Benefits
of
the
Proposed
Inter­
State
Air
Quality
Rule
EPA
452/­
03­
001
January
2004
Benefits
of
the
Proposed
Inter­
State
Air
Quality
Rule
U.
S.
Environmental
Protection
Agency
Office
of
Air
and
Radiation
Office
of
Air
Quality
Planning
and
Standards
Air
Quality
Strategies
and
Standards
Division
Innovative
Strategies
and
Economics
Group
Research
Triangle
Park,
North
Carolina
iii
CONTENTS
Section
Page
1
Executive
Summary
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1­
1
1.1
Benefit­
Cost
Comparison
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1­
3
2
Introduction
and
Background
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2­
1
2.1
Background
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2­
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2.2
Regulated
Entities
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2­
2
2.3
Control
Scenario
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2­
2
2.4
Cost
of
Emission
Controls
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2­
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2.5
Organization
of
this
Report
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2­
3
3
Emissions
and
Air
Quality
Impacts
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3­
1
3.1
Emissions
Inventories
and
Estimated
Emissions
Reductions
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3­
1
3.2
Air
Quality
Impacts
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3­
2
3.2.1
PM
Air
Quality
Estimates
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3­
4
3.2.1.1
Modeling
Domain
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3­
6
3.2.1.2
Simulation
Periods
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3­
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3.2.1.3
Model
Inputs
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3­
8
iv
3.2.1.4
Model
Performance
for
Particulate
Matter
(
PM)
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3­
9
3.2.1.5
Converting
REMSAD
Outputs
to
Benefits
Inputs
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3­
11
3.2.1.6
PM
Air
Quality
Results
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3­
12
3.2.2
Ozone
Air
Quality
Estimates
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3­
13
3.2.2.1
Modeling
Domain
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3­
15
3.2.2.2
Simulation
Periods
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3­
15
3.2.2.3
Non­
emissions
Modeling
Inputs
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3­
16
3.2.2.4
Model
Performance
for
Photochemical
Ozone
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3­
17
3.2.2.5
Converting
CAMx
Outputs
to
Full­
Season
Profiles
for
Benefits
Analysis
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3­
19
3.2.2.6
Ozone
Air
Quality
Results
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3­
19
3.2.3
Visibility
Degradation
Estimates
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3­
20
3.2.3.1
Residential
Visibility
Improvements
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3­
22
3.2.3.2
Recreational
Visibility
Improvements
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3­
22
4
Benefits
Analysis
and
Results
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4­
1
4.1
Benefit
Analysis­
Data
and
Methods
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4­
10
4.1.1
Valuation
Concepts
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4­
15
4.1.2
Growth
in
WTP
Reflecting
National
Income
Growth
Over
Time
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4­
18
4.1.3
Methods
for
Describing
Uncertainty
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4­
20
4.1.4
Demographic
Projections
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4­
25
4.1.5
Health
Benefits
Assessment
Methods
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4­
26
4.1.5.1
Selecting
Health
Endpoints
and
Epidemiological
Effect
Estimates
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4­
27
v
4.1.5.2
Uncertainties
Associated
with
Health
Impact
Functions
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4­
43
4.1.5.3
Baseline
Health
Effect
Incidence
Rates
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4­
47
4.1.5.4
Accounting
for
Potential
Health
Effect
Thresholds
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4­
51
4.1.5.5
Selecting
Unit
Values
for
Monetizing
Health
Endpoints
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4­
54
4.1.5.6
Unquantified
Health
Effects
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4­
68
4.1.6
Human
Welfare
Impact
Assessment
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4­
69
4.1.6.1
Visibility
Benefits
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4­
69
4.1.6.2
Agricultural,
Forestry
and
other
Vegetation
Related
Benefits
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4­
72
4.1.6.3
Benefits
from
Reductions
in
Materials
Damage
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4­
75
4.1.6.4
Benefits
from
Reduced
Ecosystem
Damage
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4­
75
4.2
Benefits
Analysis
 
Results
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4­
76
4.3
Discussion
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4­
79
5
Qualitative
Assessment
of
Nonmonetized
Benefits
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5­
1
5.1
Introduction
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5­
1
5.2
Atmospheric
Deposition
of
Sulfur
and
Nitrogen
 
Impacts
on
Aquatic,

Forest,
and
Coastal
Ecosystems
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5­
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5.2.1
Freshwater
Acidification
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5­
2
5.2.1.1
Water/
Watershed
Modeling
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5­
4
5.2.1.2
Description
of
the
MAGIC
Model
and
Methods
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5­
5
5.2.1.3
Model
Structure
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5­
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5.2.1.4
Model
Implementation
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5­
6
vi
5.2.1.5
Calibration
Procedure
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5­
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5.2.1.6
MAGIC
Modeling
Results
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5­
9
5.2.2
Forest
Ecosystems
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5­
10
5.2.3
Coastal
Ecosystems
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5­
11
5.3
Benefits
of
Reducing
Mercury
Emissions
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5­
12
6
Comparison
of
Benefits
and
Costs
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6­
1
References
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R­
1
vii
LIST
OF
FIGURES
Number
Page
2­
1
States
Identified
as
Having
Significant
Contribution
to
PM2.5
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2­
3
2­
2
States
Identified
as
Having
Significant
Contribution
to
Ozone
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2­
4
3­
1
REMSAD
Modeling
Domain
for
Continental
United
States
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3­
7
3­
2
Example
of
REMSAD
36
x
36km
Grid­
cells
for
Maryland
Area
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3­
8
3­
3
CAMx
Eastern
U.
S.
Modeling
Domain
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3­
16
3­
4
Recreational
Visibility
Regions
for
Continental
U.
S.
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3­
23
4­
1
Key
Steps
in
Air
Quality
Modeling
Based
Benefits
Analysis
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4­
3
4­
2
Visibility
Improvements
in
Southeastern
Class
I
Areas
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4­
3
5­
1
How
Emissions
of
Mercury
Can
Affect
Human
Health
and
Ecosystems
.
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5­
14
5­
2
Mercury
Reductions
by
State
after
IAQR
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5­
16
5­
3
Concentrations
of
Mercury
in
Blood
of
Women
of
Child­
Bearing
Age
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5­
17
viii
LIST
OF
TABLES
Number
Page
1­
1
Estimated
Reductions
in
Incidence
of
Health
Effects
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1­
2
1­
2
Estimated
Monetary
Value
of
Reductions
in
Incidence
of
Health
and
Welfare
Effects
(
millions
of
1999$)
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1­
4
1­
3
Summary
of
Benefits,
Costs,
and
Net
Benefits
of
the
Inter­
State
Air
Quality
Rule
.
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.
.
.
1­
5
1­
4
Additional
Nonmonetized
Benefits
of
the
Inter­
State
Air
Quality
Rule
.
.
.
.
.
.
1­
6
3­
1
Emissions
Sources
and
Basis
for
Current
and
Future­
Year
Inventories
.
.
.
.
.
.
3­
2
3­
2
Summary
of
Modeled
Baseline
Emissions
for
Lower
48
States
.
.
.
.
.
.
.
.
.
.
.
.
3­
3
3­
3
Summary
of
Modeled
Emissions
Changes
for
the
Proposed
Transport
Rule
.
.
3­
4
3­
4
Model
Performance
Statistics
for
REMSAD
PM
2.5
Species
Predictions:
1996
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
3­
11
3­
5
Summary
of
Base
Case
PM
Air
Quality
and
Changes
due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
13
3­
6
Distribution
of
PM
2.5
Air
Quality
Improvements
over
Population
due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
14
3­
7
Model
Performance
Statistics
for
Hourly
Ozone
in
the
Eastern
U.
S.

CAMx
Ozone
Simulations:
1995
Base
Case
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
18
ix
3­
8
Summary
of
CAMx
Derived
Population­
Weighted
Ozone
Air
Quality
Metrics
Due
to
Proposed
Transport
Rule
for
Health
Benefits
Endpoints:

Eastern
U.
S.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
.
3­
20
3­
9
Distribution
of
Populations
Experiencing
Visibility
Improvements
due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
21
3­
10
Summary
of
Baseline
Residential
Visibility
and
Changes
by
Region:

2010
and
2015
(
annual
average
deciviews)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
22
3­
11
Summary
of
Baseline
Recreational
Visibility
and
Changes
by
Region:

2010
and
2015
(
annual
average
deciviews)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
23
4­
1
Estimated
Monetized
Benefits
of
the
Proposed
IAQR
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
10
4­
2
Human
Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
IAQR
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
4­
11
4­
3
Elasticity
Values
Used
to
Account
for
Projected
Real
Income
Growth
.
.
.
.
.
4­
19
4­
4
Adjustment
Factors
Used
to
Account
for
Projected
Real
Income
Growth
.
.
.
4­
21
4­
5
Primary
Sources
of
Uncertainty
in
the
Benefit
Analysis
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
22
4­
6
Summary
of
Considerations
Used
in
Selecting
C­
R
Functions
.
.
.
.
.
.
.
.
.
.
.
.
4­
28
4­
7
Endpoints
and
Studies
Used
to
Calculate
Total
Monetized
Health
Benefits
.
.
4­
32
4­
8
Studies
Examining
Health
Impacts
in
the
Asthmatic
Population
Evaluated
for
Use
in
the
Benefits
Analysis
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
44
4­
9
Baseline
Incidence
Rates
and
Population
Prevalence
Rates
for
Use
in
Impact
Functions,
General
Population
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
49
4­
10
Asthma
Prevalence
Rates
Used
to
Estimate
Asthmatic
Populations
in
Impact
Functions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
52
4­
11
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
1999$)
.
.
.
.
4­
55
x
4­
12
Expected
Impact
on
Estimated
Benefits
of
Premature
Mortality
Reductions
of
Differences
Between
Factors
Used
in
Developing
Applied
VSL
and
Theoretically
Appropriate
VSL
.
.
.
.
.
.
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.
.
.
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.
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.
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.
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.
.
.
.
.
.
.
.
.
.
4­
60
4­
13
Alternative
Direct
Medical
Cost
of
Illness
Estimates
for
Nonfatal
Heart
Attacks
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
66
4­
14
Estimated
Costs
Over
a
5­
Year
Period
(
in
2000$)
of
a
Nonfatal
Myocardial
Infarction
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
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.
.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
66
4­
15
Women
with
Children:
Number
and
Percent
in
the
Labor
Force,
2000,
and
Weighted
Average
Participation
Rate
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
68
4­
16
Reductions
in
Incidence
of
Adverse
Health
Effects
Associated
with
Reductions
in
Particulate
Matter
and
Ozone
Associated
with
the
Proposed
IAQR
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
77
4­
17
Results
of
Human
Health
and
Welfare
Benefits
Valuation
for
the
Proposed
IAQR
(
millions
of
1999
dollars)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
78
5­
1
Acidification
Changes
in
Water
Bodies
as
a
Result
of
the
Inter­
State
Air
Quality
Rule
.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
10
6­
1
Summary
of
Benefits,
Costs,
and
Net
Benefits
of
the
Inter­
State
Air
Quality
Rule
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
6­
2
1­
1
SECTION
1
EXECUTIVE
SUMMARY
The
Clean
Air
Act
(
CAA)
contains
a
number
of
requirements
to
address
nonattainment
of
the
fine
particulate
matter
(
PM
2.5)
and
the
8­
hour
ozone
national
ambient
air
quality
standards
(
NAAQS),
including
requirements
that
States
address
interstate
transport
contributing
to
such
nonattainment.
CAA
Section
110(
a)(
2)(
D)
requires
that
the
State
Implementation
Plans
(
SIPs)
necessary
to
meet
these
standards
contain
adequate
provisions
to
prohibit
air
pollutant
emissions
within
those
States
from
"
contribut[
ing]
significantly
to
nonattainment
in,
or
interfer[
ing]
with
maintenance
by,"
a
downwind
State.
The
EPA
is
proposing
a
rule
to
reduce
interstate
transport
of
fine
particulate
matter
and
ozone
(
Inter­
State
Air
Quality
Rule
hereinafter
referred
to
as
IAQR)
in
29
States
and
the
District
of
Columbia
to
ensure
that
SIPs
provide
for
necessary
regional
reductions
of
emissions
of
sulfur
dioxide
(
SO
2)
and/
or
nitrogen
oxides
(
NO
x),
that
are
important
precursors
of
PM
2.5
(
NO
x
and
SO
2)
and
ozone
(
NO
x).
The
EPA
is
proposing
that
emissions
reductions
be
implemented
in
two
phases,
with
the
first
phase
in
2010
and
the
second
phase
in
2015.

This
document
presents
the
health
and
welfare
benefits
of
the
IAQR
and
compares
the
benefits
of
this
proposal
to
the
estimated
costs
of
implementing
the
rule
in
2010
and
2015.

Significant
health
and
welfare
benefits
are
likely
to
occur
as
a
result
of
this
rule.
Thousands
of
deaths
and
other
serious
health
effects
would
be
prevented
each
year.
The
EPA
is
able
to
monetize
annual
benefits
of
approximately
$
58
billion
in
2010
and
approximately
$
84
billion
in
2015.
Table
1­
1
presents
the
primary
estimates
of
reduced
incidence
of
PM­
and
ozonerelated
health
effects
for
the
years
2010
and
2015
for
the
regulatory
control
strategy.
In
interpreting
the
results,
it
is
important
to
keep
in
mind
the
limited
set
of
effects
we
are
able
to
monetize.
Specifically,
the
table
lists
the
PM­
and
ozone­
related
benefits
associated
with
the
reduction
of
ambient
PM
and
ozone
levels.
These
benefits
are
substantial
both
in
incidence
and
dollar
value.
In
2010,
we
estimate
that
reduction
in
exposure
to
PM
2.5
will
result
in
approximately
9,600
fewer
premature
deaths
annually
associated
with
PM
2.5,
as
well
as
5,200
fewer
cases
of
chronic
bronchitis,
13,000
fewer
nonfatal
heart
attacks
(
acute
myocardial
infarctions),
8,900
fewer
hospitalizations
(
for
respiratory
and
cardiovascular
disease
combined),
and
significant
reductions
in
days
of
restricted
activity
due
to
respiratory
illness
1­
2
(
with
an
estimate
of
6.4
million
fewer
cases).
We
also
estimate
substantial
health
improvements
for
children
from
reductions
in
upper
and
lower
respiratory
illnesses,
acute
bronchitis,
and
asthma
attacks.
Ozone
health­
related
benefits
are
expected
to
occur
during
the
summer
ozone
season
(
usually
ranging
from
May
to
September
in
the
eastern
U.
S.).
Based
on
modeling
for
2010,
ozone­
related
health
benefits
are
expected
to
include
1,000
fewer
hospital
admissions
for
respiratory
illnesses,
120
fewer
emergency
room
admissions
for
asthma,

280,000
fewer
days
with
restricted
activity
levels,
and
180,000
fewer
days
where
children
are
absent
from
school
because
of
illnesses.
In
addition,
recent
reports
by
Thurston
and
Ito
(
2001)
and
the
World
Health
Organization
(
WHO)
support
an
independent
ozone
mortality
impact,
and
the
EPA
Science
Advisory
Board
has
recommended
that
the
EPA
reevaluate
the
ozone
mortality
literature
for
possible
inclusion
in
the
estimate
of
total
benefits.
Based
on
these
new
analyses
and
recommendations,
EPA
is
sponsoring
three
independent
meta­
analyses
of
the
ozone­
mortality
epidemiology
literature
to
inform
a
determination
on
inclusion
of
this
important
health
endpoint.
Upon
completion
and
peer­
review
of
the
meta­
analyses,
EPA
will
Table
1­
1.
Estimated
Reductions
in
Incidence
of
Health
Effects
Endpoint
Constituent
2010
Estimated
Reduction
2015
Estimated
Reduction
Premature
Mortality­
adult
PM2.5
9,600
13,000
Mortality­
infant
PM2.5
22
29
Chronic
bronchitis
PM2.5
5,200
6,900
Acute
myocardial
infarction­
total
PM2.5
13,000
18,000
Hospital
admissions
­
respiratory
PM2.5,
O3
5,200
8,100
Hospital
admissions
­
cardiovascular
PM2.5
3,700
5,000
Emergency
room
visits,
respiratory
PM2.5,
O3
7,100
9,400
Acute
bronchitis
PM2.5
12,000
16,000
Lower
respiratory
symptoms
PM2.5
140,000
190,000
Upper
respiratory
symptoms
PM2.5
490,000
620,000
Asthma
exacerbation
PM2.5
190,000
240,000
Acute
respiratory
symptoms
(
MRADs)
PM2.5,
O3
6,400,000
8,500,000
Work
loss
days
PM2.5
1,000,000
1,300,000
School
loss
days
O3
180,000
390,000
MRADs
=
minor
restricted
activity
days
1­
3
determine
whether
benefits
of
reductions
in
ozone­
related
mortality
will
be
included
in
the
benefits
analysis
for
the
final
IAQR.

Table
1­
2
presents
the
estimated
monetary
value
of
reductions
in
the
incidence
of
health
and
welfare
effects.
PM­
related
health
benefits
and
ozone
benefits
are
estimated
to
be
approximately
$
56.9
billion
and
$
82.4
billion
annually
in
2010
and
2015,
respectively.

Estimated
annual
visibility
benefits
in
Southeastern
Class
I
areas
brought
about
by
the
IAQR
are
estimated
to
be
$
880
million
in
2010
and
$
1.4
billion
in
2015.
All
monetized
estimated
values
are
stated
in
1999$.
Table
1­
3
presents
the
total
annual
monetized
benefits
for
the
years
2010
and
2015.
This
table
also
indicates
with
a
"
B"
those
additional
health
and
environmental
effects
that
we
were
unable
to
quantify
or
monetize.
These
effects
are
additive
to
the
estimate
of
total
benefits,
and
the
EPA
believes
there
is
considerable
value
to
the
public
of
the
benefits
that
could
not
be
monetized.
A
listing
of
the
benefit
categories
that
could
not
be
quantified
or
monetized
in
our
estimate
is
provided
in
Table
1­
4.
Major
benefits
not
quantified
for
this
proposed
rule
include
the
value
of
increases
in
yields
of
agricultural
crops
and
commercial
forests,
value
of
improvements
in
visibility
in
places
where
people
live
and
work
and
recreational
areas
outside
of
federal
Class
I
areas,
and
value
of
reductions
in
nitrogen
and
acid
deposition
and
the
resulting
changes
in
ecosystem
functions.

In
summary,
EPA's
primary
estimate
of
the
annual
benefits
of
the
rule
is
approximately
$
58
+
B
billion
in
2010.
In
2015,
total
monetized
annual
benefits
are
approximately
$
84
+
B
billion.
These
estimates
account
for
growth
in
the
willingness
to
pay
for
reductions
in
environmental
health
risks
with
growth
in
real
gross
domestic
product
(
GDP)
per
capita
between
the
present
and
the
years
2010
and
2015.

1.1
Benefit­
Cost
Comparison
The
estimated
annual
social
benefits
of
the
rule
are
compared
to
the
annual
estimated
cost
to
implement
the
proposed
rule
in
Table
1­
3.
Estimates
of
the
annual
costs
of
implementing
the
rule
are
$
3
and
$
4
billion
in
2010
and
2015,
respectively
(
1999$).
For
further
information
concerning
the
costs
of
the
proposed
rule,
please
see
"
Preliminary
Analysis
of
the
Costs
of
the
Inter­
State
Air
Quality
Rule
 
January
2004."
1­
4
Table
1­
2.
Estimated
Monetary
Value
of
Reductions
in
Incidence
of
Health
and
Welfare
Effects
(
millions
of
1999$)

Endpoint
Constituent
2010
Estimated
Monetary
Value
of
Reductions
2015
Estimated
Monetary
Value
of
Reductions
Preamature
Mortality­
adult
PM2.5
$
53,000
$
77,000
Chronic
bronchitis
PM2.5
$
1,900
$
2,700
Acute
myocardial
infarction
PM2.5
$
1,100
$
1,500
Acute
respiratory
symptoms
(
MRADs)
PM2.5,
O3
$
320
$
440
Work
loss
days
PM2.5
$
140
$
170
Mortality­
infant
PM2.5
$
130
$
180
Hospital
admissions,
respiratory
PM2.5,
O3
$
85
$
130
Hospital
admissions,
cardiovascular
PM2.5
$
78
$
110
School
loss
days
O3
$
13
$
28
Worker
productivity
O3
$
8.0
$
17
Asthma
exacerbation
PM2.5
$
8.0
$
11
Acute
bronchitis
PM2.5
$
4.3
$
5.7
Lower
respiratory
symptoms
PM2.5
$
2.3
$
3.0
Upper
respiratory
symptoms
PM2.5
$
13
$
17
Emergency
room
visits,
respiratory
PM2.5,
O3
$
2.0
$
2.6
Visibility,
Southeastern
Class
I
areas
Light
extinction
$
880
$
1,400
TOTAL
+
B*
$
58,000
$
84,000
MRADs=
minor
restricted
activity
days
B=
nonmonetized
benefits
*
Note
total
dollar
benefits
are
rounded
to
the
nearest
billion
and
column
totals
may
not
add
due
to
rounding.
1­
5
Thus,
the
annual
net
benefit
(
social
benefits
minus
social
costs)
of
the
program
is
approximately
$
55
+
B
billion
in
2010
and
$
80
+
B
billion
in
2015.
Therefore,

implementation
of
the
proposed
rule
is
expected
to
provide
society
with
a
net
gain
in
social
welfare
based
on
economic
efficiency
criteria.
As
Table
1­
2
shows,
although
mortality
benefits
account
for
over
90
percent
of
total
monetized
benefits,
the
economic
value
of
morbidity
benefits
alone
exceed
the
cost
of
the
proposed
rule.
As
discussed
in
section
IX
of
the
notice
for
this
rulemaking,
we
did
not
complete
air
quality
modeling
that
precisely
matches
the
IAQR
region.
We
anticipate
that
any
differences
in
the
estimates
presented
due
to
the
modeling
region
analyzed
will
be
small.

Every
benefit­
cost
analysis
examining
the
potential
effects
of
a
change
in
environmental
protection
requirements
is
limited
to
some
extent
by
data
gaps,
limitations
in
model
capabilities
Table
1­
3.
Summary
of
Annual
Benefits,
Costs,
and
Net
Benefits
of
the
Inter­
State
Air
Quality
Rule
Description
2010
(
billions
of
1999
dollars)
2015
(
billions
of
1999
dollars)

Social
costsa
$
2.9
$
3.7
Social
benefits
b,
c
Ozone­
related
benefits
$
0.1
$
0.1
PM­
related
health
benefits
$
56.8
+
B
$
82.3
+
B
Visibility
benefits
$
0.9
$
1.4
Net
benefits
(
benefits­
costs)
c,
d
$
55
+
B
$
80
+
B
a
Note
that
costs
are
the
annual
total
costs
of
reducing
pollutants
including
NOx
and
SO2.

b
As
the
table
indicates,
total
benefits
are
driven
primarily
by
PM­
related
health
benefits.
The
reduction
in
premature
fatalities
each
year
accounts
for
over
90
percent
of
total
benefits.
Benefits
in
this
table
are
associated
with
NOx
and
SO2
reductions.

c
Not
all
possible
benefits
or
disbenefits
are
quantified
and
monetized
in
this
analysis.
B
is
the
sum
of
all
unquantified
benefits
and
disbenefits.
Potential
benefit
categories
that
have
not
been
quantified
and
monetized
are
listed
in
Table
1­
4.

d
Net
benefits
are
rounded
to
the
nearest
billion.
Columnar
totals
may
not
sum
due
to
rounding.
1­
6
Table
1­
4.
Additional
Nonmonetized
Benefits
of
the
Inter­
State
Air
Quality
Rule
Pollutant
Unquantified
Effects
Ozone
Health
Premature
mortalitya
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
Ozone
Welfare
Decreased
yields
for
commercial
forests
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
PM
Health
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
PM
Welfare
Visibility
in
many
Class
I
areas
Residential
and
recreational
visibility
in
non­
Class
I
areas
Soiling
and
materials
damage
Damage
to
ecosystem
functions
Nitrogen
and
Sulfate
Deposition
Welfare
Impacts
of
acidic
sulfate
and
nitrate
deposition
on
commercial
forests
Impacts
of
acidic
deposition
to
commercial
freshwater
fishing
Impacts
of
acidic
deposition
to
recreation
in
terrestrial
ecosystems
Reduced
existence
values
for
currently
healthy
ecosystems
Impacts
of
nitrogen
deposition
on
commercial
fishing,
agriculture,
and
forests
Impacts
of
nitrogen
deposition
on
recreation
in
estuarine
ecosystems
Damage
to
ecosystem
functions
Mercury
Health
Neurological
disorders
Learning
disabilities
Developmental
delays
Potential
cardiovascular
effects*
Altered
blood
pressure
regulation*
Increased
heart
rate
variability*
Myocardial
infarction*
Potential
reproductive
effects*

Mercury
Deposition
Welfare
Impact
on
birds
and
mammals
(
e.
g.,
reproductive
effects)
Impacts
to
commercial,
subsistence,
and
recreational
fishing
Reduced
existence
values
for
currently
healthy
ecosystems
a
Premature
mortality
associated
with
ozone
is
not
separately
included
in
this
analysis.
*
These
are
potential
effects
as
the
literature
is
either
contradictory
or
incomplete.
1­
7
(
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
quantitative
changes
in
health
and
environmental
effects,
such
as
potential
increases
in
fish
populations
due
to
reductions
in
nitrogen
loadings
in
sensitive
estuaries.
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.
Although
these
general
uncertainties
in
the
underlying
scientific
and
economics
literatures
(
that
can
cause
the
valuations
to
be
higher
or
lower)
are
discussed
in
detail
in
the
economic
analyses
and
its
supporting
documents
and
references,
the
key
uncertainties
that
have
a
bearing
on
the
results
of
the
benefit­
cost
analysis
of
this
proposed
rule
include
the
following:


the
exclusion
of
potentially
significant
benefit
categories
(
such
as
health
and
ecological
benefits
of
reductions
in
mercury
emissions),


errors
in
measurement
and
projection
for
variables
such
as
population
growth
and
baseline
incidence
rates,


uncertainties
in
the
estimation
of
future­
year
emissions
inventories
and
air
quality,


variability
in
the
estimated
relationships
of
health
and
welfare
effects
to
changes
in
pollutant
concentrations,


uncertainties
in
exposure
estimation,


uncertainties
in
the
size
of
the
effect
estimates
linking
air
pollution
and
health
endpoints,


uncertainties
about
relative
toxicity
of
different
components
within
the
complex
mixture,
and

uncertainties
associated
with
the
effect
of
potential
future
actions
to
limit
emissions.

Despite
these
uncertainties,
we
believe
the
benefit­
cost
analysis
provides
a
reasonable
indication
of
the
expected
economic
benefits
of
the
proposed
rulemaking
in
future
years
under
a
set
of
reasonable
assumptions.

In
addition,
in
valuing
reductions
in
premature
fatalities
associated
with
PM,
we
used
a
value
of
$
5.5
million
per
statistical
life.
This
represents
a
central
value
consistent
with
a
range
of
values
from
$
1
to
$
10
million
suggested
by
recent
meta­
analyses
of
the
wage­
risk
value
of
statistical
life
(
VSL)
literature.
1­
8
The
benefits
estimates
generated
for
the
Proposed
IAQR
are
subject
to
a
number
of
assumptions
and
uncertainties,
which
are
discussed
throughout
the
document.
As
Table
1­
2
indicates,
total
benefits
are
driven
primarily
by
the
reduction
in
premature
fatalities
each
year,

which
account
for
over
90
percent
of
total
benefits.
For
example,
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.

We
were
unable
to
quantify
or
monetize
a
number
of
health
and
environmental
effects.
A
full
appreciation
of
the
overall
economic
consequences
of
the
proposed
rule
requires
consideration
of
all
benefits
and
costs
expected
to
result
from
the
proposed
rule,
not
just
those
benefits
and
costs
that
could
be
expressed
here
in
dollar
terms.
A
listing
of
the
benefit
categories
that
could
not
be
quantified
or
monetized
in
our
estimate
is
provided
in
Table
1­
4.

These
effects
are
denoted
by
"
B"
in
Table
1­
3
above
and
are
additive
to
the
estimates
of
benefits.
1­
9
We
are
unable
to
quantify
changes
in
levels
of
methylmercury
contamination
in
fish
associated
with
reductions
in
mercury
emissions
for
this
proposal.
However,
this
proposal
is
anticipated
to
decrease
annual
EGU
mercury
emissions
nationwide
by
10.6
tons
in
2010
or
approximately
23.5
percent,
by
11.8
tons
in
2015
or
26.3
percent,
and
by
14.3
tons
or
32
percent
in
2020.
Emission
reduction
percentage
decreases
are
based
upon
expected
mercury
emissions
changes
from
fossil­
fired
EGUs
larger
than
25
megawatt
capacity.
In
a
separate
action,
EPA
is
proposing
to
regulate
mercury
and
nickel
from
certain
types
of
electric
generating
units
using
the
maximum
achievable
control
technology
(
MACT)
provisions
of
section
112
of
the
CAA
or,
in
the
alternative,
using
the
performance
standards
provisions
under
section
111
of
the
CAA.
This
proposal
will
have
implications
for
mercury
reductions,

and
potential
interactions
may
exist
between
the
rulemakings.
Information
concerning
potential
interactions
in
the
two
rulemakings
is
discussed
in
the
notice
for
proposed
rulemaking
for
the
CAA
Section
112
proposal
and
in
the
document
Benefit
Analysis
of
the
CAA
Section
112
Proposal
to
Reduce
Mercury
Emissions
included
in
the
docket
for
the
rulemaking.
2­
1
SECTION
2
INTRODUCTION
AND
BACKGROUND
For
this
rulemaking,
the
EPA
has
assessed
the
role
that
transported
emissions
from
upwind
States
play
in
contributing
to
unhealthy
levels
of
PM
2.5
and
8­
hour
ozone
in
downwind
States.
Based
on
this
assessment,
the
EPA
is
proposing
emissions
reduction
requirements
that
would
apply
to
upwind
States
under
the
Clean
Air
Act.
This
report
assesses
the
health
and
welfare
benefits
of
the
proposed
rule.
This
document
presents
the
health
and
welfare
benefits
of
the
IAQR
and
compares
the
benefits
of
this
proposal
to
the
estimated
costs
of
implementing
the
rule
in
2010
and
2015.
Significant
health
and
welfare
benefits
are
likely
to
occur
as
a
result
of
this
rule,
and
these
benefits
are
enumerated
in
this
document.
This
chapter
contains
background
information
relative
to
the
rule
and
an
outline
of
the
chapters
of
the
report.

2.1
Background
Congress
recognized
that
interstate
pollution
transport
from
upwind
States
can
contribute
to
unhealthy
pollution
levels
in
downwind
States.
Therefore,
the
CAA
contains
provisions
in
section
110(
a)(
2)(
D)
that
require
upwind
States
to
eliminate
emissions
that
contribute
significantly
to
nonattainment
downwind.
Under
section
110(
a)(
2)
States
are
required
to
submit
plans
to
the
EPA
within
3
years
of
issuance
of
a
revised
National
Ambient
Air
Quality
Standard.
Among
other
requirements,
these
plans
are
required
to
prohibit
emissions
in
the
State
that
contribute
significantly
to
nonattainment
downwind.

The
EPA's
proposal
finds
that
29
States
and
the
District
of
Columbia
contribute
significantly
to
nonattainment,
or
interfere
with
maintenance,
of
the
NAAQS
for
PM
2.5
and/
or
8­
hour
ozone
in
downwind
States.
The
EPA
is
proposing
to
require
these
upwind
States
to
revise
their
State
Implementation
Plans
to
include
control
measures
to
reduce
emissions
of
SO
2
and/
or
NO
x.
SO
2
is
a
precursor
to
PM
2.5
formation,
and
NO
x
is
a
precursor
to
both
ozone
and
PM
2.5
formation.
Reducing
upwind
precursor
emissions
will
assist
the
downwind
PM
2.5
and
8­
hour
ozone
nonattainment
areas
in
achieving
the
NAAQS.
Moreover,
attainment
would
be
achieved
in
a
more
equitable,
cost­
effective
manner
than
if
each
nonattainment
area
attempted
to
achieve
attainment
by
implementing
local
emissions
reductions
alone.
The
2­
2
relevant
regions
for
PM2.5
and
ozone
significant
contribution
are
depicted
in
Figures
2­
1
and
2­
2,
respectively.

2.2
Regulated
Entities
This
action
does
not
propose
to
directly
regulate
emissions
sources.
Instead,
it
proposes
to
require
States
to
revise
their
SIPs
to
include
control
measures
to
reduce
emissions
of
NO
x
and
SO
2.
The
proposed
emission
reduction
requirements
that
would
be
assigned
to
the
States
are
based
on
controls
that
are
known
to
be
highly
cost
effective
for
electric
generation
units
(
EGUs).
However,
States
would
have
the
flexibility
to
choose
what
sources
to
control.

While
the
EPA
is
soliciting
comments
on
the
potential
for
pollution
control
from
other
sources,
the
analysis
conducted
assumes
controls
for
EGUs
only.

2.3
Control
Scenario
The
analysis
conducted
assumes
that
a
cap­
and­
trade
program
will
be
used
to
achieve
the
level
of
emission
control
requirements
desired.
The
EPA
would
establish
regional
emission
budget
determinations
for
SO
2
and
NO
x
to
address
the
transport
problem.
In
this
proposal,

these
requirements
would
effectively
establish
emission
caps
in
2010
for
SO
2
and
NO
x
of
3.9
million
tons
and
1.6
million
tons,
respectively.
These
budgets
would
be
lowered
in
2015
to
provide
SO
2
and
NO
x
emission
caps
of
2.7
million
tons
and
1.3
million
tons,
respectively
in
the
proposed
control
region.
These
quantities
were
derived
by
calculating
the
amount
of
emissions
of
SO
2
and
NO
x
that
the
EPA
believes
can
be
controlled
from
large
EGUs
in
a
highly
cost­
effective
manner.
When
fully
implemented,
this
would
result
in
nationwide
SO
2
emissions
of
approximately
3.5
million
tons.
This
is
significantly
lower
than
the
8.95
million
tons
of
SO
2
emissions
allowed
under
the
current
Title
IV
Acid
Rain
SO
2
Trading
Program.

The
EPA
expects
that
States
will
elect
to
join
a
regional
cap­
and­
trade
program
for
these
pollutants.

2.4
Cost
of
Emission
Controls
The
EPA
analyzed
the
costs
of
IAQR
using
the
Integrated
Planning
Model
(
IPM).
The
EPA
has
used
the
IPM
to
analyze
the
impacts
of
regulations
on
the
power
sector.
A
description
of
the
methodology
used
to
model
the
costs
and
economic
impacts
to
the
power
sector
may
be
obtained
in
"
Preliminary
Analysis
of
the
Costs
of
the
Inter­
State
Air
Quality
Rule"
January
2004.
It
is
estimated
that
the
annual
cost
of
implementing
this
proposal
in
2010
is
$
2.9
billion
and
in
2015
is
$
3.7
billion
in
the
transport
region
(
1999$).
2­
3
States
where
NO
x
control
is
not
needed
for
ozone,
only
PM.

States
where
NO
x
control
is
needed
for
ozone
and
PM
Figure
2­
1.
States
Identified
as
Having
Significant
Contribution
to
PM2.5
2.5
Organization
of
this
Report
This
document
describes
the
health
and
welfare
benefits
of
the
proposed
rule.
The
document
is
organized
as
follows:


Chapter
3,
Emissions
and
Air
Quality
Impacts,
describes
emission
inventories
and
air
quality
modeling
that
are
essential
inputs
into
the
benefits
assessment.
2­
4
Figure
2­
2.
States
Identified
as
Having
Significant
Contribution
to
Ozone

Chapter
4,
Benefits
Analysis
and
Results,
describes
the
methodology
and
results
of
the
benefits
analysis.


Chapter
5,
Qualitative
Assessment
of
Nonmonetized
Benefits,
describes
benefits
that
are
not
monetized
for
this
rulemaking.


Chapter
6,
Comparison
of
Benefits
and
Costs,
provides
a
comparison
of
the
monetized
benefits
and
estimated
annual
costs
of
the
proposed
control
scenario.
3­
1
SECTION
3
EMISSIONS
AND
AIR
QUALITY
IMPACTS
This
chapter
summarizes
the
emissions
inventories
and
air
quality
modeling
that
serve
as
the
inputs
to
the
benefits
analysis
of
this
proposed
rule
as
detailed
in
Chapter
4.
In
summary,

given
baseline
and
post­
control
emissions
inventories
for
the
emission
species
expected
to
impact
ambient
air
quality,
we
use
sophisticated
photochemical
air
quality
models
to
estimate
baseline
and
post­
control
ambient
concentrations
of
ozone
and
PM
and
deposition
of
nitrogen
and
sulfur
for
each
year.
The
estimated
changes
in
ambient
concentrations
are
then
combined
with
monitoring
data
to
estimate
population
level
exposures
to
changes
in
ambient
concentrations
for
use
in
estimating
health
effects.
Modeled
changes
in
ambient
data
are
also
used
to
estimate
changes
in
visibility
and
changes
in
other
air
quality
statistics
that
are
necessary
to
estimate
welfare
effects.

The
initial
section
of
this
chapter
provides
a
summary
of
the
baseline
emissions
inventories
and
the
emissions
reductions
that
were
modeled
for
this
rule.
The
next
section
provides
a
summary
of
the
methods
for
and
results
of
estimating
air
quality
for
the
2010
and
2015
base
cases
and
control
scenarios
for
the
purposes
of
the
benefit
analysis.
There
are
separate
sections
for
PM,
ozone,
and
visibility.

3.1
Emissions
Inventories
and
Estimated
Emissions
Reductions
The
technical
support
document
for
emissions
inventories
discusses
the
development
of
the
2001,
2010
and
2015
baseline
emissions
inventories
for
the
benefits
analysis
of
this
proposed
rule.
The
emission
sources
and
the
basis
for
current
and
future­
year
inventories
are
listed
in
Table
3­
1.
Tables
3­
2
and
3­
3
summarize
the
baseline
emissions
of
NO
X
and
SO
2
and
the
change
in
the
emissions
from
EGUs
that
were
used
in
modeling
air
quality
changes.
3­
2
3.2
Air
Quality
Impacts
This
section
summarizes
the
methods
for
and
results
of
estimating
air
quality
for
the
2010
and
2015
base
cases
and
control
scenarios
for
the
purposes
of
the
benefit
analysis.
EPA
has
focused
on
the
health,
welfare,
and
ecological
effects
that
have
been
linked
to
air
quality
changes.
These
air
quality
changes
include
the
following:

1.
Ambient
particulate
matter
(
PM
10
and
PM
2.5)
 
as
estimated
using
a
national­
scale
version
of
the
REgional
Modeling
System
for
Aerosols
and
Deposition
(
REMSAD);

2.
Ambient
ozone
 
as
estimated
using
regional­
scale
applications
of
the
Comprehensive
Air
Quality
Model
with
Extensions
(
CAMx);
and
Table
3­
1.
Emissions
Sources
and
Basis
for
Current
and
Future­
Year
Inventories
Emissions
Source
2001
Base
Year
Future­
Year
Base
Case
Projections
Utilities
2001
CEM
data
Integrated
Planning
Model
(
IPM)

Non­
Utility
Point
and
Area
sources
Straight­
line
projections
from
1996
NEI
Version
3.12
(
point)

Version
3.11
(
area)
BEA
growth
projections
Highway
vehicles
MOBILE5b
model
with
MOBILE6
adjustment
factors
for
VOC
and
NOX;

PART5
model
for
PM
VMT
projection
data
Nonroad
engines
(
except
locomotives,
commercial
marine
vessels,
and
aircraft)
NONROAD2002
model
BEA
and
Nonroad
equipment
growth
projections
Note:
Full
description
of
data,
models,
and
methods
applied
for
emissions
inventory
development
and
modeling
are
provided
in
Emissions
Inventory
TSD
(
EPA,
2003a).
3­
3
3.
Visibility
degradation
(
i.
e.,
regional
haze),
as
developed
using
empirical
estimates
of
light
extinction
coefficients
and
efficiencies
in
combination
with
REMSAD
modeled
reductions
in
pollutant
concentrations.
3­
4
Table
3­
2.
Summary
of
Modeled
Baseline
Emissions
for
Lower
48
States
Pollutant
Emissions
(
tons)

Source
NOX
SO2
2001
Baseline
EGUs
4,824,967
10,714,558
Non­
EGUs
3,180,835
3,696,048
Area
2,220,728
1,379,810
Mobile
8,694,038
261,526
Nonroad
4,059,278
531,203
Total,
All
Sources
22,979,846
16,583,145
2010
Base
Case
EGUs
3,943,438
9,856,926
Non­
EGUs
3,228,201
3,799,163
Area
2,225,898
1,367,643
Mobile
4,931,947
29,790
Nonroad
3,404,962
236,446
Total,
All
Sources
17,734,447
15,289,969
2015
Base
Case
EGUs
4,008,241
9,222,097
Non­
EGUs
3,307,415
3,893,813
Area
2,235,712
1,369,925
Mobile
3,458,279
32,551
Nonroad
2,903,048
232,644
Total,
All
Sources
15,912,695
14,751,030
3­
5
The
air
quality
estimates
in
this
section
are
based
on
the
emission
changes
summarized
in
the
preceding
section.
These
air
quality
results
are
in
turn
associated
with
human
populations
and
ecosystems
to
estimate
changes
in
health
and
welfare
effects.
In
Section
3.2.1,
we
describe
the
estimation
of
PM
air
quality
using
REMSAD,
and
in
Section
3.2.2,
we
cover
the
estimation
of
ozone
air
quality
using
CAMx.
Lastly,
in
Section
3.2.3,
we
discuss
the
estimation
of
visibility
degradation.

3.2.1
PM
Air
Quality
Estimates
We
use
the
emissions
inputs
summarized
above
with
a
national­
scale
version
of
the
REgional
Model
System
for
Aerosols
and
Deposition
(
REMSAD)
to
estimate
PM
air
quality
in
the
contiguous
U.
S.
REMSAD
is
a
three­
dimensional
grid­
based
Eulerian
air
quality
model
designed
to
estimate
annual
particulate
concentrations
and
deposition
over
large
spatial
scales
(
e.
g.,
over
the
contiguous
U.
S.).
Consideration
of
the
different
processes
that
affect
primary
(
directly
emitted)
and
secondary
(
formed
by
atmospheric
processes)
PM
at
the
regional
scale
Table
3­
3.
Summary
of
Modeled
Emissions
Changes
for
the
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
Pollutant
Item
NOX
SO2
2010
Emission
Changesa
Absolute
Tons
1,373,919
3,750,219
Percentage
of
EGU
Emissions
34.8%
38.1%

Percentage
of
All
Manmade
Emissions
7.8%
24.5%

2015
Emission
Changesa
Absolute
Tons
1,704,065
3,820,393
Percentage
of
EGU
Emissions
42.5%
41.4%

Percentage
of
All
Manmade
Emissions
10.7%
25.9%

a
Note
that
the
emission
changes
only
occur
within
the
affected
transport
region;
however,
the
percent
reductions
reflect
the
change
as
a
share
of
baseline
emissions
for
the
lower
48
states
as
presented
in
Table
3­
2.
1Given
the
focus
of
this
rule
on
secondarily
formed
particles
it
is
important
to
employ
a
Eulerian
model
such
as
REMSAD.
The
impact
of
secondarily
formed
pollutants
typically
involves
primary
precursor
emissions
from
a
multitude
of
widely
dispersed
sources,
and
chemical
and
physical
processes
of
pollutants
are
best
addressed
using
an
air
quality
model
that
employs
an
Eulerian
grid
model
design.

3­
6
in
different
locations
is
fundamental
to
understanding
and
assessing
the
effects
of
proposed
pollution
control
measures
that
affect
ozone,
PM
and
deposition
of
pollutants
to
the
surface.
1
Because
it
accounts
for
spatial
and
temporal
variations
as
well
as
differences
in
the
reactivity
of
emissions,
REMSAD
is
useful
for
evaluating
the
impacts
of
the
proposed
rule
on
U.
S.
PM
concentrations.

REMSAD
was
peer­
reviewed
in
1999
for
EPA
as
reported
in
"
Scientific
Peer­
Review
of
the
Regulatory
Modeling
System
for
Aerosols
and
Deposition"
(
Seigneur
et
al.,
1999).

Earlier
versions
of
REMSAD
have
been
employed
for
the
EPA's
Prospective
812
Report
to
Congress,
EPA's
Heavy
Duty
(
HD)
Engine/
Diesel
Fuel
rule,
and
EPA's
air
quality
assessment
of
the
Clear
Skies
Initiative.
Version
7.06
of
REMSAD
was
employed
for
this
analysis
and
is
fully
described
in
the
air
quality
modeling
technical
support
document
(
EPA,
2003b).
This
version
reflects
updates
in
the
following
areas
to
improve
performance
and
address
comments
from
the
1999
peer­
review:

1.
Gas
phase
chemistry
updates
to
"
micro­
CB4"
mechanism
including
new
treatment
for
the
NO
3
and
N
2
O
5
species
and
the
addition
of
several
reactions
to
better
account
for
the
wide
ranges
in
temperature,
pressure,
and
concentrations
that
are
encountered
for
regional
and
national
applications.

2.
PM
chemistry
updates
to
calculate
particulate
nitrate
concentrations
through
use
of
the
MARS­
A
equilibrium
algorithm
and
internal
calculation
of
secondary
organic
aerosols
from
both
biogenic
(
terpene)
and
anthropogenic
(
estimated
aromatic)

VOC
emissions.

3.
Aqueous
phase
chemistry
updates
to
incorporate
the
oxidation
of
SO
2
by
O3
and
O2
and
to
include
the
cloud
and
rain
liquid
water
content
from
MM5
meteorological
data
directly
in
sulfate
production
and
deposition
calculations.

4.
Calculation
of
the
production
of
secondary
organic
aerosols
(
SOA)
due
to
atmospheric
chemistry
processes
has
been
added
for
both
anthropogenic
and
biogenic
organics.
3­
7
As
discussed
in
the
Air
Quality
Modeling
TSD,
the
model
tends
to
underestimate
observed
PM
2.5
concentrations
nationwide.

Our
analysis
applies
the
modeling
system
to
the
entire
U.
S.
for
the
six
emissions
scenarios:

a
1996
baseline
year
for
performance
evaluation,
a
2001
baseline
projection,
a
2010
baseline
projection
and
a
2010
projection
with
controls,
a
2015
baseline
projection
and
a
2015
projection
with
controls.
REMSAD
simulates
every
hour
of
every
day
of
the
year
and,
thus,

requires
a
variety
of
input
files
that
contain
information
pertaining
to
the
modeling
domain
and
simulation
period.
These
include
gridded,
1­
hour
average
emissions
estimates
and
meteorological
fields,
initial
and
boundary
conditions,
and
land­
use
information.
As
applied
to
the
contiguous
U.
S.,
the
model
segments
the
area
within
the
region
into
square
blocks
called
grids
(
roughly
equal
in
size
to
counties),
each
of
which
has
several
layers
of
air
conditions.

Using
this
data,
REMSAD
generates
predictions
of
1­
hour
average
PM
concentrations
for
every
grid.
As
discussed
in
the
Air
Quality
Modeling
TSD,
we
use
the
relative
predictions
from
the
model
by
combining
the
2001
base­
year
and
each
future­
year
scenario
with
speciated
ambient
air
quality
observations
to
determine
the
expected
change
in
2010
or
2015
concentrations
due
to
the
rule.
After
completing
this
process,
we
then
calculated
daily
and
seasonal
PM
air
quality
metrics
as
inputs
to
the
health
and
welfare
C­
R
functions
of
the
benefits
analysis.
The
following
sections
provide
a
more
detailed
discussion
of
each
of
the
steps
in
this
evaluation
and
a
summary
of
the
results.

3.2.1.1
Modeling
Domain
The
PM
air
quality
analyses
employed
the
modeling
domain
used
previously
in
support
of
Clear
Skies
air
quality
assessment.
As
shown
in
Figure
3­
1,
the
modeling
domain
encompasses
the
lower
48
States
and
extends
from
126
degrees
to
66
degrees
west
longitude
and
from
24
degrees
north
latitude
to
52
degrees
north
latitude.
The
model
contains
horizontal
grid­
cells
across
the
model
domain
of
roughly
36
km
by
36
km.
There
are
12
vertical
layers
of
atmospheric
conditions
with
the
top
of
the
modeling
domain
at
16,200
meters.
The
36
by
36
km
horizontal
grid
results
in
a
120
by
84
grid
(
or
10,080
grid­
cells)
for
each
vertical
layer.
Figure
3­
2
illustrates
the
horizontal
grid­
cells
for
Maryland
and
surrounding
areas.
3­
8
Figure
3­
1.
REMSAD
Modeling
Domain
for
Continental
United
States
Note:
Gray
markings
define
individual
grid­
cells
in
the
REMSAD
model.

3.2.1.2
Simulation
Periods
For
use
in
this
benefits
analysis,
the
simulation
periods
modeled
by
REMSAD
included
separate
full­
year
application
for
each
of
the
six
emissions
scenarios,
i.
e.,
1996
and
2001
baseline
years
and
the
2010
and
2015
base
cases
and
control
scenarios.
3­
9
Figure
3­
2.
Example
of
REMSAD
36
x
36km
Grid­
cells
for
Maryland
Area
3.2.1.3
Model
Inputs
REMSAD
requires
a
variety
of
input
files
that
contain
information
pertaining
to
the
modeling
domain
and
simulation
period.
These
include
gridded,
1­
hour
average
emissions
estimates
and
meteorological
fields,
initial
and
boundary
conditions,
and
land­
use
information.

Separate
emissions
inventories
were
prepared
for
the
1996
and
2001
baseline
years
and
each
of
the
future­
year
base
cases
and
control
scenarios.
All
other
inputs
were
specified
for
the
1996
baseline
model
application
and
remained
unchanged
for
each
future­
year
modeling
scenario.

REMSAD
requires
detailed
emissions
inventories
containing
temporally
allocated
emissions
for
each
grid­
cell
in
the
modeling
domain
for
each
species
being
simulated.
The
previously
described
annual
emission
inventories
were
preprocessed
into
model­
ready
inputs
3­
10
through
the
SMOKE
emissions
preprocessing
system.
Details
of
the
preprocessing
of
emissions
through
SMOKE
as
provided
in
the
emissions
inventory
TSD.
Meteorological
inputs
reflecting
1996
conditions
across
the
contiguous
U.
S.
were
derived
from
Version
5
of
the
Mesoscale
Model
(
MM5).
These
inputs
included
horizontal
wind
components
(
i.
e.,
speed
and
direction),
temperature,
moisture,
vertical
diffusion
rates,
and
rainfall
rates
for
each
grid
cell
in
each
vertical
layer.
Details
of
the
annual
1996
MM5
modeling
are
provided
in
Olerud
(
2000).

A
postprocessor
called
MM5REMSAD
was
developed
to
convert
the
MM5
data
into
the
appropriate
REMSAD
grid
coordinate
systems
and
file
formats.
This
postprocessor
was
used
to
develop
the
hourly
average
meteorological
input
files
from
the
MM5
output.

Documentation
of
the
MM5REMSAD
code
and
further
details
on
the
development
of
the
input
files
are
contained
in
Mansell
(
2000).
A
more
detailed
description
of
the
development
of
the
meteorological
input
data
is
provided
in
the
Air
Quality
TSD,
which
is
located
in
the
docket
for
this
rule.

The
modeling
specified
initial
species
concentrations
and
lateral
boundary
conditions
to
approximate
background
concentrations
of
the
species;
for
the
lateral
boundaries
the
concentrations
varied
(
decreased
parabolically)
with
height.
These
initial
conditions
reflect
relatively
clean
background
concentration
values.
Terrain
elevations
and
land
use
information
was
obtained
from
the
U.
S.
Geological
Survey
database
at
10
km
resolution
and
aggregated
to
the
roughly
36
km
horizontal
resolution
used
for
this
REMSAD
application.
The
development
of
model
inputs
is
discussed
in
greater
detail
in
the
Air
Quality
TSD,
which
is
available
in
the
docket
for
this
rule.

3.2.1.4
Model
Performance
for
Particulate
Matter
(
PM)

The
purpose
of
the
base
year
PM
air
quality
modeling
was
to
reproduce
the
atmospheric
processes
resulting
in
formation
and
dispersion
of
fine
particulate
matter
across
the
U.
S.
An
operational
model
performance
evaluation
for
PM
2.5
and
its
related
speciated
components
(
e.
g.,
sulfate,
nitrate,
elemental
carbon
etc.)
for
1996
was
performed
in
order
to
estimate
the
ability
of
the
modeling
system
to
replicate
base
year
concentrations.

This
evaluation
is
comprised
principally
of
statistical
assessments
of
model
versus
observed
pairs.
The
robustness
of
any
evaluation
is
directly
proportional
to
the
amount
and
quality
of
the
ambient
data
available
for
comparison.
Unfortunately,
there
are
few
PM
2.5
monitoring
networks
with
available
data
for
evaluation
of
the
PM
modeling.
Critical
limitations
of
the
1996
databases
are
a
lack
of
urban
monitoring
sites
with
speciated
3­
11
measurements
and
poor
geographic
representation
of
ambient
concentration
in
the
Eastern
U.
S.

The
largest
available
ambient
database
for
1996
comes
from
the
Interagency
Monitoring
of
PROtected
Visual
Environments
(
IMPROVE)
network.
IMPROVE
is
a
cooperative
visibility
monitoring
effort
between
EPA,
federal
land
management
agencies,
and
state
air
agencies.
Data
is
collected
at
Class
I
areas
across
the
United
States
mostly
at
National
Parks,

National
Wilderness
Areas,
and
other
protected
pristine
areas
(
IMPROVE
2000).
There
were
approximately
60
IMPROVE
sites
that
had
complete
annual
PM
2.5
mass
and/
or
PM
2.5
species
data
for
1996.
Using
the
100th
meridian
to
divide
the
eastern
and
western
U.
S.,
42
sites
were
located
in
the
West
and
18
sites
were
in
the
East.

As
presented
in
Table
3­
4,
the
observed
IMPROVE
data
used
for
the
performance
evaluation
consisted
of
PM
2.5
total
mass,
sulfate
ion,
nitrate
ion,
elemental
carbon,
organic
aerosols,
and
crustal
material
(
soils).
The
REMSAD
model
output
species
were
postprocessed
in
order
to
achieve
compatibility
with
the
observation
species.
The
principal
evaluation
statistic
used
to
evaluate
REMSAD
performance
is
the
"
ratio
of
the
means."
It
is
defined
as
the
ratio
of
the
average
predicted
values
over
the
average
observed
values.
The
annual
average
ratio
of
the
means
was
calculated
for
five
individual
PM2.5
species
as
well
as
for
total
PM2.5
mass.
The
metrics
were
calculated
for
all
IMPROVE
sites
across
the
country
as
well
as
for
the
East
and
West
individually.
The
following
table
shows
the
ratio
of
the
annual
means.
Numbers
greater
than
1
indicate
overpredictions
compared
to
ambient
observations
(
e.
g.,
1.23
is
a
23
percent
overprediction).
Numbers
less
than
1
indicate
underpredictions.

When
considering
annual
average
statistics
(
e.
g.,
predicted
versus
observed),
which
are
computed
and
aggregated
over
all
sites
and
all
days,
REMSAD
underpredicted
fine
particulate
mass
(
PM
2.5),
by
18
percent.
PM
2.5
in
the
Eastern
U.
S.
was
underpredicted
by
2
percent,

while
PM
2.5
in
the
West
was
underpredicted
by
33
percent.
All
PM
2.5
component
species
were
underpredicted
in
the
west.
In
the
East,
nitrate
and
crustal
material
are
overestimated.

Elemental
carbon
shows
neither
over
or
underprediction
in
the
east
with
a
bias
near
0
percent.

Eastern
sulfate
is
slightly
underpredicted
with
a
bias
of
12
percent.
Organic
aerosols
show
little
or
no
bias
in
the
East
and
West.

Given
the
state
of
the
science
relative
to
PM
modeling,
it
is
inappropriate
to
judge
PM
model
performance
using
criteria
derived
for
other
pollutants,
like
ozone.
Still,
the
performance
of
the
IAQR
PM
modeling
is
very
encouraging,
especially
considering
that
the
3­
12
results
may
be
limited
by
our
current
knowledge
of
PM
science
and
chemistry,
by
the
emissions
inventories
for
primary
PM
and
secondary
PM
precursor
pollutants,
by
the
relatively
sparse
ambient
data
available
for
comparisons
to
model
output,
and
by
uncertainties
in
monitoring
techniques.
The
model
performance
for
sulfate
is
quite
reasonable,
which
is
key
to
the
analysis
due
to
the
importance
of
SO
2
emissions
reductions
in
the
IAQR
control
strategy.
Additional
details,
including
comparisons
to
other
monitoring
networks,
can
be
found
in
the
Air
Quality
Modeling
TSD.

3.2.1.5
Converting
REMSAD
Outputs
to
Benefits
Inputs
REMSAD
generates
predictions
of
hourly
PM
concentrations
for
every
grid.
The
particulate
matter
species
modeled
by
REMSAD
include
a
primary
coarse
fraction
(
corresponding
to
PM
in
the
2.5
to
10
micron
size
range),
a
primary
fine
fraction
(
corresponding
to
PM
less
than
2.5
microns
in
diameter),
and
several
secondary
particles
(
e.
g.,
sulfates,
nitrates,
and
organics).
PM
2.5
is
calculated
as
the
sum
of
the
primary
fine
fraction
and
all
of
the
secondarily­
formed
particles.
Future­
year
estimates
of
PM
2.5
were
calculated
using
relative
reduction
factors
(
RRFs)
applied
to
2000­
2002
PM
2.5
design
values
(
EPA,
2003b).
The
procedures
for
determining
the
RRFs
are
similar
to
those
in
EPA's
draft
guidance
for
modeling
the
PM
2.5
standard
(
EPA,
1999a).
The
guidance
recommends
that
model
predictions
be
used
in
a
relative
sense
to
estimate
changes
expected
to
occur
in
each
Table
3­
4.
Model
Performance
Statistics
for
REMSAD
PM2.5
Species
Predictions:
1996
Ratio
of
the
Means
(
annual
average
concentrations)

IMPROVE
PM
Species
Nationwide
Eastern
U.
S.
Western
U.
S.

PM2.5,
total
mass
0.82
0.98
0.67
Sulfate
ion
0.79
0.88
0.59
Nitrate
ion
1.55
2.66
0.69
Elemental
carbon
0.86
1.01
0.71
Organic
aerosols
1.00
1.04
0.97
Soil/
Other
1.33
3.08
0.81
Note:
The
dividing
line
between
the
West
and
East
was
defined
as
the
100th
meridian.
3­
13
major
PM
2.5
species.
These
species
are
sulfate,
nitrate,
organic
carbon,
elemental
carbon,

crustal
and
un­
attributed
mass
which
is
defined
as
the
difference
between
measured
PM
2.5
and
the
sum
of
the
other
five
components.
The
procedure
for
calculating
future
year
PM
2.5
design
values
is
called
the
"
Speciated
Modeled
Attainment
Test
(
SMAT)".
EPA
previously
used
this
procedure
to
estimate
the
ambient
impact
of
the
Clear
Skies
Act
emissions
controls.

The
SMAT
procedure
was
performed
using
the
base
year
2001
scenario
and
each
of
the
future­
year
scenarios.
The
SMAT
approach
uses
temporally
scaled
speciated
PM2.5
monitor
data
from
2001­
2002,
reconstructed
into
total
PM2.5
mass
based
on
2000­
2002
design
values,
and
kriged
to
12
kilometer
grids
(
nested
within
the
standard
36
km
REMSAD
grid
structure).
Temporal
scaling
is
based
on
ratios
of
future
modeled
REMSAD
data
to
2001
REMSAD
model
data,
using
REMSAD
modeling
conducted
at
the
36
km
grid
resolution.

SMAT
output
files
include
both
quarterly
mean
and
annual
mean
PM2.5
mass
results,
which
are
then
manipulated
within
SAS
to
produce
a
BenMAP
input
file
containing
364
daily
values
(
created
by
replicating
the
quarterly
mean
values
for
each
day
of
the
appropriate
season).

3.2.1.6
PM
Air
Quality
Results
Table
3­
5
provides
a
summary
of
the
predicted
ambient
PM
2.5
concentrations
for
the
2010
and
2015
base
cases
and
changes
associated
with
proposed
rule.
The
REMSAD
results
indicate
that
the
predicted
change
in
PM
concentrations
is
composed
almost
entirely
of
reductions
in
fine
particulates
(
PM
2.5)
with
little
or
no
reduction
in
coarse
particles
(
PM
10
less
PM
2.5).
Therefore,
the
observed
changes
in
PM
10
are
composed
primarily
of
changes
in
PM
2.5.
In
addition
to
the
standard
frequency
statistics
(
e.
g.,
minimum,
maximum,
average),
we
provide
the
population­
weighted
average
which
better
reflects
the
baseline
levels
and
predicted
changes
for
more
populated
areas
of
the
nation.
This
measure,
therefore,
better
reflects
the
potential
benefits
of
these
predicted
changes
through
exposure
changes
to
these
populations.
As
shown,
the
average
annual
mean
concentrations
of
PM
2.5
across
populated
eastern
U.
S.
grid­
cells
declines
by
roughly
5.6
percent
(
or
0.6

g/
m3)
and
7.5
percent
(
or
0.8

g/
m3)
in
2010
and
2015,
respectively.
The
population­
weighted
average
mean
concentration
declined
by
6.1
percent
(
or
0.74

g/
m3)
in
2010
and
7.9
percent
(
or
0.94

g/
m3)
in
2015,

which
is
much
larger
in
absolute
terms
than
the
spatial
average
for
both
years.
This
indicates
the
proposed
rule
generates
greater
absolute
air
quality
improvements
in
more
populated,

urban
areas.
3­
14
Table
3­
5.
Summary
of
Base
Case
PM
Air
Quality
and
Changes
Due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
2010
2015
Statistic
Base
Case
Changea
Percent
Change
Base
Case
Changea
Percent
Change
PM2.5
(

g/
m3)

Minimum
Annual
Mean
5.24
­
0.33
­
6.3%
5.13
­
0.33
­
6.4%

Maximum
Annual
Mean
16.88
­
0.86
­
5.1%
16.79
­
1.19
­
7.1%

Average
Annual
Mean
10.82
­
0.61
­
5.6%
10.67
­
0.80
­
7.5%

Pop­
Weighted
Average
Annual
Mean
b
12.19
­
0.74
­
6.1%
11.99
­
0.94
­
7.9%

a
The
change
is
defined
as
the
control
case
value
minus
the
base
case
value.

b
Calculated
by
summing
the
product
of
the
projected
REMSAD
grid­
cell
population
and
the
estimated
PM
concentration,
for
that
grid­
cell
and
then
dividing
by
the
total
population.

Table
3­
6
provides
information
on
the
populations
in
2010
and
2015
that
will
experience
improved
PM
air
quality.
There
are
significant
populations
that
live
in
areas
with
meaningful
reductions
in
annual
mean
PM
2.5
concentrations
resulting
from
the
proposed
rule.
As
shown,

in
2015,
almost
40
percent
of
the
U.
S.
population
located
in
the
eastern
37
state
modeling
domain
are
predicted
to
experience
reductions
of
greater
than
1.0

g/
m3.
This
is
an
increase
from
the
20
percent
of
the
U.
S.
population
that
are
expected
to
experience
such
reductions
in
2010.
Furthermore,
over
7
percent
of
this
population
will
benefit
from
reductions
in
annual
mean
PM
2.5
concentrations
of
greater
than
1.5

g/
m3
and
almost
2
percent
will
live
in
areas
with
reductions
of
greater
than
1.75

g/
m3.

3.2.2
Ozone
Air
Quality
Estimates
We
use
the
emissions
inputs
summarized
earlier
in
this
chapter
with
a
regional­
scale
version
of
CAMx
to
estimate
ozone
air
quality
in
the
Eastern
and
Western
U.
S.
CAMx
is
an
Eulerian
three­
dimensional
photochemical
grid
air
quality
model
designed
to
calculate
the
concentrations
of
both
inert
and
chemically
reactive
pollutants
by
simulating
the
physical
and
chemical
processes
in
the
atmosphere
that
affect
ozone
formation.
Version
3.10
of
the
CAMx
model
was
employed
for
these
analyses.
Because
it
accounts
for
spatial
and
temporal
variations
as
well
as
differences
in
the
reactivity
of
emissions,
CAMx
is
useful
for
evaluating
3­
15
the
impacts
of
the
proposed
rule
on
U.
S.
ozone
concentrations.
Although
the
model
tends
to
underestimate
observed
ozone,
it
exhibits
less
bias
and
error
than
any
past
regional
ozone
Table
3­
6.
Distribution
of
PM2.5
Air
Quality
Improvements
Over
Population
Due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
Change
in
Annual
Mean
PM
2.5
Concentrations
(

g/
m3)
2010
Populationb
2015
Population
Number
(
millions)
Percent
(%)
Number
(
millions)
Percent
(%)

0
>
 
PM2.5
Conc

0.25
6.1
2.7%
0.0
0.0%

0.25
>
 
PM2.5
Conc

0.5
59.0
26.1%
29.9
12.8%

0.5
>
 
PM2.5
Conc

0.75
57.1
25.3%
52.9
22.6%

0.75
>
 
PM2.5
Conc

1.0
59.3
26.2%
60.6
25.9%

1.0
>
 
PM2.5
Conc

1.25
22.5
9.9%
34.6
14.8%

1.25
>
 
PM2.5
Conc

1.5
11.2
4.9%
38.0
16.2%

1.5
>
 
PM2.5
Conc

1.75
9.0
4.0%
13.9
5.9%

 
PM2.5
Conc
>
1.75
2.0
0.9%
4.2
1.8%

a
The
change
is
defined
as
the
control
case
value
minus
the
base
case
value.

b
Population
counts
and
percentages
are
for
the
fraction
of
the
national
population
located
in
the
eastern
37
state
modeling
domain
considered
in
modeling
health
benefits
for
the
rule.

modeling
application
conducted
by
EPA
(
i.
e.,
OTAG,
On­
highway
Tier­
2,
and
HD
Engine/
Diesel
Fuel).

Our
analysis
applies
the
modeling
system
separately
to
the
Eastern
U.
S.
for
six
emissions
scenarios:
a
1995
baseline
projection,
a
2001
baseline
projection,
a
2020
baseline
projection
and
a
2020
projection
with
controls,
a
2030
baseline
projection
and
a
2030
projection
with
controls.
The
model
was
applied
and
evaluated
over
three
episodes
that
occurred
during
the
summer
of
1995
base
year.
Subsequently,
episodic
ozone
model
runs
were
made
for
the
2001
base
year
scenario
and
the
2010
and
2015
base
and
control
case
scenarios
for
all
episodes.

Further
discussion
of
this
modeling,
including
evaluations
of
model
performance
relative
to
predicted
future
air
quality,
is
provided
in
the
air
quality
modeling
TSD.
As
discussed
in
chapter
4,
we
use
the
relative
predictions
from
the
model
by
combining
the
2001
base­
year
and
each
future­
year
scenario
with
current
ambient
air
quality
observations
to
determine
the
expected
change
in
2010
or
2015
ozone
concentrations
due
to
the
rule
(
Abt
Associates,

2003).
These
results
are
used
solely
in
the
benefits
analysis.
3­
16
The
CAMx
modeling
system
requires
a
variety
of
input
files
that
contain
information
pertaining
to
the
modeling
domain
and
simulation
period.
These
include
gridded,
day­
specific
emissions
estimates
and
meteorological
fields,
initial
and
boundary
conditions,
and
land­
use
information.
As
applied
to
the
Eastern
U.
S.,
the
model
segments
the
area
into
square
blocks
called
grids
(
roughly
equal
in
size
to
counties),
each
of
which
has
several
layers
of
air
conditions
that
are
considered
in
the
analysis.
Using
this
data,
the
CAMx
model
generates
predictions
of
hourly
ozone
concentrations
for
every
grid.
We
then
calibrate
the
results
of
this
process
to
develop
2010
and
2015
ozone
profiles
at
monitor
sites
by
normalizing
the
observations
to
the
observed
ozone
concentrations
at
each
monitor
site.
For
areas
(
grids)

without
ozone
monitoring
data,
we
interpolated
ozone
values
using
data
from
monitors
surrounding
the
area.
After
completing
this
process,
we
calculated
daily
and
seasonal
ozone
metrics
to
be
used
as
inputs
to
the
health
and
welfare
C­
R
functions
of
the
benefits
analysis.

The
following
sections
provide
a
more
detailed
discussion
of
each
of
the
steps
in
this
evaluation
and
a
summary
of
the
results.

3.2.2.1
Modeling
Domain
The
modeling
domain
representing
the
Eastern
U.
S.
is
the
same
as
that
used
previously
for
OTAG
and
the
On­
highway
Tier­
2
rulemaking.
As
shown
in
Figure
3­
3,
this
domain
encompasses
most
of
the
Eastern
U.
S.
from
the
East
coast
to
mid­
Texas
and
consists
of
two
grids
with
differing
resolutions.
The
modeling
domain
extends
from
99
degrees
to
67
degrees
west
longitude
and
from
26
degrees
to
47
degrees
north
latitude.
The
inner
portion
of
the
modeling
domain
shown
in
Figure
3­
3
uses
a
relatively
fine
grid
of
12
km
consisting
of
nine
vertical
layers.
The
outer
area
has
less
horizontal
resolution,
as
it
uses
a
36
km
grid
with
the
same
nine
vertical
layers.
The
vertical
height
of
the
modeling
domain
is
4,000
meters
above
ground
level
for
both
areas.

3.2.2.2
Simulation
Periods
For
use
in
this
benefits
analysis,
the
simulation
periods
modeled
by
CAMx
included
several
multi­
day
periods
when
ambient
measurements
recorded
high
ozone
concentrations.
A
simulation
period,
or
episode,
consists
of
meteorological
data
characterized
over
a
block
of
days
that
are
used
as
inputs
to
the
air
quality
model.
A
simulation
period
is
selected
to
characterize
a
variety
of
ozone
conditions
including
some
days
with
high
ozone
concentrations
in
one
or
more
portions
of
the
U.
S.
and
observed
exceedances
of
the
1­
hour
NAAQS
for
ozone
being
recorded
at
monitors.
We
focused
on
the
summer
of
1995
for
selecting
the
episodes
to
model
because
it
is
a
recent
time
period
for
which
we
had
model­
ready
3­
17
Figure
3­
3.
CAMx
Eastern
U.
S.
Modeling
Domain
Note:
The
inner
area
represents
fine
grid
modeling
at
12
km
resolution,
while
the
outer
area
represents
the
coarse
grid
modeling
at
36
km
resolution.

meteorological
inputs
and
this
timeframe
contained
several
periods
of
elevated
ozone
over
the
Eastern
U.
S.
As
detailed
in
the
air
quality
modeling
TSD,
this
analysis
used
three
multi­
day
meteorological
scenarios
during
the
summer
of
1995
for
the
model
simulations
over
the
eastern
U.
S.:
June
12­
24,
July
5­
15,
and
August
7­
21.
Each
of
the
six
emissions
scenarios
(
1995
base
year,
2001
base
year,
2010
base
and
control,
2015
base
and
control)
were
simulated
for
the
selected
episodes.
These
episodes
include
a
three
day
"
ramp­
up"
period
to
initialize
the
model,
but
the
results
for
these
days
are
not
used
in
this
analysis.

3.2.2.3
Non­
emissions
Modeling
Inputs
The
meteorological
data
required
for
input
into
CAMx
(
wind,
temperature,
vertical
mixing,
etc.)
were
developed
by
separate
meteorological
models.
The
gridded
meteorological
3­
18
data
for
the
three
historical
1995
episodes
were
developed
using
the
Regional
Atmospheric
Modeling
System
(
RAMS),
version
3b.
This
model
provided
needed
data
at
every
grid
cell
on
an
hourly
basis.
These
meteorological
modeling
results
were
evaluated
against
observed
weather
conditions
before
being
input
into
CAMx
and
it
was
concluded
that
the
model
fields
were
adequate
representations
of
the
historical
meteorology.
A
more
detailed
description
of
the
settings
and
assorted
input
files
employed
in
these
applications
is
provided
in
the
Air
Quality
TSD,
which
is
located
in
the
docket
for
this
rule.

The
modeling
assumed
background
pollutant
levels
at
the
top
and
along
the
periphery
of
the
domain
as
in
Tier
2.
Additionally,
initial
conditions
were
assumed
to
be
relatively
clean
as
well.
Given
the
ramp­
up
days
and
the
expansive
domains,
it
is
expected
that
these
assumptions
will
not
affect
the
modeling
results,
except
in
areas
near
the
boundary
(
e.
g.,

Dallas­
Fort
Worth
TX).
The
other
non­
emission
CAMx
inputs
(
land
use,
photolysis
rates,

etc.)
were
developed
using
procedures
employed
in
the
Tier
2/
OTAG
regional
modeling.
The
development
of
model
inputs
is
discussed
in
greater
detail
in
the
Air
Quality
TSD,
which
is
available
in
the
docket
for
this
rule.

3.2.2.4
Model
Performance
for
Photochemical
Ozone
The
purpose
of
the
1995
base
year
photochemical
ozone
modeling
was
to
reproduce
the
atmospheric
processes
resulting
in
the
observed
ozone
concentrations
over
these
domains
and
episodes.
One
of
the
fundamental
assumptions
in
air
quality
modeling
is
that
a
model
which
adequately
replicates
observed
pollutant
concentrations
in
the
base
year
can
be
used
to
assess
the
effects
of
future
year
emissions
controls.
A
series
of
performance
statistics
was
calculated
for
the
Eastern
U.
S.
domain
as
well
as
the
four
quadrants
and
multiple
subregions.
The
model
performance
evaluation
consisted
solely
of
comparisons
against
ambient
surface
ozone
data.

There
was
insufficient
data
available
in
terms
of
ozone
precursors
or
ozone
aloft
to
allow
for
a
more
complete
assessment
of
model
performance.
Three
primary
statistical
metrics
were
used
to
assess
the
overall
accuracy
of
the
base
year
modeling
simulations.


Mean
normalized
bias
is
defined
as
the
average
difference
between
the
hourly
model
predictions
and
observations
(
paired
in
space
and
time)
at
each
monitoring
location,
normalized
by
the
magnitude
of
the
observations.


Mean
normalized
gross
error
is
defined
as
the
average
absolute
difference
between
the
hourly
model
predictions
and
observations
(
paired
in
space
and
time)
at
each
monitoring
location,
normalized
by
the
magnitude
of
the
observations.
3­
19

Average
accuracy
of
the
peak
is
defined
as
the
average
difference
between
peak
daily
model
predictions
and
observations
at
each
monitoring
location,
normalized
by
the
magnitude
of
the
observations.

In
general,
the
model
tends
to
underestimate
observed
ozone.
When
all
hourly
observed
ozone
values
greater
than
a
60
ppb
threshold
are
compared
to
their
model
counterparts
for
the
30
episode
modeling
days
in
the
eastern
domain,
the
mean
normalized
bias
is
­
1.1
percent
and
the
mean
normalized
gross
error
is
20.5
percent.
As
shown
in
Table
3­
7,
the
model
generally
underestimates
observed
ozone
values
for
the
June
and
July
episodes,
but
predicts
higher
than
observed
amounts
for
the
August
episode.

At
present,
there
are
no
guidance
criteria
by
which
one
can
determine
if
a
regional
ozone
modeling
exercise
is
exhibiting
adequate
model
performance.
These
base
case
simulations
were
determined
to
be
acceptable
based
on
comparisons
to
previously
completed
model
rulemaking
analyses
(
e.
g.,
OTAG,
Tier­
2,
and
Heavy­
Duty
Engine).
The
modeling
completed
for
this
proposal
exhibits
less
bias
and
error
than
any
past
regional
ozone
modeling
application
done
by
EPA.
Thus,
the
model
is
considered
appropriate
for
use
in
projecting
changes
in
future
year
ozone
concentrations
and
the
resultant
health/
economic
benefits
due
to
the
proposed
emissions
reductions.

In
addition,
the
CAMx
modeling
results
were
also
evaluated
at
a
"
local"
level
to
ensure
that
areas
determined
to
need
the
emissions
reductions
based
on
projected
exceedances
of
the
ozone
standard
were
not
unduly
influenced
by
local
overestimation
of
ozone
in
the
model
base
year.
As
detailed
in
the
Air
Quality
Modeling
TSD,
performance
statistics
were
computed
for
each
of
51
local
subregions
within
the
modeling
domain.
These
performance
statistics
were
compared
to
the
recommended
performance
ranges
for
urban
attainment
modeling
(
EPA,
1991).
The
results
indicate
that
model
performance
for
the
June
episode
was
within
Table
3­
7.
Model
Performance
Statistics
for
Hourly
Ozone
in
the
Eastern
U.
S.
CAMx
Ozone
Simulations:
1995
Base
Case
Episode
Average
Accuracy
of
the
Peak
Mean
Normalized
Bias
Mean
Normalized
Gross
Error
June
1995
 
7.3
 
8.8
19.6
July
1995
 
3.3
 
5.0
19.1
August
1995
9.6
8.6
23.3
2The
ozone
season
for
this
analysis
is
defined
as
the
5­
month
period
from
May
to
September;
however,
to
estimate
certain
crop
yield
benefits,
the
modeling
results
were
extended
to
include
months
outside
the
5­
month
ozone
season.

3Based
on
AIRS,
there
were
961
ozone
monitors
with
sufficient
data,
i.
e.,
50
percent
or
more
days
reporting
at
least
9
hourly
observations
per
day
(
8
am
to
8
pm)
during
the
ozone
season.

4The
8
km
grid
squares
contain
the
population
data
used
in
the
health
benefits
analysis
model,
BenMAP.
See
Chapter
4
for
a
discussion
of
this
model.

5This
approach
is
a
generalization
of
planar
interpolation
that
is
technically
referred
to
as
enhanced
Voronoi
Neighbor
Averaging
(
EVNA)
spatial
interpolation
(
See
Abt
Associates
(
2003)
for
a
more
detailed
description).

3­
20
the
recommended
ranges
for
69
percent
of
the
local
areas
examined.
For
the
July
and
August
episodes,
the
percent
of
local
areas
with
performance
within
the
recommended
ranges
was
80
percent
and
61
percent,
respectively.

3.2.2.5
Converting
CAMx
Outputs
to
Full­
Season
Profiles
for
Benefits
Analysis
This
study
extracted
hourly,
surface­
layer
ozone
concentrations
for
each
grid­
cell
from
the
standard
CAMx
output
file
containing
hourly
average
ozone
values.
These
model
predictions
are
used
in
conjunction
with
the
observed
concentrations
obtained
from
the
Aerometric
Information
Retrieval
System
(
AIRS)
to
generate
ozone
concentrations
for
the
entire
ozone
season.
2,3
The
predicted
changes
in
ozone
concentrations
from
the
future­
year
base
case
to
future­
year
control
scenario
serve
as
inputs
to
the
health
and
welfare
C­
R
functions
of
the
benefits
analysis,
i.
e.,
the
Environmental
Benefits
Mapping
and
Analysis
Program
(
BenMAP).

In
order
to
estimate
ozone­
related
health
and
welfare
effects
for
the
contiguous
U.
S.,

fullseason
ozone
data
are
required
for
every
BenMAP
grid­
cell.
Given
available
ozone
monitoring
data,
we
generated
full­
season
ozone
profiles
for
each
location
in
the
contiguous
48
States
in
two
steps:
(
1)
we
combine
monitored
observations
and
modeled
ozone
predictions
to
interpolate
hourly
ozone
concentrations
to
a
grid
of
8
km
by
8
km
population
grid­
cells,
and
(
2)
we
converted
these
full­
season
hourly
ozone
profiles
to
an
ozone
measure
of
interest,
such
as
the
daily
average.
4,5
These
methods
are
described
in
detail
in
the
benefits
analysis
technical
support
document
(
Abt
Associates,
2003).

3.2.2.6
Ozone
Air
Quality
Results
This
section
provides
a
summary
the
predicted
ambient
ozone
concentrations
from
the
CAMx
model
for
the
2010
and
2015
base
cases
and
changes
associated
with
the
proposed
3­
21
rule.
Table
3­
8
provides
those
ozone
metrics
for
grid­
cells
in
the
Eastern
U.
S.
that
enter
the
concentration
response
functions
for
health
benefits
endpoints.
The
population­
weighted
average
reflects
the
baseline
levels
and
predicted
changes
for
more
populated
areas
of
the
Table
3­
8.
Summary
of
CAMx
Derived
Population­
Weighted
Ozone
Air
Quality
Metrics
for
Health
Benefits
Endpoints
Due
to
Proposed
Interstate
Air
Quality
Rule:
Eastern
U.
S.

2010
2015
Statistic
a
Base
Case
Change
b
Percent
Change
b
Base
Case
Change
b
Percent
Change
b
Population­
Weighted
Average
(
ppb)
d
Daily
1­
Hour
Maximum
Concentration
53.32
 
0.51
 
0.95%
52.10
 
1.05
 
2.02%

Daily
5­
Hour
Average
Concentration
44.51
 
0.42
 
0.93%
43.65
 
0.87
 
2.00%

Daily
8­
Hour
Average
Concentration
43.81
 
0.41
 
0.93%
42.97
 
0.86
 
1.99%

Daily
12­
Hour
Average
Concentration
41.28
 
0.38
 
0.92%
40.56
 
0.80
 
1.98%

Daily
24­
Hour
Average
Concentration
31.20
 
0.28
 
0.89%
30.83
 
0.59
 
1.91%

a
These
ozone
metrics
are
calculated
at
the
CAMX
grid­
cell
level
for
use
in
health
effects
estimates
based
on
the
results
of
spatial
and
temporal
Voronoi
Neighbor
Averaging.
Except
for
the
daily
24­
hour
average,
these
ozone
metrics
are
calculated
over
relevant
time
periods
during
the
daylight
hours
of
the
"
ozone
season,"
i.
e.,
May
through
September.
For
the
5­
hour
average,
the
relevant
time
period
is
10
am
to
3
pm;
for
the
8­
hr
average,
it
is
9
am
to
5
pm;
and,
for
the
12­
hr
average
it
is
8
am
to
8
pm.

b
The
change
is
defined
as
the
control
case
value
minus
the
base
case
value.
The
percent
change
is
the
"
Change"
divided
by
the
"
Base
Case,"
and
then
multiplied
by
100
to
convert
the
value
to
a
percentage.

d
Calculated
by
summing
the
product
of
the
projected
CAMx
grid­
cell
population
and
the
estimated
CAMx
grid­
cell
seasonal
ozone
concentration,
and
then
dividing
by
the
total
population.

nation.
This
measure,
therefore,
will
better
reflect
the
potential
benefits
of
these
predicted
changes
through
exposure
changes
to
these
populations.

3.2.3
Visibility
Degradation
Estimates
Visibility
degradation
is
often
directly
proportional
to
decreases
in
light
transmittal
in
the
atmosphere.
Scattering
and
absorption
by
both
gases
and
particles
decrease
light
transmittance.
To
quantify
changes
in
visibility,
our
analysis
computes
a
light­
extinction
coefficient,
based
on
the
work
of
Sisler
(
1996),
which
shows
the
total
fraction
of
light
that
is
decreased
per
unit
distance.
This
coefficient
accounts
for
the
scattering
and
absorption
of
light
by
both
particles
and
gases,
and
accounts
for
the
higher
extinction
efficiency
of
fine
particles
compared
to
coarse
particles.
Fine
particles
with
significant
light­
extinction
efficiencies
include
sulfates,
nitrates,
organic
carbon,
elemental
carbon
(
soot),
and
soil
(
Sisler,

1996).
3­
22
Based
upon
the
light­
extinction
coefficient,
we
also
calculated
a
unitless
visibility
index,

called
a
"
deciview,"
which
is
used
in
the
valuation
of
visibility.
The
deciview
metric
provides
a
scale
for
perceived
visual
changes
over
the
entire
range
of
conditions,
from
clear
to
hazy.

Under
many
scenic
conditions,
the
average
person
can
generally
perceive
a
change
of
one
deciview.
The
higher
the
deciview
value,
the
worse
the
visibility.
Thus,
an
improvement
in
visibility
is
a
decrease
in
deciview
value.

Table
3­
9
provides
the
distribution
of
visibility
improvements
across
2010
and
2015
populations
resulting
from
this
proposed
rule.
The
majority
of
the
2015
U.
S.
population
live
in
areas
with
predicted
improvement
in
annual
average
visibility
of
greater
than
0.6
deciviews
resulting
from
the
proposed
rule.
As
shown,
almost
72
percent
of
the
2015
U.
S.
population
are
predicted
to
experience
improved
annual
average
visibility
of
greater
than
0.4
deciviews.

Furthermore,
roughly
25
percent
of
the
2015
U.
S.
population
will
benefit
from
reductions
in
annual
average
visibility
of
greater
than
1
deciviews.

Because
the
visibility
benefits
analysis
distinguishes
between
general
regional
visibility
degradation
and
that
particular
to
Federally­
designated
Class
I
areas
(
i.
e.,
national
parks,

forests,
recreation
areas,
wilderness
areas,
etc.),
we
separated
estimates
of
visibility
Table
3­
9.
Distribution
of
Populations
Experiencing
Visibility
Improvements
due
to
Proposed
Interstate
Air
Quality
Rule:
2010
and
2015
2010
Population
2015
Population
Improvements
in
Visibilitya(
annual
average
deciviews)
Number
(
millions)
Percent
(%)
Number
(
millions)
Percent
(%)

0
>
 
Deciview

0.2
75.6
24.9%
74.8
23.6%

0.2
>
 
Deciview

0.4
24.1
7.9%
15.2
4.8%

0.4
>
 
Deciview

0.6
46.5
15.3%
25.3
8.0%

0.6
>
 
Deciview

0.8
87.7
28.8%
64.7
20.4%

0.8
>
 
Deciview

1.0
56.0
18.4%
57.8
18.2%

 
Deciview
>
1.0
14.3
4.7%
79.1
25.0%
6
The
visibility
calculations
presented
in
this
section
are
changes
in
the
annual
average
visibility
for
the
purpose
of
generating
monetized
benefits.
There
improvements
in
visibility
should
not
be
confused
with
the
requirements
under
the
Regional
Haze
rule
to
show
"
reasonable
progress"
for
the
20%
best
and
20%
worst
days
to
each
Class
I
area.
Example
Regional
Haze
calculations
for
the
20%
best
and
worst
days
are
contained
in
the
AQMTSD.

3­
23
degradation
into
"
residential"
and
"
recreational"
categories.
6
The
estimates
of
visibility
degradation
for
the
"
recreational"
category
apply
to
Federally­
designated
Class
I
areas,
while
estimates
for
the
"
residential"
category
apply
to
non­
Class
I
areas.
Deciview
estimates
are
estimated
using
outputs
from
REMSAD
for
the
2010
and
2015
base
cases
and
control
scenarios.

3.2.3.1
Residential
Visibility
Improvements
Air
quality
modeling
results
predict
that
the
proposed
Interstate
Air
Quality
Rule
will
create
improvements
in
visibility
through
the
country.
In
Table
3­
10,
we
summarize
residential
visibility
improvements
across
the
Eastern
U.
S.
in
2010
and
2015.
The
baseline
annual
average
visibility
for
eastern
U.
S.
counties
is
21.61
deciviews
in
2010.
The
mean
improvement
across
eastern
U.
S.
counties
is
0.69
deciviews,
or
almost
3.2
percent.
In
urban
areas
with
a
population
of
250,000
or
more,
the
mean
improvement
in
annual
visibility
was
similar
at
0.71
deciviews
in
2010
and
ranged
from
0.17
to
1.64
deciviews.
In
rural
areas,
the
mean
improvement
in
visibility
was
0.68
deciviews
in
2010
and
ranged
from
0.19
to
1.69
deciviews.

Table
3­
10.
Summary
of
Baseline
Residential
Visibility
and
Changes
by
Region:
2010
and
2015
(
annual
average
deciviews)

2010
2015
Regionsa
Base
Case
Changeb
Percent
Change
Base
Case
Changeb
Percent
Change
Eastern
U.
S.
21.61
0.69
3.17%
21.31
0.85
3.95%

Urban
22.78
0.71
3.14%
22.50
0.88
3.95%

Rural
21.14
0.68
3.18%
20.84
0.84
3.96%

a
The
dividing
line
between
the
Eastern
and
Western
U.
S.
was
defined
as
the
100th
meridian.

b
An
improvement
in
visibility
is
a
decrease
in
deciview
value.
The
change
is
defined
as
the
control
case
deciview
level
minus
the
base
case
deciview
level.
3­
24
Northwest
Rocky
Mountain
Northeast/
Midwest
Southeast
Southwest
California
Study
Region
Transfer
Region
Figure
3­
4.
Recreational
Visibility
Regions
for
Continental
U.
S.

Note:
Study
regions
were
represented
in
the
Chestnut
and
Rowe
(
1990a,
1990b)
studies
used
in
evaluating
the
benefits
of
visibility
improvements,
while
transfer
regions
used
extrapolated
study
results.
3.2.3.2
Recreational
Visibility
Improvements
In
Table
3­
11,
we
summarize
recreational
visibility
improvements
in
2010
and
2015
in
Federal
Class
I
areas
located
in
the
eastern
U.
S.
These
recreational
visibility
regions
are
shown
in
Figure
3­
4.
As
shown,
the
improvement
in
visibility
for
Federal
Class
I
areas
in
the
Eastern
U.
S.
increases
from
3.8
percent,
or
0.77
deciviews,
in
2010.
The
predicted
absolute
improvement
of
0.94
deciviews
in
2015
reflects
a
4.6
percent
change
from
2015
baseline
visibility
of
20.38
deciviews.

Table
3­
11.
Summary
of
Baseline
Recreational
Visibility
and
Changes
by
Region:
2010
and
2015
(
annual
average
deciviews)

2010
2015
Class
I
Visibility
Regionsa
Base
Case
Changeb
Percent
Change
Base
Case
Changeb
Percent
Change
Eastern
U.
S.
20.59
0.77
3.75%
20.38
0.94
4.61%

Southeast
22.04
0.91
4.11%
21.80
1.17
5.35%

Northeast/
Midwest
19.28
0.65
3.38%
19.11
0.74
3.85%

a
Regions
are
pictured
in
Figure
VI­
5
and
are
defined
in
the
technical
support
document
(
see
Abt
Associates,

2003).

b
An
improvement
in
visibility
is
a
decrease
in
deciview
value.
The
change
is
defined
as
the
control
case
deciview
level
minus
the
base
case
deciview
level.
3­
25
4­
1
SECTION
4
BENEFITS
ANALYSIS
AND
RESULTS
This
chapter
reports
the
EPA's
analysis
of
a
subset
of
the
public
health
and
welfare
impacts
and
associated
monetized
benefits
to
society
of
the
proposed
IAQR.
The
EPA
is
required
by
Executive
Order
12866
to
estimate
the
benefits
and
costs
of
major
new
pollution
control
regulations.
Accordingly,
the
analysis
presented
here
attempts
to
answer
three
questions:
1)
what
are
the
physical
health
and
welfare
effects
of
changes
in
ambient
air
quality
resulting
from
reductions
in
precursors
to
particulate
matter
(
PM)
including
(
NOx)
and
sulfur
dioxide
(
SO
2)
emissions?
2)
how
much
are
the
changes
in
these
effects
attributable
to
the
proposed
rule
worth
to
U.
S.
citizens
as
a
whole
in
monetary
terms?
and
3)
how
do
the
monetized
benefits
compare
to
the
costs?
It
constitutes
one
part
of
the
EPA's
thorough
examination
of
the
relative
merits
of
this
proposed
regulation.

The
analysis
presented
in
this
chapter
uses
a
methodology
generally
consistent
with
benefits
analyses
performed
for
the
recent
analysis
of
Nonroad
Diesel
Engines
Tier
4
Standards
and
the
proposed
Clear
Skies
Act
of
2003
(
EPA,
2003).
The
benefits
analysis
relies
on
three
major
modeling
components:

1)
Calculation
of
the
impact
that
a
set
of
preliminary
emissions
standards
for
EGUs
based
on
a
state­
level
cap
and
trade
program
would
have
on
the
national
inventory
of
precursors
to
PM
including
SO
2
and
NOx.

2)
Air
quality
modeling
for
2010
and
2015
to
determine
changes
in
ambient
concentrations
of
ozone
and
particulate
matter,
reflecting
baseline
and
post­
control
emissions
inventories.

3)
A
benefits
analysis
to
determine
the
changes
in
human
health
and
welfare,
both
in
terms
of
physical
effects
and
monetary
value,
that
result
from
the
projected
changes
in
ambient
concentrations
of
various
pollutants
for
the
modeled
standards.

A
wide
range
of
human
health
and
welfare
effects
are
linked
to
the
emissions
of
NOx
and
SOx
from
EGUs
and
the
resulting
impact
on
ambient
concentrations
of
ozone
and
PM.
7Short­
term
exposure
to
ambient
ozone
has
also
been
linked
to
premature
death.
The
EPA
is
currently
evaluating
the
epidemiological
literature
examining
the
relationship
between
ozone
and
premature
mortality,
sponsoring
three
independent
meta­
analyses
of
the
literature.
Once
this
evaluation
has
been
completed
and
peer­
reviewed,
the
EPA
will
consider
including
ozone­
related
premature
mortality
in
the
primary
benefits
analysis
for
the
final
rule.

4­
2
Potential
human
health
effects
linked
to
PM2.5
range
from
mortality
linked
to
long­
term
exposure
to
PM,
to
a
range
of
morbidity
effects
linked
to
long­
term
(
chronic)
and
shorterterm
(
acute)
exposures
(
e.
g.,
respiratory
and
cardiovascular
symptoms
resulting
in
hospital
admissions,
asthma
exacerbations,
and
acute
and
chronic
bronchitis
[
CB]).
Exposure
to
ozone
has
also
been
linked
to
a
variety
of
respiratory
effects
including
hospital
admissions
and
illnesses
resulting
in
school
absences.
7
Welfare
effects
potentially
linked
to
PM
include
materials
damage
and
visibility
impacts,
while
ozone
can
adversely
affect
the
agricultural
and
forestry
sectors
by
decreasing
yields
of
crops
and
forests.
Although
methods
exist
for
quantifying
the
benefits
associated
with
many
of
these
human
health
and
welfare
categories,

not
all
can
be
evaluated
at
this
time
due
to
limitations
in
methods
and/
or
data.
Table
4­
1
lists
the
full
complement
of
human
health
and
welfare
effects
associated
with
PM
and
ozone
and
identifies
those
effects
that
are
quantified
for
the
primary
estimate,
are
quantified
as
part
of
the
sensitivity
analysis
(
to
be
completed
for
the
supplemental
analysis),
and
remain
unquantified
because
of
to
current
limitations
in
methods
or
available
data.

Figure
4­
1
illustrates
the
major
steps
in
the
benefits
analysis.
Given
baseline
and
postcontrol
emissions
inventories
for
the
emission
species
expected
to
affect
ambient
air
quality,

we
use
sophisticated
photochemical
air
quality
models
to
estimate
baseline
and
post­
control
ambient
concentrations
of
ozone
and
PM,
and
deposition
of
nitrogen
and
sulfur
for
each
year.

The
estimated
changes
in
ambient
concentrations
are
then
combined
with
monitoring
data
to
estimate
population­
level
exposures
to
changes
in
ambient
concentrations
for
use
in
estimating
health
effects.
Modeled
changes
in
ambient
data
are
also
used
to
estimate
changes
in
visibility,

and
changes
in
other
air
quality
statistics
that
are
necessary
to
estimate
welfare
effects.

Changes
in
population
4­
3
Emissions
inventories
(
2001
CEM,
1996
NEI,

MOBILE
5b
and
6
PART5
model,
NONROAD2002)

Air
quality
monitoring
data
AIRS
(
ozone),
FRM
(
total
PM),
STN
(
speciated
PM)
Model
baseline
and
post­
control
ambient
air
quality
(
REMSAD,
CAM­
X)

Model
population
exposure
to
changes
in
ambient
concentrations
Estimate
expected
changes
in
human
health
outcomes
Estimate
monetary
value
of
changes
in
human
health
Adjust
monetary
values
for
growth
in
real
income
to
year
of
analysis
Sum
health
and
welfare
monetary
values
to
obtain
total
monetary
benefits
Concentration
response
functions
Incidence
and
prevalence
rates
for
health
endpoints
Population
and
demographic
data
(
with
growth
projections)

Valuation
functions
Interpolation
of
projected
air
concentration
surfaces
(
base
and
control)
Estimate
expected
changes
in
welfare
(
visibility)

Estimate
monetary
value
of
changes
in
welfare
effects
Valuation
functions
°
BenMAP­
derived
(
ozone)

°
SMAT­
derived
(
PM2.5)
BenMAP
integrated
model
Income
elasticities
GDP
projections
PROCESSES
INPUTS
INPUTS
Figure
4­
1.
Key
Steps
in
Air
Quality
Modeling
Based
Benefits
Analysis
8The
term
"
impact
function"
as
used
here
refers
to
the
combination
of
(
a)
an
effect
estimate
obtained
from
the
epidemiological
literature,
(
b)
the
baseline
incidence
estimate
for
the
health
effect
of
interest
in
the
modeled
population,
(
c)
the
size
of
that
modeled
population,
and
(
d)
the
change
in
the
ambient
air
pollution
metric
of
interest.
These
elements
are
combined
in
the
impact
function
to
generate
estimates
of
changes
in
incidence
of
the
health
effect.
The
impact
function
is
distinct
from
the
concentration
response
(
C­
R)
function,
which
strictly
refers
to
the
estimated
equation
from
the
epidemiological
study
relating
incidence
of
the
health
effect
and
ambient
pollution.
We
refer
to
the
specific
value
of
the
relative
risk
or
estimated
coefficients
in
the
epidemiological
study
as
the
"
effect
estimate."
In
referencing
the
functions
used
to
generate
changes
in
incidence
of
health
effects
for
this
RIA,
we
use
the
term
impact
function
rather
than
C­
R
function
because
"
impact
function"
includes
all
key
input
parameters
used
in
the
incidence
calculation.

4­
4
exposure
to
ambient
air
pollution
are
then
input
to
impact
functions8
to
generate
changes
in
incidence
of
health
effects,
or
changes
in
other
exposure
metrics
are
input
to
dose­
response
functions
to
generate
changes
in
welfare
effects.
The
resulting
effects
changes
are
then
assigned
monetary
values,
taking
into
account
adjustments
to
values
for
growth
in
real
income
out
to
the
year
of
analysis
(
values
for
health
and
welfare
effects
are
in
general
positively
related
to
real
income
levels).
Finally,
values
for
individual
health
and
welfare
effects
are
summed
to
obtain
an
estimate
of
the
total
monetary
value
of
the
changes
in
emissions.

On
September
26,
2002,
the
National
Academy
of
Sciences
(
NAS)
released
a
report
on
its
review
of
the
Agency's
methodology
for
analyzing
the
health
benefits
of
measures
taken
to
reduce
air
pollution.
The
report
focused
on
the
EPA's
approach
for
estimating
the
health
benefits
of
regulations
designed
to
reduce
concentrations
of
airborne
PM.

In
its
report,
the
NAS
said
that
the
EPA
has
generally
used
a
reasonable
framework
for
analyzing
the
health
benefits
of
PM­
control
measures.
It
recommended,
however,
that
the
Agency
take
a
number
of
steps
to
improve
its
benefits
analysis.
In
particular,
the
NAS
stated
that
the
Agency
should

include
benefits
estimates
for
a
range
of
regulatory
options;


estimate
benefits
for
intervals,
such
as
every
5
years,
rather
than
a
single
year;


clearly
state
the
projected
baseline
statistics
used
in
estimating
health
benefits,
including
those
for
air
emissions,
air
quality,
and
health
outcomes;


examine
whether
implementation
of
proposed
regulations
might
cause
unintended
impacts
on
human
health
or
the
environment;


when
appropriate,
use
data
from
non­
U.
S.
studies
to
broaden
age
ranges
to
which
current
estimates
apply
and
to
include
more
types
of
relevant
health
outcomes;
and
4­
5

begin
to
move
the
assessment
of
uncertainties
from
its
ancillary
analyses
into
its
base
analyses
by
conducting
probabilistic,
multiple­
source
uncertainty
analyses.
This
assessment
should
be
based
on
available
data
and
expert
judgment.

Although
the
NAS
made
a
number
of
recommendations
for
improvement
in
the
EPA's
approach,
it
found
that
the
studies
selected
by
the
Agency
for
use
in
its
benefits
analysis
were
generally
reasonable
choices.
In
particular,
the
NAS
agreed
with
the
EPA's
decision
to
use
cohort
studies
for
estimating
premature
mortality
benefits.
It
also
concluded
that
the
Agency's
selection
of
the
American
Cancer
Society
(
ACS)
study
for
the
evaluation
of
PMrelated
premature
mortality
was
reasonable,
although
it
noted
the
publication
of
new
cohort
studies
that
the
Agency
should
evaluate.
Since
the
publication
of
the
NAS
report,
the
EPA
has
reviewed
new
cohort
studies,
including
reanalyses
of
the
ACS
study
data
and
has
carefully
considered
these
new
study
data
in
developing
the
analytical
approach
for
the
IAQR
(
see
below).

In
addition
to
the
NAS
report,
the
EPA
has
also
received
technical
guidance
and
input
regarding
its
methodology
for
conducting
PM­
and
ozone­
related
benefits
analysis
from
two
additional
sources,
including
the
Health
Effects
Subgroup
(
HES)
of
the
SAB
Council
reviewing
the
812
blueprint
(
SAB­
HES,
2003)
and
the
Office
of
Management
and
Budget
(
OMB)
through
ongoing
discussions
regarding
methods
used
in
conducting
regulatory
impact
analyses
(
RIAs).
The
SAB
HES
recommendations
include
the
following
(
SAB­
HES,
2003):


use
of
the
updated
ACS
Pope
et
al.
(
2002)
study
rather
than
the
ACS
Krewski
et
al.
study
to
estimate
mortality
for
the
primary
analysis;


dropping
the
alternative
estimate
used
in
earlier
RIAs
and
instead
including
a
primary
estimate
that
incorporates
consideration
of
uncertainty
in
key
effects
categories
such
as
mortality
directly
into
the
estimates
(
e.
g.,
use
of
the
standard
errors
from
the
Pope
et
al.
[
2002]
study
in
deriving
confidence
bounds
for
the
adult
mortality
estimates);


addition
of
infant
mortality
(
children
under
the
age
of
one)
into
the
primary
estimate,
based
on
supporting
evidence
from
the
World
Health
Organization
Global
Burden
of
Disease
study
and
other
published
studies
that
strengthen
the
evidence
for
a
relationship
between
PM
exposure
and
respiratory
inflamation
and
infection
in
children
leading
to
death;


inclusion
of
asthma
exacerbations
for
children
in
the
primary
estimate;


expansion
of
the
age
groups
evaluated
for
a
range
of
morbidity
effects
beyond
the
narrow
band
of
the
studies
to
the
broader
(
total)
age
group
(
e.
g.,
expanding
a
9Note
that
the
SAB­
HES
comments
were
made
in
the
context
of
a
review
of
the
methods
for
the
Section
812
analysis
of
the
costs
and
benefits
of
the
Clean
Air
Act.
This
context
is
pertinent
to
our
interpretation
of
the
SAB­
HES
comments
on
the
selection
of
effect
estimates
for
hospital
admissions
associated
with
PM
(
SABHES
2003).
The
Section
812
analysis
is
focused
on
a
broad
set
of
air
quality
changes,
including
both
the
coarse
and
fine
fractions
of
PM10.
As
such,
impact
functions
that
focus
on
the
full
impact
of
PM10
are
appropriate.
However,
for
the
IAQR,
which
is
expected
to
affect
primarily
the
fine
fraction
(
PM2.5)
of
PM10,
impact
functions
that
focus
primarily
on
PM2.5
are
more
appropriate.

4­
6
study
population
for
7
to
11
year
olds
to
cover
the
entire
child
age
range
of
6
to
18
years).


inclusion
of
new
endpoints
(
school
absences
[
ozone],
nonfatal
heart
attacks
in
adults
[
PM],
hospital
admissions
for
children
under
two
[
ozone]),
and
suggestion
of
a
new
meta­
analysis
of
hospital
admissions
(
PM10)
rather
than
using
a
few
PM2.5
studies;
9
and

updating
of
populations
and
baseline
incidences.

Recommendations
from
OMB
regarding
RIA
methods
have
focused
on
the
approach
used
to
characterize
uncertainty
in
the
benefits
estimates
generated
for
RIAs,
as
well
as
the
approach
used
to
value
mortality
estimates.
The
EPA
is
currently
in
the
process
of
developing
a
comprehensive
integrated
strategy
for
characterizing
the
impact
of
uncertainty
in
key
elements
of
the
benefits
modeling
process
(
e.
g.,
emissions
modeling,
air
quality
modeling,

health
effects
incidence
estimation,
valuation)
on
the
results
that
are
generated.
A
subset
of
this
effort,
which
is
currently
underway,
involves
an
expert
elicitation
designed
to
characterize
uncertainty
in
the
estimation
of
PM­
related
mortality
resulting
from
both
short­
term
and
longer­
term
exposure.
The
EPA
will
be
evaluating
the
results
of
this
elicitation
to
determine
its
usefulness
in
characterizing
uncertainty
in
our
estimates
of
PM­
related
mortality
benefits.

As
elements
of
this
uncertainty
analysis
strategy
are
finalized,
it
may
be
possible
to
integrate
them
into
later
iterations
of
the
analysis
completed
for
the
IAQR
(
e.
g.,
the
supplemental
analysis
and
final
rule).

We
are
also
altering
the
value
of
a
statistical
life
(
VSL)
used
in
the
analysis
to
reflect
new
information
in
the
ongoing
academic
debate
over
the
appropriate
characterization
of
the
value
of
reducing
the
risk
of
premature
mortality.
In
previous
analyses,
we
used
a
distribution
of
VSL
based
on
26
VSL
estimates
from
the
economics
literature.
For
this
analysis,
we
are
characterizing
the
VSL
distribution
in
a
more
general
fashion,
based
on
two
recent
metaanalyses
of
the
wage­
risk­
based
VSL
literature.
The
new
distribution
is
assumed
to
be
normal,
with
a
mean
of
$
5.5
million
and
a
95
percent
confidence
interval
between
$
1
and
$
10
4­
7
million.
The
EPA
welcomes
public
comment
on
the
appropriate
methodology
for
valuing
reductions
in
the
risk
of
premature
death.

The
EPA
has
addressed
many
of
the
comments
received
from
the
NAS,
the
SAB­
HES,

and
OMB
in
developing
the
analytical
approach
for
the
IAQR.
We
have
also
reflected
advances
in
data
and
methods
in
air
quality
modeling,
epidemiology,
and
economics
in
developing
this
analysis.
Updates
to
the
assumptions
and
methods
used
in
estimating
PM
2.5­

related
and
ozone­
related
benefits
since
completion
of
the
Proposed
Nonroad
Diesel
Rule
include
the
following:

Air
Quality

Use
of
the
Simulated
Modeled
Attainment
Test
(
SMAT)
approach
for
developing
PM2.5
air
modeling
results.
The
nonroad
diesel
rule
used
spatially
and
temporally
scaled
total
PM2.5
mass
based
on
monitoring
data
from
1999
to
2001
(
averaged
by
season).
For
the
nonroad
diesel
rule,
spatial
scaling
was
based
on
1996
modeled
REMSAD
data
at
a
36
km
grid
resolution,
while
temporal
scaling
was
based
on
the
ratios
of
future
modeled
REMSAD
data
to
1996
modeled
REMSAD
data.
All
scaling
was
conducted
internally
by
BenMAP
(
see
below)
using
the
monitor
and
model
relative
grid
creation
option.
Resulting
gridded
outputs
were
for
binned
daily
PM2.5
averages.
For
the
IAQR,
we
used
the
SMAT
approach,
which
uses
temporally
scaled
speciated
PM2.5
monitor
data
from
2001­
2002,
reconstructed
into
total
PM2.5
mass
based
on
2000­
2002
design
values
and
kriged
to
12
kilometer
grids
(
nested
within
the
standard
36
km
REMSAD
grid
structure).
Temporal
scaling
is
based
on
ratios
of
future
modeled
REMSAD
data
to
2001
REMSAD
model
data,
using
REMSAD
modeling
conducted
at
the
36
km
grid
resolution.
SMAT
output
files
include
both
quarterly
mean
and
annual
mean
PM2.5
mass
results,
which
are
then
manipulated
within
SAS
to
produce
a
BenMAP
input
file
containing
364
daily
values
(
created
by
replicating
the
quarterly
mean
values
for
each
day
of
the
appropriate
season).
For
more
details
on
the
SMAT
approach
and
REMSAD
modeling,
see
the
air
quality
chapter
of
this
document.


For
both
PM
and
ozone,
the
interstate
air
quality
analysis
domain
will
include
only
the
eastern
United
States,
focusing
on
37
States
believed
to
contribute
significantly
to
the
long­
range
transport
of
precursors
in
the
formation
of
PM2.5.

Health
Endpoints
4­
8

Incorporation
of
updated
impact
functions
to
reflect
updated
time­
series
studies
of
hospital
admissions
to
correct
for
errors
in
application
of
the
generalized
additive
model
(
GAM)
functions
in
S­
plus.
More
information
on
this
issue
is
available
at
http://
www.
healtheffects.
org.


The
primary
analysis
will
use
an
all
cause
mortality
effect
estimate
based
on
the
Pope
et
al.
(
2002)
reanalysis
of
the
ACS
study
data.
In
addition,
we
will
provide
a
breakout
for
two
major
cause
of
death
categories
 
cardiopulmonary
and
lung
cancer.


Infant
mortality
will
be
included
in
the
primary
analysis.


Asthma
exacerbations
are
incorporated
into
the
primary
analysis.
Although
the
Nonroad
Diesel
Rule
included
asthma
exacerbations
as
a
separate
endpoint
outside
of
the
base
case
analysis,
for
the
IAQR,
we
will
include
asthma
exacerbations
in
children
6
to
18
years
of
age
as
part
of
the
primary
analysis.

Valuation

In
generating
the
monetized
benefits
for
mortality
in
the
primary
analysis,
the
VSL
will
be
entered
as
a
mean
(
best
estimate)
of
5.5
million.
Unlike
the
Nonroad
Diesel
Rule,
the
IAQR
will
not
include
a
value
of
statistical
life
year
(
VSLY)
estimate.

In
response
to
comments
from
the
SAB­
HES
as
well
as
the
NAS
panel,
rather
than
including
an
alternative
estimate
in
the
IAQR,
the
EPA
will
investigate
the
impact
of
key
assumptions
on
mortality
and
morbidity
estimates
through
a
series
of
sensitivity
analyses
(
to
be
completed
for
the
supplemental
analysis).

The
benefits
estimates
generated
for
the
Proposed
IAQR
are
subject
to
a
number
of
assumptions
and
uncertainties,
which
are
discussed
throughout
the
document.
For
example,

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
4­
9
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.

(
4)
The
forecasts
for
future
emissions
and
associated
air
quality
modeling
are
valid.

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.

In
addition
to
the
quantified
and
monetized
benefits
summarized
above,
a
number
of
additional
categories
are
not
currently
amenable
to
quantification
or
valuation.
These
include
reduced
acid
and
particulate
deposition
damage
to
cultural
monuments
and
other
materials,

reduced
ozone
effects
on
forested
ecosystems,
and
environmental
benefits
due
to
reductions
of
impacts
of
acidification
in
lakes
and
streams
and
eutrophication
in
coastal
areas.

Additionally,
we
have
not
quantified
a
number
of
known
or
suspected
health
effects
linked
with
PM
and
ozone
for
which
appropriate
health
impact
functions
are
not
available
or
which
do
not
provide
easily
interpretable
outcomes
(
i.
e.,
changes
in
forced
expiratory
volume
[
FEV1]).
As
a
result,
monetized
benefits
generated
for
the
primary
estimate
may
underestimate
the
total
benefits
attributable
to
the
proposed
regulatory
option.

Benefits
estimates
for
the
Proposed
IAQR
were
generated
using
BenMAP,
which
is
a
computer
program
developed
by
the
EPA
that
integrates
a
number
of
the
modeling
elements
used
in
previous
RIAs
(
e.
g.,
interpolation
functions,
population
projections,
health
impact
functions,
valuation
functions,
analysis
and
pooling
methods)
to
translate
modeled
air
concentration
estimates
into
health
effects
incidence
estimates
and
monetized
benefits
estimates.
BenMAP
provides
estimates
of
both
the
mean
impacts
and
the
distribution
of
impacts.

In
general,
the
chapter
is
organized
around
the
steps
illustrated
in
Figure
4­
1.
In
Section
4.1,
we
provide
an
overview
of
the
data
and
methods
that
are
used
to
quantify
and
value
health
and
welfare
endpoints
and
discuss
how
we
incorporate
uncertainty
into
our
analysis.
In
4­
10
Section
4.2,
we
report
the
results
of
the
analysis
for
human
health
and
welfare
effects
(
the
overall
benefits
estimated
for
the
Proposed
IAQR
are
summarized
in
Table
4­
1).
Details
on
the
emissions
inventory
and
air
modeling
are
presented
in
Chapter
3.0.

4.1
Benefit
Analysis­
Data
and
Methods
Environmental
and
health
economists
have
a
number
of
methods
for
estimating
the
economic
value
of
improvements
in
(
or
deterioration
of)
environmental
quality.
The
method
used
in
any
given
situation
depends
on
the
nature
of
the
effect
and
the
kinds
of
data,
time,
and
resources
that
are
available
for
investigation
and
analysis.
This
section
provides
an
overview
of
the
methods
we
selected
to
quantify
and
monetize
the
benefits
included
in
this
RIA.

Given
changes
in
environmental
quality
(
ambient
air
quality,
visibility,
nitrogen,
and
sulfate
deposition),
the
next
step
is
to
determine
the
economic
value
of
those
changes.
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
nonoverlapping
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
Table
4­
1.
Estimated
Monetized
Benefits
of
the
Proposed
IAQR
Total
Benefitsa,
b
(
billions
1999$)

2010
2015
Using
a
3%
discount
rate
$
58+
B
$
84+
B
Using
a
7%
discount
rate
$
54+
B
$
79+
B
a
For
notational
purposes,
unquantified
benefits
are
indicated
with
a
"
B"
to
represent
the
sum
of
additional
monetary
benefits
and
disbenefits.
A
detailed
listing
of
unquantified
health
and
welfare
effects
is
provided
in
Table
4­
2.

b
Results
reflect
the
use
of
two
different
discount
rates:
a
3
percent
rate,
which
is
recommended
by
the
EPA's
Guidelines
for
Preparing
Economic
Analyses
(
EPA,
2000c),
and
7
percent,
which
is
recommended
by
OMB
Circular
A­
94
(
OMB,
1992).
Results
are
rounded
to
two
significant
digits.
4­
11
endpoints).
The
"
damage­
function"
approach
is
the
standard
approach
for
most
cost­
benefit
analyses
of
environmental
quality
programs
and
has
been
used
in
several
4­
12
Table
4­
2.
Human
Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
IAQR
Pollutant/
Effect
Quantified
and
Monetized
in
Base
Estimatesa
Quantified
and/
or
Monetized
Effects
in
Sensitivity
Analyses
Unquantified
Effects
Ozone/
Health
Hospital
admissions:
respiratory
Emergency
room
visits
for
asthma
Minor
restricted
activity
days
School
loss
days
Asthma
attacks
Cardiovascular
emergency
room
visits
Premature
mortality:
acute
exposuresb
Acute
respiratory
symptoms
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
Nonasthma
respiratory
emergency
room
visits
Ozone/
Welfare
Decreased
outdoor
worker
productivity
Decreased
yields
for
commercial
crops
(
selected
species)

Decreased
eastern
commercial
forest
productivity
(
selected
species)
Decreased
western
commercial
forest
productivity
Decreased
eastern
commercial
forest
productivity
(
other
species)

Decreased
yields
for
fruits
and
vegetables
Decreased
yields
for
other
commercial
and
noncommercial
crops
Damage
to
urban
ornamental
plants
Impacts
on
recreational
demand
from
damaged
forest
aesthetics
Damage
to
ecosystem
functions
(
continued)
4­
13
Table
4­
2.
Human
Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
IAQR
(
continued)

Pollutant/
Effect
Quantified
and
Monetized
in
Base
Estimatesa
Quantified
and/
or
Monetized
Effects
in
Sensitivity
Analyses
Unquantified
Effects
PM/
Health
Premature
mortality:
long­
term
exposures
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
Minor
restricted
activity
days
Work
loss
days
Asthma
exacerbations
(
asthmatic
population)
Respiratory
symptoms
(
asthmatic
population)

Infant
mortality
Premature
mortality:
short­
term
exposures
Low
birth
weight
Changes
in
pulmonary
function
Chronic
respiratory
diseases
other
than
chronic
bronchitis
Morphological
changes
Altered
host
defense
mechanisms
Nonasthma
respiratory
emergency
room
visits
4­
14
Table
4­
2.
Human
Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
IAQR
(
continued)

Pollutant/
Effect
Quantified
and
Monetized
in
Base
Estimatesa
Quantified
and/
or
Monetized
Effects
in
Sensitivity
Analyses
Unquantified
Effects
Nitrogen
and
Sulfate
Deposition/

Welfare
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
SO2/
Health
Hospital
admissions
for
respiratory
and
cardiac
diseases
Respiratory
symptoms
in
asthmatics
NOX/
Health
Lung
irritation
Lowered
resistance
to
respiratory
infection
Hospital
admissions
for
respiratory
and
cardiac
diseases
(
continued)
4­
15
Table
4­
2.
Human
Health
and
Welfare
Effects
of
Pollutants
Affected
by
the
Proposed
IAQR
(
continued)

Pollutant/
Effect
Quantified
and
Monetized
in
Base
Estimatesa
Quantified
and/
or
Monetized
Effects
in
Sensitivity
Analyses
Unquantified
Effects
Mercury
Deposition/

Health
Neurological
disorders
Learning
disabilities
Retarded
development
Potential
cardiovascular
effects
*

Altered
blood
pressure
regulation
*

Increased
heart
rate
variability
*

Myocardial
infarctions
*

Potential
reproductive
effects
*

Mercury
Deposition/

Welfare
Impacts
on
birds
and
mammals
(
e.
g.,
reproductive
effects)
Impacts
to
commercial,
subsistence,
and
recreational
fishing
Reduced
existence
values
for
currently
healthy
ecosystems

These
are
potential
effects
as
the
literature
is
either
contradictory
or
incomplete.

a
Primary
quantified
and
monetized
effects
are
those
included
when
determining
the
primary
estimate
of
total
monetized
benefits
of
the
IAQR.
See
Section
C­
2
for
a
more
complete
discussion
of
presentation
of
benefits
estimates.

b
Premature
mortality
associated
with
ozone
is
not
currently
included
in
the
primary
analysis.
Recent
evidence
suggests
that
short­
term
exposures
to
ozone
may
have
a
significant
effect
on
daily
mortality
rates,
independent
of
exposure
to
PM.
The
EPA
is
currently
conducting
a
series
of
meta­
analyses
of
the
ozone
mortality
epidemiology
literature
and
will
reevaluate
inclusion
of
ozone­
related
mortality
in
the
primary
analysis
once
the
meta­
analyses
have
been
completed.
4­
16
recent
published
analyses
(
Banzhaf
et
al.,
2002;
Levy
et
al.,
2001;
Levy
et
al.,
1999;
Ostro
and
Chestnut,
1998).

To
assess
economic
value
in
a
damage­
function
framework,
the
changes
in
environmental
quality
must
be
translated
into
effects
on
people
or
on
the
things
that
people
value.
In
some
cases,
the
changes
in
environmental
quality
can
be
directly
valued,
as
is
the
case
for
changes
in
visibility.
In
other
cases,
such
as
for
changes
in
ozone
and
PM,
a
health
and
welfare
impact
analysis
must
first
be
conducted
to
convert
air
quality
changes
into
effects
that
can
be
assigned
dollar
values.

For
the
purposes
of
this
RIA,
the
health
impacts
analysis
is
limited
to
those
health
effects
that
are
directly
linked
to
ambient
levels
of
air
pollution
and
specifically
to
those
linked
to
ozone
and
PM.
There
may
be
other,
indirect
health
impacts
associated
with
implementing
controls
to
meet
the
preliminary
control
options,
such
as
occupational
health
impacts
for
equipment
operators.
These
impacts
may
be
positive
or
negative,
but
in
general,
for
this
set
of
control
options,
they
are
expected
to
be
small
relative
to
the
direct
air
pollution­
related
impacts.

The
welfare
impacts
analysis
is
limited
to
changes
in
the
environment
that
have
a
direct
impact
on
human
welfare.
For
this
analysis,
we
are
limited
by
the
available
data
to
examining
impacts
of
changes
in
visibility.
We
also
provide
qualitative
discussions
of
the
impact
of
changes
in
other
environmental
and
ecological
effects,
for
example,
changes
in
deposition
of
nitrogen
and
sulfur
to
terrestrial
and
aquatic
ecosystems,
but
we
are
unable
to
place
an
economic
value
on
these
changes.

We
note
at
the
outset
that
the
EPA
rarely
has
the
time
or
resources
to
perform
extensive
new
research
to
measure
either
the
health
outcomes
or
their
values
for
this
analysis.
Thus,

similar
to
Kunzli
et
al.
(
2000)
and
other
recent
health
impact
analyses,
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
for
the
environmental
quality
change
under
analysis.
Where
appropriate,
adjustments
are
made
for
the
level
of
environmental
quality
change,
the
sociodemographic
and
economic
characteristics
of
the
affected
population,
and
other
factors
to
improve
the
accuracy
and
robustness
of
benefits
estimates.
4­
17
4.1.1
Valuation
Concepts
In
valuing
health
impacts,
we
note
that
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).
In
general,
economists
tend
to
view
an
individual's
WTP
for
an
improvement
in
environmental
quality
as
the
appropriate
measure
of
the
value
of
a
risk
reduction.
An
individual's
willingness
to
accept
(
WTA)

compensation
for
not
receiving
the
improvement
is
also
a
valid
measure.
However,
WTP
is
generally
considered
to
be
a
more
readily
available
and
conservative
measure
of
benefits.

Adoption
of
WTP
as
the
measure
of
value
implies
that
the
value
of
environmental
quality
improvements
depends
on
the
individual
preferences
of
the
affected
population
and
that
the
existing
distribution
of
income
(
ability
to
pay)
is
appropriate.
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
cost
of
illness
(
COI)

estimates
generally
understate
the
true
value
of
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
(
Harrrington
and
Portnoy,
1987;
Berger,
1987).

For
many
goods,
WTP
can
be
observed
by
examining
actual
market
transactions.
For
example,
if
a
gallon
of
bottled
drinking
water
sells
for
$
1,
it
can
be
observed
that
at
least
some
people
are
willing
to
pay
$
1
for
such
water.
For
goods
not
exchanged
in
the
market,
such
as
most
environmental
"
goods,"
valuation
is
not
as
straightforward.
Nevertheless,
a
value
may
be
inferred
from
observed
behavior,
such
as
sales
and
prices
of
products
that
result
in
similar
effects
or
risk
reductions
(
e.
g.,
nontoxic
cleaners
or
bike
helmets).
Alternatively,
surveys
can
be
used
in
an
attempt
to
directly
elicit
WTP
for
an
environmental
improvement.

One
distinction
in
environmental
benefits
estimation
is
between
use
values
and
nonuse
values.
Although
no
general
agreement
exists
among
economists
on
a
precise
distinction
between
the
two
(
see
Freeman
[
1993]),
the
general
nature
of
the
difference
is
clear.
Use
values
are
those
aspects
of
environmental
quality
that
affect
an
individual's
welfare
more
or
less
directly.
These
effects
include
changes
in
product
prices,
quality,
and
availability;
changes
in
the
quality
of
outdoor
recreation
and
outdoor
aesthetics;
changes
in
health
or
life
expectancy;
and
the
costs
of
actions
taken
to
avoid
negative
effects
of
environmental
quality
changes.
10Concerns
about
the
reliability
of
value
estimates
from
CV
studies
arose
because
research
has
shown
that
bias
can
be
introduced
easily
into
these
studies
if
they
are
not
carefully
conducted.
Accurately
measuring
WTP
for
avoided
health
and
welfare
losses
depends
on
the
reliability
and
validity
of
the
data
collected.
There
are
several
issues
to
consider
when
evaluating
study
quality,
including
but
not
limited
to
1)
whether
the
sample
estimates
of
WTP
are
representative
of
the
population
WTP;
2)
whether
the
good
to
be
valued
is
comprehended
and
accepted
by
the
respondent;
3)
whether
the
WTP
elicitation
format
is
designed
to
minimize
strategic
responses;
4)
whether
WTP
is
sensitive
to
respondent
familiarity
with
the
good,
to
the
size
of
the
change
in
the
good,
and
to
income;
5)
whether
the
estimates
of
WTP
are
broadly
consistent
with
other
estimates
of
WTP
for
similar
goods;
and
6)
the
extent
to
which
WTP
responses
are
consistent
with
established
economic
principles.

4­
18
Nonuse
values
are
those
for
which
an
individual
is
willing
to
pay
for
reasons
that
do
not
relate
to
the
direct
use
or
enjoyment
of
any
environmental
benefit
but
might
relate
to
existence
values
and
bequest
values.
Nonuse
values
are
not
traded,
directly
or
indirectly,
in
markets.

For
this
reason,
the
measurement
of
nonuse
values
has
proved
to
be
significantly
more
difficult
than
the
measurement
of
use
values.
The
air
quality
changes
produced
by
the
IAQR
cause
changes
in
both
use
and
nonuse
values,
but
the
monetary
benefit
estimates
are
almost
exclusively
for
use
values.

More
frequently
than
not,
the
economic
benefits
from
environmental
quality
changes
are
not
traded
in
markets,
so
direct
measurement
techniques
cannot
be
used.
There
are
three
main
nonmarket
valuation
methods
used
to
develop
values
for
endpoints
considered
in
this
analysis:
stated
preference
(
or
contingent
valuation
[
CV]),
indirect
market
(
e.
g.,
hedonic
wage),
and
avoided
cost
methods.

The
stated
preference
or
CV
method
values
endpoints
by
using
carefully
structured
surveys
to
ask
a
sample
of
people
what
amount
of
compensation
is
equivalent
to
a
given
change
in
environmental
quality.
There
is
an
extensive
scientific
literature
and
body
of
practice
on
both
the
theory
and
technique
of
stated
preference­
based
valuation.
The
EPA
believes
that
well­
designed
and
well­
executed
stated
preference
studies
are
valid
for
estimating
the
benefits
of
air
quality
regulations.
10
Stated
preference
valuation
studies
form
the
basis
for
valuing
a
number
of
health
and
welfare
endpoints,
including
the
value
of
mortality
risk
reductions,
CB
risk
reductions,
minor
illness
risk
reductions,
and
visibility
improvements.

Indirect
market
methods
can
also
be
used
to
infer
the
benefits
of
pollution
reduction.
The
most
important
application
of
this
technique
for
our
analysis
is
the
calculation
of
the
VSL
for
use
in
estimating
benefits
from
mortality
risk
reductions.
No
market
exists
where
changes
in
the
probability
of
death
are
directly
exchanged.
However,
people
make
decisions
about
occupation,
precautionary
behavior,
and
other
activities
associated
with
changes
in
the
risk
of
11Income
elasticity
is
a
common
economic
measure
equal
to
the
percentage
change
in
WTP
for
a
1
percent
change
in
income.

4­
19
death.
By
examining
these
risk
changes
and
the
other
characteristics
of
people's
choices,
it
is
possible
to
infer
information
about
the
monetary
values
associated
with
changes
in
mortality
risk
(
see
Section
4.1.5.5.1).

Avoided
cost
methods
are
ways
to
estimate
the
costs
of
pollution
by
using
the
expenditures
made
necessary
by
pollution
damage.
For
example,
if
buildings
must
be
cleaned
or
painted
more
frequently
as
levels
of
PM
increase,
then
the
appropriately
calculated
increment
of
these
costs
is
a
reasonable
lower­
bound
estimate
(
under
most
conditions)
of
true
economic
benefits
when
PM
levels
are
reduced.
Avoided
costs
methods
are
also
used
to
estimate
some
of
the
health­
related
benefits
related
to
morbidity,
such
as
hospital
admissions
(
see
Section
4.1.5).

4.1.2
Growth
in
WTP
Reflecting
National
Income
Growth
Over
Time
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.
There
is
substantial
empirical
evidence
that
the
income
elasticity11
of
WTP
for
health
risk
reductions
is
positive,
although
there
is
uncertainty
about
its
exact
value.
Thus,

as
real
income
increases,
the
WTP
for
environmental
improvements
also
increases.
Although
many
analyses
assume
that
the
income
elasticity
of
WTP
is
unit
elastic
(
i.
e.,
10
percent
higher
real
income
level
implies
a
10
percent
higher
WTP
to
reduce
risk
changes),
empirical
evidence
suggests
that
income
elasticity
is
substantially
less
than
one
and
thus
relatively
inelastic.
As
real
income
rises,
the
WTP
value
also
rises
but
at
a
slower
rate
than
real
income.

The
effects
of
real
income
changes
on
WTP
estimates
can
influence
benefit
estimates
in
two
different
ways:
through
real
income
growth
between
the
year
a
WTP
study
was
conducted
and
the
year
for
which
benefits
are
estimated,
and
through
differences
in
income
between
study
populations
and
the
affected
populations
at
a
particular
time.
Empirical
evidence
of
the
effect
of
real
income
on
WTP
gathered
to
date
is
based
on
studies
examining
the
former.
The
Environmental
Economics
Advisory
Committee
(
EEAC)
of
the
SAB
advised
the
EPA
to
adjust
WTP
for
increases
in
real
income
over
time
but
not
to
adjust
WTP
to
account
for
cross­
sectional
income
differences
"
because
of
the
sensitivity
of
making
such
distinctions,
and
because
of
insufficient
evidence
available
at
present"
(
EPA­
SAB­
EEAC­
00­

013).
4­
20
Based
on
a
review
of
the
available
income
elasticity
literature,
we
adjust
the
valuation
of
human
health
benefits
upward
to
account
for
projected
growth
in
real
U.
S.
income.
Faced
with
a
dearth
of
estimates
of
income
elasticities
derived
from
time­
series
studies,
we
applied
estimates
derived
from
cross­
sectional
studies
in
our
analysis.
Details
of
the
procedure
can
be
found
in
Kleckner
and
Neumann
(
1999).
An
abbreviated
description
of
the
procedure
we
used
to
account
for
WTP
for
real
income
growth
between
1990
and
2010
and
2015
is
presented
below.

Reported
income
elasticities
suggest
that
the
severity
of
a
health
effect
is
a
primary
determinant
of
the
strength
of
the
relationship
between
changes
in
real
income
and
WTP.
As
such,
we
use
different
elasticity
estimates
to
adjust
the
WTP
for
minor
health
effects,
severe
and
chronic
health
effects,
and
premature
mortality.
We
also
expect
that
the
WTP
for
improved
visibility
in
Class
I
areas
would
increase
with
growth
in
real
income.
The
elasticity
values
used
to
adjust
estimates
of
benefits
in
2010
and
2015
are
presented
in
Table
4­
3.

In
addition
to
elasticity
estimates,
projections
of
real
gross
domestic
product
(
GDP)
and
populations
from
1990
to
2010
and
2015
are
needed
to
adjust
benefits
to
reflect
real
per
capita
income
growth.
For
consistency
with
the
emissions
and
benefits
modeling,
we
use
national
population
estimates
for
the
years
1990
to
1999
based
on
U.
S.
Census
Bureau
estimates
(
Hollman,
Mulder
and
Kallan,
2000).
These
population
estimates
are
based
on
application
of
a
cohort­
component
model
applied
to
1990
U.
S.
Census
data
projections
(
U.
S.
Table
4­
3.
Elasticity
Values
Used
to
Account
for
Projected
Real
Income
Growtha
Benefit
Category
Central
Elasticity
Estimate
Minor
Health
Effect
0.14
Severe
and
Chronic
Health
Effects
0.45
Premature
mortality
0.40
Visibilityb
0.90
a
Derivation
of
estimates
can
be
found
in
Kleckner
and
Neumann
(
1999)
and
Chestnut
(
1997).
COI
estimates
are
assigned
an
adjustment
factor
of
1.0.

b
No
range
was
applied
for
visibility
because
no
ranges
were
available
in
the
current
published
literature.
12U.
S.
Bureau
of
Census.
Annual
Projections
of
the
Total
Resident
Population,
Middle
Series,
1999­
2100.
(
Available
on
the
internet
at
http://
www.
census.
gov/
population/
www/
projections/
natsum­
T1.
html)

13U.
S.
Bureau
of
Economic
Analysis,
Table
2A
(
1992$).
(
Available
on
the
internet
at
http://
www.
bea.
doc.
gov/
bea/
dn/
0897nip2/
tab2a.
htm)
and
U.
S.
Bureau
of
Economic
Analysis,
Economics
and
Budget
Outlook.
Note
that
projections
for
2007
to
2010
are
based
on
average
GDP
growth
rates
between
1999
and
2007.

14Standard
and
Poor's.
2000.
"
The
U.
S.
Economy:
The
25
Year
Focus."
Winter.

15In
previous
analyses,
we
used
the
Standard
and
Poor's
projections
of
GDP
directly.
This
led
to
an
apparent
discontinuity
in
the
adjustment
factors
between
2010
and
2011.
We
refined
the
method
by
applying
the
relative
growth
rates
for
GDP
derived
from
the
Standard
and
Poor's
projections
to
the
2010
projected
GDP
based
on
the
Bureau
of
Economic
Analysis
projections.

4­
21
Bureau
of
Census,
2000).
12
For
the
years
between
2000
and
2015,
we
applied
growth
rates
based
on
the
U.
S.
Census
Bureau
projections
to
the
U.
S.
Census
estimate
of
national
population
in
2000.
We
use
projections
of
real
GDP
provided
in
Kleckner
and
Neumann
(
1999)
for
the
years
1990
to
2010.13
We
use
projections
of
real
GDP
(
in
chained
1996
dollars)
provided
by
Standard
and
Poor's14
(
2000)
for
the
years
2010
to
2015.15
Using
the
method
outlined
in
Kleckner
and
Neumann
(
1999)
and
the
population
and
income
data
described
above,
we
calculate
WTP
adjustment
factors
for
each
of
the
elasticity
estimates
listed
in
Table
4­
4.
Benefits
for
each
of
the
categories
(
minor
health
effects,
severe
and
chronic
health
effects,
premature
mortality,
and
visibility)
will
be
adjusted
by
multiplying
the
unadjusted
benefits
by
the
appropriate
adjustment
factor.
Table
4­
4
lists
the
estimated
adjustment
factors.
Note
that,
for
premature
mortality,
we
apply
the
income
adjustment
factor
ex
post
to
the
present
discounted
value
of
the
stream
of
avoided
mortalities
occurring
over
the
lag
period.
Also
note
that
no
adjustments
will
be
made
to
benefits
based
on
the
COI
approach
or
to
work
loss
days
and
worker
productivity.
This
assumption
will
also
lead
us
to
underpredict
benefits
in
future
years
because
it
is
likely
that
increases
in
real
U.
S.
income
would
also
result
in
increased
COI
(
due,
for
example,
to
increases
in
wages
paid
to
medical
workers)
and
increased
cost
of
work
loss
days
and
lost
worker
productivity
(
reflecting
that
if
worker
incomes
are
higher,
the
losses
resulting
from
reduced
worker
production
would
also
be
higher).
4­
22
4.1.3
Methods
for
Describing
Uncertainty
In
any
complex
analysis
using
estimated
parameters
and
inputs
from
numerous
models,

there
are
likely
to
be
many
sources
of
uncertainty.
This
analysis
is
no
exception.
As
outlined
both
in
this
and
preceding
chapters,
many
inputs
are
used
to
derive
the
final
estimate
of
benefits,
including
emission
inventories,
air
quality
models
(
with
their
associated
parameters
and
inputs),
epidemiological
health
effect
estimates,
estimates
of
values
(
both
from
WTP
and
COI
studies),
population
estimates,
income
estimates,
and
estimates
of
the
future
state
of
the
world
(
i.
e.,
regulations,
technology,
and
human
behavior).
Each
of
these
inputs
may
be
uncertain
and,
depending
on
their
location
in
the
benefits
analysis,
may
have
a
disproportionately
large
impact
on
final
estimates
of
total
benefits.
For
example,
emissions
estimates
are
used
in
the
first
stage
of
the
analysis.
As
such,
any
uncertainty
in
emissions
estimates
will
be
propagated
through
the
entire
analysis.
When
compounded
with
uncertainty
in
later
stages,
small
uncertainties
in
emission
levels
can
lead
to
much
larger
impacts
on
total
benefits.

Some
key
sources
of
uncertainty
in
each
stage
of
the
benefits
analysis
are
the
following:


gaps
in
scientific
data
and
inquiry;


variability
in
estimated
relationships,
such
as
epidemiological
effect
estimates,
introduced
through
differences
in
study
design
and
statistical
modeling;
Table
4­
4.
Adjustment
Factors
Used
to
Account
for
Projected
Real
Income
Growtha
Benefit
Category
2010
2015
Minor
Health
Effect
1.034
1.073
Severe
and
Chronic
Health
Effects
1.113
1.254
Premature
Mortality
1.100
1.222
Visibility
1.239
1.581
a
Based
on
elasticity
values
reported
in
Table
4­
3,
U.
S.
Census
population
projections,
and
projections
of
real
gross
domestic
product
per
capita
4­
23

errors
in
measurement
and
projection
for
variables
such
as
population
growth
rates;


errors
due
to
misspecification
of
model
structures,
including
the
use
of
surrogate
variables,
such
as
using
PM
10
when
PM
2.5
is
not
available,
excluded
variables,
and
simplification
of
complex
functions;
and

biases
due
to
omissions
or
other
research
limitations.

Some
of
the
key
uncertainties
in
the
benefits
analysis
are
presented
in
Table
4­
5.
Given
the
wide
variety
of
sources
for
uncertainty
and
the
potentially
large
degree
of
uncertainty
about
any
primary
estimate,
it
is
necessary
for
us
to
address
this
issue
in
several
ways,
based
on
the
following
types
of
uncertainty:
4­
24
a.
Quantifiable
uncertainty
in
benefits
estimates.
For
some
parameters
or
inputs
it
may
be
possible
to
provide
a
statistical
representation
of
the
underlying
uncertainty
distribution.
Quantitative
uncertainty
may
include
measurement
uncertainty
or
variation
in
estimates
across
or
within
studies.
For
example,
the
variation
in
VSL
Table
4­
5.
Primary
Sources
of
Uncertainty
in
the
Benefit
Analysis
1.
Uncertainties
Associated
With
Impact
Functions
S
The
value
of
the
ozone
or
PM
effect
estimate
in
each
impact
function.

S
Application
of
a
single
impact
function
to
pollutant
changes
and
populations
in
all
locations.

S
Similarity
of
future
year
impact
functions
to
current
impact
functions.

S
Correct
functional
form
of
each
impact
function.

S
Extrapolation
of
effect
estimates
beyond
the
range
of
ozone
or
PM
concentrations
observed
in
the
source
epidemiological
study.

S
Application
of
impact
functions
only
to
those
subpopulations
matching
the
original
study
population.

2.
Uncertainties
Associated
With
Ozone
and
PM
Concentrations
S
Responsiveness
of
the
models
to
changes
in
precursor
emissions
resulting
from
the
control
policy.

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

S
Model
chemistry
for
the
formation
of
ambient
nitrate
concentrations.

S
Lack
of
ozone
monitors
in
rural
areas
requires
extrapolation
of
observed
ozone
data
from
urban
to
rural
areas.

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
Full
ozone
season
air
quality
distributions
are
extrapolated
from
a
limited
number
of
simulation
days.

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
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
locationspecific
rates.

S
Current
baseline
incidence
rates
may
not
approximate
well
baseline
incidence
rates
in
2015.

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
and
welfare
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
and
welfare
benefits
estimates
are
limited
to
the
available
impact
functions.
Thus,
unquantified
or
unmonetized
benefits
are
not
included.
4­
25
results
across
available
meta­
analyses
provides
a
source
of
uncertainty
that
can
be
characterized
in
calculating
monetized
benefits.
Methods
typically
used
to
evaluate
the
impact
of
these
quantifiable
sources
of
uncertainty
on
benefits
and
incidence
estimates
center
on
Monte
Carlo­
based
probabilistic
simulation.
This
technique
allows
uncertainty
in
key
inputs
to
be
propagated
through
the
model
to
generate
a
single
distribution
of
results
reflecting
the
combined
impact
of
multiple
sources
of
uncertainty.
Variability
can
also
be
considered
along
with
uncertainty
using
nested
two­
stage
Monte
Carlo
simulation.

b.
Uncertainty
in
the
basis
for
quantified
estimates.
Often
it
is
possible
to
identify
a
source
of
uncertainty
(
e.
g.,
an
ongoing
debate
over
the
proper
method
to
estimate
premature
mortality)
that
is
not
readily
addressed
through
traditional
uncertainty
analysis.
In
these
cases,
it
is
possible
to
characterize
the
potential
impact
of
this
uncertainty
on
the
overall
benefits
estimates
through
sensitivity
analyses.

c.
Nonquantifiable
uncertainty.
Uncertainties
may
also
result
from
omissions
of
known
effects
from
the
benefits
calculation,
perhaps
owing
to
a
lack
of
data
or
modeling
capability.
For
example,
in
this
analysis
we
were
unable
to
quantify
the
benefits
of
avoided
airborne
nitrogen
deposition
on
aquatic
and
terrestrial
ecosystems.

It
should
be
noted
that,
even
for
individual
endpoints,
there
is
usually
more
than
one
source
of
uncertainty.
This
makes
it
difficult
to
provide
an
overall
quantified
uncertainty
estimate
for
individual
endpoints
or
for
total
benefits,
without
conducting
a
comprehensive
uncertainty
analysis
that
considers
the
aggregate
impact
of
multiple
sources
of
uncertainty
on
benefits
estimates.

The
NAS
report
on
the
EPA's
benefits
analysis
methodology
highlighted
the
need
for
the
EPA
to
conduct
rigorous
quantitative
analysis
of
uncertainty
in
its
benefits
estimates.
In
response
to
these
comments,
the
EPA
has
initiated
the
development
of
a
comprehensive
methodology
for
characterizing
the
aggregate
impact
of
uncertainty
in
key
modeling
elements
on
both
health
incidence
and
benefits
estimates.
This
methodology
will
begin
by
identifying
those
modeling
elements
that
have
a
significant
impact
on
benefits
due
to
either
the
magnitude
of
their
uncertainty
or
other
factors
such
as
nonlinearity
within
the
modeling
framework.
A
combination
of
influence
analysis
and
sensitivity
analysis
methods
may
be
used
to
focus
the
analysis
of
uncertainty
on
these
key
sources
of
uncertainty.
A
probabilistic
simulation
approach
based
on
Monte
Carlo
methods
will
be
developed
for
propagating
the
impact
of
4­
26
these
sources
of
uncertainty
through
the
modeling
framework.
Issues
such
as
correlation
between
input
parameters
and
the
identification
of
reasonable
upper
and
lower
bounds
for
input
distributions
characterizing
uncertainty
will
be
addressed
in
developing
the
approach.

One
component
of
the
EPA's
uncertainty
analysis
methodology
that
is
currently
underway
is
an
expert
elicitation
intended
to
characterize
uncertainty
in
the
effect
estimates
used
to
estimate
mortality
resulting
from
both
short­
term
(
timer
series
studies)
and
longer­
term
(
cohort
studies)
exposure
to
PM.
This
expert
elicitation
is
aimed
at
evaluating
uncertainty
in
both
the
form
of
the
mortality
impact
function
(
e.
g.,
threshold
versus
linear
models)
and
the
fit
of
a
specific
model
to
the
data
(
e.
g.,
confidence
bounds
for
specific
percentiles
of
the
mortality
effect
estimates).
Additional
issues
such
as
the
ability
of
longer­
term
cohort
studies
to
capture
mortality
resulting
from
short­
term
peak
PM
exposures
is
also
being
addressed
in
the
expert
elicitation.

EPA
will
consider
incorporating
elements
of
this
uncertainty
analysis
methodology,

including
information
from
the
expert
elicitation
addressing
the
mortality
estimate,
into
subsequent
analysis
conducted
for
the
IAQR
(
e.
g.,
the
Supplemental
Analysis
and
Final
Rule)

as
they
become
available.
For
the
Proposed
IAQR,
EPA
has
addressed
key
sources
of
uncertainty
through
a
series
of
sensitivity
analyses
(
to
be
completed
for
the
supplemental
analysis)
examining
the
impact
of
alternate
assumptions
on
the
benefits
estimates
that
are
generated.

Our
estimate
of
total
benefits
should
be
viewed
as
an
approximate
result
because
of
the
sources
of
uncertainty
discussed
above
(
see
Table
4­
5).
Uncertainty
about
specific
aspects
of
the
health
and
welfare
estimation
models
are
discussed
in
greater
detail
in
the
following
sections.
The
total
benefits
estimate
may
understate
or
overstate
actual
benefits
of
the
rule.
4­
27
In
considering
the
monetized
benefits
estimates,
the
reader
should
remain
aware
of
the
many
limitations
of
conducting
these
analyses
mentioned
throughout
this
RIA.
One
significant
limitation
of
both
the
health
and
welfare
benefits
analyses
is
the
inability
to
quantify
many
of
the
serious
effects
listed
in
Table
4­
1.
For
many
health
and
welfare
effects,
such
as
changes
in
ecosystem
functions
and
PM­
related
materials
damage,
reliable
impact
functions
and/
or
valuation
functions
are
not
currently
available.
In
general,
if
it
were
possible
to
monetize
these
benefits
categories,
the
benefits
estimates
presented
in
this
analysis
would
increase.

Unquantified
benefits
are
qualitatively
discussed
in
the
health
and
welfare
effects
sections.
In
addition
to
unquantified
benefits,
there
may
also
be
environmental
costs
that
we
are
unable
to
quantify.
These
endpoints
are
qualitatively
discussed
in
the
health
and
welfare
effects
sections
as
well.
The
net
effect
of
excluding
benefit
and
disbenefit
categories
from
the
estimate
of
total
benefits
depends
on
the
relative
magnitude
of
the
effects.

4.1.4
Demographic
Projections
Quantified
and
monetized
human
health
impacts
depend
critically
on
the
demographic
characteristics
of
the
population,
including
age,
location,
and
income.
In
previous
analyses,

we
have
used
simple
projections
of
total
population
that
did
not
take
into
account
changes
in
demographic
composition
over
time.
In
the
current
analysis,
we
use
more
sophisticated
projections
based
on
economic
forecasting
models
developed
by
Woods
and
Poole,
Inc.
The
Woods
and
Poole
(
WP)
database
contains
county­
level
projections
of
population
by
age,
sex,

and
race
out
to
2025.
Projections
in
each
county
are
determined
simultaneously
with
every
other
county
in
the
United
States
to
take
into
account
patterns
of
economic
growth
and
migration.
The
sum
of
growth
in
county­
level
populations
is
constrained
to
equal
a
previously
determined
national
population
growth,
based
on
Bureau
of
Census
estimates
(
Hollman,

Mulder
and
Kallan,
2000).
According
to
WP,
linking
county­
level
growth
projections
together
and
constraining
to
a
national­
level
total
growth
avoids
potential
errors
introduced
by
forecasting
each
county
independently.
County
projections
are
developed
in
a
four­
stage
process.
First,
national­
level
variables
such
as
income,
employment,
populations,
etc.
are
forecasted.
Second,
employment
projections
are
made
for
172
economic
areas
defined
by
the
Bureau
of
Economic
Analysis,
using
an
"
export­
base"
approach,
which
relies
on
linking
industrial
sector
production
of
nonlocally
consumed
production
items,
such
as
outputs
from
mining,
agriculture,
and
manufacturing
with
the
national
economy.
The
export­
base
approach
requires
estimation
of
demand
equations
or
calculation
of
historical
growth
rates
for
output
and
employment
by
sector.
Third,
population
is
projected
for
each
economic
area
based
on
net
migration
rates
derived
from
employment
opportunities
and
following
a
cohort­
component
4­
28
method
based
on
fertility
and
mortality
in
each
area.
Fourth,
employment
and
population
projections
are
repeated
for
counties,
using
the
economic
region
totals
as
bounds.
The
age,

sex,
and
race
distributions
for
each
region
or
county
are
determined
by
aging
the
population
by
single
year
of
age
by
sex
and
race
for
each
year
through
2015
based
on
historical
rates
of
mortality,
fertility,
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.

As
noted
above,
values
for
environmental
quality
improvements
are
expected
to
increase
with
growth
in
real
per
capita
income.
Accounting
for
real
income
growth
over
time
requires
projections
of
both
real
GDP
and
total
U.
S.
populations.
For
consistency
with
the
emissions
and
benefits
modeling,
we
use
national
population
estimates
based
on
the
U.
S.
Census
Bureau
projections.

4.1.5
Health
Benefits
Assessment
Methods
The
most
significant
monetized
benefits
of
reducing
ambient
concentrations
of
PM
and
ozone
are
attributable
to
reductions
in
health
risks
associated
with
air
pollution.
The
EPA's
Criteria
Documents
for
ozone
and
PM
list
numerous
health
effects
known
to
be
linked
to
ambient
concentrations
of
these
pollutants
(
EPA,
1996a
and
1996b).
As
illustrated
in
Figure
4­
1,
quantification
of
health
impacts
requires
several
inputs,
including
epidemiological
effect
estimates,
baseline
incidence
and
prevalence
rates,
potentially
affected
populations,
and
estimates
of
changes
in
ambient
concentrations
of
air
pollution.
Previous
sections
have
described
the
population
and
air
quality
inputs.
This
section
describes
the
effect
estimates
and
baseline
incidence
and
prevalence
inputs
and
the
methods
used
to
quantify
and
monetize
changes
in
the
expected
number
of
incidences
of
various
health
effects.
16Evidence
has
been
found
linking
ozone
exposures
with
premature
mortality
independent
of
PM
exposures.
A
recent
analysis
by
Thurston
and
Ito
(
2001)
reviewed
previously
published
time­
series
studies
of
the
effect
of
daily
ozone
levels
on
daily
mortality
and
found
that
previous
EPA
estimates
of
the
short­
term
mortality
benefits
of
the
ozone
NAAQS
(
EPA,
1997)
may
have
been
underestimated
by
up
to
a
factor
of
two,
even
when
PM
is
controlled
for
in
the
models.
In
its
September
2001
advisory
on
the
draft
analytical
blueprint
for
the
second
Section
812
prospective
analysis,
the
SAB
cited
the
Thurston
and
Ito
study
as
a
significant
advance
in
understanding
the
effects
of
ozone
on
daily
mortality
and
recommended
re­
evaluation
of
the
ozone
mortality
endpoint
for
inclusion
in
the
next
prospective
study
(
EPA­
SAB­
COUNCIL­
ADV­
01­
004,
2001).
In
addition,
a
recent
World
Health
Organization
(
WHO)
report
found
that
"
recent
epidemiological
studies
have
strengthened
the
evidence
that
there
are
short­
term
O3
effects
on
mortality
and
respiratory
morbidity
and
provided
further
information
on
exposure­
response
relationships
and
effect
modification."
(
WHO,
2002).
Based
on
these
new
analyses
and
recommendations,
the
EPA
is
currently
reevaluating
ozone­
related
mortality
for
inclusion
in
the
primary
benefits
analysis.
The
EPA
is
sponsoring
three
independent
meta­
analyses
of
the
ozone­
mortality
epidemiology
literature
to
inform
a
determination
on
inclusion
of
this
important
health
endpoint.
Upon
completion
and
peer
review
of
the
meta­
analyses,
the
EPA
will
make
its
determination
on
whether
benefits
of
reductions
in
ozone­
related
mortality
will
be
included
in
the
benefits
analysis
for
the
final
IAQR.

4­
29
4.1.5.1
Selecting
Health
Endpoints
and
Epidemiological
Effect
Estimates
Quantifiable
health
benefits
of
the
proposal
may
be
related
to
ozone
only,
PM
only,
or
both
pollutants.
Decreased
worker
productivity,
respiratory
hospital
admissions
for
children
under
two,
and
school
absences
are
related
to
ozone
but
not
PM.
PM­
only
health
effects
include
premature
mortality,
nonfatal
heart
attacks,
CB,
acute
bronchitis,
upper
and
lower
respiratory
symptoms,
asthma
exacerbations,
and
work
loss
days.
16
Health
effects
related
to
both
PM
and
ozone
include
hospital
admissions,
emergency
room
visits
for
asthma,
and
minor
restricted
activity
days.

We
relied
on
the
available
published
scientific
literature
to
ascertain
the
relationship
between
PM
and
ozone
exposure
and
adverse
human
health
effects.
We
evaluated
studies
using
the
selection
criteria
summarized
in
Table
4­
6.
These
criteria
include
consideration
of
whether
the
study
was
peer
reviewed,
the
match
between
the
pollutant
studied
and
the
pollutant
of
interest,
the
study
design
and
location,
and
characteristics
of
the
study
population,

among
other
considerations.
The
selection
of
C­
R
functions
for
the
benefits
analysis
is
guided
by
the
goal
of
achieving
a
balance
between
comprehensiveness
and
scientific
defensibility.

Recently,
the
Health
Effects
Institute
(
HEI)
reported
findings
by
health
researchers
at
Johns
Hopkins
University
and
others
that
have
raised
concerns
about
aspects
of
the
statistical
methods
used
in
a
number
of
recent
time­
series
studies
of
short­
term
exposures
to
air
pollution
and
health
effects
(
Greenbaum,
2002).
The
estimates
derived
from
the
long­
term
exposure
studies,
which
account
for
a
major
share
of
the
economic
benefits
described
in
4­
30
this
chapter,
are
not
affected.
Similarly,
the
time­
series
studies
employing
generalized
linear
models
(
GLMs)
or
other
parametric
methods,
as
well
as
case­
crossover
studies,
are
not
affected.
As
discussed
in
HEI
materials
provided
to
the
EPA
and
to
CASAC
(
Greenbaum,

2002),
researchers
working
on
the
National
Morbidity,
Mortality,
and
Air
Pollution
Study
(
NMMAPS)
found
problems
in
the
default
"
convergence
criteria"
used
in
Generalized
Additive
Models
(
GAM)
and
a
separate
issue
first
identified
by
Canadian
investigators
about
the
potential
to
underestimate
standard
errors
in
the
same
statistical
package.
Following
identification
of
the
GAM
issue,
a
number
of
time­
series
studies
were
reanalyzed
using
alternative
methods,
typically
GAM
with
more
stringent
convergence
criteria
and
an
alternative
model
such
as
generalized
linear
models
(
GLM)
with
natural
smoothing
splines,

and
the
results
of
the
reanalyses
have
been
compiled
and
reviewed
in
a
recent
HEI
publication
(
HEI,
2003a).
In
most,
but
not
all,
of
the
reanalyzed
studies,
it
was
found
that
risk
estimates
were
reduced
and
confidence
intervals
increased
with
the
use
of
GAM
with
more
stringent
convergence
criteria
or
GLM
analyses;
however,
the
reanalyses
generally
did
not
substantially
change
the
findings
of
the
original
studies,
and
the
changes
in
risk
estimates
with
alternative
analysis
methods
were
much
smaller
than
the
variation
in
effects
across
studies.
The
HEI
review
committee
concluded
the
following:

a.
Although
the
number
of
studies
showing
an
association
of
PM
with
mortality
was
slightly
smaller,
the
PM
association
persisted
in
the
majority
of
studies.

b.
In
some
of
the
large
number
of
studies
in
which
the
PM
association
persisted,
the
estimates
of
PM
effect
were
substantially
smaller.

c.
In
the
few
studies
in
which
investigators
performed
further
sensitivity
analyses,
some
showed
marked
sensitivity
of
the
PM
effect
estimate
to
the
degree
of
smoothing
and/
or
the
specification
of
weather
(
HEI,
2003b,
p.
269)

Examination
of
the
original
studies
used
in
our
benefits
analysis
found
that
the
health
endpoints
that
are
potentially
affected
by
the
GAM
issues
include
reduced
hospital
admissions
4­
31
and
reduced
lower
respiratory
symptoms.
For
the
IAQR,
we
have
incorporated
a
number
of
studies
that
have
been
updated
to
correct
for
the
GAM
issue,
including
Ito
et
al.
(
2003)
for
Table
4­
6.
Summary
of
Considerations
Used
in
Selecting
C­
R
Functions
Consideration
Comments
Peer
reviewed
research
Peer
reviewed
research
is
preferred
to
research
that
has
not
undergone
the
peer
review
process.

Study
type
Among
studies
that
consider
chronic
exposure
(
e.
g.,
over
a
year
or
longer)
prospective
cohort
studies
are
preferred
over
cross­
sectional
studies
because
they
control
for
important
individuallevel
confounding
variables
that
cannot
be
controlled
for
in
cross­
sectional
studies.

Study
period
Studies
examining
a
relatively
longer
period
of
time
(
and
therefore
having
more
data)
are
preferred,
because
they
have
greater
statistical
power
to
detect
effects.
More
recent
studies
are
also
preferred
because
of
possible
changes
in
pollution
mixes,
medical
care,
and
life
style
over
time.
However,
when
there
are
only
a
few
studies
available,
studies
from
all
years
will
be
included.

Population
attributes
The
most
technically
appropriate
measures
of
benefits
would
be
based
on
impact
functions
that
cover
the
entire
sensitive
population,
but
allow
for
heterogeneity
across
age
or
other
relevant
demographic
factors.
In
the
absence
of
effect
estimates
specific
to
age,
sex,
preexisting
condition
status,
or
other
relevant
factors,
it
may
be
appropriate
to
select
effect
estimates
that
cover
the
broadest
population,
to
match
with
the
desired
outcome
of
the
analysis,
which
is
total
nationallevel
health
impacts.

Study
size
Studies
examining
a
relatively
large
sample
are
preferred
because
they
generally
have
more
power
to
detect
small
magnitude
effects.
A
large
sample
can
be
obtained
in
several
ways,
either
through
a
large
population,
or
through
repeated
observations
on
a
smaller
population,
i.
e.
through
a
symptom
diary
recorded
for
a
panel
of
asthmatic
children.

Study
location
U.
S.
studies
are
more
desirable
than
non­
U.
S.
studies
because
of
potential
differences
in
pollution
characteristics,
exposure
patterns,
medical
care
system,
population
behavior
and
life
style.

Pollutants
included
in
model
When
modeling
the
effects
of
ozone
and
PM
(
or
other
pollutant
combinations)
jointly,
it
is
important
to
use
properly
specified
impact
functions
that
include
both
pollutants.
Use
of
single
pollutant
models
in
cases
where
both
pollutants
are
expected
to
affect
a
health
outcome
can
lead
to
double­
counting
when
pollutants
are
correlated.

Measure
of
PM
For
this
analysis,
impact
functions
based
on
PM2.5
are
preferred
to
PM10
because
the
IAQR
will
regulate
emissions
of
PM2.5
precursors
and
air
quality
modeling
was
conducted
for
this
size
fraction
of
PM.
Where
PM2.5
functions
are
not
available,
PM10
functions
are
used
as
surrogates,

recognizing
that
there
will
be
potential
downward
(
upward)
biases
if
the
fine
fraction
of
PM10
is
more
(
less)
toxic
than
the
coarse
fraction.

Economically
valuable
health
effects
Some
health
effects,
such
as
forced
expiratory
volume
and
other
technical
measurements
of
lung
function,
are
difficult
to
value
in
monetary
terms.
These
health
effects
are
not
quantified
in
this
analysis.
4­
32
respiratory­
related
hospital
admissions
(
COPD
and
pneumonia),
Shepard
et
al.
(
2003)
for
respiratory­
related
hospital
admissions
(
asthma),
Moolgavkar
(
2003)
for
cardiovascularrelated
hospital
admissions
(
ICD
codes
390­
429),
and
Ito
et
al.
(
2003)
for
cardiovascularrelated
hospital
admissions
(
ischemic
heart
disease,
dysrhythmia,
and
heart
failure).
Several
additional
hospital
admissions­
related
studies
have
not
yet
been
formally
updated
to
correct
for
the
GAM
issue.
These
include
the
lower
respiratory
symptoms
study
and
hospital
admissions
for
respiratory
and
cardiovascular
causes
in
populations
aged
20
to
64.
However,

as
discussed
above,
available
evidence
suggests
that
the
errors
introduced
into
effect
estimates
due
to
the
GAM
issue
should
not
significantly
affect
incidence
results.

It
is
important
to
reiterate
that
the
estimates
derived
from
the
long­
term
exposure
studies,

which
account
for
a
major
share
of
the
economic
benefits
described
in
this
chapter,
are
not
affected
by
the
GAM
issue.
Similarly,
the
time­
series
studies
employing
GLMs
or
other
parametric
methods,
as
well
as
case­
crossover
studies,
are
not
affected.

Although
a
broad
range
of
serious
health
effects
has
been
associated
with
exposure
to
elevated
ozone
and
PM
levels
(
as
noted
for
example
in
Table
4­
1
and
described
more
fully
in
the
ozone
and
PM
Criteria
Documents
(
EPA,
1996a,
1996b)),
we
include
only
a
subset
of
health
effects
in
this
quantified
benefit
analysis.
Health
effects
are
excluded
from
this
analysis
for
three
reasons:
the
possibility
of
double
counting
(
such
as
hospital
admissions
for
specific
respiratory
diseases);
uncertainties
in
applying
effect
relationships
based
on
clinical
studies
to
the
affected
population;
or
a
lack
of
an
established
relationship
between
the
health
effect
and
pollutant
in
the
published
epidemiological
literature.

In
general,
the
use
of
results
from
more
than
a
single
study
can
provide
a
more
robust
estimate
of
the
relationship
between
a
pollutant
and
a
given
health
effect.
However,
there
are
often
differences
between
studies
examining
the
same
endpoint,
making
it
difficult
to
pool
the
results
in
a
consistent
manner.
For
example,
studies
may
examine
different
pollutants
or
different
age
groups.
For
this
reason,
we
consider
very
carefully
the
set
of
studies
available
examining
each
endpoint
and
select
a
consistent
subset
that
provides
a
good
balance
of
population
coverage
and
match
with
the
pollutant
of
interest.
In
many
cases,
either
because
of
a
lack
of
multiple
studies,
consistency
problems,
or
clear
superiority
in
the
quality
or
comprehensiveness
of
one
study
over
others,
a
single
published
study
is
selected
as
the
basis
of
the
effect
estimate.

When
several
effect
estimates
for
a
pollutant
and
a
given
health
endpoint
have
been
selected,
they
are
quantitatively
combined
or
pooled
to
derive
a
more
robust
estimate
of
the
17The
fixed
effects
model
assumes
that
there
is
only
one
pollutant
coefficient
for
the
entire
modeled
area.
The
random
effects
model
assumes
that
different
studies
are
estimating
different
parameters;
therefore,
there
may
be
a
number
of
different
underlying
pollutant
coefficients.

4­
33
relationship.
The
benefits
Technical
Support
Document
(
TSD)
completed
for
the
nonroad
diesel
rulemaking
provides
details
of
the
procedures
used
to
combine
multiple
impact
functions
(
Abt
Associates,
2003).
In
general,
we
use
fixed
or
random
effects
models
to
pool
estimates
from
different
studies
of
the
same
endpoint.
Fixed
effects
pooling
simply
weights
each
study's
estimate
by
the
inverse
variance,
giving
more
weight
to
studies
with
greater
statistical
power
(
lower
variance).
Random
effects
pooling
accounts
for
both
within­
study
variance
and
between­
study
variability,
due,
for
example,
to
differences
in
population
susceptibility.
We
use
the
fixed
effects
model
as
our
null
hypothesis
and
then
determine
whether
the
data
suggest
that
we
should
reject
this
null
hypothesis,
in
which
case
we
would
use
the
random
effects
model.
17
Pooled
impact
functions
are
used
to
estimate
hospital
admissions
(
PM),
school
absence
days
(
ozone),
lower
respiratory
symptoms
(
PM),
asthma
exacerbations
(
PM),
and
asthma­
related
emergency
room
visits
(
ozone).
For
more
details
on
methods
used
to
pool
incidence
estimates,
see
the
benefits
TSD
for
the
nonroad
diesel
rulemaking
(
Abt
Associates,
2003).

Effect
estimates
for
a
pollutant
and
a
given
health
endpoint
are
applied
consistently
across
all
locations
nationwide.
This
applies
to
both
impact
functions
defined
by
a
single
effect
estimate
and
those
defined
by
a
pooling
of
multiple
effect
estimates.
Although
the
effect
estimate
may,
in
fact,
vary
from
one
location
to
another
(
e.
g.,
due
to
differences
in
population
susceptibilities
or
differences
in
the
composition
of
PM),
location­
specific
effect
estimates
are
generally
not
available.

The
specific
studies
from
which
effect
estimates
for
the
primary
analysis
are
drawn
are
included
in
Table
4­
7.

Premature
Mortality.
Both
long­
and
short­
term
exposures
to
ambient
levels
of
air
pollution
have
been
associated
with
increased
risk
of
premature
mortality.
The
size
of
the
mortality
risk
estimates
from
these
epidemiological
studies,
the
serious
nature
of
the
effect
itself,
and
the
high
monetary
value
ascribed
to
prolonging
life
make
mortality
risk
reduction
the
most
important
health
endpoint
quantified
in
this
analysis.

Epidemiological
analyses
have
consistently
linked
air
pollution,
especially
PM,
with
excess
mortality.
Although
a
number
of
uncertainties
remain
to
be
addressed
by
continued
research
(
NRC,
1998),
a
substantial
body
of
published
scientific
literature
documents
the
correlation
4­
34
between
elevated
PM
concentrations
and
increased
mortality
rates.
Community
epidemiological
studies
that
have
used
both
short­
term
and
long­
term
exposures
and
response
have
been
used
to
estimate
PM/
mortality
relationships.
Short­
term
studies
use
a
time­
series
approach
to
relate
short­
term
(
often
day­
to­
day)
changes
in
PM
concentrations
and
changes
in
daily
mortality
rates
up
to
several
days
after
a
period
of
elevated
PM
concentrations.

Long­
term
studies
examine
the
potential
relationship
between
community­
level
PM
exposures
over
multiple
years
and
community­
level
annual
mortality
rates.
4­
35
Table
4­
7.
Endpoints
and
Studies
Used
to
Calculate
Total
Monetized
Health
Benefits
Endpoint
Pollutant
Study
Study
Population
Premature
Mortality
Premature
Mortality
 
Longterm
exposure,
all­
cause
PM
2.5
Pope
et
al.
(
2002)
>
29
years
Premature
Mortality
 
Longterm
exposure,
all­
cause
PM
2.5
Woodruff
et
al.,
1997
Infant
(<
1
yr)

Chronic
Illness
Chronic
Bronchitis
PM
2.5
Abbey,
et
al.
(
1995)
>
26
years
Non­
fatal
Heart
Attacks
PM
2.5
Peters
et
al.
(
2001)
Adults
Hospital
Admissions
Respiratory
Ozone
Pooled
estimate:

Schwartz
(
1995)
­
ICD
460­
519
(
all
resp)

Schwartz
(
1994a,
1994b)
­
ICD
480­
486
(
pneumonia)

Moolgavkar
et
al.
(
1997)
­
ICD
480­
487
(
pneumonia)

Schwartz
(
1994b)
­
ICD
491­
492,
494­
496
(
COPD)

Moolgavkar
et
al
(
1997)
­
ICD
490­
496
(
COPD)
>
64
years
Ozone
Burnett
et
al.
(
2001)
<
2
years
PM
2.5
Pooled
estimate:

Moolgavkar
(
2003)
­
ICD
490­
496
(
COPD)

Ito
(
2003)
­
ICD
490­
496
(
COPD)
>
64
years
PM
2.5
Moolgavkar
(
2000)
­
ICD
490­
496
(
COPD)
20­
64
years
PM
2.5
Ito
(
2003)
­
ICD
480­
486
(
pneumonia)
>
64
years
PM
2.5
Sheppard,
et
al.
(
2003)
­
ICD
493
(
asthma)
<
65
years
Cardiovascular
PM
2.5
Pooled
estimate:

Moolgavkar
(
2003)
­
ICD
390­
429
(
all
cardiovascular)

Ito
(
2003)
­
ICD
410­
414,
427­
428
(
ischemic
heart
disease,
dysrhythmia,
heart
failure)
>
64
years
PM
2.5
Moolgavkar
(
2000)
­
ICD
390­
429
(
all
cardiovascular)
20­
64
years
Asthma­
Related
ER
Visits
Ozone
Pooled
estimate:
Weisel
et
al.
(
1995),
Cody
et
al.
(
1992),
Stieb
et
al.
(
1996)
All
ages
PM
2.5
Norris
et
al.
(
1999)
0­
18
years
(
continued)
4­
36
Researchers
have
found
statistically
significant
associations
between
PM
and
premature
mortality
using
both
types
of
studies.
In
general,
the
risk
estimates
based
on
the
long­
term
exposure
studies
are
larger
than
those
derived
from
short­
term
studies.
Cohort
analyses
are
better
able
to
capture
the
full
public
health
impact
of
exposure
to
air
pollution
over
time
Table
4­
7.
Endpoints
and
Studies
Used
to
Calculate
Total
Monetized
Health
Benefits
(
continued)

Endpoint
Pollutant
Study
Study
Population
Other
Health
Endpoints
Acute
Bronchitis
PM2.5
Dockery
et
al.
(
1996)
8­
12
years
Upper
Respiratory
Symptoms
PM10
Pope
et
al.
(
1991)
Asthmatics,
9­
11
years
Lower
Respiratory
Symptoms
PM2.5
Schwartz
and
Neas
(
2000)
7­
14
years
Asthma
Exacerbations
PM2.5
Pooled
estimate:

Ostro
et
al.
(
2001)
(
cough,
wheeze
and
shortness
of
breath)

Vedal
et
al.
(
1998)
Cough
6­
18
yearsa
Work
Loss
Days
PM2.5
Ostro
(
1987)
18­
65
years
School
Absence
Days
Ozone
Pooled
estimate:

Gilliland
et
al
(
2001)

Chen
et
al
(
2000)
9­
10
years
6­
11
years
Worker
Productivity
Ozone
Crocker
and
Horst
(
1981)
Outdoor
workers,

18­
65
Minor
Restricted
Activity
Days
PM2.5,
Ozone
Ostro
and
Rothschild
(
1989)
18­
65
years
a
The
original
study
populations
were
8
to
13
for
the
Ostro
et
al
(
2001)
study
and
6
to
13
for
the
Vedal
et
al.

(
1998)
study.
Based
on
advice
from
the
SAB­
HES,
we
have
extended
the
applied
population
to
6
to
18,

reflecting
the
common
biological
basis
for
the
effect
in
children
in
the
broader
age
group.
18The
EPA
recognizes
that
the
ACS
cohort
also
is
not
completely
representative
of
the
demographic
mix
in
the
general
population.
The
ACS
cohort
is
almost
entirely
white,
and
has
higher
income
and
education
levels
relative
to
the
general
population.
The
EPA's
approach
to
this
problem
is
to
match
populations
based
on
4­
37
(
Kunzli,
2001;
NRC,
2002).
This
section
discusses
some
of
the
issues
surrounding
the
estimation
of
premature
mortality.

Over
a
dozen
studies
have
found
significant
associations
between
various
measures
of
long­
term
exposure
to
PM
and
elevated
rates
of
annual
mortality,
beginning
with
Lave
and
Seskin
(
1977).
Most
of
the
published
studies
found
positive
(
but
not
always
statistically
significant)
associations
with
available
PM
indices
such
as
total
suspended
particles
(
TSP),

however,
exploration
of
alternative
model
specifications
sometimes
raised
questions
about
causal
relationships
(
e.
g.,
Lipfert,
[
1989]).
These
early
"
cross­
sectional"
studies
(
e.
g.,
Lave
and
Seskin
[
1977];
Ozkaynak
and
Thurston
[
1987])
were
criticized
for
a
number
of
methodological
limitations,
particularly
for
inadequate
control
at
the
individual
level
for
variables
that
are
potentially
important
in
causing
mortality,
such
as
wealth,
smoking,
and
diet.

More
recently,
several
long­
term
studies
have
been
published
that
use
improved
approaches
and
appear
to
be
consistent
with
the
earlier
body
of
literature.
These
new
"
prospective
cohort"
studies
reflect
a
significant
improvement
over
the
earlier
work
because
they
include
individual­
level
information
with
respect
to
health
status
and
residence.
The
most
extensive
study
and
analyses
has
been
based
on
data
from
two
prospective
cohort
groups,
often
referred
to
as
the
Harvard
"
Six­
City
Study"
(
Dockery
et
al.,
1993)
and
the
"
American
Cancer
Society
or
ACS
study"
(
Pope
et
al.,
1995);
these
studies
have
found
consistent
relationships
between
fine
particle
indicators
and
premature
mortality
across
multiple
locations
in
the
United
States.

A
third
major
data
set
comes
from
the
California
based
7th
Day
Adventist
Study
(
e.
g.,
Abbey
et
al,
1999),
which
reported
associations
between
long­
term
PM
exposure
and
mortality
in
men.
Results
from
this
cohort,
however,
have
been
inconsistent
and
the
air
quality
results
are
not
geographically
representative
of
most
of
the
United
States.
More
recently,
a
cohort
of
adult
male
veterans
diagnosed
with
hypertension
has
been
examined
(
Lipfert
et
al.,
2000).

The
characteristics
of
this
group
differ
from
the
cohorts
in
the
ACS,
Six­
Cities,
and
7th
Day
Adventist
studies
with
respect
to
income,
race,
health
status,
and
smoking
status.
Unlike
previous
long­
term
analyses,
this
study
found
some
associations
between
mortality
and
ozone
but
found
inconsistent
results
for
PM
indicators.
Because
of
the
selective
nature
of
the
population
in
the
veteran's
cohort,
which
may
have
resulted
in
estimates
of
relative
risk
that
are
biased
relative
to
a
relative
risk
for
the
general
population,
we
have
chosen
not
to
include
any
effect
estimates
from
the
Lipfert
et
al.
(
2000)
study
in
our
benefits
assessment.
18
the
potential
for
demographic
characteristics
to
modify
the
effect
of
air
pollution
on
mortality
risk.
Thus,
for
the
various
ACS­
based
models,
we
are
careful
to
apply
the
effect
estimate
only
to
ages
matching
those
in
the
original
studies,
because
age
has
a
potentially
large
modifying
impact
on
the
effect
estimate,
especially
when
younger
individuals
are
excluded
from
the
study
population.
For
the
Lipfert
analysis,
the
applied
population
should
be
limited
to
that
matching
the
sample
used
in
the
analysis.
This
sample
was
all
male,
veterans,
and
diagnosed
hypertensive.
There
are
also
a
number
of
differences
between
the
composition
of
the
sample
and
the
general
population,
including
a
higher
percentage
of
African
Americans
(
35
percent),
and
a
much
higher
percentage
of
smokers
(
81
percent
former
smokers,
57
percent
current
smokers)
than
the
general
population
(
12
percent
African
American,
24
percent
current
smokers).
These
composition
differences
cannot
be
controlled
for,
but
should
be
recognized
as
adding
to
the
potential
extrapolation
bias.
The
EPA
recognizes
the
difficulty
in
controlling
for
composition
of
income
and
education
levels.
However,
in
or
out
criterion
such
as
age,
veteran
status,
hypertension,
race
and
sex
are
all
controllable
by
applying
filters
to
the
population
data.
The
EPA
has
traditionally
only
controlled
for
age,
because
the
ACS
study
used
only
age
as
a
screen.

4­
38
Given
their
consistent
results
and
broad
geographic
coverage,
the
Six­
City
and
ACS
data
have
been
particularly
important
in
benefits
analyses.
The
credibility
of
these
two
studies
is
further
enhanced
by
the
fact
that
they
were
subject
to
extensive
reexamination
and
reanalysis
by
an
independent
team
of
scientific
experts
commissioned
by
HEI
(
Krewski
et
al.,
2000).

The
final
results
of
the
reanalysis
were
then
independently
peer
reviewed
by
a
Special
Panel
of
the
HEI
Health
Review
Committee.
The
results
of
these
reanalyses
confirmed
and
expanded
those
of
the
original
investigators.
This
intensive
independent
reanalysis
effort
was
occasioned
both
by
the
importance
of
the
original
findings
as
well
as
concerns
that
the
underlying
individual
health
effects
information
has
never
been
made
publicly
available.

The
HEI
re­
examination
lends
credibility
to
the
original
studies
and
highlights
sensitivities
concerning
the
relative
impact
of
various
pollutants,
the
potential
role
of
education
in
mediating
the
association
between
pollution
and
mortality,
and
the
influence
of
spatial
correlation
modeling.
Further
confirmation
and
extension
of
the
overall
findings
using
more
recent
air
quality
and
a
longer
follow­
up
period
for
the
ACS
cohort
was
recently
published
in
the
Journal
of
the
American
Medical
Association
(
Pope
et
al.,
2002).

In
developing
and
improving
the
methods
for
estimating
and
valuing
the
potential
reductions
in
mortality
risk
over
the
years,
the
EPA
has
consulted
with
the
SAB­
HES.
That
panel
recommended
use
of
long­
term
prospective
cohort
studies
in
estimating
mortality
risk
reduction
(
EPA­
SAB­
COUNCIL­
ADV­
99­
005,
1999).
This
recommendation
has
been
confirmed
by
a
recent
report
from
the
National
Research
Council,
which
stated
that
"
it
is
essential
to
use
the
cohort
studies
in
benefits
analysis
to
capture
all
important
effects
from
air
pollution
exposure"
(
NAS,
2002,
p.
108).
More
specifically,
the
SAB
recommended
emphasis
on
the
ACS
study
because
it
includes
a
much
larger
sample
size
and
longer
exposure
4­
39
interval
and
covers
more
locations
(
e.
g.,
50
cities
compared
to
the
Six
Cities
Study)
than
other
studies
of
its
kind.
As
explained
in
the
regulatory
impact
analysis
for
the
Heavy­
Duty
Engine/
Diesel
Fuel
rule
(
EPA,
2000a),
more
recent
EPA
benefits
analyses
have
relied
on
an
improved
specification
of
the
ACS
cohort
data
that
was
developed
in
the
HEI
reanalysis
(
Krewski
et
al.,
2000).
The
latest
reanalysis
of
the
ACS
cohort
data
(
Pope
et
al.,
2002),

provides
additional
refinements
to
the
analysis
of
PM­
related
mortality
by
(
a)
extending
the
follow­
up
period
for
the
ACS
study
subjects
to
16
years,
which
triples
the
size
of
the
mortality
data
set;
(
b)
substantially
increasing
exposure
data,
including
consideration
for
cohort
exposure
to
PM2.5
following
implementation
of
PM2.5
standard
in
1999;
(
c)
controlling
for
a
variety
of
personal
risk
factors
including
occupational
exposure
and
diet;
and
(
d)
using
advanced
statistical
methods
to
evaluate
specific
issues
that
can
adversely
affect
risk
estimates
including
the
possibility
of
spatial
autocorrelation
of
survival
times
in
communities
located
near
each
other.
Because
of
these
refinements,
the
SAB­
HES
recommends
using
the
Pope
et
al.
(
2002)
study
as
the
basis
for
the
primary
mortality
estimate
for
adults
and
suggests
that
alternate
estimates
of
mortality
generated
using
other
cohort
and
time
series
studies
could
be
included
as
part
of
the
sensitivity
analysis
(
SAB­
HES,
2003).

The
SAB­
HES
also
recommended
using
the
estimated
relative
risks
from
the
Pope
et
al.

(
2002)
study
based
on
the
average
exposure
to
PM2.5,
measured
by
the
average
of
two
PM2.5
measurements,
over
the
periods
1979­
1983,
and
1999­
2000.
In
addition
to
relative
risks
for
all­
cause
mortality,
the
Pope
et
al.
(
2002)
study
provides
relative
risks
for
cardiopulmonary,
lung
cancer,
and
all
other
cause
mortality.
Because
of
concerns
regarding
the
statistical
reliability
of
the
all­
other
cause
mortality
relative
risk
estimates,
we
calculate
mortality
impacts
for
the
primary
analysis
based
on
the
all­
cause
relative
risk.
However,
we
provide
separate
estimates
of
cardiopulmonary
and
lung
cancer
deaths
to
show
how
these
important
causes
of
death
are
affected
by
reductions
in
PM2.5.

In
previous
RIAs,
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
analysis
for
RIAs.
However,
recently
published
studies
have
strengthened
the
case
for
an
association
between
PM
exposure
and
respiratory
inflamation
and
infection
leading
to
premature
mortality
in
children
under
5
years
of
age.
Specifically,
the
SAB­
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
(
SAB­
HES,
2003).
The
SAB­
HES
also
cites
the
study
by
Belanger
et
al.
(
2003)
as
corroborating
findings
linking
PM
exposure
to
increased
respiratory
4­
40
inflamation
and
infections
in
children.
Recently,
a
study
by
Chay
and
Greenstone
(
2003)

found
that
reductions
in
TSP
caused
by
the
recession
of
1981­
1982
were
related
to
reductions
in
infant
mortality
at
the
county
level.
With
regard
to
the
cohort
study
conducted
by
Woodruff
et
al.
(
1997),
the
SAB­
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
infants
(
e.
g.,
maternal
educational
level,
maternal
ethnicity,
parental
marital
status,
and
maternal
smoking
status).
Based
on
these
findings,
the
SAB­
HES
recommends
that
the
EPA
incorporate
infant
mortality
into
the
primary
benefits
estimate
and
that
infant
mortality
be
evaluated
using
a
impact
function
developed
from
the
Woodruff
et
al.
(
1997)
study
(
SAB­
HES,
2003).

Chronic
Bronchitis.
CB
is
characterized
by
mucus
in
the
lungs
and
a
persistent
wet
cough
for
at
least
3
months
a
year
for
several
years
in
a
row.
CB
affects
an
estimated
5
percent
of
the
U.
S.
population
(
American
Lung
Association,
1999).
A
limited
number
of
studies
have
estimated
the
impact
of
air
pollution
on
new
incidences
of
CB.
Schwartz
(
1993)
and
Abbey
et
al.(
1995)
provide
evidence
that
long­
term
PM
exposure
gives
rise
to
the
development
of
CB
in
the
United
States.
Because
the
Inter­
State
Air
Quality
regulations
are
expected
to
reduce
primarily
PM
2.5,
this
analysis
uses
only
the
Abbey
et
al
(
1995)
study,
because
it
is
the
only
study
focusing
on
the
relationship
between
PM
2.5
and
new
incidences
of
CB.

Nonfatal
Myocardial
Infarctions
(
heart
attacks).
Nonfatal
heart
attacks
have
been
linked
with
short­
term
exposures
to
PM2.5
in
the
United
States
(
Peters
et
al.,
2001)
and
other
countries
(
Poloniecki
et
al.
,1997).
We
use
a
recent
study
by
Peters
et
al.
(
2001)
as
the
basis
for
the
impact
function
estimating
the
relationship
between
PM2.5
and
nonfatal
heart
attacks.

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
nonfatal
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
United
States.
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
19Note
that
the
Moolgavkar
(
2000)
study
has
not
been
updated
to
reflect
the
more
stringent
GAM
convergence
criteria.
However,
given
that
no
other
estimates
are
available
for
this
age
group,
we
have
chosen
to
use
the
existing
study.
Given
the
very
small
(<
5
percent)
difference
in
the
effect
estimates
for
65
and
older
cardiovascular
hospital
admissions
between
the
original
and
reanalyzed
results,
we
do
not
expect
there
to
be
much
bias
introduced
by
this
choice.

4­
41
attacks
and
other
coronary
heart
diseases
(
Carthenon
et
a.
l,
2002;
Dekker
et
al.,
2000;
Liao
et
al.,
1997,
Tsuji
et
al.,
1996).
As
such,
significant
impacts
of
PM
on
heart
rate
variability
are
consistent
with
an
increased
risk
of
heart
attacks.

Hospital
and
Emergency
Room
Admissions.
Because
of
the
availability
of
detailed
hospital
admission
and
discharge
records,
there
is
an
extensive
body
of
literature
examining
the
relationship
between
hospital
admissions
and
air
pollution.
Because
of
this,
many
of
the
hospital
admission
endpoints
use
pooled
impact
functions
based
on
the
results
of
a
number
of
studies.
In
addition,
some
studies
have
examined
the
relationship
between
air
pollution
and
emergency
room
(
ER)
visits.
Because
most
ER
visits
do
not
result
in
an
admission
to
the
hospital
(
the
majority
of
people
going
to
the
ER
are
treated
and
return
home),
we
treat
hospital
admissions
and
ER
visits
separately,
taking
account
of
the
fraction
of
ER
visits
that
are
admitted
to
the
hospital.

Hospital
admissions
require
the
patient
to
be
examined
by
a
physician
and,
on
average,

may
represent
more
serious
incidents
than
ER
visits.
The
two
main
groups
of
hospital
admissions
estimated
in
this
analysis
are
respiratory
admissions
and
cardiovascular
admissions.

There
is
not
much
evidence
linking
ozone
or
PM
with
other
types
of
hospital
admissions.
The
only
type
of
ER
visits
that
have
been
consistently
linked
to
ozone
and
PM
in
the
United
States
are
asthma­
related
visits.

To
estimate
avoided
incidences
of
cardiovascular
hospital
admissions
associated
with
PM2.5,
we
use
studies
by
Moolgavkar
(
2003)
and
Ito
et
al.
(
2003).
There
are
additional
published
studies
showing
a
statistically
significant
relationship
between
PM10
and
cardiovascular
hospital
admissions.
However,
given
that
the
preliminary
control
options
we
are
analyzing
are
expected
to
reduce
primarily
PM2.5,
we
have
chosen
to
focus
on
the
two
studies
focusing
on
PM2.5.
Both
of
these
studies
provide
an
effect
estimate
for
populations
over
65,
allowing
us
to
pool
the
impact
functions
for
this
age
group.
Only
Moolgavkar
(
2000)
provided
a
separate
effect
estimate
for
populations
20
to
64.19
Total
cardiovascular
hospital
admissions
are
thus
the
sum
of
the
pooled
estimate
for
populations
over
65
and
the
single
study
estimate
for
populations
20
to
64.
Cardiovascular
hospital
admissions
include
admissions
for
myocardial
infarctions.
To
avoid
double
counting
benefits
from
reductions
in
20Note
that
the
Moolgavkar
(
2000)
study
has
not
been
updated
to
reflect
the
more
stringent
GAM
convergence
criteria.
However,
given
that
no
other
estimates
are
available
for
this
age
group,
we
have
chosen
to
use
the
existing
study.
Given
the
very
small
(<
10
percent)
difference
in
the
effect
estimates
for
65
and
older
COPD
hospital
admissions
between
the
original
and
reanalyzed
results,
we
do
not
expect
there
to
be
much
bias
introduced
by
this
choice.

4­
42
myocardial
infarctions
when
applying
the
impact
function
for
cardiovascular
hospital
admissions,
we
first
adjusted
the
baseline
cardiovascular
hospital
admissions
to
remove
admissions
for
myocardial
infarctions.

To
estimate
total
avoided
incidences
of
respiratory
hospital
admissions,
we
use
impact
functions
for
several
respiratory
causes,
including
chronic
obstructive
pulmonary
disease
(
COPD),
pneumonia,
and
asthma.
As
with
cardiovascular
admissions,
there
are
additional
published
studies
showing
a
statistically
significant
relationship
between
PM10
and
respiratory
hospital
admissions.
We
use
only
those
focusing
on
PM2.5.
Both
Moolgavkar
(
2000)
and
Ito
et
al.
(
2003)
provide
effect
estimates
for
COPD
in
populations
over
65,
allowing
us
to
pool
the
impact
functions
for
this
group.
Only
Moolgavkar
(
2000)
provided
a
separate
effect
estimate
for
populations
20
to
6420.
Total
COPD
hospital
admissions
are
thus
the
sum
of
the
pooled
estimate
for
populations
over
65
and
the
single
study
estimate
for
populations
20
to
64.
Only
Ito
et
al
(
2003)
estimated
pneumonia,
and
only
for
the
population
65
and
older.
In
addition,
Sheppard
et
al.
(
2003)
provided
an
effect
estimate
for
asthma
hospital
admissions
for
populations
under
age
65.
Total
avoided
incidences
of
PM­
related
respiratory­
related
hospital
admissions
is
the
sum
of
COPD,
pneumonia,
and
asthma
admissions.

To
estimate
the
effects
of
PM
air
pollution
reductions
on
asthma­
related
ER
visits,
we
use
the
effect
estimate
from
a
study
of
children
18
and
under
by
Norris
et
al.
(
1999).
As
noted
earlier,
there
is
another
study
by
Schwartz
examining
a
broader
age
group
(
less
than
65),
but
the
Schwartz
study
focused
on
PM10
rather
than
PM2.5.
We
selected
the
Norris
et
al.
(
1999)

effect
estimate
because
it
better
matched
the
pollutant
of
interest.
Because
children
tend
to
have
higher
rates
of
hospitalization
for
asthma
relative
to
adults
under
65,
we
will
likely
capture
the
majority
of
the
impact
of
PM2.5
on
asthma
ER
visits
in
populations
under
65,

although
there
may
still
be
significant
impacts
in
the
adult
population
under
65.

To
estimate
avoided
incidences
of
respiratory
hospital
admissions
associated
with
ozone,

we
use
a
number
of
studies
examining
hospital
admissions
for
a
range
of
respiratory
illnesses,

including
pneumonia
and
COPD.
Two
age
groups,
adults
over
65
and
children
under
2,
are
examined.
For
adults
over
65,
Schwartz
(
1995)
provides
effect
estimates
for
two
different
cities
relating
ozone
and
hospital
admissions
for
all
respiratory
causes
(
defined
as
ICD
codes
21See
http://
www.
nlm.
nih.
gov/
medlineplus/
ency/
article/
000124.
htm,
accessed
January
2002.

4­
43
460­
519).
Impact
functions
based
on
these
studies
are
pooled
first
before
being
pooled
with
other
studies.
Two
studies
(
Moolgavkar
et
al.,
1997;
Schwartz,
1994a)
examined
ozone
and
pneumonia
hospital
admissions
in
Minneapolis.
One
additional
study
(
Schwartz,
1994b)

examined
ozone
and
pneumonia
hospital
admissions
in
Detroit.
The
impact
functions
for
Minneapolis
are
pooled
together
first,
and
the
resulting
impact
function
is
then
pooled
with
the
impact
function
for
Detroit.
This
avoids
assigning
too
much
weight
to
the
information
coming
from
one
city.
For
COPD
hospital
admissions,
there
are
two
available
studies,

Moolgavkar
et
al.
(
1997),
conducted
in
Minneapolis,
and
Schwartz
(
1994b),
conducted
in
Detroit.
These
two
studies
are
pooled
together.
To
estimate
total
respiratory
hospital
admissions
for
adults
over
65,
COPD
admissions
are
added
to
pneumonia
admissions,
and
the
result
is
pooled
with
the
Schwartz
(
1995)
estimate
of
total
respiratory
admissions.
Burnett
et
al.
(
2001)
is
the
only
study
providing
an
effect
estimate
for
respiratory
hospital
admissions
in
children
under
2.

Acute
Health
Events
and
School/
Work
Loss
Days.
As
indicated
in
Table
4­
1,
in
addition
to
mortality,
chronic
illness,
and
hospital
admissions,
a
number
of
acute
health
effects
not
requiring
hospitalization
are
associated
with
exposure
to
ambient
levels
of
ozone
and
PM.

The
sources
for
the
effect
estimates
used
to
quantify
these
effects
are
described
below.

Around
4
percent
of
U.
S.
children
between
ages
5
and
17
experience
episodes
of
acute
bronchitis
annually
(
American
Lung
Association,
2002).
Acute
bronchitis
is
characterized
by
coughing,
chest
discomfort,
slight
fever,
and
extreme
tiredness,
lasting
for
a
number
of
days.

According
to
the
MedlinePlus
medical
encyclopedia,
21
with
the
exception
of
cough,
most
acute
bronchitis
symptoms
abate
within
7
to
10
days.
Incidence
of
episodes
of
acute
bronchitis
in
children
between
the
ages
of
5
and
17
are
estimated
using
an
effect
estimate
developed
from
Dockery
et
al.
(
1996).

Incidences
of
lower
respiratory
symptoms
(
e.
g.,
wheezing,
deep
cough)
in
children
aged
7
to
14
are
estimated
using
an
effect
estimate
from
Schwartz
and
Neas
(
2000).

Because
asthmatics
have
greater
sensitivity
to
stimuli
(
including
air
pollution),
children
with
asthma
can
be
more
susceptible
to
a
variety
of
upper
respiratory
symptoms
(
e.
g.,
runny
or
stuffy
nose;
wet
cough;
and
burning,
aching,
or
red
eyes).
Research
on
the
effects
of
air
pollution
on
upper
respiratory
symptoms
has
thus
focused
on
effects
in
asthmatics.
Incidences
4­
44
of
upper
respiratory
symptoms
in
asthmatic
children
aged
9
to
11
are
estimated
using
an
effect
estimate
developed
from
Pope
et
al.
(
1991).

Health
effects
from
air
pollution
can
also
result
in
missed
days
of
work
(
either
from
personal
symptoms
or
from
caring
for
a
sick
family
member).
Work
loss
days
due
to
PM2.5
are
estimated
using
an
effect
estimate
developed
from
Ostro
(
1987).
Children
may
also
be
absent
from
school
due
to
respiratory
or
other
diseases
caused
by
exposure
to
air
pollution.

Most
studies
examining
school
absence
rates
have
found
little
or
no
association
with
PM2.5,

but
several
studies
have
found
a
significant
association
between
ozone
levels
and
school
absence
rates.
We
use
two
recent
studies,
Gilliland
et
al.
(
2001)
and
Chen
et
al.
(
2000),
to
estimate
changes
in
absences
(
school
loss
days)
due
to
changes
in
ozone
levels.
The
Gilliland
et
al.
study
estimated
the
incidence
of
new
periods
of
absence,
while
the
Chen
et
al.
study
examined
absence
on
a
given
day.
We
convert
the
Gilliland
estimate
to
days
of
absence
by
multiplying
the
absence
periods
by
the
average
duration
of
an
absence.
We
estimate
an
average
duration
of
school
absence
of
1.6
days
by
dividing
the
average
daily
school
absence
rate
from
Chen
et
al.
(
2000)
and
Ransom
and
Pope
(
1992)
by
the
episodic
absence
rate
from
Gilliland
et
al.
(
2001).
This
provides
estimates
from
Chen
et
al.
(
2000)
and
Gilliland
et
al.

(
2000),
which
can
be
pooled
to
provide
an
overall
estimate.

Minor
restricted
activity
days
(
MRAD)
result
when
individuals
reduce
most
usual
daily
activities
and
replace
them
with
less
strenuous
activities
or
rest,
yet
not
to
the
point
of
missing
work
or
school.
For
example,
a
mechanic
who
would
usually
be
doing
physical
work
most
of
the
day
will
instead
spend
the
day
at
a
desk
doing
paper
and
phone
work
due
to
difficulty
breathing
or
chest
pain.
The
effect
of
PM2.5
and
ozone
on
MRAD
is
estimated
using
an
effect
estimate
derived
from
Ostro
and
Rothschild
(
1989).

In
previous
RIAs,
we
have
not
included
estimates
of
asthma
exacerbations
in
the
asthmatic
population
in
the
primary
analysis
because
of
concerns
over
double
counting
of
benefits
and
difficulties
in
differentiating
asthma
symptoms
for
purposes
of
first
developing
impact
functions
that
cover
distinct
endpoints
and
then
establishing
the
baseline
incidence
estimates
required
for
predicting
incidence
reductions.
Concerns
over
double
counting
stem
from
the
fact
that
studies
of
the
general
population
also
include
asthmatics,
so
estimates
based
solely
on
the
asthmatic
population
cannot
be
directly
added
to
the
general
population
numbers
without
double
counting.
In
one
specific
case
(
upper
respiratory
symptoms
in
children),
the
only
study
available
was
limited
to
asthmatic
children,
so
this
endpoint
can
be
readily
included
in
the
calculation
of
total
benefits.
However,
other
endpoints,
such
as
lower
respiratory
symptoms
and
MRADs,
are
estimated
for
the
total
population
that
includes
asthmatics.
Therefore,
to
4­
45
simply
add
predictions
of
asthma­
related
symptoms
generated
for
the
population
of
asthmatics
to
these
total
population­
based
estimates
could
result
in
double
counting,
especially
if
they
evaluate
similar
endpoints.
The
SAB­
HES,
in
commenting
on
the
analytical
blueprint
for
812
acknowledged
these
challenges
in
evaluating
asthmatic
symptoms
and
appropriately
adding
them
into
the
primary
analysis
(
SAB­
HES,
2003).
However,
despite
these
challenges,
the
SAB­
HES
recommends
the
addition
of
asthma­
related
symptoms
(
i.
e.,
asthma
exacerbations)

to
the
primary
analysis,
provided
that
the
studies
use
the
panel
study
approach
and
that
they
have
comparable
design
and
baseline
frequencies
in
both
asthma
prevalence
and
exacerbation
rates.
Note
also,
that
the
SAB­
HES,
while
supporting
the
incorporation
of
asthma
exacerbation
estimates,
does
not
believe
that
the
association
between
ambient
air
pollution,

including
ozone
and
PM,
and
the
new
onset
of
asthma
is
sufficiently
strong
to
support
inclusion
of
this
asthma­
related
endpoint
in
the
primary
estimate.
For
the
IAQR,
we
have
followed
the
SAB­
HES
recommendations
regarding
asthma
exacerbations
in
developing
the
primary
estimate.
To
prevent
double
counting,
we
are
focusing
the
estimation
on
asthma
exacerbations
occurring
in
children
and
are
excluding
adults
from
the
calculation.
Asthma
exacerbations
occurring
in
adults
are
assumed
to
be
captured
in
the
general
population
endpoints
such
as
work
loss
days
and
MRADs.
Consequently,
if
we
had
included
an
adultspecific
asthma
exacerbation
estimate,
we
would
likely
double
count
incidence
for
this
endpoint.
However,
because
the
general
population
endpoints
do
not
cover
children
(
with
regard
to
asthmatic
effects),
an
analysis
focused
specifically
on
asthma
exacerbations
for
children
(
6
to
18
years
of
age)
could
be
conducted
without
concern
for
double
counting.

To
characterize
asthma
exacerbations
in
children,
we
selected
two
studies
(
Ostro
et
al.,

2001
and
Vedal
et
al.,
1998)
that
followed
panels
of
asthmatic
children.
Ostro
et
al.
(
2001)

followed
a
group
of
138
African­
American
children
in
Los
Angeles
for
13
weeks,
recording
daily
occurrences
of
respiratory
symptoms
associated
with
asthma
exacerbations
(
e.
g.,

shortness
of
breath,
wheeze,
and
cough).
This
study
found
a
statistically
significant
association
between
PM2.5,
measured
as
a
12­
hour
average,
and
the
daily
prevalence
of
shortness
of
breath
and
wheeze
endpoints.
Although
the
association
was
not
statistically
significant
for
cough,
the
results
were
still
positive
and
close
to
significance;
consequently,
we
decided
to
include
this
endpoint,
along
with
shortness
of
breath
and
wheeze,
in
generating
incidence
estimates
(
see
below).
Vedal
et
al.
(
1998)
followed
a
group
of
elementary
school
children,
including
74
asthmatics,
located
on
the
west
coast
of
Vancouver
Island
for
18
months
including
measurements
of
daily
peak
expiratory
flow
(
PEF)
and
the
tracking
of
respiratory
symptoms
(
e.
g.,
cough,
phlegm,
wheeze,
chest
tightness)
through
the
use
of
daily
diaries.
Association
between
PM10
and
respiratory
symptoms
for
the
asthmatic
population
4­
46
was
only
reported
for
two
endpoints:
cough
and
PEF.
Because
it
is
difficult
to
translate
PEF
measures
into
clearly
defined
health
endpoints
that
can
be
monetized,
we
only
included
the
cough­
related
effect
estimate
from
this
study
in
quantifying
asthma
exacerbations.
We
employed
the
following
pooling
approach
in
combining
estimates
generated
using
effect
estimates
from
the
two
studies
to
produce
a
single
asthma
exacerbation
incidence
estimate.

First,
we
pooled
the
separate
incidence
estimates
for
shortness
of
breath,
wheeze,
and
cough
generated
using
effect
estimates
from
the
Ostro
et
al
study,
because
each
of
these
endpoints
is
aimed
at
capturing
the
same
overall
endpoint
(
asthma
exacerbations)
and
there
could
be
overlap
in
their
predictions.
The
pooled
estimate
from
the
Ostro
et
al.
study
is
then
pooled
with
the
cough­
related
estimate
generated
using
the
Vedal
study.
The
rationale
for
this
second
pooling
step
is
similar
to
the
first;
both
studies
are
attempting
to
quantify
the
same
overall
endpoint
(
asthma
exacerbations).

Additional
epidemiological
studies
are
available
for
characterizing
asthma­
related
health
endpoints
(
the
full
list
of
epidemiological
studies
considered
for
modeling
asthma­
related
incidence
are
presented
in
Table
4­
8).
However,
based
on
recommendations
from
the
SABHES
we
decided
not
to
use
these
additional
studies
in
generating
the
primary
estimate.
In
particular,
the
Yu
et
al.
(
2000)
estimates
show
a
much
higher
baseline
incidence
rate
than
other
studies,
which
may
lead
to
an
overstatement
of
the
expected
impacts
in
the
overall
asthmatic
population.
The
Whittemore
and
Korn
(
1980)
study
did
not
use
a
well­
defined
endpoint,
instead
focusing
on
a
respondent­
defined
"
asthma
attack."
Other
studies
looked
at
respiratory
symptoms
in
asthmatics
but
did
not
focus
on
specific
exacerbations
of
asthma.

4.1.5.2
Uncertainties
Associated
with
Health
Impact
Functions
Within­
Study
Variation.
Within­
study
variation
refers
to
the
precision
with
which
a
given
study
estimates
the
relationship
between
air
quality
changes
and
health
effects.
Health
effects
studies
provide
both
a
"
best
estimate"
of
this
relationship
plus
a
measure
of
the
statistical
uncertainty
of
the
relationship.
This
size
of
this
uncertainty
depends
on
factors
such
as
the
number
of
subjects
studied
and
the
size
of
the
effect
being
measured.
The
results
of
even
the
most
well­
designed
epidemiological
studies
are
characterized
by
this
type
of
uncertainty,

though
well­
designed
studies
typically
report
narrower
uncertainty
bounds
around
the
best
estimate
than
do
studies
of
lesser
quality.
In
selecting
health
endpoints,
we
generally
focus
on
endpoints
where
a
statistically
significant
relationship
has
been
observed
in
at
least
some
studies,
although
we
may
pool
together
results
from
studies
with
both
statistically
significant
and
insignificant
estimates
to
avoid
selection
bias.
4­
47
Across­
Study
Variation.
Across­
study
variation
refers
to
the
fact
that
different
published
studies
of
the
same
pollutant/
health
effect
relationship
typically
do
not
report
identical
findings;
in
some
instances
the
differences
are
substantial.
These
differences
can
exist
even
between
equally
reputable
studies
and
may
result
in
health
effect
estimates
that
vary
considerably.
Across­
study
variation
can
result
from
two
possible
causes.
One
possibility
is
that
studies
report
different
estimates
of
the
single
true
relationship
between
a
given
pollutant
and
a
health
effect
due
to
differences
in
study
design,
random
chance,
or
other
factors.
For
example,
a
hypothetical
study
conducted
in
New
York
and
one
conducted
in
Seattle
may
report
different
C­
R
functions
for
the
relationship
between
PM
and
mortality,
in
part
because
of
differences
between
these
two
study
populations
(
e.
g.,
demographics,
4­
48
Table
4­
8.
Studies
Examining
Health
Impacts
in
the
Asthmatic
Population
Evaluated
for
Use
in
the
Benefits
Analysis
Endpoint
Definition
Pollutant
Study
Study
Population
Asthma
Attack
Indicators1
Shortness
of
breath
Prevalence
of
shortness
of
breath;
incidence
of
shortness
of
breath
PM2.5
Ostro
et
al.
(
2001)
African­
American
asthmatics,
8­
13
Cough
Prevalence
of
cough;
incidence
of
cough
PM2.5
Ostro
et
al.
(
2001)
African­
American
asthmatics,
8­
13
Wheeze
Prevalence
of
wheeze;

incidence
of
wheeze
PM2.5
Ostro
et
al.
(
2001)
African­
American
asthmatics,
8­
13
Asthma
exacerbation

1
mild
asthma
symptom:
wheeze,
cough,
chest
tightness,

shortness
of
breath)
PM10,
PM1.0
Yu
et
al.
(
2000)
Asthmatics,
5­
13
Cough
Prevalence
of
cough
PM10
Vedal
et
al.
(
1998)
Asthmatics,
6­
13
Other
symptoms/
illness
endpoints
Upper
respiratory
symptoms

1
of
the
following:
runny
or
stuffy
nose;
wet
cough;

burning,
aching,
or
red
eyes
PM10
Pope
et
al.
(
1991)
Asthmatics
9­
11
Moderate
or
worse
asthma
Probability
of
moderate
(
or
worse)
rating
of
overall
asthma
status
PM2.5
Ostro
et
al.
(
1991)
Asthmatics,
all
ages
Acute
bronchitis

1
episodes
of
bronchitis
in
the
past
12
months
PM2.5
McConnell
et
al.

(
1999)
Asthmatics,
9­
15*

Phlegm
"
Other
than
with
colds,
does
this
child
usually
seem
congested
in
the
chest
or
bring
up
phlegm?"
PM2.5
McConnell
et
al.

(
1999)
Asthmatics,
9­
15*

Asthma
attacks
Respondent­
defined
asthma
attack
PM2.5,

ozone
Whittemore
and
Korn
(
1980)
Asthmatics,
all
ages
22Although
we
are
not
able
to
use
region­
specific
effect
estimates,
we
use
region­
specific
baseline
incidence
rates
where
available.
This
allows
us
to
take
into
account
regional
differences
in
health
status,
which
can
have
a
significant
impact
on
estimated
health
benefits.

4­
49
activity
patterns).
Alternatively,
study
results
may
differ
because
these
two
studies
are
in
fact
estimating
different
relationships;
that
is,
the
same
reduction
in
PM
in
New
York
and
Seattle
may
result
in
different
reductions
in
premature
mortality.
This
may
result
from
a
number
of
factors,
such
as
differences
in
the
relative
sensitivity
of
these
two
populations
to
PM
pollution
and
differences
in
the
composition
of
PM
in
these
two
locations.
In
either
case,
where
we
identified
multiple
studies
that
are
appropriate
for
estimating
a
given
health
effect,
we
generated
a
pooled
estimate
of
results
from
each
of
those
studies.

Application
of
C­
R
Relationship
Nationwide.
Regardless
of
the
use
of
impact
functions
based
on
effect
estimates
from
a
single
epidemiological
study
or
multiple
studies,
each
impact
function
was
applied
uniformly
throughout
the
United
States
to
generate
health
benefit
estimates.
However,
to
the
extent
that
pollutant/
health
effect
relationships
are
region­
specific,

applying
a
location­
specific
impact
function
at
all
locations
in
the
United
States
may
result
in
overestimates
of
health
effect
changes
in
some
locations
and
underestimates
of
health
effect
changes
in
other
locations.
It
is
not
possible,
however,
to
know
the
extent
or
direction
of
the
overall
effect
on
health
benefit
estimates
introduced
by
application
of
a
single
impact
function
to
the
entire
United
States.
This
may
be
a
significant
uncertainty
in
the
analysis,
but
the
current
state
of
the
scientific
literature
does
not
allow
for
a
region­
specific
estimation
of
health
benefits.
22
Extrapolation
of
Impact
Functions
Across
Populations.
Epidemiological
studies
often
focus
on
specific
age
ranges,
either
due
to
data
availability
limitations
(
e.
g.,
most
hospital
admission
data
come
from
Medicare
records,
which
are
limited
to
populations
65
and
older),

or
to
simplify
data
collection
(
e.
g.,
some
asthma
symptom
studies
focus
on
children
at
summer
camps,
which
usually
have
a
limited
age
range).
We
have
assumed
for
the
primary
analysis
that
most
impact
functions
should
be
applied
only
to
those
populations
with
ages
that
strictly
match
the
populations
in
the
underlying
epidemiological
studies.
However,
in
many
cases,

there
is
no
biological
reason
why
the
observed
health
effect
would
not
also
occur
in
other
populations
within
a
reasonable
range
of
the
studied
population.
For
example,
Dockery
et
al.

(
1996)
examined
acute
bronchitis
in
children
aged
8
to
12.
There
is
no
biological
reason
to
expect
a
very
different
response
in
children
aged
6
or
14.
By
excluding
populations
outside
the
range
in
the
studies,
we
may
be
underestimating
the
health
impact
in
the
overall
population.
In
response
to
recommendations
from
the
SAB­
HES,
where
there
appears
to
be
a
4­
50
reasonable
physiological
basis
for
expanding
the
age
group
associated
with
a
specific
effect
estimate
beyond
the
study
population
to
cover
the
full
age
group
(
e.
g.,
expanding
from
a
study
population
of
7
to
11
year
olds
to
the
full
6to
18
year
child
age
group),
we
have
done
so
and
used
those
expanded
incidence
estimates
in
the
primary
analysis.

Uncertainties
in
the
PM
Mortality
Relationship.
Health
researchers
have
consistently
linked
air
pollution,
especially
PM,
with
excess
mortality.
A
substantial
body
of
published
scientific
literature
recognizes
a
correlation
between
elevated
PM
concentrations
and
increased
mortality
rates.
However,
much
about
this
relationship
is
still
uncertain.
These
uncertainties
include
the
following:


Causality:
A
substantial
number
of
published
epidemiological
studies
find
an
association
between
elevated
PM
concentrations
and
increased
mortality
rates;
however,
these
epidemiological
studies
are
not
designed
to
definitively
prove
causation.
For
the
analysis
of
the
IAQ
rulemaking,
we
assumed
a
causal
relationship
between
exposure
to
elevated
PM
and
premature
mortality,
based
on
the
consistent
evidence
of
a
correlation
between
PM
and
mortality
reported
in
the
substantial
body
of
published
scientific
literature.


Other
Pollutants:
PM
concentrations
are
correlated
with
the
concentrations
of
other
criteria
pollutants,
such
as
ozone
and
CO,
and
it
is
unclear
how
much
each
of
these
pollutants
may
influence
mortality
rates.
Recent
studies
(
see
Thurston
and
Ito
[
2001])
have
explored
whether
ozone
may
have
mortality
effects
independent
of
PM,
but
we
do
not
view
the
evidence
as
conclusive
at
this
time.
The
EPA
is
currently
evaluating
the
epidemiological
literature
on
the
relationship
between
ozone
and
mortality
and
will
determine
whether
to
include
ozone
mortality
as
a
separate
impact
in
the
analysis
of
the
final
IAQR
based
on
the
results
of
our
evaluation.
To
the
extent
that
the
C­
R
functions
we
use
to
evaluate
the
preliminary
control
options
in
fact
capture
mortality
effects
of
other
criteria
pollutants
besides
PM,
we
may
be
overestimating
the
benefits
of
reductions
in
PM.
However,
we
are
not
providing
separate
estimates
of
the
mortality
benefits
from
the
ozone
and
CO
reductions
likely
to
occur
due
to
the
preliminary
control
options.


Shape
of
the
C­
R
Function:
The
shape
of
the
true
PM
mortality
C­
R
function
is
uncertain,
but
this
analysis
assumes
the
C­
R
function
to
have
a
log­
linear
form
(
as
derived
from
the
literature)
throughout
the
relevant
range
of
exposures.
If
this
is
not
the
correct
form
of
the
C­
R
function,
or
if
certain
scenarios
predict
concentrations
well
above
the
range
of
values
for
which
the
C­
R
function
was
fitted,
avoided
mortality
may
be
mis­
estimated.


Regional
Differences:
As
discussed
above,
significant
variability
exists
in
the
results
of
different
PM/
mortality
studies.
This
variability
may
reflect
regionally
specific
C­
R
23
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.

4­
51
functions
resulting
from
regional
differences
in
factors
such
as
the
physical
and
chemical
composition
of
PM.
If
true
regional
differences
exist,
applying
the
PM/
mortality
C­
R
function
to
regions
outside
the
study
location
could
result
in
mis­
estimation
of
effects
in
these
regions.


Exposure/
Mortality
Lags:
There
is
a
potential
time
lag
between
changes
in
PM
exposures
and
changes
in
mortality
rates.
For
the
chronic
PM/
mortality
relationship,
the
length
of
the
lag
is
unknown
and
may
be
dependent
on
the
kind
of
exposure.
The
existence
of
such
a
lag
is
important
for
the
valuation
of
premature
mortality
incidence
because
economic
theory
suggests
that
benefits
occurring
in
the
future
should
be
discounted.
There
is
no
specific
scientific
evidence
of
the
existence
or
structure
of
a
PM
effects
lag.
However,
current
scientific
literature
on
adverse
health
effects
similar
to
those
associated
with
PM
(
e.
g.,
smoking­
related
disease)
and
the
difference
in
the
effect
size
between
chronic
exposure
studies
and
daily
mortality
studies
suggests
that
all
incidences
of
premature
mortality
reduction
associated
with
a
given
incremental
change
in
PM
exposure
probably
would
not
occur
in
the
same
year
as
the
exposure
reduction.
The
smoking­
related
literature
also
implies
that
lags
of
up
to
a
few
years
or
longer
are
plausible.
Adopting
the
lag
structure
used
in
the
Tier
2/
Gasoline
Sulfur
and
Heavy­
Duty
Engine/
Diesel
Fuel
RIAs
and
endorsed
by
the
SAB
(
EPA­
SAB­
COUNCIL­
ADV­
00­
001,
1999),
we
assume
a
5­
year
lag
structure.
23
This
approach
assumes
that
25
percent
of
PM­
related
premature
deaths
occur
in
each
of
the
first
2
years
after
the
exposure
and
the
rest
occur
in
equal
parts
(
approximately
17
percent)
in
each
of
the
ensuing
3
years.


Cumulative
Effects:
As
a
general
point,
we
attribute
the
PM/
mortality
relationship
in
the
underlying
epidemiological
studies
to
cumulative
exposure
to
PM.
However,
the
relative
roles
of
PM
exposure
duration
and
PM
exposure
level
in
inducing
premature
mortality
remain
unknown
at
this
time.

4.1.5.3
Baseline
Health
Effect
Incidence
Rates
The
epidemiological
studies
of
the
association
between
pollution
levels
and
adverse
health
effects
generally
provide
a
direct
estimate
of
the
relationship
of
air
quality
changes
to
the
relative
risk
of
a
health
effect,
rather
than
an
estimate
of
the
absolute
number
of
avoided
cases.
For
example,
a
typical
result
might
be
that
a
10
µ
g/
m3
decrease
in
daily
PM
2.5
levels
might
decrease
hospital
admissions
by
3
percent.
The
baseline
incidence
of
the
health
effect
is
necessary
to
convert
this
relative
change
into
a
number
of
cases.
The
baseline
incidence
rate
4­
52
provides
an
estimate
of
the
incidence
rate
(
number
of
cases
of
the
health
effect
per
year,

usually
per
10,000
or
100,000
general
population)
in
the
assessment
location
corresponding
to
baseline
pollutant
levels
in
that
location.
To
derive
the
total
baseline
incidence
per
year,
this
rate
must
be
multiplied
by
the
corresponding
population
number
(
e.
g.,
if
the
baseline
incidence
rate
is
number
of
cases
per
year
per
100,000
population,
it
must
be
multiplied
by
the
number
of
100,000s
in
the
population).

Some
epidemiological
studies
examine
the
association
between
pollution
levels
and
adverse
health
effects
in
a
specific
subpopulation,
such
as
asthmatics
or
diabetics.
In
these
cases,
it
is
necessary
to
develop
not
only
baseline
incidence
rates,
but
also
prevalence
rates
for
the
defining
condition
(
e.
g.,
asthma).
For
both
baseline
incidence
and
prevalence
data,
we
use
age­
specific
rates
where
available.
Impact
functions
are
applied
to
individual
age
groups
and
then
summed
over
the
relevant
age
range
to
provide
an
estimate
of
total
population
benefits.

In
most
cases,
because
of
a
lack
of
data
or
methods,
we
have
not
attempted
to
project
incidence
rates
to
future
years,
instead
assuming
that
the
most
recent
data
on
incidence
rates
is
the
best
prediction
of
future
incidence
rates.
In
recent
years,
better
data
on
trends
in
incidence
and
prevalence
rates
for
some
endpoints,
such
as
asthma,
have
become
available.
We
are
working
to
develop
methods
to
use
these
data
to
project
future
incidence
rates.
However,
for
our
primary
benefits
analysis
of
the
proposed
IAQR,
we
will
continue
to
use
current
incidence
rates.
We
will
examine
the
impact
of
using
projected
mortality
rates
and
asthma
prevalence
in
sensitivity
analyses.

Table
4­
9
summarizes
the
baseline
incidence
data
and
sources
used
in
the
benefits
analysis.
In
most
cases,
a
single
national
incidence
rate
is
used,
due
to
a
lack
of
more
spatially
disaggregated
data.
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.
However,

for
hospital
admissions,
regional
rates
are
available,
and
for
premature
mortality,
county­
level
data
are
available.

Age,
cause,
and
county­
specific
mortality
rates
were
obtained
from
the
U.
S.
Centers
for
Disease
Control
(
CDC)
for
the
years
1996
through
1998.
CDC
maintains
an
online
data
repository
of
health
statistics,
CDC
Wonder,
accessible
at
http://
wonder.
cdc.
gov/.
The
mortality
4­
53
Table
4­
9.
Baseline
Incidence
Rates
and
Population
Prevalence
Rates
for
Use
in
Impact
Functions,
General
Population
Endpoint
Parameter
Rates
Value
Sourcea
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
filesb
Asthma
ER
visits
Daily
asthma
ER
visit
rate
Age,
Region
specific
visit
rate
2000
NHAMCS
public
use
data
filesc;
1999
NHDS
public
use
data
filesb
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
filesb;
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)

(
continued)
4­
54
Table
4­
9.
Baseline
Incidence
Rates
and
Population
Prevalence
Rates
for
Use
in
Impact
Functions,
General
Population
(
continued)

Endpoint
Parameter
Rates
Value
Sourcea
Lower
Respiratory
Symptoms
Daily
lower
respiratory
symptom
incidence
among
childrend
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)

Minor
Restricted
Activity
Days
Daily
MRAD
incidence
rate
per
person
0.02137
Ostro
and
Rothschild
(
1989,
p.
243)

School
Loss
Dayse
Daily
school
absence
rate
per
person
0.055
National
Center
for
Education
Statistics
(
1996)

Daily
illness­
related
school
absence
rate
per
persone
Northeast
Midwest
South
Southwest
0.0136
0.0146
0.0142
0.0206
1996
HIS
(
Adams
et
al.,
1999,
Table
47);
estimate
of
180
school
days
per
year
Daily
respiratory
illnessrelated
school
absence
rate
per
person
Northeast
Midwest
South
West
0.0073
0.0092
0.0061
0.0124
1996
HIS
(
Adams
et
al.,
1999,
Table
47);
estimate
of
180
school
days
per
year
a
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.

b
See
ftp://
ftp.
cdc.
gov/
pub/
Health_
Statistics/
NCHS/
Datasets/
NHDS/

c
See
ftp://
ftp.
cdc.
gov/
pub/
Health_
Statistics/
NCHS/
Datasets/
NHAMCS/

d
Lower
Respiratory
Symptoms
are
defined
as

2
of
the
following:
cough,
chest
pain,
phlegm,
wheeze
4­
55
rates
provided
are
derived
from
U.
S.
death
records
and
U.
S.
Census
Bureau
postcensal
population
estimates.
Mortality
rates
were
averaged
across
3
years
(
1996
through
1998)
to
provide
more
stable
estimates.
When
estimating
rates
for
age
groups
that
differed
from
the
CDC
Wonder
groupings,
we
assumed
that
rates
were
uniform
across
all
ages
in
the
reported
age
group.
For
example,
to
estimate
mortality
rates
for
individuals
ages
30
and
up,
we
scaled
the
25­
to
34­
year
old
death
count
and
population
by
one­
half
and
then
generated
a
population­
weighted
mortality
rate
using
data
for
the
older
age
groups.
Note
that
we
have
not
projected
any
changes
in
mortality
rates
over
time.
We
are
aware
that
the
U.
S.
Census
projections
of
total
and
age­
specific
mortality
rates
used
in
our
population
projections
are
based
on
projections
of
declines
in
mortality
rates
for
younger
populations
and
increases
in
mortality
rates
for
older
populations
over
time.
We
are
evaluating
the
most
appropriate
way
to
incorporate
these
projections
of
changes
in
overall
national
mortality
rates
into
our
database
of
county­
level
cause­
specific
mortality
rates.
In
the
interim,
we
have
not
attempted
to
adjust
future
mortality
rates.
This
will
lead
to
an
overestimate
of
mortality
benefits
in
future
years,
with
the
overestimation
bias
increasing
the
further
benefits
are
projected
into
the
future.
We
do
not
at
this
time
have
a
quantified
estimate
of
the
magnitude
of
the
potential
bias
in
the
years
analyzed
for
this
rule
(
2010
and
2015).

For
the
set
of
endpoints
affecting
the
asthmatic
population,
in
addition
to
baseline
incidence
rates,
prevalence
rates
of
asthma
in
the
population
are
needed
to
define
the
applicable
population.
Table
4­
9
lists
the
baseline
incidence
rates
and
their
sources
for
asthma
symptom
endpoints.
Table
4­
10
lists
the
prevalence
rates
used
to
determine
the
applicable
population
for
asthma
symptom
endpoints.
Note
that
these
reflect
current
asthma
prevalence
and
assume
no
change
in
prevalence
rates
in
future
years.
As
noted
above,
we
are
investigating
methods
for
projecting
asthma
prevalence
rates
in
future
years.

4.1.5.4
Accounting
for
Potential
Health
Effect
Thresholds
When
conducting
clinical
(
chamber)
and
epidemiological
studies,
functions
may
be
estimated
with
or
without
explicit
thresholds.
Air
pollution
levels
below
the
threshold
are
assumed
to
have
no
associated
adverse
health
effects.
When
a
threshold
is
not
assumed,
as
is
often
the
case
in
epidemiological
studies,
any
exposure
level
is
assumed
to
pose
a
nonzero
risk
of
response
to
at
least
one
segment
of
the
population.

The
possible
existence
of
an
effect
threshold
is
a
very
important
scientific
question
and
issue
for
policy
analyses
such
as
this
one.
The
EPA
SAB
Advisory
Council
for
Clean
Air
Compliance,
which
provides
advice
and
review
of
the
EPA's
methods
for
assessing
the
4­
56
benefits
and
costs
of
the
Clean
Air
Act
under
Section
812
of
the
Clean
Air
Act,
has
advised
the
EPA
that
there
is
currently
no
scientific
basis
for
selecting
a
threshold
of
15
µ
g/
m3
or
any
other
specific
threshold
for
the
PM­
related
health
effects
considered
in
typical
benefits
analyses
(
EPA­
SAB­
Council­
ADV­
99­
012,
1999).
This
is
supported
by
the
recent
literature
on
health
effects
of
PM
exposure
(
Daniels
et
al.,
2000;
Pope,
2000;
Rossi
et
al.,
1999;

Schwartz,
2000)
that
finds
in
most
cases
no
evidence
of
a
nonlinear
relationship
between
PM
4­
57
and
health
effects
and
certainly
does
not
find
a
distinct
threshold.
The
most
recent
draft
of
the
EPA
Air
Quality
Criteria
for
Particulate
Matter
(
EPA,
2002)
reports
only
one
study,
analyzing
data
from
Phoenix,
AZ,
that
reported
even
limited
evidence
suggestive
of
a
possible
threshold
for
PM2.5
(
Smith
et
al.,
2000).

Recent
cohort
analyses
by
HEI
(
Krewski
et
al.,
2000)
and
Pope
et
al.
(
2002)
provide
additional
evidence
of
a
quasi­
linear
relationship
between
long­
term
exposures
to
PM
2.5
and
mortality.
According
to
the
latest
draft
PM
criteria
document,
Krewski
et
al.
(
2000)
found
a
"
visually
near­
linear
relationship
between
all­
cause
and
cardiopulmonary
mortality
residuals
and
mean
sulfate
concentrations,
near­
linear
between
cardiopulmonary
mortality
and
mean
PM
2.5,
but
a
somewhat
nonlinear
relationship
between
all­
cause
mortality
residuals
and
mean
PM
2.5
concentrations
that
flattens
above
about
20
µ
g/
m3.
The
confidence
bands
around
the
fitted
curves
are
very
wide,
however,
neither
requiring
a
linear
relationship
nor
precluding
a
nonlinear
relationship
if
suggested
by
reanalyses."
Table
4­
10.
Asthma
Prevalence
Rates
Used
to
Estimate
Asthmatic
Populations
in
Impact
Functions
Population
Group
Asthma
Prevalence
Rates
Value
Source
All
Ages
0.0386
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
<
18
0.0527
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
5­
17
0.0567
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
18­
44
0.0371
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
45­
64
0.0333
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
65+
0.0221
American
Lung
Association
(
2002c,
Table
7)
 
based
on
1999
HIS
Male,
27+
0.021
2000
HIS
public
use
data
filesa
African­
American,
5
to
17
0.0726
American
Lung
Association
(
2002c,
Table
9)
 
based
on
1999
HIS
African­
American,
<
18
0.0735
American
Lung
Association
(
2002c,
Table
9)
 
based
on
1999
HIS
a
See
ftp://
ftp.
cdc.
gov/
pub/
Health_
Statistics/
NCHS/
Datasets/
NHIS/
2000/
4­
58
The
Pope
et
al.
(
2002)
analysis,
which
represented
an
extension
to
the
Krewski
et
al.

analysis,
found
that
the
functions
relating
PM2.5
and
mortality
"
were
not
significantly
different
from
linear
associations."

Daniels
et
al.
(
2000)
examined
the
presence
of
thresholds
in
PM
10
C­
R
relationships
for
daily
mortality
using
the
largest
20
U.
S.
cities
for
1987­
1994.
The
results
of
their
models
suggest
that
the
linear
model
was
preferred
over
spline
and
threshold
models.
Thus,
these
results
suggest
that
linear
models
without
a
threshold
may
well
be
appropriate
for
estimating
the
effects
of
PM
10
on
the
types
of
mortality
of
main
interest.
Schwartz
and
Zanobetti
(
2000)

investigated
the
presence
of
threshold
by
simulation
and
actual
data
analysis
of
10
U.
S.
cities.

In
the
analysis
of
real
data
from
10
cities,
the
combined
C­
R
curve
did
not
show
evidence
of
a
threshold
in
the
PM
10­
mortality
associations.
Schwartz,
Laden,
and
Zanobetti
(
2002)

investigated
thresholds
by
combining
data
on
the
PM2.5­
mortality
relationships
for
six
cities
and
found
an
essentially
linear
relationship
down
to
2
µ
g/
m3,
which
is
at
or
below
anthropogenic
background
in
most
areas.
They
also
examined
just
traffic­
related
particles
and
again
found
no
evidence
of
a
threshold.
The
Smith
et
al.
(
2000)
study
of
associations
between
daily
total
mortality
and
PM
2.5
and
PM
10­
2.5
in
Phoenix,
AZ,
(
during
1995­
1997)
also
investigated
the
possibility
of
a
threshold
using
a
piecewise
linear
model
and
a
cubic
spline
model.
For
both
the
piecewise
linear
and
cubic
spline
models,
the
analysis
suggested
a
threshold
of
around
20
to
25
µ
g/
m3.
However,
the
C­
R
curve
for
PM
2.5
presented
in
this
publication
suggests
more
of
a
U­
or
V­
shaped
relationship
than
the
usual
"
hockey
stick"

threshold
relationship.

Based
on
the
recent
literature
and
advice
from
the
SAB,
we
assume
there
are
no
thresholds
for
modeling
health
effects.
Although
not
included
in
the
primary
analysis,
the
potential
impact
of
a
health
effects
threshold
on
avoided
incidences
of
PM­
related
premature
mortality
is
explored
as
a
key
sensitivity
analysis
and
is
presented
in
Appendix
9­
B
(
to
be
completed
for
the
supplemental
analysis).

Our
assumptions
regarding
thresholds
are
supported
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
concluded
that
there
is
no
evidence
for
any
departure
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
shortand
long­
term
exposure
models
and
the
similar
effects
found
in
cities
with
low
and
high
ambient
concentrations
of
PM.
4­
59
4.1.5.5
Selecting
Unit
Values
for
Monetizing
Health
Endpoints
The
appropriate
economic
value
of
a
change
in
a
health
effect
depends
on
whether
the
health
effect
is
viewed
ex
ante
(
before
the
effect
has
occurred)
or
ex
post
(
after
the
effect
has
occurred).
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
ex
ante
WTP
for
changes
in
risk.
However,
epidemiological
studies
generally
provide
estimates
of
the
relative
risks
of
a
particular
health
effect
avoided
due
to
a
reduction
in
air
pollution.
A
convenient
way
to
use
this
data
in
a
consistent
framework
is
to
convert
probabilities
to
units
of
avoided
statistical
incidences.
This
measure
is
calculated
by
dividing
individual
WTP
for
a
risk
reduction
by
the
related
observed
change
in
risk.
For
example,
suppose
a
measure
is
able
to
reduce
the
risk
of
premature
mortality
from
2
in
10,000
to
1
in
10,000
(
a
reduction
of
1
in
10,000).
If
individual
WTP
for
this
risk
reduction
is
$
100,
then
the
WTP
for
an
avoided
statistical
premature
mortality
amounts
to
$
1
million
($
100/
0.0001
change
in
risk).
Using
this
approach,
the
size
of
the
affected
population
is
automatically
taken
into
account
by
the
number
of
incidences
predicted
by
epidemiological
studies
applied
to
the
relevant
population.
The
same
type
of
calculation
can
produce
values
for
statistical
incidences
of
other
health
endpoints.

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.
For
example,
for
the
valuation
of
hospital
admissions
we
use
the
avoided
medical
costs
as
an
estimate
of
the
value
of
avoiding
the
health
effects
causing
the
admission.
These
COI
estimates
generally
understate
the
true
value
of
reductions
in
risk
of
a
health
effect.
They
tend
to
reflect
the
direct
expenditures
related
to
treatment
but
not
the
value
of
avoided
pain
and
suffering
from
the
health
effect.
Table
4­
11
summarizes
the
value
estimates
per
health
effect
that
we
used
in
this
analysis.
Values
are
presented
both
for
a
1990
base
income
level
and
adjusted
for
income
growth
in
the
two
future
analysis
years,
2010
and
2015.
Note
that
the
unit
values
for
hospital
admissions
are
the
weighted
averages
of
the
ICD­
9
code­
specific
values
for
the
group
of
ICD­
9
codes
included
in
the
hospital
admission
categories.
A
discussion
of
the
valuation
methods
for
premature
mortality
and
CB
is
provided
here
because
of
the
relative
importance
of
these
effects.
Discussions
of
the
methods
used
to
value
nonfatal
myocardial
infarctions
(
heart
attacks)
and
school
absence
days
are
provided
because
these
endpoints
have
only
recently
been
added
to
the
analysis
and
the
valuation
methods
are
still
under
development.
We
welcome
comment
on
these
valuation
methods.
In
the
following
4­
60
discussions,
unit
values
are
presented
at
1990
levels
of
income
for
consistency
with
previous
analyses.
Equivalent
future
year
values
can
be
obtained
from
Table
4­
11.
4­
61
Table
4­
11.
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
1999$)

Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2015
Income
Level
Premature
Mortality
(
Value
of
a
Statistical
Life)
$
5,500,000
$
6,000,000
$
6,400,000
Point
estimate
is
the
mean
of
a
normal
distribution
with
a
95
percent
confidence
interval
between
$
1
and
$
10
million.
Confidence
interval
is
based
on
two
metaanalyses
of
the
wage­
risk
VSL
literature.
$
1
million
represents
the
lower
end
of
the
interquartile
range
from
the
Mrozek
and
Taylor
(
2000)
meta­
analysis.
$
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
$
380,000
$
400,000
Point
estimate
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.

Nonfatal
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
$
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)
(
continued)
4­
62
Table
4­
11.
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
1999$)
(
continued)

Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2015
Income
Level
Hospital
Admissions
Chronic
Obstructive
Pulmonary
Disease
(
COPD)

(
ICD
codes
490­
492,
494­
496)
$
12,378
$
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
$
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).

Asthma
admissions
$
6,634
$
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
$
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
$
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.
(
continued)
4­
63
Table
4­
11.
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
1999$)
(
continued)

Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2015
Income
Level
Respiratory
Ailments
Not
Requiring
Hospitalization
Upper
Respiratory
Symptoms
(
URS)
$
25
$
26
$
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.

Lower
Respiratory
Symptoms
(
LRS)
$
16
$
17
$
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.

Asthma
Exacerbations
$
42
$
43
$
44
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.

Acute
Bronchitis
$
360
$
370
$
380
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.

(
continued)
4­
64
Table
4­
11.
Unit
Values
Used
for
Economic
Valuation
of
Health
Endpoints
(
1999$)
(
continued)

Health
Endpoint
Central
Estimate
of
Value
Per
Statistical
Incidence
Derivation
of
Estimates
1990
Income
Level
2010
Income
Level
2015
Income
Level
Restricted
Activity
and
Work/
School
Loss
Days
Work
Loss
Days
(
WLDs)
Variable
(
national
median
=
)
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.

School
Absence
Days
$
75
$
75
$
75
Based
on
expected
lost
wages
from
parent
staying
home
with
child.
Estimated
daily
lost
wage
(
if
a
mother
must
stay
at
home
with
a
sick
child)
is
based
on
the
median
weekly
wage
among
women
age
25
and
older
in
2000
(
U.
S.
Census
Bureau,
Statistical
Abstract
of
the
United
States:
2001,
Section
12:
Labor
Force,
Employment,
and
Earnings,
Table
No.
621).
This
median
wage
is
$
551.

Dividing
by
5
gives
an
estimated
median
daily
wage
of
$
103.

The
expected
loss
in
wages
due
to
a
day
of
school
absence
in
which
the
mother
would
have
to
stay
home
with
her
child
is
estimated
as
the
probability
that
the
mother
is
in
the
workforce
times
the
daily
wage
she
would
lose
if
she
missed
a
day
=
72.85%
of
$
103,
or
$
75.

Worker
Productivity
$
0.95
per
worker
per
10%

change
in
ozone
per
day
$
0.95
per
worker
per
10%

change
in
ozone
per
day
$
0.95
per
worker
per
10%

change
in
ozone
per
day
Based
on
$
68
 
median
daily
earnings
of
workers
in
farming,
forestry
and
fishing
 
from
Table
621,
Statistical
Abstract
of
the
United
States
("
Full­
Time
Wage
and
Salary
Workers
 
Number
and
Earnings:
1985
to
2000")
(
Source
of
data
in
table:
U.
S.
Bureau
of
Labor
Statistics,
Bulletin
2307
and
Employment
and
Earnings,
monthly).

Minor
Restricted
Activity
Days
(
MRADs)
$
51
$
53
$
54
Median
WTP
estimate
to
avoid
one
MRAD
from
Tolley,
et
al.
(
1986)
.
24The
choice
of
a
discount
rate,
and
its
associated
conceptual
basis,
is
a
topic
of
ongoing
discussion
within
the
federal
government.
The
EPA
adopted
a
3
percent
discount
rate
for
its
base
estimate
in
this
case
to
reflect
reliance
on
a
"
social
rate
of
time
preference"
discounting
concept.
We
have
also
calculated
benefits
and
costs
using
a
7
percent
rate
consistent
with
an
"
opportunity
cost
of
capital"
concept
to
reflect
the
time
value
of
resources
directed
to
meet
regulatory
requirements.
In
this
case,
the
benefit
and
cost
estimates
were
not
significantly
affected
by
the
choice
of
discount
rate.
Further
discussion
of
this
topic
appears
in
the
EPA's
Guidelines
for
Preparing
Economic
Analyses
(
in
press).

4­
65
4.1.5.5.1
Valuing
Reductions
in
Premature
Mortality
Risk.
We
estimate
the
monetary
benefit
of
reducing
premature
mortality
risk
using
the
"
value
of
statistical
lives
saved"
(
VSL)
approach,
which
is
a
summary
measure
for
the
value
of
small
changes
in
mortality
risk
experienced
by
a
large
number
of
people.
The
VSL
approach
applies
information
from
several
published
value­
of­
life
studies
to
determine
a
reasonable
benefit
of
preventing
premature
mortality.
The
mean
value
of
avoiding
one
statistical
death
is
assumed
to
be
$
5.5
million
in
1999
dollars.
This
represents
a
central
value
consistent
with
the
range
of
values
suggested
by
recent
meta­
analyses
of
the
wage­
risk
VSL
literature.
The
distribution
of
VSL
is
characterized
by
a
confidence
interval
from
$
1
to
$
10
million,
based
on
two
meta­
analyses
of
the
wage­
risk
VSL
literature.
The
$
1
million
lower
confidence
limit
represents
the
lower
end
of
the
interquartile
range
from
the
Mrozek
and
Taylor
(
2000)

meta­
analysis.
The
$
10
million
upper
confidence
limit
represents
the
upper
end
of
the
interquartile
range
from
the
Viscusi
and
Aldy
(
2003)
meta­
analysis.

In
previous
analyses,
we
used
an
estimate
of
mean
VSL
equal
to
$
6.3
million,
based
on
a
distribution
fitted
to
the
estimates
from
26
value­
of­
life
studies
identified
in
the
Section
812
reports
as
"
applicable
to
policy
analysis."
The
EPA
welcomes
comments
on
the
departure
from
this
approach
for
the
current
analysis.

As
indicated
in
the
previous
section
on
quantification
of
premature
mortality
benefits,

we
assume
for
this
analysis
that
some
of
the
incidences
of
premature
mortality
related
to
PM
exposures
occur
in
a
distributed
fashion
over
the
5
years
following
exposure.
To
take
this
into
account
in
the
valuation
of
reductions
in
premature
mortality,
we
apply
an
annual
3
percent
discount
rate
to
the
value
of
premature
mortality
occurring
in
future
years.
24
The
economics
literature
concerning
the
appropriate
method
for
valuing
reductions
in
premature
mortality
risk
is
still
developing.
The
adoption
of
a
value
for
the
projected
reduction
in
the
risk
of
premature
mortality
is
the
subject
of
continuing
discussion
within
the
economics
and
public
policy
analysis
community.
Regardless
of
the
theoretical
economic
considerations,
the
EPA
prefers
not
to
draw
distinctions
in
the
monetary
value
assigned
to
the
4­
66
lives
saved
even
if
they
differ
in
age,
health
status,
socioeconomic
status,
gender,
or
other
characteristic
of
the
adult
population.

Following
the
advice
of
the
EEAC
of
the
SAB,
the
EPA
currently
uses
the
VSL
approach
in
calculating
the
primary
estimate
of
mortality
benefits,
because
we
believe
this
calculation
provides
the
most
reasonable
single
estimate
of
an
individual's
willingness
to
trade
off
money
for
reductions
in
mortality
risk
(
EPA­
SAB­
EEAC­
00­
013).
Although
there
are
several
differences
between
the
labor
market
studies
the
EPA
uses
to
derive
a
VSL
estimate
and
the
PM
air
pollution
context
addressed
here,
those
differences
in
the
affected
populations
and
the
nature
of
the
risks
imply
both
upward
and
downward
adjustments.
Table
4­
12
lists
some
of
these
differences
and
the
expected
effect
on
the
VSL
estimate
for
air
pollution­
related
mortality.
In
the
absence
of
a
comprehensive
and
balanced
set
of
adjustment
factors,
the
EPA
believes
it
is
reasonable
to
continue
to
use
the
$
5.5
million
value
while
acknowledging
the
significant
limitations
and
uncertainties
in
the
available
literature.

Some
economists
emphasize
that
the
VSL
is
not
a
single
number
relevant
for
all
situations.
Indeed,
the
VSL
estimate
of
$
5.5
million
(
1999
dollars)
is
itself
the
central
tendency
of
a
number
of
estimates
of
the
VSL
for
some
rather
narrowly
defined
populations.

When
there
are
significant
differences
between
the
population
affected
by
a
particular
health
risk
and
the
populations
used
in
the
labor
market
studies,
as
is
the
case
here,
some
economists
prefer
to
adjust
the
VSL
estimate
to
reflect
those
differences.
Table
4­
12.
Expected
Impact
on
Estimated
Benefits
of
Premature
Mortality
Reductions
of
Differences
Between
Factors
Used
in
Developing
Applied
VSL
and
Theoretically
Appropriate
VSL
Attribute
Expected
Direction
of
Bias
Age
Uncertain,
perhaps
overestimate
Life
expectancy/
health
status
Uncertain,
perhaps
overestimate
Attitudes
toward
risk
Underestimate
Income
Uncertain
Voluntary
vs.
Involuntary
Uncertain,
perhaps
underestimate
Catastrophic
vs.
protracted
death
Uncertain,
perhaps
underestimate
4­
67
The
SAB­
EEAC
advised
the
EPA
"
continue
to
use
a
wage­
risk­
based
VSL
as
its
primary
estimate,
including
appropriate
sensitivity
analyses
to
reflect
the
uncertainty
of
these
estimates,"
and
that
"
the
only
risk
characteristic
for
which
adjustments
to
the
VSL
can
be
made
is
the
timing
of
the
risk"
(
EPA­
SAB­
EEAC­
00­
013,
EPA,
2000b).
In
developing
our
primary
estimate
of
the
benefits
of
premature
mortality
reductions,
we
have
followed
this
advice
and
discounted
over
the
lag
period
between
exposure
and
premature
mortality.

Uncertainties
Specific
to
Premature
Mortality
Valuation.
The
economic
benefits
associated
with
premature
mortality
are
the
largest
category
of
monetized
benefits
of
the
proposed
IAQR.
In
addition,
in
prior
analyses,
the
EPA
has
identified
valuation
of
mortality
benefits
as
the
largest
contributor
to
the
range
of
uncertainty
in
monetized
benefits
(
see
EPA
[
1999]).
Because
of
the
uncertainty
in
estimates
of
the
value
of
premature
mortality
avoidance,
it
is
important
to
adequately
characterize
and
understand
the
various
types
of
economic
approaches
available
for
mortality
valuation.
Such
an
assessment
also
requires
an
understanding
of
how
alternative
valuation
approaches
reflect
that
some
individuals
may
be
more
susceptible
to
air
pollution­
induced
mortality
or
reflect
differences
in
the
nature
of
the
risk
presented
by
air
pollution
relative
to
the
risks
studied
in
the
relevant
economics
literature.

The
health
science
literature
on
air
pollution
indicates
that
several
human
characteristics
affect
the
degree
to
which
mortality
risk
affects
an
individual.
For
example,

some
age
groups
appear
to
be
more
susceptible
to
air
pollution
than
others
(
e.
g.,
the
elderly
and
children).
Health
status
prior
to
exposure
also
affects
susceptibility.
An
ideal
benefits
estimate
of
mortality
risk
reduction
would
reflect
these
human
characteristics,
in
addition
to
an
individual's
WTP
to
improve
one's
own
chances
of
survival
plus
WTP
to
improve
other
individuals'
survival
rates.
The
ideal
measure
would
also
take
into
account
the
specific
nature
of
the
risk
reduction
commodity
that
is
provided
to
individuals,
as
well
as
the
context
in
which
risk
is
reduced.
To
measure
this
value,
it
is
important
to
assess
how
reductions
in
air
pollution
reduce
the
risk
of
dying
from
the
time
that
reductions
take
effect
onward,
and
how
individuals
value
these
changes.
Each
individual's
survival
curve,
or
the
probability
of
surviving
beyond
a
given
age,
should
shift
as
a
result
of
an
environmental
quality
improvement.
For
example,

changing
the
current
probability
of
survival
for
an
individual
also
shifts
future
probabilities
of
that
individual's
survival.
This
probability
shift
will
differ
across
individuals
because
survival
curves
depend
on
such
characteristics
as
age,
health
state,
and
the
current
age
to
which
the
individual
is
likely
to
survive.

Although
a
survival
curve
approach
provides
a
theoretically
preferred
method
for
valuing
the
benefits
of
reduced
risk
of
premature
mortality
associated
with
reducing
air
4­
68
pollution,
the
approach
requires
a
great
deal
of
data
to
implement.
The
economic
valuation
literature
does
not
yet
include
good
estimates
of
the
value
of
this
risk
reduction
commodity.

As
a
result,
in
this
study
we
value
avoided
premature
mortality
risk
using
the
VSL
approach.

Other
uncertainties
specific
to
premature
mortality
valuation
include
the
following:


Across­
study
variation:
There
is
considerable
uncertainty
as
to
whether
the
available
literature
on
VSL
provides
adequate
estimates
of
the
VSL
saved
by
air
pollution
reduction.
Although
there
is
considerable
variation
in
the
analytical
designs
and
data
used
in
the
existing
literature,
the
majority
of
the
studies
involve
the
value
of
risks
to
a
middle­
aged
working
population.
Most
of
the
studies
examine
differences
in
wages
of
risky
occupations,
using
a
wage­
hedonic
approach.
Certain
characteristics
of
both
the
population
affected
and
the
mortality
risk
facing
that
population
are
believed
to
affect
the
average
WTP
to
reduce
the
risk.
The
appropriateness
of
a
distribution
of
WTP
based
on
the
current
VSL
literature
for
valuing
the
mortality­
related
benefits
of
reductions
in
air
pollution
concentrations
therefore
depends
not
only
on
the
quality
of
the
studies
(
i.
e.,
how
well
they
measure
what
they
are
trying
to
measure),
but
also
on
the
extent
to
which
the
risks
being
valued
are
similar
and
the
extent
to
which
the
subjects
in
the
studies
are
similar
to
the
population
affected
by
changes
in
pollution
concentrations.


Level
of
risk
reduction:
The
transferability
of
estimates
of
the
VSL
from
the
wage­
risk
studies
to
the
context
of
the
Interstate
Air
Quality
Rulemaking
analysis
rests
on
the
assumption
that,
within
a
reasonable
range,
WTP
for
reductions
in
mortality
risk
is
linear
in
risk
reduction.
For
example,
suppose
a
study
estimates
that
the
average
WTP
for
a
reduction
in
mortality
risk
of
1/
100,000
is
$
50,
but
that
the
actual
mortality
risk
reduction
resulting
from
a
given
pollutant
reduction
is
1/
10,000.
If
WTP
for
reductions
in
mortality
risk
is
linear
in
risk
reduction,
then
a
WTP
of
$
50
for
a
reduction
of
1/
100,000
implies
a
WTP
of
$
500
for
a
risk
reduction
of
1/
10,000
(
which
is
10
times
the
risk
reduction
valued
in
the
study).
Under
the
assumption
of
linearity,
the
estimate
of
the
VSL
does
not
depend
on
the
particular
amount
of
risk
reduction
being
valued.
This
assumption
has
been
shown
to
be
reasonable
provided
the
change
in
the
risk
being
valued
is
within
the
range
of
risks
evaluated
in
the
underlying
studies
(
Rowlatt
et
al.,
1998).


Voluntariness
of
risks
evaluated:
Although
job­
related
mortality
risks
may
differ
in
several
ways
from
air
pollution­
related
mortality
risks,
the
most
important
difference
may
be
that
job­
related
risks
are
incurred
voluntarily,
or
generally
assumed
to
be,
whereas
air
pollution­
related
risks
are
incurred
involuntarily.
Some
evidence
suggests
that
people
will
pay
more
to
reduce
involuntarily
incurred
risks
than
risks
incurred
voluntarily.
If
this
is
the
case,
WTP
estimates
based
on
4­
69
wage­
risk
studies
may
understate
WTP
to
reduce
involuntarily
incurred
air
pollution­
related
mortality
risks.


Sudden
versus
protracted
death:
A
final
important
difference
related
to
the
nature
of
the
risk
may
be
that
some
workplace
mortality
risks
tend
to
involve
sudden,
catastrophic
events,
whereas
air
pollution­
related
risks
tend
to
involve
longer
periods
of
disease
and
suffering
prior
to
death.
Some
evidence
suggests
that
WTP
to
avoid
a
risk
of
a
protracted
death
involving
prolonged
suffering
and
loss
of
dignity
and
personal
control
is
greater
than
the
WTP
to
avoid
a
risk
(
of
identical
magnitude)
of
sudden
death.
To
the
extent
that
the
mortality
risks
addressed
in
this
assessment
are
associated
with
longer
periods
of
illness
or
greater
pain
and
suffering
than
are
the
risks
addressed
in
the
valuation
literature,
the
WTP
measurements
employed
in
the
present
analysis
would
reflect
a
downward
bias.


Self­
selection
and
skill
in
avoiding
risk.
Recent
research
(
Shogren
et
al.,
2002)
suggests
that
VSL
estimates
based
on
hedonic
wage
studies
may
overstate
the
average
value
of
a
risk
reduction.
This
is
based
on
the
fact
that
the
risk­
wage
tradeoff
revealed
in
hedonic
studies
reflects
the
preferences
of
the
marginal
worker
(
i.
e.,
that
worker
who
demands
the
highest
compensation
for
his
risk
reduction).
This
worker
must
have
either
higher
risk,
lower
risk
tolerance,
or
both.
However,
the
risk
estimate
used
in
hedonic
studies
is
generally
based
on
average
risk,
so
the
VSL
may
be
upwardly
biased
because
the
wage
differential
and
risk
measures
do
not
match.

4.1.5.5.2
Valuing
Reductions
in
the
Risk
of
Chronic
Bronchitis.
The
best
available
estimate
of
WTP
to
avoid
a
case
of
CB
comes
from
Viscusi
et
al.
(
1991).
The
Viscusi
et
al.

study,
however,
describes
a
severe
case
of
CB
to
the
survey
respondents.
We
therefore
employ
an
estimate
of
WTP
to
avoid
a
pollution­
related
case
of
CB,
based
on
adjusting
the
Viscusi
et
al.
(
1991)
estimate
of
the
WTP
to
avoid
a
severe
case.
This
is
done
to
account
for
the
likelihood
that
an
average
case
of
pollution­
related
CB
is
not
as
severe.
The
adjustment
is
made
by
applying
the
elasticity
of
WTP
with
respect
to
severity
reported
in
the
Krupnick
and
Cropper
(
1992)
study.
Details
of
this
adjustment
procedure
are
provided
in
the
benefits
TSD
for
the
nonroad
diesel
rulemaking
(
Abt
Associates,
2003).

We
use
the
mean
of
a
distribution
of
WTP
estimates
as
the
central
tendency
estimate
of
WTP
to
avoid
a
pollution­
related
case
of
CB
in
this
analysis.
The
distribution
incorporates
uncertainty
from
three
sources:
the
WTP
to
avoid
a
case
of
severe
CB,
as
described
by
Viscusi
et
al.;
the
severity
level
of
an
average
pollution­
related
case
of
CB
(
relative
to
that
of
the
case
described
by
Viscusi
et
al.);
and
the
elasticity
of
WTP
with
respect
to
severity
of
the
illness.
Based
on
assumptions
about
the
distributions
of
each
of
these
three
uncertain
4­
70
components,
we
derive
a
distribution
of
WTP
to
avoid
a
pollution­
related
case
of
CB
by
statistical
uncertainty
analysis
techniques.
The
expected
value
(
i.
e.,
mean)
of
this
distribution,

which
is
about
$
331,000
(
2000$),
is
taken
as
the
central
tendency
estimate
of
WTP
to
avoid
a
PM­
related
case
of
CB.

4.1.5.5.3
Valuing
Reductions
in
Non­
Fatal
Myocardial
Infarctions
(
Heart
Attacks).

The
Agency
has
recently
incorporated
into
its
analyses
the
impact
of
air
pollution
on
the
expected
number
of
nonfatal
heart
attacks,
although
it
has
examined
the
impact
of
reductions
in
other
related
cardiovascular
endpoints.
We
were
not
able
to
identify
a
suitable
WTP
value
for
reductions
in
the
risk
of
nonfatal
heart
attacks.
Instead,
we
propose
a
cCOI
unit
value
with
two
components:
the
direct
medical
costs
and
the
opportunity
cost
(
lost
earnings)

associated
with
the
illness
event.
Because
the
costs
associated
with
an
myocardial
infarction
extend
beyond
the
initial
event
itself,
we
consider
costs
incurred
over
several
years.
Using
age­
specific
annual
lost
earnings
estimated
by
Cropper
and
Krupnick
(
1990)
and
a
3
percent
discount
rate,
we
estimated
a
present
discounted
value
in
lost
earnings
(
in
2000$)
over
5
years
due
to
an
myocardial
infarction
of
$
8,774
for
someone
between
the
ages
of
25
and
44,

$
12,932
for
someone
between
the
ages
of
45
and
54,
and
$
74,746
for
someone
between
the
ages
of
55
and
65.
The
corresponding
age­
specific
estimates
of
lost
earnings
(
in
2000$)
using
a
7
percent
discount
rate
are
$
7,855,
$
11,578,
and
$
66,920,
respectively.
Cropper
and
Krupnick
(
1990)
do
not
provide
lost
earnings
estimates
for
populations
under
25
or
over
65.

As
such,
we
do
not
include
lost
earnings
in
the
cost
estimates
for
these
age
groups.

We
found
three
possible
sources
in
the
literature
of
estimates
of
the
direct
medical
costs
of
myocardial
infarction:


Wittels
et
al.
(
1990)
estimated
expected
total
medical
costs
of
myocardial
infarction
over
5
years
to
be
$
51,211
(
in
1986$)
for
people
who
were
admitted
to
the
hospital
and
survived
hospitalization.
(
There
does
not
appear
to
be
any
discounting
used.)
Wittels
et
al.
was
used
to
value
coronary
heart
disease
in
the
812
Retrospective
Analysis
of
the
Clean
Air
Act.
Using
the
CPI­
U
for
medical
care,
the
Wittels
estimate
is
$
109,474
in
year
2000$.
This
estimated
cost
is
based
on
a
medical
cost
model,
which
incorporated
therapeutic
options,
projected
outcomes,
and
prices
(
using
"
knowledgeable
cardiologists"
as
consultants).
The
model
used
medical
data
and
medical
decision
algorithms
to
estimate
the
probabilities
of
certain
events
and/
or
medical
procedures
being
used.
The
authors
note
that
the
average
length
of
hospitalization
for
acute
myocardial
infarction
has
decreased
over
time
(
from
an
average
of
12.9
days
in
1980
to
an
average
of
11
days
in
1983).
Wittels
et
al.
used
10
days
as
the
average
in
their
study.
It
is
unclear
how
much
further
the
length
of
stay
for
myocardial
infarction
may
have
4­
71
decreased
from
1983
to
the
present.
The
average
length
of
stay
for
ICD
code
410
(
myocardial
infarction)
in
the
year­
2000
AHQR
HCUP
database
is
5.5
days.
However,
this
may
include
patients
who
died
in
the
hospital
(
not
included
among
our
nonfatal
myocardial
infarction
cases),
whose
length
of
stay
was
therefore
substantially
shorter
than
it
would
be
if
they
had
not
died.


Eisenstein
et
al.
(
2001)
estimated
10­
year
costs
of
$
44,663
in
1997$,
or
$
49,651
in
2000$
for
myocardial
infarction
patients,
using
statistical
prediction
(
regression)
models
to
estimate
inpatient
costs.
Only
inpatient
costs
(
physician
fees
and
hospital
costs)
were
included.


Russell
et
al.
(
1998)
estimated
first­
year
direct
medical
costs
of
treating
nonfatal
myocardial
infarction
of
$
15,540
(
in
1995$)
and
$
1,051
annually
thereafter.
Converting
to
year
2000$,
that
would
be
$
23,353
for
a
5­
year
period
(
without
discounting)
or
$
29,568
for
a
10­
year
period.

In
summary,
the
three
different
studies
provided
significantly
different
values
(
see
Table
4­
13).

As
noted
above,
the
estimates
from
these
three
studies
are
substantially
different,
and
we
have
not
adequately
resolved
the
sources
of
differences
in
the
estimates.
Because
the
wage­
related
opportunity
cost
estimates
from
Cropper
and
Krupnick
(
1990)
cover
a
5­
year
period,
we
use
estimates
for
medical
costs
that
similarly
cover
a
5­
year
period
(
i.
e.,
estimates
from
Wittels
et
al.
(
1990)
and
Russell
et
al.
(
1998).
We
use
a
simple
average
of
the
two
5­

year
estimates,
or
$
65,902,
and
add
it
to
the
5­
year
opportunity
cost
estimate.
The
resulting
estimates
are
given
in
Table
4­
14.
4­
72
Table
4­
13.
Alternative
Direct
Medical
Cost
of
Illness
Estimates
for
Nonfatal
Heart
Attacks
Study
Direct
Medical
Costs
(
2000$)
Over
an
x­
Year
Period,
for
x
=

Wittels
et
al.
(
1990)
$
109,474a
5
Russell
et
al.
(
1998)
$
22,331b
5
Eisenstein
et
al.
(
2001)
$
49,651b
10
Russell
et
al.
(
1998)
$
27,242b
10
a
Wittels
et
al.
did
not
appear
to
discount
costs
incurred
in
future
years.

b
Using
a
3
percent
discount
rate.

Table
4­
14.
Estimated
Costs
Over
a
5­
Year
Period
(
in
2000$)
of
a
Nonfatal
Myocardial
Infarction
Age
Group
Opportunity
Cost
Medical
Costa
Total
Cost
0
­
24
$
0
$
65,902
$
65,902
25­
44
$
8,774b
$
65,902
$
74,676
45
­
54
$
12,253b
$
65,902
$
78,834
55
­
65
$
70,619b
$
65,902
$
140,649
>
65
$
0
$
65,902
$
65,902
a
An
average
of
the
5­
year
costs
estimated
by
Wittels
et
al.,
1990,
and
Russell
et
al.,
1998.

b
From
Cropper
and
Krupnick,
1990,
using
a
3
percent
discount
rate.
4­
73
4.1.5.5.4
Valuing
Reductions
in
School
Absence
Days.
School
absences
associated
with
exposure
to
ozone
are
likely
to
be
due
to
respiratory­
related
symptoms
and
illnesses.

Because
the
respiratory
symptom
and
illness
endpoints
we
are
including
are
all
PM­
related
rather
than
ozone­
related,
we
do
not
have
to
be
concerned
about
double
counting
of
benefits
if
we
aggregate
the
benefits
of
avoiding
ozone­
related
school
absences
with
the
benefits
of
avoiding
PM­
related
respiratory
symptoms
and
illnesses.

One
possible
approach
to
valuing
a
school
absence
is
using
a
parental
opportunity
cost
approach.
This
method
requires
two
steps:
estimate
the
probability
that,
if
a
school
child
stays
home
from
school,
a
parent
will
have
to
stay
home
from
work
to
care
for
the
child,
and
value
the
lost
productivity
at
the
person's
wage.
Using
this
method,
we
would
estimate
the
proportion
of
families
with
school­
age
children
in
which
both
parents
work,
and
value
a
school
loss
day
as
the
probability
of
a
work
loss
day
resulting
from
a
school
loss
day
(
i.
e.,
the
proportion
of
households
with
school­
age
children
in
which
both
parents
work)
times
some
measure
of
lost
wages
(
whatever
measure
we
use
to
value
work
loss
days).
There
are
two
significant
problems
with
this
method,
however.
First,
it
omits
WTP
to
avoid
the
symptoms/
illness
that
resulted
in
the
school
absence.
Second,
it
effectively
gives
zero
value
to
school
absences
which
do
not
result
in
a
work
loss
day
(
unless
we
derive
an
alternative
estimate
of
the
value
of
the
parent's
time
for
those
cases
in
which
the
parent
is
not
in
the
labor
force).
We
are
investigating
approaches
using
WTP
for
avoid
the
symptoms/
illnesses
causing
the
absence.
In
the
interim,
we
will
use
the
parental
opportunity
cost
approach.

For
the
parental
opportunity
cost
approach,
we
make
an
explicit,
conservative
assumption
that
in
married
households
with
two
working
parents,
the
female
parent
will
stay
home
with
a
sick
child.
From
the
U.
S.
Census
Bureau,
Statistical
Abstract
of
the
United
States:
2001,
we
obtained
(
1)
the
numbers
of
single,
married,
and
"
other"
(
i.
e.,
widowed,

divorced,
or
separated)
women
with
children
in
the
workforce,
and
(
2)
the
rates
of
participation
in
the
workforce
of
single,
married,
and
"
other"
women
with
children.
From
these
two
sets
of
statistics,
we
inferred
the
numbers
of
single,
married,
and
"
other"
women
with
children,
and
the
corresponding
percentages.
These
percentages
were
used
to
calculate
a
weighted
average
participation
rate,
as
shown
in
Table
4­
15.

Our
estimated
daily
lost
wage
(
if
a
mother
must
stay
at
home
with
a
sick
child)
is
based
on
the
median
weekly
wage
among
women
age
25
and
older
in
2000
(
U.
S.
Census
Bureau,
Statistical
Abstract
of
the
United
States:
2001,
Section
12:
Labor
Force,

Employment,
and
Earnings,
Table
No.
621).
This
median
wage
is
$
551.
Dividing
by
5
gives
an
estimated
median
daily
wage
of
$
103.
25In
a
very
recent
article,
Hall,
Brajer,
and
Lurmann
(
2003)
use
a
similar
methodology
to
derive
a
mid­
estimate
value
per
school
absence
day
for
California
of
between
$
70
and
$
81,
depending
on
differences
in
incomes
between
three
counties
in
California.
Our
national
average
estimate
of
$
75
per
absence
is
consistent
with
these
published
values.

4­
74
The
expected
loss
in
wages
due
to
a
day
of
school
absence
in
which
the
mother
would
have
to
stay
home
with
her
child
is
estimated
as
the
probability
that
the
mother
is
in
the
workforce
times
the
daily
wage
she
would
lose
if
she
missed
a
day
=
72.85%
of
$
103,
or
$
75.25
4.1.5.6
Unquantified
Health
Effects
Table
4­
15.
Women
with
Children:
Number
and
Percent
in
the
Labor
Force,
2000,
and
Weighted
Average
Participation
Ratea
Number
(
in
millions)
in
Labor
Force
(
1)
Participation
Rate
(
2)
Implied
Total
Number
in
Population
(
in
millions)

(
3)
=
(
1)/(
2)
Implied
Percent
in
Population
(
4)
Weighted
Average
Participation
Rate
[=
sum
(
2)*(
4)
over
rows]

Single
3.1
73.9%
4.19
11.84%

Married
18.2
70.6%
25.78
72.79%

Otherb
4.5
82.7%
5.44
15.36%

Total:
35.42
72.85%

a
Data
in
columns
(
1)
and
(
2)
are
from
U.
S.
Census
Bureau,
Statistical
Abstract
of
the
United
States:
2001,

Section
12:
Labor
Force,
Employment,
and
Earnings,
Table
No.
577.

b
Widowed,
divorced,
or
separated.
4­
75
In
addition
to
the
health
effects
discussed
above,
there
is
emerging
evidence
that
human
exposure
to
ozone
may
be
associated
with
premature
mortality
(
Ito
and
Thurston,

1996;
Samet,
et
al.
1997,
Ito
and
Thurston,
2001),
PM
and
ozone
with
increased
emergency
room
visits
for
non­
asthma
respiratory
causes
(
US
EPA,
1996a;
1996b),
ozone
with
impaired
airway
responsiveness
(
US
EPA,
1996a),
ozone
with
increased
susceptibility
to
respiratory
infection
(
US
EPA,
1996a),
ozone
with
acute
inflammation
and
respiratory
cell
damage
(
US
EPA,
1996a),
ozone
and
PM
with
premature
aging
of
the
lungs
and
chronic
respiratory
damage
(
US
EPA,
1996a;
1996b),
ozone
with
onset
of
asthma
in
exercising
children
(
McConnell
et
al.
2002),
and
PM
with
reduced
heart
rate
variability
and
other
changes
in
cardiac
function.
An
improvement
in
ambient
PM
and
ozone
air
quality
may
reduce
the
number
of
incidences
within
each
effect
category
that
the
U.
S.
population
would
experience.

Although
these
health
effects
are
believed
to
be
PM
or
ozone­
induced,
effect
estimates
are
not
available
for
quantifying
the
benefits
associated
with
reducing
these
effects.
The
inability
to
quantify
these
effects
lends
a
downward
bias
to
the
monetized
benefits
presented
in
this
analysis.

4.1.6
Human
Welfare
Impact
Assessment
PM
and
ozone
have
numerous
documented
effects
on
environmental
quality
that
affect
human
welfare.
These
welfare
effects
include
direct
damages
to
property,
either
through
impacts
on
material
structures
or
by
soiling
of
surfaces,
direct
economic
damages
in
the
form
of
lost
productivity
of
crops
and
trees,
indirect
damages
through
alteration
of
ecosystem
functions,
and
indirect
economic
damages
through
the
loss
in
value
of
recreational
experiences
or
the
existence
value
of
important
resources.
EPA's
Criteria
Documents
for
PM
and
ozone
list
numerous
physical
and
ecological
effects
known
to
be
linked
to
ambient
concentrations
of
these
pollutants
(
US
EPA,
1996a;
1996b).
This
section
describes
individual
effects
and
how
we
quantify
and
monetize
them.
These
effects
include
changes
in
commercial
crop
and
forest
yields,
visibility,
and
nitrogen
deposition
to
estuaries.

4.1.6.1
Visibility
Benefits
Changes
in
the
level
of
ambient
particulate
matter
caused
by
the
reduction
in
emissions
from
the
IAQR
will
change
the
level
of
visibility
in
much
of
the
Eastern
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
Great
Smokey
Mountains
National
Park.
This
section
discusses
the
measurement
of
the
economic
benefits
of
visibility.
26A
change
of
less
than
10
percent
in
the
light
extinction
budget
represents
a
measurable
improvement
in
visibility,
but
may
not
be
perceptible
to
the
eye
in
many
cases.
Some
of
the
average
regional
changes
in
visibility
are
less
than
one
deciview
(
i.
e.
less
than
10
percent
of
the
light
extinction
budget),
and
thus
less
than
perceptible.
However,
this
does
not
mean
that
these
changes
are
not
real
or
significant.
Our
assumption
is
then
that
individuals
can
place
values
on
changes
in
visibility
that
may
not
be
perceptible.
This
is
quite
plausible
if
individuals
are
aware
that
many
regulations
lead
to
small
improvements
in
visibility
which
when
considered
together
amount
to
perceptible
changes
in
visibility.

27The
Clean
Air
Act
designates
156
national
parks
and
wilderness
areas
as
Class
I
areas
for
visibility
protection.

4­
76
It
is
difficult
to
quantitatively
define
a
visibility
endpoint
that
can
be
used
for
valuation.

Increases
in
PM
concentrations
cause
increases
in
light
extinction.
Light
extinction
is
a
measure
of
how
much
the
components
of
the
atmosphere
absorb
light.
More
light
absorption
means
that
the
clarity
of
visual
images
and
visual
range
is
reduced,
ceteris
paribus.
Light
absorption
is
a
variable
that
can
be
accurately
measured.
Sisler
(
1996)
created
a
unitless
measure
of
visibility
based
directly
on
the
degree
of
measured
light
absorption
called
the
deciview.
Deciviews
are
standardized
for
a
reference
distance
in
such
a
way
that
one
deciview
corresponds
to
a
change
of
about
10
percent
in
available
light.
Sisler
characterized
a
change
in
light
extinction
of
one
deciview
as
"
a
small
but
perceptible
scenic
change
under
many
circumstances."
Air
quality
models
were
used
to
predict
the
change
in
visibility,
measured
in
deciviews,
of
the
areas
affected
by
the
preliminary
control
options.
26
EPA
considers
benefits
from
two
categories
of
visibility
changes:
residential
visibility
and
recreational
visibility.
In
both
cases
economic
benefits
are
believed
to
consist
of
both
use
values
and
non­
use
values.
Use
values
include
the
aesthetic
benefits
of
better
visibility,

improved
road
and
air
safety,
and
enhanced
recreation
in
activities
like
hunting
and
birdwatching.
Non­
use
values
are
based
on
people's
beliefs
that
the
environment
ought
to
exist
free
of
human­
induced
haze.
Non­
use
values
may
be
a
more
important
component
of
value
for
recreational
areas,
particularly
national
parks
and
monuments.

Residential
visibility
benefits
are
those
that
occur
from
visibility
changes
in
urban,

suburban,
and
rural
areas,
and
also
in
recreational
areas
not
listed
as
federal
Class
I
areas.
27
For
the
purposes
of
this
analysis,
recreational
visibility
improvements
are
defined
as
those
that
occur
specifically
in
federal
Class
I
areas.
A
key
distinction
between
recreational
and
residential
benefits
is
that
only
those
people
living
in
residential
areas
are
assumed
to
receive
benefits
from
residential
visibility,
while
all
households
in
the
U.
S.
are
assumed
to
derive
some
28For
details
of
the
visibility
estimates
discussed
in
this
chapter,
please
refer
to
the
benefits
technical
support
document
for
the
Nonroad
Diesel
rulemaking
(
Abt
Associates
2003).

29
An
SAB
advisory
letter
indicates
that"
many
members
of
the
Council
believe
that
the
Chestnut
and
Rowe
study
is
the
best
available."
(
EPA­
SAB­
COUNCIL­
ADV­
00­
002,
1999)
However,
the
committee
did
not
formally
approve
use
of
these
estimates
because
of
concerns
about
the
peer­
reviewed
status
of
the
study.
EPA
believes
the
study
has
received
adequate
review
and
has
been
cited
in
numerous
peer­
reviewed
publications
(
Chestnut
and
Dennis,
1997).

4­
77
benefit
from
improvements
in
Class
I
areas.
Values
are
assumed
to
be
higher
if
the
Class
I
area
is
located
close
to
their
home.
28
Only
two
existing
studies
provide
defensible
monetary
estimates
of
the
value
of
visibility
changes.
One
is
a
study
on
residential
visibility
conducted
in
1990
(
McClelland,
et.

al.,
1993)
and
the
other
is
a
1988
survey
on
recreational
visibility
value
(
Chestnut
and
Rowe,

1990a;
1990b).
While
there
are
a
number
of
other
studies
in
the
literature,
they
were
conducted
in
the
early
1980s
and
did
not
use
methods
that
are
considered
defensible
by
current
standards.
Both
the
Chestnut
and
Rowe
and
McClelland
et
al
studies
utilize
the
contingent
valuation
method.
There
has
been
a
great
deal
of
controversy
and
significant
development
of
both
theoretical
and
empirical
knowledge
about
how
to
conduct
CV
surveys
in
the
past
decade.
In
EPA's
judgment,
the
Chestnut
and
Rowe
study
contains
many
of
the
elements
of
a
valid
CV
study
and
is
sufficiently
reliable
to
serve
as
the
basis
for
monetary
estimates
of
the
benefits
of
visibility
changes
in
recreational
areas.
29
This
study
serves
as
an
essential
input
to
our
estimates
of
the
benefits
of
recreational
visibility
improvements
in
the
primary
benefits
estimates.
Consistent
with
SAB
advice,
EPA
has
designated
the
McClelland,

et
al.
study
as
significantly
less
reliable
for
regulatory
benefit­
cost
analysis,
although
it
does
provide
useful
estimates
on
the
order
of
magnitude
of
residential
visibility
benefits
(

EPASAB
COUNCIL­
ADV­
00­
002,
1999).
Residential
visibility
benefits
are
therefore
only
included
as
a
sensitivity
estimate
in
Appendix
9­
B
(
to
be
completed
for
the
Supplemental
Analysis).

The
Chestnut
and
Rowe
study
measured
the
demand
for
visibility
in
Class
I
areas
managed
by
the
National
Park
Service
(
NPS)
in
three
broad
regions
of
the
country:

California,
the
Southwest,
and
the
Southeast.
Respondents
in
five
states
were
asked
about
their
willingness
to
pay
to
protect
national
parks
or
NPS­
managed
wilderness
areas
within
a
particular
region.
The
survey
used
photographs
reflecting
different
visibility
levels
in
the
specified
recreational
areas.
The
visibility
levels
in
these
photographs
were
later
converted
to
deciviews
for
the
current
analysis.
The
survey
data
collected
were
used
to
estimate
a
WTP
4­
78
equation
for
improved
visibility.
In
addition
to
the
visibility
change
variable,
the
estimating
equation
also
included
household
income
as
an
explanatory
variable.

The
Chestnut
and
Rowe
study
did
not
measure
values
for
visibility
improvement
in
Class
I
areas
outside
the
three
regions.
Their
study
covered
86
of
the
156
Class
I
areas
in
the
U.
S.
We
can
infer
the
value
of
visibility
changes
in
the
other
Class
I
areas
by
transferring
values
of
visibility
changes
at
Class
I
areas
in
the
study
regions.
However,
these
values
are
not
as
defensible
and
are
thus
presented
only
as
a
sensitivity
analysis
(
to
be
completed
for
the
Supplemental
Analysis).
A
complete
description
of
the
benefits
transfer
method
used
to
infer
values
for
visibility
changes
in
Class
I
areas
outside
the
study
regions
is
provided
in
the
benefits
TSD
for
the
Nonroad
Diesel
rulemaking
(
Abt
Associates,
2003).

The
estimated
relationship
from
the
Chestnut
and
Rowe
study
is
only
directly
applicable
to
the
populations
represented
by
survey
respondents.
EPA
used
benefits
transfer
methodology
to
extrapolate
these
results
to
the
population
affected
by
the
proposed
IAQR.
A
general
willingness
to
pay
equation
for
improved
visibility
(
measured
in
deciviews)
was
developed
as
a
function
of
the
baseline
level
of
visibility,
the
magnitude
of
the
visibility
improvement,
and
household
income.
The
behavioral
parameters
of
this
equation
were
taken
from
analysis
of
the
Chestnut
and
Rowe
data.
These
parameters
were
used
to
calibrate
WTP
for
the
visibility
changes
resulting
from
the
IAQR.
The
method
for
developing
calibrated
WTP
functions
is
based
on
the
approach
developed
by
Smith,
et
al.
(
2002).
Available
evidence
indicates
that
households
are
willing
to
pay
more
for
a
given
visibility
improvement
as
their
income
increases
(
Chestnut,
1997).
The
benefits
estimates
here
incorporate
Chestnut's
estimate
that
a
1
percent
increase
in
income
is
associated
with
a
0.9
percent
increase
in
WTP
for
a
given
change
in
visibility.

Using
the
methodology
outlined
above,
EPA
estimates
that
the
total
WTP
for
the
visibility
improvements
in
Southeastern
Class
I
areas
brought
about
by
the
IAQR
is
$
880
million
in
2010
and
$
1,400
million
in
2015.
This
value
includes
the
value
to
households
living
in
the
same
state
as
the
Class
I
area
as
well
as
values
for
all
households
in
the
U.
S.
living
outside
the
state
containing
the
Class
I
area,
and
the
value
accounts
for
growth
in
real
income.

We
examine
the
impact
of
expanding
the
visibility
benefits
analysis
to
other
areas
of
the
country
in
a
sensitivity
analysis
to
be
completed
for
the
Supplemental
Analysis.
4­
79
The
benefits
resulting
from
visibility
improvements
in
Southeastern
Class
I
areas
under
the
Proposed
IAQR
are
presented
in
Figure
4­
2.
This
figure
presents
these
benefits
both
in
terms
of
the
total
benefits
modeled
for
each
of
the
Class
I
areas
(
i.
e.,
the
"
Park
Benefits"
map)

and
the
benefits
realized
by
the
populations
in
each
of
the
48
contiguous
states
(
i.
e.,
the
"
State
Benefits"
map).
The
latter
results
reflect
the
willingness
to
pay
of
state
residents
for
visibility
improvements
occuring
in
Class
I
areas
in
the
Southeastern
United
States.

One
major
source
of
uncertainty
for
the
visibility
benefit
estimate
is
the
benefits
transfer
process
used.
Judgments
used
to
choose
the
functional
form
and
key
parameters
of
the
estimating
equation
for
willingness
to
pay
for
the
affected
population
could
have
significant
effects
on
the
size
of
the
estimates.
Assumptions
about
how
individuals
respond
to
changes
in
visibility
that
are
either
very
small,
or
outside
the
range
covered
in
the
Chestnut
and
Rowe
study,
could
also
affect
the
results.

4.1.6.2
Agricultural,
Forestry
and
other
Vegetation
Related
Benefits
The
Ozone
Criteria
Document
notes
that
"
ozone
affects
vegetation
throughout
the
United
States,
impairing
crops,
native
vegetation,
and
ecosystems
more
than
any
other
air
pollutant"
(
US
EPA,
1996).
Changes
in
ground
level
ozone
resulting
from
the
preliminary
control
options
are
expected
to
impact
crop
and
forest
yields
throughout
the
affected
area.

Well­
developed
techniques
exist
to
provide
monetary
estimates
of
these
benefits
to
agricultural
producers
and
to
consumers.
These
techniques
use
models
of
planting
decisions,

yield
response
functions,
and
agricultural
products
supply
and
demand.
The
resulting
welfare
measures
are
based
on
predicted
changes
in
market
prices
and
production
costs.
Models
also
exist
to
measure
benefits
to
silvicultural
producers
and
consumers.
However,
these
models
have
not
been
adapted
for
use
in
analyzing
ozone
related
forest
impacts.
As
such,
our
analysis
(
to
be
completed
for
the
Supplemental
Analysis)
provides
monetized
estimates
of
agricultural
benefits,
and
a
discussion
of
the
impact
of
ozone
changes
on
forest
productivity,
but
does
not
monetize
commercial
forest
related
benefits.

4.1.6.2.1
Agricultural
Benefits.
Laboratory
and
field
experiments
have
shown
reductions
in
yields
for
agronomic
crops
exposed
to
ozone,
including
vegetables
(
e.
g.,
lettuce)

and
field
crops
(
e.
g.,
cotton
and
wheat).
The
most
extensive
field
experiments,
conducted
under
the
National
Crop
Loss
Assessment
Network
(
NCLAN)
examined
15
species
and
numerous
cultivars.
The
4­
80
2015
V
is
ibility
B
e
n
e
f
its
In
M
illio
n
s
o
f
D
o
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0
.05­
0.1
0
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­
0
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0
.25­
0.4
0
.4
­
1
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____
___
____

0­
1
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1
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­
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4
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­
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­
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1
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­
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S
tate
P
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Shenandoah
Jam
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e
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in
v
ille
G
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rg
e
G
reat
Sm
o
ky
M
o
u
n
tain
s
Sw
an
Q
uarter
S
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in
in
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R
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c
k
Jo
y
c
e
K
ilm
e
r­
S
lick
rock
C
o
h
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tta
S
ipsey
W
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Is
la
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O
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fenokee
S
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M
arks
C
hassahow
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Ev
e
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M
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C
h
a
n
g
e
in
D
e
c
iv
iew
S
ta
te
le
v
e
l
dollar
v
a
lues
reflect
the
w
illingne
s
s
to
p
ay
o
f
s
ta
te
re
s
id
e
n
ts
fo
r
v
is
ibility
im
p
ro
v
em
ents
o
ccurrin
g
in
C
lass
I
are
a
s
in
the
southea
s
te
rn
United
S
ta
te
s
.
T
he
re
a
re
16
C
lass
I
a
reas
in
the
southe
a
s
t
w
hich
acco
u
n
t
for
ap
p
ro
xim
ately
17%
o
f
total
p
ark
v
is
its
in
the
United
S
ta
tes.
S
ta
te
dollar
b
enefits
a
re
d
riven
b
y
p
o
p
u
latio
n
and
p
ro
xim
ity
to
p
a
rks.

Pa
rk
d
o
lla
r
b
e
n
e
f
its
a
re
d
riv
e
n
b
y
deg
re
e
o
f
v
is
ibility
im
provem
en
t
and
p
a
rk
visitatio
n
.

S
ta
te
B
en
efits
P
ark
B
en
e
f
its
Figure
4.2.
Visib
ility
Improvements
in
Southeastern
Class
I
Areas
4­
81
NCLAN
results
show
that
"
several
economically
important
crop
species
are
sensitive
to
ozone
levels
typical
of
those
found
in
the
U.
S."
(
US
EPA,
1996).
In
addition,
economic
studies
have
shown
a
relationship
between
observed
ozone
levels
and
crop
yields
(
Garcia,
et
al.,
1986).

Due
to
data
limitations,
we
were
unable
to
assess
ozone­
related
agricultural
benefits
associated
with
the
proposed
IAQR.
However,
we
will
be
assessing
these
benefits
for
the
Supplemental
Analysis
and
for
the
analysis
of
the
final
IAQR.

4.1.6.2.2
Forestry
Benefits.
Ozone
also
has
been
shown
conclusively
to
cause
discernible
injury
to
forest
trees
(
US
EPA,
1996;
Fox
and
Mickler,
1996).
In
our
previous
analysis
of
the
HD
Engine/
Diesel
Fuel
rule,
we
were
able
to
quantify
the
effects
of
changes
in
ozone
concentrations
on
tree
growth
for
a
limited
set
of
species.
Due
to
data
limitations,
we
were
not
able
to
quantify
such
impacts
for
this
analysis.
We
plan
to
assess
both
physical
impacts
on
tree
growth
and
the
economic
value
of
those
physical
impacts
in
our
analysis
of
the
final
rule.
We
will
use
econometric
models
of
forest
product
supply
and
demand
to
estimate
changes
in
prices,
producer
profits
and
consumer
surplus.
These
benefits
will
be
estimated
for
the
final
IAQR.

4.1.6.2.3
Other
Vegetation
Effects.
An
additional
welfare
benefit
expected
to
accrue
as
a
result
of
reductions
in
ambient
ozone
concentrations
in
the
U.
S.
is
the
economic
value
the
public
receives
from
reduced
aesthetic
injury
to
forests.
There
is
sufficient
scientific
information
available
to
reliably
establish
that
ambient
ozone
levels
cause
visible
injury
to
foliage
and
impair
the
growth
of
some
sensitive
plant
species
(
US
EPA,
1996c,
p.
5­
521).

However,
present
analytic
tools
and
resources
preclude
EPA
from
quantifying
the
benefits
of
improved
forest
aesthetics.

Urban
ornamentals
represent
an
additional
vegetation
category
likely
to
experience
some
degree
of
negative
effects
associated
with
exposure
to
ambient
ozone
levels
and
likely
to
impact
large
economic
sectors.
In
the
absence
of
adequate
exposure­
response
functions
and
economic
damage
functions
for
the
potential
range
of
effects
relevant
to
these
types
of
vegetation,
no
direct
quantitative
economic
benefits
analysis
has
been
conducted.
It
is
estimated
that
more
than
$
20
billion
(
1990
dollars)
are
spent
annually
on
landscaping
using
ornamentals
(
Abt
Associates,
1995),
both
by
private
property
owners/
tenants
and
by
governmental
units
responsible
for
public
areas.
This
is
therefore
a
potentially
important
welfare
effects
category.
However,
information
and
valuation
methods
are
not
available
to
allow
for
plausible
estimates
of
the
percentage
of
these
expenditures
that
may
be
related
to
impacts
associated
with
ozone
exposure.
4­
82
The
EGU
standards,
by
reducing
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
share
of
nitrogen
requirements
provided
by
this
deposition
is
small,
and
the
marginal
cost
of
providing
this
nitrogen
from
alternative
sources
is
quite
low.

In
some
areas,
agricultural
lands
suffer
from
nitrogen
over­
saturation
due
to
an
abundance
of
on­
farm
nitrogen
production,
primarily
from
animal
manure.
In
these
areas,
reductions
in
atmospheric
deposition
of
nitrogen
represent
additional
agricultural
benefits.

Information
on
the
effects
of
changes
in
passive
nitrogen
deposition
on
forests
and
other
terrestrial
ecosystems
is
very
limited.
The
multiplicity
of
factors
affecting
forests,

including
other
potential
stressors
such
as
ozone,
and
limiting
factors
such
as
moisture
and
other
nutrients,
confound
assessments
of
marginal
changes
in
any
one
stressor
or
nutrient
in
forest
ecosystems.
However,
reductions
in
deposition
of
nitrogen
could
have
negative
effects
on
forest
and
vegetation
growth
in
ecosystems
where
nitrogen
is
a
limiting
factor
(
US
EPA,

1993).

On
the
other
hand,
there
is
evidence
that
forest
ecosystems
in
some
areas
of
the
United
States
are
nitrogen
saturated
(
US
EPA,
1993).
Once
saturation
is
reached,
adverse
effects
of
additional
nitrogen
begin
to
occur
such
as
soil
acidification
which
can
lead
to
leaching
of
nutrients
needed
for
plant
growth
and
mobilization
of
harmful
elements
such
as
aluminum.

Increased
soil
acidification
is
also
linked
to
higher
amounts
of
acidic
runoff
to
streams
and
lakes
and
leaching
of
harmful
elements
into
aquatic
ecosystems.

4.1.6.3
Benefits
from
Reductions
in
Materials
Damage
The
preliminary
control
options
that
we
modeled
are
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.
4­
83
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
base
estimate.
We
calculate
household
soiling
damages
in
a
sensitivity
estimate
that
will
be
completed
as
part
of
the
Supplemental
Analysis.

EPA
is
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
PMrelated
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
benefits
could
be
an
order
of
magnitude
larger
than
household
soiling
benefits.

4.1.6.4
Benefits
from
Reduced
Ecosystem
Damage
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
final
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
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
functions
and
on
estimates
of
the
value
of
environmental
responses.
Because
neither
appropriate
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.

If
better
models
of
ecological
effects
can
be
defined,
EPA
believes
that
progress
can
be
made
in
estimating
WTP
measures
for
ecosystem
functions.
These
estimates
would
be
superior
to
avoided
cost
estimates
in
placing
economic
values
on
the
welfare
changes
associated
with
air
pollution
damage
to
ecosystem
health.
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.
4­
84
4.2
Benefits
Analysis
 
Results
Applying
the
impact
and
valuation
functions
described
in
Section
C
to
the
estimated
changes
in
ozone
and
PM
described
in
Section
B
yields
estimates
of
the
changes
in
physical
damages
(
i.
e.
premature
mortalities,
cases,
admissions,
change
in
light
extinction,
etc.)
and
the
associated
monetary
values
for
those
changes.
Estimates
of
physical
health
impacts
are
presented
in
Table
4­
16.
Monetized
values
for
both
health
and
welfare
endpoints
are
presented
in
Table
4­
17,
along
with
total
aggregate
monetized
benefits.
All
of
the
monetary
benefits
are
in
constant
year
1999
dollars.

Not
all
known
PM­
and
ozone­
related
health
and
welfare
effects
could
be
quantified
or
monetized.
The
monetized
value
of
these
unquantified
effects
is
represented
by
adding
an
unknown
"
B"
to
the
aggregate
total.
The
estimate
of
total
monetized
health
benefits
is
thus
equal
to
the
subset
of
monetized
PM­
and
ozone­
related
health
and
welfare
benefits
plus
B,

the
sum
of
the
nonmonetized
health
and
welfare
benefits.

Total
monetized
benefits
are
dominated
by
benefits
of
mortality
risk
reductions.
The
primary
analysis
estimate
projects
that
the
proposed
rule
will
result
in
9,600
avoided
premature
deaths
in
2010
and
13,000
avoided
premature
deaths
in
2015.
The
increase
in
benefits
from
2010
to
2015
reflects
additional
emission
reductions
from
the
standards,
as
well
as
increases
in
total
population
and
the
average
age
(
and
thus
baseline
mortality
risk)
of
the
population.
Note
that
unaccounted
for
changes
in
baseline
mortality
rates
over
time
may
lead
to
reductions
in
the
estimated
number
of
avoided
premature
mortalities.
4­
85
Table
4­
16.
Reductions
in
Incidence
of
Adverse
Health
Effects
Associated
with
Reductions
in
Particulate
Matter
and
Ozone
Associated
with
the
Proposed
IAQRa
Endpoint
2010
2015
PM­
related
Endpoints
Premature
mortalityb
Long­
term
exposure
(
adults,
30
and
over)

Long­
term
exposure
(
infant,
<
1
yr)
9,600
13,000
22
29
Chronic
bronchitis
(
adults,
26
and
over)
5,200
6,900
Non­
fatal
myocardial
infarctions
(
adults,
18
and
older)
13,000
18,000
Hospital
admissions
 
Respiratory
(
all
ages)
c
4,200
5,800
Hospital
admissions
 
Cardiovascular
(
adults,
>
18)
d
3,700
5,000
Emergency
Room
Visits
for
Asthma
(
18
and
younger)
7,000
9,200
Acute
bronchitis
(
children,
8­
12)
12,000
16,000
Lower
respiratory
symptoms
(
children,
7­
14)
140,000
190,000
Upper
respiratory
symptoms
(
asthmatic
children,
9­
18)
490,000
620,000
Asthma
Exacerbations
(
asthmatic
children,
6­
18)
190,000
240,000
Work
loss
days
(
adults,
18­
65)
1,000,000
1,300,000
Minor
restricted
activity
days
(
adults,
age
18­
65)
6,100,000
7,900,000
Ozone­
related
Endpoints
Hospital
Admissions
 
Respiratory
Causes
(
adults,
65
and
older)
e
630
1,500
Hospital
Admissions
 
Respiratory
Causes
(
children,
under
2
years)
380
840
Emergency
Room
Visits
for
Asthma
(
all
ages)
120
250
Minor
restricted
activity
days
(
adults,
age
18­
65)
280,000
610,000
School
absence
days
(
children,
age
6­
18)
180,000
390,000
a
Incidences
are
rounded
to
two
significant
digits.

b
Premature
mortality
associated
with
ozone
is
not
separately
included
in
this
analysis.
It
is
assumed
that
the
Impact
function
for
premature
mortality
captures
both
PM
mortality
benefits
and
any
mortality
benefits
associated
with
other
air
pollutants.

c
Respiratory
hospital
admissions
for
PM
includes
admissions
for
COPD,
pneumonia,
and
asthma.

d
Cardiovascular
hospital
admissions
for
PM
includes
total
cardiovascular
and
subcategories
for
ischemic
heart
disease,
dysrhythmias,
and
heart
failure.

e
Respiratory
hospital
admissions
for
ozone
includes
admissions
for
all
respiratory
causes
and
subcategories
for
COPD
and
pneumonia.
4­
86
Table
4­
17.
Results
of
Human
Health
and
Welfare
Benefits
Valuation
for
the
Proposed
IAQR
(
millions
of
1999
dollars)
a,
b
Endpoint
Pollutant
2010
2015
Premature
mortalityc
Long­
term
exposure,
(
adults,
>
30yrs)

3%
discount
rate
PM
$
53,000
$
77,000
7%
discount
rate
$
50,000
$
72,000
Long­
term
exposure
(
child
<
1yr)
$
130
$
180
Chronic
bronchitis
(
adults,
26
and
over)
PM
$
1,900
$
2,700
Non­
fatal
myocardial
infarctions
3%
discount
rate
PM
$
1,100
$
1,500
7%
discount
rate
$
1,000
$
1,400
Hospital
Admissions
from
Respiratory
Causes
O3
and
PM
$
85
$
130
Hospital
Admissions
from
Cardiovascular
Causes
PM
$
78
$
110
Emergency
Room
Visits
for
Asthma
O3
and
PM
$
2.0
$
2.6
Acute
bronchitis
(
children,
8­
12)
PM
$
4.3
$
5.7
Lower
respiratory
symptoms
(
children,
7­
14)
PM
$
2.3
$
3.0
Upper
respiratory
symptoms
(
asthmatic
children,
9­
11)
PM
$
13
$
17
Asthma
exacerbations
PM
$
8.0
$
10
Work
loss
days
(
adults,
18­
65)
PM
$
140
$
170
Minor
restricted
activity
days
(
adults,
age
18­
65)
O3
and
PM
$
320
$
440
School
absence
days
(
children,
age
6­
11)
O3
$
13
$
28
Worker
productivity
(
outdoor
workers,
age
18­
65)
O3
$
8.1
$
17
Recreational
visibility
(
Southeastern
Class
I
areas)
PM
$
880
$
1,400
Monetized
Totald
Base
estimate
3%
discount
rate
O3
and
PM
$
58,000+
B
$
84,000+
B
7%
discount
rate
$
54,000+
B
$
79,000+
B
a
Monetary
benefits
are
rounded
to
two
significant
digits.

b
Monetary
benefits
are
adjusted
to
account
for
growth
in
real
GDP
per
capita
between
1990
and
the
analysis
year
(
2010
or
2015).

c
Valuation
assumes
the
5
year
distributed
lag
structure
described
earlier.
Results
reflect
the
use
of
two
different
discount
rates;
a
3
percent
rate
which
is
recommended
by
EPA's
Guidelines
for
Preparing
Economic
Analyses
(
US
EPA,
2000c),
and
7
percent
which
is
recommended
by
OMB
Circular
A­
94
(
OMB,
1992).

d
B
represents
the
monetary
value
of
the
nonmonetized
health
and
welfare
benefits.
A
detailed
listing
of
unquantified
PM,
ozone,
and
mercury
related
health
effects
is
provided
in
Table
XI­
B.
1.
4­
87
Our
estimate
of
total
monetized
benefits
in
2010
for
the
proposed
rule
is
$
58
billion
using
a
3
percent
discount
rate
and
$
54
billion
using
a
7
percent
discount
rate.
In
2015,
the
monetized
benefits
are
estimated
at
$
84
billion
using
a
3
percent
discount
rate
and
$
79
billion
using
a
7
percent
discount
rate.
Health
benefits
account
for
98
percent
of
total
benefits,

mainly
because
we
are
unable
to
quantify
most
of
the
non­
health
benefits.
The
monetized
benefit
associated
with
reductions
in
the
risk
of
premature
mortality,
which
accounts
for
$
53
billion
in
2010
and
$
77
billion
in
2015,
is
over
90
percent
of
total
monetized
health
benefits.

The
next
largest
benefit
is
for
reductions
in
chronic
illness
(
CB
and
non­
fatal
heart
attacks),

although
this
value
is
more
than
an
order
of
magnitude
lower
than
for
premature
mortality.

Hospital
admissions
for
respiratory
and
cardiovascular
causes,
visibility,
minor
restricted
activity
days,
work
loss
days,
school
absence
days,
and
worker
productivity
account
for
the
majority
of
the
remaining
benefits.
The
remaining
categories
account
for
less
than
$
10
million
each,
however,
they
represent
a
large
number
of
avoided
incidences
affecting
many
individuals.

A
comparison
of
the
incidence
table
to
the
monetary
benefits
table
reveals
that
there
is
not
always
a
close
correspondence
between
the
number
of
incidences
avoided
for
a
given
endpoint
and
the
monetary
value
associated
with
that
endpoint.
For
example,
there
are
100
times
more
work
loss
days
than
premature
mortalities,
yet
work
loss
days
account
for
only
a
very
small
fraction
of
total
monetized
benefits.
This
reflects
the
fact
that
many
of
the
less
severe
health
effects,
while
more
common,
are
valued
at
a
lower
level
than
the
more
severe
health
effects.
Also,
some
effects,
such
as
hospital
admissions,
are
valued
using
a
proxy
measure
of
WTP.
As
such
the
true
value
of
these
effects
may
be
higher
than
that
reported
in
Table
4­
16.

Ozone
benefits
are
in
aggregate
positive
for
the
nation.
However,
due
to
ozone
increases
occurring
during
certain
hours
of
the
day
in
some
urban
areas,
there
is
a
dampening
of
overall
ozone
benefits
in
both
2010
and
2015,
although
the
net
incidence
and
benefits
estimates
for
all
health
effects
categories
are
net
positive.
Overall,
ozone
benefits
are
low
relative
to
PM
benefits
for
similar
endpoint
categories
because
of
the
increases
in
ozone
concentrations
during
some
hours
of
some
days
in
certain
urban
areas.

4.3
Discussion
This
analysis
has
estimated
the
health
and
welfare
benefits
of
reductions
in
ambient
concentrations
of
particulate
matter
and
ozone
resulting
from
reduced
emissions
of
NOx
and
SO
2
from
affected
EGUs.
The
result
suggests
there
will
be
significant
health
and
welfare
4­
88
benefits
arising
from
the
regulation
of
emissions
from
EGUs
in
the
U.
S.
Our
estimate
that
13,000
premature
mortalities
would
be
avoided
in
2015,
when
emission
reductions
from
the
regulation
are
fully
realized,
provides
additional
evidence
of
the
important
role
that
pollution
from
the
EGU
sector
plays
in
the
public
health
impacts
of
air
pollution.

To
examine
the
importance
of
specific
assumptions
and
analytical
choices
we
made
for
this
analysis,
we
will
be
providing
a
number
of
sensitivity
analyses
in
an
appendix
to
be
completed
for
the
upcoming
Supplemental
Analysis
of
the
proposed
rule.
In
addition,
there
are
other
uncertainties
that
we
could
not
quantify,
such
as
the
importance
of
unquantified
effects
and
uncertainties
in
the
modeling
of
ambient
air
quality.
Inherent
in
any
analysis
of
future
regulatory
programs
are
uncertainties
in
projecting
atmospheric
conditions,
and
sourcelevel
emissions,
as
well
as
population,
health
baselines,
incomes,
technology,
and
other
factors.
The
assumptions
used
to
capture
these
elements
are
reasonable
based
on
the
available
evidence.
However,
data
limitations
prevent
an
overall
quantitative
estimate
of
the
uncertainty
associated
with
estimates
of
total
economic
benefits.
If
one
is
mindful
of
these
limitations,
the
magnitude
of
the
benefit
estimates
presented
here
can
be
useful
information
in
expanding
the
understanding
of
the
public
health
impacts
of
reducing
air
pollution
from
EGUs.

The
U.
S.
EPA
will
continue
to
evaluate
new
methods
and
models
and
select
those
most
appropriate
for
the
estimation
the
health
benefits
of
reductions
in
air
pollution.
It
is
important
to
continue
improving
benefits
transfer
methods
in
terms
of
transferring
economic
values
and
transferring
estimated
Impact
functions.
The
development
of
both
better
models
of
current
health
outcomes
and
new
models
for
additional
health
effects
such
as
asthma
and
high
blood
pressure
will
be
essential
to
future
improvements
in
the
accuracy
and
reliability
of
benefits
analyses
(
Guo
et
al.,
1999;
Ibald­
Mulli
et
al.,
2001).
Enhanced
collaboration
between
air
quality
modelers,
epidemiologists,
and
economists
should
result
in
a
more
tightly
integrated
analytical
framework
for
measuring
health
benefits
of
air
pollution
policies.
The
Agency
welcomes
comments
on
how
we
can
improve
the
quantification
and
monetization
of
health
and
welfare
effects
and
on
methods
for
characterizing
uncertainty
in
our
estimates.
5­
1
SECTION
5
QUALITATIVE
ASSESSMENT
OF
NONMONETIZED
BENEFITS
5.1
Introduction
This
proposal
will
result
in
benefits
in
addition
to
the
enumerated
human
health
and
welfare
benefits
resulting
from
reductions
in
ambient
levels
of
PM
and
ozone.
This
rule
will
also
result
in
benefits
that
we
were
unable
to
monetize.
This
chapter
discusses
welfare
benefits
associated
with
reduced
acid
deposition,
reduced
eutrophication
in
water
bodies,
and
the
reduced
health
and
welfare
effects
due
to
the
deposition
of
mercury.
Welfare
benefits
including
visibility
benefits,
agricultural,
forestry
and
other
benefits
due
to
reductions
in
ozone
levels,
and
benefits
from
reductions
in
materials
damage
are
discussed
in
chapter
4
of
this
report.
In
contrast
to
the
benefits
discussed,
it
is
also
possible
that
this
proposal
will
lessen
the
benefits
of
passive
fertilization
for
forest
and
terrestrial
ecosystems
where
nutrients
are
a
limiting
factor
and
for
some
croplands.

5.2
Atmospheric
Deposition
of
Sulfur
and
Nitrogen
 
Impacts
on
Aquatic,
Forest,
and
Coastal
Ecosystems
Atmospheric
deposition
of
sulfur
and
nitrogen,
more
commonly
known
as
acid
rain,

occurs
when
emissions
of
SO
2
and
NO
x
react
in
the
atmosphere
(
with
water,
oxygen,
and
oxidants)
to
form
various
acidic
compounds.
These
acidic
compounds
fall
to
earth
in
either
a
wet
form
(
rain,
snow,
and
fog)
or
a
dry
form
(
gases
and
particles).
Prevailing
winds
transport
the
acidic
compounds
hundreds
of
miles,
often
across
state
and
national
borders.
Acidic
compounds
(
including
small
particles
such
as
sulfates
and
nitrates)
cause
many
negative
environmental
effects.
These
pollutants

acidify
lakes
and
streams,


harm
sensitive
forests,
and

harm
sensitive
coastal
ecosystems.

The
effect
of
atmospheric
deposition
of
acids
on
freshwater
and
forest
ecosystems
depends
largely
on
the
ecosystem's
ability
to
neutralize
the
acid
(
Driscoll
et
al.,
2001).
This
is
referred
5­
2
to
as
an
ecosystem's
acid
neutralizing
capacity
(
ANC).
Acid
neutralization
occurs
when
positively
charged
ions
such
as
calcium,
potassium,
sodium,
and
magnesium,
collectively
known
as
base
cations,
are
released.
As
water
moves
through
a
watershed,
two
important
chemical
processes
act
to
neutralize
acids.
The
first
involves
cation
exchange
in
soils,
a
process
by
which
hydrogen
ions
from
the
acid
deposition
displace
other
cations
from
the
surface
of
soil
particles,
releasing
these
cations
to
soil
and
surface
water.
The
second
process
is
mineral
weathering,
where
base
cations
bound
in
the
mineral
structure
of
rocks
are
released
as
the
minerals
gradually
break
down
over
long
time
periods.
As
the
base
cations
are
released
by
weathering,
they
neutralize
acidity
and
increase
the
pH
level
in
soil
water
and
surface
waters.
Acid
deposition,
because
it
consists
of
acid
anions
(
e.
g.,
sulfate,
nitrate),
leaches
some
of
the
accumulated
base
cation
reserves
from
the
soils
into
drainage
waters.
The
leaching
rate
of
these
base
cations
may
accelerate
to
the
point
where
it
significantly
exceeds
the
resupply
via
weathering
(
Driscoll
et
al.,
2001).

Soils,
forests,
surface
waters
and
aquatic
biota
(
fish,
algae,
and
the
rest),
and
coastal
ecosystems
share
water,
nutrients,
and
other
essential
ecosystem
components
and
are
inextricably
linked
by
the
chemical
processes
described
above.
For
example,
the
same
base
cations
that
help
to
neutralize
acidity
in
lakes
and
streams
are
also
essential
nutrients
in
forest
soils,
meaning
that
cation
depletion
both
increases
freshwater
acidification
and
decreases
forest
productivity.
Similarly,
the
same
nitrogen
atom
that
contributes
to
stream
acidification
can
ultimately
contribute
to
coastal
eutrophication
as
it
travels
downstream
to
an
estuarine
environment.
Therefore,
to
understand
the
full
effects
of
atmospheric
deposition,
it
is
necessary
to
recognize
the
interactions
between
all
of
these
systems.

5.2.1
Freshwater
Acidification
Acid
deposition
causes
acidification
of
surface
waters.
In
the
1980s,
acid
rain
was
found
to
be
the
dominant
cause
of
acidification
in
75
percent
of
acidic
lakes
and
50
percent
of
acidic
streams.
Areas
especially
sensitive
to
acidification
include
portions
of
the
Northeast
(
particularly
the
Adirondack
and
Catskill
Mountains,
portions
of
New
England,
and
streams
in
the
mid­
Appalachian
highlands)
and
Southeastern
streams.
Some
high
elevation
Western
lakes,
particularly
in
the
Rocky
Mountains,
have
become
acidic,
especially
during
snowmelt.

However,
although
many
Western
lakes
and
streams
are
sensitive
to
acidification,
they
are
not
subject
to
continuously
high
levels
of
acid
deposition
and
so
have
not
become
chronically
acidified
(
NAPAP,
1990).
5­
3
ANC,
a
key
indicator
of
the
ability
of
the
water
and
watershed
soil
to
neutralize
the
acid
deposition
it
receives,
depends
largely
on
the
watershed's
physical
characteristics:

geology,
soils,
and
size.
Waters
that
are
sensitive
to
acidification
tend
to
be
located
in
small
watersheds
that
have
few
alkaline
minerals
and
shallow
soils.
Conversely,
watersheds
that
contain
alkaline
minerals,
such
as
limestone,
tend
to
have
waters
with
a
high
ANC.

As
acidity
increases,
aluminum
leached
from
the
soil
flows
into
lakes
and
streams
and
can
be
toxic
to
aquatic
species.
The
lower
pH
levels
and
higher
aluminum
levels
that
result
from
acidification
make
it
difficult
for
some
fish
and
other
aquatic
species
to
survive,
grow,

and
reproduce.
In
some
waters,
the
number
of
species
of
fish
able
to
survive
has
been
directly
correlated
to
water
acidity.
Acidification
can
also
decrease
fish
population
density
and
individual
fish
size
(
U.
S.
Department
of
the
Interior
2003).

Recent
watershed
mass
balance
studies
in
the
Northeast
reveal
that
loss
of
sulfate
from
the
watershed
exceeds
atmospheric
sulfur
deposition
(
Driscoll
et
al.,
2001).
This
suggests
that
these
soils
have
become
saturated
with
sulfur,
meaning
that
the
supply
of
sulfur
from
deposition
exceeds
the
sulfur
demands
of
the
ecosystem.
As
a
result,
sulfur
is
gradually
being
released
or
leached
from
the
watershed
into
the
surface
waters
as
sulfate.
Scientists
now
expect
that
the
release
of
sulfate
that
previously
accumulated
in
watersheds
will
delay
the
recovery
of
surface
waters
in
the
Northeast
that
is
anticipated
in
response
to
the
recent
SO
2
emission
controls
(
Driscoll
et
al.,
2001).

A
recent
study
at
a
stream
in
the
Catskill
Mountains
found
that
stream
nitrate
concentrations
were
positively
correlated
to
mean
annual
air
temperature
but
not
to
annual
nitrogen
deposition
(
Murdoch
et
al.,
1998).
This
research
suggests
that,
in
nitrogen­
saturated
soils,
microbial
processes
(
nitrogen
mineralization
and
nitrification),
which
are
sensitive
to
changes
in
temperature
and
moisture,
are
the
primary
factors
controlling
nitrate
leaching,

rather
than
atmospheric
deposition
or
vegetation
uptake
of
nitrogen.
Therefore,
declines
in
nitrogen
deposition
in
nitrogen­
saturated
soils
may
not
immediately
lead
to
improvements
in
stream
water
chemistry
(
Murdoch
et
al.,
1998).

A
major
study
of
the
ecological
response
to
acidification
is
taking
place
in
the
Bear
Brook
Watershed
in
Maine.
Established
in
1986
as
part
of
the
EPA's
Watershed
Manipulation
Project,
the
project
has
found
that
experimental
additions
of
sulfur
and
nitrogen
to
the
watershed
increased
the
concentrations
of
both
sulfate
and
nitrate
in
the
West
Bear
Brook
stream.
Stream
water
concentrations
of
several
other
ions,
including
base
cations,

aluminum,
and
ANC,
changed
substantially
as
well
(
Norton
et
al.,
1999).
During
the
first
year
5­
4
of
treatment,
94
percent
of
the
nitrogen
added
experimentally
to
the
Bear
Brook
watershed
was
retained,
while
the
remainder
leached
into
streams
as
nitrate.
Nitrogen
retention
decreased
to
about
82
percent
in
subsequent
years
(
Kahl
et
al.,
1993,
1999).
Although
the
forest
ecosystem
continued
to
accumulate
nitrogen,
nitrate
leaching
into
the
stream
continued
at
elevated
levels
throughout
the
length
of
the
experiment.
This
nitrate
contributed
to
both
episodic
and
chronic
acidification
of
the
stream.
This
and
other
similar
studies
have
allowed
scientists
to
quantify
acidification
and
recovery
relationships
in
eastern
watersheds
in
much
more
detail
than
was
possible
in
1990.

The
Appalachian
Mountain
region
receives
some
of
the
highest
rates
of
acid
deposition
in
the
United
States
(
Herlihy
et
al.,
1993).
The
acid­
base
status
of
stream
waters
in
forested
upland
watersheds
in
the
Appalachian
Mountains
was
extensively
investigated
in
the
early
1990s
(
e.
g.,
Church
et
al.
[
1992],
Herlihy
et
al.
[
1993],
Webb
et
al.
[
1994],
van
Sickle
and
Church
[
1995]).
A
more
recent
assessment
of
the
southern
Appalachian
region
from
West
Virginia
to
Alabama
identified
watersheds
that
are
sensitive
to
acid
deposition
using
geologic
bedrock
and
the
associated
buffering
capacity
of
soils
to
neutralize
acid.
The
assessment
found
that
approximately
59
percent
of
all
trout
stream
length
in
the
region
is
in
areas
that
are
highly
vulnerable
to
acidification,
and
that
27
percent
is
in
areas
that
are
moderately
vulnerable
(
SAMAB,
1996).
Another
study
estimated
that
18
percent
of
potential
brook
trout
streams
in
the
mid­
Appalachian
Mountains
are
too
acidic
for
brook
trout
survival
(
Herlihy
et
al.,
1996).
Perhaps
the
most
important
study
of
acid­
base
chemistry
of
streams
in
the
Appalachian
region
in
recent
years
has
been
the
Virginia
Trout
Stream
Sensitivity
Study
(
Webb
et
al.,
1994).
Trend
analyses
of
these
streams
indicate
that
few
long­
term
sampling
sites
are
recovering
from
acidification,
most
are
continuing
to
acidify,
and
the
continuing
acidification
is
at
levels
that
are
biologically
significant
for
brook
trout
populations
(
Webb
et
al.,
2000).

5.2.1.1
Water/
Watershed
Modeling
Researchers
have
used
models
to
help
them
understand
and
predict
atmospheric,

environmental,
and
human
health
responses
to
acid
deposition
for
well
over
20
years.
Since
1990,
watershed
modeling
capabilities
have
also
improved
as
researchers
are
continuing
to
refine
and
expand
models
that
project
acidification
of
waterbodies.
Unlike
the
response
of
air
quality
and
deposition
to
changes
in
emissions,
lakes
and
streams
take
years
to
decades
to
fully
reflect
reductions
in
acid
deposition.
In
some
cases,
soil
chemistry
has
been
significantly
altered
and
ions
must
either
build
up
or
be
leached
out
before
the
chemistry
can
return
to
its
pre­
acidification
status.
Therefore,
lake
and
stream
conditions
are
presented
for
2030.
5­
5
5.2.1.2
Description
of
the
MAGIC
Model
and
Methods
A
number
of
mathematical
models
of
soil
and
surface
water
acidification
in
response
to
atmospheric
deposition
were
developed
in
the
early
1980s
(
e.
g.,
Christopherson
and
Wright
[
1981];
Christopherson
et
al.
[
1982];
Schnoor
et
al.
[
1984];
Booty
and
Kramer
[
1984];

Goldstein
et
al.
[
1984];
Cosby
et
al.
[
1985a,
b,
c]).
These
models
were
based
on
process­
level
information
about
the
acidification
process
and
were
built
for
a
variety
of
purposes
ranging
from
estimating
transient
water
quality
responses
for
individual
storm
events
to
estimating
chronic
acidification
of
soils
and
base
flow
surface
water.
One
of
these
models
(
MAGIC
 
the
Model
of
Acidification
of
Groundwater
In
Catchments;
Cosby
et
al.
[
1985a,
b,
c])
has
been
in
use
now
for
more
than
15
years.
MAGIC
has
been
applied
extensively
in
North
America
and
Europe
to
both
individual
sites
and
regional
networks
of
sites
and
has
also
been
used
in
Asia,

Africa,
and
South
America.
The
utility
of
MAGIC
for
simulating
a
variety
of
water
and
soil
acidification
responses
at
the
laboratory,
plot,
hillslope,
and
catchment
scales
has
been
tested
using
long­
term
monitoring
data
and
experimental
manipulation
data.
MAGIC
has
been
widely
used
in
policy
and
assessment
activities
in
the
United
States
and
in
several
countries
in
Europe.

5.2.1.3
Model
Structure
MAGIC
is
a
lumped­
parameter
model
of
intermediate
complexity,
developed
to
predict
the
long­
term
effects
of
acidic
deposition
on
surface
water
chemistry.
The
model
simulates
soil
solution
chemistry
and
surface
water
chemistry
to
predict
the
monthly
and
annual
average
concentrations
of
the
major
ions
in
these
waters.
MAGIC
consists
of
the
following:
1)
a
section
in
which
the
concentrations
of
major
ions
are
assumed
to
be
governed
by
simultaneous
reactions
involving
sulfate
adsorption,
cation
exchange,

dissolutionprecipitation
speciation
of
aluminum,
and
dissolution­
speciation
of
inorganic
carbon;
and
2)
a
mass
balance
section
in
which
the
flux
of
major
ions
to
and
from
the
soil
is
assumed
to
be
controlled
by
atmospheric
inputs,
chemical
weathering,
net
uptake,
and
loss
in
biomass
and
losses
to
runoff.
At
the
heart
of
MAGIC
is
the
size
of
the
pool
of
exchangeable
base
cations
in
the
soil.
As
the
fluxes
to
and
from
this
pool
change
over
time
owing
to
changes
in
atmospheric
deposition,
the
chemical
equilibria
between
soil
and
soil
solution
shift
to
give
changes
in
surface
water
chemistry.
The
degree
and
rate
of
change
of
surface
water
acidity
thus
depend
both
on
flux
factors
and
the
inherent
characteristics
of
the
affected
soils.

Cation
exchange
is
modeled
using
equilibrium
(
Gaines­
Thomas)
equations
with
selectivity
coefficients
for
each
base
cation
and
aluminum.
Sulfate
adsorption
is
represented
5­
6
by
a
Langmuir
isotherm.
Aluminum
dissolution
and
precipitation
are
assumed
to
be
controlled
by
equilibrium
with
a
solid
phase
of
aluminum
trihydroxide.
Aluminum
speciation
is
calculated
by
considering
hydrolysis
reactions
as
well
as
complexation
with
sulfate,
fluoride,

and
dissolved
organic
compounds.
Effects
of
carbon
dioxide
on
pH
and
on
the
speciation
of
inorganic
carbon
are
computed
from
equilibrium
equations.
Organic
acids
are
represented
in
the
model
as
tri­
protic
analogues.
Weathering
rates
are
assumed
to
be
constant.
Two
alternate
mechanisms
are
offered
for
simulation
of
nitrate
and
ammonium
in
soils:
either
1)
first
order
equations
representing
net
uptake
and
retention
or
2)
a
set
of
equations
and
compartments
describing
process­
based
nitrogen
dynamics
in
soils
controlled
by
soil
nitrogen
pools.
Input­
output
mass
balance
equations
are
provided
for
base
cations
and
strong
acid
anions,
and
charge
balance
is
required
for
all
ions
in
each
compartment.
Given
a
description
of
the
historical,
current,
and
expected
future
deposition
at
a
site,
the
model
equations
are
solved
numerically
to
give
long­
term
reconstructions
of
surface
water
chemistry
(
for
complete
details
of
the
model
see
Cosby
et
al.
[
1985
a,
b,
c],
[
2001]).

MAGIC
has
been
used
to
reconstruct
the
history
of
acidification,
to
examine
current
patterns
of
recovery,
and
to
simulate
the
future
trends
in
stream
water
acidity
in
both
individual
catchment
and
regional
applications
at
a
large
number
of
sites
across
North
America
and
Europe
(
e.
g.,
Beier
et
al.
[
1995];
Cosby
et
al.
[
1985b,
1990,
1995,
1996,
1998];

Ferrier,
et
al.
[
2001];
Hornberger
et
al.
[
1989];
Jenkins
et
al.
[
1990];
Moldan
et
al.
[
1998];

Norton
et
al.
[
1992];
Whitehead
et
al.
[
1988,
1997];
Wright
et
al.
[
1990,
1994,
1998]).

5.2.1.4
Model
Implementation
Atmospheric
deposition
and
net
uptake­
release
fluxes
for
the
base
cations
and
strong
acid
anions
are
required
as
inputs
to
the
model.
These
inputs
are
generally
assumed
to
be
uniform
over
the
catchment.
Atmospheric
fluxes
are
calculated
from
concentrations
of
the
ions
in
precipitation
and
the
rainfall
volume
into
the
catchment.
The
atmospheric
fluxes
of
the
ions
must
be
corrected
for
dry
deposition
of
gas,
particulates,
and
aerosols
and
for
inputs
in
cloud/
fog
water.
The
volume
discharge
for
the
catchment
must
also
be
provided
to
the
model.
In
general,
the
model
is
implemented
using
average
hydrologic
conditions
and
meteorological
conditions
in
annual
or
seasonal
simulations
(
i.
e.,
mean
annual
or
mean
monthly
deposition);
precipitation
and
lake
discharge
are
used
to
drive
the
model.
Values
for
soil
and
surface
water
temperature,
partial
pressure
of
carbon
dioxide,
and
organic
acid
concentrations
must
also
be
provided
at
the
appropriate
temporal
resolution.
5­
7
As
implemented
in
this
project,
the
model
is
a
two­
compartment
representation
of
a
catchment.
Atmospheric
deposition
enters
the
soil
compartment,
and
the
equilibrium
equations
are
used
to
calculate
soil
water
chemistry.
The
water
is
then
routed
to
the
stream
compartment,
and
the
appropriate
equilibrium
equations
are
reapplied
to
calculate
runoff
chemistry.

Once
initial
conditions
(
initial
values
of
variables
in
the
equilibrium
equations)
have
been
established,
the
equilibrium
equations
are
solved
for
soil
water
and
surface
water
concentrations
of
the
remaining
variables.
These
concentrations
are
used
to
calculate
the
lake
discharge
output
fluxes
of
the
model
for
the
first
time
step.
The
mass
balance
equations
are
(
numerically)
integrated
over
the
time
step,
providing
new
values
for
the
total
amounts
of
base
cations
and
strong
acid
anions
in
the
system.
These
in
turn
are
used
to
calculate
new
values
of
the
remaining
variables,
new
lake
discharge
fluxes,
and
so
forth.
The
output
from
MAGIC
is
thus
a
time
trace
for
all
major
chemical
constituents
for
the
period
of
time
chosen
for
the
integration.

5.2.1.5
Calibration
Procedure
The
aggregated
nature
of
the
model
requires
that
it
be
calibrated
to
observed
data
from
a
system
before
it
can
be
used
to
examine
potential
system
response.
Calibration
is
achieved
by
setting
the
values
of
certain
parameters
within
the
model
that
can
be
directly
measured
or
observed
in
the
system
of
interest
(
called
"
fixed"
parameters).
The
model
is
then
run
(
using
observed
atmospheric
and
hydrologic
inputs)
and
the
simulated
values
of
surface
water
and
soil
chemical
variables
(
called
"
criterion"
variables)
are
compared
to
observed
values
of
these
variables.
If
the
observed
and
simulated
values
differ,
the
values
of
another
set
of
parameters
in
the
model
(
called
"
optimized"
parameters)
are
adjusted
to
improve
the
fit.

After
a
number
of
iterations,
the
simulated­
minus­
observed
values
of
the
criterion
variables
usually
converge
to
zero
(
within
some
specified
tolerance).
The
model
is
then
considered
calibrated.
If
new
assumptions
(
or
values)
for
any
of
the
fixed
variables
or
inputs
to
the
model
are
subsequently
adopted,
the
model
must
be
recalibrated
by
readjusting
the
optimized
parameters
until
the
simulated­
minus­
observed
values
of
the
criterion
variables
again
fall
within
the
specified
tolerance.

Calibrations
are
based
on
volume
weighted
mean
annual
or
seasonal
fluxes
for
a
given
period
of
observation.
The
length
of
the
period
of
observation
used
for
calibration
is
not
arbitrary.
Model
output
will
be
more
reliable
if
the
annual
flux
estimates
used
in
calibration
are
based
on
a
number
of
years
rather
than
just
1
year.
There
is
a
lot
of
year­
to­
year
5­
8
variability
in
atmospheric
deposition
and
catchment
runoff.
Averaging
over
a
number
of
years
reduces
the
likelihood
that
an
"
outlier"
year
(
very
dry,
etc.)
is
the
primary
data
on
which
model
forecasts
are
based.
On
the
other
hand,
averaging
over
too
long
a
period
may
remove
important
trends
in
the
data
that
the
model
needs
to
simulate.

The
calibration
procedure
requires
that
stream
water
quality,
soil
chemical
and
physical
characteristics,
and
atmospheric
deposition
data
be
available
for
each
catchment.
The
water
quality
data
needed
for
calibration
are
the
concentrations
of
the
individual
base
cations
(
Ca,
Mg,
Na,
and
K)
and
acid
anions
(
Cl,
SO
4,
and
NO
3)
and
the
pH.
The
soil
data
used
in
the
model
include
soil
depth
and
bulk
density,
soil
pH,
soil
cation­
exchange
capacity,
and
exchangeable
bases
on
the
soil
(
Ca,
Mg,
Na,
and
K).
The
atmospheric
deposition
inputs
to
the
model
must
be
estimates
of
total
deposition,
not
just
wet
deposition.
In
some
instances,

direct
measurements
of
either
atmospheric
deposition
or
soil
properties
may
not
be
available
for
a
given
site
with
stream
water
data.
In
these
cases,
the
required
data
can
often
be
estimated
by
assigning
soil
properties
based
on
some
landscape
classification
of
the
catchment
and
assigning
deposition
using
model
extrapolations
from
some
national
or
regional
atmospheric
deposition
monitoring
network.

Soil
Physical
and
Chemical
Properties.
Soil
data
for
model
calibration
are
usually
derived
as
a
really
averaged
values
of
soil
parameters
within
a
catchment.
If
soils
data
for
a
given
location
are
vertically
stratified,
the
soils
data
for
the
individual
soil
horizons
at
that
sampling
site
can
be
aggregated
based
on
horizon,
depth,
and
bulk
density
to
obtain
single
vertically
aggregated
values
for
the
site,
or
the
stratified
data
can
be
used
directly
in
the
model.

Total
Atmospheric
Deposition.
Total
atmospheric
deposition
consists
of
three
components:
wet
deposition,
the
flux
of
ions
occurring
in
precipitation;
dry
deposition,

resulting
from
gaseous
and
particulate
fluxes;
and
cloud/
fog
deposition
(
which
can
be
particularly
important
in
mountainous
inland
areas
or
moderate
highlands
in
areas
adjacent
to
oceans
or
seas).
Estimates
of
precipitation
volume
and
ionic
concentrations
in
precipitation
can
be
used
to
calculate
wet
deposition
for
a
site.
Observations
of
dry
deposition
or
cloud/
fog
deposition
are
very
infrequent.
The
approach
usually
used
to
quantify
these
components
relies
on
some
estimate
of
the
ratio
of
estimated
total
deposition
to
the
observed
wet
deposition
for
important
ions
(
e.
g.,
sulphate,
nitrate,
and
ammonium
ions).
These
ratios
(
called
dry
deposition
factors)
are
then
used
to
calculate
total
deposition
from
the
observed
wet
deposition
data.
5­
9
Historical
Loading.
Calibration
of
the
model
(
and
estimation
of
the
historical
changes
at
the
sites)
requires
a
temporal
sequence
of
historical
anthropogenic
deposition.
Our
current
understanding
of
ecosystem
responses
to
acidic
deposition
suggests
that
future
ecosystem
responses
can
be
strongly
conditioned
by
historical
acidic
loadings.
Thus,
as
part
of
the
model
calibration
process,
the
model
should
be
constrained
by
some
measure
of
historical
deposition
to
the
site.
However,
such
long­
term,
continuous
historical
deposition
data
do
not
exist.
The
usual
approach
is
to
use
historical
emissions
data
as
a
surrogate
for
deposition.
The
emissions
for
each
year
in
the
historical
period
can
be
normalized
to
emissions
in
a
reference
year
(
a
year
for
which
observed
deposition
data
are
available).
Using
this
scaled
sequence
of
emissions,

historical
deposition
can
be
estimated
by
multiplying
the
total
deposition
estimated
for
each
site
in
reference
year
by
the
emissions
scale
factor
for
any
year
in
the
past
to
obtain
deposition
for
that
year.

5.2.1.6
MAGIC
Modeling
Results
Watershed
modeling
undertaken
for
IAQR
projects
that,
under
IAQR,
1
percent
of
northeastern
lakes
would
be
chronically
acidic
in
2030.
In
contrast,
the
same
model
used
to
analyze
existing
control
programs
projects
6
percent
of
northeastern
lakes
would
be
chronically
acidic
in
2030.
The
modeling
projects
that,
under
IAQR,
28
percent
of
northeastern
lakes
would
be
episodically
acidic
in
2030,
compared
to
25
percent
in
2030
under
existing
control
programs.
For
Adirondack
lakes,
a
subset
of
northeastern
lakes,
the
signals
of
surface
water
chemical
recovery
are
much
stronger.
Under
IAR,
no
Adirondack
lakes
would
be
chronically
acidic,
and
64
percent
would
be
episodically
acidic
in
2030,
as
opposed
to
12
percent
chronically
acidic
and
52
percent
episodically
acidic
in
2030
under
current
control
programs.

Because
of
the
age
and
types
of
soils
in
many
high
elevation
areas
of
the
southeast,

streams
in
that
region
are
more
frequently
characterized
by
a
delayed
response
to
changes
in
deposition.
For
the
ecosystems
modeled
in
this
region,
17
percent
of
streams
are
currently
chronically
acidic,
and
this
level
stays
the
same
under
IAQR
2030;
the
proportion
of
episodically
acidic
streams
increases
from
19
percent
under
current
conditions
to
23
percent
under
IQAR,
which
reflects
a
decrease
in
the
proportion
of
nonacidic
streams
from
64
percent
under
current
conditions
to
60
percent
under
IQAR
in
2030.
It
is
important
to
note
that,

under
the
Base
Case,
the
proportion
of
nonacidic
streams
decreases
even
further,
dropping
from
64
percent
under
current
conditions
to
58
percent
in
2030.
Thus,
in
the
southeast,

IQAR
would
slow
the
deterioration
of
stream
health
(
episodically
acidic)
expected
under
the
5­
10
Base
Case
and
would
prevent
additional
streams
from
becoming
chronically
acidic.
Results
of
the
MAGIC
modeling
are
summarized
in
Table
5­
1.

5.2.2
Forest
Ecosystems
Our
current
understanding
of
the
effects
of
acid
deposition
on
forest
ecosystems
has
come
to
focus
increasingly
on
the
effects
of
biogeochemical
processes
that
affect
plant
uptake,

retention,
and
cycling
of
nutrients
within
forested
ecosystems.
Research
results
from
the
1990s
indicate
that
documented
decreases
in
base
cations
(
calcium,
magnesium,
potassium,

and
others)
from
soils
in
the
northeastern
and
southeastern
United
States
are
at
least
partially
attributable
to
acid
deposition
(
Lawrence
et
al.,
1997;
Huntington
et
al.,
2000).
Base
cation
depletion
is
a
cause
for
concern
because
of
the
role
these
ions
play
in
acid
neutralization
and,

in
the
case
of
calcium,
magnesium,
and
potassium,
their
importance
as
essential
nutrients
for
tree
growth.
It
has
been
known
for
some
time
that
depletion
of
base
cations
from
the
soil
interferes
with
the
uptake
of
calcium
by
roots
in
forest
soils
(
Shortle
and
Smith
1988).
Recent
research
indicates
it
also
leads
to
aluminum
mobilization
(
Lawrence
et
al.,
1995),
which
can
have
harmful
effects
on
fish
(
US
Dept.
of
Interior
2003).

The
plant
physiological
processes
affected
by
reduced
calcium
availability
include
cell
wall
structure
and
growth,
carbohydrate
metabolism,
stomatal
regulation,
resistance
to
plant
pathogens,
and
tolerance
of
low
temperatures
(
DeHayes
et
al.,
1999).
Soil
structure,
macro
and
micro
fauna,
decomposition
rates,
and
nitrogen
metabolism
are
also
important
processes
that
are
significantly
influenced
by
calcium
levels
in
soils.
The
importance
of
calcium
as
an
indicator
of
forest
ecosystem
function
is
due
to
its
diverse
physiological
roles,
coupled
with
the
fact
that
calcium
mobility
in
plants
is
very
limited
and
can
be
further
reduced
by
tree
age,

competition,
and
reduced
soil
water
supply
(
McLaughlin
and
Wimmer
1999).

A
clear
link
has
now
been
established
in
red
spruce
stands
between
acid
deposition,

calcium
supply,
and
sensitivity
to
abiotic
stress.
Red
spruce
uptake
and
retention
of
calcium
is
affected
by
acid
deposition
in
two
main
ways:
leaching
of
important
stores
of
calcium
from
needles
(
DeHayes
et
al.,
1999)
and
decreased
root
uptake
of
calcium
due
to
calcium
depletion
from
the
soil
and
aluminum
mobilization
(
Smith
and
Shortle,
2001;
Shortle
et
al.,
1997;

Lawrence
et
al.,
1997).
Acid
deposition
leaches
calcium
from
mesophyll
cells
of
1­
year
old
red
spruce
needles
(
Schaberg
et
al.,
2000),
which
in
turn
reduces
freezing
tolerance
(
DeHayes
et
al.,
1999).
These
changes
increase
the
sensitivity
of
red
spruce
to
winter
injuries
5­
11
under
normal
winter
conditions
in
the
Northeast,
result
in
the
loss
of
needles,
and
impair
the
overall
health
of
forest
ecosystems
(
DeHayes
et
al.,
1999).
Red
spruce
must
also
expend
more
metabolic
energy
to
acquire
calcium
from
soils
in
areas
with
low
calcium/
aluminum
ratios,
resulting
in
slower
tree
growth
(
Smith
and
Shortle,
2001).

Losses
of
calcium
from
forest
soils
and
forested
watersheds
have
now
been
documented
as
a
sensitive
early
indicator
of
the
soil
response
to
acid
deposition
for
a
wide
range
of
forest
soils
in
the
United
States
(
Lawrence
et
al.,
1999;
Huntington
et
al.,
2000).

There
is
a
strong
relationship
between
acid
deposition
and
leaching
of
base
cations
from
hardwood
forest
(
e.
g.,
maple,
oak)
soils,
as
indicated
by
long­
term
data
on
watershed
mass
balances
(
Likens
et
al.,
1996;
Mitchell
et
al.,
1996),
plot­
and
watershed­
scale
acidification
experiments
in
the
Adirondacks
(
Mitchell
et
al.,
1994)
and
in
Maine
(
Norton
et
al.,
1994;

Rustad
et
al.,
1996),
and
studies
of
soil
solution
chemistry
along
an
acid
deposition
gradient
from
Minnesota
to
Ohio
(
MacDonald
et
al.,
1992).

Although
sulfate
is
the
primary
cause
of
base
cation
leaching,
nitrate
is
a
significant
contributor
in
watersheds
that
are
nearly
nitrogen
saturated
(
Adams
et
al.,
1997).
Recent
studies
of
the
decline
of
sugar
maples
in
the
Northeast
demonstrate
a
link
between
low
base
cation
availability,
high
levels
of
aluminum
and
manganese
in
the
soil,
and
increased
levels
of
tree
mortality
due
to
native
defoliating
insects
(
Horsley
et
al.,
2000).
The
chemical
composition
of
leaves
and
needles
may
also
be
altered
by
acid
deposition,
resulting
in
changes
in
organic
matter
turnover
and
nutrient
cycling.

5.2.3
Coastal
Ecosystems
Since
1990,
a
large
amount
of
research
has
been
conducted
on
the
impact
of
nitrogen
deposition
to
coastal
waters.
It
is
now
known
that
nitrogen
deposition
is
a
significant
source
of
nitrogen
to
many
estuaries
(
Valigura
et
al.,
2001;
Howarth
1998).
The
amount
of
nitrogen
entering
estuaries
due
to
atmospheric
deposition
varies
widely,
depending
on
the
size
and
location
of
the
estuarine
watershed
and
other
sources
of
nitrogen
in
the
watershed.
For
a
handful
of
estuaries,
atmospheric
deposition
of
nitrogen
contributes
well
over
40
percent
of
the
total
nitrogen
load;
however,
in
most
estuaries
for
which
estimates
exist,
the
contribution
from
atmospheric
deposition
ranges
from
15
to
30
percent.
The
area
with
the
highest
deposition
rates
stretches
from
Massachusetts
to
the
Chesapeake
Bay
and
along
the
central
Gulf
of
Mexico
coast.

Nitrogen
is
often
the
limiting
nutrient
in
coastal
ecosystems.
Increasing
the
levels
of
nitrogen
in
coastal
waters
can
cause
significant
changes
to
those
ecosystems.
Approximately
5­
12
60
percent
of
estuaries
in
the
United
States
(
65
percent
of
the
estuarine
surface
area)
suffer
from
overenrichment
of
nitrogen,
a
condition
known
as
eutrophication
(
Bricker
et
al.,
1999).

Symptoms
of
eutrophication
include
changes
in
the
dominant
species
of
plankton
(
the
primary
food
source
for
many
kinds
of
marine
life)
that
can
cause
algal
blooms,
low
levels
of
oxygen
in
the
water
column,
fish
and
shellfish
kills,
and
cascading
population
changes
up
the
food
chain.
Many
of
the
most
highly
eutrophic
estuaries
are
along
the
Gulf
and
mid­
Atlantic
coasts,
overlapping
many
of
the
areas
with
the
highest
nitrogen
deposition,
but
there
are
eutrophic
estuaries
in
every
region
of
the
coterminous
U.
S.
coastline.

5.3
Benefits
of
Reducing
Mercury
Emissions
According
to
baseline
emission
estimates,
the
sources
affected
by
this
proposal
would
emit
approximately
45.1
tons
of
mercury
per
year
in
2010.
This
estimate
is
specific
to
fossilfired
electric
generating
units
in
excess
of
25
megawatt
capacity.
The
proposed
regulation
would
reduce
approximately
10.6
tons
of
mercury
(
or
23.5
percent)
from
the
2010
baseline,

11.8
tons
of
mercury
(
or
26.3
percent)
from
the
2015
baseline,
and
14.3
tons
(
or
32
percent)

from
the
2020
baseline
at
affected
electric
generating
units.

Mercury
emitted
from
utilities
and
other
natural
and
man­
made
sources
is
carried
by
winds
through
the
air
and
eventually
is
deposited
to
water
and
land.
Recent
estimates
(
which
are
highly
uncertain)
of
annual
total
global
mercury
emissions
from
all
sources
(
natural
and
anthropogenic)
are
about
5,000
to
5,500
tons
per
year
(
tpy).
Of
this
total,
about
1,000
tpy
are
estimated
to
be
natural
emissions
and
about
2,000
tpy
are
estimated
to
be
contributions
through
the
natural
global
cycle
of
re­
emissions
of
mercury
associated
with
past
anthropogenic
activity.
Current
anthropogenic
emissions
account
for
the
remaining
2,000
tpy.

Point
sources
such
as
fuel
combustion;
waste
incineration;
industrial
processes;
and
metal
ore
roasting,
refining,
and
processing
are
the
largest
point
source
categories
on
a
world­
wide
basis.
Given
the
global
estimates
noted
above,
U.
S.
anthropogenic
mercury
emissions
are
estimated
to
account
for
roughly
3
percent
of
the
global
total,
and
U.
S.
utilities
are
estimated
to
account
for
about
1
percent
of
total
global
emissions.
Mercury
exists
in
three
forms:

elemental
mercury,
inorganic
mercury
compounds
(
primarily
mercuric
chloride),
and
organic
mercury
compounds
(
primarily
methylmercury).
Mercury
is
usually
released
in
an
elemental
form
and
later
converted
into
methylmercury
by
bacteria.
Methylmercury
is
more
toxic
to
humans
than
other
forms
of
mercury,
in
part
because
it
is
more
easily
absorbed
in
the
body
(
EPA,
1996).
5­
13
If
the
deposition
is
directly
to
a
water
body,
then
the
processes
of
aqueous
fate,

transport,
and
transformation
begin.
If
deposition
is
to
land,
then
terrestrial
fate
and
transport
processes
occur
first
and
then
aqueous
fate
and
transport
processes
occur
once
the
mercury
has
cycled
into
a
water
body.
In
both
cases,
mercury
may
be
returned
to
the
atmosphere
through
resuspension.
In
water,
mercury
is
transformed
to
methylmercury
through
biological
processes
and
for
exposures
affected
by
this
rulemaking,
methylmercury
is
considered
to
be
the
form
of
greatest
concern.
Once
mercury
has
been
transformed
into
methylmercury,
it
can
be
ingested
by
the
lower
trophic
level
organisms
where
it
can
bioaccumulate
in
fish
tissue
(
i.
e.,

concentrations
of
mercury
remain
in
the
fish's
system
for
a
long
period
of
time
and
accumulates
in
the
fish
tissue
as
predatory
fish
consume
other
species
in
the
food
chain).
Fish
and
wildlife
at
the
top
of
the
food
chain
can,
therefore,
have
mercury
concentrations
that
are
higher
than
the
lower
species,
and
they
can
have
concentrations
of
mercury
that
are
higher
than
the
concentration
found
in
the
water
body
itself.
In
addition,
when
humans
consume
fish
contaminated
with
methylmercury,
the
ingested
methymercury
is
almost
completely
absorbed
into
the
blood
and
distributed
to
all
tissues
(
including
the
brain);
it
also
readily
passes
through
the
placenta
to
the
fetus
and
fetal
brain
(
EPA,
2001a).

Based
on
the
findings
of
the
National
Research
Council,
EPA
has
concluded
that
benefits
of
Hg
reductions
would
be
most
apparent
at
the
human
consumption
stage,
as
consumption
of
fish
is
the
major
source
of
exposure
to
methylmercury.
At
lower
levels,

documented
Hg
exposure
effects
may
include
more
subtle,
yet
potentially
important,

neurodevelopmental
effects.
Figure
5­
1
shows
how
emissions
of
mercury
can
transport
from
the
air
to
water
and
impact
human
health
and
ecosystems.
30
Cardiovascular,
immune,
and
reproductive
system
problems
in
adults
are
potential
effects
as
the
literature
is
either
contradictory
or
incomplete.

5­
14
Emissions
Reductions
Reduce
Atmospheric
Transport
and
Deposition
Reduce
Ecosystem
Transport
and
Methylation
Reduce
Human
and
Wildlife
Exposure
Reduce
Health
Impacts
Fishing
°
commercial
°
recreational
°
subsistence
Mercury
transforms
into
methylmercury
in
soils
and
water,
then
can
bioaccumulate
in
fish
Atmospheric
deposition
Lake
methylation
Ocean
methylation
volatilization
volatilization
Largest
impacts
on
young
children
Impacts
include:

°
Impaired
motor
and
cognitive
skills
°
Potential
cardiovascular,

immune,
and
reproductive
system
problems
in
adults
Power
Plant
Emissions
Humans
and
wildlife
affected
primarily
by
eating
contaminated
fish
Wet
and
Dry
Deposition
Figure
5­
1.
How
Emissions
of
Mercury
Can
Impact
Human
Health
and
Ecosystems30
5­
15
Some
subpopulations
in
the
U.
S.,
such
as:
Native
Americans,
Southeast
Asian
Americans,
and
lower
income
subsistence
fishers,
may
rely
on
fish
as
a
primary
source
of
nutrition
and/
or
for
cultural
practices.
Therefore,
they
consume
larger
amounts
of
fish
than
the
general
population
and
may
be
at
a
greater
risk
to
the
adverse
health
effects
from
Hg
due
to
increased
exposure.
In
pregnant
women,
methylmercury
can
be
passed
on
to
the
developing
fetus,
and
at
sufficient
exposure
may
lead
to
a
number
of
neurological
disorders
in
children.
Thus,
children
who
are
exposed
to
low
concentrations
of
methylmercury
prenatally
may
be
at
increased
risk
of
poor
performance
on
neurobehavioral
tests,
such
as
those
measuring
attention,
fine
motor
function,
language
skills,
visual­
spatial
abilities
(
like
drawing),

and
verbal
memory.
The
effects
from
prenatal
exposure
can
occur
even
at
doses
that
do
not
result
in
effects
in
the
mother.
Mercury
may
also
affect
young
children
who
consume
fish
contaminated
with
Hg.
Consumption
by
children
may
lead
to
neurological
disorders
and
developmental
problems,
which
may
lead
to
later
economic
consequences.

Monitoring
the
concentrations
of
mercury
in
the
blood
of
women
of
child­
bearing
age
can
help
identify
the
proportion
of
children
who
may
be
at
risk.
EPA's
reference
dose
(
RfD)

for
methylmercury
is
0.1
micrograms
per
kilogram
body
weight
per
day,
which
is
approximately
equivalent
to
a
concentration
of
5.8
parts
per
billion
mercury
in
blood.

Although
the
prenatal
period
is
the
most
sensitive
period
of
exposure,
exposure
to
mercury
during
childhood
also
could
pose
a
potential
health
risk
(
NAS,
2000).

Figure
5­
2
shows
reported
concentrations
of
mercury
in
blood
of
women
of
childbearing
age
from
the
National
Health
and
Nutrition
Examination
Survey
(
NHANES)

(
EPA,
2003b).
The
data
presented
are
for
total
mercury,
which
includes
methylmercury
and
other
forms
of
mercury.
Total
blood
mercury
is
a
reasonable
indicator
of
methylmercury
exposure
in
people
who
consume
fish
and
have
no
significant
exposure
to
inorganic
or
elemental
mercury
(
JAMA,
April
2003).
Thus
the
measured
concentrations
are
a
good
indication
of
methylmercury
concentrations.
From
this
survey,
about
8
percent
of
women
of
child­
bearing
age
had
at
least
5.8
parts
per
billion
of
mercury
in
their
blood
in
1999­
2000.
5­
16
Figure
5­
2.
Concentrations
of
Mercury
in
Blood
of
Women
of
Childbearing
Age
5­
17
50
58
BMDL
85
BMD
5.8
RfD
Figure
5­
3
shows
relative
values
of
the
BMD,
BMDL
and
the
RfD.
The
data
show
a
Benchmark
Dose
(
BMD)
BMD
at
85
ppb.
The
BMD
is
the
dose
or
concentration
that
produced
a
doubling
of
the
number
of
children
with
a
response
at
the
5th
percentile
of
the
population.
In
this
case,
the
changes
evaluated
were
changes
on
neuropsychological
testing
batteries
(
i.
e.
the
Boston
Naming
Test).
In
determining
the
RfD,
EPA
started
with
the
BMD
(
85
ppb)
and
then
used
the
95%
lower
confidence
limit
to
arrive
at
the
58
ppb
BMDL.
EPA
then
applied
a
composite
uncertainty
factor
of
10
to
calculate
a
final
RfD
of
5.8
ppb.
The
uncertainty
factor
adjustment
was
used
to
account
for
pharmacokinetic
and
pharmacodynamic
uncertainty
and
variability.

Figure
5­
3.
Relative
Values
of
BMD,
BMDL,
and
the
RfD
(
Values
in
ppb)

In
response
to
potential
risks
of
mercury­
contaminated
fish
consumption,
EPA
and
FDA
have
issued
fish
consumption
advisories
which
provide
recommended
limits
on
consumption
of
certain
fish
species
for
different
populations.
EPA
and
FDA
are
currently
developing
a
joint
advisory
that
has
been
released
in
draft
form.
This
newest
draft
FDA­
EPA
fish
advisory
recommends
that
women
and
young
children
reduce
the
risks
of
Hg
consumption
in
their
diet
by
moderating
their
fish
consumption,
diversifying
the
types
of
fish
they
consume,

and
by
checking
any
local
advisories
that
may
exist
for
local
rivers
and
streams.
This
collaborative
FDA­
EPA
effort
will
greatly
assist
in
educating
the
most
susceptible
populations.
Additionally,
the
reductions
of
Hg
from
this
regulation
may
potentially
lead
to
fewer
fish
consumption
advisories
(
both
from
federal
or
state
agencies),
which
will
benefit
the
fishing
community.
As
Figure
5­
4
shows,
currently
44
states
have
issued
fish
consumption
advisories
for
non­
commercial
fish
for
some
or
all
of
their
waters
due
to
contamination
of
mercury.
The
scope
of
FCA
issued
by
states
varies
considerably,
with
some
warnings
applying
to
all
water
bodies
in
a
state
and
others
applying
only
to
individual
lakes
and
streams.
Note
5­
18
that
the
absence
of
a
state
advisory
does
not
necessarily
indicate
that
there
is
no
risk
of
exposure
to
unsafe
levels
of
mercury
in
recreationally
caught
fish.
Likewise,
the
presence
of
a
state
advisory
does
not
indicate
that
there
is
a
risk
of
exposure
to
unsafe
levels
of
mercury
in
recreationally
caught
fish,
unless
people
consume
these
fish
at
levels
greater
than
those
recommended
by
the
fish
advisory.

Reductions
in
methylmercury
concentrations
in
fish
should
reduce
exposure,

subsequently
reducing
the
risks
of
mercury­
related
health
effects
in
the
general
population,
to
children,
and
to
certain
subpopulations.
Fish
consumption
advisories
(
FCA)
issued
by
the
States
may
also
help
to
reduce
exposures
to
potential
harmful
levels
of
methylmercury
in
fish
(
although
some
studies
have
shown
limited
knowledge
of
and
compliance
with
advisories
by
at
risk
populations
(
May
and
Burger,
1996;
Burger,
2000)).
To
the
extent
that
reductions
in
mercury
emissions
reduces
the
probability
that
a
water
body
will
have
a
FCA
issued,
there
are
a
number
of
benefits
that
will
result
from
fewer
advisories,
including
increased
fish
consumption,
increased
fishing
choices
for
recreational
fishers,
increased
producer
and
consumer
surplus
for
the
commercial
fish
market,
and
increased
welfare
for
subsistence
fishing
populations.
5­
19
Figure
5­
4.
Mercury
Reductions
By
State
In
2015
There
is
a
great
deal
of
variability
among
individuals
in
fish
consumption
rates;

however,
critical
elements
in
estimating
methylmercury
exposure
and
risk
from
fish
consumption
include
the
species
of
fish
consumed,
the
concentrations
of
methylmercury
in
the
fish,
the
quantity
of
fish
consumed,
and
how
frequently
the
fish
is
consumed.
The
typical
U.
S.

consumer
eating
a
wide
variety
of
fish
from
restaurants
and
grocery
stores
is
not
in
danger
of
consuming
harmful
levels
of
methylmercury
from
fish
and
is
not
advised
to
limit
fish
consumption.
Those
who
regularly
and
frequently
consume
large
amounts
of
fish,
either
marine
or
freshwater,
are
more
exposed.
Because
the
developing
fetus
may
be
the
most
sensitive
to
the
effects
from
methylmercury,
women
of
child­
bearing
age
are
regarded
as
the
population
of
greatest
interest.
The
EPA,
Food
and
Drug
Administration,
and
many
States
have
issued
fish
consumption
advisories
to
inform
this
population
of
protective
consumption
levels.

The
EPA's
1997
Mercury
Study
RTC
supports
a
plausible
link
between
anthropogenic
releases
of
Hg
from
industrial
and
combustion
sources
in
the
U.
S.
and
methylmercury
in
fish.

However,
these
fish
methylmercury
concentrations
also
result
from
existing
background
concentrations
of
Hg
(
which
may
consist
of
Hg
from
natural
sources,
as
well
as
Hg
which
has
been
re­
emitted
from
the
oceans
or
soils)
and
deposition
from
the
global
reservoir
(
which
5­
20
includes
Hg
emitted
by
other
countries).
Given
the
current
scientific
understanding
of
the
environmental
fate
and
transport
of
this
element,
it
is
not
possible
to
quantify
how
much
of
the
methylmercury
in
locally­
caught
fish
consumed
by
the
U.
S.
population
is
contributed
by
U.
S.

emissions
relative
to
other
sources
of
Hg
(
such
as
natural
sources
and
re­
emissions
from
the
global
pool).
As
a
result,
the
relationship
between
Hg
emission
reductions
from
Utility
Units
and
methylmercury
concentrations
in
fish
cannot
be
calculated
in
a
quantitative
manner
with
confidence.
In
addition,
there
is
uncertainty
regarding
over
what
time
period
these
changes
would
occur.
This
is
an
area
of
ongoing
study.

Given
the
present
understanding
of
the
Hg
cycle,
the
flux
of
Hg
from
the
atmosphere
to
land
or
water
at
one
location
is
comprised
of
contributions
from:
the
natural
global
cycle;

the
cycle
perturbed
by
human
activities;
regional
sources;
and
local
sources.
Recent
advances
allow
for
a
general
understanding
of
the
global
Hg
cycle
and
the
impact
of
the
anthropogenic
sources.
It
is
more
difficult
to
make
accurate
generalizations
of
the
fluxes
on
a
regional
or
local
scale
due
to
the
site­
specific
nature
of
emission
and
deposition
processes.
Similarly,
it
is
difficult
to
quantify
how
the
water
deposition
of
Hg
leads
to
an
increase
in
fish
tissue
levels.

This
will
vary
based
on
the
specific
characteristics
of
the
individual
lake,
stream,
or
ocean.
6­
1
SECTION
6
COMPARISON
OF
BENEFITS
AND
COSTS
The
estimated
social
costs
to
implement
the
proposed
IAQR,
as
described
in
the
cost
analysis
document,
are
approximately
$
2.9
billion
annually
and
$
3.7
billion
annually
for
2010
and
2015,
respectively
(
1999$).
Thus,
the
net
benefits
(
social
benefits
minus
social
costs)
of
the
program
in
2010
are
approximately
$
55
+
B
billion
annually
in
2010
and
$
80
+
B
billion
annually
in
2015
(
1999$).
(
B
represents
the
sum
of
all
unquantified
benefits
and
disbenefits.)

Therefore,
implementation
of
the
proposed
rule
is
expected,
based
purely
on
economic
efficiency
criteria,
to
provide
society
with
a
significant
net
gain
in
social
welfare,
even
given
the
limited
set
of
health
and
environmental
effects
we
were
able
to
quantify.
Addition
of
ozone­,
directly
emitted
PM
2.5­,
mercury­,
acidification­,
and
eutrophication­
related
impacts
would
increase
the
net
benefits
of
the
proposed
rule.
As
discussed
in
section
IX
of
the
notice
for
this
rulemaking,
we
did
not
complete
air
quality
modeling
that
precisely
matches
the
IAQR
region.
We
anticipate
that
any
differences
in
estimates
presented
due
to
the
modeling
region
analyzed
will
be
small.
Table
6­
1
presents
a
summary
of
the
benefits,
costs,
and
net
benefits
of
the
proposed
rule.
6­
2
Table
6­
1.
Summary
of
Annual
Benefits,
Costs,
and
Net
Benefits
of
the
Inter­
State
Air
Quality
Rule
Description
2010
(
billions
of
1999
dollars)
2015
(
billions
of
1999
dollars)

Social
costsa
$
2.9
$
3.7
Social
benefits
b,
c
Ozone­
related
benefits
$
0.1
$
0.1
PM­
related
health
benefits
$
56.8
+
B
$
82.3
+
B
Visibility
benefits
$
0.9
$
1.4
Net
benefits
(
benefits­
costs)
b,
c,
d
$
55
+
B
$
80
+
B
a
Note
that
costs
are
the
annual
total
costs
of
reducing
pollutants
including
NOx
and
SO2.

b
As
the
table
indicates,
total
benefits
are
driven
primarily
by
PM­
related
health
benefits.
The
reduction
in
premature
fatalities
each
year
accounts
for
over
90
percent
of
total
benefits.
Benefits
in
this
table
are
associated
with
NOx
and
SO2
reductions.

c
Not
all
possible
benefits
or
disbenefits
are
quantified
and
monetized
in
this
analysis.
B
is
the
sum
of
all
unquantified
benefits
and
disbenefits.
Potential
benefit
categories
that
have
not
been
quantified
and
monetized
are
listed
in
Table
1­
4.

d
Net
benefits
are
rounded
to
the
nearest
billion.
Columnar
totals
may
not
sum
due
to
rounding.
R­
1
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