Document ID: EPA-HQ-OAR-2002-0056-0104
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2004-02-04T05:00Z

2­
22
Table
2­
1.

Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
Utility
MACT
Standard
Pollutant/
Effect
Quantified
and
Monetized
Potential
Unquantified
Effects
PM/
Health
Premature
mortality
­
adults
Premature
mortality
­
infants
Bronchitis
­
chronic
and
acute
Hospital
admissions
­
respiratory
and
cardiovascular
Emergency
room
visits
for
asthma
Non­
fatal
heart
attacks
(
myocardial
infarction)

Lower
and
upper
respiratory
illness
Asthma
exacerbations
Minor
restricted
activity
days
Work
loss
days
Low
birth
weight
Changes
in
pulmonary
function
Chronic
respiratory
diseases
other
than
chronic
bronchitis
Morphological
changes
Altered
host
defense
mechanisms
Non­
asthma
respiratory
emergency
room
visits
Changes
in
cardiac
function
(
e.
g.
heart
rate
variability)

Allergic
responses
(
to
diesel
exhaust)

PM/
Welfare
Visibility
in
Class
I
areas
Visibility
in
residential
and
non­
Class
I
areas
Household
soiling
Ozone/
Health
Increased
airway
responsiveness
to
stimuli
Inflammation
in
the
lung
Chronic
respiratory
damage
Premature
aging
of
the
lungs
Acute
inflammation
and
respiratory
cell
damage
Increased
susceptibility
to
respiratory
infection
Non­
asthma
respiratory
emergency
room
visits
Hospital
admissions
­
respiratory
Emergency
room
visits
for
asthma
Minor
restricted
activity
days
School
loss
days
Asthma
attacks
Cardiovascular
emergency
room
visits
Premature
mortality
 
acute
exposures
Acute
respiratory
symptoms
Pollutant/
Effect
Quantified
and
Monetized
Potential
Unquantified
Effects
2­
23
Ozone/
Welfare
Decreased
commercial
forest
productivity
Decreased
yields
for
fruits
and
vegetables
Decreased
yields
for
commercial
and
non­
commercial
crops
Damage
to
urban
ornamental
plants
Impacts
on
recreational
demand
from
damaged
forest
aesthetics
Damage
to
ecosystem
functions
Decreased
outdoor
worker
productivity
Nitrogen
and
Sulfate
Deposition/

Welfare
Costs
of
nitrogen
controls
to
reduce
eutrophication
in
selected
eastern
estuaries
Impacts
of
acidic
sulfate
and
nitrate
deposition
on
commercial
forests
Impacts
of
acidic
deposition
on
commercial
freshwater
fishing
Impacts
of
acidic
deposition
on
recreation
in
terrestrial
ecosystems
Impacts
of
nitrogen
deposition
on
commercial
fishing,
agriculture,

and
forests
Impacts
of
nitrogen
deposition
on
recreation
in
estuarine
ecosystems
Reduced
existence
values
for
currently
healthy
ecosystems
SO
2/
Health
Hospital
admissions
for
respiratory
and
cardiac
diseases
Respiratory
symptoms
in
asthmatics
NO
x/
Health
Lung
irritation
Lowered
resistance
to
respiratory
infection
Hospital
Admissions
for
respiratory
and
cardiac
diseases
Mercury
Health
Neurological
disorders
Learning
disabilities
Neonatal
development
delays
Potential
Cardiovascular
effects*

Altered
blood
pressure
regulation*

Increased
heart
rate
variability*

Myocardial
infarctions*

Potential
Reproductive
effects*

Mercury
Deposition
Welfare
Deposition
Impacts
on
birds
and
mammals
(
e.
g.
reproductive
effects)

Impacts
to
commercial,
subsistence,
and
recreational
fishing
Reduced
existence
values
for
currently
healthy
ecosystems
2­
24
*
These
are
potential
effects
as
the
literature
is
either
contradictory
or
incomplete.
2­
25
2.1
Emission
Changes
Expected
to
Result
from
Implementation
of
the
Proposed
Standard
The
proposed
standards
have
various
cost
and
emission
related
components,
as
described
in
the
"
Economic
and
Energy
Impact
Analysis
for
the
Utility
MACT
Proposed
Rulemaking"
memo
available
in
the
docket
for
this
proposal.
The
controls
and
emission
reductions
are
expected
to
be
implemented
by
2008.
Our
benefits
analysis
provide
a
snapshot
of
the
expected
human
health
impacts
and
dollar
benefits
in
2010.
We
chose
2010
due
to
the
availability
of
air
quality
modeling
for
Clear
Skies
in
2010.

Table
2­
2
summarizes
the
expected
changes
in
emissions
of
SO
2
and
NO
x,
based
on
the
IPM
modeling
for
2010.
Over
99
percent
of
emission
reductions
for
SO
2
and
NO
x
are
predicted
to
occur
in
the
Eastern
U.
S.
As
such,
our
omission
of
benefits
occurring
in
the
Western
U.
S.
will
not
result
in
a
large
downward
bias
in
our
national
benefits
estimate.

Table
2­
2.
Summary
of
2010
Reductions
in
Emissions
of
SO2
and
NOx
Predicted
from
Utility
MACT
IPM
Modeling
Tons
Reduced
(%
of
baseline)

NO
x
SO
2
Eastern
U.
S.
899,179
590,846
26.8%
6.3%

Western
U.
S.
2,742
592
0.5%
0.2%

Total
901,921
591,438
23.2%
6.1%

2.2
Development
of
Benefits
Scaling
Factors
Based
on
Differences
in
Emission
Impacts
Between
Proposed
MACT
and
Clear
Skies
The
Clear
Skies
benefits
analysis
was
based
on
the
pattern
of
reductions
in
emissions
of
SO
2
and
NO
x
occurring
as
a
result
of
a
nationwide
cap
and
trade
program.
Under
the
Clear
Skies
proposal,
emissions
of
NO
x
were
expected
to
be
reduced
by
about
1.7
million
tons,
while
SO
2
emissions
were
expected
to
be
reduced
by
3.5
million
tons.
The
pattern
of
projected
emission
reductions
for
the
proposed
MACT
standard
is
somewhat
different
than
that
for
Clear
Skies.
The
main
difference
is
that
the
MACT
standard
are
expected
to
see
over
85
percent
of
the
emissions
2­
26
reductions
for
NO
x
and
SO
2
in
the
Ohio
Valley,
the
Southeast,
and
the
Mid­
Atlantic
(
48
percent
of
NO
x
reductions
and
75
percent
of
SO
2
reductions
were
in
the
Ohio
Valley
alone).
Very
little
emissions
reductions
are
predicted
for
the
Midwest
or
Western
states.
In
contrast,
only
57
percent
of
NO
x
emission
reductions
and
74
percent
of
SO
2
emission
reductions
occurred
in
these
regions
based
on
Clear
Skies,
and
even
within
these
regions,
were
much
more
spread
out.
We
have
attempted
to
minimize
these
differences
somewhat
by
focusing
only
on
the
results
for
the
Eastern
U.
S.,
however,
it
is
likely
that
our
benefits
estimates
will
have
some
remaining
biases
due
to
the
differences
in
emission
reductions
patterns
in
the
Eastern
U.
S.
Because
the
reductions
under
the
Utility
MACT
are
more
concentrated
in
areas
that
are
upwind
of
major
population
centers,
we
expect
that
benefits
per
ton
of
emissions
reduced
will
be
somewhat
higher
on
average
for
the
Utility
MACT
than
for
Clear
Skies.
As
such,
we
are
likely
to
underestimate
the
benefits
of
the
Utility
MACT
by
transferring
benefits
from
the
Clear
Skies
analysis.
However,
we
are
not
able
to
account
for
this
quantitatively
in
our
estimates.
Table
2­
3
summarizes
the
reductions
in
emissions
of
NO
x
and
SO
2
from
baseline
for
Clear
Skies
and
the
proposed
standard,
the
difference
between
the
two,
and
the
ratio
of
emissions
reductions
from
the
proposed
standard
to
Clear
Skies.
The
ratios
presented
in
the
last
column
of
Table
2­
3
are
the
basis
for
the
benefits
scaling
approach
discussed
below.

Table
2­
3.
Comparison
of
Modeled
Emission
Reductions
in
2010
Between
Clear
Skies
and
the
Proposed
Utility
MACT
Standard
(
Eastern
U.
S.
Only)

Emissions
Species
Reduction
from
Baseline
Difference
in
Reductions
(
Proposed
MACTClear
Skies)
Ratio
of
Reductions
(
Proposed
MACT/
Clear
Skies)
Clear
Skies
Proposed
MACT
NO
x
1,764,882
901,918
­
862,964
0.511
SO
2
3,526,491
591,459
­
2,935,032
0.168
2.3
Summary
of
Modeled
Benefits
and
Apportionment
Method
Based
on
the
air
quality
modeling
conducted
for
the
Clear
Skies
analysis,
we
conducted
a
benefits
analysis
to
determine
human
health
benefits
resulting
from
the
reductions
in
emissions
of
NO
x
and
SO
2.
We
used
the
air
quality
modeling
results
from
the
Clear
Skies
assessment.
However,
we
have
updated
the
health
impact
and
valuation
approaches
to
be
consistent
with
those
used
in
the
upcoming
proposed
Interstate
Air
Quality
rule
analysis.
The
Clear
Skies
analysis
is
available
on
the
internet
at
http://
www.
epa.
gov/
clearskies.
The
benefits
analysis
for
the
proposed
Interstate
Air
Quality
rule
is
documented
in
U.
S.
EPA,
2003b.

The
reductions
in
emissions
of
NO
x
and
SO
2
from
fossil­
fuel
fired
utilities
in
the
United
States
are
expected
to
result
in
wide­
spread
overall
reductions
in
ambient
concentrations
of
PM
2.5.
2­
27
These
improvements
in
air
quality
are
expected
to
result
in
substantial
health
benefits,
based
on
the
body
of
epidemiological
evidence
linking
PM
with
health
effects
such
as
premature
mortality,
cardiovascular
disease,
chronic
lung
disease,
hospital
admissions,
and
acute
respiratory
symptoms.
Based
on
modeled
changes
in
ambient
concentrations
of
PM
2.5,
we
estimate
changes
in
the
incidence
of
each
health
effect
using
health
impact
functions
derived
from
the
epidemiological
literature
with
appropriate
baseline
populations
and
incidence
rates.
We
then
apply
estimates
of
the
dollar
value
of
each
health
effect
to
obtain
a
monetary
estimate
of
the
total
PM­
related
health
benefits
of
the
rule.

2.3.1
Overview
of
Analytical
Approach
This
section
summarizes
our
analysis
of
the
modeled
air
quality
changes
from
the
Clear
Skies
assessment
to
determine
the
changes
in
human
health
and
welfare,
both
in
terms
of
physical
effects
and
monetary
value
that
result
from
modeled
changes
in
PM
2.5.
The
methodology
closely
follows
that
used
in
the
analyses
of
the
proposed
Nonroad
Diesel
rule
and
proposed
Interstate
Air
Quality
rule.
Details
of
the
analytical
approach
can
be
found
in
the
Regulatory
Impact
Analyses
for
these
rules
(
U.
S.
EPA,
2003a,
2003b)
and
in
the
User's
Manual
for
the
environmental
Benefits
Mapping
and
Analysis
Program
(
BenMAP)
(
Abt
Associates,
2003).

We
follow
a
"
damage­
function"
approach
in
calculating
total
benefits
of
the
modeled
changes
in
environmental
quality.
This
approach
estimates
changes
in
individual
health
and
welfare
endpoints
(
specific
effects
that
can
be
associated
with
changes
in
air
quality)
and
assigns
values
to
those
changes
assuming
independence
of
the
individual
values.
Total
benefits
are
calculated
simply
as
the
sum
of
the
values
for
all
non­
overlapping
health
and
welfare
endpoints.
This
imposes
no
overall
preference
structure,
and
does
not
account
for
potential
income
or
substitution
effects,
i.
e.
adding
a
new
endpoint
will
not
reduce
the
value
of
changes
in
other
endpoints.
The
"
damage­
function"
approach
is
the
standard
approach
for
most
cost­
benefit
analyses
of
regulations
affecting
environmental
quality,
and
it
has
been
used
in
several
recent
published
analyses
(
Banzhaf
et
al.,
2002;
Levy
et
al,
2001;
Kunzli
et
al,
2000;
Levy
et
al,
1999;
Ostro
and
Chestnut,
1998).
Time
and
resource
constraints
prevented
us
from
performing
extensive
new
research
to
measure
either
the
health
outcomes
or
their
values
for
this
analysis.
Thus,
similar
to
these
studies,
our
estimates
are
based
on
the
best
available
methods
of
benefits
transfer.
Benefits
transfer
is
the
science
and
art
of
adapting
primary
research
from
similar
contexts
to
obtain
the
most
accurate
measure
of
benefits
available
for
the
environmental
quality
change
under
analysis.

There
are
significant
categories
of
PM­
related
benefits
that
cannot
be
monetized
(
or
in
many
cases
even
quantified),
and
thus
they
are
not
included
in
our
accounting
of
health
and
welfare
benefits.
These
unquantified
effects
include
low
birth
weight,
changes
in
pulmonary
function,
chronic
respiratory
diseases
other
than
chronic
bronchitis,
morphological
changes,
altered
host
defense
mechanisms,
non­
fatal
cancers,
and
non­
asthma
respiratory
emergency
room
visits.
A
complete
discussion
of
PM
related
health
effects
can
be
found
in
the
PM
Criteria
Document
(
U.
S.
EPA,
1996).
Since
many
health
effects
overlap,
such
as
minor
restricted
activity
days
and
asthma
symptoms,
we
made
assumptions
intended
to
reduce
the
chances
of
"
double­
2­
28
counting"
health
benefits,
which
may
result
in
an
underestimate
of
the
total
health
benefits
of
the
pollution
controls.

2.3.2
Health
Impact
Functions
Health
impact
functions
are
derived
from
the
epidemiology
literature.
A
standard
health
impact
function
has
four
components:
an
effect
estimate
from
a
particular
epidemiological
study,
a
baseline
incidence
rate
for
the
health
effect
(
obtained
from
either
the
epidemiology
study
or
a
source
of
public
health
statistics
like
the
Centers
for
Disease
Control),
the
affected
population,
and
the
estimated
change
in
the
relevant
ozone
summary
measure.

A
typical
health
impact
function
might
look
like:

 
 
y
y
e
x
=
 
 
 
0
1
(
),
 
where
y
0
is
the
baseline
incidence,
equal
to
the
baseline
incidence
rate
times
the
potentially
affected
population,
 
is
the
effect
estimate,
and
 
x
is
the
estimated
change
in
the
summary
PM
2.5
measure.
There
are
other
functional
forms,
but
the
basic
elements
remain
the
same.

Integral
to
the
estimation
of
the
impact
functions
are
reasonable
estimates
of
future
population
projections.
The
underlying
data
used
to
create
county­
level
2010
population
projections
is
based
on
county
level
allocations
of
national
population
projections
from
the
U.
S.
Census
Bureau
(
Hollman,
Mulder
and
Kallan,
2000).
County­
level
allocations
of
populations
by
age,
race,
and
sex
are
based
on
economic
forecasting
models
developed
by
Woods
and
Poole,
Inc
(
WP),
which
account
for
patterns
of
economic
growth
and
migration.

The
WP
projections
of
county
level
population
are
based
on
historical
population
data
from
1969­
1999,
and
do
not
include
the
2000
Census
results.
Given
the
availability
of
detailed
2000
Census
data,
we
constructed
adjusted
county
level
population
projections
for
each
future
year
using
a
two
stage
process.
First,
we
constructed
ratios
of
the
projected
WP
populations
in
a
future
year
to
the
projected
WP
population
in
2000
for
each
future
year
by
age,
sex,
and
race.
Second,
we
multiplied
the
block
level
2000
Census
population
data
by
the
appropriate
age,
sex,
and
race
specific
WP
ratio
for
the
county
containing
the
census
block,
for
each
future
year.
This
results
in
a
set
of
future
population
projections
that
is
consistent
with
the
most
recent
detailed
census
data.

Specific
populations
matching
the
study
populations
in
each
epidemiological
study
are
constructed
by
accessing
the
appropriate
age­
specific
projections
from
the
overall
population
database.
For
some
endpoints,
such
as
asthma
attacks,
we
further
limit
the
population
by
applying
disease
prevalence
rates
to
the
overall
population.
We
do
not
have
sufficient
information
to
quantitatively
characterize
uncertainty
in
the
population
estimates.

Fundamental
to
the
estimation
of
health
benefits
was
our
utilization
of
the
PM
2­
29
epidemiology
literature.
We
rely
upon
effect
estimates
derived
from
published
epidemiological
studies
that
relate
health
effects
to
ambient
concentrations
of
PM.
The
specific
studies
from
which
effect
estimates
are
drawn
are
listed
in
Table
2­
4.
While
a
broad
range
of
serious
health
effects
have
been
associated
with
exposure
to
elevated
PM
levels,
we
include
only
a
subset
of
health
effects
in
this
benefit
analysis
due
to
limitations
in
available
effect
estimates
and
concerns
about
double­
counting
of
overlapping
effects
(
U.
S.
EPA,
1996).
For
the
most
part,
we
use
the
same
set
of
effect
estimates
as
we
used
in
the
analysis
of
the
proposed
Nonroad
Diesel
Engines
rule.
However,
based
on
recent
advice
from
the
Science
Advisory
Board,
we
use
an
updated
effect
estimate
for
premature
mortality
and
include
two
additional
health
effects,
infant
mortality
and
asthma
exacerbations.
Because
of
their
significance
in
the
analysis,
we
provide
a
more
detailed
discussion
of
premature
mortality
and
chronic
illness
endpoints
below.
Complete
details
on
the
effect
estimates
used
in
the
analysis
can
be
found
in
the
benefits
analysis
for
the
proposed
Interstate
Air
Quality
rule
(
U.
S.
EPA,
2003)
and
the
BenMAP
User's
Manual
(
Abt
Associates,
2003).

To
generate
health
outcomes,
projected
changes
in
ambient
PM
concentrations
were
entered
into
BenMAP,
a
customized
geographic
information
system
based
program.
BenMAP
aggregates
populations
to
air
quality
model
grids
and
calculates
changes
in
air
pollution
metrics
(
e.
g.,
daily
averages)
for
input
into
health
impact
functions.
BenMAP
uses
grid
cell
level
population
data
and
changes
in
pollutant
concentrations
to
estimate
changes
in
health
outcomes
for
each
grid
cell.
Details
on
the
BenMAP
program
can
be
found
in
the
BenMAP
User's
Manual
(
Abt
Associates,
2003).

The
baseline
incidences
for
health
outcomes
used
in
our
analyses
are
selected
and
adapted
to
match
the
specific
populations
studied.
For
example,
we
use
age­
and
county­
specific
baseline
total
mortality
rates
in
the
estimation
of
PM­
related
premature
mortality.
County­
level
incidence
rates
are
not
available
for
other
endpoints.
We
used
national
incidence
rates
whenever
possible,
because
these
data
are
most
applicable
to
a
national
assessment
of
benefits.
However,
for
some
studies,
the
only
available
incidence
information
comes
from
the
studies
themselves;
in
these
cases,
incidence
in
the
study
population
is
assumed
to
represent
typical
incidence
at
the
national
level.
Sources
of
baseline
incidence
rates
are
reported
in
Table
2­
5.

In
this
assessment
we
made
analytical
judgements
affecting
both
the
selection
of
effect
estimates
and
the
application
of
those
estimates
in
formulating
health
impact
functions.
In
general,
we
selected
effect
estimates
that
1)
most
closely
match
the
pollutants
of
interest,
i.
e.
PM
2.5)
cover
the
broadest
potentially
exposed
population
(
i.
e.
all
ages
functions
would
be
preferred
to
adults
27
to
35),
3)
have
appropriate
model
specification
(
e.
g.
control
for
confounding
pollutants),
4)
have
been
peer­
reviewed,
and
5)
are
biologically
plausible.
Other
factors
may
also
affect
our
selection
of
effect
estimates
for
specific
endpoints,
such
as
premature
mortality.
Some
of
the
more
important
of
these
relating
to
premature
mortality
and
chronic
illness
are
discussed
below.
Alternative
assumptions
about
these
judgements
may
lead
to
substantially
different
results.

While
there
is
a
consistent
body
of
evidence
supporting
a
relationship
between
a
number
2­
30
of
adverse
health
effects
and
ambient
PM
levels,
there
is
often
only
a
single
study
of
a
specific
endpoint
covering
a
specific
age
group.
There
may
be
multiple
estimates
examining
subgroups
(
i.
e.
asthmatic
children).
However,
for
the
purposes
of
assessing
national
population
level
benefits,
we
chose
the
most
broadly
applicable
effect
estimate
to
more
completely
capture
health
benefits
in
the
general
population.

Based
on
a
review
of
the
recent
literature
on
health
effects
of
PM
exposure
(
Daniels
et
al.,
2000;
Pope
et
al,
2002;
Rossi
et
al.,
1999;
Schwartz,
2000),
we
chose
for
the
purposes
of
this
analysis
to
assume
that
PM­
related
health
effects
occur
down
to
natural
background
(
i.
e.
there
is
no
health
effects
threshold).
We
assume
that
all
of
the
health
impact
functions
are
continuous
and
differentiable
down
to
natural
background
levels.
Our
assumptions
regarding
thresholds
are
supported
as
being
plausible
by
the
National
Research
Council
in
its
recent
review
of
methods
for
estimating
the
public
health
benefits
of
air
pollution
regulations.
In
their
review,
the
National
Research
Council
did
not
find
evidence
for
departing
from
linearity
in
the
observed
range
of
exposure
to
PM
10
or
PM
2.5,
nor
any
indication
of
a
threshold.
They
cite
the
weight
of
evidence
available
from
both
short
and
long
term
exposure
models
and
the
similar
effects
found
in
cities
with
low
and
high
ambient
concentrations
of
PM.

Premature
Mortality
As
recommended
by
the
SAB­
HES
(
2003),
we
focus
on
the
prospective
cohort
long­
term
exposure
studies
in
deriving
the
health
impact
function
for
our
base
estimate
of
premature
mortality.
We
selected
an
effect
estimate
from
the
extended
analysis
of
the
American
Cancer
Society
(
ACS)
cohort
(
Pope
et
al.,
2002).
This
effect
estimate
quantifies
the
relationship
between
annual
mean
PM
2.5
levels
and
all­
cause
mortality
in
adults
30
and
older.
We
selected
the
effect
estimate
based
on
the
measure
of
PM
representing
average
exposure
over
the
follow­
up
period,
calculated
as
the
average
of
1979­
1984
and
1999­
2000
PM2.5
levels.
EPA
is
investigating
ways
of
characterizing
the
uncertainty
in
the
concentration­
response
function
estimates.

In
previous
analyses,
infant
mortality
has
not
been
evaluated
as
part
of
the
primary
analysis.
Instead,
benefits
estimates
related
to
reduced
infant
mortality
have
been
included
as
part
of
the
sensitivity
analyses.
However
recently
published
studies
have
strengthened
the
case
for
an
association
between
PM
exposure
and
respiratory
inflamation
and
infection
leading
to
premature
mortality
in
infants
under
five
years
of
age.
Specifically,
the
SAB's
Health
Effects
Subcommittee
(
HES)
noted
the
release
of
the
World
Health
Organization
Global
Burden
of
Disease
Study
focusing
on
ambient
air
which
cites
several
recently­
published
time­
series
studies
relating
daily
PM
exposure
to
mortality
in
children
(
EPA­
SAB­
COUNCIL­
ADV­
03­
00x).
The
HES
also
cites
the
study
by
Belanger
et
al.,
(
2003)
as
corroborating
findings
linking
PM
exposure
to
increased
respiratory
inflamation
and
infections
in
children.
With
regard
to
the
cohort
study
conducted
by
Woodruff
et
al.
(
1997),
the
HES
notes
several
strengths
of
the
study
including
the
use
of
a
larger
cohort
drawn
from
a
large
number
of
metropolitan
areas
and
efforts
to
control
for
a
variety
of
individual
risk
factors
in
children
(
e.
g.,
maternal
educational
level,
maternal
ethnicity,
parental
marital
status
and
maternal
smoking
status).
We
follow
the
HES
recommendation
to
include
infant
mortality
in
the
primary
benefits
estimate
using
the
effect
estimate
from
the
Woodruff
et
al.
2­
31
(
1997)
study.

Chronic
Illness
Although
there
are
several
studies
examining
the
relationship
between
PM
of
different
size
fractions
and
incidence
of
chronic
bronchitis,
we
use
a
study
by
Abbey
et
al
(
1995)
to
obtain
our
estimate
of
avoided
incidences
of
chronic
bronchitis
in
adults
aged
25
and
older,
because
Abbey
et
al
(
1995)
is
the
only
available
estimate
of
the
relationship
between
PM
2.5
and
chronic
bronchitis.
Based
on
the
Abbey
et
al
study,
we
estimate
the
number
of
new
chronic
bronchitis
cases
that
will
"
reverse"
over
time
and
subtract
these
reversals
from
the
estimate
of
avoided
chronic
bronchitis
incidences.
Reversals
refer
to
those
cases
of
chronic
bronchitis
that
were
reported
at
the
start
of
the
Abbey
et
al.
survey,
but
were
subsequently
not
reported
at
the
end
of
the
survey.
Since
we
assume
that
chronic
bronchitis
is
a
permanent
condition,
we
subtract
these
reversals.
Given
the
relatively
high
value
assigned
to
chronic
bronchitis,
this
ensures
that
we
do
not
overstate
the
economic
value
of
this
health
effect.

Non­
fatal
heart
attacks
have
been
linked
with
short
term
exposures
to
PM
2.5
in
the
U.
S.
(
Peters
et
al,
2001)
and
other
countries
(
Poloniecki
et
al,
1997).
We
use
a
recent
study
by
Peters
et
al.
(
2001)
as
the
source
for
the
effect
estimate
quantifying
the
relationship
between
PM
2.5
and
non­
fatal
heart
attacks
in
adults.
Peters
et
al
is
the
only
available
U.
S.
study
to
provide
a
specific
estimate
for
heart
attacks.
Other
studies,
such
as
Samet
et
al
(
2000)
and
Moolgavkar
et
al
(
2000)
show
a
consistent
relationship
between
all
cardiovascular
hospital
admissions,
including
for
nonfatal
heart
attacks,
and
PM.
Given
the
lasting
impact
of
a
heart
attack
on
longer­
term
health
costs
and
earnings,
we
choose
to
provide
a
separate
estimate
for
non­
fatal
heart
attacks
based
on
the
single
available
U.
S.
effect
estimate.
The
finding
of
a
specific
impact
on
heart
attacks
is
consistent
with
hospital
admission
and
other
studies
showing
relationships
between
fine
particles
and
cardiovascular
effects
both
within
and
outside
the
U.
S.
These
studies
provide
a
weight
of
evidence
for
this
type
of
effect.
Several
epidemiologic
studies
(
Liao
et
al,
1999;
Gold
et
al,
2000;
Magari
et
al,
2001)
have
shown
that
heart
rate
variability
(
an
indicator
of
how
much
the
heart
is
able
to
speed
up
or
slow
down
in
response
to
momentary
stresses)
is
negatively
related
to
PM
levels.
Heart
rate
variability
is
a
risk
factor
for
heart
attacks
and
other
coronary
heart
diseases
(
Carthenon
et
al,
2002;
Dekker
et
al,
2000;
Liao
et
al,
1997,
Tsuji
et
al.
1996).
As
such,
significant
impacts
of
PM
on
heart
rate
variability
is
consistent
with
an
increased
risk
of
heart
attacks.

2.3.3
Economic
Values
for
Health
Outcomes
Reductions
in
ambient
concentrations
of
air
pollution
generally
lower
the
risk
of
future
adverse
health
affects
by
a
fairly
small
amount
for
a
large
population.
The
appropriate
economic
measure
is
therefore
willingness­
to­
pay
(
WTP)
for
changes
in
risk
prior
to
the
regulation
(
Freeman,
1993).
For
some
health
effects,
such
as
hospital
admissions,
WTP
estimates
are
generally
not
available.
In
these
cases,
we
use
the
cost
of
treating
or
mitigating
the
effect
as
a
primary
estimate.
These
costs
of
illness
(
COI)
estimates
generally
understate
the
true
value
of
4
The
SAB­
HES
has
recently
recommended
that
EPA
rethink
the
use
of
a
5­
year
lag.
They
recommend
that
a
more
complex
lag
structure
be
considered
incorporation
components
dealing
with
short­
term
(
0­
6
months),
intermediate
(
1­
2
years)
and
long­
term
(
15­
25
years)
exposures.
EPA
is
evaluating
techniques
for
characterizing
lag
structures
and
will
incorporate
new
methods
as
they
become
available.

2­
32
reductions
in
risk
of
a
health
effect,
reflecting
the
direct
expenditures
related
to
treatment
but
not
the
value
of
avoided
pain
and
suffering
from
the
health
effect
(
Harrington
and
Portney,
1987;
Berger,
1987).
Unit
values
for
health
endpoints
are
provided
in
Table
2­
6.
All
values
are
in
constant
year
1999
dollars.

The
size
of
the
delay
between
changes
in
chronic
PM
exposures
and
changes
in
mortality
rates
is
unknown.
The
size
of
such
a
time
lag
is
important
for
the
valuation
of
premature
mortality
incidences
as
economic
theory
suggests
benefits
occurring
in
the
future
should
be
discounted
relative
to
benefits
occurring
today.
Although
there
is
no
specific
scientific
evidence
of
the
size
of
PM
effects
lag,
current
scientific
literature
on
adverse
health
effects
associated
with
smoking
and
the
difference
in
the
effect
size
between
chronic
exposure
studies
and
daily
mortality
studies
suggest
that
all
incidences
of
premature
mortality
reduction
associated
with
a
given
incremental
change
in
PM
exposure
would
not
occur
in
the
same
year
as
the
exposure
reduction.
This
literature
implies
that
lags
of
a
few
years
or
longer
are
plausible.
For
our
analysis,
we
have
assumed
a
five­
year
distributed
lag
structure,
with
25
percent
of
premature
deaths
occurring
in
the
first
year,
another
25
percent
in
the
second
year,
and
16.7
percent
in
each
of
the
remaining
three
years4.
To
account
for
the
preferences
of
individuals
for
current
risk
reductions
relative
to
future
risk
reductions,
we
discount
the
value
of
avoided
premature
mortalities
occurring
beyond
the
analytical
year
(
2010)
using
three
and
seven
percent
discount
rates.

Our
analysis
accounts
for
expected
growth
in
real
income
over
time.
Economic
theory
argues
that
WTP
for
most
goods
(
such
as
environmental
protection)
will
increase
if
real
incomes
increase.
The
economics
literature
suggests
that
the
severity
of
a
health
effect
is
a
primary
determinant
of
the
strength
of
the
relationship
between
changes
in
real
income
and
WTP
(
Alberini,
1997;
Miller,
2000;
Evans
and
Viscusi,
1993).
As
such,
we
use
different
factors
to
adjust
the
WTP
for
minor
health
effects,
severe
and
chronic
health
effects,
and
premature
mortality.
Adjustment
factors
used
to
account
for
projected
growth
in
real
income
from
1990
to
2010
are
1.03
for
minor
health
effects,
1.11
for
severe
and
chronic
health
effects,
and
1.10
for
premature
mortality.

2.3.4
Treatment
of
Uncertainty
In
any
complex
analysis,
there
are
likely
to
be
many
sources
of
uncertainty.
This
analysis
is
no
exception.
Many
inputs
are
used
to
derive
the
final
estimate
of
economic
benefits,
including
emission
inventories,
air
quality
models
(
with
their
associated
parameters
and
inputs),
epidemiological
effect
estimates,
estimates
of
values,
population
estimates,
income
estimates,
and
estimates
of
the
future
state
of
the
world
(
i.
e.,
regulations,
technology,
and
human
behavior).
Some
of
the
key
uncertainties
in
the
benefits
analysis
are
presented
in
Table
2­
7.
For
some
parameters
or
inputs
it
may
be
possible
to
provide
a
statistical
representation
of
the
underlying
2­
33
uncertainty
distribution.
For
other
parameters
or
inputs,
the
necessary
information
is
not
available.

In
addition
to
uncertainty,
the
annual
benefit
estimates
presented
in
this
analysis
are
also
inherently
variable
due
to
the
truly
random
processes
that
govern
pollutant
emissions
and
ambient
air
quality
in
a
given
year.
Factors
such
as
electricity
demand
and
weather
display
constant
variability
regardless
of
our
ability
to
accurately
measure
them.
As
such,
the
estimates
of
annual
benefits
should
be
viewed
as
representative
of
the
magnitude
of
benefits
expected,
rather
than
the
actual
benefits
that
would
occur
every
year.

A
key
source
of
uncertainty
for
this
analysis
is
the
scaling
approach
used
to
estimate
the
benefits
associated
with
the
emission
reductions
for
the
Utility
MACT.
As
noted
earlier,
while
we
believe
this
to
be
a
valid
approach,
we
are
unable
to
quantify
any
uncertainties
related
to
the
scaling
approach.
To
the
extent
that
the
effectiveness
in
reducing
ambient
PM
2.5
of
each
ton
of
NO
x
and
SO
2
reduced
by
the
Utility
MACT
over
or
understates
the
effectiveness
of
the
tons
reduced
by
Clear
Skies,
the
benefits
of
the
Utility
MACT
will
be
over
or
underestimated.

2.3.5
Results
of
Revised
Clear
Skies
Analysis
In
order
to
generate
benefits
estimates
consistent
with
the
analytical
assumptions
underlying
the
benefits
estimates
for
the
Interstate
Air
Quality
Rule,
we
have
revised
the
Clear
Skies
benefits
analysis
to
use
a
consistent
set
of
assumptions.
Based
on
the
application
of
the
health
impact
functions
to
the
modeled
changes
in
ambient
PM2.5,
we
estimated
the
change
in
incidence
and
economic
value
of
health
effects
for
the
updated
set
of
health
endpoints
listed
in
Table
2­
4.
The
results
of
the
estimation
are
provided
in
Table
2­
8.
Compared
to
the
original
Clear
Skies
benefits
analysis,
the
updated
estimates
show
an
increase
in
avoided
cases
of
premature
mortality
due
to
the
change
in
the
effect
estimate
and
slight
changes
in
other
endpoints,
due
to
minor
changes
in
the
set
of
air
quality
monitoring
data
used
in
defining
the
change
in
ambient
PM
2.5.
2­
34
Table
2­
4.
Endpoints
and
Studies
Used
to
Calculate
Total
Monetized
Health
Benefits
Endpoint
Study
Study
Population
Premature
Mortality
Premature
Mortality
­
Adult,
all­
cause
Pope
et
al.
(
2002)
>
29
years
Premature
Mortality
­
Infant
Woodruff
et
al.
(
1997)
<
1
Chronic
Illness
Chronic
Bronchitis
Abbey,
et
al.
(
1995)
>
26
years
Non­
fatal
Heart
Attacks
Peters
et
al.
(
2001)
Adults
Hospital
Admissions
Respiratory
Pooled
estimate:
Moolgavkar
(
2003)
­
ICD
490­
496
(
COPD)
Ito
(
2003)
­
ICD
490­
496
(
COPD)
>
64
years
Moolgavkar
(
2000)
­
ICD
490­
496
(
COPD)
20­
64
years
Ito
(
2003)
­
ICD
480­
486
(
pneumonia)
>
64
years
Sheppard,
et
al.
(
2003)
­
ICD
493
(
asthma)
<
65
years
Cardiovascular
Pooled
estimate:
Moolgavkar
(
2003)
­
ICD
390­
429
(
all
cardiovascular)
Ito
(
2003)
­
ICD
410­
414,
427­
428
(
ischemic
heart
disease,
dysrhythmia,
heart
failure)
>
64
years
Moolgavkar
(
2000)
­
ICD
390­
429
(
all
cardiovascular)
20­
64
years
Asthma­
Related
ER
Visits
Norris
et
al.
(
1999)
0­
18
years
Other
Health
Endpoints
Acute
Bronchitis
Dockery
et
al.
(
1996)
8­
12
years
Upper
Respiratory
Symptoms
Pope
et
al.
(
1991)
Asthmatics,
9­
11
years
Lower
Respiratory
Symptoms
Schwartz
and
Neas
(
2000)
7­
14
years
Asthma
Exacerbations
Pooled
estimate:
Ostro
et
al.
(
2001)
Cough
Ostro
et
al.
(
2001)
Wheeze
Ostro
et
al.
(
2201)
Shortness
of
breath
Vedal
et
al.
(
1998)
Cough
6­
18
yearsA
Work
Loss
Days
Ostro
(
1987)
18­
65
years
Minor
Restricted
Activity
Days
Ostro
and
Rothschild
(
1989)
18­
65
years
B
The
original
study
populations
were
8­
13
for
the
Ostro
et
al
(
2001)
study
and
6­
13
for
the
Vedal
et
al.
(
1998)
study.
Based
on
advice
from
the
SAB­
HES,
we
have
extended
the
applied
population
to
6­
18,
reflecting
the
2­
35
common
biological
basis
for
the
effect
in
children
in
the
broader
age
group.
2­
36
Table
2­
5
Baseline
Incidence
Rates
and
Population
Prevalence
Rates
for
Use
in
Impact
Functions
Endpoint
Parameter
Rates
Value
Source1
Mortality
Daily
or
annual
mortality
rate
Age,
cause,
and
county­
specific
rate
CDC
Wonder
(
1996­
1998)

Hospitalizations
Daily
hospitalization
rate
Age,
region,
cause­
specific
rate
1999
NHDS
public
use
data
files2
Asthma
ER
visits
Daily
asthma
ER
visit
rate
Age,
Region
specific
visit
rate
2000
NHAMCS
public
use
data
files3;
1999
NHDS
public
use
data
files2
Chronic
Bronchitis
Annual
prevalence
rate
per
person
Age
18­
44
Age
45­
64
Age
65
and
older
0.0367
0.0505
0.0587
1999
HIS
(
American
Lung
Association,
2002b,
Table
4)

Annual
incidence
rate
per
person
0.00378
Abbey
et
al.
(
1993,
Table
3)

Nonfatal
MI
(
heart
attacks)
Daily
nonfatal
myocardial
infarction
incidence
rate
per
person,
18+
Northeast
Midwest
South
West
0.0000159
0.0000135
0.0000111
0.0000100
1999
NHDS
public
use
data
files2;
adjusted
by
0.93
for
prob.
of
surviving
after
28
days
(
Rosamond
et
al.,
1999)

Asthma
Exacerbations
Incidence
(
and
prevalence)
among
asthmatic
African
American
children
­
daily
wheeze
­
daily
cough
­
daily
dyspnea
0.076
(
0.173)
0.067
(
0.145)
0.037
(
0.074)
Ostro
et
al.
(
2001)

Prevalence
among
asthmatic
children
­
daily
wheeze
­
daily
cough
­
daily
dyspnea
0.038
0.086
0.045
Vedal
et
al.
(
1998)

Acute
Bronchitis
Annual
bronchitis
incidence
rate,
children
0.043
American
Lung
Association
(
2002a,
Table
11)

Lower
Respiratory
Symptoms
Daily
lower
respiratory
symptom
incidence
among
children4
0.0012
Schwartz
(
1994,
Table
2)

Upper
Respiratory
Symptoms
Daily
upper
respiratory
symptom
incidence
among
asthmatic
children
0.3419
Pope
et
al.
(
1991,
Table
2)

Work
Loss
Days
Daily
WLD
incidence
rate
per
person
(
18­
65)
Age
18­
24
Age
25­
44
Age
45­
64
0.00540
0.00678
0.00492
1996
HIS
(
Adams
et
al.,
1999,
Table
41);
U.
S.
Bureau
of
the
Census
(
2000)
Endpoint
Parameter
Rates
Value
Source1
2­
37
Minor
Restricted
Activity
Days
Daily
MRAD
incidence
rate
per
person
0.02137
Ostro
and
Rothschild
(
1989,
p.
243)

1.
The
following
abbreviations
are
used
to
describe
the
national
surveys
conducted
by
the
National
Center
for
Health
Statistics:
HIS
refers
to
the
National
Health
Interview
Survey;
NHDS
­
National
Hospital
Discharge
Survey;
NHAMCS
­
National
Hospital
Ambulatory
Medical
Care
Survey.
2.
See
ftp://
ftp.
cdc.
gov/
pub/
Health_
Statistics/
NCHS/
Datasets/
NHDS/
3.
See
ftp://
ftp.
cdc.
gov/
pub/
Health_
Statistics/
NCHS/
Datasets/
NHAMCS/
4.
Lower
Respiratory
Symptoms
are
defined
as

2
of
the
following:
cough,
chest
pain,
phlegm,
wheeze
2­
38
Table
2­
6
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
2000$)

Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
Premature
Mortality
$
5,500,000
$
6,100,000
Point
estimate
is
the
mean
of
a
normal
distribution
with
a
95%
confidence
interval
between
$
1
and
$
10
million.
Confidence
interval
is
based
on
two
meta­
analyses
of
the
wage­
risk
VSL
literature.
$
1
million
represents
the
lower
end
of
the
interquartile
range
from
the
Mrozek
and
Taylor
(
2000)

metaanalysis
$
10
million
represents
the
upper
end
of
the
interquartile
range
from
the
Viscusi
and
Aldy
(
2003)
meta­
analysis.
The
VSL
represents
the
value
of
a
small
change
in
mortality
risk
aggregated
over
the
affected
population.

Chronic
Bronchitis
(
CB)
$
340,000
$
370,000
Base
value
is
the
mean
of
a
generated
distribution
of
WTP
to
avoid
a
case
of
pollution­
related
CB.
WTP
to
avoid
a
case
of
pollution­
related
CB
is
derived
by
adjusting
WTP
(
as
described
in
Viscusi
et
al.,
1991)
to
avoid
a
severe
case
of
CB
for
the
difference
in
severity
and
taking
into
account
the
elasticity
of
WTP
with
respect
to
severity
of
CB.
Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2­
39
Non­
fatal
Myocardial
Infarction
(
heart
attack)

3%
discount
rate
Age
0­
24
Age
25­
44
Age
45­
54
Age
55­
65
Age
66
and
over
7%
discount
rate
Age
0­
24
Age
25­
44
Age
45­
54
Age
55­
65
Age
66
and
over
$
66,902
$
74,676
$
78,834
$
140,649
$
66,902
$
65,293
$
73,149
$
76,871
$
132,214
$
65,293
$
66,902
$
74,676
$
78,834
$
140,649
$
66,902
$
65,293
$
73,149
$
76,871
$
132,214
$
65,293
Age
specific
cost­
of­
illness
values
reflecting
lost
earnings
and
direct
medical
costs
over
a
5
year
period
following
a
non­
fatal
MI.
Lost
earnings
estimates
based
on
Cropper
and
Krupnick
(
1990).
Direct
medical
costs
based
on
simple
average
of
estimates
from
Russell
et
al.
(
1998)
and
Wittels
et
al.
(
1990).

Lost
earnings:

Cropper
and
Krupnick
(
1990).
Present
discounted
value
of
5
yrs
of
lost
earnings:

age
of
onset:
at
3%
at
7%

25­
44
$
8,774
$
7,855
45­
54
$
12,932
$
11,578
55­
65
$
74,746
$
66,920
Direct
medical
expenses:
An
average
of:

1.
Wittels
et
al.,
1990
($
102,658
 
no
discounting)

2.
Russell
et
al.,
1998,
5­
yr
period.
($
22,331
at
3%
discount
rate;
$
21,113
at
7%
discount
rate)

Hospital
Admissions
Chronic
Obstructive
Pulmonary
Disease
(
COPD)

(
ICD
codes
490­
492,
494­
496)
$
12,378
$
12,378
The
COI
estimates
(
lost
earnings
plus
direct
medical
costs)
are
based
on
ICD­
9
code
level
information
(
e.
g.,
average
hospital
care
costs,
average
length
of
hospital
stay,
and
weighted
share
of
total
COPD
category
illnesses)
reported
in
Agency
for
Healthcare
Research
and
Quality,
2000
(
www.
ahrq.
gov).

Pneumonia
(
ICD
codes
480­
487)
$
14,693
$
14,693
The
COI
estimates
(
lost
earnings
plus
direct
medical
costs)
are
based
on
ICD­
9
code
level
information
(
e.
g.,
average
hospital
care
costs,
average
length
of
hospital
stay,
and
weighted
share
of
total
pneumonia
category
illnesses)

reported
in
Agency
for
Healthcare
Research
and
Quality,
2000
(
www.
ahrq.
gov).
Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2­
40
Asthma
admissions
$
6,634
$
6,634
The
COI
estimates
(
lost
earnings
plus
direct
medical
costs)
are
based
on
ICD­
9
code
level
information
(
e.
g.,
average
hospital
care
costs,
average
length
of
hospital
stay,
and
weighted
share
of
total
asthma
category
illnesses)
reported
in
Agency
for
Healthcare
Research
and
Quality,
2000
(
www.
ahrq.
gov).

All
Cardiovascular
(
ICD
codes
390­
429)
$
18,387
$
18,387
The
COI
estimates
(
lost
earnings
plus
direct
medical
costs)
are
based
on
ICD­
9
code
level
information
(
e.
g.,
average
hospital
care
costs,
average
length
of
hospital
stay,
and
weighted
share
of
total
cardiovascular
category
illnesses)

reported
in
Agency
for
Healthcare
Research
and
Quality,
2000
(
www.
ahrq.
gov).

Emergency
room
visits
for
asthma
$
286
$
286
Simple
average
of
two
unit
COI
values:

(
1)
$
311.55,
from
Smith
et
al.,
1997,
and
(
2)
$
260.67,
from
Stanford
et
al.,
1999.

Respiratory
Ailments
Not
Requiring
Hospitalization
Asthma
Exacerbations
$
42
$
43
Asthma
exacerbations
are
valued
at
$
42
per
incidence,
based
on
the
mean
of
average
WTP
estimates
for
the
four
severity
definitions
of
a
"
bad
asthma
day,"

described
in
Rowe
and
Chestnut
(
1986).
This
study
surveyed
asthmatics
to
estimate
WTP
for
avoidance
of
a
"
bad
asthma
day,"
as
defined
by
the
subjects.

For
purposes
of
valuation,
an
asthma
attack
is
assumed
to
be
equivalent
to
a
day
in
which
asthma
is
moderate
or
worse
as
reported
in
the
Rowe
and
Chestnut
(
1986)
study.

Upper
Respiratory
Symptoms
(
URS)
$
25
$
26
Combinations
of
the
3
symptoms
for
which
WTP
estimates
are
available
that
closely
match
those
listed
by
Pope,
et
al.
result
in
7
different
"
symptom
clusters,"
each
describing
a
"
type"
of
URS.
A
dollar
value
was
derived
for
each
type
of
URS,
using
mid­
range
estimates
of
WTP
(
IEc,
1994)
to
avoid
each
symptom
in
the
cluster
and
assuming
additivity
of
WTPs.
The
dollar
value
for
URS
is
the
average
of
the
dollar
values
for
the
7
different
types
of
URS.
Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2­
41
Lower
Respiratory
Symptoms
(
LRS)
$
16
$
17
Combinations
of
the
4
symptoms
for
which
WTP
estimates
are
available
that
closely
match
those
listed
by
Schwartz,
et
al.
result
in
11
different
"
symptom
clusters,"
each
describing
a
"
type"
of
LRS.
A
dollar
value
was
derived
for
each
type
of
LRS,
using
mid­
range
estimates
of
WTP
(
IEc,
1994)
to
avoid
each
symptom
in
the
cluster
and
assuming
additivity
of
WTPs.
The
dollar
value
for
LRS
is
the
average
of
the
dollar
values
for
the
11
different
types
of
LRS.

Acute
Bronchitis
$
360
$
370
Assumes
a
6
day
episode,
with
daily
value
equal
to
the
average
of
low
and
high
values
for
related
respiratory
symptoms
recommended
in
Neumann,
et
al.

1994.

Restricted
Activity
and
Work
Loss
Days
Work
Loss
Days
(
WLDs)
Variable
(
national
median
=
$
115
)
Variable
(
national
median
=

$
115)
County­
specific
median
annual
wages
divided
by
50
(
assuming
2
weeks
of
vacation)
and
then
by
5
 
to
get
median
daily
wage.
U.
S.
Year
2000
Census,

compiled
by
Geolytics,
Inc.

Minor
Restricted
Activity
Days
(
MRADs)
$
51
$
53
Median
WTP
estimate
to
avoid
one
MRAD
from
Tolley,
et
al.
(
1986)
.
Table
2­
7
Primary
Sources
of
Uncertainty
in
the
Benefit
Analysis
1.
Uncertainties
Associated
With
Health
Impact
Functions
S
The
value
of
the
PM
effect
estimate
in
each
impact
function.

S
Application
of
a
single
effect
estimate
to
pollutant
changes
and
populations
in
all
locations.

S
Similarity
of
future
year
effect
estimates
to
current
effect
estimates.

S
Correct
functional
form
of
each
impact
function.

S
Application
of
effect
estimates
to
changes
in
PM
outside
the
range
of
PM
concentrations
observed
in
the
study.

S
Application
of
effect
estimates
only
to
those
subpopulations
matching
the
original
study
population.

2.
Uncertainties
Associated
With
PM
Concentrations
S
Responsiveness
of
the
models
to
changes
in
precursor
emissions.

S
Projections
of
future
levels
of
precursor
emissions,
especially
ammonia
and
crustal
materials.

S
Model
chemistry
for
the
formation
of
ambient
nitrate
concentrations.

S
Use
of
separate
air
quality
models
for
ozone
and
PM
does
not
allow
for
a
fully
integrated
analysis
of
pollutants
and
their
interactions.

S
Comparison
of
model
predictions
of
particulate
nitrate
with
observed
rural
monitored
nitrate
levels
indicates
that
REMSAD
overpredicts
nitrate
in
some
parts
of
the
Eastern
US
and
underpredicts
nitrate
in
parts
of
the
Western
US.

3.
Uncertainties
Associated
with
PM
Mortality
Risk
S
Limited
scientific
literature
supporting
a
direct
biological
mechanism
for
observed
epidemiological
evidence.

S
Direct
causal
agents
within
the
complex
mixture
of
PM
have
not
been
identified.

S
The
extent
to
which
adverse
health
effects
are
associated
with
low
level
exposures
that
occur
many
times
in
the
year
versus
peak
exposures.

S
The
extent
to
which
effects
reported
in
the
long­
term
exposure
studies
are
associated
with
historically
higher
levels
of
PM
rather
than
the
levels
occurring
during
the
period
of
study.

S
Reliability
of
the
limited
ambient
PM
2.5
monitoring
data
in
reflecting
actual
PM
2.5
exposures.

4.
Uncertainties
Associated
With
Possible
Lagged
Effects
S
The
portion
of
the
PM­
related
long­
term
exposure
mortality
effects
associated
with
changes
in
annual
PM
levels
would
occur
in
a
single
year
is
uncertain
as
well
as
the
portion
that
might
occur
in
subsequent
years.

5.
Uncertainties
Associated
With
Baseline
Incidence
Rates
S
Some
baseline
incidence
rates
are
not
location­
specific
(
e.
g.,
those
taken
from
studies)
and
may
therefore
not
accurately
represent
the
actual
location­
specific
rates.

S
Current
baseline
incidence
rates
may
not
approximate
well
baseline
incidence
rates
in
2010.

S
Projected
population
and
demographics
may
not
represent
well
future­
year
population
and
demographics.

6.
Uncertainties
Associated
With
Economic
Valuation
S
Unit
dollar
values
associated
with
health
endpoints
are
only
estimates
of
mean
WTP
and
therefore
have
uncertainty
surrounding
them.

S
Mean
WTP
(
in
constant
dollars)
for
each
type
of
risk
reduction
may
differ
from
current
estimates
due
to
differences
in
income
or
other
factors.

7.
Uncertainties
Associated
With
Aggregation
of
Monetized
Benefits
S
Health
benefits
estimates
are
limited
to
the
available
effect
estimates.
Thus,
unquantified
or
unmonetized
benefits
are
not
included.
2­
43
Table
2­
8.
Results
of
Revised
Clear
Skies
Benefits
Analysis
Endpoint
Cases
AvoidedA
Economic
Value
(
Millions
of
2000$)

Premature
mortality
­

Long­
term
exposure
(
adults,
30
and
over)
B
9,800
$
60,000
Long­
term
exposure
(
infant,
<
1
yr)
23
$
140
Chronic
bronchitis
(
adults,
26
and
over)
5,300
$
2,000
Non­
fatal
myocardial
infarctions
(
adults,
18
and
older)
C
13,000
$
1,100
Hospital
admissions
 
Respiratory
(
all
ages)
D
4,300
$
76
Hospital
admissions
 
Cardiovascular
(
adults,
20
and
older)
E
3,700
$
82
Emergency
Room
Visits
for
Asthma
(
18
and
younger)
7,400
$
2.1
Acute
bronchitis
(
children,
8­
12)
12,000
$
4.5
Lower
respiratory
symptoms
(
children,
7­
14)
150,000
$
2.4
Upper
respiratory
symptoms
(
asthmatic
children,
9­
11)
110,000
$
3.0
Asthma
exacerbations
190,000
$
8.5
Work
loss
days
(
adults,
18­
65)
1,000,000
$
130
Minor
restricted
activity
days
(
adults,
age
18­
65)
6,200,000
$
320
Total
Economic
Value
of
Health
BenefitsF
$
64,000
A
Incidences
and
values
are
rounded
to
two
significant
digits.
B
Economic
value
calculated
using
a
3
percent
discount
rate.
Economic
value
using
a
7
percent
discount
rate
is
$
57,000
million.
C
Economic
value
calculated
using
a
3
percent
discount
rate.
Economic
value
using
a
7
percent
discount
rate
is
also
$
1,100
million
(
difference
is
within
the
margin
of
rounding).
D
Respiratory
hospital
admissions
for
PM
includes
admissions
for
COPD,
pneumonia,
and
asthma.
E
Cardiovascular
hospital
admissions
for
PM
includes
total
cardiovascular
and
subcategories
for
ischemic
heart
disease,
dysrhythmias,
and
heart
failure.
F
Total
economic
value
calculated
using
3
percent
discount
rate
results.
Total
economic
value
using
a
7
percent
discount
rate
is
$
61,000
million.

2.3.6
Apportionment
of
Benefits
to
NOx
and
SO2
Emissions
Reductions
In
order
to
develop
benefits
estimates
for
the
set
of
emission
reductions
expected
to
result
from
the
proposed
MACT
standard,
it
is
necessary
for
us
to
scale
the
Clear
Skies
based
benefits
to
reflect
the
difference
in
emissions
reductions
between
the
proposed
MACT
standards
and
the
Clear
Skies
analysis.
In
order
to
do
so,
however,
we
must
first
apportion
total
benefits
to
the
NO
x
2­
44
and
SO
2
reductions
for
the
modeled
Clear
Skies
scenario.
This
apportionment
is
necessary
due
to
the
differential
contribution
of
each
emission
species
to
the
total
change
in
ambient
PM
and
total
benefits.

PM
is
a
complex
mixture
of
particles
of
varying
species,
including
nitrates,
sulfates,
and
primary
particles,
including
organic
and
elemental
carbon.
These
particles
are
formed
in
complex
chemical
reactions
from
emissions
of
precursor
pollutants,
including
NO
x,
SO
2,
ammonia,
hydrocarbons,
and
directly
emitted
particles.
Different
emissions
species
contribute
to
the
formation
of
PM
in
different
amounts,
so
that
a
ton
of
emissions
of
NO
x
contributes
to
total
ambient
PM
mass
differently
than
a
ton
of
SO
2.
As
such,
it
is
inappropriate
to
scale
benefits
by
simply
scaling
the
sum
of
all
precursor
emissions.
A
more
appropriate
scaling
method
is
to
first
apportion
total
PM
benefits
to
the
changes
in
underlying
emission
species
and
then
scale
the
apportioned
benefits.

PM
formation
relative
to
any
particular
reduction
in
an
emission
species
is
a
highly
nonlinear
process,
depending
on
meteorological
conditions
and
baseline
conditions,
including
the
amount
of
available
ammonia
to
form
ammonium
nitrate
and
ammonium
sulfate.
Given
the
limited
air
quality
modeling
conducted
for
this
analysis,
we
make
several
simplifying
assumptions
about
the
contributions
of
emissions
reductions
for
specific
species
to
changes
in
particle
species.
For
this
exercise,
we
assume
that
changes
in
sulfate
particles
are
attributable
to
changes
in
SO
2
emissions,
and
changes
in
nitrate
and
secondary
organic
particles
are
attributable
to
changes
in
NO
x
emissions.
These
assumptions
essentially
assume
independence
between
SO
2
and
NO
x
in
the
formation
of
ambient
PM.
This
is
a
potentially
significant
source
of
uncertainty,
as
SO
2
and
NO
x
emissions
interact
with
other
compounds
in
the
atmosphere
to
form
PM
2.5.
For
example,
ammonia
reacts
with
SO
2
first
to
form
ammonium
sulfate.
If
there
is
remaining
ammonia,
it
reacts
with
NO
x
to
form
ammonium
nitrate.
When
SO
2
alone
is
reduced,
ammonia
is
freed
to
react
with
any
NO
x
that
has
not
been
used
in
forming
ammonium
nitrate.
If
NO
x
is
also
reduced,
then
there
will
be
less
available
NO
x
to
form
ammonium
nitrate
from
the
newly
available
ammonia.
Thus,
reducing
SO
2
can
potentially
lead
to
decreased
ammonium
sulfate
and
increased
nitrate,
so
that
overall
ambient
PM
benefits
are
less
than
the
reduction
in
sulfate
particles.
If
NO
x
alone
is
reduced,
there
will
be
a
direct
reduction
in
ammonium
nitrate,
although
the
amount
of
reduction
depends
on
whether
an
area
is
ammonia
limited.
If
there
is
not
enough
ammonia
in
an
area
to
use
up
all
of
the
available
NO
x,
then
NO
x
reductions
will
only
have
an
impact
if
they
reduce
emissions
to
the
point
where
ammonium
nitrate
formation
will
be
affected.
NO
x
reductions
will
not
result
in
any
offsetting
increases
in
ambient
PM
under
most
conditions.
The
implications
of
this
for
apportioning
benefits
between
NO
x
and
SO
2,
is
that
some
of
the
sulfate
related
benefits
will
be
offset
by
reductions
in
nitrate
benefits,
so
benefits
from
SO
2
reductions
will
be
overstated,
while
NO
x
benefits
will
be
understated.
It
is
not
immediately
apparent
the
size
of
this
bias.

The
measure
of
change
in
ambient
particle
mass
that
is
most
related
to
health
benefits
is
the
population­
weighted
change
in
PM
2.5
µ
g/
m3,
because
health
benefits
are
driven
both
by
the
size
of
the
change
in
PM
2.5
and
the
populations
exposed
to
that
change.
We
calculate
the
proportional
share
of
total
change
in
mass
accounted
for
by
sulfate
particles
and
the
sum
of
nitrate
and
secondary
organic
particles.
Results
of
these
calculations
for
the
2010
Clear
Skies
REMSAD
2­
45
modeling
analysis
are
presented
in
Table
2­
9.
The
sulfate
percentage
of
total
change
is
used
to
represent
the
SO
2
contribution
to
health
benefits
and
the
nitrate
plus
secondary
organics
percentage
is
used
to
represent
the
NO
x
contribution
to
health
benefits.
These
percentages
are
then
applied
to
the
PM­
related
health
benefits
estimates
from
the
analysis
of
the
Clear
Skies
PM
2.5
air
quality
modeling
and
combined
with
the
emission
scaling
factors
developed
in
section
2.2
to
estimate
benefits
for
the
proposed
Utility
MACT
standard.

Table
2­
9.
Apportionment
of
Population
Weighted
Change
in
Ambient
PM2.5
to
Nitrate,
Sulfate,
and
Secondary
Organic
Particles
Population­
weighted
Change
(
µ
g/
m3)
Percent
of
Total
Change
Total
PM2.5
0.610
Sulfate
0.520
85.2%

Nitrate
0.081
13.4%

Secondary
Organic
0.008
1.4%

2.4
Estimated
Benefits
of
Proposed
MACT
Standard
in
2010
To
estimate
the
benefits
of
the
NO
x
and
SO
2
emission
reductions
from
the
proposed
standard
in
2010,
we
apply
the
emissions
scaling
factors
derived
in
section
2.2
and
the
apportionment
factors
described
in
section
2.3.6
to
the
benefits
estimates
for
2010
estimated
using
the
Clear
Skies
PM
2.5
air
quality
modeling.
The
scaled
avoided
incidence
estimate
for
any
particular
health
endpoint
is
calculated
using
the
following
equation:

,
Scaled
Incidence
Modeled
Incidence
R
A
i
i
i
=

*

where
Modeled
Incidence
is
the
estimated
change
in
incidence
of
the
health
effect
from
the
updated
Clear
Skies
analysis
from
Table
2­
8,
R
i
is
the
emissions
ratio
for
emission
species
i
from
Table
2­
3,
and
A
i
is
the
health
benefits
apportionment
factor
for
emission
species
i,
from
Table
2­
9.
Essentially,
benefits
are
scaled
using
a
weighted
average
of
the
species
specific
emissions
ratios.
For
example,
the
calculation
of
the
avoided
incidence
of
premature
mortality
in
2010
is:

Scaled
Premature
Mortality
Incidence
=
9,800
*
(
0.852*
0.168
+
0.147*
0.574)
=
2,200
The
economic
value
for
each
endpoint
is
obtained
by
scaling
the
estimated
Clear
Skies
economic
value
from
Table
2­
8
using
the
same
function.
The
estimated
changes
in
incidence
and
economic
value
of
PM­
related
health
effects
in
2010
for
the
proposed
Utility
MACT
standard
based
on
application
of
the
weighted
scaling
factors
are
presented
in
Table
2­
10.
2­
46
The
benefits
estimates
generated
for
the
proposed
rule
are
subject
to
a
number
of
assumptions
and
uncertainties,
which
are
discussed
throughout
the
document.
As
the
table
indicates,
total
benefits
are
driven
primarily
by
the
reduction
in
premature
fatalities
each
year,
which
account
for
over
90
percent
of
total
benefits.
Key
assumptions
underlying
the
primary
estimate
for
the
mortality
category
include
the
following:

(
1)
Inhalation
of
fine
particles
is
causally
associated
with
premature
death
at
concentrations
near
those
experienced
by
most
Americans
on
a
daily
basis.
Although
biological
mechanisms
for
this
effect
have
not
yet
been
definitively
established,
the
weight
of
the
available
epidemiological
evidence
supports
an
assumption
of
causality.
(
2)
All
fine
particles,
regardless
of
their
chemical
composition,
are
equally
potent
in
causing
premature
mortality.
This
is
an
important
assumption,
because
PM
produced
via
transported
precursors
emitted
from
EGUs
may
differ
significantly
from
direct
PM
released
from
automotive
engines
and
other
industrial
sources,
but
no
clear
scientific
grounds
exist
for
supporting
differential
effects
estimates
by
particle
type.
(
3)
The
C­
R
function
for
fine
particles
is
approximately
linear
within
the
range
of
ambient
concentrations
under
consideration.
Thus,
the
estimates
include
health
benefits
from
reducing
fine
particles
in
areas
with
varied
concentrations
of
PM,
including
both
regions
that
are
in
attainment
with
fine
particle
standard
and
those
that
do
not
meet
the
standard.

Although
recognizing
the
difficulties,
assumptions,
and
inherent
uncertainties
in
the
overall
enterprise,
these
analyses
are
based
on
peer­
reviewed
scientific
literature
and
up­
to­
date
assessment
tools,
and
we
believe
the
results
are
highly
useful
in
assessing
this
proposal.
2­
47
Table
2­
10.
Estimated
PM­
related
Health
Benefits
of
the
Proposed
Utility
MACT
Standards
in
2010
Endpoint
Cases
AvoidedA
Economic
Value
(
Millions
of
1999$)

Premature
mortality
­

Long­
term
exposure
(
adults,
30
and
over)
B
2,200
$
14,000
Long­
term
exposure
(
infant,
<
1
yr)
5
$
32
Chronic
bronchitis
(
adults,
26
and
over)
1,200
$
460
Non­
fatal
myocardial
infarctions
(
adults,
18
and
older)
C
2,900
$
250
Hospital
admissions
 
Respiratory
(
all
ages)
D
980
$
17
Hospital
admissions
 
Cardiovascular
(
adults,
20
and
older)
E
850
$
19
Emergency
Room
Visits
for
Asthma
(
18
and
younger)
1,700
$
0.48
Acute
bronchitis
(
children,
8­
12)
2,800
$
1.0
Lower
respiratory
symptoms
(
children,
7­
14)
33,000
$
0.54
Upper
respiratory
symptoms
(
asthmatic
children,
9­
11)
25,000
$
0.69
Asthma
exacerbations
43,000
$
1.9
Work
loss
days
(
adults,
18­
65)
240,000
$
31
Minor
restricted
activity
days
(
adults,
age
18­
65)
1,400,000
$
74
Total
Economic
Value
of
Health
BenefitsF
$
15,000
A
Incidences
and
values
are
rounded
to
two
significant
digits.
B
Economic
value
calculated
using
a
3
percent
discount
rate.
Economic
value
using
a
7
percent
discount
rate
is
$
13,000
million.
C
Economic
value
calculated
using
a
3
percent
discount
rate.
Economic
value
using
a
7
percent
discount
rate
is
$
250
million.
D
Respiratory
hospital
admissions
for
PM
includes
admissions
for
COPD,
pneumonia,
and
asthma.
E
Cardiovascular
hospital
admissions
for
PM
includes
total
cardiovascular
and
subcategories
for
ischemic
heart
disease,
dysrhythmias,
and
heart
failure.
F
Total
economic
value
calculated
using
3
percent
discount
rate
results.
Total
economic
value
using
a
7
percent
discount
rate
is
$
14,000
million.

2.5
Welfare
Effects
There
are
a
number
of
environmental
resources
which
may
be
adversely
affected
by
emissions
of
NO
x
and
SO
2
or
ambient
PM
2.5
Changes
in
these
environmental
resources
may
affect
human
welfare,
but
due
to
a
lack
of
appropriate
physical
effects
or
valuation
methods,
we
are
unable
to
quantify
or
monetize
these
effects
for
our
analysis
of
the
proposed
MACT
standard.
Qualitative
discussions
of
these
benefits
are
provided
in
Section
1.
A
brief
discussion
of
some
of
2­
48
the
benefits
which
are
known
to
have
significant
economic
value
is
provided
below.

Changes
in
the
level
of
ambient
particulate
matter
caused
by
the
reduction
in
emissions
from
fossil­
fuel
fired
utility
sources
will
change
the
level
of
visibility
in
much
of
the
U.
S.
Visibility
directly
affects
people's
enjoyment
of
a
variety
of
daily
activities.
Individuals
value
visibility
both
in
the
places
they
live
and
work,
in
the
places
they
travel
to
for
recreational
purposes,
and
at
sites
of
unique
public
value,
such
as
the
Grand
Canyon.

The
effects
of
air
pollution
on
the
health
and
stability
of
ecosystems
are
potentially
very
important,
but
are
at
present
poorly
understood
and
difficult
to
measure.
The
reductions
in
NO
x
caused
by
the
proposed
rule
could
produce
significant
benefits.
Excess
nutrient
loads,
especially
of
nitrogen,
cause
a
variety
of
adverse
consequences
to
the
health
of
estuarine
and
coastal
waters.
These
effects
include
toxic
and/
or
noxious
algal
blooms
such
as
brown
and
red
tides,
low
(
hypoxic)
or
zero
(
anoxic)
concentrations
of
dissolved
oxygen
in
bottom
waters,
the
loss
of
submerged
aquatic
vegetation
due
to
the
light­
filtering
effect
of
thick
algal
mats,
and
fundamental
shifts
in
phytoplankton
community
structure
(
Bricker
et
al.,
1999).
Direct
impact
functions
relating
changes
in
nitrogen
loadings
to
changes
in
estuarine
benefits
are
not
available.
The
preferred
WTP
based
measure
of
benefits
depends
on
the
availability
of
these
impact
functions
and
on
estimates
of
the
value
of
environmental
responses.
Because
neither
appropriate
impact
functions
nor
sufficient
information
to
estimate
the
marginal
value
of
changes
in
water
quality
exist
at
present,
calculation
of
a
WTP
measure
is
not
possible.

Reductions
in
NO
x
emissions
will
also
reduce
nitrogen
deposition
on
agricultural
land
and
forests.
There
is
some
evidence
that
nitrogen
deposition
may
have
positive
effects
on
agricultural
output
through
passive
fertilization.
Holding
all
other
factors
constant,
farmers'
use
of
purchased
fertilizers
or
manure
may
increase
as
deposited
nitrogen
is
reduced.
Estimates
of
the
potential
value
of
this
possible
increase
in
the
use
of
purchased
fertilizers
are
not
available,
but
it
is
likely
that
the
overall
value
is
very
small
relative
to
other
health
and
welfare
effects.

The
proposed
Utility
MACT
standard
are
also
expected
to
produce
economic
benefits
in
the
form
of
reduced
materials
damage.
There
are
two
important
categories
of
these
benefits.
Household
soiling
refers
to
the
accumulation
of
dirt,
dust,
and
ash
on
exposed
surfaces.
Criteria
pollutants
also
have
corrosive
effects
on
commercial/
industrial
buildings
and
structures
of
cultural
and
historical
significance.
The
effects
on
historic
buildings
and
outdoor
works
of
art
are
of
particular
concern
because
of
the
uniqueness
and
irreplaceability
of
many
of
these
objects.

Previous
EPA
benefit
analyses
have
been
able
to
provide
quantitative
estimates
of
household
soiling
damage.
Consistent
with
SAB
advice,
we
determined
that
the
existing
data
(
based
on
consumer
expenditures
from
the
early
1970'
s)
are
too
out
of
date
to
provide
a
reliable
enough
estimate
of
current
household
soiling
damages
(
EPA­
SAB­
Council­
ADV­
003,
1998)
to
include
in
our
primary
estimate.
We
are
also
unable
to
estimate
any
benefits
to
commercial
and
industrial
entities
from
reduced
materials
damage.
Nor
is
EPA
able
to
estimate
the
benefits
of
reductions
in
PM­
related
damage
to
historic
buildings
and
outdoor
works
of
art.
Existing
studies
of
damage
to
this
latter
category
in
Sweden
(
Grosclaude
and
Soguel,
1994)
indicate
that
these
2­
49
benefits
could
be
an
order
of
magnitude
larger
than
household
soiling
benefits.

If
better
models
of
ecological
effects
can
be
defined,
EPA
believes
that
progress
can
be
made
in
estimating
WTP
measures
for
ecosystem
functions.
For
example,
if
nitrogen
or
sulfate
loadings
can
be
linked
to
measurable
and
definable
changes
in
fish
populations
or
definable
indexes
of
biodiversity,
then
CV
studies
can
be
designed
to
elicit
individuals'
WTP
for
changes
in
these
effects.
This
is
an
important
area
for
further
research
and
analysis,
and
will
require
close
collaboration
among
air
quality
modelers,
natural
scientists,
and
economists.

2.6
Comparison
of
Costs
and
Benefits
Table
2­
11
summarizes
the
results
of
the
benefit­
cost
analysis
of
the
proposed
section
112
MACT
scenario
and
compares
them
with
estimates
of
the
range
of
potential
costs
and
benefits
associated
with
an
alternative
scenario
that
addresses
combined
implementation
of
section
111
Hg
requirements
in
coordination
with
proposed
SO2
and
NOx
requirements
in
the
proposed
IAQR.
The
potential
influence
of
such
a
combined
scenario
is
illustrated
in
the
second
column
of
Table
2­
11,
which
assumes
the
proposed
section
111
requirements
are
implemented
in
combination
with
the
IAQR.
The
IAQR
analysis
projects
that
the
Hg
reductions
associated
with
implementing
the
SO2/
NOx
requirements
in
the
Eastern
U.
S.
in
2010
would
be
approximately
10.6
tons
per
year,
which
is
almost
identical
to
those
estimated
from
the
proposed
section
112
MACT­
only
scenario.

If
the
goal
for
the
proposed
section
111
program
in
2010
is
limited
to
these
co­
control
reductions,
there
might
be
no
additional
costs
or
benefits
to
the
program,
over
those
achieved
by
the
IAQR
 
this
is
indicated
in
the
lower
portion
of
the
ranges
in
Table
2­
11.
By
contrast,
if
the
proposed
section
111
regulation
adopts
a
2010
goal
similar
to
the
Phase
I
Clear
Skies
Hg
cap,
additional
Hg
reductions
would
be
required
over
those
forecast
for
the
IAQR.
Based
on
a
multipollutant
analyses
conducted
for
Clear
Skies
(
p
D­
9,
Technical
appendix
D,
at
www.
epa.
gov/
airmarkets/
epa­
ipm),
power
generators
would
likely
opt
for
some
additional
SO2
and
NOx
controls
beyond
those
needed
for
the
IAQR,
as
well
as
considering
additional
direct
Hg
controls.
Although
the
actual
results
are
uncertain,
the
Clear
Skies
results
suggest
that
the
costs
and
benefits
associated
with
a
section
112
MACT­
only
approach
may
reflect
a
reasonable
lower
bound
for
the
additional
costs
and
benefits.
These
potential
additional
costs
and
benefits
related
to
additional
Hg
controls
are
reflected
in
the
upper
end
of
the
ranges
in
Table
2­
11.
In
the
decade
beyond
2010,
the
proposed
section
111
program
would
establish
a
15
ton
cap
for
Hg
in
2018,
similar
to
Clear
Skies.
Based
on
Clear
Skies
analyses,
this
would
result
in
further
Hg
controls,
which
would
likely
include
at
least
some
additional
SO2/
NOx
controls
as
well
as
direct
Hg
controls.
The
IAQR
program
alone
produces
only
small
additional
reductions
in
Hg
emissions
in
2020.
The
Hg
reductions
estimated
for
the
proposed
section
112
MACT
and
the
proposed
section
111
and
proposed
IAQR
programs
are
summarized
in
Table
2­
12.
These
forecasts
are
based
on
IPM
analyses
of
the
proposed
section
112
MACT
scenario
outlined
above,
the
proposed
IAQR
analysis,
and
estimates
derived
from
earlier
analyses
of
the
Clear
Skies
program.

Every
benefit­
cost
analysis
examining
the
potential
effects
of
a
change
in
environmental
2­
50
protection
requirements
is
limited,
to
some
extent,
by
data
gaps,
limitations
in
model
capabilities
(
such
as
geographic
coverage),
and
uncertainties
in
the
underlying
scientific
and
economic
studies
used
to
configure
the
benefit
and
cost
models.
Deficiencies
in
the
scientific
literature
often
result
in
the
inability
to
estimate
changes
in
health
and
environmental
effects.
Deficiencies
in
the
economics
literature
often
result
in
the
inability
to
assign
economic
values
even
to
those
health
and
environmental
outcomes
that
can
be
quantified.
While
these
general
uncertainties
in
the
underlying
scientific
and
economics
literatures
are
discussed
in
detail
in
the
RIA
and
its
supporting
documents
and
references,
the
key
uncertainties
which
have
a
bearing
on
the
results
of
the
benefitcost
analysis
of
today's
action
are
the
following:

1.
The
exclusion
of
potentially
significant
benefit
categories
(
e.
g.,
health
and
ecological
benefits
of
reduction
in
hazardous
air
pollutants
emissions);

2.
Errors
in
measurement
and
projection
for
variables
such
as
population
growth;

3.
Uncertainties
in
the
estimation
of
future
year
emissions
inventories
and
air
quality;

4.
Uncertainties
associated
with
the
extrapolation
of
air
quality
monitoring
data
to
some
unmonitored
areas
required
to
better
capture
the
effects
of
the
standards
on
the
affected
population;

5.
Variability
in
the
estimated
relationships
of
health
and
welfare
effects
to
changes
in
pollutant
concentrations;
and
6.
Uncertainties
associated
with
the
benefit
transfer
approach.

Despite
these
uncertainties,
we
believe
the
benefit­
cost
analysis
provides
a
reasonable
indication
of
the
expected
economic
benefits
of
the
proposed
actions
under
a
given
set
of
assumptions.

Based
on
estimated
compliance
costs
(
control
+
administrative
costs
associated
with
Paperwork
Reduction
Act
requirements
associated
with
the
proposed
rule
and
predicted
changes
in
the
price
and
output
of
electricity),
the
estimated
social
costs
of
the
proposed
section
112
MACT­
only
scenario
are
$
1.6
billion
(
1999$).
Social
costs
are
different
from
compliance
costs
in
that
social
costs
take
into
account
the
interactions
between
affected
producers
and
the
consumers
of
affected
products
in
response
to
the
imposition
of
the
compliance
costs.
In
this
action,
coalfired
utilities
are
the
affected
producers
and
users
of
electricity
are
the
consumers
of
the
affected
product.

As
explained
above,
we
estimate
$
15
billion
in
benefits
from
the
proposed
section
112
MACT,
compared
to
less
than
$
2
billion
in
costs.
It
is
important
to
put
the
results
of
this
analysis
in
the
proper
context.
The
large
benefit
estimate
is
not
attributable
to
reducing
human
and
environmental
exposure
to
Hg.
It
arises
from
ancillary
reductions
in
SO2
and
NOx
that
result
from
controls
aimed
at
complying
with
the
proposed
MACT.
Although
consideration
of
ancillary
2­
51
benefits
is
reasonable,
we
note
that
these
benefits
are
not
uniquely
attributable
to
Hg
regulation.
Under
the
IAQR,
coal­
fired
units
would
achieve
much
larger
reductions
in
SO2
and
NOx
emissions
than
they
would
under
the
proposed
section
112
MACT.
In
the
years
ahead,
as
the
Agency
and
the
States
develop
rules,
guidance
and
policies
to
implement
the
new
air
quality
standards
for
ozone
and
PM,
coal­
fired
power
plants
will
be
required
to
implement
additional
controls
to
reduce
SO2
and
NOx
(
e.
g.,
scrubbers,
SCR
units,
year­
round
NOx
controls
in
place
of
summertime
only
controls,
conversion
to
low­
sulfur
coals,
and
so
forth).
Thus,
most
or
all
of
the
ancillary
benefits
of
Hg
control
would
be
achieved
anyway,
regardless
of
whether
a
section
112
MACT
is
promulgated.
Based
on
analysis
of
the
Clear
Skies
legislation,
EPA
believes
that
the
proposed
2018
Hg
cap
in
the
proposed
section
111
rule
would
result
in
additional
SO2
and
NOx
reductions
beyond
those
that
would
be
required
under
the
proposed
IAQR.
Thus,
the
section
111
approach,
unlike
the
section
112
approach,
may
achieve
SO2
and
NOx
reduction
benefits
beyond
those
that
would
be
achieved
under
the
IAQR.
We
believe,
however,
that
even
if
no
Hg
controls
were
imposed,
most
major
coal­
fired
units
would
still
have
to
reduce
their
SO2
and
NOx
emissions
as
part
of
the
efforts
to
bring
the
nation
into
attainment
with
the
new
air
quality
standards.
In
light
of
these
considerations,
the
Agency
believes
that
the
key
rationale
for
controlling
Hg
is
to
reduce
public
and
environmental
exposure
to
Hg,
thereby
reducing
risk
to
public
health
and
wildlife.
Although
the
available
science
does
not
support
quantification
of
these
benefits
at
this
time,
the
Agency
believes
the
qualitative
benefits
are
large
enough
to
justify
substantial
investment
in
Hg
emission
reductions.

It
should
be
recognized,
however,
that
this
analysis
does
not
account
for
many
of
the
potential
benefits
that
may
result
from
these
actions.
The
net
benefits
would
be
greater
if
all
the
benefits
of
the
Hg,
Ni,
and
other
pollutant
reductions
could
be
quantified.
Notable
omissions
to
the
net
benefits
include
all
benefits
of
HAP
reductions,
including
reduced
cancer
incidences,
toxic
morbidity
effects,
and
cardiovascular
and
CNS
effects,
and
all
health
and
welfare
effects
from
reduction
of
ambient
NO
x
and
SO
2.

Table
2­
11.
Summary
of
Monetized
Benefits,
Costs,
and
Net
Benefits
under
the
Proposed
Section
112
MACT
Standard,
and
the
Proposed
Section
111
Rule
and
the
Proposed
IAQRA
($
billions/
yr)

MACT­
only
Scenario
Sec.
111
plus
IAQR
CombinedD
Social
CostsB
$
1.6
$
2.9
to
4.5+

Social
BenefitsC:
PM­
related
Health
benefits
$
15+
B
$
58
to
73+
B
Net
Benefits
(
Benefits­
Costs)
C
$
13+
B
$
55
to
$
68+
B
A
All
costs
and
benefits
are
rounded
to
two
significant
digits.

B
Note
that
costs
are
the
total
costs
of
reducing
all
pollutants,
including
Hg
and
other
metallic
air
toxics,
as
well
as
NOx
and
SO2.
Benefits
in
this
table
are
associated
only
with
NOx
and
SO2
reductions.
2­
52
C
Not
all
possible
benefits
or
disbenefits
are
quantified
and
monetized
in
this
analysis.
In
particular,
ozone
health
and
welfare
and
PM
welfare
benefits
are
omitted.
Other
potential
benefit
categories
that
have
not
been
quantified
and
monetized
are
listed
in
Table
2­
1.
B
is
the
sum
of
all
unquantified
benefits
and
disbenefits.

D
Estimated
combined
benefits
of
Sec.
111
plus
IAQR
costs
and
benefits
in
2010.
Ranges
do
not
reflect
actual
analyses
of
combined
programs.
Rough
estimates
based
on
consideration
of
available
IAQR,
MACT,
and
Clear
Skies
analyses.
See
text.
2­
53
Table
2­
12.
Forecast
Mercury
Emissions
under
the
Proposed
Section
112
MACT,
and
the
Proposed
Section
111
Rule
and
the
Proposed
IAQRA
Program/
Year
2010
2020
MACT
only
34
31
IAQR
only
34
30
IAQR
and
section
111
caps
B
18
­
22
A
Annual
reductions
from
base
case
forecast
under
current
programs
to
reduce
Utility
Unit
emissions.
MACT
only
value
for
2015
based
on
interpolation
of
2010
and
2015.
Lower
bound
of
IAQR
and
section
111
caps
in
2010
assumes
Hg
cap
is
set
at
co­
control
level
achieved
by
IAQR.
Upper
bound
in
2010
and
ranges
thereafter
estimates
derived
from
Clear
Skies
analyses.
B
Mercury
emissions
will
reflect
the
level
of
emissions
resulting
from
the
co­
benefits
of
controlling
SO2
and
NOx.
See
section
IV.
B.
1
for
a
detailed
discussion.
2­
54
2.7
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D.
E.,
B.
L.
Hwang,
R.
J.
Burchette,
T.
Vancuren,
and
P.
K.
Mills.
1995.
Estimated
Long­
Term
Ambient
Concentrations
of
PM(
10)
and
Development
of
Respiratory
Symptoms
in
a
Nonsmoking
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2):
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152.

Abbey,
D.
E.,
F.
Petersen,
P.
K.
Mills,
and
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Beeson.
1993.
Long­
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Total
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Particulates,
Ozone,
and
Sulfur
Dioxide
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Respiratory
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in
a
Nonsmoking
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D.
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D.
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3(
2):
181­
202.

Abbey,
D.
E.,
N.
Nishino,
W.
F.
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R.
J.
Burchette,
S.
F.
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term
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pollutants
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to
mortality
in
nonsmokers
[
see
comments].
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J
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159(
2):
373­
82.

Abt
Associates,
Inc.
2003.
BenMAP
User's
Manual.
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for
EPA
Office
of
Air
Quality
Planning
and
Standards,
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Adams,
P.
F.,
G.
E.
Hendershot
and
M.
A.
Marano.
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the
National
Health
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Vital
Health
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10(
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212.

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for
Healthcare
Research
and
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Utilization
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American
Lung
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TECHNICAL
REPORT
DATA
(
Please
read
Instructions
on
reverse
before
completing)

1.
REPORT
NO.

EPA­
452/
R­
03­
021
2.
3.
RECIPIENT'S
ACCESSION
NO.

4.
TITLE
AND
SUBTITLE
Benefit
Analysis
for
the
Section
112
Utility
Rule
5.
REPORT
DATE
January
2004
6.
PERFORMING
ORGANIZATION
CODE
7.
AUTHOR(
S)
8.
PERFORMING
ORGANIZATION
REPORT
NO.

9.
PERFORMING
ORGANIZATION
NAME
AND
ADDRESS
U.
S.
Environmental
Protection
Agency
Office
of
Air
Quality
Planning
and
Standards
Air
Quality
Strategies
and
Standards
Division
Research
Triangle
Park,
NC
27711
10.
PROGRAM
ELEMENT
NO.

11.
CONTRACT/
GRANT
NO.

12.
SPONSORING
AGENCY
NAME
AND
ADDRESS
Director
Office
of
Air
Quality
Planning
and
Standards
Office
of
Air
and
Radiation
U.
S.
Environmental
Protection
Agency
Research
Triangle
Park,
NC
27711
13.
TYPE
OF
REPORT
AND
PERIOD
COVERED
14.
SPONSORING
AGENCY
CODE
EPA/
200/
04
15.
SUPPLEMENTARY
NOTES
16.
ABSTRACT
This
document
is
a
benefits
analysis
of
the
Section
112
(
MACT)
proposal
to
reduce
mercury
emissions
from
power
plants.
The
analysis
provided
qualitative
and
quantitative
estimates
of
the
benefits
from
the
emission
reductions,
which
include
reductions
of
nitrogen
oxides
and
sulfur
dioxide
as
well
as
mercury.
Extensive
background
on
the
health
effects
from
mercury
exposure
is
also
included.

17.
KEY
WORDS
AND
DOCUMENT
ANALYSIS
a.
DESCRIPTORS
b.
IDENTIFIERS/
OPEN
ENDED
TERMS
c.
COSATI
Field/
Group
Benefits
Health
Effects
Emissions
Air
Pollution
Control
18.
DISTRIBUTION
STATEMENT
Release
Unlimited
19.
SECURITY
CLASS
(
Report)

Unclassified
21.
NO.
OF
PAGES
71
20.
SECURITY
CLASS
(
Page)

Unclassified
22.
PRICE
EPA
Form
2220­
1
(
Rev.
4­
77)
PREVIOUS
EDITION
IS
OBSOLETE
United
States
Office
of
Air
Quality
Planning
and
Standards
Publication
No.
EPA­
452/
R­
03­
021
Environmental
Protection
Air
Quality
Strategies
and
Standards
Division
January
2004
Agency
Research
Triangle
Park,
NC
27711