MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
INTRODUCTION
Section
IV
of
this
EEBA
focuses
on
the
state
of
Ohio
as
a
case
study
of
the
MP
&
M
regulation s
expected
benefits
and
costs.
Ohio
has
a
diverse
water
resource
base,
a
relatively
large
number
of
MP&
M
industry
facilities,
and
a
more
extensive
water
quality
ecological
database
than
many
other
states.
EPA
gathered
extensive
data
on
MP&
M
facilities
and
on
Ohio s
baseline
water
quality
conditions
and
water­
based
recreation
activities
to
support
the
case
study
analysis.

These
data
characterize
current
water
quality
conditions,

water
quality
changes
expected
from
the
regulation,
and
the
expected
welfare
changes
from
water
quality
improvements
at
water
bodies
affected
by
MP&
M
discharges.

The
case
study
analysis
supplements
the
national­
level
analysis
performed
for
the
MP&
M
regulation
in
two
important
ways.
First,
the
case
study
used
improved
data
and
methods
to
determine
MP&
M
pollutant
discharges
from
both
MP&
M
facilities
and
other
sources.
In
particular,
EPA
administered
1,600
screener
questionnaires
in
the
state
of
Ohio
to
augment
information
on
Ohio
MP&
M
facilities.
The
Agency
also
used
information
from
the
sampled
MP&
M
facilities
to
assign
discharge
characteristics
to
non­
sampled
MP&
M
facilities1.
Second,
the
analysis
used
an
original
travel
cost
study
to
value
four
recreational
uses
of
water
resources
affected
by
the
regulation:
swimming,
fishing,

boating,
and
near­
water
activities.
The
added
detail
provides
a
more
complete
and
reliable
analysis
of
water
quality
changes
from
reduced
MP&
M
discharges.
The
case
study
analysis
therefore
provides
more
complete
estimates
of
changes
in
human
welfare
resulting
from
reduced
health
risk,

enhanced
recreational
opportunities,
and
improved
economic
productivity.
Chapter
20:
Baseline
Conditions
in
Ohio
CHAPTER
CONTENTS
20.1
iew
of
Ohio s
Geography,
Population,
and
Economy
.................
........
20­
2
20.2
MP&
M
Facilities
in
Ohio
...........
20­
3
20.3
.................
...
20­
6
20.3.1
.................
....
20­
8
20.3.2
r
Recreation
in
Ohio
.............
20­
11
20.3.3
ercial
Fishing
in
Ohio
..........
20­
12
20.3.4
e
Water
Withdrawals
...........
20­
12
20.4
e
Water
Quality
in
Ohio
..............
20­
12
20.4.1
in
Ohio
.................
............
20­
13
20.4.2
Attainment
.................
.........
20­
13
20.4.3
Non­
Attainment
in
Ohio
................
20­
14
20.5
Impairments
on
Water
Resource
Services
.................
....
20­
15
20.5.1
ity
Impairment
on
Life
Support
for
Animals
and
Plants
..........
20­
15
20.5.2
ect
of
Water
Quality
Impairment
on
Recreational
Services
.................
.
20­
17
20.6
and
Distribution
of
Endangered
and
Threatened
Species
in
Ohio
.............
20­
18
20.6.1
.................
........
20­
19
20.6.2
sks
.................
....
20­
19
20.6.3
...........
20­
20
Glossary
.................
.................
..
20­
24
Acronyms
.................
.................
.
20­
27
References
.................
.................
20­
28
Overv
Profile
of
Ohio s
Water
Resources
Aquatic
Life
Use
Wate
Comm
Surfac
Surfac
Use
Attainment
in
Streams
and
Rivers
Lake
Erie
and
Other
Lakes
Use
Causes
and
Sources
of
Use
Effects
of
Water
Quality
Effect
of
Water
Qual
Eff
Presence
E&
T
Fish
E&
T
Mollu
Other
Aquatic
E&
T
Species
The
statewide
case
study
of
recreational
benefits
from
the
MP&
M
regulation
combines
water
quality
modeling
with
a
random
utility
model
(
RUM)
to
assess
how
changes
in
water
quality
from
the
regulation
will
affect
consumer
valuation
of
water
resources.
The
study
addresses
a
wide
range
of
pollutant
types
and
effects,
including
water
quality
measures
not
often
addressed
in
past
recreational
benefits
studies.
The
estimated
model
supports
a
more
complete
analysis
of
recreational
benefits
from
reductions
in
nutrients
and
 
toxic 
pollutants.
2
1
Appendix
H
provides
a
detailed
discussion
on
the
approach
used
to
estimate
discharge
characteristics
for
non­
sampled
MP&
M
facilities.

2
The
term
 
toxic 
used
here
refers
to
the
126
priority
or
toxic
pollutants
specifically
defined
as
such
by
EPA,
as
well
as
nonconventional
pollutants
that
have
a
toxic
effect
on
human
health
or
aquatic
organisms.

20­
1
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
This
and
the
next
two
chapters
present
the
Ohio
case
study.
This
chapter
provides
background
information
on
the
state
of
Ohio,
the
following
chapter
presents
the
results
from
the
recreational
benefits
analysis,
and
the
last
chapter
summarizes
social
costs
and
benefits
of
the
final
regulation
for
the
state
of
Ohio.

20.1
OVERVIEW
OF
OHIO S
GEOGRAPHY,
POPULATION,
AND
ECONOMY
Table
20.1
summarizes
general
information
on
Ohio.
Ohio
is
large,
heavily­
industrialized,
and
densely­
populated.
The
state
covers
a
total
surface
area
of
44,828
sq.
mi.
(
106,607
sq.
km.),
of
which
water
represents
3,875
sq.
mi.
(
10,036
sq.
km.).

About
90
percent
of
the
water
surface
area
consists
of
Lake
Erie;
the
remainder
includes
inland
waters,
such
as
lakes,

reservoirs,
and
rivers
(
including
the
Ohio
River).
The
state
housed
11,353,140
people
in
2000.
The
three
largest
metropolitan
areas
are
located
on
Lake
Erie
(
Toledo
and
Cleveland)
and
the
Ohio
River
(
Cincinnati).

20­
2
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.1:
Facts
about
the
State
of
Ohio
Geography
Location
Midwest
United
States,
northeast
part:
south
of
Lake
Erie
east
of
Indiana
north
of
the
Ohio
River
Total
land
area
40,953
sq.
(
106,607
sq.
.)

Of
the
26,451,000
acres
of
terrestrial
surface
area
in
Ohio:
97
percent
is
non­
federal
land
(
National
Resources
Inventory
(
NRI))

3,558,000
acres,
representing
13.5
percent
of
the
total
area
of
Ohio,
are
developed
The
remaining
non­
federal
lands
are
rural
land,
classified
mostly
as
crop
land,
forest,
and
pasture
lands.

Total
water
surface
area
3,875
sq.
(
10,036
sq.
.)

Approximately
90
percent
is
represented
by
Lake
Erie,
and
10
percent
are
inland
waters
including
rivers,
lakes,
and
reservoirs.
a
Total
area
44,828
sq.
(
116,104
sq.
.)

Demographics
Population
11,353,140
in
2000,
approximately
4
percent
of
total
U.
S.
population
(
U.
S.
Census
Bureau)
Population
increase:
4.7
percent
from
1990
to
2000,
compared
to
a
13.1
percent
increase
in
the
U.
S.
population
overall.

Most
densely
populated
part
of
the
state:
northeastern
Ohio,
both
urban
and
rural
areas.

Largest
cities:
Cleveland,
Cincinnati,
and
Toledo.

Economics
Ohio
Midwest
U.
S.

Per
capita
income
(
1996$)
$
23,537
Rank
in
per
capital
income
in
the
U.
S.:
21
$
24,166
$
24,231
Percent
of
population
below
the
poverty
level
(
1995
Current
Population
Survey
data,
DOC
1996)
11.5%
N/
A
13.8%

Ohio
per
capita
income
increased
by
16
percent
from
1986
to
1996.

Income
growth
is
consistent
with
other
midwestern
states
and
is
2
percent
greater
than
overall
U.
S.
per
capita
income
growth.

Gross
State
Product
(
GSP)
$
303,569,000,000
(
1996$),
representing
4
percent
of
Gross
Domestic
Product
(
GDP)
for
the
U.
S.
in
1996.

Percent
increase
in
GSP/
GDP
from
1986
to
1996
(
in
adjusted
1996$)
Ohio
GSP
U.
S.
GDP
25%
29%
mi.
km
(
USDA,
1992a)

mi.
km
mi.
km
a
Total
water
surface
areas
are
estimated
by
the
USDA s
National
Resources
Inventory
(
NRI)
(
USDA
1992b).

(
http://
www.
ftw.
nrcs.
usda.
gov/
nri_
data.
html)

Source:
U.
S.
EPA
analysis.

20.2
PROFILE
OF
MP&
M
FACILITIES
IN
OHIO
EPA
selected
Ohio
as
the
case
study
state
because
MP&
M
industries
account
for
a
large
share
of
the
state's
economy
(
see
Table
20.2).
Data
from
the
1997
Economic
Censuses
show
that
industries
containing
MP&
M
facilities
employ
19.8
percent
of
Ohio's
total
industrial
workers
and
produce
21.2
percent
of
industrial
worker
output
by
value.
MP&
M
industries
also
account
for
22.1
percent
of
payroll
payments,
indicating
that
jobs
in
MP&
M
industries
are
more
highly
paid
than
industrial
20­
3
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
jobs
on
average
in
Ohio.
The
discussion
below
explains
the
sources
and
methodology
EPA
used,
and
then
presents
detailed
results
and
caveats.

Table
20.2:
&
M
Share
of
Industrial
Output
and
Employment
in
Ohio,
1997
Total
Employment
Payroll
Value
of
Output
MP&
M
827,507
$
23,233,857,000
$
132,117,226,000
Total
4,087,393
$
112,777,104,000
$
677,978,137,000
MP&
M
Share
19.8%
22.1%
21.2%
MP
Source:
Department
of
Commerce
1992
Economic
Censuses.

EPA
obtained
employment,
payroll,
and
output
data
from
the
1997
Economic
Census
CD­
ROM,
drawing
from
the
eight
economic
censuses
in
Table
20.3.
Employment
and
payroll
numbers
include
all
employees
(
i.
e.,
production
plus
non­

production
workers).
The
measure
of
output
differs
according
to
the
source,
but
in
each
case
the
output
measures
shown
in
Table
20.2
correspond
conceptually
to
total
revenue.
EPA
extracted
the
EMPLOYEE,
PAYROLL,
and
VALUE
fields
for
each
4­
digit
SIC
industry
in
the
MP&
M
category
and
for
the
entire
state
of
Ohio.
Industries
include
both
in­
scope
and
out­
of­

scope
facilities.

Table
20.3:
e
Economic
Censuses
Source
Measure
of
Output
Census
of
Retail
Trade
Value
of
sales
Census
of
Wholesale
Trade
Value
of
sales
Census
of
Service
Industriesa
Value
of
receipts
Census
of
Transportation,
Communications,
and
Utilities
Value
of
revenue
Financial,
Insurance,
and
Real
Estate
Industries
Value
of
receipts
Census
of
Manufacturers
Value
of
shipments
Census
of
Mineral
Industries
Value
of
shipments
Census
of
Construction
Industries
Value
of
construction
work
Th
a
Includes
both
taxable
and
non­
taxable
establishments.

Source:
Department
of
Commerce
1997
Economic
Censuses.

The
MP&
M
industries
include
facilities
to
which
the
MP&
M
rule
may
not
apply.
For
example,
MP&
M
industries
include
non­
dischargers,
but
census
data
do
not
distinguish
between
in­
scope
and
out­
of­
scope
facilities.
In
addition,
EPA
substantially
revised
the
scope
of
the
final
regulation
by
excluding
from
the
final
regulation
all
indirect
dischargers
and
direct
dischargers
in
all
subcategories
except
for
Oily
Wastes.
Definition
of
MP&
M
subcategories
is
provided
in
Section
4.1
of
this
report.
The
final
rule
applies
to
an
estimated
172
direct
discharging
facilities
in
Ohio.

Also,
the
analysis
examines
only
the
industrial
sectors
for
which
the
Department
of
Commerce
compiles
statistics
in
the
Econom
ic
Censuses.
Published
industrial
employment
and
output
measures
often
exclude
military
and
other
government
personnel
and
farm
output
and
employment,
whether
those
exclusions
are
noted
or
not.
The
analysis
excludes
$
4.7
billion
in
value
of
agricultural
products
sold
in
1997
by
farms
in
Ohio,
according
to
the
U.
S.
Department
of
Agriculture's
1997
Census
of
Agriculture.
The
Ohio
analysis
also
excludes
the
government
sector,
which
employed
approximately
760,000
people
in
20­
4
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Ohio
in
1997,
according
to
the
U.
S.
Bureau
of
Labor
Statistics.
3
These
exclusions
are
normal
when
economists
compare
the
size
of
industrial
groups.

If
total
employment
in
Ohio
includes
the
government
sector,
then
MP&
M
industries
account
for
only
16.7
percent,
rather
than
19.8,
percent
of
employment.
If
total
industrial
manufacturing
and
non­
manufacturing
output
in
Ohio
includes
the
agricultural
sector,
then
MP&
M
industries
account
for
only
21.0,
rather
than
21.1,
percent
of
output.
This
said,
data
from
the
Bureau
of
Labor
Statistics
and
USDA
are
not
completely
consistent
with
the
Economic
Census
data.

EPA
augmented
information
on
MP&
M
facilities
available
from
published
data
sources
and
the
Section
308
survey
by
oversampling
the
state
of
Ohio
with
1,600
screeners.
The
Agency
used
information
from
the
Section
308
survey
and
the
1,600
screeners
to
characterize
discharges
from
MP&
M
facilities
in
Ohio
and
to
assess
the
economic
impact
of
the
final
regulation
at
the
state
level.
Figure
20.1
depicts
locations
of
the
Ohio
facilities
included
in
the
case
study
analysis.

The
map
of
facility
locations
shows
that
the
additional
information
from
1,600
screeners
enabled
EPA
to
perform
the
benefits
assessment
with
a
greater
level
of
detail
than
is
possible
at
the
national
level.
The
added
detail
results
in
a
more
complete
and
reliable
analysis
of
changes
in
human
welfare
resulting
from
reduced
health
risk
and
improved
recreational
opportunities.

3
U.
S.
Bureau
of
the
Census,
Statistical
Abstract
of
the
United
States,
1993,
Washington,
D.
C.,
1993.

20­
5
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Figure
20.1:
Location
of
Sample
MP&
M
Facilities
in
Ohio
Source:
U.
S.
EPA
analysis.

20.3
OHIO S
WATER
RESOURCES
The
benefits
of
enhanced
water
quality
stem
directly
from
enhancing
water
quality
and/
or
quantity
of
services
provided
by
water
resources.
To
aid
in
understanding
the
analysis
of
benefits
from
the
final
rule
in
Ohio,
this
section
summarizes
environmental
services
provided
by
Ohio s
water
resources.

Ohio
is
a
water­
rich
state:

 
24,000+
miles
of
named
and
designated
rivers
and
streams;

 
451­
mile
border
on
the
Ohio
River;

 
200,000
acres
among
450
lakes,
ponds,
rivers,
and
reservoirs;
and
 
230+
miles
of
Lake
Erie
shoreline.

20­
6
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
These
water
resources
provide
three
broad
categories
of
services:
in­
stream,
withdrawal,
and
existence
services.

Water
resources
provide
in­
stream
services
prior
to
the
withdrawal
of
water
from
the
water
body.
Major
in­
stream
services
include
life
support
for
animals
and
plants,
water­
based
recreation,
commercial
fishing
and
navigation,
water
storage,
and
aesthetics.
Withdrawal
services
include
uses
of
water
resources
after
the
water
is
withdrawn
from
the
water
body.
These
uses
include
drinking
water
supply,
irrigation,
production
and
processing
services,
and
sanitary
services.
Existence
services
are
not
linked
to
current
uses
of
water
bodies,
and
arise
from
knowing
that
species
diversity
or
the
natural
beauty
of
a
given
water
body
is
preserved.

The
Ohio
Environmental
Protection
Agency
(
Ohio
EPA)
assesses
surface
waters
in
their
Ohio
Water
Resource
Inventory
(
OWRI)
report
based
on
water
resource
services
provided
by
the
assessed
water
body.
The
main
focus
of
this
assessment
is
on
beneficial
uses
associated
with
Ohio s
water
resources,
including
aquatic
life
use,
recreation,
and
public
water
supply.

Table
20.4
shows
how
Ohio
surface
waters
fall
into
these
use
designations.

Table
20.4:
Summary
of
Designated
Life
Uses
for
Ohio
Surface
Waters
(
1996)

Use
Designation
Stream/
River
(
Miles)
a
Lakes
/

Reservoir
(
Acres)
a
Lake
Erie
(
Shore
Miles)
a
Total
43,917
200,000
236
Aquatic
Life
Usea
Exceptional
Warmwater
Habitat
(
EWH)
Warmwater
Habitat
(
WWH)

Other
24,067
3,217
18,318
2,532
193,903
193,903
236
236
Recreation
Primary
Contact
(
PCR)
b
Secondary
Contact
(
SCR)
224,96
1,188
200,000
236
Public
Water
Supply
118,801
a
Total
river/
stream
miles
are
based
on
Ohio
EPA
estimates.
U.
S.
EPA
estimates
61,532
total
river
miles
and
29,113
total
perennial
miles
based
on
RF3,
which
includes
many
smaller
undesignated
streams.

b
Note
that
some
water
bodies
have
more
than
one
designated
use
(
e.
g.,
aquatic
life
and
primary
recreation).

Source:
Ohio
EPA,
OWRI,
1996.

The
aquatic
life
use
category
is
further
subdivided
into
seven
categories.
The
most
widely­
applied
aquatic
use
designation
in
Ohio
is
Warmwater
Habitat
(
WWH),
accounting
for
18,318
(
76
percent)
stream
and
river
miles
(
Ohio
EPA,
OWRI,
1996).

The
second
most
widely
applied
designation
is
Exceptional
Warmwater
Habitat
(
EWH)
,
accounting
for
3,
217
stream
and
river
miles
(
13
percent),
236
Lake
Erie
shore
miles
(
100
percent),
and
193,903
acres
of
inland
lakes
(
100
percent).
Other
aquatic
life
categories
include:

 
Modified
Warmwater
Habitat
(
MWH),

 
Limited
Resource
Waters
(
LRW)
,

 
Limited
Warmwater
Habitat
(
LWH),

 
Seasonal
Salmonid
Habitat
(
SSH)
,
and
 
Coldwater
Habitat
(
CWH)
.

20­
7
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Recreational
uses
are
subdivided
into
Primary
Contact
Recreation
(
PCR)
and
Secondary
Contact
Recreation
(
SCR):

 
Primary
Contact
Recreation
(
PCR)
rivers
and
streams
deep
enough
for
full
human
body
immersion
activities,

such
as
swimming.

 
Secondary
Contact
Recreation
(
SCR)
only
deep
enough
to
permit
wading
and
incidental
contact,
such
as
boating.

Approximately
half
of
the
designated
stream
miles,
all
inland
lakes,
and
all
of
the
Lake
Erie
shore
miles
are
designated
for
PCR
(
see
Table
20.4).
In
addition,
three
percent
of
the
designated
stream
miles
(
1,188
miles)
are
suitable
for
SCR.

The
following
sections
detail
each
category
of
water
resource
use.

20.3.1
Aquatic
Life
Use
The
Ohio
water
resources
support
hundreds
of
aquatic
species
and
plants.
Ohio
water
resources
are
also
home
to
a
number
of
endangered
and
threatened
species.
Suitable
stream
and
lake
habitat
are
essential
for
both
resident
and
transient
animal
populations,
including
imperiled
aquatic
species.
Habitats
include
specific
biotic
components
(
e.
g.,
assemblages
of
plant
and
animal
species)
and
physical
(
e.
g.,
dissolved
oxygen
(
DO
)
content
and
temperature
range)
components.
Water
quality
impairments
associated
with
siltation,
excess
nutrients,
or
low
DO
can
adversely
affect
habitats
that
support
important
activities,
such
as
reproduction,
foraging,
migration,
and
overwintering.

The
following
sections
briefly
introduce
water­
dependent
biological
resources
in
Ohio.
Water
quality
effects
on
life
support
for
animals
and
plants
are
discussed
in
Section
20.5
a.
Ohio
fish
species
Fish
are
found
throughout
Ohio
in
almost
every
inland
surface
water
body
and
Lake
Erie.
Many
fish
species
serve
important
recreational
or
commercial
functions,
while
others
are
important
forage
for
birds,
other
fish,
and
land­
based
species.

Ecosystem
well­
being
therefore
depends
on
the
health
of
fish
and
other
aquatic
species
populations.
The
Ohio
EPA
monitors
biological
data,
especially
those
on
sensitive
aquatic
species,
to
determine
the
aquatic
life
use
attainment
of
surface
waters.

The
state
gives
high
priority
to
healthy
aquatic
ecosystem
maintenance.

Ohio s
rivers
and
lakes
offer
a
variety
of
man­
made
and
natural
habitats
that
offer
excellent
fishing
opportunities
for
numerous
gamefish
species.
The
state
of
Ohio
spends
significant
resources
on
fishery
management,
trout
stocking,
and
recreational
area
maintenance
to
enhance
these
fish
populations.
Table
20.5
below
provides
brief
summaries
of
the
habitat
and
diet
of
major
recreational
and
commercial
fish
species
in
Ohio
(
Ohio
DNR
,
1999).

Table
20.5:
Recreationally
or
Commercially
Valuable
Fish
Species
in
Ohio
Fish
Native
or
introduced?
Habitat
Spawning
season
Diet
Bass
Most
native
bass
(
e.
g.,
largemouth,
smallmouth,

spotted,
and
sock)
Ponds,
lakes,
rivers,
and
streams
in
every
county;
Lake
Erie
Mid­
April
to
mid­
June
Frogs,
crayfish,
insects,
and
other
fish
Bullhead
Native
Throughout
Ohio;
concentrations
in
northern
and
west
central
Ohio
Mid­
May
to
June
Insect
larvae,
crayfish,
snails,
dead
animals
Burbot
Native
Lakes
and
rivers;
prefer
deep
waters,
but
move
inshore
to
spawn
Winter
Minnows
and
the
young
of
other
fish
species
Carp
Introduced
Warm
lakes,
streams,
and
ponds
with
abundant
organic
matter,
in
every
county
Late
April
to
June
Insect
larvae,
mollusks,
fish,

crustaceans
Catfish
(
channel,
flathead)
Native
Throughout
Ohio's
rivers
and
lakes;

tolerate
a
wide
range
of
conditions
When
waters
reach
70
°
F
in
temperature
Bottom
feeders
with
a
diet
of
insect
larvae,
mollusks,
and
fish
both
dead
and
alive
20­
8
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.5:
Recreationally
or
Commercially
Valuable
Fish
Species
in
Ohio
Fish
Native
or
introduced?
Habitat
Spawning
season
Diet
Crappie,

white
Larger
ponds,
reservoirs,
and
rivers,

including
near­
shore
habitats
of
Lake
Erie,
in
most
areas
of
Ohio
May
and
June
Insects
and
small
fish
Crappie,
black
Same
general
habitat
as
white
crappie,
slightly
less
widely
distributed
May
and
June
Insects
and
small
fish
Drum
Native
Lake
Erie;
drums
support
a
commercial
fishery
Spring
into
late
summer
Mollusks,
crayfish,
fish,
insects
Lamprey
Lake
Erie
and
tributaries;
Ohio
River
and
larger
tributaries
Some
species
parasitize
other
fish
by
attaching
themselves
to
a
larger
host's
flank
and
feeding
on
its
flesh
Muskellunge
(
Muskie)
Native
Historically
found
in
Lake
Erie
bays
and
tributaries
and
streams
of
Ohio
River
drainage;
now
also
found
in
several
impoundments
April
and
early
May,
when
temperatures
reach
low­
to
mid­
50s
Suckers,
gizzard
shard,
and
other
soft­
rayed
fish
Perch,
white
Introduced
Lake
Erie
and
tributaries
April
and
May
Insects,
crustaceans,
other
fishPerch,
yellow
Native
Lakes,
impoundments,
ponds,
slow­
moving
rivers
April
and
May
Pike
Native
Historically
abundant
in
Lake
Erie
and
tributaries;
today
distributed
in
a
small
portion
of
Lake
Erie,
Sandusky
Bay,
Maumee
Bay,
and
their
tributary
streams
in
marshes,
bays,
and
pools
with
abundant
vegetation
As
ice
breaks
in
late
February
and
early
March
Pike
is
a
popular
ice­
fishing
species
Mostly
fish,
but
are
opportunistic
feeders;
will
occasionally
eat
frogs,
muskrats,
small
ducks
Salmon
(
chinook
and
coho)
Introduced
Stocked
in
Lake
Erie
for
both
recreational
and
commercial
fishing
purposes
Sauger
Native
Lake
Erie
and
its
tributaries;
Ohio
River
Spring,
when
water
temperatures
reach
high
40s
Insects,
crayfish,
other
small
fish
during
low
light
(
dawn
and
dusk)

Saugeye
(
cross
between
sauger
and
walleye)
Introduced
Stocked
into
many
Ohio
impoundments
Sucker,
white
Native
Every
county;
Lake
Erie
April
to
May
Bottom
feeders,
consuming
various
plant
and
animal
species
Sunfish
Bluegill,

pumpkinseed,

green,
warmouth,

and
longear
sunfish
are
native;

redear
sunfish
are
introduced
Rivers,
streams,
and
lakes
throughout
Ohio,
and
Lake
Erie
Between
May
and
August
Adults
feed
mostly
on
smaller
fish,
insects,
crustaceans
20­
9
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.5:
Recreationally
or
Commercially
Valuable
Fish
Species
in
Ohio
Fish
Native
or
introduced?
Habitat
Spawning
season
Diet
Trout
Lake
and
brook
trout
are
native;
rainbow
and
brown
trout
are
introduced
and
maintained
by
stocking
Lake
trout
populations
are
stocked
in
Pennsylvania
and
New
York
and
are
not
highly
prevalent
in
Ohio
and
Lake
Erie
waters;
Brook
trout
are
stocked
in
several
locations
throughout
Ohio
Walleye
Native
Historically
found
in
Lake
Erie,
but
has
been
stocked
in
the
Ohio
River
and
reservoirs
throughout
the
state
April
Shiners,
gizzard
shad,
alewives,

rainbow
smelt
Whitefish
Native
Shallow
bays
of
Lake
Erie's
western
basin
Bottom
feeders
with
a
diet
of
mollusks
and
insect
larvae
Source:
U.
S.
EPA
analysis.

b.
Other
species
dependent
on
aquatic
resources
Resident
and
migratory
bird
species
make
extensive
use
of
Ohio
waters.
Areas
along
the
banks
or
shorelines
of
rivers,

streams,
lakes,
ponds,
and
reservoirs
provide
high
quality
nesting
areas;
the
waters
themselves
are
an
abundant
source
of
food.

Ohio
waters
also
serve
as
important
staging
areas
for
birds
migrating
to
or
from
points
north
or
south.
Wading
or
aquatic
birds
are
generally
unaffected
by
water
quality
impairments
directly.
They
are
affected
indirectly,
however,
through
feeding
on
fish
or
invertebrates
whose
populations
may
be
affected
by
point
and
non­
point
pollution
sources.
The
regulations
aimed
at
protecting
aquatic
species
will
therefore
benefit
wading
and
aquatic
bird
species
indirectly.
More
than
130
aquatic
bird
species
rely
on
Lake
Erie
and
its
tributaries.
Many
species
are
also
found
near
inland
surface
waters.
Major
classifications
of
birds
in
Ohio
include
(
Ohio
DNR
,
1999):

 
Waterfowl,
residing
year­
round
in
Ohio
waters,
especially
Lake
Erie.
Large
groups
of
migrating
and
breeding
birds
are
also
found
elsewhere
in
the
state.
More
than
30
species
are
associated
with
the
Great
Lakes
area
alone.
All
species
depend
on
fish
and
crustaceans
or
aquatic
plants
for
feeding.
Waterfowl
include
loons,
grebes,
swans,
ducks
and
geese.
The
trumpeter
swan
is
of
particular
interest
to
Ohio,
which
became
one
of
several
states
involved
in
efforts
to
restore
these
birds
to
the
Midwest
beginning
in
1996
(
Ohio
DNR,
1999).

 
Wading
birds,
including
bitterns,
herons,
and
egrets.
These
species
both
reside
in
Ohio
waters
and
use
them
as
breeding
grounds.
They
use
 
stand­
and­
wait 
methods
to
catch
fish
or
other
aquatic
organisms
in
shallow
waters.

Many
wading
birds,
such
as
the
great
egret,
black­
crowned
night
heron,
and
American
bittern,
frequent
Lake
Erie
and
surrounding
areas.

 
Marsh
birds,
including
rails,
moorhens,
coots,
and
gallinules.
They
may
feed
on
insects,
crustaceans,
mollusks,

frogs,
invertebrates,
and
small
fish.
These
bird
populations
suffer
from
excessive
development
and
habitat
destruction.
Ohio
surface
waters,
especially
those
around
Lake
Erie,
can
serve
as
important
breeding
grounds
for
these
and
other
bird
species.

 
Shore
birds,
including
42
species
of
plovers,
sandpipers,
gulls,
and
terns,
in
the
Lake
Erie
and
other
Ohio
areas.

Many
of
them
feed
on
aquatic
organisms
from
Lake
Erie.

 
Raptors,
including
the
bald
eagle
and
osprey.
These
birds
of
prey
rely
on
fishing
for
a
large
part
of
their
diet.
Bald
eagles
are
also
a
nationally­
listed
threatened
species.

 
The
belted
kingfisher,
which
relies
on
fish
in
Ohio
waters
as
a
main
source
of
food.

Ohio s
biological
resources
also
includes
reptiles.
Several
species
of
lizards,
snakes,
and
turtles
depend
on
aquatic
habitats
for
food
and
breeding.
These
reptiles
include:

 
Lizards
­
The
five­
fined
skink,
reported
in
areas
along
Lake
Erie,
can
be
found
throughout
Ohio.

20­
10
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
 
Snakes
­
The
eastern
fox
snake,
Eastern
massasasuga,
eastern
ribbon
snake,
copperbelly
water
snake,
Lake
Erie
water
snake,
and
northern
water
snake
feed
within
aquatic
habitats.

 
Turtles
­
The
midland
smooth
softshell
turtle
and
eastern
spiny
softshell
turtle,
found
in
the
Ohio
River
and
tributaries,
are
among
Ohio
turtles
requiring
aquatic
habitats.

20.3.2
Water
Recreation
in
Ohio
EPA
used
the
1994
Survey
of
National
Demand
for
Water­
based
Recreation
(
NDS)
(
U.
S.
EPA,
1994)
to
characterize
recreational
uses
of
Ohio s
water
resources.
The
1994
survey
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
previous
12
months,
including
the
primary
purpose
of
their
trips
(
e.
g.,
fishing,
boating,
swimming,
and
viewing),
total
number
of
trips,
trip
length,
distance
to
the
recreation
site(
s),

and
number
of
participants.
EPA
estimated
recreational
water
use
in
Ohio
by
taking
the
following
steps:

 
estimate
the
percentage
of
survey
respondents
that
visited
Ohio,
by
state;

 
apply
this
percentage
to
the
total
number
of
state
residents
aged
16
and
over,
to
yield
the
total
number
of
participants
from
each
state;

 
estimate
the
total
number
of
recreation
trips
during
the
12­
month
period
for
in­
state
and
out­
of­
state
participants;

 
estimate
the
total
number
of
recreation
trips
for
out­
of­
state
participants
by
multiplying
an
average
number
of
trips
per
Ohio
water
body
visitor
by
the
total
number
of
participants
from
each
state;

 
estimate
the
average
number
of
annual
trips
per
out­
of­
state
visitor
based
on
the
number
of
times
the
respondents
visited
the
site
of
their
last
recreational
trip
(
i.
e.,
Ohio
water
body).
4
EPA
assumed
that
Ohio
residents
whose
last
recreation
trip
was
in­
state
used
Ohio
water
bodies
for
all
of
their
recreation
trips
during
the
12­
month
period;
and
 
estimate
the
total
number
of
in­
state
trips,
summing
the
weighted
number
of
recreation
trips
over
all
Ohio
respondents.

EPA
found
that:

 
An
estimated
one
million
individuals
made
about
6.3
million
boating
trips
to
Ohio
waters
in
1993.
In­
state
residents
made
90
percent
of
the
boating
trips.

 
Approximately
one
million
people
visited
Ohio
water
bodies
for
recreational
fishing.
5
These
visitors
accounted
for
about
15.6
million
fishing
trips
to
the
area.
Recreational
fishermen
from
Ohio
were
the
most
frequent
users
of
the
state
water
resources,
representing
approximately
97
percent
of
all
visitors.

 
Approximately
972,000
and
896,000
visitors
used
the
Ohio
water
bodies
for
near­
water
viewing
and
swimming,

respectively,
in
1993.
These
visitors
account
for
approximately
9.4
and
7.8
million
viewing
and
swimming
trips
to
the
area.
Ohio
residents
account
for
89
percent
of
viewers
and
93
percent
of
swimmers.

 
Most
out­
of­
state
recreational
users
came
from
the
states
surrounding
Ohio,
such
as
Indiana,
Michigan,
and
Pennsylvania.

4
NDS
collected
information
only
on
the
last
site
visited.
Its
numbers
do
not
reflect
people
whose
last
visit
was
to
a
different
area,
but
who
may
have
also
visited
an
Ohio
water
body
on
a
previous
trip
during
the
year.
See
Section
21.3
for
detail
on
the
NDS
data.

5
EPA
compared
the
estimated
number
of
participants
with
total
fishing
licenses
issued
by
Ohio
in
1996.
Ohio
issued
a
total
of
895,770
licenses
for
resident
and
nonresident
fishing.
The
NDS
data
therefore
provide
relatively
accurate
information
on
participation
rates.

20­
11
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
20.3.3
Commercial
Fishing
in
Ohio
Commercial
fishing
is
a
minor
activity
in
Lake
Erie:
12
license
holders
share
a
total
of
19
licenses
(
LECBA
2003).

Commercial
catch
data
compiled
by
the
Great
Lakes
Fishery
Commission
are
summarized
in
Table
20.6
(
Baldwin
et
al.

2002).

Table
20.6:
Commercial
Catches
for
Ohio
Lake
Erie
Waters
(
1990)

Fish
Catch
(
1990
lbs)

Yellow
perch
1,559,000
Carp
1,190,000
White
perch
786,000
Sheepshead
640,000
White
bass
392,000
ChannelcCatfish
365,000
Quillback
134,000
Buffalo
132,000
Bullheads
59,000
Suckers
41,000
Goldfish
31,000
Gizzard
shad
19,000
Lake
whitefish
10,000
Rock
bass
1000
Source:
Baldwin
et
al.
(
2002)

Yellow
perch
represents
about
half
of
the
dockside
value
for
the
entire
commercial
fishery
in
the
Ohio
waters
of
Lake
Erie.

The
value
of
this
fishery
ranged
from
$
1.3
million
to
$
2.5
million
between
1993
and
1998.
Overfishing
and
pollution
have
decreased
the
yellow
perch
population
throughout
Lake
Erie
dramatically
over
the
past
30+
years.
Annual
catches
averaged
around
20
million
pounds
during
the
1960s
and
70s.
The
Lake
Erie
Committee
set
the
1998
lakewide
total
allowable
catch
(
TAC)
quota
for
this
species
at
7.44
million
pounds.
The
yellow
perch
fishery
rebounded
somewhat
over
the
past
couple
of
years,
due
to
strong
annual
recruitment,
strict
commercial
catch
restrictions,
and
a
strict
creel
limit
of
30
fish
per
day
for
the
sport
angler
(
LECB
A
2003
).

20.3.4
Surface
Water
Withdrawals
Water
resources
provide
a
wide
range
of
services
upon
being
withdrawn
(
removed)
from
the
water
body.
Once
used,
water
can
be
returned
to
its
original
sources,
returned
to
another
water
body,
or
consumed
(
e.
g.,
for
human
drinking
water).
Water
withdrawals
from
surface
water
averaged
9,615
mgd
in
1995
(
USGS
1995).
The
majority
of
this
water
is
used
in
power
generation,
accounting
for
85
percent
of
all
surface
water
withdrawals.
Public
water
supply
accounts
for
ten
percent
of
all
withdrawals.
Industrial
and
commercial
water
use
account
for
one
and
four
percent
of
the
total,
respectively.
Water
quality
and
quantity
impairments
can
have
substantial
impacts
on
the
key
withdrawal
services
that
water
provides
to
a
wide
range
of
economic
entities.

20.4
SURFACE
WATER
QUALITY
IN
OHIO
This
section
describes
current
water
quality
conditions
in
Ohio
and
the
effects
of
water
quality
impairments
on
beneficial
uses
of
Ohio s
water
resources.
Ohio
EPA
assessed
designated
use
attainment
in
approximately
42
percent
of
Ohio
streams
and
20­
12
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
rivers;
approximately
64
percent
of
lakes,
ponds,
and
reservoirs;
and
all
of
the
Lake
Erie
shoreline
(
Ohio
EPA,
OWRI,
1996).

The
OWRI
report
summarizes
the
results
of
this
assessment.
This
report
provides
information
on
designated
use
support
by
water
type
and
use
designation,
identifies
major
pollutant/
stressors
that
affect
the
quality
of
surface
water
bodies
and
prevent
designated
use
attainment,
and
lists
major
sources
of
impairment.
The
following
three
sections
summarize
findings
from
the
1996
OWRI
report.

20.4.1
Use
Attainment
in
Streams
and
Rivers
in
Ohio
Most
water
bodies
are
designated
for
several
uses
and
more
than
one
use
can
be
impaired
at
a
time.
The
most
commonly
occurring
sole
impairment
in
fresh
water
bodies
is
to
aquatic
life
support.
The
Ohio
EPA
used
an
ecosystem
approach
that
relies
on
various
tools
to
determine
aquatic
life
use
attainment.
Water
chemistry,
physical
and
habitat
assessment,
and
direct
sampling
of
biota
all
contribute
to
determine
whether
a
water
body
meets
an
attainment
status.
Field
data
yield
biological
indices
that
eventually
determine
a
final
attainment
score.

Ohio
EPA
assessed
6,560
perennial
river
miles
for
aquatic
life
use
attainment.
Of
the
6,560
river
miles
assessed
for
aquatic
life
use:

 
38.5
percent
(
2,536
miles)
are
in
full
attainment
(
i.
e.,
all
water
quality
indicators
meet
criteria
for
specific
water
bodies);

 
10.8
percent
(
708
miles)
are
in
full
attainment,
but
are
threatened
by
pollution
and
other
sources;

 
23.3
percent
(
1,528
miles
)
are
in
partial
attainment
(
i.
e.,
one
of
two,
or
two
water
quality
indicators
do
not
meet
criteria);
and
 
27.4
percent
(
1,797
miles)
are
in
non­
attainment
(
i.
e.,
no
criteria
are
met
or
the
river
experiences
a
severe
toxic
impact).

Fecal
coliform
bacteria
counts
determine
recreational
use
attainment.
Such
counts
are
less
stringent
for
Secondary
Contact
Recreation
than
for
Primary
Contact
Recreation.
Ohio
EPA
has
assessed
2,402
river
miles
for
recreation
use
since
1988
(
Ohio
EPA,
OW
RI,
1996).
Of
the
2,402
river
miles
assessed
for
recreation
use:

 
57
percent
(
1,370.3
miles)
of
the
sampled
rivers
and
streams
are
in
full
attainment
(
i.
e.,
a
water
body
meets
all
chemical
criteria
for
recreational
use
and
human
contact);

 
19.7
percent
(
474.1
miles)
are
in
partial
attainment
(
i.
e.,
a
water
body
only
partially
meets
human
contact
criteria);

and
 
23.2
percent
(
557.4
miles)
are
in
non­
attainment
(
i.
e.,
a
water
body
fails
to
meet
human
contact
criteria).

20.4.2
Lake
Erie
and
Other
Lakes
Use
Attainment
Lake
Erie,
which
has
a
history
of
pollution
problems,
currently
has
fish
consumption
advisories
for
carp
and
channel
catfish
(
Ohio
DN
R,
1999).
Ohio
E
PA
assesses
Lake
Erie
as
having
partial
use
attainment
for
aquatic
life
and
fish
consumption,
and
full
attainment
for
recreation.
6
Ohio
EPA
used
parameters
specified
by
the
Ohio
EPA
Lake
Condition
Index
(
LCI)
to
develop
use
attainment
for
other
lakes.
Only
approximately
two
percent
of
all
lakes
are
in
full
use
attainment
for
aquatic
life,

recreation,
and
fish
consumption.
Approximately
82,
50,
and
53
percent
are
in
full
attainment
for
aquatic
life,
recreation,
and
fish
consumption,
respectively,
but
are
threatened
by
pollution
for
these
categories.
High
percentages
of
lake
acres
are
in
partial
attainment
for
recreation
(
38.8
percent)
and
public
supply
(
43.8
percent)
use
designations.
Table
20.7
shows
use
attainment
for
Lake
Erie
and
other
lakes,
ponds,
and
reservoirs.

6
Further
methodologies
to
better
assess
use
attainment
in
Lake
Erie
are
still
under
development
by
the
Ohio
EPA.

20­
13
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.7:
Use
Attainment
Summary
for
Lake
Erie
ane
Other
Lakes
Use
Category
%
of
Total
Units
Assessed
Full
Attainment
Full
Attainment,

Threatened
Partial
Attainment
Non­
Attainment
%
Units
%
Units
%
Units
%
Units
%

Lake
Erie
(
Unit:
Shore
Miles)
a
Aquatic
Life
(
EWH)
100
236
100
Recreation
100
231
98
5
2
Fish
Consumption
100
236
100
Lakes,
Ponds,
&
Reservoirs
(
Unit:
Acres)

Aquatic
Life
(
EWH)
64.7
1,651
2.2
63,174
82.2
10,686
13.9
1,302
1.7
Recreation
(
PCR)
64.4
1,392
1.8
38,499
50.3
29,793
38.9
6,582
9.0
Public
Water
Supply
64.1
1,301
1.7
40,846
53.6
33,365
43.8
673
0.9
a
Assessments
are
based
on
unit
of
measure
presented
in
parentheses.

Source:
Ohio
EPA,
OWRI
1996.

20.4.3
Causes
and
Sources
of
Use
Non­
Attainment
in
Ohio
Ohio
EPA
assessed
the
causes
and
sources
of
impairment
to
Ohio
surface
waters
and
examined
trends
in
major
causes
and
sources
from
previous
assessment
cycles.
The
following
discussion
summarizes
findings
from
the
1996
OWRI
report
(
Ohio
EPA,
1996).

a.
Causes
Causes
are
the
agents
responsible
for
damage
and
threats
to
aquatic
life.
The
major
causes
of
impairment
in
Ohio
surface
waters
include:

 
organic
enrichment/
low
DO,

 
habitat
modifications,

 
siltation,

 
flow
alteration,

 
nutrients,
and
 
metals.

Ohio
EPA
examined
trends
in
these
major
causes
from
previous
assessment
cycles
through
1996.
They
found
that
point
source­
related
causes
declined,
while
non­
point
sources
became
major
contributors.
Ohio
EPA
concluded
that
this
trend
 
reflects
the
relative
effectiveness
of
the
programs
to
control
point
sources
compared
to
general
lack
of
measures
to
control
many
[
non­
point
sources] 
(
Ohio
EPA,
OWRI,
1996).

Organic
enrichment,
which
alters
DO
levels
and
affects
aquatic
communities,
is
the
main
cause
of
impairment
in
Ohio s
rivers
and
streams.
Inadequate
wastewater
treatment
from
municipal
and
industrial
point
sources
account
for
most
of
this
impairment.
Metals
are
a
major
cause
of
impairment
to
approximately
226
river
miles,
a
moderate
cause
of
impairment
to
179
river
miles,
and
a
minor
cause
of
impairment
or
threat
to
165
river
miles.

Nutrients,
resulting
mostly
from
agricultural
non­
point
sources,
are
the
main
cause
of
impairment
in
lakes.
Metals
are
a
major
cause
for
impairment
in
approximately
250
acres
of
Ohio s
lakes,
ponds,
and
reservoirs,
and
form
the
main
cause
of
impairment
in
Lake
Erie,
the
major
water
resource
in
Ohio
(
90
percent
of
the
surface
water
volume).
Highly
developed
areas
20­
14
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
bordering
the
lake
contribute
urban
runoff,
along
with
discharges
from
industrial
and
municipal
sources.
Other
causes
of
impairment
in
Lake
Erie
include
priority
organics,
DO,
and
nutrients.
7
b.
Sources
Sources
are
the
origins
of
the
agents
responsible
for
damage
and
threats
to
water
resources.
The
major
sources
of
impairment
to
Ohio
surface
waters
include:

 
municipal
and
industrial
discharges,

 
hydromodification,

 
agricultural
runoff,

 
urban
runoff,
and
 
mining.

Point
source­
caused
impairment
has
declined
over
time,
while
that
from
non­
point
sources,
such
as
agricultural
and
urban
runoff,
has
increased.
Point
sources
remain
a
major
source
of
impairment
in
almost
900
miles,
or
25
percent,
of
Ohio s
affected
rivers
and
streams.
Point
sources
are
the
major
source
of
impairment
for
Lake
Erie.
They
form
a
major
source
of
impairment
for
24
shore
miles,
and
a
moderate
source
of
impairment
for
an
additional
281
shore
miles
of
Lake
Erie.
In
addition,
point
sources
adversely
affect
1,678
lake
acres.

Non­
point
sources
related
to
agricultural
and
urban
runoff
form
the
major
source
of
impairment
for
some
9,000
acres,
or
two­

thirds
of
Ohio s
lakes,
ponds,
and
reservoirs.
In
addition,
46
Lake
Erie
shore
miles
list
non­
point
sources
as
their
major
impairment
source.

20.5
EFFECTS
OF
WATER
QUALITY
IMPAIRMENTS
ON
WATER
RESOURCE
SERVICES
Water
resource
services
are
negatively
affected
by
pollutants
that
impair
the
aquatic
ecosystems.
Certain
pollutants
can
adversely
affect
aquatic
species
directly
by
increasing
species
morbidity
and/
or
impairing
reproductive
success,
or
indirectly
by
adversely
altering
food
chain
interactions.
These
direct
and
indirect
impacts
can
change
quantity
and
type
of
fish
and
other
species
in
the
aquatic
ecosystem.
In
the
worst
case
scenario,
an
impaired
ecosystem
no
longer
supports
any
aquatic
life.
High
pathogen
counts
or
excessive
eutrophication
in
water
bodies
that
are
suitable
for
swimming
may
force
swimmers
to
go
elsewhere
or
forego
swimming
altogether.
Any
aesthetic
degradation
decreases
the
value
of
each
individual s
recreational
experience.
In
severe
cases,
the
affected
water
bodies
become
unsuitable
for
recreation.
Water
quality
impairments
also
increase
the
cost
of
treating
water
to
meet
drinking
water
standards.

This
section
details
the
effects
of
water
quality
impairments
on
in­
stream
services
provided
by
Ohio s
water
resources.

20.5.1
Effect
of
Water
Quality
Impairment
on
Life
Support
for
Animals
and
Plants
Deficiencies
in
water
quantity
and
quality
can
impair
the
health
of
aquatic
ecosystems.
In
worst
case
scenarios,
the
ecosystem
may
no
longer
support
aquatic
life
at
all.
The
major
causes
of
water
quality
impairment
in
Ohio
include
high
biological
oxygen
demand
(
BOD)
from
organic
enrichment,
habitat
and
flow
alterations,
nutrients,
siltation
and
turbidity,

metals,
pH,
ammonia,
and
priority
organics.
Habitat,
flow
alterations,
and
thermal
discharges
are
unrelated
to
MP&
M
effluents
and
are
not
discussed
here.
MP&
M
effluents
contribute
to
the
remaining
major
causes
of
water
quality
impairment,

with
the
ecological
effects
outlined
below.

a.
BOD/
COD
BOD
and
chemical
oxygen
demand
(
COD)
are
two
methods
to
determine
the
oxygen
requirements
of
pollutants
in
wastewater.
Low
oxygen
level
is
the
primary
cause
of
impairment
in
Ohio s
rivers
and
streams
and
a
major
source
of
7
Major,
moderate,
and
minor
impacts
refer
to
the
high,
moderate,
and
slight
magnitude
codes
specified
by
the
U.
S.
EPA
for
the
301(
b)
report.

20­
15
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
impairments
in
Ohio s
lakes.
When
bacteria
decompose
excess
organic
matter,
they
consume
DO
in
surface
waters.
Oxygen
is
needed
to
chemically
(
abiotically)
oxidize
the
pollutants
present
in
wastewater.
When
too
much
oxygen
is
needed
to
oxidize
pollutants,
hypoxic
(
oxygen
deficient)
or
anoxic
(
oxygen
depleted)
conditions
result.
Sources
of
high
oxygen
demand
include
effluents
from
municipal
treatment
plants
and
certain
industries,
and
runoff
from
feedlots
or
farms.
Another
source
is
eutrophication
caused
by
excessive
nutrient
input.
The
nutrients
stimulate
algal
blooms.
Bacteria
consume
the
algae
when
they
die,
decreasing
DO
in
the
water
column.
DO
is
a
critical
variable
for
fish
and
invertebrate
survival.
If
oxygen
concentrations
drop
below
a
minimum
level,
organisms
suffocate
and
either
move
out
or
die
(
EPA,
1986).
This
effect
can
drastically
reduce
the
amount
of
useable
aquatic
habitat.

b.
Nutrients
Nutrients
are
the
leading
causes
of
impairment
in
Ohio
lakes
and
comprise
one
of
the
major
causes
of
impairment
in
rivers,

streams,
and
Lake
Erie.
The
overabundance
of
nitrogen
and
phosphorus
is
one
of
the
most
documented
forms
of
aquatic
ecosystem
pollution.
Although
both
compounds
are
essential
nutrients
for
phytoplankton
(
free­
floating
algae)
and
periphyton
(
attached
algae),
which
form
the
base
of
the
aquatic
food
web,
too
much
nutrient
input
overstimulates
primary
productivity
and
results
in
eutrophication.
The
impact
of
these
compounds
has
contributed
significantly
to
water
quality
decline
in
the
United
States
(
EPA,
1992).
Phosphorus
is
a
limiting
nutrient
in
most
freshwater
systems
(
Wetzel,
1983),
whereas
nitrogen
is
typically
limited
in
estuarine
and
marine
systems.

In
freshwater,
excess
phosphate
(
PO4
)
has
been
linked
to
eutrophication
and
nuisance
growth
of
algae
and
aquatic
weeds
(
Wetzel,
1983),
even
though
direct
toxicity
to
fish
and
other
aquatic
species
is
not
a
major
concern.
DO
in
the
water
column
decreases,
however,
when
algae
and
other
aquatic
plants
die
off,
and
certain
toxins
may
be
produced,
both
of
which
can
contribute
to
fish
kills.

c.
Siltation
and
turbidity
Siltation
and
turbidity
are
the
third
leading
causes
of
impairments
in
Ohio
rivers
and
lakes,
except
Lake
Erie.
Siltation
is
the
most
important
factor
in
surface
water
degradation
in
the
U.
S.
(
EPA,
1992).
Major
sources
include
urban
and
stormwater
runoff,
mining
and
logging
activities,
and
runoff
from
plowed
fields
(
EPA,
1992).
All
these
inputs
create
cloudy
water
with
increased
turbidity
and
decreased
visibility
and
light
penetration.
High
primary
productivity
by
phytoplankton
following
excessive
nutrient
input
can
also
increase
turbidity.
Excess
suspended
matter
decreases
the
amount
of
light
penetrating
the
water
column,
which
can
reduce
primary
productivity.
This
turbidity
can
eliminate
or
displace
fish
species
requiring
clear
water
to
live,
feed,
or
reproduce.

d.
Metals
Metals
are
the
leading
cause
of
impairment
in
Lake
Erie
and
comprise
one
of
the
major
causes
of
impairment
in
inland
lakes
and
rivers.
Metals
are
naturally­
occurring
inorganic
constituents
of
the
earth s
crust.
Priority
pollutant
metals
commonly
found
in
the
aquatic
environment
include
antimony,
arsenic,
cadmium,
chromium,
copper,
lead,
mercury,
nickel,
selenium,

silver,
thallium
and
zinc
(
EPA,
1998a).
These
compounds
enter
the
aquatic
environment
via
urban
stormwater
runoff,

industrial
and
municipal
effluents,
and
atmospheric
deposition.
As
a
group,
metals
can
be
highly
toxic:
water
quality
criteria
(
WQC)
for
acute
toxicity
range
from
around
1,100
 
g/
l
(
chromium
VI
in
saltwater)
to
around
1
 
g/
l
(
mercury
in
freshwater);
WQC
for
chronic
toxicity
range
from
120
 
g/
l
(
zinc
in
freshwater)
to
<
1.0
 
g/
l
(
mercury
in
salt­
and
freshwater)

and
are
therefore
an
order
of
magnitude
lower
(
EPA,
1998a).

Once
metals
reach
the
aquatic
environment,
they
tend
to
associate
with
organic
and
inorganic
particulates
in
the
water
column.
Sediments
become
long­
term
sinks
for
metals,
which
accumulate
in
the
bottom.
Metals
can
enter
the
food
chain
when
ingested
by
benthic
invertebrates
or
other
burrowing
organisms.
Most
metals
have
bioconcentration
factors
(
BCFs)
ranging
from
100
to
10,000
and
can
therefore
bioaccumulate
in
aquatic
organisms.
A
few,
including
selenium,
lead,

and
mercury,
may
reach
hazardous
levels
in
fish
or
wildlife
receptors
and
result
in
avian
developmental
or
neurological
abnormalities.

e.
Organic
chemicals
Priority
organics
are
the
second
most
frequent
cause
of
impairment
in
Lake
Erie
and
comprise
one
of
the
major
causes
of
impairment
in
rivers
and
streams.
Thousands
of
different
compounds
exist
as
organic
chemicals,
including
petroleum
hydrocarbons
and
myriad
industrial
chemicals.
They
enter
the
aquatic
environments
via
municipal
and
industrial
effluents,

stormwater
runoff,
contaminated
groundwater,
atmospheric
deposition,
illegal
dumping,
or
accidental
releases.
Aquatic
toxicities
vary
by
orders
of
magnitude
depending
on
the
compound.
Factors
influencing
toxicity
and
long­
term
ecological
effects
include
water
solubility,
volatility,
biodegradation
potential,
and
bioaccumulation
potential.

20­
16
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Excessive
amounts
of
organic
chemicals
degrade
surface
water
quality
by
causing
acute
or
(
more
typically)
chronic
toxicity.

This
toxicity
impairs
growth,
development,
and/
or
reproductive
success
in
fish
and
aquatic
invertebrates.
Persistent
and
low
water­
soluble
organic
chemicals
accumulate
in
sediments
and
are
taken
up
into
local
aquatic
food
chains.
They
can
reach
dangerous
concentrations
in
fish
and
avian
receptors,
resulting
in
reproductive
failures
or
other
avian
health
effects.

f.
pH
Approximately
180
river
miles
are
pH­
impaired
in
Ohio.
pH
is
a
measure
of
acidity.
Acid
reaches
surface
waters
via
atmospheric
deposition
( 
acid
rain ),
industrial
effluents,
and
leachates
from
mine
overburdens
or
spoils.
Acidity
by
itself
is
a
key
variable
shaping
aquatic
communities:
it
is
a
toxicant
in
its
own
right
but
also
controls
metal
solubility,
and
the
toxicity
of
several
metals
and
ammonia.

Aquatic
species
vary
widely
in
their
sensitivity
to
pH:
the
most
sensitive
vertebrate
and
invertebrate
species
die
off
when
average
pH
ranges
between
6.0
and
6.5.
Most
fish
species
are
eliminated
when
pH
reaches
5.0.
Only
a
few
can
survive
at
pH
4.5
(
U.
S.
EPA,
1999).
Macro
invertebrates
exhibit
the
same
pattern,
except
that
hardy
species
can
survive
down
to
a
pH
of
about
3.5.

g.
Ammonia
Large
amounts
of
ammonia
enter
lakes
and
rivers
via
wastewater
treatment
plants
and
industrial
effluents,
atmospheric
deposition,
and
non­
point
source
surface
runoff.
Approximately
150
river
miles
in
Ohio
are
ammonia­
impaired.
This
compound,
unique
among
regulated
pollutants,
is
also
produced
naturally
inside
fish
as
a
metabolic
waste
product.
Excess
ammonia
usually
diffuses
rapidly
out
of
the
blood
stream
and
into
the
surrounding
water
via
the
gills.
High
concentrations
of
external
un­
ionized
ammonia
(
NH3)
reduce
or
reverse
this
diffusive
gradient
and
allow
ammonia
to
build
up
to
toxic
levels
inside
the
organism
(
EPA,
1998c).

Ammonia
in
surface
water
exists
in
two
major
forms:
un­
ionized
ammonia
(
NH3),
which
is
highly
toxic
to
fish
or
invertebrates,
and
ammonium
ion
(
NH4
+
),
which
is
much
less
toxic.
Which
form
prevails
depends
mainly
upon
the
pH
level;

temperature
and
ionic
composition
play
a
smaller
role.
EPA
calculated
a
WQ
C
that
becomes
more
severe
with
decreasing
acidity.
For
example,
the
acute
criteria
for
surface
waters
containing
salmonids
equals
36.7
mg/
l
at
pH=
6.0
but
only
2.14
mg/
l
at
pH=
8.5.
For
surface
waters
without
salmon,
the
acute
criteria
for
the
same
pH
equal
55.0
mg/
l
and
3.2
mg/
l,

respectively
(
EPA,
1998c).

20.5.2
Effect
of
Water
Quality
Impairment
on
Recreational
Services
Healthy
surface
waters
are
essential
to
support
a
diversity
of
recreational
uses,
including
viewing
and
other
near­
water
activities.
Industrial
or
other
human
activities
impair
surface
water
quality.
Certain
metals
and
chlorinated
compounds
can
bioaccumulate
in
aquatic
food
chains
and
reach
unhealthy
levels
in
carnivorous
fish
or
shellfish.
Health
advisories
to
limit
or
avoid
their
consumption
may
result.
High
concentrations
of
toxic
compounds
can
also
lead
to
human
contact
advisories.
The
release
of
untreated
or
poorly
treated
sewage
can
cause
high
levels
of
pathogenic
bacteria
in
water
and
result
in
swimming
advisories
or
beach
closures.
All
of
these
actions
limit
the
full
use
of
surface
waters
and
can
have
significant
local
economic
impacts.

a.
Fish
consumption
advisories
In
1997,
the
Ohio
Department
of
Health
(
ODH)
issued
a
statewide
fish
consumption
advisory
to
protect
women
of
childbearing
age
and
children
six
years
or
younger
against
mercury s
neurological
and
developmental
effects.
The
advisory,

which
applies
only
to
these
two
population
groups,
recommended
that
these
women
and
children
eat
no
more
than
one
meal
per
week
of
any
fish
caught
in
Ohio
waters.
The
advisory
covers
all
state
waters
because
most
of
the
mercury
measured
in
fish
tissues
originates
from
region­
wide
fossil
fuel
combustion
processes.
The
mercury
reaches
surface
waters
via
atmospheric
deposition
on
the
surrounding
landscape
(
Ohio
DNR,
1999).

Since
1983,
the
ODH
has
developed
numerous
water
body­
specific
fish
consumption
advisories
for
approximately
174
water
body
segments
(
rivers
and
lakes)
and
Lake
Erie.
These
water
bodies
represent
a
relatively
small
fraction
of
Ohio s
5,000
discrete
water
body
segments,
as
determined
by
Ohio
EPA.
The
contaminants
of
greatest
concern
include
polychlorinated
biphenyls
(
PCBs)
,
mercury,
polycyclic
aromatic
hydrocarbons
(
PAHs)
,
lead,
organometallics,
Mirex,
phthalate
esters,
Chlordane,
and
hexachlorobenzene.
Of
these,
four
mercury,
PAHs,
lead,
and
phthalates
are
included
on
the
MP&
M
list
of
pollutants
of
concern
(
POCs)
.
As
a
group,
these
contaminants
are
generally
characterized
as
lipophilic
(
i.
e.,
fat
loving),
resistant
to
biological
degradation
or
cellular
metabolism,
and
toxic.
Once
they
reach
surface
water,
they
20­
17
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
concentrate
in
sediments
and
bioaccumulate
or
biomagnify
through
aquatic
food
chains.
These
compounds
can
linger
for
decades
in
aquatic
systems.

The
kind
of
sports
or
recreational
fish
species
affected
by
the
consumption
advisories
varies
by
water
body
segment.
More
than
23
different
species
are
covered
by
advisories,
including
walleye,
common
carp,
sauger,
saugeye,
white
crappie,

freshwater
drum,
and
various
species
of
bass,
perch,
catfish,
salmon,
trout,
suckers,
and
sunfish.
Restrictions
vary
depending
on
the
pollutant,
the
fish
species
concerned,
and
the
concentrations
measured
in
edible
tissues.
The
ODH
developed
maximum
recommended
rates
of
fish
consumption
that
include
outright
consumption
bans,
one
meal
every
two
months,
one
meal
a
month,
or
one
meal
a
week.
The
same
water
body
segments
can
commonly
have
different
advisories
for
different
fish
species
(
Ohio
DNR,
1999).

b.
Contact
advisories
The
ODH
also
issued
human
contact
advisories
for
nine
water
body
segments
in
Ohio
located
on
the
Black
River,
Little
Scioto
River,
Mahoning
River,
the
middle
fork
of
the
Little
Beaver
Creek,
and
the
Ottawa
River.
Swimming
or
wading
is
prohibited
due
to
the
presence
of
high
levels
of
PAHs,
PCB
s,
Mirex,
phthalate
esters,
and/
or
Chlordane.
Of
these,
PAHs
and
phthalates
are
included
on
the
list
of
MP&
M
POCs.
Fish
consumption
advisories
also
cover
all
of
these
segments
(
Ohio
DNR,
1999).

c.
Beach
closures
Beach
closures
typically
occur
during
the
summer
months
when
high
levels
of
fecal
coliform
bacteria
or
other
disease­
causing
organisms
(
e.
g.,
Escherichia
coli)
proliferate
in
surface
waters.
Such
waters
can
become
contaminated
from
several
sources,

including:
agricultural
runoff,
sewer
overflows,
boating
wastes,
and
poor
hygienic
practices
by
some
bathers.
Excessive
levels
of
indicator
pathogens
in
surface
waters
can
indicate
a
serious
threat
to
human
health
and
may
cause
health
departments
to
post
warnings,
restrict
access,
or
forbid
swimming
altogether.
The
MP&
M
regulation
is
not
expected
to
reduce
beach
closures
during
summer
months.

Numerous
public
bathing
beaches
dot
Ohio s
262­
mile
shoreline
along
Lake
Erie.
The
ODH
has
developed
a
composite
metric
based
on
E.
coli
counts
in
surface
waters
at
11
selected
beaches
along
Ohio s
north
coast.
The
metric
tracks
the
average
number
of
days
that
swimming
advisories
are
posted
at
the
11
beaches
for
a
15
week
period
beginning
around
Memorial
Day
and
continuing
through
Labor
Day.
The
most
recent
data
available
show
that
the
11
beaches
were
under
advisement
an
average
of
21
days
during
the
summer
months
(
minimum
of
0
days
and
maximum
of
49
days)
in
1996.

The
ODH
developed
a
4­
tiered
scale
to
score
and
track
the
average
number
of
days
that
the
11
public
beaches
are
under
advisement
from
one
year
to
the
next.
Between
1990
and
1996,
the
average
(
based
on
a
five­
year
running
average)
number
of
beach
advisories
scored
in
the
 
fair 
category
consistently,
meaning
that
the
beaches
were
under
advisement
between
20
and
30
days
in
the
summer
(
State
of
Ohio,
1998).

Ohio s
lakes,
ponds,
and
reservoirs
(
excluding
Lake
Erie)
yielded
no
quantitative
data
on
beach
closures.
The
1996
Ohio
Water
Resource
Inventory
of
Public
Lakes,
Ponds
and
Reservoirs
provides
a
breakdown
of
the
portion
of
Ohio s
446
public
lakes
that
are
threatened
or
impaired
as
a
result
of
high
levels
of
fecal
coliform
bacteria.

20.6
PRESENCE
AND
DISTRIBUTION
OF
ENDANGERED
AND
THREATENED
SPECIES
IN
OHIO
Many
factors
can
affect
the
survival
of
endangered
and
threatened
(
E&
T)
species.
Some
factors
are
species­
specific;

others
result
from
one
or
more
anthropogenic
stressors.
Inherent
vulnerability
factors
include
narrow
geographic
distribution,

slow
reproductive
rates,
or
requirements
for
large
areas.
Major
anthropogenic
stressors
include
intentional
taking
(
e.
g.,

fishing),
incidental
taking,
physically
altering
habitat
(
e.
g.,
converting
wetlands
into
agricultural
land),
water
pollution,
and
introducing
alien
species.
A
single
stressor
or
a
set
of
stressors
can
contribute
to
a
species'
decline
or
extinction.
Previous
studies
reported
that
more
than
40
percent
of
endangered
aquatic
species
were
affected
by
five
or
more
environmental
stressors,
and
only
seven
percent
of
federally­
listed
species
had
a
single
threat
to
their
survival.
Although
stressors
seldom
act
alone,
water
pollution
is
one
of
the
major
hazards
to
E&
T
aquatic
species,
cited
as
responsible
for
the
decline
of
19
(
54
percent)
out
of
35
E&
T
fish
species
in
Ohio
(
Ohio
DN
R,
1998).

20­
18
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
The
following
sections
provide
an
overview
of
E&
T
species
found
in
Ohio,
their
distribution,
and
the
major
hazards
threatening
their
survival.
Species
discussed
below
include
those
listed
under
both
the
federal
Endangered
Species
Act
(
50
CFR
Part
17)
and
the
Ohio
Department
of
Natural
Resources 
(
DNR)
Division
of
Natural
Areas
and
Preserves.
The
MP&
M
regulation
concentrates
on
water­
related
benefits;
these
sections
therefore
describe
only
those
species
associated
with
aquatic
environments.
8
The
DNR
list
includes
90
E&
T
species
with
a
total
of
1,227
observations
throughout
Ohio.

 
Observations 
refers
to
locations
where
species
were
observed;
most
species
have
multiple
observations.
This
analysis
includes
observations
spanning
the
years
1980
to
1988.

20.6.1
E&
T
Fish
E&
T
fish
inhabit
almost
every
major
water
body
in
Ohio,
including
Lake
Erie
and
the
Ohio
River
and
its
tributaries.
The
Ohio
DNR
lists
35
total
state­
listed
E&
T
fish
species,
of
which
13
are
threatened
and
22
endangered.
The
list
includes
only
one
federally­
listed
species,
the
scioto
madtom.

Of
the
total
E&
T
fish,
approximately
12
species
use
Lake
Erie
as
a
possible
habitat
and
nine
use
the
Ohio
River.
Most
of
the
species
listed
live
in
riverine
habitats.
Approximately
28
species
were
identified
in
a
river
system
in
Ohio,
including
the
Ohio,
Scioto,
Muskingham,
Miami,
Walhondig,
and
Maumee
River
systems.
MP&
M
facilities
are
found
on
all
these
major
river
systems.

The
DNR
lists
384
observations
of
E&
T
fish
in
Ohio,
of
which
240
observations
of
30
different
species
have
been
reported
since
1980.
Figure
20.2
maps
the
observations
of
E&
T
fish
in
Ohio
and
shows
the
extent
to
which
these
observations
were
reported
in
the
state.
Multiple
observations
can
occur
for
a
single
species.
In
southern
Ohio,
most
observations
come
from
the
Muskingham
and
Scioto
River
systems
and
the
Ohio
River.
Most
observations
in
northern
Ohio
came
from
Lake
Erie
tributaries
or
the
lake
itself.

In
addition
to
water
pollution,
cited
above
as
major
hazard
to
E&
T
aquatic
species,
other
major
hazards
to
E&
T
fish
include
siltation
and
impoundments.
Approximately
two­
thirds
of
E&
T
fish
species
are
threatened
by
siltation,
and
17
percent
are
threatened
by
impoundments
or
dams.
MP
&
M
regulations
can
improve
affected
ecosystems
or
habitats
by
reducing
discharges
from
MP&
M
facilities.
These
improvements
can
then
help
reduce
siltation
and
restore
some
of
the
E&
T
fish
populations.

Many
obscure
E&
T
fish
species
have
a
pure
existence
value.
Some
E&
T
species,
like
brook
trout
and
lake
sturgeon,
have
high
potential
for
consumptive
uses.
Restoring
their
populations
and
those
of
other
commercial
and
recreational
fish
species
may
enhance
recreational
fishing
opportunities.
Table
20.8
lists
E&
T
fish
in
Ohio,
their
habitat
locations,
and
the
cause
for
their
E&
T
listing.
The
table
lists
species
alphabetically
by
scientific
name.

20.6.2
E&
T
Mollusks
Mollusks
yield
the
largest
number
of
reported
observations
of
aquatic
E&
T
species
in
Ohio,
representing
48
percent
of
the
total
1,227
observations.
The
Ohio
DNR
lists
29
E&
T
mollusk
species,
four
threatened
and
25
endangered.
Of
these,
five
mollusk
species
are
on
the
federal
endangered
species
list:
catspaw,
clubshell,
fanshell,
white
catspaw,
and
pink
mucket.

Ohio s
E&
T
mollusks
concentrate
in
five
major
areas:
Lake
Erie
and
the
Grand
River
tributary,
Scioto
River
and
Big
Arby
tributary,
Muskingham
River,
Little
Miami
River,
and
the
Ohio
River.
E&
T
mollusk
populations
reside
mostly
along
the
mainstems
of
large
rivers
and
in
Lake
Erie,
but
are
also
found
in
the
St.
Joseph,
Sandusky,
and
Cuyahoga
Rivers.

8
 
Aquatic
species 
were
identified
by
the
Ohio
Department
of
Natural
Resources,
Division
of
Natural
Areas
and
Preserves.
These
species
include
any
species
that
are
 
closely
associated
with
aquatic
habitats
through
their
breeding
or
feeding
requirements. 

20­
19
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Figure
20.2:
E&
T
Fish
Observances
in
Ohioa
(
1980­
1997)

a
Each
$
represents
an
observance.

Source:
U.
S.
EPA
analysis.

20.6.3
Other
Aquatic
E&
T
Species
Improved
water
quality
resulting
from
the
MP
&
M
regulation
may
also
benefit
other
aquatic
E&
T
species.
Unlike
fish
and
mollusks,
whose
primary
habitat
is
a
surface
water
body
at
all
times,
these
species
may
use
surface
water­
related
habitats
only
for
breeding
or
feeding.
Improved
water
quality
may
benefit
these
populations
indirectly
by
enhancing
the
quality
and
quantity
of
aquatic
biological
resources.

Other
aquatic­
associated
E&
T
species
of
Ohio
include:

 
Birds
ten
state­
listed
species,
one
threatened
and
nine
endangered,
include
one
federally­
listed
threatened
species,

the
bald
eagle.
The
state­
listed
species
include:
American
and
least
bitterns,
common
and
black
terns,
yellow­
and
black­
Crowned
night­
herons,
king
rail,
osprey,
and
snowy
egret.
These
species
are
observed
mostly
along
the
Lake
Erie
coast.
The
bald
eagle
is
observed
mostly
in
Ohio s
northeast
corner.

20­
20
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
 
Amphibians
three
state­
listed
endangered
species:
blue­
spotted
salamander,
observed
in
the
very
northwest
section
of
the
state
along
small
streams
and
near
the
Maumee
River;
eastern
spadefoot,
found
near
the
Ohio
and
Muskingham
Rivers;
and
eastern
hellbender,
observed
along
the
Muskingham
and
Scioto
River
systems
and
tributaries
of
the
Ohio
River.

 
Reptiles
two
species:
the
copperbelly
water
snake,
a
state­
listed
endangered
and
federally­
listed
threatened
species
found
in
lakes
and
ponds
in
the
northwest
corner
of
Ohio;
and
the
Lake
Erie
water
snake,
state­
listed
as
threatened
and
a
proposed
threatened
species
for
the
federal
list,
found
only
along
the
edges
of
the
Lake
Erie
islands.

 
Mammals
the
river
otter
is
state­
listed
as
endangered.
Sparse
observations
of
the
animal
come
from
various
small
creeks
and
lakes
in
the
eastern
part
of
Ohio.

 
Crustaceans
the
state­
listed
endangered
Sloan s
crayfish
has
been
observed
in
several
small
tributaries
of
the
Great
Miami
River
system.

 
Insects
nine
state­
listed
species,
one
threatened
and
eight
endangered,
are
reported
throughout
the
state.

20­
21
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.8:
Endangered
and
Threatened
Fish
Species
of
Ohio
Common
Name
Scientific
Name
Number
of
Observations
Last
Observed
Federal
Status
State
Status
Habitat
Causes
for
Listing
Lake
sturgeon
Acipenser
fulvescens
3
1979
E
Lake
Erie,
spawning
in
larger
rivers
such
as
Maumee
and
Auglaize
Pollution
and
dams
Longnose
sucker
Catostomus
catostomus
1
1950
E
Lake
Erie
Pollution
creating
low
oxygen
levels
Rosyside
dace
Clinostomus
funduloides
53
1997
T
Small,
upland
streams
of
Teays
and
Little
Scioto
River
systems
Runoff
and
siltation
Cisco
Coregonus
artedi
1
1976
E
Lake
Erie
Pollution
and
overfishing
Blue
sucker
Cycleptus
elongatus
2
1985
E
Ohio
River
and
lower
reaches
of
large
tributaries
Pollution,
dams,
increased
turbidity
and
siltation
Lake
chubsucker
Erimyzon
sucetta
28
1994
T
Lakes
(
not
Erie)
and
larger
streams
Increased
turbidity
and
siltation
Bluebreast
darter
Etheostoma
camurum
19
1995
T
Scioto
and
Muskingham
River
systems,

large
streams
Pollution
and
siltation
Spotted
darter
Etheostoma
maculatum
8
1992
E
Large
streams
of
Muskingham
and
Scioto
systems
Pollution
and
siltation
Tippecanoe
darter
Etheostoma
tippecanoe
11
1994
T
Muskingham
and
Scioto
River
systems
Tonguetied
minnow
Exoglossum
laurae
16
1996
T
Great
Miami
River
system
Undetermined,
likely
pollution
and
siltation
Western
banded
killifish
Fundulus
diaphanus
menona
9
1994
E
Lake
Erie
and
larger
tributaries
Siltation
Goldeye
Hiodon
alosoides
16
1989
E
Ohio
River
and
lower
reaches
of
large
tributaries
Pollution
Mississippi
silvery
minnow
Hybognathus
nuchalis
1
1983
E
Ohio
River
and
tributaries
Siltation
Ohio
lamprey
Ichthyomyzon
bdellium
4
1992
E
Ohio
River
and
lower
reaches
of
large
tributaries
Pollution
and
siltation
Northern
brook
lamprey
Ichthyomyzon
fossor
25
1992
E
Small
streams,
tributaries
of
Grand
and
Scioto
rivers
Pollution,
siltation,
and
dams
Mountain
brook
lamprey
Ichthyomyzon
greeleyi
6
1993
E
Mahoning
River
and
tributaries
Pollution,
siltation,
and
dams
Silver
lamprey
Ichthyomyzon
unicuspis
40
1993
T
Lake
Erie
and
larger
tributaries
Pollution,
siltation,
and
dams
Blue
catfish
Ictalurus
furcatus
1
1987
E
Scioto
River
Spotted
gar
Lepisosteus
oculatus
1
1978
E
Lake
Erie
Siltation
and
dredging
20­
22
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Table
20.8:
Endangered
and
Threatened
Fish
Species
of
Ohio
Common
Name
Scientific
Name
Number
of
Observations
Last
Observed
Federal
Status
State
Status
Habitat
Causes
for
Listing
Shortnose
gar
Lepisosteus
platostomus
9
1981
E
Scioto
River
and
tributaries
Pollution
and
siltation
Speckled
chub
Macrhybopsis
aestivalis
1
1990
E
Ohio
and
Muskingham
rivers,
large
rivers
Pollution
and
siltation
Greater
redhorse
Moxostoma
valenciennesi
12
1989
T
Maumee
river
system,
large
streams
Pollution
and
siltation
Popeye
shiner
Notropis
ariommus
4
1993
E
Extirpated
from
Ohio,
creeks
and
small
rivers
of
Maumee
system
Siltation
Bigeye
shiner
Notropis
boops
22
1995
T
Great
Miami
River
and
Ohio
River
systems,
upland
streams
Siltation
and
impoundments
Bigmouth
shiner
Notropis
dorsalis
16
1994
T
Black
and
Rocky
River
systems,
brooks
and
small
streams
Competition
with
silver
minnow
Blackchin
shiner
Notropis
heterodon
2
1983
E
Lake
Erie
and
other
lakes
Increased
turbidity
and
siltation
Blacknose
shiner
Notropis
heterolepis
7
1983
E
Lake
Erie
and
other
lakes
Siltation
Mountain
madtom
Noturus
eleutherus
11
1991
E
Ohio
River
tributaries,
larger
streams
and
rivers
Pollution
and
siltation
Northern
madtom
Noturus
stigmosus
10
1989
E
Muskingham,
Little
Miami,
Walhondig
Rivers
Scioto
madtom
Noturus
trautmani
1
1957
E
E
Big
Darby
Creek,
tributary
of
Scioto
Pollution
and
siltation
Pugnose
minnow
Opsopoeodus
emiliae
6
1982
E
Lakes,
canals,
streams,
and
Lake
Erie
Increased
turbidity
and
siltation
Channel
darter
Percina
copelandi
18
1991
T
Lake
Erie
and
Ohio
River
Siltation
River
darter
Percina
shumardi
8
1989
T
Lake
Erie
and
larger
tributaries
of
Ohio
River
Pollution
and
siltation
Paddlefish
Polyodon
spathula
11
1996
T
Ohio
River
tributaries,
larger
streams
and
rivers
Pollution
and
siltation
Brook
trout
Salvelinus
fontinalis
1
1997
T
Tributaries
of
Lake
Erie
Habitat
destruction
­

timbering
and
non­
native
species
Source:
Division
of
Natural
Areas
and
Preserves,
Ohio
Department
of
Natural
Resources,
Natural
Heritage
Program
1998.
20­
23
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
GLOSSARY
ammonia:
a
compound
of
nitrogen
and
hydrogen
(
NH3
).
It
is
a
colorless,
pungent
gas.

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

bioconcentration
factors
(
BCFs):
indicators
of
the
potential
for
chemicals
dissolved
in
the
water
column
to
be
taken
up
by
aquatic
biota
across
external
surface
membranes,
usually
gills.

biotic:
pertaining
to
the
characteristics
of
a
naturally
occurring
assemblage
of
plants
and
animals
that
live
in
the
same
environment
and
are
mutually
sustaining
and
interdependent.

chemical
oxygen
demand
(
COD):
the
amount
of
oxygen
consumed
in
the
complete
chemical
oxidation
of
matter,
both
organic
and
inorganic,
present
in
polluted
water.

Coldwater
Habitat
(
CWH):
a
designation
assigned
to
a
water
body
based
on
the
potential
aquatic
assemblage.

dissolved
oxygen
(
DO):
oxygen
freely
available
in
water,
vital
to
fish
and
other
aquatic
life
and
for
the
prevention
of
odors.
DO
levels
are
considered
a
most
important
indicator
of
a
water
body's
ability
to
support
desirable
aquatic
life.

Secondary
and
advanced
waste
treatment
are
generally
designed
to
ensure
adequate
DO
in
waste­
receiving
waters.

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

endangered
and
threatened
(
E&
T):
animals,
birds,
fish,
plants,
or
other
living
organisms
threatened
with
extinction
by
anthropogenic
(
i.
e.,
man­
caused)
or
other
natural
changes
in
their
environment.
The
Endangered
Species
Act
contains
requirements
for
declaring
a
species
endangered.

Endangered
Species
Act:
federal
legislation
enacted
in
1973
that
protects
animals,
birds,
fish,
plants,
or
other
living
organisms
threatened
with
extinction
by
anthropogenic
or
other
natural
changes
in
their
environment.
For
a
species
to
be
protected
under
this
act
it
must
be
"
listed"
as
either
an
"
endangered"
or
"
threatened"
species.

eutrophication:
process
by
which
bodies
of
water
receive
increased
amounts
of
dissolved
nutrients,
such
as
nitrogen
and
phosphorus,
that
encourage
excessive
plant
growth
and
result
in
oxygen
depletion.

Exceptional
Warmwater
Habitat
(
EWH):
the
aquatic
life
use
designed
to
protect
aquatic
communities
of
exceptional
diversity
and
biotic
integrity.
Such
communities
typically
have
a
high
species
richness;
often
include
strong
populations
of
rare,
endangered,
threatened,
and
declining
species;
and/
or
are
exceptional
sport
fisheries.

existence
services:
services
that
are
not
linked
to
current
uses
of
water
bodies.
They
arise
from
the
knowledge
that
species
diversity
or
the
natural
beauty
of
a
given
water
body
is
being
preserved.

in­
stream
services:
water
use
taking
place
within
the
stream
channel
for
purposes
such
as
life
support
for
animals
and
plants,
water­
based
recreation,
hydroelectric
power
generation,
navigation,
commercial
fishing,
water
storage,
and
aesthetics.

Limited
Resource
Waters
(
LRW):
an
aquatic
life
use
assigned
to
streams
with
very
limited
aquatic
life
potential,
usually
restricted
to
highly
acidic
mine
drainage
streams,
or
highly
modified
small
streams
(<
3
sq.
mi.
drainage
area)
in
urban
or
agricultural
areas
with
little
or
no
water
during
the
summer
months.

Limited
Warmwater
Habitat
(
LWH):
see
limited
resource
waters.

metals:
inorganic
compounds,
generally
non­
volatile
(
with
the
notable
exception
of
mercury),
that
cannot
be
broken
down
by
biodegradation
processes.
They
are
of
particular
concern
due
to
their
prevalence
in
MP&
M
effluents.
Metals
can
accumulate
in
biological
tissues,
sequester
into
sewage
sludge
in
POTW
s,
and
contaminate
soils
and
sediments
when
released
into
the
environment.
Some
metals
are
quite
toxic
even
when
present
at
relatively
low
levels.

 
g/
l:
micrograms
per
liter.

20­
24
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
Modified
Warmwater
Habitat
(
MWH):
aquatic
life
use
assigned
to
streams
that
have
irretrievable,
extensive,
man­

induced
modifications
that
preclude
attainment
of
the
Warmwater
Habitat
use,
but
which
harbor
the
semblance
of
an
aquatic
community.
Such
waters
are
characterized
by
poor
chemical
quality
(
low
and
fluctuating
dissolved
oxygen),
degraded
habitat
conditions
(
siltation,
habitat
simplification),
and
species
that
are
tolerant
of
these
effects.

nonconventional
pollutants:
a
catch­
all
category
that
includes
everything
not
classified
as
either
a
priority
or
conventional
pollutant.

nutrients:
any
substance,
assimilated
by
living
things,
that
promotes
growth.
The
term
is
generally
applied
to
nitrogen
and
phosphorus
in
wastewater,
but
is
also
applied
to
other
essential
and
trace
elements.

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

Ohio
EPA
Lake
Condition
Index
(
LCI):
an
ecologically­
based
index
that
aggregates
results
across
ten
ecological
metrics.

Ohio
Water
Resource
Inventory
(
OWRI):
a
biennial
report
to
U.
S.
EPA
and
Congress
required
by
Section
305(
b)
of
the
Clean
Water
Act.
The
report
is
composed
of
four
major
sections:
(
1)
inland
rivers
and
streams,
wetlands,
Lake
Erie,
and
water
program
description;
(
2)
fish
tissue
contaminants;
(
3)
inland
lakes,
ponds,
and
reservoirs;
and
(
4)
groundwater.

overburdens:
rock
and
soil
cleared
away
before
mining.

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

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
131
contaminants
identified
by
EPA
as
being
of
potential
concern
for
this
rule
and
that
are
currently
being
discharged
by
MP&
M
facilities.
EPA
used
fate
and
toxicity
data,
in
conjunction
with
various
modeling
techniques,
to
identify
these
pollutants
and
assess
their
potential
environmental
impacts
on
receiving
water
bodies
and
POTWs.
MP&
M
pollutants
of
concern
include
43
priority
pollutants,
3
conventional
pollutants,
and
86
nonconventional
pollutants.

polychlorinated
biphenyls
(
PCBs):
a
group
of
toxic,
persistent
chemicals
that
are
mixtures
of
chlorinated
biphenyl
compounds
having
various
percentages
of
chlorine.
PCBs
are
industrial
chemicals
formerly
used
in
electrical
transformers
and
capacitors
for
insulating
purposes,
and
in
gas
pipeline
systems
as
a
lubricant.

polycyclic
aromatic
hydrocarbons
(
PAHs):
a
class
of
organic
compounds
with
a
fused­
ring
aromatic
structure.
PAHs
result
from
incomplete
combustion
of
organic
carbon
(
including
wood),
municipal
solid
waste,
and
fossil
fuels,
as
well
as
from
natural
or
anthropogenic
introduction
of
uncombusted
coal
and
oil.
PAHs
include
benzo(
a)
pyrene,
fluoranthene,
and
pyrene.

Primary
Contact
Recreation
(
PCR):
water
recreation
activities
requiring
full
human
body
immersion,
such
as
swimming,
diving,
water
skiing,
and
surfing.

priority
organics:
prority
pollutants
that
are
organic
chemicals.

priority
pollutants:
126
individual
chemicals
that
EPA
routinely
analyzes
when
assessing
contaminated
surface
water,

sediment,
groundwater,
or
soil
samples.

random
utility
model
(
RUM):
a
model
of
consumer
behavior.
The
model
contains
observable
determinants
of
consumer
behavior
and
a
random
element.

Secondary
Contact
Recreation
(
SCR):
water
recreation
activities
requiring
some
direct
contact
with
water
but
where
swallowing
of
water
is
unlikely,
such
as
paddling,
wading,
and
boating.

siltation:
deposition
of
finely
divided
soil
and
rock
particles
on
the
bottom
of
stream
and
river
beds
and
in
reservoirs.

Survey
of
National
Demand
for
Water­
based
Recreation
(
NDS):
a
U.
S.
EPA
survey
of
recreational
behavior.
The
1993
survey
collected
data
on
socioeconomic
characteristics
and
water­
based
recreation
behavior
using
a
nationwide
stratified
random
sample
of
13,059
individuals
aged
16
and
over.
(
http://
www.
epa.
gov/
opei)

20­
25
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
total
allowable
catch
(
TAC):
amount
of
fish
permitted
to
be
removed
under
a
fishery
management
regime
in
which
the
total
catch
allowed
of
a
certain
species
for
a
fishing
season
has
been
fixed
in
advance.

 
toxic 
pollutants:
refers
to
the
126
priority
or
toxic
pollutants
specifically
defined
as
such
by
EPA,
as
well
as
nonconventional
pollutants
that
have
a
toxic
effect
on
human
health
or
aquatic
organisms.

turbidity:
cloudy
condition
in
water
that
interferes
with
the
passage
of
light
through
the
water
column.
It
is
caused
by
the
presence
of
suspended
silt
or
organic
matter
in
the
water
body.

un­
ionized:
neutral
form
of
an
ionizable
compound.
With
reference
to
ammonia,
it
is
the
neutral
form
of
ammonia­
nitrogen
in
water,
usually
occurring
as
NH4
OH.
Un­
ionized
ammonia
is
the
principal
form
of
ammonia
that
is
toxic
to
aquatic
life.
The
relative
proportion
of
un­
ionized
to
ionized
ammonia
(
NH4+
)
is
controlled
by
water
temperature
and
pH.

Warmwater
Habitat
(
WWH):
a
designation
assigned
to
a
water
body
based
on
the
potential
aquatic
assemblage.

water
quality
criteria
(
WQC):
specific
levels
of
water
quality
that,
if
reached,
are
expected
to
render
a
body
of
water
suitable
for
certain
designated
uses.

withdrawal
services:
services
associate
with
water
removed
from
the
ground
or
diverted
from
a
surface­
water
source
for
uses
such
as
drinking
water
supply,
irrigation,
production
and
processing
services,
and
sanitary
services.

20­
26
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
ACRONYMS
BCFs:
bioconcentration
factors
BOD:
biological
oxygen
demand
COD:
chemical
oxygen
demand
CWH:
Coldwater
Habitat
DO:
dissolved
oxygen
E&
T:
endangered
and
threatened
EWH:
Exceptional
Warmwater
Habitat
LRW:
Limited
Resource
Waters
LWH:
Limited
Warmwater
Habitat
MWH:
Modified
Warmwater
Habitat
ODH:
Ohio
Department
of
Health
DNR:
Ohio
Department
of
Natural
Resources
LCI:
Ohio
EPA
Lake
Condition
Index
OWRI:
Ohio
Water
Resource
Inventory
POCs:
pollutants
of
concern
PCBs:
polychlorinated
biphenyls
PAHs:
polycyclic
aromatic
hydrocarbons
PCR:
Primary
Contact
Recreation
RUM:
random
utility
model
SSH:
Seasonal
Salmonid
Habitat
SCR:
Secondary
Contact
Recreation
NDS:
Survey
of
National
Demand
for
W
ater­
based
Recreation
TAC:
total
allowable
catch
WWH:
Warmwater
Habitat
WQC:
water
quality
criteria
20­
27
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
20:
Baseline
Conditions
in
Ohio
REFERENCES
Baldwin,
N.
A.,
R.
W.
Saalfeld,
M.
R.
Dochoda,
H.
J.
Buettner,
and
R.
L.
Eshenroder.
August
2002.
Commercial
Fish
Production
in
the
Great
Lakes
1867­
2000.
http://
www.
glfc.
org/
databases/
commercial/
commerc.
asp
Lake
Erie
Charter
Boat
Association
(
LECB
A).
2003.
www.
lecba.
org.

Ohio
Department
of
Natural
Resources,
Ohio
Division
of
Wildlife.
1999.
Fish
Consumption
Advisories.

http://
www.
dnr.
state.
oh.
us/
odnr/
wildlife/
index.
html.

Ohio
Department
of
Natural
Resources,
Division
of
Natural
Areas
and
Preserves.
1998.
Database
File
of
Aquatic
and
Associated
Aquatic
Endangered
&
Threatened
Animals.

Ohio
Environmental
Protection
Agency.
1998.
State
of
the
Lake
Report
(
www.
epa.
ohio.
gov/
oleo/
leqi/
leqi.
html)

Ohio
Environmental
Protection
Agency.
1996.
Ohio
Water
Resource
Inventory.
Volume
1:
Summary,
Status
,
and
Trends
and
3:
Ohio s
Public
Lakes,
Ponds,
and
Reservoirs.
www.
chagrin.
epa.
state.
oh.
us/
document_
index.

U.
S.
Department
of
Agriculture
(
USDA).
1992a.
Agricultural
Waste
Management
Field
Handbook.
National
Engineering
Handbook
Series,
Part
651.
210­
AWM
FH,
4/
92.

U.
S.
Department
of
Agriculture.
1992b.
National
Resources
Inventory.
National
Resources
Conservation
Services.

http://
www.
ftw.
nrcs.
usda.
gov/
nri_
data.
html.

U.
S.
Department
of
Commerce,
Bureau
of
the
Census.
1992.
Census
of
Manufactures,
Census
of
Transportation,
Census
of
Wholesale
Trade,
Census
of
Retail
Trade,
Census
of
Service
Industries.

U.
S.
Department
of
Commerce,
Bureau
of
the
Census.
1999.
Ohio
Population,
Demographic,
and
Housing
Statistics.

http://
www.
census.
gov/
cgi­
bin/
datamap/
state?
39.

United
States
Geological
Survey
(
USGS).
1995.
Water
Use
in
the
United
States.
http://
water.
usgs.
gov/
watuse.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1986.
Ambient
Water
Quality
Criteria
for
Dissolved
Oxygen.
EPA
440/
5­
86­
003.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1992.
Managing
Non­
point
Source
Pollution:
Final
Report
to
Congress.
EPA­
506/
9­
90.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1994.
National
Demand
for
Water­
Based
Recreation
Survey.

Washington,
D.
C.:
Office
of
Policy
Evaluation
and
Information.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998a.
National
Recommended
Water
Quality
Criteria;
Notice;

Republication.
63(
237:
68354­
68364).

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998b.
Condition
of
the
Mid­
Atlantic
Estuaries.
EPA
600­
R­
98­
147.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1998c.
1988
Update
of
Ambient
Water
Quality
Criteria
for
Ammonia.

EPA
822­
R­
98­
008.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1999.
Progress
Report
on
the
EPA
Acid
Rain
Program.
U.
S.
EPA
Office
of
Air
and
radiation.
EPA
430­
R­
99­
011.

Wetzel,
R.
G.
1983.
Limnology,
2nd
ed.
Saunders
College
Publishing.

20­
28
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
INTRODUCTION
The
recreational
benefits
analysis
outlined
in
this
chapter
focuses
on
Ohio
as
a
case
study
of
the
MP&
M
regulation's
expected
benefits.
EPA
combined
water
quality
modeling
and
a
random
utility
model
of
consumer
behavior
(
RUM)

to
assess
how
changes
in
water
quality
from
the
MP&
M
regulation
will
affect
consumer
valuation
of
water
resources
for
recreational
uses.
The
RUM
analysis
provides
a
framework
for
estimating
the
effect
of
ambient
water
quality
and
other
site
characteristics
on
the
total
number
of
trips
taken
for
different
water­
based
recreation
activities
and
the
allocation
of
these
trips
among
particular
sites.

The
Agency
used
this
case
study
to
address
limitations
inherent
in
the
benefits
transfer
method
used
in
the
analysis
of
recreational
benefits
at
the
national
level
(
see
Chapter
15
for
detail).
The
RUM
model
assesses
water
quality
characteristics
directly
affected
by
the
MP&
M
regulation,

such
as
presence
of
ambient
water
quality
criteria
(
AWQC)
exceedances
and
nonconventional
nutrient
Total
Kjeldahl
Nitrogen
(
TKN)
concentrations
and
their
effect
on
recreation
behavior.
The
direct
link
between
the
water
quality
measures
included
in
the
RUM
model
and
the
water
quality
measures
affected
by
the
regulation,
as
well
as
the
site
specific
nature
of
the
analysis
reduce
uncertainty
in
benefit
estimates.
In
general,
RUM
models
are
well­
regarded
in
the
economic
literature
and
when
these
models
are
appropriately
applied,
the
results
are
thought
to
be
quite
reliable.
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
CHAPTER
CONTENTS
21.1
Methodology
.................
...........
21­
2
21.1.1
.................
.........
21­
2
21.1.2
he
Site
Choice
Decision
......
21­
3
21.1.3
rip
Participation
............
21­
6
21.1.4
ting
Welfare
Changes
from
Water
Quality
Improvements
.................
.
21­
9
21.1.5
the
State
Level
.
.
21­
10
21.2
.................
.................
.
21­
10
21.2.1
.................
....
21­
11
21.2.2
Visits
to
Sites
.
.
.
21­
14
21.2.3
.................
21­
14
21.3
................
21­
17
21.3.1
l
.................
....
21­
18
21.3.2
.................
....
21­
19
21.3.3
.................
..
21­
20
21.3.4
Activity)
Model
.
.
.
21­
20
21.4
el
.................
..
21­
20
21.5
ating
Benefits
from
Reduced
MP&
M
Discharges
in
Ohio
.................
...
21­
23
21.5.1
iting
Reaches
in
Ohio
...........
21­
23
21.5.2
ating
creational
Benefits
in
Ohio
21­
24
21.6
itations
and
Uncertainty
................
21­
25
21.6.1
.................
21­
25
21.6.2
One­
Day
Trips
Only
.........
21­
26
21.6.3
nefits
.................
...
21­
26
21.6.4
ial
Sources
of
Survey
Bias
.......
21­
26
Glossary
.................
.................
..
21­
28
Acronyms
.................
.................
.
21­
30
References
.................
.................
21­
31
Overview
Modeling
t
Modeling
T
Calcula
Extrapolating
Results
to
Data
The
Ohio
Data
Estimating
the
Price
of
Site
Characteristics
Site
Choice
Model
Estimates
Fishing
Mode
Boating
Model
Swimming
Model
Viewing
(
Near­
water
Trip
Participation
Mod
Estim
Benef
EstimRe
Lim
One­
State
Approach
Including
Nonuse
Be
Potent
Benefits
transfer
results
are
subject
to
uncertainty
because
water
quality
changes
evaluated
in
available
recreation
demand
studies
are
only
roughly
comparable
with
water
quality
measures
considered
in
regulatory
development.
This
case
study
analysis
improves
upon
previous
recreation
demand
studies
that
focused
mainly
on
directly
observable
water
quality
effects,

e.
g.,
designated
use
support
(
i.
e.,
whether
a
water
body
supports
fishing),
the
presence
of
fish
advisories,
an
oil
sheen,
or
eutrophication.
The
Ohio
case
study
includes
unobservable
water
quality
effects
as
well.
The
MP&
M
regulation
affects
a
broad
range
of
pollutants,
many
of
which
are
toxic
to
human
and
aquatic
life
but
are
not
directly
observable
(
i.
e.,
priority
and
nonconventional
pollutants.
These
unobservable
toxic
pollutants
degrade
aquatic
habitats,
decrease
the
size
and
abundance
of
fish
and
other
aquatic
species,
increase
fish
deformities,
and
change
watershed
species
composition.
Water
quality
changes
(
i.
e.,
changes
in
toxic
pollutant
concentrations)
affect
consumers 
water
resource
valuation
for
recreation,

even
if
consumers
are
unaware
of
changes
in
ambient
pollutant
concentrations.

This
study
allows
for
a
more
complete
estimate
of
recreational
benefits
from
reduced
discharges
of
MP&
M
pollutants.
In
addition
to
estimates
of
recreational
benefits
from
reduced
frequency
of
AWQC
exceedances,
the
Ohio
case
study
evaluated
changes
in
the
water
resource
values
from
reduced
discharges
of
TKN
.
The
analysis
also
values
additional
recreational
uses
not
addressed
in
the
national
analysis,
such
as
swimming.

21­
1
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
The
study
used
data
from
the
National
Demand
Survey
for
Water­
Based
Recreation
(
NDS)
,
conducted
by
U.
S.
EPA
and
the
National
Forest
Service,
to
examine
the
effects
of
in­
stream
pollutant
concentrations
on
consumer
decisions
to
visit
a
particular
water
body
(
U.
S.
EPA,
1994).

21.1
METHODOLOGY
21.1.1
Overview
The
Ohio
study
combines
direct
simulation
and
inferential
analyses
to
assess
how
changes
in
water
quality
will
affect
consumers 
valuation
of
water
resources.

The
direct
simulation
analysis
component
estimates
baseline
and
post­
compliance
water
quality
at
recreation
sites
actually
visited
by
the
surveyed
consumers
and
all
other
sites
within
the
consumers 
choice
set,
visited
or
not.

The
inferential
analysis
component,
a
RUM
analysis
of
consumer
behavior,
estimates
the
effect
of
ambient
water
quality
and
other
site
characteristics
on
the
total
number
of
trips
taken
for
different
water­
based
recreation
activities
and
the
allocation
of
these
trips
among
particular
recreational
sites.
The
RUM
analysis
is
a
travel
cost
model
(
TCM),
in
which
the
cost
to
travel
to
a
particular
recreational
site
represents
the
 
price 
of
a
visit.

The
main
advantage
of
the
RUM
model
is
inclusion
of
the
effect
of
substitute
sites
on
site
values.
For
any
particular
site,

assuming
that
it
is
not
totally
unique
in
nature,
the
availability
of
substitutes
makes
the
value
for
that
site
lower
than
it
would
be
without
available
substitutes.

EPA
modeled
two
consumer
decisions:

 
how
many
water­
based
recreational
trips
to
take
during
the
recreational
season
(
the
trip
participation
model);
and
 
conditional
on
the
first
decision,
which
recreation
site
to
choose
(
the
site
choice
model).

The
econometric
estimation
proceeded
in
two
steps,
each
corresponding
to
the
above
decisions.
The
Agency
estimated
these
decisions
in
reverse
order
(
i.
e.,
EPA
modeled
the
second
decision,
site
choice,
first).

 
Modeling
the
Site
Choice
Decision.
Assuming
that
a
consumer
decides
to
take
a
water­
based
recreation
trip,
EPA
estimated
the
likelihood
that
the
consumer
will
choose
a
particular
site
as
a
function
of
site
characteristics,
the
price
paid
per
site
visit,
and
household
income.
A
consumer
weighs
the
attributes
for
various
"
choice
set"
sites
against
the
travel
costs
to
each
site.
These
travel
costs
include
both
the
cost
of
operating
a
vehicle
and
the
opportunity
costs
of
time
spent
traveling.
The
consumer
then
weighs
the
value
given
to
the
site's
attributes
against
the
cost
of
getting
to
the
site
when
making
a
site
selection.
The
site
choice
model
estimates
how
recreational
users
value
access
to
specific
sites,
and
estimates
per
trip
economic
values
for
changes
in
water
quality
at
recreational
sites
in
the
study
area.

EPA
estimated
the
site
choice
model
using
a
two­
level
nested
multinomial
logit
(
NMNL)
model,
which
groups
sites
with
similar
characteristics.
The
nested
logit
model
assumes
that
individuals
first
choose
the
group
of
sites
and
then
a
site
within
that
group.
This
study
assumes
that
individuals
first
choose
a
water
body
type
(
Lake
Erie,
rivers,
or
small
lakes)
and
then
a
specific
site.
EPA
used
the
estimated
site­
choice
model
coefficients
to
estimate
the
value
to
the
consumer
of
being
able
to
choose
among
Ohio
recreation
sites
on
a
given
day.
This
measure
is
referred
to
as
the
 
inclusive
value. 

 
Modeling
Trip
Frequency.
The
site
choice
models
estimated
in
the
previous
step
treat
the
total
number
of
recreational
trips
taken
each
season
as
exogenous
to
the
site
selection.
The
Agency
estimated
the
expected
number
of
trips
taken
during
the
recreation
season
using
a
Negative
Binomial
Poisson
model
(
Hausman
et
al.,

1995;
Feather
et
al.,
1995;
and
Creel
and
Loomis,
1992),
which
treats
trip
frequency
as
a
pre­
season
decision
regarding
total
participation
in
a
given
recreation
activity.

EPA
estimated
the
total
number
of
trips
during
the
recreation
season
as
a
function
of
the
expected
maximum
utility
(
inclusive
value)
from
recreational
activity
participation
on
a
trip,
and
socioeconomic
characteristics
affecting
demand
for
recreation
trips
(
e.
g.,
number
of
children
in
the
household).
The
coefficient
of
the
individual s
expected
21­
2
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
maximum
utility
of
taking
a
trip)
provid
ed
a
m
eans
o
f
estimating
the
seas
onal
welfare
effect
of
water
qua
lity
improvements,
beca
use
ch
anges
in
water
quality
ch
ange
the
value
of
av
ailable
recre
ation
sites
.

Estimating
the
site
choice
and
total
trip
p
articipa
tion
mode
ls
jointly
is
theo
retically
possible,
but
co
mpu
tational
requirements
make
an
integrated
utility­
theoretic
model
infeasible.
EPA
estimated
separate
site
choice
and
trip
frequency
models
for
the
four
recreational
activities:
boating,
swimming,
fishing,
and
near­
water
recreation
(
e.
g.,
viewing
wildlife).
1
The
Agency
used
estimated
coefficients
of
the
indirect
utility
function
with
estima
ted
ch
anges
in
water
quality
to
calcula
te
per­
trip
chang
es
in
co
nsum
er
welfare
from
impro
ved
water
q
uality
at
rec
reation
sites
within
e
ach
c
onsu
mer
c
hoice
set.
Trip
frequency
per
season
increases
if
site
water
quality
changes
are
substantial.
A
sample
consumer s
expected
seasonal
welfare
gain
is
therefore
a
function
of
both
welfare
gain
per
trip
and
the
estimated
change
in
number
of
trips
per
season.

Combin
ing
the
trip
freque
ncy
mo
del s
p
redic
tion
of
trip
s
unde
r
the
ba
seline
an
d
po
st­
com
plianc
e
and
the
site
cho
ice
mo
del s
corresponding
per­
trip
welfare
measure
yields
the
total
se
aso
nal
w
elfare
measure.

EP
A
ca
lculated
each
individ
ual s
seasona
l
welfare
g
ain
for
e
ach
re
creatio
n
activity
fro
m
po
st­
com
plianc
e
wate
r
qua
lity
changes,
and
then
used
Census
population
data
to
aggregate
the
estimated
welfare
change
to
the
state
level.
The
sum
of
estimated
welfare
changes
over
the
four
recreation
activities
yielded
estimates
of
total
welfare
gain.

To
analyze
water
quality
improveme
nt
benefits
in
the
RUM
framewo
rk,
EPA
used
available
discharge,
ambient
concentration,

and
other
relevant
data
to
measure
baseline
and
post­
compliance
water
quality
at
the
impact
sites.
H
provides
detail
on
water
q
uality
modeling
used
in
this
analysis.

21.1.2
EPA
used
the
RUM
framework
to
estimate
the
probability
of
a
consumer
visiting
a
recreation
site.
ramework
is
based
on
the
assumption
that
a
consumer
derives
utility
from
the
recreational
activity
at
each
recreation
site.
Each
visit
decision
involves
cho
osing
one
site
and
excluding
o
thers.

The
consumer s
decision
involves
comparing
each
site
and
choosing
the
site
that
produces
the
maximum
utility.
An
observer
cannot
measure
all
potential
determinants
of
consumer
utility,
so
the
indirect
utility
function
will
have
a
non­
random
element
(
V)
and
a
random
error
term
(
 
)
,
such
tha
t
the
actua
l
determ
inants
of
consumer
utility
V
 
=
V
+
 
.
e
pro
bab
ility
(
 
jn)
that
site
j
will
be
visited
by
an
individual
n
is
defined
as:

(
21.1)

where:

Vjn
+
 
jn
=
utility
of
visiting
site
j,
and
Vsn
+
 
sn
=
utility
of
visiting
a
substitute
site.

Estimating
the
m
ode
l
requires
spe
cifying
the
fun
ctiona
l
form
o
f
the
indire
ct
utility
function
,
V,
in
wh
ich
site
ch
oice
is
mod
eled
a
s
a
functio
n
of
site
ch
aracte
ristics
and
the
 
pric
e 
to
v
isit
particular
sites.
r
exam
ple,
a
se
t
of
con
ditiona
l
utility
functions
(
one
for
each
site
alternative
j
in
the
choice
set)
can
b
e
determ
ined
as
follows:

(
21.2)

where:

Vjn
=
the
utility
realized
from
a
conventional
budget­
constrained,
utility
maximization
model
conditional
on
choice
of
site
j
by
consumer
n;

 
M
=
marginal
utility
of
income;

M
jn
=
the
income
of
individual
n
availab
le
to
visit
site
j;
Appendix
Modeling
the
Site
Choice
Decision
This
f
Th
Fo
1
The
Agency
also
attempted
a
model
structure
that
allows
for
interaction
among
the
choice
of
recreational
activities.
In
this
model,
a
person
first
chooses
a
recreational
activity
and
then
chooses
a
site.
This
model
did
not
perform
very
well
because
less
than
ten
percent
of
recreational
users
included
in
the
dataset
participate
in
all
four
activities.

21­
3
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Pjn
=
a
composite
measure
of
travel
and
time
costs
for
consumer
n
on
site
alternative
j;

 
=
a
vector
of
coefficients
representing
the
marginal
utility
of
a
specified
site
characteristic
to
be
estimated
along
with
 
M
(
e.
g.,
size
of
the
water
body,
presence
of
boating
ramps);
and
Xjn
=
a
vector
of
site
characteristics
for
site
alternative
j
as
perceived
by
consumer
n.
These
characteristics
include
the
actual
monitored
and/
or
modeled
water
quality
parameters
that
are
hypothesized
to
be
determinants
of
consumer
valuation
of
water­
based
recreation
resources,
and
that
may
also
be
affected
by
the
MP&
M
regulation.

The
magnitude
of
the
coefficients
in
Equation
21.2
reflects
the
relative
importance
of
site
characteristics
when
consumers
decide
which
site
to
visit.
The
coefficients
(
 
)
of
water
quality
characteristics
of
recreation
sites
are
expected
to
be
positive;

that
is,
all
else
being
equal,
consumers
of
water­
based
recreation
would
prefer
"
cleaner"
recreation
sites.
The
coefficient
on
travel
cost
is
expected
to
be
negative,
i.
e.,
consumers
prefer
lower
travel
costs.

To
estimate
the
site
choice
probabilities,
EPA
specified
and
estimated
a
nested
multinomial
logit
model
(
NM
NL)
for
fishing,

boating,
and
swimming
activities.
The
nested
structure
explicitly
groups
similar
alternatives,
which
allows
for
a
richer
pattern
of
substitution
among
alternative
sites.
The
NMNL
is
based
on
the
assumption
that
an
individual
chooses
first
between
groups
of
alternatives
and
then,
within
the
chosen
group,
between
individual
alternatives.
For
this
analysis,
EPA
grouped
all
recreational
sites
in
Ohio
by
water
body
type
based
on
site
similarities.
EPA
tested
various
alternative
site
groupings,
but
the
models
presented
here
were
most
successful
at
explaining
the
probability
of
selecting
a
site.
The
best
model
used
the
following
activity­
specific
site
groupings:
2
 
Fishing
model:

 
Group
1:
Lake
Erie
sites;

 
Group
2:
river
sites;

 
Group
3:
small
lakes
and
reservoirs;

 
Boating
model:

 
Group
1:
Lake
Erie
sites;

 
Group
2:
inland
sites,
including
rivers,
small
lakes,
and
reservoirs;

 
Swimming
model:

 
Group
1:
Lake
Erie
sites;

 
Group
2:
inland
sites,
including
rivers,
small
lakes,
and
reservoirs;

 
Viewing
model:
EPA
used
a
non­
nested
model
in
which
an
individual
compares
all
sites
and
chooses
the
one
offering
the
highest
utility
level
for
each
trip
occasion.

First,
the
Agency
attempted
to
estimate
a
nested
model
based
on
the
three
water
body
types
lakes,
rivers,
and
Lake
Erie
for
all
four
recreational
activities
included
in
the
analysis.
This
structure,
however,
performed
well
only
for
fishing.
A
two­
nested
model
that
included
inland
and
Lake
Erie
sites
seemed
to
perform
better
for
the
boating
and
swimming
models.

None
of
the
nested
structures
performed
well
for
participants
in
near­
water/
wildlife
viewing
activities.

This
finding
is
not
surprising
because
sites
are
grouped
based
on
their
similarities
within
a
given
nest.
It
is
reasonable
to
assume
that
inland
lakes,
rivers,
and
Lake
Erie
sites
are
dissimilar
from
an
angler's
point
of
view,
because
each
of
the
three
water
body
types
is
likely
to
support
different
fish
species.
Lake
sites
may
therefore
not
be
close
substitutes
for
rivers
sites.

For
other
activities,
differences
in
fishery
resources
across
water
body
types
are
unlikely
to
be
important.
Water
body
size
and
the
presence
of
recreational
amenities
are
likely
to
play
a
more
important
role
than
differences
in
fish
species
and
the
type
of
aquatic
habitat.
Lake
and
river
sites
may
therefore
be
regarded
as
substitutes
for
each
other
by
boaters
and
swimmers.

Lake
Erie,
on
the
other
hand,
is
a
unique
water
resource
that
differs
from
inland
water
bodies
because
of
its
physical
characteristics
(
e.
g.,
size
and
water
temperature);
river
and
lake
sites
are
therefore
not
likely
to
be
considered
substitutes
for
Lake
Erie
sites.
Finally,
participants
in
near­
water
recreation
use
water
resources
indirectly
and
are
therefore
more
likely
to
regard
recreational
sites
located
on
different
water
body
types
as
close
substitutes
to
each
other.
For
this
reason,
the
viewing
model
is
a
simple
logit
model
without
a
nested
structure.

2
Three
of
the
four
models
(
fishing,
boating,
and
swimming)
passed
specification
tests
for
appropriateness
of
a
nested
structure
(
see
Section
21.3
for
detail).
Test
results
showed
that
only
two
site
groups
are
appropriate
for
the
boating
and
swimming
models
inland
sites
(
rivers,
small
lakes,
and
reservoirs)
and
Lake
Erie
sites
in
Ohio.
The
fourth
activity,
wildlife
viewing,
did
not
pass
specification
tests
for
a
nested
structure
and
was
estimated
as
a
flat
multinomial
logit
(
MNL)
model.

21­
4
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
The
models
assume
that
an
individual
first
decides
to
visit
a
specific
water
body
grouping
(
hereafter,
region),
then
decides
which
site
within
that
group
to
visit.
An
individual
probability
of
visiting
site
j,
given
the
choice
of
region
R,
is
a
simple
multinomial
logit.
If
the
random
terms
 
nj
for
individual
n
at
site
j
are
independently
and
identically
distributed
and
have
an
extreme
value
Weilbull
distribution,
then
 
jn
takes
the
form
(
McFadden,
1981):

(
21.3)

where:

 
jn|
r
=
probab
ility
of
selecting
site
j
in
region
r;

=
the
consumer s
utility
from
visiting
site
j;

r
=
regions
­­ 
Lake
Erie, 
 
rivers, 
etc.
as
specified
above
for
a
given
activity;
and
=
the
sum
of
the
consumer s
utility
at
each
site
j
for
all
sites
in
the
opportunity
set
for
region
r.

Estimated
parameters
of
the
indirect
utility
function
are
then
used
to
estimate
the
inclusive
value.
n,
the
inclusive
value
measures
the
overall
quality
of
recreational
opportunities
for
each
water­
based
activity
and
represents
the
expected
maximum
utility
of
taking
a
trip.
although
EPA
used
a
rando
m
draw
from
the
oppo
rtunity
set
for
the
purp
ose
o
f
estimating
the
mo
del
param
eters,
the
Agency
calculated
the
inclusive
value
(
i.
e.,
the
ex
pected
m
aximu
m
utility)

using
all
recreation
sites
in
the
consumer's
opportunity
set
in
a
given
region.

The
inclusive
value
is
calculated
as
the
log
of
the
denominator
in
Equation
21.2
(
M
cFadden,
198
1).

(
21.4)

where:

Ir
=
inclusive
value
for
sites
associated
with
region
R;

=
individual
n s
utility
from
visiting
site
j;
and
W
=
a
vector
o
f
baseline
water
qua
lity
characteristics.

The
prob
ability
of
choosing
a
p
articular
region
is:

(
21.5)

where:

 
r
=
probability
of
selecting
region
r;

Ir
=
the
inclusive
values
for
a
given
region;

 r
=
the
coefficient
on
the
inclusive
value
for
a
given
region;

r
=
activity­
specific
regions
(
e.
g.,
 
Lake
Erie, 
 
rivers, 
and
 
small
lakes 
for
fishing).

To
estimate
the
mo
del
d
escrib
ed
b
y
Eq
uation
s
21.2
and
2
1.5,
E
PA
used
a
stand
ard
sta
tistical
softwa
re
pa
ckag
e,
LIMDEP.
For
consumer
Note
that,

and
21­
5
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
21.1.3
Modeling
Trip
Participation
After
modeling
the
site
choice
decision,
the
next
step
modeled
the
determinants
of
the
number
of
water­
based
recreation
trips
a
consumer
takes
during
a
season.
To
link
the
quality
of
available
recreation
sites
with
consumer
demand
for
recreation
trips,

EPA
modeled
the
number
of
recreation
trips
taken
during
the
recreation
season
as
a
function
of
the
inclusive
value
estimated
in
the
previous
step
and
socioeconomic
characteristics
affecting
demand
for
recreation
activities.
The
dependent
variable,
the
number
of
recreation
trips
taken
by
an
individual
during
the
recreation
season,
is
an
integer
value
greater
than
or
equal
to
zero.
To
account
for
the
non­
negative
property
of
the
dependent
variable,
EPA
used
count
data
models
based
on
probability
densities
that
have
the
non­
negative
integers
as
their
domain.

One
of
the
simplest
count
data
models
is
a
Poisson
estimation
process,
which
is
commonly
used
with
count
data,
such
as
number
of
recreation
trips
taken
during
the
recreation
season.
Inherent
in
the
model
specification
is
the
assumption
that
each
observation
of
a
number
of
trips
is
drawn
from
a
Poisson
distribution.
Such
a
distribution
favors
a
large
number
of
observations
with
small
values
(
e.
g.,
two
trips,
four
trips)
or
zeros,
resulting
in
its
being
skewed
toward
the
lower
end.
Due
to
the
nature
of
the
observed
number
of
trips,
it
is
quite
reasonable
to
assume
that
the
underlying
distribution
can
be
characterized
as
a
Poisson
distribution.
Figure
21.1
shows
the
number
of
recreation
trips
taken
per
year
and
the
number
of
respondents
who
reported
taking
that
number
of
trips.

21­
6
MP&
M
EEBA
Part
V:
Ohio
Case
StudyChapter
21:
Modeling
Recreational
Benefits
in
Ohio
With
a
RUM
Model
21­
7
Figure
21.1:
Number
of
Trips
Per
Year
By
Activity
Type
Source:
U.
S.
EPA
analysis
of
NDS
data
(
U.
S.
EPA,
1994)
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Estimating
the
Poisson
model
is
similar
to
estimating
a
nonlinear
regression.
The
single
parameter
of
the
Poisson
distribution
is
 
,
which
is
both
the
mean
and
variance
of
yn
.
The
probability
that
the
actual
number
of
trips
taken
is
equal
to
the
estimated
number
of
trips
is
estimated
as
follows
(
Green,
1993):

(
21.6)

where:

Yn
=
the
actual
number
of
trips
taken
by
an
individual
in
the
sample;

yn
=
the
estimated
number
of
trips
taken
by
an
individual
in
the
sample;

n
=
1,
2,...,
N,
the
number
of
individuals
in
the
sample;
and
 
n
=
 
 
X,
expected
numb
er
of
trips
for
an
individual
in
the
sample,
where
X
is
a
vector
of
variables
affecting
the
demand
for
recreational
trips
(
e.
g.,
inclusive
values
and
socioeconomic
characteristics)
and
 
is
the
vector
of
estimated
co
efficients.

From
Equation
21.6,
the
expected
number
of
water­
based
recreation
trips
per
recreation
activity
season
taken
by
an
individual
is
given
b
y:

(
21.7)

where:

E[
yn|
xn]
=
the
exp
ected
numb
er
of
trips,
yn,
given
xn;
Var[
yn|
xn]
=
the
variance
of
the
numbe
r
trips,
yn,
given
xn;
 
=
a
vector
of
coefficients
on
x;
and
x
=
a
matrix
of
socioec
ono
mic
va
riables
and
inclusive
values.

An
empirical
drawback
of
the
Poisson
model
is
that
the
variance
of
the
number
of
trips
taken
must
be
equal
to
the
mean
numb
er
of
trips,
and
this
equa
lity
is
not
always
supp
orted
by
actual
data.
ticular,
the
ND
S
survey
data
exhibit
overdispersion,
a
condition
where
variance
exceeds
the
mean.
The
estimated
variance­
to­
mean
ratios
of
the
number
of
trips
in
the
NDS
d
ata
sample
are
31,
2
7.9,
3
5.6,
and
10.5
fo
r
fishing,
swim
ming,
viewing,
and
boating
trips,
resp
ectively.

Overdisp
ersion
is
therefo
re
pre
sent
in
the
data
se
t.

To
address
the
problem
of
overdispersion,
EPA
used
the
negative
binom
ial
regression
mo
del,
an
extension
of
the
Poisson
regression
model,
which
allows
the
variance
of
the
number
of
trips
to
differ
from
the
mean.
In
the
negative
binomial
mod
el,
 
is
respecified
so
that
(
Green,
1993):

(
21.8)

where
the
error
term
(
 
)
has
a
gamm
a
distribution,
E[
exp(
 
i)]
is
equal
to
1.0,
and
the
variance
of
 
is
 
.

Th
e
resulting
probab
ility
distributio
n
is:

(
21.9)

where:

yn
=
0,1,2
...
numb
er
of
trips
taken
b
y
individual
n
in
the
sample;

n
=
1,2,...,
N
number
of
individuals
in
the
sample;
and
 
n
=
expected
number
of
trips
for
an
individual
in
the
sample.
In
par
21­
8
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Integrating
 
from
E
quation
21
.9
pro
duces
the
un
conditiona
l
distribution
of
yn.
ive
binomial
model
has
an
add
itional
p
aram
eter,
 ,
which
is
the
over
dispe
rsion
p
aram
eter,
suc
h
that:

(
21.10)

The
overd
ispersion
rate
is
then
given
by
the
following
equation:

(
21.11)

EPA
used
the
negative
binomial
model
to
predict
the
seasonal
number
of
recreation
trips
for
each
recreation
activity
based
on
the
inclusive
value,
individ
ual
soc
ioeco
nom
ic
characteristics,
and
the
ove
rdispersion
parameter,
 
.
the
inclusive
value
has
the
anticipated
positive
sign,
then
increases
in
the
inclusive
value
stemming
from
improved
ambient
water
quality
at
recreation
sites
will
lead
to
an
incre
ase
in
the
numb
er
of
trips.
e
com
bined
MNL
mod
el
site
cho
ice
and
coun
t
data
trip
participation
models
allowed
the
Agency
to
account
for
changes
in
per­
trip
welfare
values,
and
for
increased
trip
participation
in
response
to
improve
d
amb
ient
water
quality
at
recreation
sites.

21.1.4
EPA
estimated
the
welfare
change
associated
with
water
quality
improvements
from
the
baseline
to
post­
compliance
conditions
as
a
compensating
variation
(
CV),
which
equates
the
expected
value
of
realized
utility
under
the
baseline
and
post­
compliance
conditions.
d
seasonal
change
in
welfare
attributed
to
the
quality
improvements
for
an
individual
n
in
the
sample
co
nsists
of
two
comp
onents:

 
per
trip
welfare
gain,
and
 
increased
numb
er
of
trips
unde
r
the
po
st­
compliance
water
quality
cond
ition.

The
Age
ncy
first
calculated
the
welfare
gain
from
water
quality
improvement
for
each
consumer
on
a
given
day
by
using
a
CV
m
easure
for
consumer
n
(
Kling
and
Thom
pson,
1996):

(
21.12)

where:

CVn
=
the
compe
nsating
variation
for
individual
n
at
site
j
on
a
given
d
ay;

r
=
 
Lake
Erie, 
 
inland, 
etc.

j
=
1,...
Jr
represents
a
set
of
alternative
sites
for
a
given
recreational
activity
in
region
r;

=
the
inclusive
value
index
(
I);

W
0
=
a
vector
of
info
rmatio
n
describing
base
line
wate
r
qua
lity;

W
1
=
a
vector
of
information
describing
post­
compliance
water
quality;
and
 
M
=
the
implicit
coefficient
on
income
that
influences
recreation
behavior.

In
deriving
E
quation
21
.12,
E
PA
assumed
that
the
marginal
utility
of
inc
ome
,
 
M
,
is
constant
across
alternatives
(
as
well
as
across
quality
change
s).
ption
doe
s
not
ap
ply,
the
derivatio
n
of
E
q.
21
.12
is
m
ore
c
omp
licated
(
Ha
usma
n
et
al.,

1995).
The
negat
If
Th
Calculating
Welfare
Changes
from
Water
Quality
Improvements
The
expecte
If
this
assum
21­
9
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
EP
A
then
estimated
the
lo
w
and
high
values
of
the
seasonal
welfare
gain
for
individual
n
in
the
sam
ple
as
fo
llows:
3
(
21.13)

(
21.14)

where:

W
low,
n
=
lower
bound
estimate
o
f
the
seaso
nal
welfare
gain
for
individual
n;

W
high,
n
=
upper
bound
estimate
of
the
seasonal
welfare
gain
for
individual
n;

I1
=
the
post­
policy
inclusive
value;

Y1
=
the
estim
ated
n
umb
er
of
trip
s
after
wa
ter
qua
lity
impro
vement;

I0
=
the
baseline
inclusive
value;

Y0
=
the
estimated
number
of
trips
in
the
baseline;
and
 
 
=
the
implicit
coefficient
on
income
that
influences
recreation
behavior.

These
estimates
are
per
individual
in
the
population
for
those
individuals
meeting
qualifications
for
inclusion
in
the
NDS
respo
nse
set
(
i.
e.,
respo
ndents
who
se
home
state
is
Oh
io
and
respo
ndents
from
the
neighbo
ring
states
whos
e
last
trip
w
as
to
Ohio s
sites).
4
EPA
extrapolated
the
estimates
of
value
per
individual
to
the
Ohio
state
level
based
on
Census
data
(
U.
S.

Bure
au
of
the
Cen
sus,
2000
).
section
details
the
extrapo
lation
method
used
in
the
analysis.

21.1.5
Extrapolating
Results
to
the
State
Level
EPA
used
a
simplified
extrapolation
technique
to
estimate
the
state­
level
benefits.
EPA
first
estimated
the
number
of
participants
in
fishing,
swimming,
boating,
and
wildlife
viewing
in
Ohio,
based
on
the
estimated
percentage
of
the
NDS
survey
respo
ndents
residing
in
O
hio
who
pa
rticipate
in
a
given
activity
and
the
state
adult
po
pulatio
n.
e
20
00
C
ensus
d
ata
provide
information
on
the
number
of
Ohio
residents
aged
16
and
older.
EPA
then
multiplied
the
estimated
average
seasonal
welfare
gain
per
participant
in
a
given
recreational
activity
by
the
corresponding
number
of
recreational
users.
The
total
welfare
gain
to
the
users
of
water­
based
recreation
in
Ohio
is
the
sum
of
fishing,
swimming,
boating,
and
wildlife
viewing
benefits.

21.2
DATA
This
section
describes
the
data
and
supporting
analyses
required
to
implement
the
RUM
analysis.
ollowing
general
categories
of
data
and
supporting
analyses
are
required:

 
information
on
the
consumers
of
water­
based
recreation
responding
to
the
NDS
in
Ohio;

 
recreation
sites
identified
for
the
water
quality
and
RUM
analyses,
including
the
sites
visited
by
consumers
of
water­

based
recreational
activity
and
sup
plemen
tal
sites
in
their
choice
sets;

 
estimated
price
of
visiting
the
sites.
isit
price 
is
estimated
as
a
function
of
travel
distance
(
and
travel
time)

between
each
consumer s
hometown
and
each
site
in
the
choice
set;
and
 
information
on
site
characteristics
likely
to
be
important
determinants
of
consumer
behavior.
Of
particular
importan
ce
to
this
analysis
are
the
water
qua
lity
and
related
characteristics
of
sites
in
the
choice
set,
and
h
ow
those
characteristics
may
be
expected
to
change
as
a
result
of
regulation.
The
following
Th
The
f
The
 
v
3
EPA
selected
this
approach
for
calculating
seasonal
welfare
gain
per
individual
based
on
Dr.
Parsons 
recommendation
(
G.
R.
Parsons,
1999).

4
Section
21.2.1
provides
a
detailed
description
of
the
data
sample
used
in
the
analysis.

21­
10
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
The
following
sections
discuss
each
category
of
data
and/
or
supporting
analysis
below.

21.2.1
The
Ohio
Data
EPA
obtained
information
on
survey
respondent
socioeconomic
characteristics
and
recreation
behavior,
including
last
trip
profile
and
the
annual
number
of
trips
associated
with
each
water­
based
activity,
from
the
NDS
(
U.
S.
EPA,
1994).
The
1994
survey
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
(
e.
g.,
fishing,
boating,
swimming,
and
viewing),
total
number
of
trips,
trip
length,
distance
to
the
recreation
site(
s),
and
number
of
participants.
Where
fishing
was
the
primary
purpose
of
a
trip,
respondents
were
also
asked
to
state
the
number
of
fish
caught.
Table
21.1
shows
the
number
of
trips
taken
per
year
by
primary
recreation
activity,
as
reported
in
the
NDS.

EPA
selected
case
study
observations
for
Ohio
residents
who
took
trips
within
or
outside
of
the
state.
Trips
to
Ohio
recreation
sites
by
residents
of
neighboring
states
were
also
included
in
the
site
choice
models,
but
not
in
the
trip
participation
models.
5
All
four
activity
models
included
single­
day
trips
only.
EPA
included
only
activity
participants
with
valid
hometown
ZIP
co
des,
whose
destination
site
was
uniquely
identified.
The
Agency
used
data
on
both
Ohio
participants
and
Ohio
non­
participants
to
estimate
total
seasonal
trips,
but
included
only
Ohio
participants
and
several
residents
of
nearby
states
in
the
site
choice
models.
Although
they
could
not
be
used
in
the
site
choice
model,
participant
observations
from
Ohio
with
missing
location
information
were
used
to
analyze
the
number
of
trips.
Tables
21.1
and
21.2
list
valid
observations
by
activity,
residence,
and
model
type.
Figure
21.2
illustrates
the
distribution
of
the
sample
observations
in
relation
to
the
location
of
MP&
M
facilities
affected
by
the
rule
in
Ohio.

Table
21.1:
lassification
of
Sample
Observations
for
Estimation
of
the
Site
Choice
Models
Total
Ohio
Residents
Ohio
Residents
with
Last
Trip
In­
State
Valid
Ohio
Residents
with
Last
Trip
In­

State
Valid
Ohio
Residents
with
Last
Trip
Outside
State
Valid
Nonresidents
with
Last
Trip
in
Ohio
Valid
for
Site
Choice
Model
Participants
(
Total)
609
408
237
35
11
297
Fishing
122
103
66
9
0
84
Swimming
147
100
58
14
2
76
Viewing
231
126
64
2
7
73
Boating
109
79
49
10
2
64
C
Source:
U.
S.
EPA
analysis.

5
These
additional
observations
total
11
across
the
four
activities
and
thus
represent
only
a
small
fraction
of
total
observations.

Including
only
Ohio
respondents
in
the
trip
participation
models
underestimates
the
benefits
associated
with
water
quality
improvements,

because
the
welfare
gains
to
recreators
from
neighboring
states
are
ignored.

21­
11
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Table
21.2:
Classification
of
Sample
Observations
for
Estimation
of
the
Trip
Participation
Models
Ohio
Residents
Total
Residents
with
Last
Trip
In­
State
Residents
with
Last
Trip
Outside
State
Valid
for
Trip
Participation
Model
Non­
Participants
300
291
Participants
(
Total)
609
408
34
322
Fishing
122
103
4
84
Swimming
147
100
9
78
Viewing
231
126
7
75
Boating
109
79
14
85
Total
Observations
909
408
34
613
Source:
U.
S.
EPA
analysis.

21­
12
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Figure
21.2:
Location
of
MP&
M
Facilities
in
Relation
to
the
Visited
Sites
Source:
U.
S.
EPA
analysis.

21­
13
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
21.2.2
Estimating
the
Price
of
Visits
to
Sites
EPA
estimated
trip
 
price 
for
each
consumer
of
water­
based
recreation
as
the
sum
of
travel
costs
plus
the
opportunity
cost
of
time,
following
the
procedure
described
in
Haab
et
al.
(
2000).
and
Kealy
(
1992),
this
study
assumed
that
time
sp
ent
 
on
­
site 
is
constant
ac
ross
sites
a
nd
ca
n
be
ignore
d
in
the
p
rice
ca
lculation
.

To
estimate
consumers 
travel
costs,
EPA
first
used
ZipFip
software
to
calculate
the
one­
way
distance
to
each
site
for
each
participant.
6
The
average
estimated
one­
way
distance
to
the
site
visited
is
37.5
6
miles.
EP
A
then
multiplied
round­
trip
distance
by
average
motor
vehicle
cost
per
mile
($
0.29,
1993
do
llars).
7,8
The
model
adds
the
opportunity
cost
of
travel
time,

measured
in
terms
of
wages
lost,
to
the
travel
cost
for
those
who
would
have
lost
income
by
taking
the
recreation
trip.
For
these
consumers
the
dummy
variable
LOSEINC
equals
one.
times
equal
the
round­
trip
distance
divided
by
a
travel
speed
of
40
mph
and
multiplied
by
the
individual s
hourly
wage
as
calculated
below.

The
travel
cost
variable
in
the
mo
del
was
calculated
as
follows:

(
21.15)

Individuals
not
losing
income
(
e.
g.,
individuals
taking
vacation
or
a
weekend
trip
or
individuals
whose
work
schedule
is
not
flexible)
do
not
face
lost
wages
as
a
result
of
the
trip
and
inclusion
of
the
opportunity
cost
of
time
would
be
inappropriate.

These
consumers
still
have
an
opportunity
cost
for
their
travel
time,
which
could
otherwise
be
spent
doing
something
else,

like
fishing.
istance
traveled
allows
for
a
longer
time
spe
nt
at
the
recreation
site.

consum
ers,
the
analysis
included
an
ad
ditional
round
­
trip
travel
time
variable
calculated
a
s:

(
21.16)

The
average
o
ne­
way
estimated
trave
l
time
to
the
visited
site
is
56.34
minutes.
9
21.2.3
Site
Characteristics
EPA
identified
1,954
recreation
sites
on
1,631
reaches
in
the
universal
opportunity
set.
hese,
580
observations
are
known
recreational
sites
(
e.
g.
parks);
1,366
observations
are
Reach
File
1
(
RF1)
reaches
without
a
known
recreational
site;
and
eight
observ
ations
a
re
neithe
r
located
in
R
F1
nor
ide
ntified
as
known
recreation
sites
but
w
ere
visited
by
an
N
DS
respo
ndent.
Based
on
Parsons
Travel
In
other
words,
a
shorter
d
For
these
Of
t
6
The
program
was
created
by
Daniel
Hellerstein
and
is
available
through
the
USDA
at
http://
usda.
maunlib.
cornell.
edu/
datasets/
general/
93014.

7
Note
that
all
expenditures
are
in
1993
dollars
because
the
NDS
trip
choices
and
the
associated
expenditure
occurred
in
1993.

8
The
estimate
of
motor
vehicle
cost
per
mile
was
based
on
estimates
compiled
by
the
Insurance
Information
Institute.

9
The
average
travel
time
to
the
visited
site
was
fairly
uniform
across
the
activities.
Average
one­
way
time
to
the
visited
site
was
51.38
minutes,
71.64
minutes,
43.76
minutes,
and
58.57
minutes
for
fishing,
boating,
swimming,
and
viewing,
respectively.

21­
14
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Each
consumer
choice
set
theoretically
includes
hundreds
of
substitutable
recreation
sites
in
Ohio
and
in
the
neighboring
states.
To
prevent
the
recreation
site
analysis
from
becoming
unmanageable,
EPA
analyzed
a
sample
of
recreation
sites
for
each
consumer
observation.
The
Agency
then
created
a
randomly­
drawn
reduced
choice
set
for
each
recreational
activity
as
follows:
10
 
Fishing.
The
reduced
choice
set
consists
of
20
Lake
Erie
sites,
20
river
sites,
and
20
small
lakes/
reservoirs.
Thus,
a
total
individual
choice
set
consists
of
60
alternatives
(
including
the
chosen
site);

 
Boating.
The
reduced
choice
set
consists
of
20
Lake
Erie
sites
and
20
inland
recreation
sites
(
including
rivers
and
lakes/
reservoirs).
A
total
individual
choice
set
consists
of
40
alternative
sites
(
including
the
chosen
site);

 
Swimming.
Similar
to
boating,
the
reduced
choice
set
consists
of
20
Lake
Erie
sites
and
20
inland
recreation
sites
(
including
rivers
and
lakes/
reservoirs).
A
total
individual
choice
set
consists
of
40
alternative
sites
(
including
the
chosen
site);

 
Wildlife
Viewing.
The
reduced
choice
set
consists
of
40
sites,
including
Lake
Erie,
river,
and
small
lake/
reservoir
sites.

Each
participant
choice
set,
by
definition,
includes
the
site
actually
visited
by
the
respondent.
For
each
consumer,
EPA
drew
the
additional
sites
from
a
geographic
area
defined
by
a
distance
constraint
(
and
the
water
body
types
listed
above).
The
Agency
used
a
120­
mile
distance
limit
for
inland
recreation
sites
(
Ohio
rivers,
small
lakes,
or
reservoirs).
All
Lake
Erie
sites
are
eligible
for
inclusion
in
the
choice
sets
for
all
models.
EPA
assumed
that
consumers
of
water­
based
recreation
would
be
willing
to
travel
farther
to
visit
Lake
Erie
sites,
because
this
water
resource
presents
unique
recreational
opportunities.
11
EPA
used
the
resulting
aggregate
choice
set
of
sites
for
all
individuals
participating
in
a
given
recreation
activity
to
model
consumer
decisions
regarding
trip
allocation
across
recreation
sites.

The
Agency
used
two
classes
of
characteristics
to
estimate
site
choice:

 
those
unaffected
by
the
MP&
M
regulation,
but
likely
to
determine
valuation
of
water­
based
recreational
resources;

and
 
those
affected
by
the
regulation
and
hypothesized
to
be
significant
in
explaining
recreation
behavior
and
resource
valuation.

Regulation­
independent
site
characteristics
include
water
body
type
and
size,
location
characteristics,
and
the
presence
of
site
amenities
(
e.
g.,
boat
ramps,
swimming
beaches,
picnic
areas).
Regulation­
dependent
site
characteristics
include
regulation­

affected
water
quality
variables.

a.
Regulation­
independent
site
characteristics
Site
characteristics
that
are
likely
to
be
important
determinants
of
consumer
valuation
of
water­
based
recreational
resources
but
that
are
independent
of
the
MP&
M
regulation
include
general
site
descriptors.
These
descriptors
include
the
type
and
size
of
the
water
body
and
location
characteristics,
and
the
presence
of
site
amenities.
EPA
obtained
data
on
regulation­

independent
site
characteristics
from
two
main
sources,
RF1
and
the
Ohio
Department
of
Natural
Resources
(
ODNR).

RF1
provided
water
body
type
(
i.
e.,
lake,
river,
or
reservoir)
and
physical
dimension
(
i.
e.,
length,
width,
and
depth).
The
dummy
variables,
LAKE
ERIE,
RIVER,
and
LAKE
characterize
water
body
types.
If
a
site
is
located
on
Lake
Erie,
LAKE
ERIE
takes
the
value
of
1;
0
otherwise.
If
a
site
is
located
on
river,
RIVER
takes
the
value
of
1;
0
otherwise.
Finally,
if
a
site
is
located
on
a
small
lake
or
reservoir,
LAKE
takes
the
value
1;
0
otherwise.
Water
body
size
was
determined
by
the
length
of
the
reach
segment
in
miles
for
rivers
and
Lake
Erie
sites.
For
small
lakes
and
reservoirs,
the
appropriate
water
body
size
is
the
water
body
area
in
acres.
The
site
choice
models
use
the
logarithm
of
water
body
size
as
a
measure
of
site
importance,

10
McFadden
(
1981)
has
shown
that
estimating
a
model
using
random
draws
can
give
unbiased
estimates
of
the
model
with
the
full
set
of
alternatives.

11
Travel
distance
from
respondent s
hometown
to
the
Lake
Erie
sites
did
not
exceed
250
miles.

21­
15
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
because
people
are
more
likely
to
be
aware
of
large
water
bodies.
12
Water
body
size
data
for
sites
not
located
in
RF1
came
from
the
ODNR.

ODN
R,
supplemented
by
the
Ohio
Atlas
and
Gazetteer,
provided
data
on
recreational
amenities
and
site
setting
(
e.
g.,

presence/
absence
of
boat
ramps,
swimming
beaches,
or
picnic
areas;
public
accessibility;
and
size
of
land
available
for
recreation).
EPA
used
land
available
for
recreation,
LN(
LAND),
(
e.
g.,
acreage
of
state
park,
fishing,
hunting,
and
other
recreation
areas)
to
approximate
site
setting
and
attractiveness.
Dummy
variables
represent
the
presence
of
three
recreational
amenities:
BEACH
is
a
swimming
beach;
RAMP
is
a
boating
ramp;
and
PARK
indicates
a
park.

b.
Regulation­
dependent
site
characteristics
Selecting
regulation­
dependent
site
characteristic
variables
that
are
both
policy­
relevant
and
significant
in
explaining
recreation
behavior
proved
challenging.
MP&
M
facilities
discharge
many
pollutants,
most
of
them
unlikely
to
have
visible
indicators
of
degraded
water
quality
(
e.
g.,
odor,
reduced
turbidity,
etc).
EPA
hypothesized
that
pollutant
loadings
can,

nonetheless,
reduce
the
likelihood
of
selecting
a
recreation
site.
Reduced
pollutant
discharges
improve
water
quality
and
aquatic
habitat,
thereby
increasing
fish
populations
and
enhancing
the
recreational
fishing
experience.
In
addition,
in­
stream
nutrient
concentrations
are
good
predictors
of
eutrophication,
which
causes
aesthetic
losses
and
may
thus
affect
the
utility
of
a
water
resource
for
all
four
recreational
uses.

The
connection
between
the
policy
variables
(
i.
e.,
the
change
in
concentrations
of
MP&
M
pollutants)
and
the
effects
perceived
by
consumers
(
e.
g.,
increased
catch
rate,
increased
size
of
fish,
greater
diversity
of
species,
or
improved
aesthetic
qualities
of
the
water
body)
are
not
modeled
directly,
but
are
captured
implicitly
in
the
differential
valuation
of
water
resources
as
reflected
in
the
RUM
analyses.

EPA
considered
two
types
of
pollutant
effects
in
defining
water
quality
variables
for
model
inclusion:

 
visible
or
otherwise
directly
perceivable
effects
(
e.
g.,
water
turbidity);
and
 
unobservable
toxic
effects
likely
to
impact
aquatic
habitat
and
species
adversely.

The
Agency
accounted
for
directly
observable
effects
using
the
ambient
concentrations
of
nutrients
(
e.
g.,
TKN
)
as
an
explanatory
variable.

Rather
than
include
the
concentrations
of
all
toxic
pollutants
separately,
EPA
constructed
a
variable
to
reflect
the
adverse
impact
potential
of
toxic
pollutants
on
aquatic
habitat.
EPA
identified
recreation
sites
at
which
estimated
concentrations
of
one
or
more
MP&
M
pollutants
exceeds
AWQC
limits
for
aquatic
life
protection,
to
assess
the
likely
adverse
impacts
on
aquatic
organisms.
A
dummy
variable,
AWQC_
EX,
takes
the
value
of
1
if
in­
stream
concentrations
of
at
least
one
MP&
M
pollutant
exceed
AWQC
limits
for
aquatic
life
protection,
0
otherwise.
This
approach
accounts
for
the
fact
that
adverse
effects
on
aquatic
habitat
are
not
likely
to
occur
below
a
certain
threshold
level.

c.
Biological
factors
Numerous
biological
parameters
(
e.
g.,
abundance
of
sport
fish)
that
are
a
function
of
the
availability
and
quality
of
suitable
habitat
for
breeding
and
feeding
are
also
likely
to
affect
recreation
behavior.
To
account
for
biological
parameters
affecting
the
demand
for
water­
based
recreation,
EPA
used
relative
fish
abundance
(
Biomass)
obtained
from
the
Ohio
Water
Resource
Inventory
(
OWRI)
database
(
OH
EPA,
1996).
Relative
fish
abundance
is
measured
as
the
total
fish
weight
(
in
kg)
per
300
meters.
Because
this
variable
reflects
presence
of
both
tolerant
and
intolerant
fish
species,
it
is
less
correlated
with
the
two
regulation­
dependent
water
quality
variables
(
i.
e.,
TKN
and
AW
QC)
included
in
the
analysis
compared
to
the
index
of
well­
being
(
IWB2)
used
in
the
proposed
rule
analysis.

Chemical
properties
of
the
waters
(
e.
g.,
pollutant
concentrations)
are
likely
to
affect
the
diversity
and
abundance
of
the
fishery
resources.
Biological
parameters
may
also
be
affected
by
numerous
anthropogenic
stressors
unrelated
to
water
quality,
such
as
over­
fishing,
physical
alteration
of
habitat,
invasion
of
exotic
species,
etc.
Although
EPA
used
the
baseline
values
of
relative
fish
abundance
to
estimate
the
site
choice
models,
the
Agency
did
not
estimate
changes
in
biological
parameters
caused
by
the
regulation
analysis
due
to
data
limitations
and
the
challenges
posed
by
modeling
population
impacts
of
a
broad
spectrum
of
pollutants
at
hundreds
of
recreation
sites.

12
EPA
uses
the
logarithm
of
water
body
size
because
it
expects
the
effect
of
water
body
size
on
utility
to
diminish
as
that
size
increases.

21­
16
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
d.
Presence
of
fish
advisories
Another
important
factor
that
may
affect
a
recreational
consumer s
decision
to
visit
a
particular
site
is
presence
of
fish
consumption
or
contact
advisories
(
FCAs)
.
EPA
obtained
information
on
fish
consumption
advisories
and
contact
advisories
at
reaches
in
Ohio
from
the
ODNR
(
Ohio
DNR,
1999).
Fish
consumption
advisories
and
contact
advisories
were
listed
by
the
name
of
the
stream
or
river
with
the
consumption
advisory.
An
advisory
that
applied
to
only
part
of
the
river
included
the
names
of
cities,
towns,
or
highways
to
identify
the
stretch
of
the
reach
for
which
the
advisory
was
relevant.
The
name
of
the
river
and
the
other
geographic
identifiers
were
used
to
assign
reach
numbers
from
RF1
to
the
consumption
advisories.
EPA
created
a
dummy
variable
for
each
type
of
advisory
(
i.
e.,
fish
advisories
and
contact
advisories).
The
variable
takes
the
value
of
1
if
the
relevant
advisories
are
present;
0
otherwise.

21.3
SITE
CHOICE
MODEL
ESTIMATES
EPA
estimated
four
separate
models
of
recreational
demand:
fishing,
boating,
swimming,
and
viewing.
The
Agency
classified
trips
by
the
primary
activity
listed
by
the
respondent.
All
four
activity
models
cover
single­
day
trips.
EPA
estimated
the
site
choice
model
using
the
site
actually
visited
and
randomly­
drawn
sites
from
the
choice
set
for
each
recreation
activity
as
described
in
Section
21.2­
3
above.

EPA
estimated
activity
models
for
five
alternative
choice
sets
(
i.
e.,
five
random
draws
from
the
universal
choice
set),

producing
five
sets
of
estimated
coefficients.
Mean
estimates
from
the
five
alternative
draws
represent
EPA s
best
estimate
of
actual
coefficient
values.
Table
21.3
lists
the
variables
used
as
arguments
in
the
utility
function
and
presents
the
mean
estimation
results
for
the
four
models.
In
estimating
site
choice
models
for
fishing,
boating,
swimming,
and
viewing,
the
Agency
restricted
the
coefficient
on
travel
cost
to
be
equal
across
all
four
models
to
ensure
a
constant
marginal
utility
of
income
across
all
four
activities.

The
following
sections
provide
a
short
description
of
the
results
of
the
site
choice
model
corresponding
to
each
recreation
activity.

21­
17
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Table
21.3:
Site
Choice
Model
Estimation
Results
(
Mean
parameter
estimates
from
five
random
draws)
a
Variable
Activity
Fishing
Boating
Swimming
Viewing
TRCOST
b
­
0.044
(­
22.704)
­
0.044
(­
22.704)
­
0.044
(­
22.704)
­
0.044
(­
22.704)

TIME
c
­
1.474
(­
7.482)
­
0.362
(­
4.27)
­
0.436
(­
7.007)
­
0.719
(­
12.647)

RAMP
d
0.878
(
7.509)
N/
A
N/
A
N/
A
LN(
LAND)
e
N/
A
N/
A
0.058
(
2.431)
0.162
(
7.471)

PARK
f
N/
A
N/
A
0.753
(
3.79)
0.787
(
4.638)

BEACH
g
N/
A
N/
A
0.491
(
2.96)
N/
A
LN(
SIZE)
h
All
N/
A
0.502
(
5.777)
­
0.273
(­
6.083)
N/
A
Lake
Erie
0.908
(
6.639)
N/
A
N/
A
0.665
(
10.474)

River
0.171(
1.993)
N/
A
N/
A
­
0.261
(­
4.937)

Lake
0.050
(­
0.348)
N/
A
N/
A
­
0.429
(­
4.329)

Biomass
i
Lake
Erie
N/
A
­
0.130
(­
1.777)
N/
A
N/
A
River
0.068
(
2.328)
0.017
(
0.4432)
N/
A
N/
A
TKN
j
­
0.584
(­
3.763)
­
1.187
(­
6.863)
­
0.660
(­
4.631)
­
0.711
(­
4.401)

AWQC
k
­
0.573
(­
3.698)
­
0.172
(­
1.179)
N/
A
N/
A
Inclusive
Values
ERIE
0.811(
9.895)
0.296
(
6.098)
0.730
(
7.466)
N/
A
Inland
N/
A
0.088
(
2.525)
0.275
(
6.302)
N/
A
RIVER
0.591
(
6.945)
N/
A
N/
A
N/
A
LAKE
0.429
(
2.629)
N/
A
N/
A
N/
A
Adj.
R2
0.467
0.280
0.408
a
EPA
performed
this
analysis
based
on
five
alternative
draws
to
assess
sensitivity
of
the
estimated
coefficients
with
respect
to
random
draws.

b
Travel
Cost
is
calculated
as
0.29
*
round­
trip
distance.

c
Travel
Time
is
(
round­
trip
distance
/
40)*
Wage.

d
1
if
a
boating
ramp
is
present,
and
0
otherwise.

e
Log
of
the
number
of
land
acres.

f
1
if
the
site
is
a
park,
and
0
otherwise.

g
1
if
a
swimming
beach
is
present,
and
0
otherwise.

h
Log
of
the
size
of
the
water
body.
For
rivers
and
Lake
Erie,
this
is
the
log
of
the
reach
segment
length
or
Lake
Erie
shore
segment
in
miles.
For
lakes,
this
is
log
of
the
lake
circumference.

i
Biomass
is
measured
as
the
total
fish
weight
(
in
kg)
per
300
meters.

j
In­
stream
concentrations
of
TKN
(
mg/
l).

k
1
for
any
reach
if
in­
stream
concentrations
of
at
least
one
MP&
M
pollutant
exceeds
the
AWQC
limits
for
protection
of
aquatic
life,
and
0
otherwise.

Note:
T­
statistic
for
test
that
the
estimated
coefficient
equals
0
is
given
in
parentheses
beside
the
coefficient
estimates.
N/
A
indicates
that
the
variable
was
not
included
in
the
estimation
for
this
activity.

Source:
U.
S.
EPA
analysis.

21.3.1
Fishing
Model
The
estimated
fishing
model
includes
travel
cost
(
TRCOST),
time
(
TIME)
spent
traveling,
and
site
characteristics.
The
Agency
included
the
following
site
characteristics
in
the
fishing
model:
boat
ramp
(
RAMP),
water
body
size
(
LN(
SIZE)),

21­
18
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
relative
fish
abundance
(
Biomass),
TKN
concentrations,
and
presence
of
AWQC
exceedances.
Table
21.3
shows
that
most
coefficients
have
the
expected
sign
and
are
significantly
different
from
zero
at
the
95th
percentile.
Travel
cost
and
travel
time
have
a
negative
effect
on
the
probability
of
selecting
a
site,
indicating
that
anglers
prefer
to
visit
sites
closer
to
their
homes
(
other
things
being
equal).

Anglers
who
fish
from
a
boat
are
likely
to
view
the
presence
of
a
boat
ramp
as
an
important
factor
that
may
affect
their
site
choice.
However,
the
presence
of
a
boat
ramp
is
unlikely
to
be
important
for
anglers
who
fish
from
shore.
Thus,
the
Agency
used
an
interaction
variable
(
RAMP
x
USE_
BOAT)
such
that
the
ramp
variable
was
turned
on
only
if
the
angler
reported
using
a
boat
on
his
last
fishing
trip.
A
positive
sign
on
the
boat
ramp
indicates
that
anglers
owning
a
boat
are
more
likely
to
choose
sites
with
a
boat
ramp.

The
water
body
size
has
a
different
effect
on
the
probability
of
selecting
a
site
in
the
Lake
Erie,
river,
and
small
lake/
reservoir
groups.
The
larger
the
river
or
the
Lake
Erie
shore
segment,
the
more
likely
that
anglers
visited
the
reach.
The
size
of
inland
lakes
and
reservoirs
does
not
have
a
significant
effect
on
the
probability
of
visiting
the
site.

The
Agency
used
the
square
root
of
the
fish
weight
per
300
meters
as
a
measure
of
fish
abundance
(
Biomass).
The
probability
of
a
river
site
visit
increases
as
the
relative
fish
abundance
at
the
site
increases.
However,
inclusion
of
this
variable
in
the
Lake
Erie
nest
was
not
significant,
which
indicates
that
relative
fish
abundance
does
not
have
a
significant
effect
on
choosing
a
Lake
Erie
site.
This
finding
is
counterintuitive
and
is
likely
to
be
due
to
the
lack
of
variation
in
the
relative
fish
abundance
variable
for
the
Lake
Erie
sites.
This
variable
was
excluded
from
the
Lake
Erie
nest
in
the
final
model
presented
here.
Data
on
relative
fish
abundance
were
not
available
for
lakes.

Finally,
higher
ambient
concentrations
of
TKN,
which
indicate
potential
eutrophication
problems,
and
presence
of
AWQC
exceedances
negatively
affect
the
probability
of
site
selection.
In
other
words,
anglers
prefer
cleaner
sites,
all
else
being
equal.

Estimated
inclusive
values
on
Lake
Erie
sites,
rivers,
and
small
lakes
lie
within
a
unit
interval
[
0,1]
and
are
significantly
13
different
from
0,
indicating
that
the
nested
choice
structure
is
appropriate.

EPA
found
other
variables,
tested
as
explanatory
variables,
to
be
insignificant,
including
the
presence
of
FCAs.
It
might
be
expected,
a
priori,
that
the
presence
of
an
FCA
decreases
a
site s
likelihood
as
a
fishing
choice.
In
fact,
the
existence
of
FCAs
did
not
significantly
affect
a
site s
probability
of
being
chosen;
59
percent
of
the
sites
actually
chosen
by
NDS
respondents
had
an
FCA
in
place.
Creel
surveys
provided
by
ODNR
indicated
that,
on
average,
anglers
released
70
percent
of
their
catch
(
ODNR,
1997).
This
finding
suggests
that
recreational
anglers
are
aware
of
FCAs,
and
catch
but
do
not
consume
fish
in
the
affected
areas.

21.3.2
Boating
Model
The
estimated
boating
model
includes
travel
cost
(
TRCOST),
time
(
TIME)
spent
traveling,
and
site
characteristics.
The
Agency
included
the
following
site
characteristics
in
the
boating
model:
water
body
size
(
LN(
SIZE)),
relative
fish
abundance
(
Biomass),
TKN
concentrations,
and
presence
of
AWQC
exceedances.
Table
21.3
shows
that
most
coefficients
have
the
expected
sign
and
are
significantly
different
from
zero
at
the
95th
percentile.

Travel
cost
and
travel
time
have
a
negative
effect
on
the
probability
of
selecting
a
site,
indicating
that
boaters
prefer
to
visit
sites
closer
to
their
homes
(
other
things
being
equal).
However,
the
magnitude
of
the
travel
time
coefficient
indicates
that
boaters
are
willing
to
travel
farther
than
participants
in
other
recreational
activities.
This
is
not
surprising,
since
motor­

boating
and
sailing
are
restricted
to
the
sites
where
these
activities
are
allowed.
The
positive
coefficient
on
the
water
body
size
variable
(
LN(
SIZE))
indicates
that
the
larger
the
water
body
the
more
likely
the
boaters
visited
it.

The
coefficients
on
water
quality
variables
(
TKN
and
AW
QC)
are
negative,
indicating
that
boaters
prefer
to
visit
cleaner
sites.
The
Biomass
coefficient
is
positive,
but
insignificant
for
inland
sites,
and
negative
for
Lake
Erie
sites.
The
negative
coefficient
on
this
variable
is
likely
to
be
due
to
the
fact
that
88
percent
of
the
sample
trips
used
in
this
model
were
motorboating
trips.
Motorboating
itself
is
likely
to
be
a
significant
environmental
stressor
for
biological
communities
due
to
noise
and
turbidity
associated
with
this
activity.
Thus,
lower
fish
abundance
at
popular
boating
sites
may
indicate
that
intensive
motorboating
may
adversely
affect
species
abundance.
As
was
the
case
with
the
fishing
model,
the
estimated
13
Inclusive
values
equal
to
1
cause
the
model
to
collapse
to
a
flat
multinomial
logit.

21­
19
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
inclusive
value
is
significantly
different
from
zero
and
lies
within
a
unit
interval
[
0.1],
supporting
the
nested
model
framework.

21.3.3
Swimming
Model
EPA
included
the
travel
cost
and
time
variables
(
TRCOST,
TIME),
physical
characteristics
of
the
site,
and
ambient
TKN
concentrations
in
the
swimming
model.
This
model
also
includes
the
presence
of
recreational
amenities
that
are
likely
to
be
important
to
swimmers:
presence
of
a
beach,
the
designation
of
the
site
as
a
park,
and
the
natural
log
of
the
land
acres.
All
estimated
coefficients
have
the
expected
sign
and
are
significantly
different
from
zero
at
the
95th
percentile.

Price,
travel
time,
the
presence
of
a
park
with
a
beach,
and
the
size
of
the
land
area
around
the
site
all
increase
the
probability
of
a
particular
site
being
chosen
for
swimming.
Swimmers
are
less
likely
to
visit
large
sites
(
referring
to
the
size
of
the
water
body)
or
sites
with
visible
water
quality
effects
as
indicated
by
higher
in­
stream
concentrations
of
TKN.
As
for
the
fishing
and
boating
models,
the
estimated
inclusive
value
is
significantly
different
from
zero
and
lies
within
a
unit
interval
[
0,1]

supporting
the
nested
model
framework.

Again,
some
variables
expected
to
be
significant,
such
the
presence
of
contact
advisories,
are
not.
This
variable s
insignificance
probably
stems
from
its
scarcity.
Of
1,954
sites
included
in
the
universal
opportunity
set,
contact
advisories
are
in
place
for
only
13.
(
None
of
the
sites
actually
visited
had
contact
advisories
in
place.)
The
probability
that
a
chosen
site
has
contact
advisories
in
place
is
very
small,
because
individual
choice
sets
are
randomly
selected.

The
fish
Biomass
variable
representing
biological
characteristics
of
a
water
body
also
did
not
have
a
significant
influence
on
consumer
decisions
to
visit
a
particular
site
and
was
dropped
from
the
model.
This
outcome
is
not
surprising,
since
abundant
aquatic
life
may,
in
fact,
interfere
with
swimming
activities.

21.3.4
Viewing
(
Near­
water
Activity)
Model
EPA
included
the
travel
cost
and
time
variables
(
TRCOST,
TIME),
physical
site
characteristics,
and
ambient
TKN
concentrations
in
the
viewing
model.
In
addition,
the
Agency
included
the
natural
log
of
the
land
acres
and
the
designation
of
the
site
as
a
park.
All
estimated
coefficients
have
the
expected
sign
and
are
significantly
different
from
zero
at
the
95th
percentile.

The
probability
of
choosing
a
site
for
near­
water
activities
is
most
significantly
related
to
visit
price,
travel
time,
land
size,

and
in­
stream
concentrations
of
TKN.
Similarly
to
the
fishing
model,
the
water
body
size
has
a
different
effect
on
the
probability
of
selecting
a
site
in
the
Lake
Erie,
river,
and
small
lake/
reservoir
groups.
The
larger
the
Lake
Erie
shore
segment,

the
more
likely
that
viewers
visited
the
site.
The
negative
coefficients
on
river
and
inland
lake
size
indicate
that
consumers
prefer
smaller
inland
water
bodies
for
near­
water
and
wildlife
viewing
activities.

21.4
TRIP
PARTICIPATION
MODEL
EPA
estimated
the
determinants
of
individual
choice
concerning
how
many
trips
to
take
during
a
recreation
season
with
a
separate
model
for
each
of
the
four
activities.
These
participation
models
rely
on
socioeconomic
data,
and
on
estimates
of
individual
utility
(
the
inclusive
value)
derived
from
the
site
choice
models.
Variables
of
importance
include
age,
ethnicity,

gender,
education,
and
the
presence
of
young
or
older
children
in
the
household.
Whether
or
not
the
individual
owns
a
boat
is
particularly
important
in
boating
participation,
and
is
included
in
the
model
for
that
activity
only.
Variable
definitions
for
the
trip
participation
model
are:

 
IVBASE:
inclusive
value,
estimated
using
the
coefficients
obtained
from
the
site
choice
models;

 
#
TRIPS:
number
of
trips
taken
by
the
individual;

 
AGE:
individual s
age.
If
the
individual
did
not
report
age,
their
age
is
set
to
the
sample
mean;

 
MALE:
equals
1
if
the
individual
is
a
male,
0
otherwise;

 
NOH
S:
equals
1
if
the
individual
did
not
complete
high
school,
0
otherwise;

21­
20
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
 
COLLEGE:
equals
1
if
the
individual
completed
college,
0
otherwise;

 
AFAM:
equals
1
if
the
individual
is
African
American,
0
otherwise;

 
YNG
KIDS:
equals
1
if
there
are
kids
6
years
or
younger,
0
otherwise;

 
OLDKIDS:
equals
1
if
there
are
kids
7
years
or
older,
0
otherwise;

 
OW
NBT
:
equals
1
if
individual
owns
a
boat,
0
otherwise;

 
Constant:
a
constant
term
representing
each
individual s
utility
associated
with
not
taking
a
trip;
and
 
 
(
alpha):
overdispersion
parameter
estimated
by
the
Negative
Binomial
Model.

Table
21.4
presents
explanatory
variables
and
a
mean
value
for
each.

Table
21.4:
Mean
Values
for
Explanatory
Variables
Used
in
the
Participation
Models
Variables
(
Mean)
Non­
Participant
(
N=
291)
Boating
(
N=
85)
Fishing
(
N=
84)
Swimming
(
N=
78)
Viewing
(
N=
75)

#
TRIPS
0.00
7.71
10.07
9.46
9.59
AGE
43.99
39.06
38.53
34.76
36.91
MALE
0.33
0.49
0.65
0.47
0.47
NOHS
0.17
0.09
0.14
0.13
0.13
COLLEGE
0.15
0.32
0.20
0.32
0.35
AFAM
0.11
0.02
0.05
0.03
0.12
YNGKIDS
0.18
0.26
0.24
0.24
0.27
OLDKIDS
0.38
0.48
0.58
0.56
0.48
OWNBT
N/
A
0.53
N/
A
N/
A
N/
A
Source:
U.
S.
EPA
analysis.

21­
21
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Table
21.5
presents
the
results
for
the
participation
models
of
the
four
recreation
activities.

Table
21.5:
Trip
Participation
Negative
Binomial
Model
Estimates
Variables/
Statistics
Boating
Fishing
Swimming
Viewing
IVBASE
0.12
0.82
0.72
0.47
(
0.71)
(
2.86
)
(
4.57)
(
3.66)

AGE
­
0.07
­
0.04
­
0.06
­
0.05
(­
4.73)
(
­
2.06)
(
­
2.24)
(
­
2.77)

MALE
1.23
2.22
1.15
0.91
(
2.75)
(
3.25)
(
1.52)
(
2.00)

NOHS
1.29
­
1.09
­
0.92
0.1
(
2.37)
(­
1.56
)
(
­
0.96)
(
0.17)

COLLEGE
­
0.19
­
0.40
0.53
1.22
(­
0.29)
(­
0.721
)
(
0.71)
(
2.05)

AFAM
­
3.74
­
1.44
­
4.07
­
1.16
(­
1.81)
(­
1.53
)
(
­
2.68)
(
­
1.34)

YNGKIDS
1.51
­
0.95
0.35
­
0.17
(
2.96)
(
­
1.26)
(
0.42
)
(
­
0.38)

OLDKIDS
­
1.67
1.11
0.4
0.8
(­
3.58)
(
2.78)
(
0.65)
(
1.81)

OWNBT
3.82
N/
A
N/
A
N/
A
(
5.26)

Constant
0.20
­
5.74
­
0.1
­
1.98
(
0.11)
(
­
3.01)
(
­
0.06)
(
­
1.6)

Alpha
 
5.77
9.03
8.92
8.17
(
5.85)
(
7.16)
(
6.78)
(
6.03)

Note:
T­
statistic
for
test
that
coefficient
equals
0
is
given
in
parentheses
below
the
coefficient
estimates.
N/
A
indicates
that
the
variable
was
not
included
in
the
estimation
for
this
activity.

Source:
U.
S.
EPA
analysis.

Parameter
estimates
of
the
inclusive
value
index
(
IVBASE)
in
the
swimming,
fishing,
and
viewing
models
are
positive
and
differ
significantly
from
zero
at
the
95th
percentile,
indicating
that
water
quality
improvements
have
a
positive
effect
on
the
number
of
trips
taken
during
a
recreation
season.

The
estimated
coefficient
on
IVBASE
in
the
boating
model,
while
positive,
was
not
statistically
significant.
Taking
a
boating
trip
often
requires
more
preparation
(
e.
g.,
taking
a
boat
to
the
water
body)
than
taking
other
trips.
Therefore,
although
water
quality
improvements
increase
the
value
of
a
boating
day,
factors
other
than
water
quality
are
likely
to
have
a
stronger
impact
on
the
number
of
boating
trips
per
season.

The
AG
E
variable
is
negative
and
significant
for
all
four
recreation
activities:
younger
people
are
likely
to
take
more
recreation
trips.
Ethnicity
and
gender
(
the
AFAM
and
MALE
variables)
also
have
a
significant
impact
on
whether
an
individual
participates
in
water­
based
recreation.
African
Americans
living
in
Ohio
are
less
likely
to
participate
in
any
of
the
four
recreation
activities
than
representatives
of
other
ethnic
groups.
Males
are
more
likely
than
females
to
participate
in
any
of
the
recreation
activities.

Education
also
influences
trip
frequency
significantly.
People
who
did
not
complete
high
school
(
NOHS=
1)
tend
to
take
fewer
fishing
or
swimming
trips.
Those
with
a
college
degree
(
COLLEGE=
1)
are
more
likely
to
participate
in
swimming
and
21­
22
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
viewing.
Respondents
who
attended
college
are
less
likely,
however,
to
participate
in
fishing
and
boating
than
those
who
completed
only
a
high
school
education.
For
the
boating
model,
the
COLLEGE
variable
is
not
significantly
different
from
zero.

The
presence
of
older
children
(
OLDKIDS)
in
the
household
is
associated
with
greater
participation
in
swimming,
viewing
(
near­
water
recreation),
and
fishing
activities,
but
is
not
a
significant
determinant
in
decisions
to
participate
in
boating.

Younger
children
in
the
household
(
YNGKIDS)
tends
to
lead
to
greater
participation
in
boating
and
swimming,
but
leads
to
fewer
fishing
or
viewing
trips.

21.5
ESTIMATING
BENEFITS
FROM
REDUCED
MP&
M
DISCHARGES
IN
OHIO
21.5.1
Benefiting
Reaches
in
Ohio
EPA
identified
reaches
where
it
expects
the
MP&
M
rule
to
eliminate
or
reduce
the
number
of
existing
AWQC
exceedances
(
hereafter,
benefiting
reaches).
The
Agency
first
identified
the
reaches
in
which
baseline
discharges
from
industrial
sources,

including
both
MP&
M
and
non­
MP&
M
facilities,
caused
one
or
more
pollutant
concentrations
to
exceed
AWQC
limits
for
aquatic
species.
A
reach
is
considered
to
benefit
from
the
MP&
M
rule
if
at
least
one
AWQC
exceedance
is
eliminated
due
to
reduced
M
P&
M
discharges.
Although
the
method
for
identifying
benefiting
reaches
is
similar
to
the
method
used
in
the
national
analysis
(
see
Chapter
15
for
detail),
there
are
three
notable
differences:

 
Unlike
the
national
analysis,
the
Ohio
case
study
incorporates
information
on
all
industrial
and
municipal
point
source
discharges
and
non­
point
sources
to
assess
in­
stream
concentrations
of
toxic
and
nonconventional
pollutants
in
the
baseline
and
post­
compliance.
Appendix
H
provides
information
on
the
data
sources
and
methods
used
to
assess
ambient
water
quality
conditions
in
Ohio.

 
The
water
quality
model
used
in
this
analysis
estimates
ambient
pollutant
concentrations
in
the
reaches
receiving
discharges
from
MP&
M
facilities
and
reaches
below
the
initial
discharge
reach.
Appendix
H
provides
detail
on
the
water
quality
model
used
in
this
analysis.

 
The
analysis
of
recreational
benefits
accounts
for
changes
in
TKN
concentrations.

EPA's
analysis
indicates
that
pollutant
concentrations
at
the
baseline
discharge
levels
from
all
industrial
sources
(
including
all
MP&
M
facilities)
exceed
acute
exposure
criteria
for
aquatic
life
on
15
reaches,
and
exceed
chronic
exposure
criteria
for
protection
of
aquatic
species
on
21
reaches.
EPA
estimates
that
reducing
pollutant
discharges
from
oily
waste
facilities
directly
discharging
to
the
receiving
streams
would
not
eliminate
all
concentrations
in
excess
of
the
acute
aquatic
life
exposure
criteria
or
the
chronic
exposure
criteria
on
any
reach
under
the
final
rule;
it
would
reduce
the
number
of
acute
and
chronic
exceedances
on
one
reach.

In
addition,
baseline
pollutant
concentrations
exceed
human
health­
based
AWQC
for
consumption
of
water
and
organisms
on
three
reaches
and
exceed
AQW
C
for
consumption
of
organisms
only
on
two
reaches.
EPA
estimates
that
reducing
pollutant
discharges
from
oily
waste
facilities
directly
discharging
to
the
receiving
streams
would
reduce
the
number
of
pollutants
exceeding
the
human
health­
based
AWQC
on
one
reach
under
the
final
rule;
it
would
not
eliminate
all
human
health­
based
AQWC
exceedances
on
any
reach
in
Ohio.
Table
21.6
summarizes
these
results.
In
addition,
the
final
regulation
is
estimated
to
reduce
in­
stream
concentrations
of
TKN
in
the
affected
reaches.
The
estimated
average
reductions
are
0.54
percent
in
lakes
and
0.45
percent
in
rivers
and
streams.

21­
23
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Table
21.6.:
Estimated
MP&
M
Discharge
Reaches
with
MP&
M
Pollutant
Concentrations
in
Excess
of
AWQC
Limits
for
the
Oily
Wastes
Subcategory
for
Protection
of
Aquatic
Species
or
Human
Health
Regulatory
Status
Number
of
Reaches
with
Concentrations
Exceeding
AWQC
Limits
for
Human
Health
Number
of
Reaches
with
Concentrations
Exceeding
AWQC
Limits
for
Aquatic
Species
Number
of
Benefiting
Reaches
All
AWQC
Exceedances
Eliminated
Reaches
with
Some
AWQC
Exceedances
Eliminated
H20
and
Organisms
Org.

Only
Acute
Chronic
Baseline
3
2
15
21
­­

Final
Regulation
3
2
15
21
0
1
Source:
U.
S.
EPA
analysis.

21.5.2
Estimating
Recreational
Benefits
in
Ohio
To
estimate
peoples 
willingness­
to­
pay
for
water
quality
improvements,
the
Agency
first
calculated
per­
person
seasonal
welfare
gain
corresponding
to
the
final
regulation.
Table
21.7
presents,
for
each
recreation
activity,
the
compensating
variation
per
trip
(
the
median
value
over
all
individuals
in
the
sample)
associated
with
the
reduced
MP&
M
discharges.

Because
the
trip
choices
and
the
associated
expenditures
occurred
in
1993,
the
welfare
gain
was
calculated
in
1993
dollars
and
then
adjusted
to
2001
dollars
based
on
the
Consumer
Price
Index
(
CPI).

The
model
indicates
that
the
reductions
in
MP&
M
discharges
from
the
final
regulation
result
in
a
modest
increase
in
per­
trip
values
for
three
of
the
four
recreation
activities
(
fishing,
viewing,
and
swimming).
There
is
no
welfare
gain
to
boaters
from
improved
water
quality
under
the
post­
compliance
scenario.
14
Table
21.7
provides
the
mean
estimates
of
welfare
gain
per
recreational
user
in
Ohio.

Table
21.7:
Welfare
Gain
per
Recreational
User
in
Ohio
(
2001$)

Per
Trip
Welfare
Gain
Average
Number
of
Trips
per
Person
per
Year
Mean
Seasonal
Welfare
Gain
Fishing
$
0.02
13.6
$
0.17
Boating
$
0.00
6.22
$
0.00
Viewing
$
0.01
9.26
$
0.11
Swimming
$
0.01
8.72
$
0.01
Source:
U.
S.
EPA
analysis.

Table
21.7
also
reports
seasonal
compensating
variation
per
individual.
The
reported
seasonal
welfare
gain
includes
both
the
increase
in
the
utility
from
better
water
quality
at
the
available
recreation
sites
receiving
MP&
M
discharges
and
the
increase
in
utility
from
greater
recreational
trip
participation.

As
noted
above,
the
Ohio
case
study
evaluated
changes
in
the
water
resource
values
from
both
reduced
discharges
of
TKN
and
reduced
frequency
of
AWQ
C
exceedances.
Changes
in
TKN
concentration
in
the
Ohio
water
bodies
resulting
from
reduced
MP&
M
discharges
from
the
Oily
Wastes
subcategory
account
for
approximately
96
percent
of
the
monetary
value
of
benefits
resulting
from
the
final
rule.

14
The
choice
set
of
recreational
sites
available
to
boaters
was
restricted
to
the
sites
where
motorboating
and
sailing
is
permitted
because
the
majority
of
Ohio
boaters
included
in
this
analysis
used
either
motor
or
sail
boats.
Water
quality
improvements
at
the
sites
where
boating
is
not
allowed
does
not
result
in
welfare
gain
to
boaters.

21­
24
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
Both
the
per­
trip
and
seasonal
welfare
estimates
are
much
lower
than
values
reported
in
the
existing
studies
of
water­
based
recreation.
This
is
not
surprising,
since
the
water
quality
changes
expected
from
the
final
rule
are
very
modest.

To
calculate
state­
level
recreational
benefits
from
the
final
rule,
EPA
first
calculated
seasonal
welfare
gain
from
water
quality
improvements
per
individual
in
the
sample.
The
Agency
then
multiplied
the
average
welfare
gain
per
individual
by
the
corresponding
number
of
participants
in
a
given
activity
(
see
Section
21.1.5
above
for
detail).
The
resulting
product
is
the
annual
benefit
from
the
final
MP&
M
rule
to
consumers
of
a
given
water­
based
recreation
activity
in
Ohio.
Table
21.8
summarizes
state­
level
results.

Table
21.8:
Estimated
Recreational
and
Nonuse
Benefits
from
Reduced
MP&
M
Discharges
from
the
Oily
Wastes
Subcategory
in
Ohio
Activity
Percentage
of
Ohio
Residents
Participating
in
Single­
Day
Trips
(
from
NDS)
Number
of
Participants
Aged
16
and
oldera
Total
Annual
Welfare
Gain
to
Recreational
Users
in
Ohio
Fishing
10.2%
892,283
$
153,102
Boating
7.7%
676,026
$
0
Viewing
9.1%
798,220
$
88,047
Swimming
9.1%
798,220
$
9,783
Total
Recreational
Use
Benefit
$
250,933
Nonuse
Benefits
$
125,466
Total
Recreational
Benefits
(
Use
+
Nonuse)
$
375,859
a
EPA
estimated
the
number
of
participants
in
each
recreation
activity
by
multiplying
the
percent
of
NDS
survey
respondents
from
Ohio
participating
in
a
single
day
trip
for
each
activity
by
the
total
adult
population
aged
16
an
older
(
8,790,969).
This
analysis
uses
the
2000
Census
data
to
estimate
current
population
in
Ohio.

Source:
U.
S.
EPA
analysis.

Under
the
final
regulation,
the
extrapolation
from
the
sample
to
the
adult
population
in
Ohio
yields
mean
annual
benefits
estimates
of
$
153,102,
$
9,783,
$
88,047,
and
$
0
(
2001$)
for
fishing,
swimming,
viewing,
and
boating,
respectively.
The
total
mean
recreational
use
benefit
is
$
250,932
(
2001$).
The
Agency
used
the
same
approach
as
in
the
national
analysis
to
estimate
nonuse
benefits
(
see
Section
15.2.3,
Nonuse
Benefits,
for
detail).
EPA
estimated
nonuse
benefits
as
one­
half
of
recreational
use
benefits
for
low,
mid,
and
high
estimates,
respectively.
The
estimated
mean
nonuse
benefit
is
$
125,466
(
2001$).

21.6
LIMITATIONS
AND
UNCERTAINTY
21.6.1
One­
State
Approach
Some
benefits
are
likely
to
be
missed
by
a
state­
level
case
study.
For
example,
residents
from
neighboring
states
undoubtedly
recreate
in
Ohio
waters,
and
residents
of
Ohio
undoubtedly
recreate
in
neighboring
states.
A
state­
by­
state
approach
that
restricts
its
analysis
to
recreation
activities
within
the
state
misses
these
categories
of
benefits.
15
This
omission
is
likely
to
be
more
significant
for
unique
locations
of
high
quality
(
e.
g.,
Lake
Erie),
where
participants
travel
significant
distances,
and
for
sites
very
close
to
state
boundaries.

15
Note
that
EPA
used
a
few
observation
on
visitors
from
neighboring
states
to
estimate
site
choice
models.
The
analysis
does
not
include
these
observations
in
calculating
state­
level
benefits
from
water
quality
improvements.

21­
25
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
21.6.2
Including
One­
Day
Trips
Only
Use
of
day­
trips
only
tends
to
understate
recreational
benefits
for
swimming,
fishing,
boating,
and
viewing,
since
recreation
as
part
of
multi­
day
trips
is
excluded.
Inclusion
of
multi­
day
trips,
however,
can
be
problematic.
Multi­
day
trips
are
frequently
multi­
activity
trips.
An
individual
might
travel
a
substantial
distance,
participate
in
several
recreation
activities
and
go
shopping
and
sightseeing,
all
as
part
of
one
trip.
Recreational
benefits
from
improved
recreational
opportunities
for
the
primary
activity
are
overstated
if
all
travel
costs
are
treated
as
though
they
are
associated
with
the
one
recreational
activity
of
interest.
The
total
benefits
per
trip
from
water
quality
improvements
are
not
overstated,
however,
if
individuals
participated
only
in
several
water­
based
activities.

21.6.3
Nonuse
Benefits
Estimating
nonuse
benefits
using
the
50
percent
rule
is
less
precise
than
using
a
more
sophisticated
benefits
transfer
approach.

However,
limiting
the
benefits
of
water
quality
improvements
only
to
recreational
benefits
would
significantly
underestimate
the
benefits
of
the
rule.
The
effects
of
using
the
simpler
approach,
e.
g.
either
overestimation
or
underestimation
of
benefits,
is
unknown.
Other
benefits
include
aesthetic
benefits
for
residents
living
near
water
bodies,
habitat
values
for
a
variety
of
species
(
in
addition
to
recreational
fish),
and
nonuse
values.
To
correct
for
this
limitation
of
using
only
a
travel
cost
model,

EPA
quantified
nonuse
values
in
proportion
to
recreation
values.
This
approach
provides
only
a
rough
approximation
of
the
value
of
water
resources
to
nonusers.
For
example,
some
natural
resources
have
high
use
values
but
small
or
negligible
nonuse
values
(
e.
g.,
cows),
while
other
species
have
very
high
nonuse
values
but
small
or
negligible
use
values
(
e.
g.,
blue
whales).

21.6.4
Potential
Sources
of
Survey
Bias
The
survey
results
could
suffer
from
bias,
such
as
recall
bias
(
e.
g.,
Westat,
1989),
nonresponse
bias,
and
sampling
effects.

a.
Recall
bias
Recall
bias
can
occur
when
respondents
are
asked
the
number
of
days
in
which
they
recreated
over
the
previous
season,
such
as
in
the
NDS
survey.
Some
researchers
believe
that
recall
bias
tends
to
lead
to
an
overstatement
of
the
number
of
recreation
days,
particularly
for
more
avid
participants.
Avid
participants
tend
to
overstate
the
number
of
recreation
days,
since
they
count
days
in
a
"
typical"
week
and
then
multiply
them
by
the
number
of
weeks
in
the
recreation
season.
16
They
often
neglect
to
consider
days
missed
due
to
bad
weather,
illness,
travel,
or
when
fulfilling
"
atypical"
obligations.
Some
studies
also
found
that
the
more
salient
the
activity,
the
more
"
optimistic"
the
respondent
tends
to
be
in
estimating
number
of
recreation
days.

Individuals
also
have
a
tendency
to
overstate
the
number
of
days
they
participate
in
activities
that
they
enjoy
and
value.

Taken
together,
these
sources
of
recall
bias
may
result
in
an
overstatement
of
the
actual
number
of
recreation
days.

b.
Nonresponse
bias
A
problem
with
sampling
bias
may
arise
when
extrapolating
sample
means
to
population
means.
This
could
happen,
for
example,
when
avid
recreation
participants
are
more
likely
to
respond
to
a
survey
than
those
who
are
not
interested
in
the
forms
of
recreation,
are
unable
to
participate,
assume
that
the
survey
is
not
meant
for
them,
or
consider
the
survey
not
worth
their
time.

c.
Sampling
effects
Recreational
demand
studies
frequently
face
two
types
of
observations
that
do
not
fit
general
recreation
patterns:

non­
participants
and
avid
participants:

Non­
participants
are
those
individuals
who
would
not
participate
in
the
recreation
activity
under
any
conditions.
This
analysis
assumes
that
an
individual
is
a
non­
participant
in
a
particular
activity
if
he
or
she
did
not
participate
in
that
activity
at
any
site.

This
assumption
tends
to
understate
benefits,
since
some
individuals
may
not
have
participated
during
the
sampling
period
simply
by
chance,
or
because
price/
quality
conditions
were
unfavorable
during
the
sampling
period.

Avid
participants
can
also
be
problematic
because
they
claim
to
participate
in
an
activity
an
inordinate
number
of
times.
This
16
Westat
(
1989)
uses
ten
or
more
activity­
days
per
year
as
an
indicator
of
an
"
avid"
user.

21­
26
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
reported
level
of
activity
is
sometimes
correct,
but
often
overstated,
perhaps
due
to
recall
bias
(
see
Westat,
1989).
Even
where
the
reports
are
correct,
these
observations
tend
to
be
overly
influential.
EPA
dropped
observations
of
participants
who
reported
more
than
100
trips
per
year
when
estimating
trip
participation
models,
to
correct
for
potential
bias
caused
by
these
observations.

21­
27
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
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)

compensating
variation
(
CV):
the
amount
of
money
a
person
would
need
to
pay
or
receive
in
order
to
leave
that
person
as
well
off
as
they
were
before
a
change.

consumer
choice
set:
the
set
of
alternatives
(
e.
g.,
alternative
recreation
sites)
from
which
a
consumer
may
choose.

exogenous:
external
to
the
inner
workings
of
a
system
or
model;
variables
are
exogenous
to
the
extent
that
they
are
"
given"

and
not
the
result
of
the
operation
of
the
system
or
anything
going
on
in
the
model
itself.

expected
maximum
utility:
see
 
inclusive
value. 

fish
biomass
(
Biomass):
measure
of
biological
factors
in
the
water
body
represented
by
the
total
fish
weight
in
kilograms
per
300
meters.

fish
consumption
advisories
(
FCAs):
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.

inclusive
value:
the
value
to
the
consumer
of
being
able
to
choose
among
X
alternatives
(
e.
g.,
among
a
number
of
recreational
sites)
on
a
given
trip
occasion.

indirect
utility
function:
gives
the
maximum
value
of
utility
for
any
given
prices
and
money
income.
The
indirect
utility
function
is
obtained
when
the
quantity
of
goods
that
maximizes
consumer
utility
subject
to
a
budget
constraint
are
substituted
into
a
utility
function.

inferential
analyses:
based
on
interpretation.

multinomial
logit
(
MNL):
a
utility
maximization
model.
In
this
model,
an
individual
is
assumed
to
have
preferences
defined
over
a
set
of
alternatives
(
e.
g.,
recreation
sites).
The
choice
model
takes
the
form
of
comparing
utilities
from
different
alternatives
and
choosing
the
one
that
produces
the
maximum
utility.
In
this
framework,
observed
data
consist
of
attributes
of
the
choices
(
e.
g.,
available
recreational
amenities
at
different
sites)
and
the
choice
actually
made.
Usually
no
characteristics
of
the
individuals
are
observed
beyond
their
actual
choice.

National
Demand
Survey
for
Water­
Based
Recreation
(
NDS):
a
U.
S.
EPA
survey
of
recreational
behavior.
The
1994
survey
collected
data
on
socioeconomic
characteristics
and
water­
based
recreation
behavior
using
a
nationwide
stratified
random
sample
of
13,059
individuals
aged
16
and
over.
(
http://
www.
epa.
gov/
opei)

negative
binomial
regression
model:
an
extension
of
the
Poisson
regression
model
that
allows
the
variance
of
the
process
to
differ
from
the
mean
(
see
also
Poisson
distribution
and
Poisson
estimation
process).

Negative
Binomial
Poisson
model:
(
see
negative
binomial
regression
model).

nested
multinomial
logit
model
(
NMNL):
an
extension
of
MNL
(
see
above).
In
this
model,
an
individual
is
assumed
to
choose
among
different
groups
of
alternatives
first
(
i.
e.,
Great
Lakes
or
inland
recreation
sites)
and
then
to
choose
specific
alternatives
(
e.
g.,
a
particular
river
reach,
lake,
or
Great
Lakes
site)
in
the
choice
set
for
each
group.

nonconventional
pollutants:
a
catch­
all
category
that
includes
all
pollutants
that
are
not
classified
as
priority
pollutants
or
conventional
pollutants.

Ohio
Water
Resource
Inventory
(
OWRI):
a
biennial
report
to
U.
S.
EPA
and
Congress
required
by
Section
305(
b)
of
the
Clean
Water
Act.
The
report
is
composed
of
four
major
sections:
(
1)
inland
rivers
and
streams,
wetlands,
Lake
Erie,
and
water
program
description;
(
2)
fish
tissue
contaminants;
(
3)
inland
lakes,
ponds,
and
reservoirs;
and
(
4)
groundwater.

21­
28
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
overdispersion:
condition
for
a
distribution
where
the
variance
exceeds
the
mean.
It
usually
signifies
a
nonrandom
dispersion,
for
example
the
case
where
a
small
minority
of
the
population
is
responsible
for
the
majority
of
recreational
trips
taken.

Poisson
distribution:
a
random
variable
X
is
defined
to
have
a
Poisson
distribution
if
the
probability
density
of
X
is
given
­
 
by
fx(
X)=
fx
(
X;
 
)
=
e
 
x
/
x!
for
x
=
0,1,2..,
and
0
otherwise.
In
this
model,
 
is
both
the
mean
and
variance
of
X.

Poisson
estimation
process:
used
to
model
discrete
random
variables.
Typically,
a
Poisson
random
variable
is
a
count
of
the
number
of
events
that
occur
in
a
certain
time
interval
or
spatial
area,
for
example,
the
number
of
recreational
trips
taken
during
a
recreational
season.

priority
pollutants:
126
individual
chemicals
that
EPA
routinely
analyzes
when
assessing
contaminated
surface
water,

sediment,
groundwater,
or
soil
samples.

random
utility
model
(
RUM):
a
model
of
consumer
behavior.
The
model
contains
observable
determinants
of
consumer
behavior
and
a
random
element.

Reach
File
1
(
RF1):
a
database
of
approximately
700,000
miles
of
streams
and
open
waters
in
the
conterminous
United
States.
The
database
contains
information
on
stream
flow,
time
travel
velocity,
reach
length,
width,
depth,
and
other
stream
attributes.

site
choice
model:
used
to
determine
which
recreational
site
is
chosen
by
the
consumer.
EPA
estimated
the
likelihood
that
the
consumer
will
choose
a
particular
site
as
a
function
of
site
characteristics,
the
price
paid
per
site
visit,
and
household
income.

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.

travel
cost
model
(
TCM):
method
to
determine
the
value
of
an
event
by
evaluating
expenditures
by
participants.
Travel
costs
are
used
as
a
proxy
for
price
in
deriving
demand
curves
for
recreation
sites.

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

total
seasonal
welfare:
see
 
welfare
effect. 

trip
participation
model:
used
to
estimate
the
number
of
water­
based
recreational
trips
taken
during
the
recreation
season.

EPA
estimated
the
total
number
of
trips
during
the
recreation
season
as
a
function
of
the
expected
maximum
utility
(
inclusive
value)
from
recreational
activity
participation
on
a
trip
and
socioeconomic
characteristics
affecting
demand
for
recreation
trips
(
e.
g.,
number
of
children
in
the
household).

utility­
theoretic:
consistent
with
the
behavioral
postulate
that
individuals
act
to
maximize
their
welfare
(
utility)
that
underlines
the
structure
of
models
of
consumer
behavior.

welfare
effect:
gain
or
loss
of
welfare
to
the
group
of
individuals
(
e.
g.,
fishermen)
as
a
whole.

21­
29
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
ACRONYMS
AWQC:
ambient
water
quality
criteria
CV:
compensating
variation
FCAs:
fish
consumption
advisories
IWB2:
index
of
well­
being
LIMDEP:
Limited
Dependent
Variable
MNL:
multinomial
logit
NDS:
National
Demand
Survey
for
Water­
Based
Recreation
NMNL:
nested
multinomial
logit
model
ODNR:
Ohio
Department
of
Natural
Resources
OWRI:
Ohio
W
ater
Resource
Inventory
RUM:
random
utility
model
RF1:
Reach
File
1
TKN:
Total
Kjeldahl
Nitrogen
TCM:
travel
cost
model
21­
30
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
REFERENCES
Bockstael,
N.
E.,
I.
E.
Strand,
and
W.
M.
Hanemann.
1987.
Time
and
the
Recreation
Demand
Model.
American
Journal
of
Agricultural
Economics
69:
213­
32.

Creel,
M.
and
J.
Loomis.
1992.
 
Recreation
Value
of
Water
to
Wetlands
in
the
San
Joaquin
Valley:
Linked
Multinomial
Logit
and
Count
Data
Trip
Frequency
Models. 
Water
Resource
Research.
Vol
28.
No
10,
October:
2597­
2606.

Feather,
Peter
M.,
Daniel
Hellerstein,
and
Theodore
Tomasi.
1995.
 
A
Discrete­
Count
Model
of
Recreation
Demand. 

Journal
of
Environmental
Economics
and
Management,
29:
214­
227.

Green,
W.
H.
1993.
Econometric
Analysis.
New
York,
NY:
Macmillan
Publishing
Company.

Hausman,
J.,
G.
Leonard,
and
D.
McFadden.
1995.
 
A
Utility­
Consistent,
Combined
Discrete
Choice
and
Count
Data
Model:
Assessing
Recreational
Use
Losses
Due
to
Natural
Resource
Damage. 
J.
of
Public
Economics
No
56
pp.
1­
30.

Jones,
C.
A.
and
Y.
D.
Sung.
1993.
Valuation
of
Environmental
Quality
at
Michigan
Recreational
Fishing
Sites:

Methodological
Issues
and
Policy
Application.
Final
Report.
EPA
Contract
No.
CR­
816247­
01­
2.
September.

Kling,
C.
L.
and
C.
J.
Thomson.
1996.
 
The
Implication
of
Model
Specification
for
Welfare
Estimation
in
Nested
Logit
Models. 
American
Journal
of
Agricultural
Economics
Association,
No.
78,
February:
103­
114.

McFadden,
D.
1981.
 
Econometric
Models
of
Probabilistic
Choice. 
In:
C.
F.
Manski
and
D.
L.
McFadden,
eds.,
Structural
Analysis
of
Discrete
Data.
Cambridge,
MA:
MIT
Press.

Ohio
Atlas
and
Gazetteer,
The.
1995.
Freeport,
ME:
Delorme.

Ohio
Department
of
Natural
Resources,
Division
of
Wildlife.
Creel
Survey
Summaries
from
1992
to
1997.

Ohio
Department
of
Natural
Resources,
Division
of
Wildlife.
1999.
Fish
Consumption
Advisories.

Ohio
Environmental
Protection
Agency.
1996.
Ohio
Waste
Resource
Inventory
Volume
1:
Summary
Status,
and
Trends;
and
Volume
3:
Ohio
Public
Lakes,
Ponds,
and
Reservoirs.
www.
Chagrin.
EPA.
State.
OH.
US/
document_
index.

Parsons,
G.
and
M.
J.
Kealy.
1992.
 
Randomly
Drawn
Opportunity
Sets
in
a
Random
Utility
Model
of
Lake
Recreation. 

Land
Economics
68
No.
4:
418­
33.

Parsons,
G.
R.
1999.
Comments
on
Assessing
the
Recreational
Benefits
of
the
MP&
M
Regulation:
A
State­
level
Case
Study
Based
on
the
Random
Utility
Model
Approach.
Memo
to
Abt
Associates
Inc.,
August.

U.
S.
Department
of
Commerce,
Bureau
of
the
Census.
2000.
http://
www.
state.
oh.
us/
odhs/
octf/
stats/
gjcs/
ohio.
pdf.

U.
S.
Environmental
Protection
Agency
(
U.
S.
EPA).
1994.
National
Demand
for
Water­
Based
Recreation
Survey.

Washington,
D.
C.:
Office
of
Policy
Evaluation
and
Information.

21­
31
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
21:
Modeling
Recreational
Benefits
in
Ohio
with
a
RUM
Model
THIS
PAGE
INTENTIONALLY
LEFT
BLANK
21­
32
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
INTRODUCTION
This
chapter
presents
estimated
benefits
and
costs
of
the
final
MP&
M
regulation
in
Ohio.
The
preceding
chapter
summarized
the
methodology
and
results
of
the
case
study
of
the
expected
recreational
benefits
from
water
quality
improvements
in
Ohio.
This
chapter
first
presents
estimates
of
the
remaining
three
benefit
categories,
including:

 
reduced
human
health
risk
from
exposure
to
carcinogens
and
systemic
health
toxicants,

 
changes
in
health
risk
from
exposure
to
lead
for
adults
and
children,
and
 
publicly­
owned
treatment
works
(
POTW)

benefits.
1
CHAPTER
CONTENTS
22.1
its
of
the
Final
Regulation
..............
22­
1
22.1.1
n
Health
Benefits
(
Other
than
Lead)
.
22­
2
22.1.2
Related
Benefits
................
22­
3
22.1.3
c
Productivity
Benefits
.........
22­
4
22.1.4
onetized
Benefits
..............
22­
4
22.2
ts
of
the
Final
Regulation
...........
22­
5
22.2.1
mpliance
Closures
.
.
22­
5
22.2.2
MP&
M
Facilities
.
.
22­
6
22.2.3
ocial
Costs
.................
...
22­
7
22.3
in
Ohio
.................
.............
22­
7
Glossary
.................
.................
...
22­
8
Acronyms
.................
.................
..
22­
9
Benef
Huma
Lead­
Economi
Total
M
Social
Cos
Baseline
and
Post­
Co
Compliance
Costs
for
Total
S
Comparison
of
Monetized
Benefits
and
Costs
The
chapter
then
presents
the
social
costs
of
the
final
regulation
for
the
state
of
Ohio
and
compares
the
aggregate
benefits
and
social
costs
estimates.
From
this
analysis,
EPA
estimates
that
the
final
regulation
will
have
net
monetizable
benefits
in
Ohio
of
$
868
thousand
annually
(
2001$).

EPA
estimated
MP&
M
costs
and
benefits
in
Ohio
using
methodologies
similar
to
those
used
for
the
national­
level
analysis
but
with
greater
detail
and
coverage
of
information.
In
addition
to
the
RUM
study
of
recreational
benefits
discussed
in
the
previous
chapter,
other
analytical
improvements
included
the
following:

 
the
use
of
more
detailed
data
on
MP&
M
facilities.
EPA
oversampled
the
state
of
Ohio
with
1,600
screeners
to
obtain
information
on
co­
occurrence
of
MP&
M
discharges;

 
the
use
of
data
on
non­
MP&
M
discharges
to
estimate
current
baseline
conditions
in
the
state;
and
 
the
use
of
a
first­
order
decay
model
to
estimate
in­
stream
concentrations
in
the
Ohio
water
bodies.
This
model
allows
the
assessment
of
the
environmental
effects
of
MP&
M
discharges
on
the
reaches
receiving
MP&
M
discharges
and
downstream
reaches.

Appendix
H
describes
the
water
quality
model
used
in
this
analysis
and
the
approach
and
data
sources
used
to
estimate
total
pollutant
loadings
from
all
industrial
and
municipal
sources
to
Ohio s
water
bodies.
The
Agency
believes
that
the
added
level
of
detail
results
in
more
robust
benefit­
cost
estimates.

22.1
BENEFITS
OF
THE
FINAL
REGULATION
EPA
estimates
that
MP&
M
facilities
in
all
subcategories
in
Ohio
discharge
approximately
127.6
million
pounds
of
pollutants
per
year
to
POTWs,
and
approximately
83.6
million
pounds
of
pollutants
directly
to
surface
water.
EPA
estimates
that
the
final
regulation
will
reduce
direct
discharges
by
approximately
0.5
million
pounds
of
pollutants
annually.

1
The
final
rule
regulates
only
direct
dischargers.
Therefore,
the
selected
option
does
not
affect
POTW
operations.

22­
1
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
22.1.1
Human
Health
Benefits
(
Other
than
Lead)

EPA
estimates
total
monetized
human
health
benefits
from
the
final
regulation
of
$
14,504
(
2001$).
Chapter
13
details
the
methodologies
used
to
estimate
human
health
benefits
from
reduced
exposure
to
carcinogens
and
systemic
health
toxicants
other
than
lead.

a.
Reduced
incidence
of
cancer
cases
from
consumption
of
contaminated
fish
and
drinking
water
Table
22.1
shows
the
number
of
cancer
cases
avoided
by
the
final
regulation
for
both
the
drinking
water
and
fish
consumption
pathways.
EPA
estimates
that
improved
water
quality
resulting
from
the
final
regulation
will
reduce
the
incidence
of
cancer
cases
via
the
drinking
water
and
fish
consumption
pathways
from
0.11
cases
in
the
baseline
to
0.10
cases
under
the
final
regulation,
with
a
total
annual
value
of
$
14,504.
Essentially
all
of
the
cancer
avoidance
occurs
via
the
fish
consumption
pathway,
which
yields
annual
cancer
avoidance
benefits
of
$
14,503.
Monetized
cancer
avoidance
benefits
from
reduced
drinking
water
contamination
are
negligible.

Table
22.1:
imated
Annual
Benefits
from
Avoided
Cancer
Cases
from
Fish
and
Drinking
Water
Consumption
Cancer
Cases
Benefits
(
2001$)

Baselinea
Drinking
Water
0.1026421
Fish
Consumption
0.00331
Total
0.11
Final
Regulation
Drinking
Water
0.1026420
negligible
b
Fish
Consumption
0.00108
$
14,503
Total
0.10
$
14,504
Est
a
The
baseline
includes
baseline
loadings
from
dischargers
in
all
subcategories.

b
Monetized
cancer
avoidance
benefits
from
reduced
drinking
water
contamination
are
approximately
$
1.

Source:
U.
S.
EPA
analysis.

b.
Systemic
health
effects
EPA s
analysis
of
in­
waterway
pollutant
concentrations
indicates
that
baseline
hazard
ratios,
for
both
the
fish
consumption
and
drinking
water
pathways,
for
the
population
associated
with
sample
facilities
only,
are
less
than
one
on
all
reaches
but
one.
For
those
reaches
with
a
baseline
hazard
ratio
of
less
than
one,
EPA s
analysis
finds
shifts
in
populations
from
higher
(
but
less
than
1.0)
to
lower
hazard
ratio
value
between
the
baseline
and
post­
compliance
cases.
For
the
single
reach
with
a
baseline
hazard
ratio
greater
than
one,
the
hazard
ratio
declined
but
did
not
fall
below
one.

c.
Reduced
frequency
of
human
health­
based
AWQC
exceedances
in
Ohio s
water
bodies
Baseline
in­
waterway
concentrations
of
MP&
M
pollutants
exceed
human
health­
based
ambient
water
quality
criteria
(
AWQC)
limits
for
consumption
of
water
or
organisms
in
three
reaches.
Two
reaches
exceeded
human
health­
based
AWQC
for
consumption
of
organisms
only.
EPA
estimates
that
the
final
regulation
will
not
eliminate
these
exceedences
of
human
health
AWQ
C
on
any
reach
but
will
reduce
the
number
of
exceedences
on
one
reach.

22­
2
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
22.1.2
Lead­
Related
Benefits
Total
monetized
lead­
related
benefits
in
Ohio
for
children
and
adults
under
the
final
regulation
are
$
422,113
(
2001$).

Chapter
14
of
this
report
describes
the
methodology
used
to
estimate
these
benefits.

a.
Estimated
benefits
to
Ohio s
children
Table
22.2
presents
lead­
related
benefits
from
the
final
regulation
for
preschool
age
children
and
pregnant
women
in
Ohio.

EPA
estimates
that
the
final
regulation
will
reduce
neonatal
mortality
by
0.024
cases
annually,
with
an
annual
monetary
value
of
$
162,094
(
2001$).

EPA
estimates
that
the
final
regulation
will
avoid
the
loss
of
an
estimated
26.96
IQ
points
among
preschool
children
in
Ohio,

with
an
annual
value
of
$
253,934
(
2001$).
The
annual
avoided
costs
of
compensatory
education
from
reduced
incidence
of
children
with
IQ
below
70
and
blood
lead
levels
above
20
 
g/
dL
amount
to
approximately
$
6,085.
In
total,
the
final
regulation
results
in
lead­
related
benefits
for
Ohio
children
of
$
422,113
annually
(
2001$).

Table
22.2:
Ohio
Child
Lead
Annual
Benefits
(
2001$):
Final
Regulation
Category
Reduced
Cases
or
IQ
Points
Monetary
Value
of
Benefits
Neonatal
mortality
0.024
$
162,094
Avoided
IQ
loss
26.96
$
253,934
Reduced
IQ
<
70
0.09
$
5,345
Reduced
PbB
>
20
 
g/
dL
0.04
$
740
Total
Benefits
$
422,113
Source:
U.
S.
EPA
analysis.

b.
Adult
benefits
Table
22.3
presents
benefit
estimates
for
reduced
lead­
related
health
effects
in
adults.
These
health
effects
include
increased
incidence
of
hypertension,
initial
non­
fatal
coronary
heart
disease
(
CHD)
,
non­
fatal
stokes
(
cerebrovascular
accidents
[
CBA]
and
brain
infarction
[
BI]
),
and
premature
mortality.
The
final
regulation
would
reduce
hypertension
in
Ohio
by
an
estimated
9.4
cases
annually
among
males,
with
annual
benefits
of
approximately
$
10,670
(
2001$).
Reducing
the
incidence
of
initial
CHD,
strokes,
and
premature
mortality
among
adult
males
and
females
in
Ohio
would
result
in
estimated
benefits
of
$
963,
$
2,115,
and
$
103,645,
respectively.
Overall,
adult
lead­
related
benefits
total
$
117,393.
This
analysis
does
not
include
other
lead­
related
health
effects
from
elevated
blood
pressure
(
BP)
or
from
effects
such
as
nervous
system
disorders,
anemia,
and
possible
cancer
effects.

22­
3
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
Table
22.3:
Ohio
Adult
Lead
Benefits
(
2001$):
Final
Regulation
Category
Final
Regulation
Reduced
Cases
Monetary
Value
of
Benefits
Men
Hypertension
8.697
$
10,670
CHD
0.011
$
693
CBA
0.005
$
947
BI
0.003
$
535
Mortality
0.015
$
79,178
Women
CHD
0.003
$
270
CBA
0.002
$
392
BI
0.001
$
241
Mortality
0.004
$
24,467
Total
Benefits
$
117,393
Source:
U.
S.
EPA
analysis.

22.1.3
Economic
Productivity
Benefits
The
selected
option
does
not
affect
POTW
operations
because
the
final
rule
regulates
only
direct
dischargers.
For
the
alternative
policy
options
that
consider
both
direct
and
indirect
dischargers,
EPA
evaluated
two
categories
of
productivity
benefits
for
POTWs:

 
reduced
interference
with
the
operations
of
POTWs,
and
 
reduced
contamination
of
sewage
sludge
(
i.
e.,
biosolids)
at
POTWs
that
receive
discharges
from
MP&
M
facilities.

Chapter
16
presents
the
methodology
for
evaluating
POTW
benefits.
EPA s
analysis
found
that
the
alternative
policy
options
did
not
yield
POTW
productivity
benefits
in
Ohio.

22.1.4
Total
Monetized
Benefits
Summing
the
monetary
values
over
all
benefit
categories
(
Chapters
21
and
22)
yields
total
monetized
benefits
in
Ohio
of
$
930,408
(
2001$)
annually
for
the
final
regulation
(
see
Table
22.4).
As
noted
in
Chapter
12,
this
benefit
estimate
is
necessarily
incomplete
because
it
omits
some
mechanisms
by
which
society
is
likely
to
benefit
from
reduced
effluent
discharges
from
the
MP&
M
industry.
Examples
of
benefit
categories
excluded
from
this
estimate
include:
non­
lead­
related,

non­
cancer
health
benefits;
improved
aesthetic
value
of
waters
near
discharge
outfalls;
benefits
from
improved
habitat
for
wildlife,
including
threatened
or
endangered
species;
tourism
benefits;
and
reduced
costs
for
drinking
water
treatment.

22­
4
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
Table
22.4:
Estimated
Annual
Benefits
in
Ohio
from
Reduced
MP&
M
Discharges
under
the
Final
Regulation
2001$)

Benefit
Category
Low
Mid
High
1.
d
Cancer
Risk:
Fish
Consumption
Water
Consumptiona
$
14,503
n/
a
$
14,503
n/
a
$
14,503
n/
a
2.
isk
from
Exposure
to
Lead:

Children
Adults
$
422,113
$
117,393
$
422,113
$
117,393
$
422,113
$
117,393
3.
ater­
Based
Recreation
$
250,932
$
250,932
$
250,932
4.
fits
$
125,466
$
125,466
$
125,466
5.
d
Sewage
Sludge
Disposal
Costs
$
0
$
0
$
0
Total
Monetized
Benefits
$
930,408
$
930,408
$
930,408
Reduce
Reduced
R
Enhanced
W
Nonuse
bene
Avoide
a
The
monetized
cancer
avoidance
benefits
from
reduced
drinking
water
contamination
are
negligible.

Source:
U.
S.
EPA
analysis.

22.2
SOCIAL
COSTS
OF
THE
FINAL
REGULATION
22.2.1
Baseline
and
Post­
Compliance
Closures
The
methodology
used
to
assess
baseline
and
post­
compliance
closures
differed
from
the
methodology
used
for
the
national
analysis
presented
in
Chapter
5.
The
screener
data
collected
for
Ohio
facilities
did
not
provide
financial
data
to
perform
an
after­
tax
cash
flow
or
net
present
value
test.
EPA
therefore
used
data
from
the
national
analysis
to
estimate
the
percentage
of
facilities
that
close
in
the
baseline
and
post­
compliance.
EPA
assumed
that
the
frequency
of
Ohio
facility
closures
would
be
the
same
as
that
found
in
the
national
analysis
for
facilities
with
the
same
discharge
status,
subcategory,
and
flow
category.

For
example,
2
percent
of
Oily
Wastes
facilities
discharging
less
than
one
million
gallons
per
year
close
in
the
baseline
in
the
national
analysis,
and
this
same
percentage
is
assumed
for
Ohio
screener
direct
dischargers
in
that
flow
size
category.

Table
22.5
summarizes
the
numbers
of
facilities
in
Ohio
closing
or
excluded
from
the
final
regulation
by
discharge
status.
All
indirect
dischargers
operating
post­
regulation
are
excluded
from
requirements
by
subcategory
exclusions.
Of
the
198
direct
dischargers
operating
post­
regulation,
85
(
or
43
percent)
are
excluded
from
requirements
by
subcategory
exclusions.
A
total
of
113
direct
discharging
facilities
in
the
Oily
Wastes
subcategory
are
therefore
subject
to
requirements
under
the
final
regulation.

22­
5
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
Table
22.5:
Regulatory
Impacts
for
Ohio
MP&
M
Facilities
by
Discharge
Type
Indirect
Direct
Total
Number
of
MP&
M
facilities
operating
in
the
baseline
1,682
198
1,880
Number
of
MP&
M
facilities
with
subcategory
exclusions
1,682
85
1,767
Number
of
MP&
M
facilities
operating
in
the
baseline
estimated
subject
to
regulatory
requirements
0
113
113
Number
of
regulatory
closures
0
0
0
Percent
of
MP&
M
facilities
operating
in
the
baseline
and
subject
to
regulatory
requirements
that
are
regulatory
closures
0.0%
0.0%
0.0%

Source:
U.
S.
EPA
analysis.

22.2.2
Compliance
Costs
for
MP&
M
Facilities
The
calculation
of
annualized
compliance
costs
in
Ohio
uses
the
methodology
presented
in
Chapter
11.
These
compliance
costs
are
not
adjusted
for
the
effect
of
taxes
or
for
recovery
of
costs
through
price
increases,
and
therefore
represent
the
social
value
of
resources
used
for
compliance.
EPA
annualized
compliance
costs
using
a
social
discount
rate
of
seven
percent
over
an
estimated
15­
year
useful
life
of
compliance
equipment.

In
calculating
compliance
costs
for
Ohio
facilities,
EPA
combined
the
compliance
cost
estimates
developed
for
the
 
detailed
questionnaire 
Ohio
facilities
included
the
national
analysis
with
compliance
costs
estimated
for
the
additional
 
screener
questionnaire 
facilities
included
in
the
Ohio
analysis.
The
Agency
estimated
compliance
costs
for
each
Ohio
screener
facility
and
then
calculated
an
annualized
compliance
cost
by
subcategory,
flow
range,
and
discharge
status
for
the
Ohio
facilities.
These
costs
included
facilities
that
might
be
assessed
as
baseline
closures
and
thus
would
overstate
expected
compliance
costs
to
the
extent
that
some
facilities
are
expected
to
close
and
not
incur
compliance
costs.
Because
EPA
estimated
closures
among
Ohio
screener
facilities
based
on
the
closure
rates
from
the
national
analysis,
it
was
not
possible
to
identify
specific
Ohio
screener
facilities
as
baseline
or
post­
regulation
closures
and
to
remove
their
compliance
costs
from
the
total
compliance
cost
estimates
on
a
facility­
specific
basis.
Instead,
EPA
reduced
the
total
compliance
costs,
by
facility
category,
by
the
estimated
fraction
of
facilities
assessed
as
baseline
closures
from
the
national
analysis.
EPA
added
these
costs
for
the
 
screener
questionnaire 
facilities
to
the
estimated
compliance
costs
for
the
 
detailed
questionnaire 
facilities
to
calculate
total
compliance
costs
for
Ohio
MP&
M
facilities.

Table
22.6
reports
the
estimated
resource
value
of
compliance
costs
by
discharge
status
and
subcategory.
The
total
estimated
annualized
compliance
costs
are
$
62
thousand.

Table
22.6:
Resource
Value
of
Compliance
Costs
in
Ohio
(
2001$)

Subcategory
Indirect
Direct
Total
General
Metals
$
0
$
0
$
0
MF
Job
Shop
$
0
$
0
$
0
Non
Chromium
Anodizer
$
0
$
0
$
0
Oily
Wastes
$
0
$
62,232
$
62,232
Printed
Wiring
Boards
$
0
$
0
$
0
Railroad
Line
Maintenance
$
0
$
0
$
0
Steel
Forming
&
Finishing
$
0
$
0
$
0
Total
$
0
$
62,232
$
62,232
Source:
U.
S.
EPA
analysis.

22­
6
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
22.2.3
Total
Social
Costs
As
discussed
in
Chapter
11,
the
regulation s
social
costs
include
the
resource
cost
of
compliance
(
e.
g.,
labor,
equipment,

material,
and
other
economic
resources
needed
to
comply
with
the
rule),
costs
to
governments
administering
the
regulation,

and
the
social
costs
associated
with
unemployment
resulting
from
facility
closure.
EPA
estimated
that
the
final
rule
will
not
result
in
social
costs
of
unemployment
and
that
governments
will
not
incur
additional
costs
in
administering
the
regulation.

Accordingly,
as
shown
in
Table
22.7,
EPA s
estimate
of
the
final
rule s
social
costs
in
Ohio
is
the
same
as
that
reported
for
the
resource
cost
of
compliance,
$
62
thousand
(
2001$)
annually.

Table
22.7:
Annual
Social
Costs
for
the
Final
Regulation
in
Ohio
(
2001$,
costs
annualized
at
7%)

Component
of
Social
Costs
Final
Rule
Resource
value
of
compliance
costs
$
62,232.0
Government
administrative
costs
$
0.0
Social
cost
of
unemployment
$
0.0
Total
Social
Cost
$
62,232.0
Source:
U.
S.
EPA
analysis.

22.3
COMPARISON
OF
MONETIZED
BENEFITS
AND
COSTS
IN
OHIO
EPA
cannot
perform
a
complete
cost­
benefit
comparison
because
not
all
of
the
benefits
resulting
from
the
final
rule
can
be
valued
in
dollar
terms.
As
reported
above,
for
Ohio,
EPA
estimated
the
final
rule s
social
cost
at
$
62
thousand
annually
(
2001$)
and
estimated
monetizable
benefits
of
$
930
thousand
annually
(
2001$).
Subtracting
the
social
costs
from
social
benefits
yields
a
net
monetizable
benefit
of
$
868
thousand
annually
(
2001$).

In
contrast
to
the
national
estimates
of
costs
and
benefits
for
the
final
regulation,
the
Ohio
case
study
shows
substantial
net
positive
benefits
even
for
the
lower­
bound
benefits
estimate.
This
difference
results
mainly
from
the
more
complete
assessment
of
benefits
from
reduced
MP&
M
pollutant
discharges
and
more
detailed
water
quality
modeling.
In
addition
to
estimating
recreational
benefits
from
reduced
frequency
of
AWQC
exceedences,
the
Ohio
case
study
estimated
changes
in
water
resource
values
from
reduced
discharges
of
TKN.
Changes
in
TKN
concentration
in
Ohio
water
bodies
account
for
approximately
96
percent
of
the
monetary
value
of
recreational
and
nonuse
benefits
from
the
final
rule.
EPA
also
included
an
additional
recreational
benefit
category
in
the
Ohio
analysis:
swimming.
Although
the
estimated
per­
trip
welfare
gain
to
swimmers
is
less
than
the
gain
for
participants
in
other
water­
based
recreational
activities,
this
benefit
category
accounts
for
a
sizable
portion
of
the
state­
level
benefits.
Other
factors
that
affect
the
Ohio
benefit­
cost
comparison
include:
the
presence
of
unique
water
resources
such
as
Lake
Erie;
use
of
a
more
sophisticated
water
quality
model,
which
estimates
water
quality
changes
in
reaches
downstream
from
the
discharge
reach;
and
a
more
accurate
account
of
baseline
water
quality
conditions.

The
presence
of
unique
water
resources,
such
as
Lake
Erie,
and
other
numerous
recreational
opportunities
(
e.
g.,
inland
lakes,

rivers,
and
reservoirs),
suggest
that
the
estimated
benefits
for
Ohio
are
likely
to
be
higher
than
the
average
of
benefits
for
other
states.

22­
7
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
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)

blood
pressure
(
BP):
the
pressure
of
the
blood
on
the
walls
of
the
arteries.

brain
infarction
(
BI):
stroke.

cerebrovascular
accidents
(
CBA):
stroke.

coronary
heart
disease
(
CHD):
disorder
that
restricts
blood
supply
to
the
heart;
occurs
when
coronary
arteries
become
narrowed
or
clogged
due
to
the
build
up
of
cholesterol
and
fat
on
the
inside
walls
and
are
unable
supply
enough
blood
to
the
heart.

interference:
the
obstruction
of
a
routine
treatment
process
of
POTWs
that
is
caused
by
the
presence
of
high
levels
of
toxics,
such
as
metals
and
cyanide
in
wastewater
discharges.
These
toxic
pollutants
kill
bacteria
used
for
microbial
degradation
during
wastewater
treatment
(
see:
microbial
degradation).

publicly­
owned
treatment
works
(
POTW):
a
treatment
works
as
defined
by
Section
212
of
the
Act,
which
is
owned
by
a
state
or
municipality.
This
definition
includes
any
devices
or
systems
used
in
the
storage,
treatment,
recycling,
and
reclamation
of
municipal
sewage
or
industrial
wastes
of
a
liquid
nature.

(
http://
www.
epa.
gov/
owm/
permits/
pretreat/
final99
.
pdf)

22­
8
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
ACRONYMS
AWQC:
ambient
water
quality
criteria
BI:
brain
infarction
BP:
blood
pressure
CBA:
cerebrovascular
accidents
CHD:
coronary
heart
disease
POTW:
publicly­
owned
treatment
works
22­
9
MP&
M
EEBA
Part
V:
Ohio
Case
Study
Chapter
22:
MP&
M
Benefit­
Cost
Analysis
in
Ohio
THIS
PAGE
INTENTIONALLY
LEFT
BLANK
22­
10
