%
44%
56%
0%
100%
0%
100%
0%

MT
0%
8%
92%
96%
4%
0%
0%
100%
N/
A
N/
A
N/
A
NC
0%
23%
77%
0%
12%
88%
0%
100%
40%
41%
19%

ND
0%
30%
70%
0%
32%
68%
0%
100%
N/
A
N/
A
N/
A
NE
0%
0%
100%
0%
0%
100%
0%
100%
0%
5%
95%

NH
0%
48%
52%
0%
61%
39%
0%
100%
0%
100%
0%

NJ
15%
10%
74%
8%
48%
44%
26%
74%
35%
47%
18%

NM
0%
8%
92%
0%
49%
51%
91%
9%
98%
2%
0%

NV
0%
19%
81%
0%
67%
33%
0%
100%
0%
32%
68%

NY
23%
29%
48%
5%
36%
58%
44%
56%
5%
76%
19%
N­
23
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
9:
Distribution
of
Direct
Costs
for
Single­
Day
Trips
State
Boating
(%
of
total
expenditures)
Fishing
(%
of
total
expenditures)
Swimminga
(%
of
total
expenditures)
Viewing
(%
of
total
expenditures)

Transb
Enter
Fee
Boat
Rental
Trans
Enter
Fee
Boat
Rental
Trans
Enter
Fee
Trans
Enter
Fee
Boat
Rental
OH
28%
6%
66%
7%
14%
79%
0%
100%
43%
11%
46%

OK
0%
27%
73%
58%
9%
33%
0%
100%
0%
0%
100%

OR
0%
17%
83%
0%
25%
75%
0%
100%
0%
87%
13%

PA
0%
11%
89%
0%
22%
78%
94%
6%
49%
41%
10%

RI
0%
0%
100%
0%
1%
99%
0%
100%
0%
100%
0%

SC
0%
66%
34%
0%
10%
90%
0%
100%
95%
2%
3%

SD
0%
19%
81%
0%
39%
61%
0%
100%
0%
100%
0%

TN
26%
0%
73%
0%
22%
78%
0%
100%
98%
2%
0%

TX
0%
2%
98%
0%
20%
80%
0%
100%
0%
33%
67%

UT
0%
42%
59%
0%
60%
40%
0%
100%
0%
84%
16%

VA
9%
25%
66%
0%
25%
75%
0%
100%
20%
80%
0%

VT
0%
8%
92%
0%
0%
100%
0%
100%
0%
100%
0%

WA
0%
18%
82%
45%
16%
39%
0%
100%
17%
56%
28%

WI
0%
19%
81%
1%
20%
79%
2%
98%
48%
36%
16%

WV
0%
33%
67%
0%
0%
100%
78%
22%
88%
6%
6%

WY
0%
47%
53%
0%
53%
48%
N/
A
N/
A
0%
16%
84%

a
Swimming
expenditures
do
not
include
boat
rental.

b
Transportation
expenses
include
expenditures
on
plane,
train,
bus,
or
ship
taken
on
the
trip
only
and
do
not
reflect
travel
costs.

N/
A
­
Not
Available
Source:
U.
S.
EPA
analysis.

N­
24
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
25
Table
N.
10:
Distribution
of
Direct
Costs
for
Multiple­
Day
Trips
State
Boating
(%
of
total
expenditures)
Fishing
(%
of
total
expenditures)
Swimminga
(%
of
total
expenditures)
Viewing
(%
of
total
expenditures)

TransbEnter
Fee
Lodg­

ingc
Boat
Rental
Trans
Enter
Fee
Lodging
Boat
Rental
Trans
Enter
Fee
LodgingTrans
Enter
Fee
Lodging
Boat
Rental
AK0%
2%
15%
84%
0%
3%
77%
20%
N/
AN/
AN/
A0%
0%
71%
29%

AL0%
0%
20%
80%
0%
0%
37%
63%
4%
0%
96%
0%
0%
99%
1%

AR0%
1%
51%
48%
0%
7%
73%
20%
0%
0%
100%
8%
8%
84%
0%

AZ0%
5%
55%
40%
0%
10%
65%
24%
31%
0%
69%
29%
1%
69%
1%

CA28%
6%
53%
13%
12%
16%
58%
14%
23%
2%
76%
20%
11%
63%
5%

CO0%
7%
77%
16%
20%
0%
69%
10%
12%
2%
87%
23%
1%
72%
3%

CT22%
0%
16%
63%
0%
1%
95%
4%
17%
1%
83%
11%
0%
88%
0%

DC100%
0%
0%
0%
N/
AN/
AN/
AN/
A0%
0%
100%
22%
6%
42%
30%

DE0%
0%
77%
23%
0%
0%
100%
0%
8%
0%
92%
0%
1%
99%
0%

FL10%
1%
71%
18%
0%
2%
16%
81%
0%
2%
98%
7%
5%
88%
0%

GA14%
7%
51%
28%
8%
4%
69%
19%
0%
0%
99%
9%
9%
77%
5%

HI0%
0%
91%
9%
N/
AN/
AN/
AN/
A0%
0%
100%
0%
0%
100%
0%

IA0%
21%
63%
16%
0%
0%
64%
36%
18%
0%
82%
26%
1%
71%
1%

ID0%
1%
92%
7%
0%
8%
78%
15%
0%
3%
97%
20%
14%
65%
1%

IL18%
6%
41%
35%
14%
2%
79%
5%
33%
4%
63%
21%
1%
74%
3%

IN8%
3%
58%
31%
7%
5%
81%
7%
17%
57%
26%
20%
37%
42%
1%

KS0%
2%
30%
68%
0%
1%
74%
25%
24%
0%
76%
16%
0%
82%
1%

KY0%
3%
12%
85%
21%
3%
48%
27%
0%
0%
100%
14%
0%
86%
0%

LA0%
3%
81%
16%
0%
31%
62%
8%
9%
0%
91%
1%
3%
94%
2%

MA0%
1%
38%
61%
0%
14%
78%
9%
19%
0%
81%
21%
5%
73%
0%

MD0%
0%
56%
44%
0%
0%
98%
2%
12%
0%
88%
13%
2%
83%
2%

ME0%
72%
22%
6%
0%
15%
80%
6%
0%
4%
96%
0%
6%
94%
0%

MI9%
6%
58%
26%
5%
4%
83%
8%
17%
6%
77%
33%
3%
63%
2%

MN17%
0%
78%
5%
0%
0%
73%
27%
0%
1%
99%
44%
0%
54%
2%

MO0%
3%
74%
23%
0%
6%
64%
30%
30%
0%
70%
24%
8%
67%
1%

MS0%
1%
65%
34%
0%
0%
69%
31%
0%
0%
100%
0%
2%
97%
1%

MT0%
0%
100%
0%
0%
0%
92%
8%
5%
0%
95%
0%
5%
95%
0%

NC12%
8%
55%
25%
18%
4%
69%
9%
8%
0%
92%
7%
0%
93%
0%

ND0%
0%
16%
84%
0%
0%
100%
0%
N/
AN/
AN/
AN/
AN/
AN/
AN/
A
NE0%
6%
66%
28%
30%
6%
64%
0%
0%
0%
100%
60%
0%
40%
0%

NH0%
13%
0%
87%
N/
AN/
AN/
AN/
A9%
0%
91%
0%
0%
100%
0%

NJ34%
0%
60%
6%
0%
37%
25%
39%
20%
0%
79%
21%
2%
76%
1%

NM0%
7%
52%
41%
0%
5%
83%
12%
0%
18%
82%
17%
1%
82%
1%

NV0%
3%
37%
60%
0%
0%
100%
0%
45%
2%
53%
7%
1%
92%
1%
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
10:
Distribution
of
Direct
Costs
for
Multiple­
Day
Trips
State
Boating
(%
of
total
expenditures)
Fishing
(%
of
total
expenditures)
Swimminga
(%
of
total
expenditures)
Viewing
(%
of
total
expenditures)

Transb
Enter
Fee
Lodgingc
Boat
Rental
Trans
Enter
Fee
Lodging
Boat
Rental
Trans
Enter
Fee
Lodging
Trans
Enter
Fee
Lodging
Boat
Rental
NY
20%
13%
58%
8%
0%
5%
75%
20%
9%
1%
90%
16%
0%
82%
1%

OH
8%
2%
49%
41%
5%
1%
79%
15%
6%
24%
70%
17%
0%
79%
4%

OK
16%
0%
62%
22%
0%
81%
19%
0%
18%
4%
78%
30%
1%
68%
2%

OR
4%
1%
43%
52%
0%
2%
87%
11%
41%
0%
59%
5%
3%
91%
1%

PA
37%
4%
36%
23%
1%
1%
61%
37%
19%
1%
81%
9%
0%
89%
1%

RI
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
20%
0%
79%
0%

SC
10%
0%
24%
67%
0%
10%
87%
3%
0%
0%
100%
21%
1%
76%
1%

SD
0%
0%
96%
4%
0%
12%
69%
18%
0%
0%
100%
28%
0%
72%
0%

TN
0%
0%
53%
46%
0%
0%
38%
63%
12%
0%
88%
7%
0%
91%
2%

TX
6%
5%
56%
33%
6%
5%
70%
19%
21%
8%
72%
20%
1%
76%
3%

UT
0%
0%
66%
34%
0%
60%
40%
0%
0%
1%
99%
34%
0%
66%
0%

VA
54%
3%
30%
14%
0%
10%
60%
31%
2%
1%
97%
8%
2%
90%
1%

VT
0%
50%
50%
0%
N/
A
N/
A
N/
A
N/
A
0%
0%
100%
60%
0%
40%
0%

WA
10%
4%
46%
40%
57%
0%
18%
25%
23%
4%
73%
24%
1%
73%
1%

WI
9%
4%
35%
52%
26%
21%
48%
6%
13%
0%
87%
18%
1%
81%
0%

WV
0%
0%
80%
20%
0%
3%
97%
0%
0%
0%
100%
0%
13%
87%
0%

WY
0%
0%
71%
29%
0%
4%
96%
0%
N/
A
N/
A
N/
A
0%
10%
90%
0%

a
Swimming
expenditures
do
not
include
boat
rental.

b
Transportation
expenses
include
expenditures
on
plane,
train,
bus,
or
ship
taken
on
the
trip
only
and
do
not
reflect
travel
costs.

c
Total
expenses
for
multiple­
day
trips
include
lodging,
while
total
expenditures
for
single­
day
trips
do
not.

N/
A
­
Not
Available
Source:
NDS;
U.
S.
EPA
analysis.

N­
26
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
8
PROFILE
OF
BOATING
TRIPS
This
analysis
provides
a
profile
of
sample
boater
characteristics
by
state
of
residence.
Table
N.
11
shows
distribution
of
boaters
by
type
of
boating
in
which
they
participated
on
their
last
trip
and
the
source
of
the
boat
used
on
their
most
recent
boating
trip.

Boating
types
include:

 
motorboating;

 
sailing;

 
white
water
kayaking
and
canoeing;

 
other
kayaking
or
canoeing;

 
rowing,
rafting,
tubing,
or
floating;

 
wind
surfing;
and
 
other.

Boat
sources
include:

 
boaters
who
used
their
own
boat,
including
those
who
indicated
using
either
their
own
boat
or
one
belonging
to
someone
in
their
immediate
family
on
their
last
trip;

 
boat
renters,
including
those
who
either
rented
or
chartered
a
boat
on
their
last
trip;
and
 
other,
including
respondents
who
did
not
indicate
either
using
their
own
boat
or
renting
a
boat.

Dividing
the
number
of
respondents
who
participated
in
each
boating
type
on
their
last
trip
by
the
total
sample
of
boaters
provided
an
estimate
of
the
percent
participating
in
each
type.

N­
27
MP&
M
EEBA:
AppendicesAppendix
N:
Analysis
of
the
National
Demand
for
Water­
based
RecreationSurvey
N­
28
Table
N.
11:
Profile
of
Boating
Trips
State
Total
Number
of
Boaters
Source
Boat
Used
on
Last
Tripa
(
Percent
of
Boaters)
Type
of
Boating
on
Last
Tripb
(
Percent
of
Boaters)

NDS
Sample
Sample
Weighted
OwnRentOtherMotorSail
White
Water
Kayak
Other
Kayak
RowRaftWind
SurfOther
AK14220,97236%
36%
29%
71%
0%
21%
7%
0%
0%
0%
0%

AL39617,48651%
21%
28%
79%
0%
8%
0%
0%
3%
0%
10%

AR25404,80948%
20%
32%
88%
0%
0%
0%
0%
4%
0%
8%

AZ21461,00057%
14%
29%
67%
14%
0%
10%
10%
0%
0%
0%

CA2695,244,63431%
23%
46%
66%
14%
2%
1%
1%
4%
0%
11%

CO27423,14344%
22%
33%
70%
4%
4%
4%
4%
7%
4%
4%

CT32533,62641%
34%
25%
50%
22%
6%
9%
0%
6%
0%
6%

DC453,5510%
50%
50%
50%
50%
0%
0%
0%
0%
0%
0%

DE10119,66160%
10%
30%
80%
10%
10%
0%
0%
0%
0%
0%

FL1522,925,61431%
32%
37%
74%
8%
3%
5%
1%
1%
0%
7%

GA691,156,29730%
32%
38%
77%
9%
7%
0%
0%
3%
0%
4%

HI11189,8379%
36%
55%
36%
36%
9%
9%
0%
0%
0%
9%

IA32426,85425%
31%
44%
81%
3%
9%
3%
0%
0%
0%
3%

ID25291,91756%
12%
32%
72%
4%
0%
4%
4%
16%
0%
0%

IL861,758,81634%
28%
38%
66%
5%
2%
6%
0%
6%
0%
15%

IN62967,69435%
26%
39%
84%
10%
2%
0%
0%
0%
0%
5%

KS18274,46544%
22%
33%
83%
0%
0%
6%
11%
0%
0%
0%

KY35505,22854%
17%
29%
89%
0%
0%
3%
0%
3%
0%
6%

LA38682,56347%
18%
34%
87%
3%
3%
0%
3%
0%
0%
5%

MA581,166,52428%
45%
28%
53%
19%
9%
12%
0%
0%
0%
7%

MD49778,91720%
47%
33%
65%
10%
4%
0%
2%
8%
2%
8%

ME24336,75821%
46%
33%
63%
8%
8%
13%
0%
0%
0%
8%

MI1411,867,31230%
43%
26%
76%
9%
4%
3%
1%
1%
1%
6%

MN59910,96439%
29%
32%
83%
2%
2%
3%
0%
0%
0%
10%

MO60938,32638%
28%
33%
75%
3%
5%
12%
0%
0%
0%
5%

MS25385,74436%
32%
32%
80%
12%
0%
8%
0%
0%
0%
0%

MT12153,03867%
0%
33%
42%
0%
17%
17%
0%
8%
0%
17%

NC57881,07530%
30%
40%
70%
9%
7%
2%
2%
5%
0%
5%

ND11138,09855%
27%
18%
82%
0%
0%
0%
0%
0%
9%
9%

NE17266,12618%
12%
71%
88%
0%
12%
0%
0%
0%
0%
0%

NH15225,13913%
53%
33%
80%
7%
13%
0%
0%
0%
0%
0%

NJ641,207,23417%
53%
30%
70%
13%
0%
5%
2%
0%
0%
11%

NM20260,97855%
10%
35%
65%
5%
0%
15%
0%
10%
0%
5%

NV17348,59041%
12%
47%
82%
0%
6%
0%
6%
0%
6%
0%

NY1372,619,15821%
50%
28%
64%
11%
1%
6%
4%
2%
0%
12%
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
11:
Profile
of
Boating
Trips
State
Total
Number
of
Boaters
Source
Boat
Used
on
Last
Tripa
(
Percent
of
Boaters)
Type
of
Boating
on
Last
Tripb
(
Percent
of
Boaters)

NDS
Sample
Sample
Weighted
Own
Rent
Other
Motor
Sail
White
Water
Kayak
Other
Kayak
Row
Raft
Wind
Surf
Other
OH
109
1,473,937
30%
37%
33%
75%
7%
6%
3%
0%
3%
0%
6%

OK
29
540,650
21%
34%
45%
66%
7%
7%
3%
0%
0%
3%
14%

OR
57
702,199
49%
21%
30%
70%
9%
0%
4%
2%
9%
0%
7%

PA
111
1,450,179
28%
40%
32%
72%
6%
1%
4%
2%
1%
1%
14%

RI
9
130,654
33%
56%
11%
33%
44%
0%
11%
11%
0%
0%
0%

SC
34
585,163
53%
21%
26%
71%
3%
6%
9%
0%
0%
0%
12%

SD
11
151,221
18%
36%
45%
82%
0%
9%
0%
0%
0%
0%
9%

TN
67
1,006,355
45%
25%
30%
84%
6%
0%
0%
1%
0%
0%
9%

TX
118
2,805,077
41%
20%
39%
80%
5%
1%
3%
2%
2%
0%
8%

UT
22
316,826
59%
9%
32%
86%
0%
0%
0%
0%
0%
0%
14%

VA
72
1,023,443
36%
43%
21%
57%
22%
1%
8%
0%
1%
0%
10%

VT
8
112,768
50%
50%
0%
50%
13%
13%
13%
0%
13%
0%
0%

WA
114
1,601,852
37%
26%
37%
70%
11%
0%
6%
3%
4%
0%
6%

WI
65
903,611
42%
38%
20%
68%
8%
5%
15%
2%
3%
0%
0%

WV
17
196,359
47%
6%
47%
59%
0%
6%
6%
0%
0%
0%
29%

WY
8
98,550
38%
0%
63%
63%
0%
13%
13%
0%
13%
0%
0%

a
Own
includes
those
who
used
their
own
boat
or
one
belonging
to
someone
in
their
immediate
family.

Rent
includes
those
who
rented
or
chartered
a
boat.

Other
includes
those
who
did
not
indicate
either
using
own
boat
or
renting
a
boat.

b
Kayak
includes
kayak
or
canoe;
raft
includes
rafting,
tubing,
or
floating;
other
includes
other
or
type
not
indicated.

N/
A
­
Not
Available
Source:
U.
S.
EPA
analysis;
NDS.
N­
29
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
N.
9
PROFILE
OF
FISHING
TRIPS
This
analysis
provides
a
profile
of
fishing
trips,
including
angling
success
rate,
average
catch,
and
type
of
fisheries
targeted
on
the
last
trip
by
state
of
residence.
The
success
rate
equals
the
total
number
of
fishermen
who
report
catching
at
least
one
fish
on
their
last
trip
divided
by
the
total
number
of
fishermen
in
each
state.
The
average
catch
equals
the
total
fish
caught
by
all
fishermen
divided
by
the
total
number
of
fishermen
in
the
state.
Average
catch
therefore
includes
those
who
did
not
indicate
catching
any
fish.
Similarly,
the
percent
of
fishermen
who
fished
from
a
boat
equals
the
total
number
of
fishermen
who
reported
fishing
from
a
boat
on
their
last
trip,
divided
by
the
total
number
of
fishermen.
Finally,
the
percent
of
fishermen
who
participated
in
each
type
of
fishing
equals
the
total
number
of
fishermen
who
reported
fishing
in
either
cold,
warm,
salt,

anadromous,
or
other
water
divided
by
the
total
number
of
fishermen.
Other
includes
both
those
who
indicated
other
and
missing
values.
Results
of
the
analysis
are
presented
below
in
Table
N.
12.

N­
30
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
12:
Profile
of
Fishing
Trips
State
Sample
Weighted
Number
of
Fishermen
Fish
Catch
on
Last
Tripa
Fished
from
a
Boat
on
Last
Trip
(%
of
fishermen)
Type
of
Water
Fished
on
Last
Tripb
Average
Number
of
Fish
Caught
Success
Rate
(%
of
fishermen
)
Cold
(%
of
fishermen)
Warm
(%
of
fishermen)
Salt
(%
of
fishermen)
Anadromous
(%
of
fishermen)
Other
(%
of
fishermen)

AK
268,323
9
65%
65%
41%
0%
53%
6%
0%

AL
870,813
7
67%
71%
22%
45%
20%
2%
11%

AR
761,041
7
85%
62%
36%
60%
0%
0%
4%

AZ
790,286
4
67%
39%
44%
47%
3%
3%
3%

CA
5,556,583
5
73%
47%
47%
14%
28%
5%
6%

CO
1,253,757
4
65%
16%
79%
13%
5%
0%
4%

CT
466,922
3
71%
50%
29%
21%
43%
7%
0%

DC
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
N/
A
DE
119,661
6
70%
50%
20%
20%
60%
0%
0%

FL
3,156,584
5
70%
57%
14%
23%
50%
4%
9%

GA
1,457,940
6
74%
47%
32%
33%
24%
0%
10%

HI
189,837
21
91%
36%
0%
18%
82%
0%
0%

IA
546,907
7
76%
37%
51%
41%
0%
0%
7%

ID
385,331
3
73%
21%
85%
0%
0%
9%
6%

IL
1,942,878
5
74%
44%
38%
45%
4%
2%
11%

IN
1,201,814
6
74%
45%
39%
47%
4%
1%
9%

KS
579,427
6
74%
39%
21%
74%
0%
0%
5%

KY
952,715
5
76%
39%
26%
65%
2%
3%
5%

LA
1,149,580
8
80%
56%
19%
47%
25%
2%
8%

MA
1,086,074
4
72%
43%
39%
19%
30%
2%
11%

MD
842,502
5
77%
62%
38%
17%
38%
0%
8%

ME
308,695
3
68%
55%
64%
18%
18%
0%
0%

MI
2,052,719
6
75%
61%
54%
28%
3%
7%
9%

MN
1,281,526
5
70%
63%
53%
34%
0%
2%
11%

MO
1,141,630
4
74%
37%
51%
36%
3%
3%
8%

MS
586,331
8
82%
55%
24%
58%
13%
0%
5%

MT
293,322
3
78%
22%
87%
9%
4%
0%
0%

NC
1,592,117
10
77%
42%
27%
22%
42%
4%
5%

ND
163,207
4
69%
46%
54%
46%
0%
0%
0%

NE
266,126
9
88%
41%
41%
53%
0%
0%
6%

NH
150,093
2
40%
60%
50%
30%
10%
10%
0%

NJ
1,244,960
5
73%
45%
18%
21%
48%
2%
11%

NM
300,125
3
61%
22%
74%
17%
4%
0%
4%
N­
31
MP&
M
EEBA:
Appendices
Appendix
N:
Analysis
of
the
National
Demand
for
Water­
based
Recreation
Survey
Table
N.
12:
Profile
of
Fishing
Trips
State
Sample
Weighted
Number
of
Fishermen
Fish
Catch
on
Last
Tripa
Fished
from
a
Boat
on
Last
Trip
(%
of
fishermen)
Type
of
Water
Fished
on
Last
Tripb
Average
Number
of
Fish
Caught
Success
Rate
(%
of
fishermen
)
Cold
(%
of
fishermen)
Warm
(%
of
fishermen)
Salt
(%
of
fishermen)
Anadromous
(%
of
fishermen)
Other
(%
of
fishermen)

NV
328,084
4
75%
13%
63%
19%
6%
0%
13%

NY
2,236,799
4
80%
49%
46%
19%
26%
2%
7%

OH
1,649,727
5
72%
48%
41%
45%
4%
3%
7%

OK
857,583
6
70%
33%
26%
57%
9%
0%
9%

OR
960,904
3
51%
40%
62%
8%
13%
12%
6%

PA
2,064,218
4
61%
44%
51%
25%
16%
3%
5%

RI
174,205
5
50%
50%
33%
17%
33%
0%
17%

SC
912,165
8
81%
60%
32%
34%
25%
6%
4%

SD
151,221
7
64%
45%
73%
27%
0%
0%
0%

TN
1,141,537
4
76%
46%
36%
51%
5%
3%
5%

TX
4,564,193
5
66%
55%
23%
45%
24%
1%
7%

UT
360,030
3
56%
16%
84%
8%
0%
0%
8%

VA
1,421,449
7
73%
46%
28%
21%
39%
3%
9%

VT
42,288
5
100%
33%
33%
67%
0%
0%
0%

WA
1,250,568
2
56%
60%
49%
8%
22%
15%
6%

WI
1,209,448
9
69%
59%
57%
34%
0%
1%
7%

WV
358,067
6
58%
16%
68%
26%
0%
0%
6%

WY
221,738
4
72%
28%
89%
6%
0%
0%
6%

a
Missing
values
for
fish
catch
were
included
as
zero
in
both
the
mean
and
the
median.

b
Other
includes
both
those
that
indicated
other
and
missing
values.

N/
A
­
Not
Available
Source:
NDS;
U.
S.
EPA
analysis.

N­
32