United States Office of Air and Radiation November 1999

Environmental Protection Office of Policy EPA-410-R-99-001

Agency

The Benefits and Costs

of the Clean Air Act

1990 to 2010

EPA Report to Congress

November 1999
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxxii

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Executive Summary

i

Executive

Summary

Section 812 of the Clean Air Act Amendments

of 1990 requires the Environmental Protection

Agency to periodically assess the effect of the Clean

Air Act on the ìpublic health, economy, and environment of the United States,î and to report the

findings and results of its assessments to the Congress. This Report to Congress, the first of a series

of prospective studies we plan to produce every two

years, presents the results and conclusions of our

analysis of the benefits and costs of the Clean Air

Act during the period from 1990 to 2010. The main

goal of this report is to provide Congress and the

public with comprehensive, up-to-date information

on the Clean Air Actís social costs and benefits, including improvements in human health, welfare, and

ecological resources.

The first report that the EPA created under the

section 812 authority, The Benefits and Costs of the

Clean Air Act: 1970 to 1990, was published and conveyed to Congress in October 1997. This retrospective analysis comprehensively assessed the benefits

and costs of all requirements of the 1970 Clean Air

Act and the 1977 Amendments, up to the passage of

the Clean Air Act Amendments of 1990. The results of the retrospective analysis showed that the

nationís investment in clean air was more than justified by the substantial benefits that were gained in

the form of increased health, environmental quality, and productivity.

The Clean Air Act Amendments of 1990 built

upon the significant progress made by the original

Clean Air Act of 1970 and its 1977 amendments in

improving the nationís air quality. The amendments

utilized the existing structure of the Clean Air Act,

but strengthened those requirements to tighten and

clarify implementation goals and timing, increase the

stringency of some requirements, revamp the hazardous air pollutant regulatory program, refine and

streamline permitting requirements, and introduce

new programs for the control of acid rain precursors and stratospheric ozone depleting substances.

Because the 1990 Amendments represent an incremental improvement to the nationís clean air program, the analysis summarized in this report was

designed to estimate the costs and benefits of the 1990

Amendments incremental to those assessed in the

retrospective analysis. Our intent is that this report

and its predecessor, the retrospective, together provide a comprehensive assessment of current and expected future clean air regulatory programs and their

costs and benefits.

This first prospective analysis consists of a sequence of six steps. These six steps, listed in order

of completion, are:

(1) estimate air pollutant emissions in 1990,

2000, and 2010;

(2) estimate the cost of emission reductions arising from the Clean Air Act Amendments;

(3) model air quality based on emissions estimates;

(4) quantify air quality related health and environmental effects;

(5) estimate the economic value of cleaner air;

and

(6) aggregate results and characterize uncertainties.

The methodology and results for each step are

summarized below and described in detail in the

chapters of this report.

Air Pollutant Emissions

Estimation of reductions in pollutant emissions

afforded by the 1990 Clean Air Act Amendments

(CAAA) serves as the starting point for this studyís

subsequent benefit and cost estimates. We focused

our emissions analysis on six major pollutants: volatile organic compounds (VOCs), nitrogen oxides
The Benefits and Costs of the Clean Air Act, 1990 to 2010

ii

(NOx), sulfur dioxide (SO2), carbon monoxide (CO),

coarse particulate matter (PM10), and fine particulate matter (PM2.5). For each of these pollutants we

forecast emissions for the years 2000 and 2010 under two different scenarios: a) the Pre-CAAA scenario that assumes no additional control requirements would be implemented beyond those that

were in place when the 1990 CAAA were passed;

and b) the Post-CAAA scenario that incorporates

the effects of controls which, when we formulated

the scenario, we expected would be likely to occur

as a result of implementing the 1990 Amendments.

Emissions estimates for both the Pre-CAAA and

Post-CAAA scenarios reflect expected growth in

population, transportation, electric power generation, and other economic activity by 2000 and 2010.

We compare the emissions estimates under each of

these scenarios to estimate the effect of the CAAA

requirements on future emissions.

The results of the emissions phase of the assessment indicate that the 1990 Clean Air Act Amendments significantly reduce future emissions of air

pollutants. Substantial reductions will be achieved

for the two major precursors of ambient groundlevel ozone: volatile organic compounds (VOCs) and

oxides of nitrogen (NOx

). Relative to the Pre-CAAA

scenario, estimated VOC emissions under the PostCAAA case are 35 percent lower by 2010. This

change in emissions is due largely to VOC reductions from motor vehicles and area sources (e.g., dry

cleaners, commercial bakeries, and other widely dispersed sources).

The NOx

 emission reduction under the PostCAAA scenario represents the greatest proportional

emissions change estimated in our analysis. For the

year 2010, the Post-CAAA NOx

 emissions estimate

is 39 percent lower than the Pre-CAAA estimate,

representing a decrease in emissions of almost 11

million tons. Nearly half of this reduction is from

utilities, largely as a result of the particular NOx

emissions cap and trading program we assumed under the Post-CAAA scenario. The remaining reductions are attributable to cuts in motor vehicle and

non-utility point source emissions.

Carbon monoxide (CO) emissions contribute

directly to concentrations of carbon monoxide in

the environment. The 2010 Post-CAAA estimate

for CO emissions is 81.9 million tons, 23 percent

lower than the Pre-CAAA projection. The reduction in CO emissions is mostly due to motor vehicle emission controls.

The CAAA also will achieve a substantial reduction in precursors of fine particulate matter

(PM2.5). Sulfur dioxide (SO2

) is an important precursor of PM. By 2010, SO2

 emissions are 31 percent

lower under the Post-CAAA scenario. Of the 8.2

million ton difference between Pre- and Post-CAAA

SO2

 estimates, 96 percent is attributable to additional

control of utility emissions through a national capand-trade program involving marketable SO2

 emission allowances. Oxides of nitrogen, discussed above,

are also important fine PM precursors.

We project the 1990 Clean Air Act Amendments

to have more modest effects on emissions of particulate material which is emitted in solid form (i.e.,

ìprimaryî or ìdirectî PM10 and PM2.5 emissions).

Overall, emissions of primary PM10 and PM2.5 are

each approximately four percent lower in 2010 under the Post-CAAA scenario than under the PreCAAA scenario. Although the incremental effects

of the Clean Air Act Amendments on primary PM

emissions will be relatively small, PM in the atmosphere is comprised of both directly emitted primary

particles and particles that form in the atmosphere

through secondary processes as a result of emissions

of SO2

, NOx, and organic compounds. These PM

species, formed by the conversion of gaseous pollutant emissions, are referred to collectively as ìsecondaryî PM. Because, as noted above, the 1990 Amendments achieve substantial reductions in these gaseous

precursor emissions, the Amendments have a much

larger effect on PM10 and PM2.5 levels in the atmosphere than might be apparent if only the changes

in directly emitted primary particles are considered.

Compliance Costs

Our estimate of the costs of the Clean Air Act

Amendment provisions is based on an evaluation of

the increases in expenditures incurred by various

entities to meet the additional control requirements

incorporated in the Post-CAAA case. These costs

include operation and maintenance (O&M) expenditures ówhich includes research and development

(R&D) and other similarly recurring expendituresó

plus amortized capital costs (i.e., depreciation plus
Executive Summary

iii

interest costs associated with the existing capital

stock). Relative to the Pre-CAAA case, Post-CAAA

scenario total annual compliance costs for Titles I

through V are approximately $19 billion higher by

the year 2000, rising to $27 billion by the year 2010.

Compliance with Title I, Provisions for Attainment and Maintenance of National Ambient Air

Quality Standards (NAAQS), accounts for $14.5

billion, or over half, of the estimated increase in year

2010 compliance costs. Compliance with mobile

source emissions control provisions under Title II

of the Clean Air Act Amendments accounts for an

additional 30 percent of the total costs, or $9 billion

annually by 2010. Provisions to control acid deposition and emissions of stratospheric ozone depleting substances account for most of the remainder of

the costs.

These direct compliance costs provide a good,

but incomplete, measure of the total effect of the

Clean Air Act Amendments on the U.S. economy.

A complete picture of the indirect impacts of these

costs would include changes in employment and

prices as well as impacts that might be experienced

among customers of the firms that must incur these

costs. While these indirect effects could be important, we believe the direct cost estimates provide a

good initial measure of the effect of the Clean Air

Act Amendments on the U.S. economy, as well as

an appropriate metric for comparison with the direct benefits reported here.

Table ES-1

Summary Comparison of Benefits and Costs (Estimates in millions 1990$)

Titles I through V

Annual Estimates

2000 2010

Monetized Direct Costs:

Low a

Central $19,000 $27,000

High a

Monetized Direct Benefits:

Lowb $16,000 $26,000

Central $71,000 $110,000

Highb $160,000 $270,000

Net Benefits:

Low ($3,000) ($1,000)

Central $52,000 $83,000

High $140,000 $240,000

Benefit/Cost Ratio:

Lowc less than 1/1 less than 1/1

Central 4/1 4/1

Highc more than 8/1 more than 10/1

a The cost estimates for this analysis are based on assumptions about future changes in factors such as consumption

patterns, input costs, and technological innovation. We recognize that these assumptions introduce significant

uncertainty into the cost results; however the degree of uncertainty or bias associated with many of the key factors cannot

be reliably quantified. Thus, we are unable to present specific low and high cost estimates.

b Low and high benefits estimates are based on primary results and correspond to 5th and 95th percentile results from

statistical uncertainty analysis, incorporating uncertainties in physical effects and valuation steps of benefits analysis.

Other significant sources of uncertainty not reflected include the value of unquantified or unmonetized benefits that are

not captured in the primary estimates and uncertainties in emissions and air quality modeling.

c

 The low benefit/cost ratio reflects the ratio of the low benefits estimate to the central costs estimate, while the high ratio

reflects the ratio of the high benefits estimate to the central costs estimate. Because we were unable to reliably quantify

the uncertainty in cost estimates, we present the low estimate as "less than X," and the high estimate as "more than Y",

where X and Y are the low and high benefit/cost ratios, respectively.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

iv

Human Health and

Environmental Benefits

To estimate benefits, the results of the emissions

analysis served as the principal input to a linked series of models. We used these models to estimate

changes in air quality, human health effects, ecological effects, and, ultimately, the net economic benefits of the Clean Air Act Amendments. The goals

of these steps in the analysis were to estimate the

implications of changes in emissions resulting from

compliance with the Clean Air Act Amendments

on criteria pollutant air quality throughout the lower

48 states, and the impacts on human health and the

environment that result from these changes.

We focused our air quality modeling efforts on

estimating the impact of Pre- and Post-CAAA emissions on ambient concentrations of ozone, PM10,

PM2.5, SO2

, NOx, and CO and on acid deposition

and visibility in future years. We found that the

majority of the total monetized benefits, however,

is attributable to changes in particulate matter concentrations and, more specifically, to the effect of

these ambient air quality changes on avoidance of

premature mortality. We estimate that 2010 PostCAAA PM10 and PM2.5 concentrations in the eastern U.S. will average about 5 to 10 percent lower

than 2010 Pre-CAAA concentrations, with some

areas of the eastern U.S. experiencing much greater

reductions of up to 30 percent. The air quality modeling also indicates a substantial overall reduction in

future-year PM10 and PM2.5 concentrations throughout the western U.S., including most population

centers, following implementation of the Clean Air

Act Amendments.

The direct benefits of the air quality improvements we estimated under the Post-CAAA scenario

include reduced incidence of a number of adverse

human health effects, improvements in visibility, and

avoided damage to agricultural crops. The estimated

annual economic value of these benefits in the year

2010 ranges from $26 to $270 billion, in 1990 dollars, with a central estimate, or mean, of $110 billion. These estimates do not include a number of

other potentially important effects which could not

be readily quantified and monetized (i.e., converted

to dollar terms). These excluded effects include a

wide range of ecosystem changes, air toxics-related

human health effects, and a number of additional

health effects associated with criteria pollutants.

In addition, these results reflect the particular

choices we made with respect to interpretations of

the available scientific and economic literature and

adoption of paradigms for representing health and

environmental changes in economic terms. We refer to these results, then, as our ìprimaryî estimates;

however, in the text of this report we also present

some alternative results which reflect other available

choices for models or assumptions.

One particularly important assumption of our

primary analysis is that correlations between increased air pollution exposures and adverse health

outcomes found by epidemiological studies indicate

causal relationships between the pollutant exposures

and the adverse health effects. Future research may

lead to revisions in this assumption as well as other

key assumptions, data, and models we use to estimate the benefits and costs of the Clean Air Act.

Such revisions may in turn imply significant changes

in the estimates of Clean Air Act costs and benefits

presented here and in past and future assessments.

In our judgment, however, the primary results reflect the best currently available science and the most

up-to-date tools and data we had at our disposal ó

and the most reasonable assumptions we could

adoptó as each step of the analysis was implemented.

Cleaner air also yields benefits to ecological systems. This first section 812 prospective analysis devotes a great deal of effort to characterizing and,

where possible, quantifying and monetizing the impacts of air pollutants on natural systems. Our increased effort is in part a result of the findings of the

retrospective analysis, where we identified a better

understanding of ecological effects as an important

research direction for the first prospective and subsequent analyses. Quantified benefits of CAAA programs reflected in the overall monetized benefits

include: increased agricultural and timber yields; reduced effects of acid rain on aquatic ecosystems; and

reduced effects of nitrogen deposited to coastal estuaries. Many ecological benefits, however, remain

difficult or impossible to quantify, or can only be

quantified for a limited geographic area. The magnitude of quantified benefits and the wide range of

unquantified benefits nonetheless suggest that as we

learn more about ecological systems and can conduct more comprehensive ecological benefits assessments, estimates of these benefits could be substantially greater.
Executive Summary

v

We developed separate estimates for the Title

VI provisions of the CAAA designed to protect

stratospheric ozone. Stratospheric ozone is the layer

of the atmosphere that protects the planet from the

harmful effects of ultraviolet radiation (UV-b). Our

primary estimate of the cumulative benefits of Title

VI is $530 billion. Using the same uncertainty estimation procedure as for other parts of the analysis,

we estimate Primary Low and Primary High estimates of $100 billion to $900 billion, respectively.

These estimates partially reflect potential averting

behaviors, such as remaining indoors or increasing

use of sunscreens or hats, which may mitigate the

effects of the UV-b exposure increases estimated in

the Pre-CAAA case.

Comparing Costs to Benefits

Based on the specific tools and techniques we

employed, our primary estimate of the net benefit

(benefits minus costs) over the entire 1990 to 2010

period of the additional criteria pollutant control

programs incorporated in the Post-CAAA case is

$510 billion. Our results imply that the monetizable

benefits alone exceeded the direct compliance costs

by four to one. For many of the factors contributing to this net benefit estimate (especially physical

effects and economic valuation estimates), we were

able to generate quantitative estimates of uncertainty.

By statistically combining these uncertain estimates,

we were able to develop a range of net benefit estimates which provide a partial indication of the overall uncertainty surrounding the central estimate of

net benefits. This range, reflecting a 90 percent probability range around the mean, or central estimate,

is negative $20 billion (implying a small probability

that costs could exceed monetized benefits) to positive $1.4 trillion.

The estimates for Title VI also indicate that cumulative benefits ($500 billion) well exceed cumulative costs ($27 billion). The time period of our Title

VI analysis (175 years) suggests that these estimates

are very uncertain. Nonetheless, the conclusion that

benefits well exceed costs holds even at our Primary

Low estimate of benefits (the low end of the 90 percent probability range, or $100 billion), and regardless of discount rate used to generate the cumulative

estimates from the perspective of the present.

The assumptions necessitated by data limitations,

by the current state of the art in each phase of the

analytical approach, by the need to predict future

conditions, and by the state of current research on

air pollutionís effects imply that both the mean estimate and the 90 percent probability range around

the central estimate are uncertain. While alternative choices for data, models, modeling assumptions,

and valuation paradigms may yield results outside

the range projected in our primary analysis, we believe based on the magnitude of the difference between the estimated benefits and costs that it is unlikely that eliminating uncertainties or adopting reasonable alternative assumptions would change the

fundamental conclusion of this study: the Clean Air

Act Amendmentsí total benefits to society exceed

its costs.

The uncertainties in the primary estimates and

the controversies which persist regarding model

choices and valuation paradigms nonetheless highlight the need for a variety of new and continued

research efforts. Based on the findings of this study,

the highest priority research needs are:

ï Improved emissions inventories and inventory management systems

ï A more geographically comprehensive air

quality monitoring network, particularly for

fine particles and hazardous air pollutants

ï Use of integrated air quality modeling tools

based on an open, consistent model architecture

ï Development of tools and data to assess the

significance of wetland, aquatic, and terrestrial ecosystem changes associated with air

pollution

ï Increased basic and targeted research on the

health effects of air pollution, especially particulate matter

ï Continued development of economic valuation methods and data, particularly valuation of changes in risks of premature mortality associated with air pollution

Properly directed and funded, such research

would improve the results of future analyses of the

benefits and costs of the Clean Air Act.

Review Process

The CAA requires EPA to consult with an outside panel of experts during the development and

interpretation of the 812 studies. This panel of ex-
The Benefits and Costs of the Clean Air Act, 1990 to 2010

vi

perts was organized in 1991 under the auspices of

EPAís Science Advisory Board (SAB) as the Advisory Council on Clean Air Act Compliance Analysis (hereafter, the Council). Organizing the review

committee under the SAB ensured that highly qualified experts would review the section 812 studies in

an objective, rigorous, and publicly open manner

consistent with the requirements and procedures of

the Federal Advisory Committee Act (FACA).

Council review of the present study began in 1993

with a review of the analytical design plan. Since

the initial June 1993 meeting, the Council has met

many times to review proposed data, proposed methodologies, and interim results. While the full Council retains overall review responsibility for the section 812 studies, some specific issues concerning

physical effects and air quality modeling were referred to subcommittees comprised of both Council

members and members of other SAB committees.

The Councilís Health and Ecological Effects Subcommittee (HEES) met several times and provided

its own review findings to the full Council. Similarly, the Councilís Air Quality Modeling Subcommittee (AQMS) held in-person and teleconference

meetings to review methodology proposals and

modeling results and conveyed its review recommendations to the parent committee.

An interagency review was conducted, during

which a number of analytical issues were discussed.

Conducting a benefit/cost analysis of a major statute such as the Clean Air Act requires scores of methodological decisions. Many of these issues are the

subject of continuing discussion within the economic

and policy analysis communities and within the

Administration. Key issues include the treatment

of uncertainty in the relationship between particulate matter exposure and mortality; the valuation of

premature mortality; the treatment of tax interaction effects; the assessment of stratospheric ozone

recovery; and the treatment of ecological and welfare effects. These issues could not be resolved within

the constraints of this review. Thus, this report reflects the findings of the EPA and not necessarily

other agencies of the Administration.
Table of Contents

vii

Table of Contents

Executive Summary ........................................................................................ i

Air Pollutant Emissions.......................................................................................................................... i

Compliance Costs ................................................................................................................................... ii

Human Health and Environmental Benefits................................................................................... iv

Comparing Costs to Benefits................................................................................................................ v

Review Process......................................................................................................................................... v

Chapter 1: Introduction................................................................................. 1

Background and Purpose ....................................................................................................................... 1

Relationship of This Report to Other Regulatory Analyses........................................................ 1

Requirements of the 1990 Clean Air Act Amendments................................................................ 2

Analytical Design and Review ............................................................................................................. 3

Target Variable ........................................................................................................................................... 3

Key Assumptions ......................................................................................................................................... 3

Analytic Sequence ....................................................................................................................................... 4

Review Process......................................................................................................................................... 6

Report Organization .............................................................................................................................. 7

Chapter 2: Emissions ................................................................................... 9

Overview Of Approach......................................................................................................................... 9

Scenario Development ......................................................................................................................... 11

Emissions Estimation Results............................................................................................................. 11

Comparison of Emissions Estimates With Other Existing Data .............................................. 18

Uncertainty In Emission Estimates .................................................................................................. 19

Chapter 3: Direct Costs .............................................................................. 23

Approach to Estimating Direct Compliance Costs...................................................................... 23

Direct Compliance Cost Results ....................................................................................................... 24

Selected Provisions .................................................................................................................................... 26

Characterization of Other Economic Impacts............................................................................... 27

Uncertainty in the Cost Estimates.................................................................................................... 30

Overview .................................................................................................................................................... 30

Quantitative Sensitivity Tests ................................................................................................................ 30

Qualitative Analysis of Key Factors Contributing to Uncertainty ................................................. 31

Chapter 4: Air Quality Modeling ................................................................ 35

Overview of Air Quality Models...................................................................................................... 35

General Methodology .......................................................................................................................... 38
The Benefits and Costs of the Clean Air Act, 1990 to 2010

viii

Air Quality Model Results.................................................................................................................. 39

Ozone .......................................................................................................................................................... 39

Particulate Matter ..................................................................................................................................... 41

Visibility ..................................................................................................................................................... 42

Acid Deposition ......................................................................................................................................... 45

SO2, NO, NO2, and CO ........................................................................................................................ 45

Uncertainty in the Air Quality Estimates ...................................................................................... 47

Chapter 5: Human Health Effects of Criteria Pollutants .......................... 51

Analytical Approach ............................................................................................................................ 51

Air Quality ................................................................................................................................................ 51

Population .................................................................................................................................................. 52

Concentration-Response Functions........................................................................................................ 52

Key Analytical Assumptions.............................................................................................................. 52

Exposure Analysis...................................................................................................................................... 54

Selection and Application

of C-R Functions ........................................................................................................................ 55

PM-Related Mortality ............................................................................................................................... 57

Health Effects Modeling Results ....................................................................................................... 60

Avoided Premature Mortality Estimates .............................................................................................. 60

Non-Fatal Health Impacts ....................................................................................................................... 62

Avoided Health Effects of Other Pollutants .................................................................................. 62

Avoided Effects of Air Toxics .................................................................................................................. 62

Avoided Health Effects for Provisions to Protect Stratospheric Ozone........................................... 63

Uncertainty in the Health Effects Analysis.................................................................................... 65

Chapter 6: Economic Valuation of Human Health Effects....................... 69

Valuation of Benefit Estimates ........................................................................................................... 69

Valuation of Premature Mortality......................................................................................................... 70

Valuation of Specific Health Effects....................................................................................................... 72

Stratospheric Ozone Provisions .............................................................................................................. 74

Results of Benefits Valuation.............................................................................................................. 74

Valuation Uncertainties....................................................................................................................... 75

Mortality Risk Benefits Transfer ............................................................................................................ 76

Chapter 7: Ecological and Other Welfare Effects .................................... 81

Overview of Approach ........................................................................................................................ 81

Characterization of Impacts of Air Pollution on Ecological Systems...................................... 82

Effects of Mercury and Ozone ................................................................................................................. 83

Effects of Nitrogen Deposition ................................................................................................................ 84

Effects of Acid Deposition ........................................................................................................................ 85

Selection of Service Flows Potentially Amenable to Economic Analysis............................... 87

Results ...................................................................................................................................................... 89

Estuarine Eutrophication Associated with Airborne Nitrogen Deposition ................................... 89

Acidification of Freshwater Fisheries ..................................................................................................... 91

Reduced Timber Growth Associated with Ozone Exposure ............................................................. 92

Reduced Carbon Sequestration Associated with Reduced Timber Growth ................................... 93

Other Categories of Ecological Benefits................................................................................................. 94

Valuation of Other Effects....................................................................................................................... 94

Stratospheric Ozone Provisions .............................................................................................................. 96
Table of Contents

ix

Summary of Quantitative Results ..................................................................................................... 97

Uncertainty ............................................................................................................................................. 98

Chapter 8: Comparison of Costs and Benefits......................................... 99

Monetized Benefits of the CAAA ..................................................................................................... 99

Overview of Benefits Analyses................................................................................................................ 99

Summary of Monetized Benefits for Human Health and Welfare Effects .................................... 100

Annual Benefits Estimates..................................................................................................................... 101

Aggregate Monetized Benefits ............................................................................................................... 103

Aggregate Benefits of Title VI Provisions ........................................................................................... 103

Comparison of Monetized Benefits and Costs............................................................................. 104

Cost-Effectiveness Evaluation ............................................................................................................... 106

Major Sources of Uncertainty .......................................................................................................... 106

Quantitative Analysis of Physical Effects and Valuation Uncertainties ...................................... 107

Measurement Error and Uncertainty in Direct Cost Inputs........................................................... 109

PM Mortality Valuation Based on Life-Years Lost ........................................................................... 109

PM Mortality Incidence Using the Dockery Study............................................................................ 110

Uncertainties in Title VI Health Benefits Analysis .......................................................................... 110

Uncertainties in Emissions and Air Quality Steps ........................................................................... 111

Omission of Potentially Important Benefits Categories................................................................... 113

Alternative Discount Rates ................................................................................................................... 113

Appendix A: Emissions Analysis

Scenario Development ....................................................................................................................... A-2

Comparison of the Base Year Inventory and Emissions Projections

with Other Existing Data ....................................................................................................... A-7

Post-CAAA Emissions Estimates and EPA Trends Data ................................................................ A-7

Prospective Analysis and GCVTC Emissions Estimates ............................................................... A-12

Prospective Analysis PM2.5 Emissions Estimates and Observed Data ....................................... A-13

Industrial Point Sources...................................................................................................................A-14

Overview of Approach ......................................................................................................................... A-14

Base Year Emissions.............................................................................................................................. A-15

Growth Projections ............................................................................................................................... A-15

Control Scenarios .................................................................................................................................. A-16

Emissions Summary ............................................................................................................................. A-18

Utilities ................................................................................................................................................A-21

Overview of Approach ......................................................................................................................... A-21

Base Year Emissions.............................................................................................................................. A-21

Control Scenarios .................................................................................................................................. A-22

Emissions Summary ............................................................................................................................. A-22

Nonroad Engines/Vehicles.............................................................................................................A-24

Overview of Approach ......................................................................................................................... A-24

Base Year Emissions.............................................................................................................................. A-24

Growth Projections ............................................................................................................................... A-24

Control Scenarios .................................................................................................................................. A-25

Emissions Summary ............................................................................................................................. A-27

Motor Vehicles...................................................................................................................................A-29

Overview of Approach ......................................................................................................................... A-29

Base Year Emissions.............................................................................................................................. A-29
The Benefits and Costs of the Clean Air Act, 1990 to 2010

x

Growth Projections ............................................................................................................................... A-30

Control Scenarios .................................................................................................................................. A-30

Emissions Summary ............................................................................................................................. A-31

Area Sources .......................................................................................................................................A-35

Overview of Approach ......................................................................................................................... A-35

Base Year Emissions.............................................................................................................................. A-35

Growth Projections ............................................................................................................................... A-36

Control Scenarios .................................................................................................................................. A-37

Emissions Summary ............................................................................................................................. A-37

Reasonable Further Progress Requirements...............................................................................A-41

Mercury Emission Estimates..........................................................................................................A-48

Medical Waste Incinerators (MWI) .................................................................................................... A-48

Municipal Waste Combustors (MWCs) ............................................................................................. A-48

Electric Utility Generation ................................................................................................................. A-49

Hazardous Waste Combustion ........................................................................................................... A-49

Chlor-alkali Plants ................................................................................................................................ A-49

Uncertainties in the Emission Estimates .....................................................................................A-51

Base Year Emission Estimates............................................................................................................. A-51

Growth Forecasts ................................................................................................................................... A-52

Future Year Control Assumptions ..................................................................................................... A-53

References............................................................................................................................................A-55

Appendix B: Direct Costs .......................................................................... B-1

Introduction.......................................................................................................................................... B-1

Summary of Methods ......................................................................................................................... B-1

ERCAM Model ......................................................................................................................................... B-1

IPM Model ................................................................................................................................................. B-2

Additional Methods ................................................................................................................................. B-2

Annalization of Costs.............................................................................................................................. B-2

CAAA Costs ........................................................................................................................................ B-2

Industrial Point Sources .......................................................................................................................... B-3

Utility Sources........................................................................................................................................... B-8

Non-Road Engines/Vehicles ............................................................................................................... B-11

Motor Vehicles ....................................................................................................................................... B-12

Area Sources ........................................................................................................................................... B-18

Reasonable Further Progress Requirements ..................................................................................... B-19

Costs by Title.......................................................................................................................................... B-23

Social Costs ......................................................................................................................................... B-29

Limitations and Uncertainties........................................................................................................ B-31

Emissions Projections ........................................................................................................................... B-33

Draft Rules ............................................................................................................................................. B-33

Economic Incentive Provisions .......................................................................................................... B-33

Reasonable Further Progress (RFP) and Attainment Costs ........................................................... B-34

Future Year Control Cost Assumptions............................................................................................ B-34

Discount Rate Assumptions ................................................................................................................ B-34

Source-Specific Cost Equations ........................................................................................................... B-34

Sensitivity Analyses to Quantify Key Uncertainties ...................................................................... B-35
Table of Contents

xi

Appendix C: Air Quality Modeling ............................................................ C-1

Introduction.......................................................................................................................................... C-1

Overview of Section 812 Prospective Modeling Analysis......................................................... C-2

Air Quality Models and Databases ...................................................................................................... C-2

Methodology for the Combined Use of Observations and Air Quality Modeling Results ......... C-3

Estimating the Effects of the CAAA on Ozone Air Quality .................................................. C-4

Overview of the UAM and UAM-V Photochemical Modeling Systems.............................. C-5

UAM .......................................................................................................................................................... C-5

UAM-V ..................................................................................................................................................... C-5

Regional-Scale Modeling of the Eastern U.S....................................................................................... C-6

Regional-Scale Modeling of the Western U.S. .................................................................................. C-12

Urban-Scale Modeling of the San Francisco Bay Area .................................................................... C-15

Urban-Scale Modeling of the Los Angeles Area ............................................................................... C-18

Urban-Scale Modeling of the Maricopa County (Phoenix Area) ................................................... C-20

Calculation of Ozone Air Quality Profiles ................................................................................C-23

Overview of the Methodology ............................................................................................................. C-23

Description of the Observation Dataset ............................................................................................ C-23

Calculation of Percentile-Based Adjusted Factors ........................................................................... C-24

Use of Adjustment Factors to Modify Observed Concentrations ................................................. C-25

Calculation of Ozone Profiles............................................................................................................. C-25

Estimating the Effects of the CAAA on Particulate Matter ...................................................C-38

Overview of the RADM/RPM Modeling System............................................................................ C-38

Application of RADM/RPM for the Eastern U.S. .......................................................................... C-38

Overview of the REMSAD Modeling System .................................................................................. C-41

Application of REMSAD for the Western U.S. ............................................................................... C-44

Calculation of PM Air Quality Profiles ............................................................................................ C-48

Estimating the Effects of the CAAA on Visibility ...................................................................C-64

RADM/RPM and Visibility ...........................................................................................................C-64

RADM/RPM Modeling Results.......................................................................................................... C-64

REMSAD and Visibility ..................................................................................................................C-66

REMSAD Modeling Results ................................................................................................................ C-66

Acid Deposition.................................................................................................................................C-69

Overview of the RADM Modeling System ....................................................................................... C-69

RADM Modeling Results ..................................................................................................................... C-69

Estimating the Effects of the CAAA on Sulfur Dioxide,

Oxides of Nitrogen, and Carbon Monoxide..................................................................................... C-77

Methodology for Estimating Future-Year SO2, NO, NO2, and CO Concentrations ............. C-77

Emission-Based Ratios for SO2, NO, NO2, and CO.................................................................... C-80

Comparison of the Pre- and Post-CAAA Ratios ............................................................................. C-80

Attributes and Limitations of the Modeling Analysis Methodology ...................................C-81

Conclusions and Recommendations for Further Research ....................................................C-82

References............................................................................................................................................C-84

Appendix D: Human Health

and Visibility Effects of Criteria Pollutants .................................... D-1

Introduction..........................................................................................................................................D-1

Health Effects Modeling Approach ................................................................................................D-1

Quantifying Changes in Pollutant Exposures ................................................................................... D-1

Quantifying Human Health Effects of Exposure .............................................................................. D-5
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xii

Types of Health Studies .....................................................................................................................D-5

Chamber Studies ..................................................................................................................................... D-5

Epidemiological Studies ......................................................................................................................... D-6

Selection of C-R Functions ...............................................................................................................D-7

C-R Function General Issues................................................................................................................. D-7

C-R Function Selection Criteria ........................................................................................................D-12

Mortality .................................................................................................................................................D-16

Chronic Illness .......................................................................................................................................D-26

Hospital Administration .....................................................................................................................D-30

Minor Illness...........................................................................................................................................D-48

C-R Functions Linking Air Pollution and Adverse Health Effects .................................... D-58

Carbon Monoxide .................................................................................................................................D-58

Nitrogen Dioxide ..................................................................................................................................D-61

Ozone ......................................................................................................................................................D-64

Particulate Matter .................................................................................................................................D-70

Sulfur Dioxide........................................................................................................................................D-80

Modeling Results .............................................................................................................................. D-82

Uncertainty ............................................................................................................................................D-82

Sensitivity Analyses ..............................................................................................................................D-83

References........................................................................................................................................... D-93

Appendix E: Ecological Effects of Criteria Pollutants ............................ E-1

Introduction.......................................................................................................................................... E-1

Ecological Overview of the Impacts of Air Pollutants Regulated by the CAAA............... E-3

Effects of Atomospheric Pollutants on Natural Systems....................................................................E-3

Acidic Deposition .....................................................................................................................................E-4

Nitrogen Deposition ................................................................................................................................E-4

Hazardous Air Pollutant Deposition ...................................................................................................E-5

Troposheric Ozone ...................................................................................................................................E-7

Multiple Stresses and Patterns of Exposure ..........................................................................................E-8

Summary of Ecological Impacts from Air Pollutants Regulated by the CAAA...........................E-9

Methodological Overview............................................................................................................... E-14

Using Service Flow Endpoints for Valuation .................................................................................. E-15

Defensible Links and Quantitative Modeling Requirements ....................................................... E-16

Extending Future Analyses .................................................................................................................. E-20

Eutrophication of Estuaries ............................................................................................................ E-20

Impacts of Nitrogen Deposition on Estuaries................................................................................... E-20

Economic Benefits of Decreasing Atmospheric Deposition of Nitrogen ...................................... E-21

GIS-Based Deposition and Loadings Estimates ............................................................................... E-21

Displaced Costs from Reducing Atmospheric Deposition to Estuaries ........................................ E-23

Avoided Damages to Estuarine Ecosystems...................................................................................... E-30

Caveats and Uncertainties .................................................................................................................. E-33

Acidification of Freshwater Fisheries .......................................................................................... E-33

Acidification of Surface Waters and Ecological Impacts ............................................................... E-34

Modeling Acidification ........................................................................................................................ E-35

Acidification Results............................................................................................................................. E-39

Economic Results .................................................................................................................................. E-40

Avoiding Cost of Liming .................................................................................................................... E-42

Caveats and Uncertainties .................................................................................................................. E-43

Emissions, Depositions, and Acidification Estimates ..................................................................... E-43
Table of Contents

xiii

Timber Production Impacts From Tropospheric Ozone ....................................................... E-45

Ecological Effects of Ozone.................................................................................................................. E-45

Modeling Timber Impacts from Ozone............................................................................................. E-46

Ecological Results .................................................................................................................................. E-47

Economic Impacts ................................................................................................................................. E-48

Caveats and Uncertainties .................................................................................................................. E-48

Carbon Sequestration Effects .............................................................................................................. E-50

Caveats and Uncertainties .................................................................................................................. E-52

Aesthetic Degradation of Forests .................................................................................................. E-52

Forest Aesthetic Effects from Air Pollutants..................................................................................... E-53

Economic Value of Changes in Forest Aesthetics ............................................................................ E-58

Extending Economic Estimates to a Broader Area ......................................................................... E-59

Caveats and Uncertainties .................................................................................................................. E-61

Toxification of Freshwater Fisheries............................................................................................ E-61

Impacts of Toxic Air Emissions .......................................................................................................... E-62

Illustration of Economic Cost to Anglers ......................................................................................... E-63

Caveats and Uncertainties .................................................................................................................. E-65

Conclusion and Implications.......................................................................................................... E-65

Summary of Quantitative Results ..................................................................................................... E-66

Recommendations of Future Research .............................................................................................. E-68

References............................................................................................................................................ E-70

Appendix F: Effects of Criteria Pollutants on Agriculture ..................... F-1

Introduction...........................................................................................................................................F-1

Ozone Concentration Data ...............................................................................................................F-1

Calculation of the SUM06 Statistic....................................................................................................... F-2

October to April Ozone Concentration Data .................................................................................... F-2

Yield Change Estimates ......................................................................................................................F-3

Exposure-Response Functions ................................................................................................................. F-3

Calculation of Ozone Indices................................................................................................................. F-5

Calculation of County Weights ............................................................................................................. F-5

Calculation of Percent Change in Yield .............................................................................................. F-6

Economic Impact Estimates ..............................................................................................................F-7

Conclusions ...........................................................................................................................................F-8

References...............................................................................................................................................F-9

Appendix G: Stratospheric Ozone Assessment...................................... G-1

Introduction..........................................................................................................................................G-1

History of Stratospheric Ozone Protection and the CAAA ...................................................G-1

Cost and Benefit Approaches .................................................................................................... .......G-8

Cost Approach in RIAs .......................................................................................................................... G-8

Benefits Approach in RIAs ..................................................................................................................G-11

Emissions Modeling ..............................................................................................................................G-13

Stratospheric Ozone Depletion Modeling and Global Warming.................................................G-14

Physical Effects .......................................................................................................................................G-15

Valuation................................................................................................................................................G-22

Adjustments to Estimates From Existing Analyses ................................................................. G-24

Discount Rate ........................................................................................................................................G-24

Value of Statistical Life ........................................................................................................................G-25
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xiv

Cost and Benefits Results with Adjusted Parameters.............................................................. G-25

Five Percent Discount Rate .................................................................................................................G-26

Three Percent and Seven Percent Sensitivity Tests .........................................................................G-27

Two Percent Discount Rate ................................................................................................................G-30

Undiscounted Benefits ..........................................................................................................................G-30

Limitations and Uncertainties....................................................................................................... G-34

Long-Term Discounting ......................................................................................................................G-34

Costs.........................................................................................................................................................G-35

Benefits ....................................................................................................................................................G-37

References........................................................................................................................................... G-39

Appendix H: Valuation of Human Health and Welfare Effects of Criteria

Pollutants........................................................................................... H-1

Methods Used to Value Health and Welfare Effects ................................................................. H-1

Valuation of Specific Health Endpoints ....................................................................................... H-3

Valuation of Premature Mortality Avoided ...................................................................................... H-3

Mortality Valuation Methodologies ..................................................................................................... H-6

Valuation Strategy Chosen for this Analysis....................................................................................H-11

Valuation of Hospital Admissions Avoided.....................................................................................H-14

Valuation of Chronic Bronchitis Avoided .......................................................................................H-15

Valuation of Chronic Asthma Avoided............................................................................................H-16

Valuation of Other Morbidity Endpoints Avoided ........................................................................H-17

Valuation of Welfare Effects .......................................................................................................... H-18

Visibility Valuation .............................................................................................................................H-18

Results of Valuation of Health and Welfare Effects ................................................................ H-27

Uncertainties in the Valuation Estimates................................................................................... H-31

Relative Importance of Different Components of Uncertainty....................................................H-31

Economic Benefits Associated with Reducing Premature Mortality ...........................................H-32

Sensitivity Test of Benefits Due to Reduced Premature Mortality Valuation ...........................H-36

Sensitivity Test for Impact of Income Changes Over Time ..........................................................H-38

Illustrative Calculations - Morbidity Benefits Estimates ...............................................................H-40

Illustrative Calculations - VSL Estimate ..........................................................................................H-41

References........................................................................................................................................... H-42

Appendix I: Implications for Future Research...........................................I-1

Overview .................................................................................................................................................I-1

Emissions Modeling .............................................................................................................................. I-1

Cost Estimation .....................................................................................................................................I-2

Air Quality Modeling ..........................................................................................................................I-3

Human Health Effects Estimation ....................................................................................................I-3

Evaluation of the Effects of Air Toxics ...........................................................................................I-4

Ecological Effects Estimation .................................................................................................. ...........I-4

Economic Valuation .............................................................................................................................I-6
List of Tables and Figures

xv

Table ES-1 Summary Comparison of Benefits and Costs (Estimates in millions 1990$). ............... iii

Table 2-1 Major Emissions Source Categories. ...................................................................................... 10

Table 2-2 Summary of National Annual Emissions Projections ....................................................... 12

Table 2-3 Summary of Source Category of National Annual Emission Projections to 2010

(thousand tons). ........................................................................................................................... 14

Table 2-4 Airborne Mercury Emission Estimates. ................................................................................ 15

Table 2-5 Key Uncertainties Associated with Emissions Estimation. .............................................. 21

Table 3-1 Summary of Direct Costs for Titles I to V of CAAA, By Title and Selected

Provisions. .................................................................................................................................... 26

Table 3-2 Results of Quantitative Sensitivity Tests. ............................................................................. 32

Table 3-3 Key Uncertainty Associated with Cost Estimation. .......................................................... 33

Table 4-1 Overview of Air Quality Models. .......................................................................................... 37

Table 4-2 Comparison of Visibility in Selected Eastern Urban Areas ............................................. 43

Table 4-3 Comparison of Visibility in Selected Eastern National Parks......................................... 43

Table 4-4 Comparison of Visibility in Selected Western Urban Areas. .......................................... 44

Table 4-5 Comparison of Visibility in Selected Western National Parks. ...................................... 44

Table 4-6 Median Values of the Distribution of ratios of 2010 Post-CAAA/Pre-CAAA

Adjustment Factors .................................................................................................................... 46

Table 4-7 Key Uncertainties Associated with Air Quality Modeling. ............................................. 48

Table 5-1 Human Health Effects of Criteria Pollutants ...................................................................... 53

Table 5-2 Summary of Considerations Used in Selecting C-R Functions....................................... 56

Table 5-3 Change in Incidence of Adverse Health Effects Associated

with Criteria Pollutants in 2010 (Pre-CAAA minus Post-CAAA) - 48 State U.S.

Population (avoided cases per year) ........................................................................................ 61

Table 5-4 Mortality Distribution by Age in Primary Analysis (2010 only),

Based on Pope et al. (1995)........................................................................................................ 62

Table 5-5 Major Health Benefits of provisions to Protect Stratospheric Ozone. .......................... 64

Table 5-6 Key Uncertainties Associated with Human Health Effects Modeling. ......................... 65

Table 6-1 Health Effects Unit Valuation (1990 Dollars)...................................................................... 70

Table 6-2 Summary of Mortality Valuation Estimates (millions of $1990) .................................... 72

Table 6-3 Results of Human Health Benefits Valuation, Post-CAAA 2010................................... 75

Table 6-4 Valuation of CAAA Benefits: Potential Sources and Likely Direction of Bias........... 76

Table 6-5 Key Uncertainties Associated with Valuation of Health Benefits .................................. 79

Table 7-1 Classes of Pollutants and Ecological Effects ......................................................................... 83

Table 7-2 Interactions of Mercury and Ozone with Natural Systems at Various Levels of

Organization ................................................................................................................................ 84

Tables
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xvi

Table 7-3 Interactions Between Nitrogen Deposition and Natural Systems

at Various Levels of Organization .......................................................................................... 85

Table 7-4 Interactions Between Acid Deposition and Natural Systems

at Various Levels of Organization .......................................................................................... 86

Table 7-5 Ecological Effects of Air Pollutant ......................................................................................... 88

Table 7-6 Summary of Endpoints Selected For Quantitative Analysis............................................ 89

Table 7-7 Estimated Displaced Costs For Three Estuaries.................................................................. 91

Table 7-8 Annual Economic Impact of Acidification in 2010 ............................................................ 92

Table 7-9 Quantified and Unquantified Ecological and Welfare Effects

of Title VI Provisions................................................................................................................. 96

Table 7-10 Summary of Evaluated Ecological Benefits .......................................................................... 97

Table 7-11 Summary of Other Welfare Benefits...................................................................................... 97

Table 7-12 Key Uncertainties Associated with Ecological Effects Estimation ................................. 98

Table 8-1 Criteria Pollutant Health and Welfare Benefits in 2010.................................................. 102

Table 8-2 Present Value of Monetized Benefits for 48 State Population ....................................... 103

Table 8-3 Summary of Quantified primary Central Estimate Benefits and Costs ...................... 104

Table 8-4 Summary comparison of Benefits and Costs ...................................................................... 105

Table 8-5 Summary of Impact of Alternative Methods for Calculating Costs and Benefits..... 108

Table 8-6 Effect of Alternative Discount Rates on Primary Central Estimates .......................... 114

Table A-1 Base Year Inventory - Summary of Approach ................................................................... A-3

Table A-2 Analysis Approach By Major Sector .................................................................................... A-4

Table A-3 Projection Scenario Summary By Major Sector................................................................ A-5

Table A-4 Comparison of Emissions: Prospective Analysis and GCVTC Study .....................A-13

Table A-5 Industrial Point Source Control Assumptions For The Post-CAAA Scenario ......A-17

Table A-6 Industrial Point Source Emission Summaries By Pollutant

For 1990, 2000, and 2010 .......................................................................................................A-19

Table A-7 Utility Emission Summary ...................................................................................................A-23

Table A-8 BEA Growth Forecasts by Major Source Category: Nonroad Engines/Vehicles ..A-26

Table A-9 Nonroad National Emission Projections By Source Category ...................................A-28

Table A-10 Applicability of Mobile Source Control Programs ........................................................A-32

Table A-11 National Highway Vehicle Emissions By Vehicle Type...............................................A-33

Table A-12 Area Source Emission Summary By Pollutant For 1990, 2000, and 2010 ................A-39

Table A-13 2000 Rate of Progress Analysis ............................................................................................A-43

Table A-14 2010 Rate of Progress Analysis ............................................................................................A-45

Table A-15 Discretionary Control Measures Modeled For ROP/RFP ..........................................A-47

Table A-16 Airborne Mercury Emission Estimates .............................................................................A-51

Table B-1 Summary of Cost Estimates By Emissions Source ........................................................... B-3

Table B-2 Point Source VOC Cost Summary ...................................................................................... B-5

Table B-3 Point Source NOx Summary ................................................................................................. B-7

Table B-4 Differences in the Control of Utility NOx and SOx For

the Pre-CAAA and the Post-CAAA Regulatory Scenarios ........................................... B-9

Table B-5 Electric Power Industry Costs From Post-CAAA Controls For SOx and NOx... B-10

Table B-6 Cost Estimates For Non-road Engine/Vehicle CAAA Programs.............................. B-12

Table B-7 Motor Vehicle Unit Costs By Provision ........................................................................... B-16
List of Tables and Figures

xvii

Table B-8 Cost Estimates of Motor Vehicle Program....................................................................... B-17

Table B-9 Cost Summary of Area Source NOx and PM Controls ............................................... B-19

Table B-10 2000 Rate of Progress Analysis ............................................................................................ B-21

Table B-11 2010 Rate of Progress Analysis ............................................................................................ B-22

Table B-12 Summary of Cost Estimates By CAAA Title .................................................................. B-24

Table B-13 Title I National Rules, Point, and Area Source VOC Control Costs ....................... B-26

Table B-14 Summary of Costs For Title I .............................................................................................. B-26

Table B-15 Summary of Title II Motor Vehicle and Non-road

Engine/Vehicle Program Costs ........................................................................................... B-27

Table B-16 Title III, MACT Standards, Point and Area Source VOC Control Costs ............... B-28

Table B-17 Annual Cost of Title IV ........................................................................................................ B-29

Table B-18 Potential Effects of Uncertainty on Cost Estimates ....................................................... B-33

Table B-19 Factors Affecting Cost of Major CAAA Provisions ...................................................... B-37

Table B-20 Rate-of-Progress Cost Sensitivity Summary ..................................................................... B-42

Table B-21 Area Source PM Control Cost Sensitivity Analysis, Year 2000 ................................. B-43

Table B-22 Results of Sensitivity Analysis of LEV Cost ....................................................................E-45

Table B-23 Discount Rate Sensitivity Analysis For 2010 Cost Estimates ...................................... B-46

Table C-1 Emission Totals by Component for each Scenario

for the OTAG Domain (tpd) ...............................................................................................C-10

Table C-2 Emissions Totals by Component for each Scenario

for the Entire U.S. (tpd) ........................................................................................................C-13

Table C-3 Emissions Totals by Component for each Scenario

for the San Francisco Bay Area Entire U.S. (tpd) ...........................................................C-17

Table C-4 Emission Totals by Component for each Scenario for Los Angeles (tpd)................C-19

Table C-5 Emissions Totals by Component for each Scenario for Pheonix (tpd) .....................C-22

Table C-6 Comparison of CASTNet and RPM Average Concentration of SO4 ......................C-40

Table C-7 Comparison of CASTNet and RPM Average Concentrations

and Fractions of NO3 ............................................................................................................C-41

Table C-8 REMSAD Output File Species ............................................................................................C-43

Table C-9 Chemical Speciation Schemes Applied for REMSAD ...................................................C-45

Table C-10 Emission Totals by Component for Each Scenario for the Entire U.S. (tpd) .........C-46

Table C-11 Background Species Concentration used for REMSAD Initial

and Boundary Conditions.....................................................................................................C-47

Table C-12 Geographical Regions of the U.S. .......................................................................................C-50

Table C-13 Comparison of Visibility in Selected Eastern Cities, Metropolitan Areas,

and National Parks .................................................................................................................C-65

Table C-14 Median Values of the Distribution of Ratios of 2010

Post-CAAA/Pre-CAAA Adjustment Factors.................................................................C-81

Table C-15 Background Concentrations used to Prepare the SO2, NO, NO2,

and CO Profiles.......................................................................................................................C-81

Table D-1 Summary of Considerations Used in Selecting C-R Functions.................................. D-13

Table D-2a Studies and Results Selected for Meta-Analysis

of the Relationship Between Daily Mortality and Exposure

to Ambient Ozone in the United States........................................................................... D-21

Table D-2b Selected Studies and Results for Carbon Monoxide and Mortality ........................... D-23
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xviii

Table D-3 Studies and Results Selected for Adverse Effects in Fetuses, Infants,

and Young Children.............................................................................................................. D-25

Table D-4 Summary of Selected Studies for Chronic Illness .......................................................... D-28

Table D-5 Studies Used to Develop Respiratory Admissions Estimates ..................................... D-32

Table D-6 Summary of Hospital Admissions Studies - Respiratory Illnesses ............................. D-33

Table D-7 Studies Used to Develop Cardiovascular Admissions Estimates ............................... D-39

Table D-8 Summary of Hospital Admissions Studies - Cardiovascular Illness .......................... D-40

Table D-9 Studies Used to Develop Asthma Emergency Room Visits ........................................ D-42

Table D-10 Summary of Selected Studies for Emergency Room Visits

- Asthma and Acute Wheezing ........................................................................................... D-43

Table D-11 Summary of Selected Studies for Emergency Room Visits

- All-Cause, All-Respiratory, COPD, and Bronchitis................................................... D-46

Table D-12 Studies Used to Develop Minor Illness Estimates.......................................................... D-51

Table D-13 Summary of Selected Studies for Minor Illness .............................................................. D-52

Table D-14 Summary of Selected Studies for Asthmatics .................................................................. D-55

Table D-15 Summary of C-R Functions for Carbon Monoxide ...................................................... D-59

Table D-16 Summary of C-R Functions for Nitrogen Dioxide ....................................................... D-62

Table D-17 Summary of C-R Functions for Ozone ............................................................................ D-65

Table D-18 Summary of C-R Functions for Particulate Matter....................................................... D-71

Table D-19 Summary of C-R Functions for Sulfur Dioxide ............................................................. D-81

Table D-20 Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants

(Pre-CAAA minus Post- CAAA) ñ 48 State U.S. Population within 50 km of a

Monitor (avoided cases per year)........................................................................................ D-85

Table D-21 Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants

(Pre-CAAA minus Post- CAAA) ñ 48 State U.S. Population

(avoided cases per year) ......................................................................................................... D-87

Table D-22 Mortality Distribution by Age in Primary Analysis, Based

on Pope et al. (1995) .............................................................................................................. D-89

Table D-23 Illustrative Estimates of the Impact of Criteria Pollutants on Mortality

ñ 48 State U.S. Population within 50 km of a Monitor (cases per year .................... D-90

Table D-24 Illustrative Estimates of the Impact of Criteria Pollutants on Mortality

ñ 48 State U.S. Population (cases per year) ...................................................................... D-90

Table D-25 Comparison of Alternative Lag Assumptions for Premature Mortality

Associated with PM Exposure ............................................................................................ D-91

Table E-1 Classes of Pollutants and Ecological Effects ....................................................................... E-3

Table E-2 Interactions Between Acid Deposition and Natural Systems

at Various Levels of Organization ...................................................................................... E-10

Table E-3 Interactions Between Nitrogen Deposition and Natural Systems

at Various Levels of Organization ...................................................................................... E-11

Table E-4 Interactions of Mercury and Ozone With Natural Systems

at Various Levels of Organization ...................................................................................... E-12

Table E-5 Ecological Impacts with Identifiable Human Service Flows ........................................ E-16

Table E-6 Model Coverage for Candidate Endpoints for Quantitative Assessment ................. E-18

Table E-7 Summary of Endpoints Selected For Quantitative Analysis........................................ E-19

Table E-8 Service Flows Affected by Changes in Estuarine Ecosystems ..................................... E-21

Table E-9 Total Nitrogen Deposition Based on GIS Analysis........................................................ E-22
List of Tables and Figures

xix

Table E-10 Land Use Prevalence and Pass-Through Figures ............................................................. E-23

Table E-11 Nitrogen Loading From Atmospheric Deposition ........................................................ E-23

Table E-12 Estimated Avoided Costs for three Estuaries................................................................... E-27

Table E-13 Avoided Cost for Atlantic Coast ........................................................................................ E-29

Table E-14 Summary of pH-Based Effects Threshold ......................................................................... E-38

Table E-15 Acidification Results - 2010 .................................................................................................. E-40

Table E-16 Annual Economic Impact of Acidification in 2010 ........................................................ E-41

Table E-17 Cumulative Economic Benefits of Acidification from 1990 to 2010.......................... E-42

Table E-18 Cumulative Cost of pH Stabilization from 1990 to 2010 ............................................. E-43

Table E-19 Difference in Commercial Timber Growth Rates With

and Without the CAAA........................................................................................................ E-48

Table E-20 Carbon Flux B CAAA versus No-CAAA Air Quality Scenarios.............................. E-52

Table E-21 Typical Impacts of Specific Pollutants on the Visual Quality of Forests .................. E-54

Table E-22 Forests Affected by Regional Pollution............................................................................. E-55

Table E-23 Summary of Monetized Estimates of the Annual Value of Forest

Quality Changes...................................................................................................................... E-60

Table E-24 Illustrative Value of Avoiding Forest Damage in the United States .......................... E-60

Table E-25 Summary of National Data on Toxicity Sampling for Fishing Advisories.............. E-63

Table E-26 Estimates of the Welfare Cost of Toxification in New York State ............................ E-63

Table E-27 Summary of Evaluated Ecological Benefits ...................................................................... E-67

Table F-1 Ozone Exposure-Response Functions for Selected Corps (SUM06) .............................F-4

Table F-2 Relative Percent Yield Course ................................................................................................F-7

Table F-3 Ozone Exposure-Response Functions for Selected Corps (SUM06) .............................F-8

Table G-1 Six Major Sections of Title VI ................................................................................................G-3

Table G-2 Phaseout Scenario in Clean Air Act Section 604 and Phaseout Scenario

in Amendments Added Under Clean Air Act Section 606 ............................................G-5

Table G-3 Scope of Title VI Cost Estimates...........................................................................................G-9

Table G-4 Benefits of Section 604, 606, and 609 ................................................................................ G-16

Table G-5 Benefits of Section 608 .......................................................................................................... G-20

Table G-6 Benefits of Section 611 .......................................................................................................... G-21

Table G-7 Sections 604 and 606: Valuation of Total Benefits from 1990 to 2165,

With a Two Percent Discount Rate .................................................................................. G-23

Table G-8 Adjustment Strategy for Key Parameters......................................................................... G-25

Table G-9 Costs and Benefits of Title VI ............................................................................................. G-27

Table G-10 Human Health Beneftis of Section 604 and 606 ............................................................. G-28

Table G-11 Summary of Benefits for Title VI with a Two Percent Discount Rate

and $4.8 Million VSL ............................................................................................................ G-31

Table G-1 Summary of Costs for Title VI by Section with a Two Percent Discount Rate .... G-32

Table G-13 Major Limitations of Existing Cost Analysis for Title VI ........................................... G-36

Table G-14 Major Limitations of Existing Benefits Analysis for Title VI ..................................... G-38

Table H-1 Summary of Mortality Valuation Estimates ..................................................................... H-1

Table H-2 Summary of Alternative Methods for Assessing the Value

of Reduced Mortality Risk .................................................................................................... H-3
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xx

Table H-3 Unit Values Used for Economical Valuation of Health

and Welfare Endpoints ......................................................................................................... H-21

Table H-4 Primary Estimates of Health and Welfare Benefits Due

to Criteria Pollutants - Post-CAAA 2000 ........................................................................ H-29

Table H-5 Primary Estimates of Health and Welfare Benefits Due

to Criteria Pollutants - Post-CAAA 2010 ....................................................................... H-30

Table H-6 Sensitivity Analysis of Alternative Discount Rates

on the Valuation of Reduced Premature Mortality ....................................................... H-37

Table H-7 Elasticity Values for Conducting Sensitivity Analysis of Income Effect ................. H-39

Table H-8 Illustrative Adjustment to Estimates of WPT to Avoid Morbidity .......................... H-40

Table H-9 Illustrative Adjustment to Estimates of the Value of Statistical Life......................... H-41

Figures

Figure 1-1 Analytic Sequence For First Section 812 Prospective Analysis ......................................... 4

Figure 2-1 Pre- and Post-CAAA Scenario VOC Emissions Estimates .............................................. 16

Figure 2-2 Pre- and Post-CAAA Scenario NOx Emissions Estimates............................................... 16

Figure 2-3 Pre- and Post-CAAA Scenario SO2 Emissions Estimates. ............................................... 16

Figure 2-4 Pre- and Post-CAAA Scenario CO Emissions Estimates. ................................................ 17

Figure 2-5 Pre- and Post-CAAA Scenario PM10 Emissions Estimates. ............................................ 17

Figure 2-6 Pre- and Post-CAAA Scenario PM2.5 Emissions Estimates. ........................................... 17

Figure 2-7 1990 Primary PM2.5 Emissions by EPA Region. ............................................................... 19

Figure 4-1 Schematic Diagram of the Future-Year Concentration Estimation Methodology. ... 39

Figure 4-2 Distribution of Monitor-Level Ratios for 95th Percentile

Ozone Concentration: 2010 Post-CAAA/Pre-CAAA. .................................................... 40

Figure 4-3 Distribution of Combined RADM/RPM- and REMSAD-Derived

Monitor Level Ratios for Annual Average PM10 Concentrations:

2010 Post-CAAA/Pre-CAAA. ................................................................................................ 42

Figure 4-4 Distribution of Combined RADM/RPM- and REMSAD-Derived

Monitor-Level Ratios for Annual Average PM2.5 Concentrations:

2000 Post-CAAA / 2000Pre-CAAA . .................................................................................... 42

Figure 4-5 Distribution of Monitor-Level Ratios of Summer SO2 Emissions:

2010 Post-CAAA/ 2010 Pre-CAAA ...................................................................................... 45

Figure 4-6 Distribution of Monitor-Level Ratios of NO Summer Emissions:

2010 Post-CAAA/ 2010 Pre-CAAA ...................................................................................... 45

Figure 4-7 Distribution of Monitor-Level Ratios of NO2 Summer Emissions:

2010 Post-CAAA/ 2010 Pre-CAAA ...................................................................................... 46

Figure 4-8 Distribution of Monitor-Level Ratios of CO Summer Emissions:

2010 Post-CAAA/ 2010 Pre-CAAA ...................................................................................... 46
List of Tables and Figures

xxi

Figure 7-1 Annual Economic Welfare Benefit of Mitigating Ozone Impacts

on Commercial Timber: Difference Between Pre- and Post-CAAA ............................ 93

Figure 8-1 Monte Carlo Simulation Model Primary Benefits Results

for Target Years - Titles I Through V . ............................................................................... 101

Figure 8-2 Analysis of Contribution of Key Parameters to Quantified Uncertainty .................. 107

Figure A-1 Comparison of Pre-CAAA, Post-CAAA, and Trends

VOC Emissions Estimates ...................................................................................................... A-8

Figure A-2 Comparison of Pre-CAAA, Post-CAAA, and Trends

NOx Emissions Estimates ....................................................................................................A-10

Figure A-3 Comparison of Pre-CAAA, Post-CAAA, and Trends

SO Emissions Estimates ........................................................................................................A-10

Figure A-4 Comparison of Pre-CAAA, Post-CAAA, and Trends

CO Emissions Estimates .......................................................................................................A-11

Figure A-5 Comparison of Pre-CAAA, Post-CAAA, and Trends

PM10 Emissions Estimates ...................................................................................................A-11

Figure A-6 1990 Primary PM2.5 Emissions by EPA Region ............................................................A-13

Figure C-1 Schematic Diagram of the Future-year Concentration

Estimation Methodology ........................................................................................................ C-4

Figure C-2 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 15 July 1995 OTAG Episode Day: 2010 pre-CAAA90

minus base 1990 .......................................................................................................................C-26

Figure C-3 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 15 July 1995 OTAG Episode Day: 2010 post-CAAA90

minus base 1990 .......................................................................................................................C-27

Figure C-4 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 15 July 1995 OTAG Episode Day: 2010 post-CAAA90

minus pre-CAAA90 ...............................................................................................................C-28

Figure C-5 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 8 July 1995 Western U.S. Simulation Day: 2010 pre-CAAA90

minus base 1990 .......................................................................................................................C-29

Figure C-6 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 8 July 1995 Western U.S. Simulation Day: 2010 post-CAAA90

minus base 1990 .......................................................................................................................C-30

Figure C-7 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 8 July 1995 Western U.S. Simulation Day: 2010 post-CAAA90

minus pre-CAAA90 Base 1990 ...........................................................................................C-31

Figure C-8 UAM-V Modeling Domain For Western U.S. Analysis with the

High-Resolution Modeling Domains for the San Francisco Bay Area,

Los Angeles, and Phoenix .....................................................................................................C-32

Figure C-9 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 6 August 1990 Simulation Day for Northern California:

2010 post-CAAA90 Minus Pre-CAAA90 .........................................................................C-33

Figure C-10 Difference in Daily Maximum Simulated Ozone Concentration (ppb)

for the 28 July 1987 Simulation Day for Los Angeles: 2010 post-CAAA90

minus pre-CAAA90 ...............................................................................................................C-34
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxii

Figure C-11a Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2000 Pre-CAAA90/1990 Base-Year........................................C-35

Figure C-11b Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2000 Post-CAAA90/1990 Base-Year......................................C-35

Figure C-12a Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2010 Pre-CAAA/1990 Base-Year ............................................C-36

Figure C-12b Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2010 Post-CAAA/1990 Base-Year ..........................................C-36

Figure C-13a Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2000 Post-CAAA/2000 Pre-CAAA .......................................C-37

Figure C-13b Distribution of Monitor - Level Ratios for 95th Percentile

Ozone Concentration 2010 Post-CAAA/2010 Pre-CAAA .......................................C-37

Figure C-14 80-km RADM Domain..........................................................................................................C-54

Figure C-15 Comparison of Simulated and Observed Seasonal PM10 Concentration

(ug/m3) for REMSAD for the Western U.S.: Spring 1990...........................................C-55

Figure C-16 Comparison of Simulated and Observed Seasonal PM10 Concentration

(ug/m3) for REMSAD for the Western U.S.: Summer 1990 .......................................C-55

Figure C-17 Comparison of Simulated and Observed Seasonal PM10 Concentration

(ug/m3) for REMSAD for the Western U.S.: Fall 1990 ................................................C-55

Figure C-18 Comparison of Simulated and Observed Seasonal PM10 Concentration

(ug/m3) for REMSAD for the Western U.S.: Winter 1990 .........................................C-55

Figure C-19 Difference in Seasonal Average PM10 Concentration (ug/m3) for the Summer

REMSAD Simulation Period (1-10 July 1990) for 2010: post-CAAA90

minus pre-CAAA90 ...............................................................................................................C-56

Figure C-20 Difference in Seasonal Average PM25 Concentration (ug/m3) for the Summer

REMSAD Simulation Period (1-10 July 1990) for 2010: post-CAAA90

minus pre-CAAA90 ...............................................................................................................C-57

Figure C-21a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2000 Pre-CAAA90 / 1990Base-Year .................................................................................C-58

Figure C-21b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2000 Post-CAAA/1990 Base-Year.....................................................................................C-58

Figure C-22a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2010 Pre-CAAA/1990 Base-Year ......................................................................................C-59

Figure C-22b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2010 Post-CAAA/1990 Base-Year.....................................................................................C-59

Figure C-23a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration

2000 Pre-CAAA/1990 Base-Year ......................................................................................C-60

Figure C-23b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration

2000 Post-CAAA90/1990 Base-Year ................................................................................C-60
List of Tables and Figures

xxiii

Figure C-24a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration:

2010 Pre-CAAA/1990 Base-Year ......................................................................................C-61

Figure C-24b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration

2010 Post-CAAA/1990 Base-Year.....................................................................................C-61

Figure C-25a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2000 Post-CAAA/2000 Pre-CAAA..................................................................................C-62

Figure C-25b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-10 Concentration:

2010 Post-CAAA/2010 Pre-CAAA..................................................................................C-62

Figure C-26a Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration:

2000 Post-CAAA/ 2000 pre-CAAA.................................................................................C-63

Figure C-26b Distribution of Combined RADM/RPM- and REMSAD-derived

Monitor-level Ratios for Annual Average PM-2.5 Concentration

2010 Post-CAAA/2010 Pre-CAAA..................................................................................C-63

Figure C-27 Seasonal Average Deciview for the Summer REMSAD Simulation

Period (1-10 July 1990): Base 1990 (Western U.S. Only) ..............................................C-67

Figure C-28 Difference in Seasonal Average Deciview for the Summer REMSAD

Simulation Period (1-10 July 1990): 2010 Pre-CAAA90 Minus Base 1990

(Western United States Only) ..............................................................................................C-68

Figure C-29 Annual Sulfur Deposition 1990 Base Case Scenario .......................................................C-71

Figure C-30 Annual Nitrogen Deposition 1990 Base Case Scenario .................................................C-72

Figure C-31 Annual Sulfur Deposition 2010 Pre CAAA Scenario ....................................................C-73

Figure C-32 Annual Sulfur deposition 2010 Post CAAA Scenario ...................................................C-74

Figure C-33 Annual Nitrogen Deposition 2010 Pre CAAA Scenario ..............................................C-75

Figure C-34 Annual Nitrogen deposition in 2010 Post CAAA Scenario ........................................C-76

Figure C-35 Distribution of Monitor-Level Ratios of Summer SO2 Emissions:

2010 Post-CAAA / 2010 Pre-CAAA .................................................................................C-78

Figure C-36 distribution of Monitor-Level Ratios of Summer NO Emissions:

2010 Post-CAAA / 2010 Pre-CAAA .................................................................................C-78

Figure C-37 Distribution of Monitor-Level Ratios of Summer NO2 Emissions:

2010 Post-CAAA / 2010 Pre-CAAA .................................................................................C-79

Figure C-38 Distribution of Monitor-Level Ratios of Summer CO Emissions:

2010 Post-CAAA / 2010 Pre-CAAA .................................................................................C-79

Figure D-1 Location of Air Quality Monitors Section 812 Analysis ................................................D-4

Figure D-2 Long-term Mortality Based on Pope (1995): National Avoided Incidence

Estimates (2010) at Different Assumed Effect Thresholds, Based on a

50 Km Maximum Distance .................................................................................................. D-92

Figure E-1 Nitrogen Reduction Without CAAA................................................................................ E-25

Figure E-2 Nitrogen Reduction With CAAA...................................................................................... E-25

Figure E-3 Estuary Models and Ecological Impacts of Concern ...................................................... E-31

Figure E-4 Nitrogen Load vs. Seagrass Acreage in Tampa Bay....................................................... E-32
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxiv

Figure E-5 Chesapeak Bay SAV 1978-1996 ........................................................................................... E-32

Figure E-6 Percentage of Acidic Surface Waters in the National Surface

Water Survey Regions............................................................................................................ E-36

Figure E-7 Acidification of Freshwater Ecosystems ........................................................................... E-37

Figure E-8 Annual Economic Welfare Benefit of Mitigation Ozone Impacts

on Commercial Timber......................................................................................................... E-49

Figure E-9 U.S. Major Forest Types Affected By Air Pollution-Induced Visual Injuries ......... E-57

Figure G-1 Schematic of Cost and Benefit Analysis of Title VI ...................................................... G-12

Figure G-2 Annual Human Health Benefits from Sections 604 and 606 (Discounted at 5%) .. G-29

Figure G-3 Annual Undiscounted Human Health Benefits of Sections 604 and 606................. G-33

Figure H-1 Hypothetical Survival Curve Shift ...................................................................................... H-4

Figure H-2 Change in 1990 Annual Risk of Death by 25 Percent .................................................... H-5

Figure H-3 Increase in 1990 Remaining Life Expectance .................................................................... H-5

Figure H-4 Analysis of Contribution of Key Parameters to Quantified Uncertainty ............... H-31
Acronyms and Abbreviations

xxv

μEq microequivalents

μg microgram

ACT average cost per ton

AGSIM AGricultural SImulation Model

AIC Akaike information criterion

AIRS Aerometric Information Retrieval

System

ANC acid neutralizing capacity

ANOVA analysis of variance

AOD airway obstructive disease

AP-42 EPAís Compilation of Air Pollution Emission Factors

ATDM aerosol and toxics deposition

module

AQM air quality modeling

AQMS Air Quality Modeling Subcommittee

ATLAS Aggregate Timber Land Assessment

System

bext light extinction coefficient

BAAQMD Bay Area Air Quality Management

District

BACT best available control technology

BAF bioaccumulation factor

BARCT best available retrofit control

technology

BCF bioconcentration factor

BEA Bureau of Economic Analysis

BID background information document

BIES Biogenic Emissions Inventory

System

BLS Bureau of Labor Statistics

BMP best management practice

BNR biological nutrient removal

BS black smoke

C-R concentration-response

CAA Clean Air Act

CAAA Clean Air Act Amendments

CAPI Clean Air Power Initiative

CAPMS Criteria Air Pollutant Modeling

System

CARB California Air Resources Board

Acronyms and Abbreviations

CASAC Clean Air Science Advisory Board

CASTNet Clean Air Act Status and Trends

Network

CB chronic bronchitis

CEM continuous emissions monitoring

CES constant elasticity of substitution

CFC chlorofluorocarbon

CFFP Clean Fuel Fleet Program

CGE computable general equilibrium

CI compression ignition

C O carbon monoxide

C O H coefficient of haze

COI cost of illness

COPD chronic obstructive pulmonary

disease

C R C capital recovery cost

CRF capital recovery factor

CTG control technique guideline

CV contingent valuation

dbh diameter at breast height

DDT dichlorodiphenyl-trichloroethane

DOE Department of Energy

dV deciview

E-GAS Economic Growth Analysis System

EC elemental carbon

EGU electrical generating unit

EMFAC emission factors model

ER emergency room

EPA Environmental Protection Agency

EPS Emissions Processing System

ERCAM Emission Reduction and Cost

Analysis Model

ERL Environmental Research Laboratory

FACA Federal Advisory Committee Act

FAPRI Food and Agricultural Policy

Research Institute

FCM Fuel Consumption Model

FDA Food and Drug Administration

FEV1 forced expiratory volume in one

second
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxvi

FGD flue gas desulfurization

FHWA Federal Highway Administration

FMVCP Federal Motor Vehicle Control

Program

FORCARB forest carbon model

FORTRAN formula translation

FR Federal Register

GCVTC Grand Canyon Visibility Transport

Commission

GDP gross domestic product

GIRAS Geographic Information Retrieval

Analysis System

GIS geographic information system

GNP gross national product

GSP gross state product

H + hydrogen ions

ha hectare

HAP hazardous air pollutant

HARVCARB harvested carbon model

HBFC hydrobromofluorocarbons

H C hydrocarbon

HCFC hydrochlorofluorocarbon

HDDV heavy-duty diesel vehicle

HDGV heavy-duty gasoline vehicle

HDV heavy-duty vehicle

HEES Health and Ecological Effects

Subcommittee

Hg mercury

HIV-1 human immunodeficiency virus

type one

HNO3 nitric acid

HPMS Highway Performance Monitoring

System

HS2

O4 sulfuric acid

I/M inspection and maintenance

ICI industrial/commercial/institutional

ICD International Classification of

Disease

ID identification code

IMPROVE Interagency Monitoring of

PROtected Environments

IPM Integrated Planning Model

kg kilogram

km kilometer

kWh kilowatt hour

LAER lowest achievable emission rate

lb pound

LDAR leak detection and repair

LDDT light-duty diesel truck

LDDV light-duty diesel vehicle

LDGT light-duty gasoline truck

LDGV light-duty gasoline vehicle

LEV low emission vehicle

LRS lower respiratory symptom

LTO landing and takeoff operations

m meter

m3 cubic meter

MACT maximum achievable control

technology

MAG Maricopa Association of Governments

MAGIC Model of Acidification of Groundwater in Catchments

M C motorcycle

MCF methyl chloroform

MDL method detection limit

MM4 mesoscale model four

MMBtu million British thermal units

MRAD minor restricted activity day

Models-3 Third Generation Air Pollution

Modeling System

MOU memorandum of understanding

MOBILE mobile source emission factor

model

MPO metropolitan planning organization

MWC municipal waste combustor

MWI medical waste incinerator

N nitrogen

NAA nonattainment area

NAAQS National Ambient Air Quality

Standards

NAPAP National Acid Precipitation Assessment Program

NASA National Aeronautics and Space

Administration

NCAR National Center for Atmospheric

Research

NCLAN National Crop Loss Assessment

Network

NE northeast

NEMS National Energy Modeling System

NERC North American Electric Reliability Council

NESHAP National Emission Standards for

Hazardous Air Pollutants
Acronyms and Abbreviations

xxvii

NET National Emission Trend

NH3 ammonia

NHANES National Health and Nutrition

Examination

NIH National Institutes of Health

NMOC non-methane organic compound

N O nitrogen oxide

NO2 nitrogen dioxide

NOx nitrogen oxides

NP national park

NPI National Particulates Inventory

NPP net primary productivity

NPV net present value

NSPS new source performance standard

NSR new source review

NSWS National Surface Waters Survey

NYSDEC New York Department of Environmental Conservation

O3 ozone

O&M operation and maintenance

OBD onboard diagnostic

O C organic carbon

ODS ozone-depleting substance

OMB Office of Management and Budget

OMS Office of Mobile Sources

OPPE Office of Policy, Planning and

Evaluation

ORIS Office of the Regulatory Information System

OSD ozone season daily

OTAG Ozone Transport Assessment

Group

OTC Ozone Transport Commission

OTR Ozone Transport Region

P-i-G plume-in-grid

PAN peroxyacetyl nitrate

Pb lead

PCB polychlorinated biphenyl

PCDD polychlorinated dibenzo-p-dioxin

PCDF polychlorinated dibenzofurans

PCE perchloroethylene

pH logarithm of the reciprocal of

hydrogen ion concentration, a

measure of acidity

PM particulate matter (both PM10 and

PM2.5)

PM10 particulate matter less than or equal

to 10 microns in diameter

PM2.5 particulate matter less than or equal

to 2.5 microns in diameter

PnET Net Photosynthesis and EvapoTranspiration model

POC parameter occurrence code

POTW publically owned treatment works

ppb parts per billion

ppm parts per million

PRYL percentage relative yield loss

PRZM Pesticide Root Zone Model

PSU Pennsylvania State University

QALY quality adjusted life years

R&D research and development

RACT reasonable available control technology

RAD restricted activity day

RADM Regional Acid Deposition Model

RELMAP Regional Lagrangian Model of Air

Pollution

REMSAD Regulatory Modeling System for

Aerosols and Acid Deposition

RE rule effectiveness

RFG reformulated gasoline

RHC reactive hydrocarbon

RIA regulatory impact analysis

RFP reasonable further progress

RO2 peroxy radical

ROP rate of progress

RPM Regional Particulate Model

RUM Random Utility Model

RVP Reid vapor pressure

S sulfur

SAB Science Advisory Board

SAS Statistical Analysis Software

SAV submerged aquatic vegetation

SCAQMD South Coast Air Quality Management District

SCAQS South Coast Air Quality Study

SCC Source Classification Code

SCR selective catalytic reduction

SEDS State Energy Data Systems

SI spark ignition

SIC Standard Industrial Classification

SIP State Implementation Plan

SO2 sulfur dioxide
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxviii

SOA secondary organic aerosol

SoCAB South Coast Air Basin

SOCMI synthetic organic chemical manufacturing industry

SUM06 sum of hourly ozone concentrations at or above 0.06 ppm

TAC total annualized costs

TAF temporal allocation factors

TAMM Timber Assessment Market Model

TBRP Tampa Bay Estuary Program

TCDD tetrachlorodibenzo-p-dioxin

TEQ toxic equivalency

TLEV transitional low emission vehicle

tpd tons per day

TREGRO tree growth model

TSDF treatment, storage, and disposal

facility

TSP total suspended particulates

UAM Urban Airshed Model

URS upper respiratory symptoms

USDA United States Department of

Agriculture

ULEV ultra-low emission vehicle

USGS United States Geological Survey

UV ultraviolet

VMT vehicle miles traveled

VNA Voronoi neighbor averaging

VOC volatile organic compound

VR visual range

VSL value of statistical life

VSLY value of statistical life year

WEFA Wharton Economic Forecasting

Associates

WHO World Health Organization

WLD work-loss days

WTA willingness-to-accept

WTP willingness-to-pay

XO2 halogenated peroxy radical

yr year

ZEV zero emission vehicle
Acknowledgments

xxix

Acknowledgments

This project is managed under the direction of

Robert Perciasepe, Assistant Administrator and Robert D. Brenner, Deputy Assistant Administrator for

the EPA Office of Air and Radiation. The principal

project manager is Jim DeMocker, Senior Policy

Analyst, EPA Office of Air and Radiation/Office of

Policy Analysis and Review. Brian Heninger, EPA

Office of Policy/Office of Economy and Environment, directed the ecological assessment; and Sam

Napolitano, EPA Office of Air and Radiation/Office of Atmospheric Programs directed the electric

utility emissions and cost analyses. Robin Dennis,

EPA Office of Research and Development/National

Exposure Research Laboratory directed the RADMRPM air quality modeling. Al McGartland, Director of the EPA Office of Economy and Environment

in the Office of Policy, and David Gardiner, former

Assistant Administrator for the Office of Policy provided guidance and support.

Many EPA staff contributed or reviewed portions

of this document, including Bryan Hubbell, John

Bachmann, Ron Evans, Rosalina Rodriguez, Scott

Mathias, Ann Watkins, Rona Birnbaum, Karen Martin, Doris Price, Drusilla Hufford, Jeff Cohen, Joe

Somers, Carl Mazza, Brett Snyder, and Tom Gillis.

A number of contractors developed key elements

of the analysis and supporting documents. Jim

Neumann of Industrial Economics, Incorporated

managed the overall integration and coordination of

the analytical work and documentation and also made

considerable substantive analytical contributions.

Other contractor members of the 812 Project Team

included Bob Unsworth, Henry Roman, Jared

Hardner, Naomi Kleckner, Nick Livesay, Lauren

Fusfeld, Andre Cap, Stephen Everett, Jon Discher,

and Mike Hester of Industrial Economics, Incorporated; Leland Deck, Ellen Post, Lisa Akeson, Kenneth Davidson, and Don McCubbin of Abt Associates; Sharon Douglas, John Langstaff, Robert

Iwamiya, Belle Hudischewsky, and John Calcagni of

ICF Incorporated, and John Blaney of ICF Consulting; and Jim Wilson, Erica Laich, and Dianne Crocker

of Pechan-Avanti Associates.

Science Advisory Board review of this report is

supervised by Donald G. Barnes, Director of the SAB

Staff. The Designated Federal Official for the SAB

reviews is Angela Nugent. Other SAB staff who assisted in the coordination of SAB reviews include Jack

Fowle, Robert Flaak, and Jack Kooyoomjian. Diana

Pozun provided administrative support to the SAB.

The SAB Council is chaired by Maureen Cropper of the World Bank. SAB Council members serving during the final review of this report include A.

Myrick Freeman of Bowdoin College, Gardner

Brown, Jr. of the University of Washington, Paul

Lioy of the Robert Wood Johnson School of Medicine, Paulette Middleton of the RAND Center for

Environmental Sciences and Policy, Donald Fullerton of the University of Texas ñ Austin, Lawrence

Goulder of Stanford University, Jane Hall of California State University ñ Fullerton, Charles Kolstad

of the University of California at Santa Barbara, and

Lester Lave of Carnegie-Mellon University. Alan

Krupnick of Resources for the Future served as a

Consultant to the Council. In addition, several members of the SAB Council whose terms expired during

the development of the study provided valuable advice and ideas in the early stages of project design and

implementation. These former members include

Richard Schmalensee of MIT, William Nordhaus of

Yale University, Paul Portney of Resources for the

Future, Kip Viscusi of Harvard University, Ronald

Cummings of Georgia State University, Thomas

Tietenberg of Colby College, Wallace Oates of the

University of Maryland, Wayne Kachel of MELE Associates, Robert Mendelsohn of Yale University, and

Daniel Dudek of the Environmental Defense Fund.

William Smith, a liaison to the Council from the SAB

Environmental Processes and Effects Committee also

provided valuable advice regarding the ecological assessment.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

xxx

The SAB Council is supported by two technical

subcommittees. The first of these subcommittees,

the Health and Ecological Effects Subcommittee is

chaired by Paul Lioy. Members who participated in

the final review of this report included Morton

Lippmann of New York University Medical Center,

George T. Wolff of General Motors, A. Myrick Freeman, Timothy Larson of the University of Washington, Joseph Meyer of the University of Wyoming,

Robert Rowe of Stratus Consulting, George Taylor

of George Mason University, Jane Hall, Michael

Kleinman of the University of California at Irvine,

and Carl Shy of the University of North Carolina at

Chapel Hill. Several former members who provided

valuable advice in the early stages of the study include Bernard Weiss of the University of Rochester

Medical Center, David V. Bates of the University of

British Columbia, Gardner Brown, and Lester Lave.

The second technical subcommittee, the Air

Quality Modeling Subcommittee is chaired by

Paulette Middleton. Members serving during the final review of this report include Philip Hopke of

Clarkson University, James H. Price, Jr. of the Texas

Natural Resource Conservation Commission, Harvey

Jeffries of the University of North Carolina ñ Chapel

Hill, Timothy Larson, and Peter Mueller of the Electric Power Research Institute. A former member who

helped guide the analysis in its early stages was George

T. Wolff.

The project managers wish to convey special acknowledgment and appreciation for the valuable contributions of A. Myrick Freeman. As a charter member of the Council and as Vice Chair of the Health

and Ecological Effects Subcommittee, Dr. Freeman

provided wise and excellent counsel throughout the

entire course of SAB review of both this study and

the preceding retrospective study.

This report could not have been produced without the support of key administrative support staff.

The project managers are grateful to Barbara Morris,

Nona Smoke, Eunice Javis, Gloria Booker, and

Wanda Farrar for their timely and tireless support

on this project.
Chapter 1: Introduction

1

Introduction

Chapter

1

Background and Purpose

Section 812 of the 1990 Clean Air Act Amendments requires the EPA to develop periodic Reports

to Congress that estimate the benefits and costs of

the Clean Air Act (CAA). The first report EPA

created under this authority, The Benefits and Costs

of the Clean Air Act: 1970 to 1990, was published and

conveyed to Congress in October 1997. This retrospective analysis comprehensively assessed benefits

and costs of requirements of the 1970 Clean Air Act

and the 1977 Amendments, up to the passage of the

Clean Air Act Amendments of 1990. The results of

the retrospective analysis showed that the nationís

investment in clean air was more than justified by

the substantial benefits that were gained in the form

of increased health, environmental quality, and productivity. The aggregate benefits of the CAA during the 1970 to 1990 period exceeded costs by a factor of 10 to 100 times.

Before the retrospective analysis was complete,

we began the process of assessing the prospective

benefits and costs of the Clean Air Act Amendments

(CAAA), covering the period 1990 to 2010. This

report, the first of a series that we plan to produce

every two years, is the result of our prospective analysis of the 1990 Amendments.

Similar to the retrospective analysis, this document has one primary and several secondary objectives. The main goal is to provide Congress and the

public with comprehensive, up-to-date information

on the CAAAís social costs and benefits, including

health, welfare, and ecological benefits. Data and

methods derived from the retrospective analysis have

already been used to assist policy-makers in refining

clean air regulations over the last two years, and we

hope the information continues to prove useful to

Congress during future Clean Air Act reauthorizations. Beyond the statutory goals of section 812,

EPA also intends to use the results of this study to

help support decisions on future investments in air

pollution research. In addition, lessons learned in

conducting this first prospective will help better target efforts to improve the accuracy and usefulness

of future prospective analyses.

Relationship of This Report

to Other Regulatory Analyses

The Clean Air Act Amendments of 1990 augment the significant progress made in improving the

nationís air quality through the original Clean Air

Act of 1970 and its 1977 amendments. The amendments built off the existing structure of the original

Clean Air Act, but went beyond those requirements

to tighten and clarify implementation goals and timing, increase the stringency of some federal requirements, revamp the hazardous air pollutant regulatory program, refine and streamline permitting requirements, and introduce new programs for the

control of acid rain and stratospheric ozone depleters.

Because the 1990 Amendments represent an additional improvement to the nationís existing clean

air program, the analysis summarized in this report

was designed to estimate the costs and benefits of

the 1990 CAAA incremental to those costs and benefits assessed in the retrospective analysis. In economic terminology, this report addresses the marginal costs and benefits of the 1990 CAAA. Our

intent is that this report and its predecessor, the retrospective analysis, together provide a comprehensive assessment of current and expected future clean

air regulatory programs and their costs and benefits.

Because of the time and resources necessary to

conduct this type of comprehensive prospective assessment, however, and the ongoing refinements in

Clean Air Act regulatory programs, the estimates

presented in this report do not reflect some recent
The Benefits and Costs of the Clean Air Act, 1990 to 2010

2

major developments in EPAís clean air program.

The prospective analysis, for example, does not capture the benefits and costs of EPAís recent revision

of the particulate matter and ozone National Ambient Air Quality Standards (NAAQS), the recently

proposed Tier II tailpipe standards, or the recently

finalized regional haze standards. Neither costs nor

benefits of those actions are reflected in the estimates

presented here. In most cases, Regulatory Impact

Analyses (RIAs) for those actions did incorporate

the section 812 prospective Post-CAAA scenario as

their starting point, or baseline, from which the actions were assessed, and in most respects the RIAs

used a methodology consistent with that used here.1

As a result, cost and benefit estimates presented in

those RIAs can be considered incremental to the

primary estimates presented in this document.

In addition to omitting these actions from the

assessment, this first prospective analysis required

locking in a set of emissions reductions to be used in

subsequent analyses at a relatively early date (late

1996), and as a result we were compelled to forecast

the implementation outcome of several pending programs. The most important of these was the thenongoing Ozone Transport Assessment Group

(OTAG) recommendations for achieving regionalscale reductions of emissions of ground-level ozone

precursors. The NOx control program incorporated

in the Post-CAAA scenario may not reflect the NOx

controls that are actually implemented in a regional

ozone transport rule. We acknowledge and discuss

these types of discrepancies and their impact on the

outcome of our analysis in the document.

Finally, despite our efforts to comprehensively

evaluate the costs and benefits of all provisions of

the Clean Air Act and its Amendments, there remain a few categories of effects that are not addressed

by either the retrospective or prospective analyses.

For example, this first prospective analysis does not

assess the effect of CAAA provisions on lead exposures, primarily because the 1990 Amendments do

not include major new provisions for the control of

lead emissions. The vast majority of lead emissions

sources present in 1970 were addressed by programs

initiated under the original Clean Air Act and the

1977 Amendments; evaluation of the costs and health

benefits of these programs were important elements

of the retrospective analysis. In the retrospective,

however, we were unable to quantify the potentially

substantial ecological benefits of controls on lead

emissions. While this first prospective analysis reflects a significantly greater investment in quantifying ecological effects, for the reason stated above we

did not assess the ecological effects of lead in this

analysis either. As a result, the ecological effects of

this persistent pollutant, past emissions of which may

continue to be released from soils for many years,

are not captured by either the retrospective or prospective analyses. In addition, lead previously deposited to soils may be re-entrained in the air as road

dust, dust plumes from construction excavations, and

other particulate matter emission processes subject

to 1990 CAAA controls. Reductions in this re-entrainment of, and potential exposure to, pre-1990

emitted lead due to post-1990 control programs,

however, are not reflected in either the section 812

retrospective (1970 to 1990) or prospective (1990 to

2010) benefit analyses.

Requirements of the 1990

Clean Air Act Amendments

The first prospective analysis, despite the limitations discussed above, presents a comprehensive

estimate of costs and benefits of all titles of the 1990

Clean Air Act Amendments. The 1990 Amendments

consist of the following eleven titles:

ï Title I. Establishes a detailed and graduated

program for the attainment and maintenance

of the National Ambient Air Quality Standards.

ï Title II. Regulates mobile sources and establishes requirements for reformulated gasoline and clean fuel vehicles.

ï Title III. Expands and modifies regulations

of hazardous air pollutant emissions; and

establishes a list of 189 hazardous air pollutants to be regulated.

1 There are minor differences in the assumptions used to

construct the Post- CAAA scenario for this analysis and the

baseline used in the PM and ozone NAAQS RIA. For example,

the RIA baseline incorporates the effects of 7- and 10-year MACT

rules that are not reflected here, because of the timing of the

two analyses, and the RIA used a 95 percent rule-effectiveness

assumption. In most respects, however, the analyses are compatible.
Chapter 1: Introduction

3

ï Title IV. Establishes control programs for

reducing acid rain precursors.

ï Title V. Requires a new permitting system

for primary sources of air pollution.

ï Title VI. Limits emissions of chemicals that

deplete stratospheric ozone.

ï Title VII. Presents new provisions for enforcement.

ï Titles VIII through XI. Establishes miscellaneous provisions for issues such as disadvantaged business concerns, research, training, new regulation of outer continental shelf

sources, and assistance for people who lose

their jobs as a result of the Clean Air Act

Amendments.

As part of the requirements under Title VIII,

section 812 of the Clean Air Act Amendments of

1990 requires the EPA to analyze the costs and benefits to human health and the environment that are

attributable to the Clean Air Act. In addition, section 812 directs EPA to measure the effects of this

statute on economic growth, employment, productivity, cost of living, and the overall economy of the

United States.

Analytical Design and Review

Target Variable

The prospective analysis compares the overall

health, welfare, ecological and economic benefits of

the 1990 Clean Air Act Amendment programs to

the costs of these programs. By examining the overall effects of the Clean Air Act, this analysis complements the Regulatory Impact Analyses (RIAs) developed by EPA over the years to evaluate individual

regulations. Resources were used more efficiently by

recognizing that these RIAs, and other EPA analyses, provide complete information about the costs

and benefits of specific rules. Within this analysis,

costs can be reliably attributed to individual programs, but the broad-scale approach adopted in the

prospective study precludes reliable re-estimation of

the benefits of individual standards or programs.

Similar to the retrospective benefits analysis, this

study calculates the change in incidences of adverse

effects implied by changes in ambient concentrations

of air pollutants. However, pollutant emissions reductions achieved contribute to changes in ambient

concentrations of those, or secondarily formed, pollutants in ways that are highly complex, interactive,

and often nonlinear. Therefore, benefits cannot be

reliably matched to provision-specific changes in

emissions or costs.

Focusing on the broader target variables of overall costs and overall benefits of the Clean Air Act,

the EPA Project Team adopted an approach based

on construction and comparison of two distinct scenarios: a ìPre-CAAAî and a ìPost-CAAAî scenario.

The Pre-CAAA scenario essentially freezes federal,

state, and local air pollution controls at the levels of

stringency and effectiveness which prevailed in 1990.

The Post-CAAA scenario assumes that all federal,

state, and local rules promulgated pursuant to, or in

support of, the 1990 CAAA were implemented. This

analysis then estimates the differences between the

economic and environmental outcomes associated

with these two scenarios. For more information on

the scenarios and their relationship to historical

trends, see Chapter 2 and Appendix A of this document.

Key Assumptions

Similar to the retrospective analysis, we made

two key assumptions during the scenario design process to avoid miring the analytical process in endless

speculation. First, as stated above, we froze air pollution controls at 1990 levels throughout the PreCAAA scenario. Second, we assumed that the geographic distributions of population and economic

activity remain the same between the two scenarios,

although these distributions do change over time

under both scenarios to reflect expected patterns of

high and low population and economic growth

across the country.

The first assumption is an obvious simplification. In the absence of the 1990 CAAA, one would

expect to see some air pollution abatement activity,

either voluntary or due to state or local regulation.

It is conceivable that state and local regulation would

have required air pollution abatement equal to ñor

even greater thanñ that required by the 1990 CAAA;
The Benefits and Costs of the Clean Air Act, 1990 to 2010

4

particularly since some states, most notably California, have in the past done so. If one were to assume

that state and local regulations would have been

equivalent to 1990 CAAA standards, then a costbenefit analysis of the 1990 CAAA would be a meaningless exercise since both costs and benefits would

equal zero. Any attempt to predict how statesí and

localitiesí regulations would have differed from the

1990 CAAA would be too speculative to support

the credibility of the ensuing analysis. Instead, the

Pre-CAAA scenario has been structured to reflect

the assumption that states and localities would not

have invested further in air pollution control programs after 1990 in the absence of the federal CAAA.

Thus, this analysis accounts for all costs and benefits of air pollution control from 1990 to

2010 and does not speculate about the fraction of costs and benefits attributable exclusively to the federal CAAA. Nevertheless,

it is important to note that state and local

governments and private initiatives are responsible for a significant portion of these

total costs and total benefits. In the end,

the benefits of air pollution controls result

from partnerships among all levels of government and with the active participation

and cooperation of private entities and individuals.

The second assumption concerns changing demographic patterns in response to air

pollution. In the hypothetical Pre-CAAA

scenario, air quality is worse than the actual

1990 conditions and the projected air quality in the Post-CAAA scenario. It is possible that under the Pre-CAAA scenario

more people, relative to the Post-CAAA

case, would move away from the most

heavily polluted areas. Rather than speculate on the scale of population movement,

the analysis assumes no differences in demographic patterns between the two scenarios.

Similarly, the analysis assumes no differences

between the two scenarios with respect to

the spatial pattern of economic activity.

Analytic Sequence

The analysis comprises a sequence of six

basic steps, summarized below and described

in detail later in this report. These six steps, listed in

order of completion, are:

(1) emissions modeling

(2) direct cost estimation

(3) air quality modeling

(4) health and environmental effects estimation

(5) economic valuation

(6) results aggregation and uncertainty characterization

Figure 1-1 summarizes the analytical sequence

used to develop the prospective results; we describe

the analytic process in greater detail below.

Direct Cost

Estimation

Air Quality

Modeling --

Criteria Pollutants

Physical

Effects

Valuation

Analytic Design

Scenario

Development

Emissions

Profile

Development

Benefits

Analysis

Cost

Analysis

Comparison of Benefits

and Costs

Figure 1-1

Analytic Sequence for

First Section 812 Prospective Analysis
Chapter 1: Introduction

5

The first step of the analysis is the estimation of

the effect of the 1990 CAAA on emissions sources.

We generated emissions estimates through a three

step process: (1) construction of an emissions inventory for the base year (1990); (2) projection of emissions for the Pre-CAAA case for two target years,

2000 and 2010, assuming a freeze on emissions control regulation at 1990 levels and continued economic

progress, consistent with sector-specific Bureau of

Economic Analysis economic activity projections;

and (3) construction of Post-CAAA estimates for the

same two target years, using the same set of economic

activity projections used in the Pre-CAAA case but

with regulatory stringency, scope, and timing consistent with EPAís CAAA implementation plan (as

of late 1996). The analysis reflects application of

utility and other sector-specific emissions models

developed and used in various offices of EPAís Office of Air and Radiation. These emissions models

provide estimates of emissions of six criteria air pollutants2 from each of several key emitting sectors.

We provide more details in Chapter 2 and Appendix A.

The emissions modeling step is a critical component of the analysis, because it establishes consistency

between the subsequent cost and benefit estimates

that we develop. Estimates of direct compliance costs

to achieve the emissions reductions estimated in the

first step are generated as either an integral or subsequent output from the emissions estimation models, depending on the model used. For example, the

Integrated Planning Model used to estimate emissions

and compliance costs for the utility sector develops

an optimal allocation of reductions of sulfur and nitrogen oxides taking into account the regulatory flexibility inherent in the Title IV trading schemes for

emissions allocations. In a few cases, for example

the Title V permitting requirements, we estimate

public and private costs incurred to implement the

regulatory requirements through analysis of the relevant RIAs conducted to support promulgation of

the rules.

Emissions estimates also form the first step in

estimating benefits. After the emissions inventories

are developed, they are translated into estimates of

air quality conditions under each scenario. Given

the complexity, data requirements, and operating

costs of state-of-the-art air quality models, and the

projectís resource constraints, the EPA Project Team

adopts simplified, linear scaling approaches for some

gaseous pollutants. However, for particulate matter, ozone, and other air quality conditions that involve substantial non-linear formation processes and/

or long-range atmospheric transport and transformation, the EPA Project Team invests the time and

resources needed to use more sophisticated modeling systems. For example, we exercise EPAís Regional Acid Deposition Model/Regional Particulate

Model (RADM/RPM) to estimate secondarily

formed particulate matter in the eastern U.S.

Up to this point of the analysis, modeled conditions and outcomes establish the Pre-CAAA and

Post-CAAA scenarios. However, at the air quality

modeling step, the analysis returns to a foundation

based on actual historical conditions and data. Specifically, actual 1990 historical air quality monitoring data are used to define the baseline conditions

from which the Pre-CAAA and Post-CAAA scenario air quality projections are constructed. We

derive air quality conditions under the Pre-CAAA

scenario by scaling the historical data adopted for

the base-year (1990) by the ratio of the modeled PreCAAA and base-year air quality. We use the same

approach to estimate future-year air quality for the

Post-CAAA scenario. This method takes advantage

of the richness of the monitoring data on air quality, provides a realistic grounding for the benefit

measures, and yet retains analytical consistency by

using the same modeling process for both scenarios.

The outputs of this step of the analysis are profiles

for each pollutant characterizing air quality conditions at each monitoring site in the lower 48 states.

The Pre-CAAA and Post-CAAA scenario air

quality profiles serve as inputs to a modeling system

that translates air quality to physical outcomes (e.g.,

mortality, emergency room visits, or crop yield

2

 The six pollutants are particulate matter (separate estimates for each of PM10 and PM 2.5), sulfur dioxide (SO2

), nitrogen oxides (NOx

), carbon monoxide (CO), volatile organic compounds (VOCs), and ammonia (NH3

). One of the CAA criteria

pollutants, ozone (O3

), is formed in the atmosphere through the

interaction of sunlight and ozone precursor pollutants such as

NOx

 and VOCs. Ammonia is not a criteria pollutant, but is an

important input to the air quality modeling step because it affects secondary particulate formation. The sixth criteria pollutant, lead (Pb), is not included in this analysis since airborne

emissions of lead were virtually eliminated by pre-1990 Clean

Air Act programs.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

6

losses) through the use of concentration-response

functions. Scientific literature on the health and

ecological effects of air pollutants provides the source

of these concentration-response functions. At this

point, we derive estimates of the differences between

the two scenarios in terms of incidence rates for a

broad range of human health and other effects of air

pollution by year, by pollutant, and by geographic

area.

In the next step, we use economic valuation

models or coefficients to estimate the economic value

of the reduction in incidence of those adverse effects

amenable to monetization. For example, a distribution of unit values derived from the economic literature provides estimates of the value of reductions in

mortality risk. In addition, we compile and present

benefits that cannot be expressed in economic terms.

In some cases, we calculate quantitative estimates of

scenario differences in the incidence of a

nonmonetized effect. In many cases, available data

and techniques are insufficient to support anything

more than a qualitative characterization of the change

in effects.

Next, we compare costs and monetized benefits

to provide our primary estimate of the net economic

benefits of the 1990 CAAA and associated programs,

and a range of estimates around that primary estimate reflecting quantified uncertainties associated

with the physical effects and economic valuation

steps. The monetized benefits used in the net benefit calculations reflect only a portion of the total

benefits due to limitations in analytical resources,

available data and models, and the state of the science. For example, in many cases we are unable to

quantify or monetize the potentially large benefits

of air pollution controls that result from protection

of the health, structure, and function of ecosystems.

In addition, although available scientific studies demonstrate clear links between air quality changes and

changes in many human health effects, the available

studies do not always provide the data needed to

quantify and/or monetize some of these effects.

Finally, we present a limited set of alternative

benefit estimates which reflect methods, models, or

assumptions that differ from those we used to derive the primary net benefit estimate. We also quantify some of the uncertainties surrounding these alternative estimates. In addition, beyond those variables for which alternative results are estimated, we

conduct sensitivity analyses for a number of variables that may influence the primary net benefit estimate. The primary estimate and the range around

this estimate, however, reflect our current interpretation of the available literature; our judgments regarding the best available data, models, and modeling methodologies; and the assumptions we consider

most appropriate to adopt in the face of important

uncertainties.

In addition, throughout the report at the end of

the chapter we summarize the major sources of uncertainty for each analytic step. Although the impact of many of these uncertainties cannot be quantified, we qualitatively characterize the magnitude

of effect on our net benefit results by assigning one

of two classifications to each source of uncertainty:

potentially major factors could, in our estimation,

have effects of greater than five percent of the total

net benefits; and probably minor factors likely have

effects less than five percent of total net benefits.

Review Process

The CAA requires EPA to consult with an outside panel of experts during the development and

interpretation of the 812 studies. This panel of experts was organized in 1991 under the auspices of

EPAís Science Advisory Board (SAB) as the Advisory Council on Clean Air Act Compliance Analysis (hereafter, the Council). Organizing the review

committee under the SAB ensured that highly qualified experts would review the section 812 studies in

an objective, rigorous, and publicly open manner

consistent with the requirements and procedures of

the Federal Advisory Committee Act (FACA).

Council review of the present study began in 1993

with a review of the analytical design plan. Since

the initial June 1993 meeting, the Council has met

many times to review proposed data, proposed methodologies, and interim results. While the full Council retains overall review responsibility for the section 812 studies, some specific issues concerning

physical effects and air quality modeling were referred to subcommittees comprised of both Council

members and members of other SAB committees.

The Councilís Health and Ecological Effects Subcommittee (HEES) met several times and provided
Chapter 1: Introduction

7

its own review findings to the full Council. Similarly, the Councilís Air Quality Modeling Subcommittee (AQMS) held in-person and teleconference

meetings to review methodology proposals and

modeling results and conveyed its review recommendations to the parent committee.

An interagency review was conducted, during

which a number of analytical issues were discussed.

Conducting a benefit/cost analysis of a major statute such as the Clean Air Act requires scores of methodological decisions. Many of these issues are the

subject of continuing discussion within the economic

and policy analysis communities and within the

Administration. Key issues include the treatment

of uncertainty in the relationship between particulate matter exposure and mortality; the valuation of

premature mortality; the treatment of tax interaction effects; the assessment of stratospheric ozone

recovery; and the treatment of ecological and welfare effects. These issues could not be resolved within

the constraints of this review. Thus, this report reflects the findings of the EPA and not necessarily

other agencies of the Administration.

Report Organization

The remainder of the main text of this report

summarizes the key methodologies and findings our

prospective study.

ï Chapter 2 summarizes emissions modeling

and key elements of the regulatory scenarios.

ï Chapter 3 discusses the direct cost estimation.

ï Chapter 4 presents the air quality modeling

methodology and sample results.

ï Chapter 5 describes the approaches used and

principal results obtained through the human health effects estimation process.

ï Chapter 6 describes the human health effects

economic valuation methodology and results.

ï Chapter 7 summarizes the ecological and

other welfare effects analyses, including assessments of commercial timber, agriculture,

visibility, and other categories of effects.

ï Chapter 8 presents the aggregated results of

the cost and benefit estimates and describes

and evaluates important uncertainties in the

results.

Additional details regarding the methodologies

and results are presented in the appendices and in

the referenced supporting documents.

ï Appendix A provides additional detail on the

sector-specific emissions modeling effort.

ï Appendix B covers the direct costs.

ï Appendix C provides details of the air quality models used and results obtained.

ï Appendix D presents the human health effects estimation methodology and results.

ï Appendix E describes the ecological benefits

estimation methods and results.

ï Appendix F presents the agricultural benefits

estimation methodology and results.

ï Appendix G provides details of the stratospheric ozone analysis.

ï Appendix H describes the methods and assumptions used to value quantified effects

of the CAA in economic terms.

ï Appendix I describes areas of research which

may increase comprehensiveness and/or reduce uncertainties in effect estimates for future assessments.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

8

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Chapter 2: Emissions

9

Emissions

Estimation of pollutant emissions, a key comChapter

2

ponent of this prospective analysis, serves as the starting point for subsequent benefit and cost estimates.

We focused the emissions analysis on six major pollutants: volatile organic compounds (VOCs), nitrogen oxides (NOx

), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter with an aerodynamic

diameter of 10 microns or less (PM10), and fine particulate matter (PM2.5).1 For each of these pollutants we projected 1990 emissions to the years 2000

and 2010 under two different scenarios: a) the PreCAAA scenario which assumes no additional control requirements would be implemented beyond

those in place when the 1990 Amendments were

passed; and b) the Post-CAAA scenario which incorporates the effects of controls authorized by the 1990

Amendments. We compare the emissions estimates

under each of these scenarios to forecast the effect

of the CAAA requirements on future emissions.

This chapter consists of four sections. The first

section provides an overview of our approach for

developing the Pre- and Post-CAAA control scenarios and projecting emissions from 1990 levels to

2000 and 2010. The second section summarizes our

emissions projections for the years 2000 and 2010

and presents our estimates of changes in future emissions resulting from the implementation of the 1990

Amendments. The third section compares these results with other estimates that are based upon more

recent emissions data. Finally, we conclude this chapter with a summary of the key uncertainties associated with estimating emissions.

Overview Of Approach

We projected emissions for five major source

categories: industrial point sources, utilities, nonroad

engines/vehicles, motor vehicles, and area sources

(see Table 2-1).2 The basic method involves estimating emissions in the 1990 base-year, adjusting

the base-year emissions to reflect projected growth

in the level of pollution-generating activity by 2000

and 2010 in the absence of additional CAAA requirements, and modifying these projections to reflect

future-year control assumptions. The resulting estimates depend largely upon three factors: the method

for selecting the base-year inventory, the indicators

used to forecast growth and the effectiveness of future controls, and the specific regulatory programs

incorporated in the Pre- and Post-CAAA scenarios.

We constructed the base-year inventory using

1990 emissions levels. For all of the air pollutants

examined in this analysis except particulate matter,

we selected emissions levels from Version 3 of the

National Particulates Inventory (NPI) to serve as the

baseline. This inventory consists of emissions data

compiled primarily by the National Acid Precipitation Assessment Program (NAPAP), EPAís Office

of Mobile Sources (OMS), and the Federal Highway

Administration (FHWA). For both PM2.5 and PM10,

however, we updated NPI estimates to incorporate

changes in the methodology used to calculate fugitive dust emissions. Adoption of this new technique,

also used to develop EPAís National Emission Trend

1 We also estimated ammonia (NH3

) emissions. NH3 influences the formation of secondary PM (PM formed as a result

of atmospheric chemical processes). We used NH3

 emissions

estimates as an input during the air quality modeling phase of

the prospective analysis when estimating future-year ambient

PM concentrations. However, we did not examine the human

health and environmental effects of exposure to NH3

. In addition to NH3

, we also estimated mercury (Hg) emissions. We

qualitatively evaluated the effects of Hg emissions on ecological systems, but we did not examine the impact of Hg on human health. We did not estimate the effect of the CAAA on

lead (Pb) emissions. By 1990 most major airborne Pb emission

sources were already controlled and the CAAA has minimal

additional impact on Pb emissions.

2 We estimated utility and industrial point source emissions at the plant/facility level. We estimated nonroad engine/

vehicle, motor vehicle, and area source emissions at the county

level.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

10

(NET) PM2.5 and PM10 inventory, leads to lower

estimates of fugitive dust emissions and therefore of

overall primary PM.3

Once we established the base-year inventory, we

projected emissions to the years 2000 and 2010, accounting for the influences expected to cause future

emissions to differ from 1990 levels. For all but utility sources, we rely on an emissions analysis using

the Emissions Reduction and Cost Analysis Model

(ERCAM) which incorporates the effects of the level

of pollution-generating activity and the stringency

and success of regulations designed to protect air

quality. In this analysis, we view changes in economic growth as an important indicator of future

activity levels and thus, future emissions. We used

1995 Bureau of Economic Analysis (BEA) Gross

State Product (GSP) projections to forecast the

growth of emissions from industrial point sources.

We relied on BEA GSP projections as well as data

on BEA predicted changes in population to estimate

future emissions from nonroad and area sources.4

We used BEA population growth as an indicator of

the increase in nonroad emissions from recreational

marine vessels, recreational vehicles, and lawn/garden equipment as well as an indicator of the increase

in area source solvent emissions (e.g., VOC emissions from dry cleaners). For motor vehicle sources,

we estimated the growth in activity based primarily

on the projected increase in vehicle miles traveled

(VMT). We develop future VMT estimates using

the EPA MOBILE fuel consumption model.

We estimated the impact of CAAA regulations

on industrial point source, nonroad, motor vehicle,

and area source emissions based on expected control

efficiency and rule effectiveness. Control efficiency

represents the percentage reduction in emissions

anticipated as a result of the implementation of the

CAAA, assuming full compliance and successful

operation of all control mechanisms. The rule effectiveness factor accounts for equipment malfunction, non-compliance, and other circumstances that

influence the overall effectiveness of air pollution

regulations. We selected a rule effectiveness of 80

percent as the standard for this analysis which we

applied to stationary source NOx

 and VOC controls.5 Rule effectiveness was not calculated for mobile source controls as an adjustment factor separate

from the emissions rates estimated for the various

vehicle classes.

To estimate future utility source emissions, we

relied on the Integrated Planning Model (IPM). This

optimization model forecasts, for the 48 contiguous

states and the District of Columbia, emissions from

all existing utility power generation units, as well as

from independent power producers and other cogeneration facilities that sell wholesale power and

are included in the North American Electric Reliability Council (NERC) data base for reliability planning. The model considers future capacity additions

by both utilities and independent power producers

which might cause an increase in emissions. In addition, the model is capable of producing baseline air

3 Primary PM consists of directly emitted particles such as

wood smoke and road dust. Secondary PM forms in the atmosphere as a result of atmospheric chemical reactions.

4 The growth forecast for area source agricultural tilling is

based on projections of acres planted, not BEA GSP and population projections.

5 At the time we selected the general rule effectiveness for

use in this analysis, 80 percent was the standard factor applied in

air pollution modeling. More recent analyses have used higher

rule effectiveness values. If a higher rule effectiveness value had

been used in this analysis, emissions reduction estimates would

be larger and the estimated benefits associated with air quality

improvements would be greater.

Table 2-1

Major Emissions Source Categories

Source Category Examples

Industrial Point Sources boilers, cement kilns, process heaters, turbines

Utilities electricity producing utilities

Nonroad Engines/Vehicles aircraft, construction equipment, lawn and garden equipment,

locomotives, marine engines

Motor Vehicles buses, cars, trucks (sources that usually operate on roads and

highways)

Area Sources agricultural tilling, dry cleaners, open burning, wildfires
Chapter 2: Emissions

11

emissions forecasts and estimates of air emissions

levels under various control options at the national

and NERC regional and subregional level. We used

IPM to estimate base-year (1990) utility source emissions and to project future-year (2000 and 2010)

emissions under both the Pre- and Post-CAAA scenarios.

Using emissions analysis or IPM, we estimated

future emissions for each of the five major source

categories under both the Pre- and Post-CAAA scenarios. While the selection of the base-year inventory, emission growth factors, and rate of regulatory effectiveness all influence the emissions projections, the difference between Pre- and Post-CAAA

estimates is primarily determined by the difference

in control assumptions incorporated in the two projection scenarios.

Scenario Development

We developed two contrasting emissions control scenarios, the Pre-CAAA scenario and the PostCAAA scenario. The Pre-CAAA scenario maintains

the air pollution regulatory requirements which existed in 1990 through the 2000 and 2010 analytical

period and serves as a baseline against which we

measure the changes in emissions projected under

the Post-CAAA scenario.6 This latter scenario assumes the implementation of the 1990 Clean Air Act

Amendments and incorporates the influences of the

following provisions:

ï Title I VOC and NOx

 reasonably available

control technology (RACT) and reasonable

further progress (RFP) requirements for

ozone nonattainment areas;

ï Title II motor vehicle and nonroad engine/

vehicle provisions;

ï Title III 2- and 4-year maximum achievable

control technology (MACT) standards;

ï Title IV SO2

 and NOx emissions programs

for utilities;

ï Title V permitting system for primary

sources of air pollution; and

ï Title VI emissions limits for chemicals that

deplete stratospheric ozone.7

The Post-CAAA scenario also assumes the implementation of region-wide NOx

 controls and a capand-trade system designed to reduce emissions during the summer months from large utility and industrial sources in the 37 easternmost states that comprise the Ozone Transport Assessment Group

(OTAG) domain.8 In addition, the Post-CAAA scenario incorporates the effects of a similarly designed

trading program for the 11 northeast states that comprise the Ozone Transport Region (OTR). This trading program is consistent with Phase II of the Ozone

Transport Commission (OTC) Memorandum of

Understanding (MOU).9 We provide more detailed

discussion of both Pre- and Post-CAAA scenario

development in Appendix A.

Emissions Estimation

Results

The results of the Pre- and Post-CAAA projections indicate that the 1990 Clean Air Act Amendments will likely have a significant effect on future

emissions of air pollutants. Table 2-2 displays both

base-year (1990) and future-year (2000 and 2010)

emissions estimates for the modeled scenarios along

with the percent change from Pre- to Post-CAAA

VOC, NOx

, SO2, CO, PM10, and PM2.5 projections.

A more detailed breakout of 2010 Pre- and PostCAAA emissions estimates, displaying emissions for

each major source category, is contained in Table 2-

3. Figures 2-1 through 2-6 show the emissions projections for each of the pollutants examined in this

analysis.

Emissions projections for VOC, NOx

, SO2, and

CO, displayed in Figures 2-1 through 2-4, follow

6 We also attempted to incorporate in the Pre-CAAA

(baseline) scenario the non-CAAA regulations and policies we

expect will have a significant effect on emissions between 1990

and 2010. For example, the IPM, which we used to estimate

utility emissions, incorporates the effect of the deregulation of

railroad rates on SO2

 emissions. IPM accounts for the influence

of the future cost of low-sulfur coal prices expected to occur as

a result of lower railroad rates. The impact of prescribed burning policies for private and federally owned lands on PM emissions is also incorporated in the Pre-CAAA scenario.

7 For a more detailed discussion of the CAAA provisions

incorporated in the Post-CAAA scenario, see Appendix A.

8 The NOx

 control program incorporated in the PostCAAA scenario may not reflect the NOx

 controls that are actually implemented in a regional ozone transport rule..

9 The Post-CAAA scenario does not incorporate any influences of the recently revised PM and ozone NAAQS regulations or any impact of the recently proposed Tier II tailpipe

standards.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

12

similar patterns. Pre-CAAA estimates indicate emissions of these pollutants would increase, on average,

by almost 20 percent from 1990 to 2010. These increases reflect the expectation that anticipated growth

in activity levels in the relevant emitting sectors will

more than offset reductions achieved by pre-1990

control programs. While we predict relatively steady

growth in emissions in the absence of the 1990

Amendments, projections show emissions of these

four pollutants would increase at a slightly faster rate

over the last ten years of the 20 year projection period.

Post-CAAA estimates of VOC, NOx

, SO2, and

CO emissions for the modeled regulatory scenarios

decrease significantly from 1990 to 2000 and then

plateau, remaining relatively constant from 2000 to

2010. The initial decrease is triggered by the implementation of the CAAA and the associated controls.

After cleaner means of production are adopted, better emissions control technologies are implemented,

and other required changes and improvements are

made, emissions reduction slows and in some instances stops all together; emissions may even begin

to increase. Although the Post-CAAA estimates for

each of the above mentioned pollutants show little

or no change in the level of emissions from 2000 to

2010, an overall comparison of our Pre- and PostCAAA projections indicates that during this time

period the 1990 Amendments continue to have an

increasingly beneficial effect on emission levels.

Comparison of Pre- and Post- CAAA emissions

estimates reveals that by 2010, estimated VOC emissions will be 35 percent lower as a result of the implementation of the CAAA than they would have been

if no new control requirements, beyond those in

place in 1990, were mandated. This sizeable change

in emissions attributable to the Amendments is due

largely to estimated VOC reductions from motor

vehicle and area sources. The 2010 Post-CAAA estimate for these two source categories combined is

8.2 million tons lower than the Pre-CAAA projection, a total which accounts for 84 percent of the

predicted difference in VOC emissions estimated

under the two scenarios.

Based on the regulatory programs incorporated

in the Post-CAAA scenario, we project that NOx

emissions will be reduced by the greatest percentage. Comparison of projections for the year 2010

indicates the Post-CAAA NOx

 estimate is 39 percent lower than the Pre-CAAA estimate, representing a decrease in emissions of 10.8 million tons. We

project nearly half of this reduction will come from

utilities, while the remaining portions will come from

cuts in motor vehicle and non-utility point source

emissions.

Table 2-2

Summary of National Annual Emissions Projections

(thousand tons)

Pollutant

1990

BaseYear

2000

PreCAAA

2000

PostCAAA

2000

%

Change

2010

PreCAAA

2010

PostCAAA

2010

%

Change

VOC 22,715 24,410 17,874 -27% 27,559 17,877 -35%

NOx 22,747 25,021 18,414 -26% 28,172 17,290 -39%

SO2 22,361 24,008 18,013 -25% 26,216 18,020 -31%

CO 94,385 95,572 80,919 -15% 107,034 81,943 -23%

Primary

PM10

28,289 28,768 28,082 -2% 28,993 28,035 -3%

Primary

PM2.5

7,091 7,353 7,216 -2% 7,742 7,447 -4%

Notes: Totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii.

 Percent change between Pre-CAAA and Post-CAAA scenarios.
Chapter 2: Emissions

13

Figure 2-3 shows that by 2010 we anticipate SO2

levels will be 31 percent lower than they would have

been under the Pre-CAAA scenario. We project 96

percent of the 8.2 million ton difference between

Pre- and Post-CAAA estimates will result from regulation of utilities, while the remaining reduction

comes from motor vehicles.

We estimate 2010 Post-CAAA CO emissions

will be 81.9 million tons, 23 percent lower than the

Pre-CAAA projection. Much of this reduction we

project will be achieved as a result of nonattainment

(Title I) and motor vehicle provisions (Title II) of

the 1990 Amendments. The more influential programs (in order of importance) are expected to be

enhanced vehicle emission inspections, wintertime

oxygenated fuel use, and LEV program adoption.

Figures 2-5 and 2-6 indicate that the 1990 Clean

Air Act Amendments have more modest effects on

primary PM10 and PM2.5 emissions.10 For both of

these pollutants, Pre-CAAA projections increase at

a slow rate from 1990 to 2010. Post-CAAA emissions estimates for primary PM10 and PM2.5, however, follow different paths. While we estimate

implementation of the CAAA will cause primary

PM10 levels to slowly decrease from 1990 to 2010,

Post-CAAA projections indicate primary PM2.5 emissions will actually rise despite the influence of the

CAAA. Overall, however, emissions of primary

PM10 and PM2.5 both will be approximately four percent lower in 2010 than they would have been without the CAAA.11

The significant influence of area source emissions

on primary PM emissions levels, combined with the

limited regulation of this major source category,

explains the limited effect of the CAAA on primary

particulate matter emissions. According to data used

in this analysis, area sources account for over 90 percent of primary PM10 emissions and over 80 percent

of primary PM2.5 emissions.12 As a result, even the

successful reduction of motor vehicle and nonroad

emissions have only a slight impact on overall primary PM10 and PM2.5 estimates developed for this

study.13 Furthermore, the CAAAís most significant

primary PM area source controls target emissions in

counties not in compliance with the National Ambient Air Quality Standards (NAAQS).14 Currently,

however, there are fewer than 85 counties in the

country that are not in attainment with the national

standards. Emissions changes in these areas are capable of having only a minor influence on the overall primary PM level in the United States. Even

minor changes in primary PM emissions leading to

minor changes in the concentrations of this pollutant, however, are significant. In the subsequent

portions of this analysis, sizable benefits are estimated

to result from small reductions in PM concentrations in the atmosphere.

The seemingly small impact on direct PM emissions resulting from implementation of the CAAA

depicted in Figures 2-5 and 2-6 can be misleading.

While these figures illustrate the impact of the 1990

CAAA on primary PM emissions, it is important to

remember that ambient PM concentrations are influenced by the presence of both primary and secondary PM. VOCs, NOx

, and SO2, all pollutants

regulated by the CAA, are secondary PM precursors. The reduction in the emissions of these three

pollutants also leads to lower overall PM concentrations in the atmosphere. The complete impact of

the CAAA on PM thus is not fully captured by Figures 2-5 and 2-6. Additional discussion of the influence of the CAAA on PM and ambient air quality is

provided in Chapter 4 and Appendix C.

As part of this prospective analysis we also estimated future-year NH3

 emissions. The 1990 Amendments, however, do not include provisions designed

10 EPA projected PM10 and PM2.5 levels holding natural

source emissions of particulate matter constant at 1990 levels.

The estimates presented in Figures 2-5 and 2-6 have been adjusted; these estimates represent total PM emissions minus natural source emissions (wind erosion).

11 Directly emitted PM, such as fugitive dust, is referred to

as primary PM. Secondary PM is not directly emitted, but rather

forms in the atmosphere. NOs

 and SO2 are two examples of

secondary PM precursors.

12 As discussed on pages 18 and 20 and in Table 2-5, however, some recent data indicate that the composition data used

in this analysis may underestimate the contribution from motor vehicle carbonaceous emissions.

13 The difference between 2010 Pre- and Post-CAAA estimates for PM10 and PM2.5 motor vehicle emissions is 31 percent

and 39 percent respectively. The difference between 2010 Preand Post CAAA estimates for PM10 and PM2.5 nonroad emissions is 19 percent and 20 percent respectively.

14 The PM NAAQS referred to here is the 50 ug/m3

 (annual mean) 150 ug/m3

 (daily mean) standard.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

14

Table 2-3

Summary by Source Category of National Annual Emission Projections to 2010

(thousand tons)

Pollutant

Source

Category 1990

2010

Pre-CAAA

2010

Post-CAAA % Change

VOC Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

37

3,500

10,000

2,100

6,800

23,000

49

4,200

13,000

2,600

7,300

28,000

50

3,500

8,500

1,900

3,900

18,000

2%

-19%

-36%

-28%

-46%

-35%

NOx Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

7,400

2,900

2,200

2,800

7,400

23,000

9,100

3,600

3,000

3,400

9,100

28,000

3,800

2,200

3,000

2,700

5,600

17,000

-58%

-39%

-1%

-20%

-39%

-39%

CO Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

330

6,000

12,000

14,000

62,000

94,000

450

7,400

14,000

19,000

66,000

107,000

460

7,400

14,000

18,000

42,000

82,000

2%

0%

0%

-4%

-37%

-23%

SO2 Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

16,000

4,600

1,000

240

570

22,000

18,000

6,000

1,500

240

770

26,000

9,900

6,000

1,500

240

410

18,000

-44%

0%

0%

0%

-47%

-31%

Primary

PM10

Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

280

930

26,000

340

360

28,000

310

1,200

27,000

410

300

29,000

280

1,200

26,000

340

210

28,000

-9%

0%

-3%

-19%

-31%

-3%

Primary

PM2.5

Utility

Point

Area

Nonroad

Motor Vehicle

TOTAL

110

590

5,800

290

290

7,100

120

750

6,300

360

230

7,700

110

750

6,100

290

140

7,400

-8%

0%

-2%

-20%

-39%

-4%

NOTES: Table may not sum due to rounding. Percentage change was calculated prior to rounding.
Chapter 2: Emissions

15

to regulate NH3. As a result, the Pre- and PostCAAA estimates follow a similar upward trend. We

estimate NH3

 emissions will increase roughly 55

percent from 1990 to 2010. Although we do not

estimate the costs and benefits associated with NH3

controls and changes in NH3 ambient concentrations

as part of this analysis, estimation of NH3

 emissions

is an important part of the prospective study. NH3

is a secondary PM precursor, and we relied on future-year NH3

 emissions estimates as model input

to help us estimate PM concentrations.

We also estimated the effect of CAAA provisions on mercury (Hg) emissions for five separate

Hg emissions sources: medical waste incinerators

(MWI), municipal waste combustors (MWCs), electric utility plants, hazardous waste combustors, and

chlor-alkali plants.15 Together, these sources account

for 75 to 80 percent of national anthropogenic airborne Hg emissions. In this analysis we qualitatively

examine the effects of mercury emissions reductions

on ecological systems (see Chapter 7 and Appendix

E). We do not, however, evaluate the impact of Hg

on human health.

Table 2-4 displays, for each emission category,

base-year (1990) and future-year (2000 and 2010) Preand Post-CAAA emissions estimates. The table also

shows the difference between Pre- and Post-CAAA

estimates for each projection year. Overall, the results of this analysis indicate that the 1990 Amendments will provide a reduction in Hg emissions of

44.2 tons per year (tpy) in the year 2000 and a reduction of 56.2 tpy in 2010. These changes represent a

35 percent reduction in airborne mercury emissions

for the year 2000 and a 42 percent reduction for 2010.

We estimate that most of the reduction will be the

result of New Source Performance Standards for

MWI and MWCs.

15 With the exception of electric utility plant Hg emissions

that were estimated using IPM, we relied on previously generated estimates (typically from recently conducted RIAs) to evaluate the impact of the CAAA on Hg emissions. For a more complete discussion of the methodology, see Appendix A.

Table 2-4

Airborne Mercury Emission Estimates

2000 Emissions (tons) 2010 Emissions (tons)

Source Category

1990

Emissions

(tons)

PreCAAAPostCAAA Diff.PreCAAAPostCAAA Diff.

Medical Waste Incin. 50 17.9 1.3 16.6 22.6 1.6 21.0

Municipal Waste Comb. 54 31.2 5.5 25.7 33.8 6.0 27.8

Electric Utility Generation 51.3 63.0 61.1 1.9 68.5 65.4 3.1

Hazardous Waste Comb. 6.6 6.6 6.6 0 6.6 3.0 3.6

Chlor-Alkali Plants 9.8 6.0 6.0 0 2.0 1.3 0.7

Total CAAA Benefits (Reductions) 44.2 56.2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

16

Figure 2-1

Pre- and Post-CAAA Scenario VOC Emissions

Estimates

Figure 2-2

Pre- and Post-CAAA Scenario NOX Emissions

Estimates

Figure 2-3

Pre- and Post-CAAA Scenario SO2 Emissions

Estimates

0

5

10

15

20

25

30

1980 1990 2000

Year

Emissions in Short Tons 

(Millions)

Pre-CAAA Post-CAAA

0

5

10

15

20

25

30

1980 1990 2000 2010

Year

Emissions in Short Tons

(Millions)

Pre-CAAA Post-CAAA

0

5

10

15

20

25

30

1990 2000 2010

Year

Emissions in Short Tons

(Millions)

Pre-CAAA Post-CAAA
Chapter 2: Emissions

17

Figure 2-4

Pre- and Post-CAAA Scenario CO Emissions

Estimates

Figure 2-5

Pre- and Post-CAAA Scenario Primary PM10

Emissions Estimates

Figure 2-6

Pre- and Post-CAAA Scenario Primary PM2.5

Emissions Estimates

0

20

40

60

80

100

120

1980 1990 2000 2010

Year

Emissions in Short Tons

(Millions)

Pre-CAAA Post-CAAA

0

5

10

15

20

25

30

1980 1990 2000 2010

Year

Emissions in Short Tons

(Millions)

Pre-CAAA Post-CAAA

0

5

10

15

20

25

30

1980 1990 2000

Year

Emissions in Short Tons 

(Millions)

Pre-CAAA Post-CAAA
The Benefits and Costs of the Clean Air Act, 1990 to 2010

18

Comparison of Emissions

Estimates With Other

Existing Data

Comparison of the emissions projections generated by the prospective analysis to historical emissions estimates drawn from the National Air Pollutant and Emissions Trends reports (Trends) provides

a check on the reasonableness of our emissions inventories. In addition, comparison of emissions projections from the prospective analysis with those of

the Grand Canyon Visibility Transport Commission (GCVTC) study of western regional haze provides an initial test of the sensitivity of emissions

projections to base-year inventories and growth assumptions. Analysis of PM emissions and comparison of estimated and observed PM data also help us

evaluate the prospective studyís emissions estimation

methods.

Trends reports contain historical estimates of

annual VOC, NOx

, SO2, CO, and PM10, emissions.

While the most recent report only provides emissions data through the first half of the 1990s, comparison of these estimates from 1990 to 1996 with

emissions trends projected under the Post-CAAA

scenarios reveals that emissions figures from both

are similar. The disparity that does exist between

the two sets of estimates largely stems from the fact

that the Post-CAAA scenario trend lines running

from 1990 to 2000 consist of only two data points.

As a result, Post-CAAA trend lines cannot capture

yearly fluctuations in emissions and the exact timing of emissions cuts. Only for NOx

 are the Trends

and Post-CAAA estimates significantly different; this

is because the Trends report is still in the process of

incorporating the Stateís periodic emission inventory

into the NET database. As a result, Trends values

do not capture all the NOx

 emission reductions that

have occurred since 1990. For example, significant

reductions attributable to reasonable available control technology (RACT) requirements for major stationary source NOx

 emitters areas are not reflected

in the Trends figures.

The Grand Canyon Visibility Transport Commission conducted an air pollution analysis for Western States that projected emissions for selected pollutants, including NOx

, SO2, and PM2.5, from 1990

base-year levels for the year 2000 and every tenth

subsequent year up to 2040. GCVTC estimates of

future-year emissions levels differ from Post-CAAA

projections. This disparity results from the use of

different base-year inventories in the two studies and

from specific regional reductions not incorporated

in the prospective analysis scenarios. Despite the

difference in GCVTC and Post-CAAA estimates,

the change in the level of emissions from 1990 to

2010 predicted by the two studies is similar. Comparison of both sets of projections illustrates the sensitivity of future-year emissions estimates to the baseyear inventory.

The 1997 National Air Quality and Emissions

Trends Report provides a summary of PM2.5 concentration speciation data. This report shows the

relative contribution of the major PM emissions

source components (crustal material, carbonaceous

particles, nitrate, and sulfate) to ambient PM2.5 concentrations in urban and nonurban areas throughout the U.S.16 Comparison of primary PM2.5 emissions estimates generated for this analysis with the

observed concentration data presented in the 1997

report indicates that the ratio in the prospective study

of crustal material to primary carbonaceous particles

is high. At least part of this apparent overestimation of crustal material and underestimation of carbonaceous particulates, however, is due to the fact

that much of the emitted crustal material quickly

settles and does not have a quantifiable impact on

ambient air quality. In this analysis, we apply a factor of 0.2 to crustal emissions to estimate the fraction of crustal PM2.5 that makes its way into the

ìmixed layerî of the atmosphere and influences pollutant concentrations. Figure 2-7 displays the

breakout of primary PM2.5 into its adjusted crustal

and carbonaceous (elemental carbon and organic

carbon) components. The figure divides crustal

material into two subcategories, fugitive dust or industrial sources, based on the source of the material

and also shows the fraction of primary PM2.5 that is

16 Crustal material is directly emitted from fugitive dust

sources such as agricultural operations, construction, paved and

unpaved roads, and wind erosion as well as from some industrial sources such as metals processing. Carbonaceous particles,

as defined in the 1997 National Air Quality and Emissions

Trends Report,î are emitted directly and as condensed liquid

droplets from fuel combustion, burning of forests, rangelands,

and fields; off highway and highway mobile sources (gas and

diesel); and certain industrial processesî.
Chapter 2: Emissions

19

Region 10

Region 9

Region 8

Region 7

Region 6

Region 5

Region 4

Region 3

Region 2

Region 1

100,000 450,000 850,000

Crustal - Fugitive Dust Sources

Crustal - Industrial Sources

Other Primary

Elemental Carbon

Organic Carbon

neither crustal nor carbonaceous. The ratios of adjusted crustal material to primary carbonaceous particles presented in Figure 2-7 are in line with the

observed PM2.5 concentration data presented in the

1997 report.

Uncertainty In Emission

Estimates

Table 2-5 provides a list of sources of uncertainty

associated with estimating base-year emissions, the

expected direction of bias introduced by each uncertainty (if known), and the relative significance of

each uncertainty in the overall 812 benefits analysis.

The emissions estimates presented in the prospective analysis are characterized by three major sources

of uncertainty: estimation of the base-year inventory, prediction of the growth in pollution-generating activity, and assumptions about future-year controls.

Base-year emissions were estimated using emissions factors that express the relationship between a

particular human/industrial activity and the level of

emissions. The accuracy of base-year emissions estimates varies from pollutant to pollutant, depending

largely on how directly the selected activity and

emissions correlate. We likely estimated 1990 SO2

emissions with the greatest precision. Sulfur dioxide emissions are generated during combustion of

sulfur-containing fuel and are directly related to fuel

sulfur content. In addition, we were able to verify

these estimates through comparison with Continuous Emission Monitoring (CEM) data. As a result,

we were able to accurately estimate SO2

 emissions

using emissions factors based on data on fuel usage

and fuel sulfur content. Nitrogen oxides are also a

product of fuel combustion, allowing us to estimate

emissions of this pollutant using the same general

technique used to estimate SO2

 emissions. However,

the processes involved in the formation of NOx

during combustion are more complicated than those

involved in the formation of SO2

; thus, our NOx

emissions estimates are more variable and less certain than SO2

 estimates.

Volatile organic compounds, like SO2

 and NOx,

are products of fuel combustion; however, these

compounds are also a product of evaporation. To

estimate evaporative emissions of this pollutant we

Figure 2-7

1990 Primary PM2.5 Emissions by EPA Region (tons/year)
The Benefits and Costs of the Clean Air Act, 1990 to 2010

20

used emissions factors that relate changes in emissions to changes in temperature. Because future

meteorological conditions are difficult to predict,

the uncertainty associated with forecasting temperature influences the uncertainty in our VOC emissions estimates. The likely significance of this uncertainty, in terms of its impact on the overall monetary benefit present in this analysis, is probably

minor.

In this analysis we estimated primary PM2.5 emissions based on unit emissions that may not accurately reflect the composition and mobility of particles. The ratio of crustal to carbonaceous particulate material, for example, likely is high as a result of

overestimation of the fraction of crustal material,

primarily composed of fugitive dust, and underestimation of the fraction of carbonaceous material.

Because the CAAA has a greater impact on emissions sources that generate carbonaceous particles

(mobile sources) than on sources that mainly emit

crustal material (area sources), we likely underestimate the impact of the CAAA on reducing PM2.5,

thereby reducing monetary benefits estimates. The

uncertainty associated with estimating the partition

of PM2.5 emissions components could conceivably

have a major impact on the net benefit estimate;

compared to secondary PM2.5 precursor emissions,

however, changes in primary PM2.5 emissions have a

relatively small impact on PM2.5 related benefits..

We estimated future-year emissions levels based

on expected growth in pollution-generating activities. Inherent uncertainties and data inadequacies/

limitations exist in forecasting growth for any future period. Also, the growth indicators we used in

this analysis may not directly correlate with changes

in the factors that influence emissions. Both of these

factors contribute to the uncertainty associated with

this studyís emissions results. For example, the best

indicator of pollution-generating activity is fuel use

or some other measure of input/output that most

directly relates to emissions. The key BEA indicator used in this analysis, GSP, is closely correlated

with the pollution-generating activity associated with

many manufacturing industry processes (iron and

steel, petroleum refining, etc.). However, a good

portion of industrial sector emissions are from boilers and furnaces, whose activity is related to production, but not as closely as to product output. Activities such as fuel switching may produce different

emission patterns than those reflected in the results

of this study.

Our future-year control assumptions are also a

source of uncertainty. Despite our efforts to minimize this uncertainty, whether each of the PostCAAA controls will be adopted, whether PostCAAA control programs will be more or less effective than estimated, and whether unanticipated technological shifts will reduce future-year emissions are

all unknown. For example, the Post-CAAA scenario

includes implementation of a region-wide NOx

 control strategy designed to regulate the regional transport of ozone. However, the control program assumed under the Post-CAAA scenario may not reflect the NOx

 controls that are actually implemented

in a regional ozone transport rule.
Chapter 2: Emissions

21

Table 2-5

Key Uncertainties Associated with Emissions Estimation

Potential Source of Error

Direction of Potential Bias for

Net Benefits Estimate

Likely Significance Relative to Key

Uncertainties in Net Benefit

Estimate*

PM2.5 emissions are largely

based on scaling of PM10

emissions.

Overall, unable to determine

based on current information,

but current emission factors are

likely to underestimate PM2.5

emissions from combustion

sources, implying a potential

underestimation of benefits.

Potentially major. Source-specific

scaling factors reflect the most careful

estimation currently possible, using

current emissions monitoring data.

However, health benefit estimates

related to changes in PM2.5 constitute

a large portion of overall CAAA-related

benefits.

Primary PM2.5 emissions

estimates are based on unit

emissions that may not

accurately reflect composition

and mobility of the particles.

For example, the ratio of

crustal to primary

carbonaceous particulate

material likely is high.

Underestimate. The effect of

overestimating crustal emissions

and underestimating

carbonaceous when applied in

later stages of the analysis, is to

reduce the net impact of the

CAAA on primary PM2.5

emissions by underestimating

PM2.5 emissions reductions

associated with mobile source

tailpipe controls.

Potentially major. Mobile source

primary carbonaceous particles are a

significant contributor to public

exposure to PM2.5. Overall, however,

compared to secondary PM2.5

precursor emissions, changes in

primary PM2.5 emissions have only a

small impact on PM2.5 related benefits.

The Post-CAAA scenario

includes implementation of a

region-wide NOx emissions

reduction strategy to control

regional transport of ozone

that may not reflect the NOx

controls that are actually

implemented in a regional

ozone transport rule.

Unable to determine based on

current information.

Probably minor. Overall, magnitude of

estimated emissions reductions is

comparable to that in expected future

regional transport rule. In some areas

of the 37 state region, emissions

reductions are expected to be

overestimated, bur in other areas, NOx

inhibition of ozone leads to

underestimates of ozone benefits

(e.g., some eastern urban centers).

VOC emissions are dependent

on evaporation, and future

patterns of temperature are

difficult to predict.

Unable to determine based on

current information.

Probably minor. We assume future

temperature patterns are well

characterized by historic patterns, but

an acceleration of climate change

(warming) could increase emissions.

Use of average temperatures

(i.e., daily minimum and

maximum) in estimating

motor-vehicle emissions

artificially reduces variability in

VOC emissions.

Unable to determine based on

current information.

Probably minor. Use of averages will

overestimate emissions on some days

and underestimate on other days.

Effect is mitigated in Post-CAAA

scenarios because of more stringent

evaporative controls that are in place

by 2000 and 2010.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

22

Table 2-5 (continued)

Key Uncertainties Associated with Emissions Estimation

Potential Source of Error

Direction of Potential Bias for

Net Benefits Estimate

Likely Significance Relative to Key

Uncertainties in Net Benefit

Estimate*

Economic growth factors used

to project emissions are an

indicator of future economic

activity. They reflect

uncertainty in economic

forecasting as well as

uncertainty in the link to

emissions.

Unable to determine based on

current information.

Probably minor. The same set of

growth factors are used to project

emissions under both the Pre-CAAA

and Post-CAAA scenarios, mitigating

to some extent the potential for

significant errors in estimating

differences in emissions.

Uncertainties in the

stringency, scope, timing, and

effectiveness of Post-CAAA

controls included in projection

scenarios.

Unable to determine based on

current information.

Probably minor. Future controls could

be more or less stringent, widereaching (e.g., NOx reductions in

OTAG region - see above), or

effective (e.g., uncertainty in realizing

all Reasonable Further Progress

requirements) than projected. Timing

of emissions reductions may also be

affected (e.g., sulfur emissions

reductions from utility sources have

occurred more rapidly than projected

for this analysis).

* The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The

Project Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could

influence the overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or

approach is likely to change the total benefit estimate by less than five percent, the Project Team assigns a classification

of "probably minor."
Chapter 3: Direct Costs

23

Direct Costs

The costs of complying with the requirements

Chapter

3

of the Clean Air Act Amendments (CAAA) of 1990

will affect all levels of the U.S. economy. The impact, initially experienced through the direct costs

imposed by regulations promulgated under the

amendments, will also be seen in patterns of industrial production, research and development, capital

investment, productivity, employment, and consumption. The purpose of the analysis summarized

in this chapter is to estimate the incremental change

in annual compliance costs from 1990 to 2010 that

are directly attributable to the 1990 Clean Air Act

Amendments.

This chapter consists of four sections. The first

section summarizes our approach to estimating direct compliance costs. In the second section we

present the results of the cost analysis. We first report the total costs of Titles I through V and then

present estimates for major individual provisions.

We also briefly discuss our derivation of Title VI

costs. In the third section, we provide a qualitative

discussion of the potential magnitude of social costs

and other impacts associated with the Amendments

to characterize the potential welfare loss not captured in the direct cost approach. We conclude the

chapter with a discussion of the major analytic uncertainties and include the results of quantitative sensitivity tests of key data and assumptions.

Approach to Estimating

Direct Compliance Costs

As discussed in the previous chapter, the first

step of the prospective analysis required the development of emission estimates for the base-year, 1990,

and for the two target years in our analytic time

period, 2000 to 2010. We developed two scenarios,

Pre-CAAA and Post-CAAA, that reflect three key

parameters: (i) base-year inventory selection, (ii) indicators of forecasted economic growth, and (iii) effects of future year controls and selected CAAA provisions. The Pre-CAAA scenario applies the stringency and scope of air pollution regulations as they

existed in 1990 and projects emissions and costs to

2000 and 2010. This scenario establishes a baseline

that represents projected emission levels and control costs in the absence of the 1990 Amendments.

Under the Post-CAAA scenario, costs are based on

compliance with selected CAAA provisions. Together these two scenarios form the foundation upon

which the incremental costs and benefits of complying with the 1990 Amendments are estimated. For

more information on the development of these scenarios, see Chapter 2.

We closely integrate the modeling of direct compliance costs with emissions projections by maintaining consistency among control assumptions (i.e.

emissions scenarios) used as inputs in the cost estimation modeling and in the analysis of emissions

projections and benefits. We use two models to estimate costs, Emission Reduction and Cost Analysis

Model (ERCAM) and Integrated Planning Model

(IPM). These models generate cost estimates for the

Post-CAAA scenarios in two projection years, 2000

and 2010. The estimates are calculated relative to

costs under the same year Pre-CAAA scenario, so

estimates represent incremental costs of compliance

with the 1990 Amendments.

We use ERCAM to estimate costs associated with

regulating particulate matter (PM), volatile organic

compounds (VOCs), and non-utility source oxides

of nitrogen (NOx

).1 The model is essentially a costaccounting tool that provides a structure for modifying and updating changes in inputs while main1

 This model was developed by E. H. Pechan & Associates, Inc. to facilitate EPAís analysis of emissions control.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

24

taining consistency with the emission and cost analyses. Cost scenarios and assumptions are developed

for each non-utility source category (e.g., point, area,

nonroad, and motor vehicle sources) and in response

to specific provisions and emission targets. The

model estimates costs based on inputs such as cost

per ton, source-specific cost equations, incremental

production, and operating cost estimates. For this

analysis, we collected data and inputs from information presented in regulatory impact assessments

(RIAs), background information documents (BIDs),

regulatory support documents, and Federal Register notices.

To estimate the costs of reducing utility NOx

and sulfur dioxide (SO2) emissions, we use the Integrated Planning Model (IPM). IPM allows us to estimate the control costs of several pollutants while

maintaining consistent control scenarios and economic forecasts of the electric power industry. It

assesses the optimal mix of pollution control strategies subject to a series of specified constraints. Key

inputs and constraints in the model include targeted

emissions reductions (on a seasonal or annual basis),

costs and constraints of control technology, and economic parameters (e.g., forecasted demand for electricity, power plant availability/capacity, costs of

fuel, etc.)

To assess the costs of reducing emission of pollutants or sectors not covered by our two models,

we estimate costs using the best available cost equations or other types of analyses. For example, we

estimate non-utility SO2

 emission control costs for

point sources by applying source-specific cost equations for flue gas desulfurization (FGD)/scrubber

technology to affected sources in 2000 and 2010.

While we do not explicitly model CO attainment

costs, we include in the analysis the costs of programs designed to reduce CO emissions, such as oxygenated fuels and a cold temperature CO motor vehicle emission standard. Finally, to estimate costs

of the rate of progress/reasonable further progress

(ROP/RFP) provisions, requirements under Title I

that require ozone nonattainment areas to make

steady progress toward attainment, we first estimate

the emissions reduction shortfall that must be

achieved in each target year in each nonattainment

area, and then apply a cost per ton estimate from a

schedule of measures that could be applied locally

to meet the necessary ROP/RFP requirement. For

more detail on the specific methods used to estimate

compliance costs for each pollutant and source category, see Appendix B.

The cost estimates in this chapter are the incremental costs of the 1990 Amendments (i.e. the difference between pre- and Post-CAAA cost estimates).

We present the results as total annualized costs (TAC)

in 2000 and 2010. Annualized costs include both

capital costs, such as costs of control equipment, and

operation and maintenance (O&M) costs.2 They

do not represent actual cash flow in a given year,

but are rather an estimate of average annual burden

over the period during which firms will incur costs.

In annualizing costs, we convert total capital investment to a uniform series of total per-year equivalent

payments over a given time period using an assumed

real cost-of-capital at five percent. We then add

O&M and other reoccurring costs to the annualized

capital cost to arrive at TAC. The discounted sum

of these annual expenditures is equal to the net

present value of total costs incurred over the time

period of this analysis.3

Direct Compliance Cost

Results

Total annual compliance costs for Titles I

through V of the 1990 Amendments in the year 2000

will be approximately $19.4 billion; the estimate increases to $26.8 billion in the year 2010. These costs

reflect ìannualizedî operation and maintenance

(O&M) expenditures (which includes research and

development (R&D) and other similarly recurring

expenditures) plus amortized capital costs (i.e., depreciation plus interest costs associated with the ex2

 For a few VOC source categories, we estimate that capital investment will not be necessary; for these sources, compliance costs reflect O&M costs only.

3

 We recalculate the control cost estimates from regulatory

documents that use a seven or ten percent discount rate so that

the costs will be consistent with the five percent discount rate

assumption used in this analysis. We also calculate cost using

three percent and seven percent discount rates, as sensitivity tests;

for detail see the discussion of uncertainty later in this chapter,

in Chapter 8, and in Appendix B.
Chapter 3: Direct Costs

25

isting capital stock) for the particular year.4 We

present cost estimates by title and emissions source

category (point sources, area sources, utilities,

nonroad engines and vehicles, and motor vehicles)

in Table 3-1.

In some cases, assigning costs to a single CAAA

title is complicated by the fact that there are rules

issued pursuant to more than one title.5 In addition, with the passage of the 1990 Amendments, the

States were given greater discretion in developing

CAAA compliance strategies. For example, the

States can determine how best to meet progress requirements and are responsible for creating permit

programs (under Title V). As a result, a significant

portion of the costs also represent State-level strategies and decisions for reducing emissions.

Title I, Provisions for Attainment and Maintenance of National Ambient Air Quality Standards

(NAAQS), represents pollution controls (of VOC,

NOx

, and PM emissions) implemented primarily by

point and area sources. Title I provisions also account for State programs designed to meet progress

requirements. By 2010, we project the costs of Title

I provisions will account for over half of total CAAA

direct compliance costs ($14.5 billion). An additional

34 percent of estimated total costs ($9 billion) is attributed to regulating mobile source emissions under Title II. Collectively, the combined direct compliance costs of these two titles is $16 billion in 2000

and $23 billion by 2010.

The remaining three titles account for less than

20 percent of total CAAA direct costs. We estimate

that Title III provisions, which target hazardous air

pollutant (HAP) emissions, will cost $840 million

by the year 2010. This estimate represents total annualized capital costs (TACs) for individual two- and

four-year MACT standards. While the majority of

this estimated cost reflects reducing VOC emissions

(since HAP emissions were not included as part of

the Section 812 base- year inventory), Title III costs

do include some costs of final MACT rules that regulate non-VOC HAP emissions.

In order to estimate the costs associated with

Title IV, we considered the implications of pollution abatement controls (for SO2

 and NOx) on the

electric power industryís operation of generation

units and how, over time, this would affect the demand for electricity. The annual compliance estimate for Title IV costs is $2.3 billion in 2000. This

estimate decreases to $2.0 billion by 2010. This decrease reflects, in part, the future compliance cost

savings resulting from the SO2

 allowance trading

program.

Title V costs are associated with new operating

permit programs. The estimate accounts for approximately one percent of total costs projected under

the Post-CAAA 2010 scenario. States are expected

to implement Title V permit programs by 2005. The

estimate reflects the costs of State-developed programs during the first five-year implementation period. These costs include incremental administrative costs incurred by the permitted sources, State

and local permitting agencies, and EPA. The estimate excludes federally-implemented State programs

and state programs which were already established

in the baseline.

Our presentation of cost estimates for the stratospheric ozone protection provisions of Title VI is,

by necessity, different from other titles. Ideally, one

should compare the costs of actions taken in a given

year to the benefits attributable to these actions. For

Title VI, a cost-benefit comparison of any given year

requires assumptions that result in potentially misleading figures. The difficulty is due to the differing

time horizons and the complexity of the process by

which ozone-depleting substances (ODSs) cause adverse effects on human health and the environment.

Title VI provisions incur costs over significantly

varying time horizons; for example, the cost analysis of Sections 604 and 606 provisions spans 85 years

(from 1990 to 2075). At the same time, the analysis

of Section 611 extends from 1994 to 2015. In response to this analytic difficulty, we base our comparison of Title VI costs to Title VI benefits on net

present values.

4 Capital expenditures are investments, generating a stream

of benefits and opportunity costs over an investmentís lifetime.

In a cost-benefit analysis, the appropriate accounting technique

is to annualize capital expenditures. This technique involves

spreading the costs of capital equipment uniformly over the useful life of the equipment, by using a discount rate to account for

the time value of money. In this analysis, all capital expenditures were annualized using a real five percent interest rate.

5 In those cases, we generally assigned costs to a single title

based upon implementation dates and the year by which emission reductions are expected.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

26

The net present value of Title VI program costs

reflect selected actions and their associated costs from

Sections 604, 606, 608, 609, and 611. Examples of

these actions include: replacement of ozone-depleting chemicals with alternative technologies and materials; recycling and storage of unused chlorofluorocarbons; labeling; training; and administration.

Using a discount rate of five percent and a 85-year

time horizon (from 1990 to 2075), we estimate the

net present value of Title VI costs is $27 billion. For

illustrative purposes, we calculated an annualized

estimate of Title VI costs. It is, however, important

to recognize that these estimates may overestimate

actual compliance costs in those years, especially in

the year 2000, because of the phased nature of implementationó see Appendix G for more details. Our

annualized estimate of total Title VI costs is $1.4

billion. This value reflects an annualized equivalent

value of costs incurred over 85 years (from 1990 to

2075) using a five percent discount rate.

Selected Provisions

Our analysis indicates eight provisions will account for approximately 54 percent of the total direct compliance costs estimate for 2010. Six are Title

I provisions that affect stationary sources and vehicle

Table 3-1

Summary of Direct Costs for Titles I to V of CAAA, By Title and Selected Provisions

(Annual Costs in million 1990$)

Title/Provision

Primary Cost

Estimate

2000

Percentage of

Total Costs

Primary Cost

Estimate

2010

Percentage of

Total Costs

Title I- Provisions for Attainment and Maintenance of NAAQS

Stationary NOx Controls, Utility Industry $ 790 4% $ 2,500 9%

Progress Requirements 1,200 6% 2,500 9%

PM NAAQS Controls 1,900 10% 2,200 8%

California LEV 320 2% 1,100 4%

National LEV 180 1% 1,100 4%

High Enhanced I/M 1,100 6% 1,400 5%

Other Title I Programs 3,100 16% 3,700 14%

Title I: Total Costs $ 8,600 44% $ 14,500 54%

Title II- Provisions Relating to Mobile Sources

California Reformulated Gasoline $ 2,000 10% $ 2,400 9%

NOx Tailpipe/Extended Useful

Life Standard

1,500 8% 1,700 6%

Other Title II Programs 3,900 20% 4,900 18%

Title II: Total Costs $ 7,400 38% $ 9,050 34%

Title III- Hazardous Air Pollutants

Title III: Total Costs $ 780 4% $ 840 3%

Title IV- Acid Deposition Control

Title IV: Total Costs $ 2,300 12% $ 2,040 8%

Title V- Permits

Title V: Total Costs $ 300 2% $ 300 1%

Total Annual Cost $ 19,400 100% $ 26,800 100%

Note: Totals may not sum due to rounding. Only major provisions are listed under each title - other, less costly provisions not

listed here are nonetheless included in the totals by title and the overall total.
Chapter 3: Direct Costs

27

emissions. The remaining two provisions target

mobile sources under Title II. These provisions are:

ï PM NAAQS controls6 ,

ï Electric power industry compliance (stationary NOx

 control),

ï Progress Requirements,

ï California Low Emission Vehicle

(LEV)program,

ï National Low Emission Vehicle (LEV) program,

ï High Enhanced Inspection and Maintenance

(I/M) program,

ï California Reformulated Gasoline, and

ï NOx

 Tailpipe/Extended Useful Life Standard.

The 1990 CAAA regulates stationary source

emissions primarily under Title I. Among the relevant provisions, PM NAAQS, utility industry compliance with NOx

 standards, and progress requirements are the main sources of Title I costs. From

2000 to 2010, we estimate the control costs of all

three provisions will increase by at least a factor of

two. Under the Post-CAAA scenario developed for

the emissions analysis, the utility industryís compliance with NOx

 emission standards affects all electric

generation units using fossil fuels. Existing oil and

gas units face Reasonable Available Control Technology (RACT) requirements and all new units must

comply with more stringent New Source Performance Standards (NSPS) and New Source Review

(NSR) requirements. By 2010, estimated costs for

stationary NOx

 controls more than triple ($790 million to $2,500 million). The cost estimate indicates

that the provision will be the single largest source of

CAAA direct costs. The second largest component

of total costs in 2010 is attributed to progress requirements. Annual compliance costs with progress

requirements double from 2000 to 2010 ($1.2 billion and $2.5 billion, respectively). Among the three

provisions, the annual costs associated with PM

NAAQS compliance exhibits the least amount of

growth. We estimate annual costs for PM NAAQS

compliance will grow from $1.9 billion in 2000 to

$2.2 billion in 2010.

Among the provisions regulating vehicle emissions, only the national and California LEV programs exhibit a trend of increasing direct costs of

the same magnitude as seen with the costs of regulating stationary sources under Title I. The combined cost of national and California LEV programs

is $2.2 billion in 2010. For the California LEV program, the increase in cost is largely a function of

higher per vehicle cost estimates (e.g., zero emission

vehicles (ZEV) are mandated in the year 2003). Our

cost analysis of the national LEV program assumes

that only the Northeast Ozone Transport Region

(OTR) states will incur costs in the year 2000. By

2010, however, we expect that the program will affect areas outside of the OTR. As a result, 2010

national LEV costs increase with the expected expansion and increased volume of vehicle sales. Unlike many of the other provisions, high enhanced I/

M costs do not exhibit significant growth from 2000

to 2010. We estimate this provision accounts for

approximately six percent of total costs in 2000 and

five percent in 2010. These costs, however, are uncertain pending State decisions regarding the design

of their programs.

Among the analyzed Title II provisions, we attribute nearly 15 percent of total annual direct costs

to the California reformulated gasoline (RFG) program and NOx

 Tailpipe/Extended Useful Life Standard. Although the reformulated gasoline program

affects only California, the state accounts for nearly

ten percent of annual gasoline sales in the United

States. We estimate compliance costs of $1.9 billion

in the year 2000. As the program enters Phase 2,

estimated costs grow to $2.4 billion. The trend in

cost associated with NOx

 Tailpipe/Extended Useful

Life Standard is very different. While costs increase

slightly between the years 2000 and 2010, the

provisionís share of total cost slightly decreases.

Characterization of Other

Economic Impacts

In an ideal setting, a cost-benefit analysis would

not only identify, but also quantify and monetize,

an exhaustive list of social costs associated with a

regulatory action. This would include assessing how

regulatory actions targeting a specific industry or set

of facilities can alter the level of production and consumption in the directly affected market and related

6 We estimate the PM NAAQS provision costs based on

compliance with standards that were in effect prior to 1997 revisions (62 Fed. Reg. 38,652, 1997).
The Benefits and Costs of the Clean Air Act, 1990 to 2010

28

markets. For example, regulation of emissions from

the electric utility industry that results in higher electricity rates would have both supply-side and demand-side responses. In secondary markets, the increased electricity rates affect production costs for

various industries and initiate behavioral changes

(e.g., using alternative fuels as a substitute for electric power). With each affected market, there are

also associated externalities that should be included

in estimating social costs. Returning to the utilities

example, the externalities associated with electric

power generation versus nuclear power generation

can be very different. The mix of externalities could

change as consumers substitute nuclear power for

electric power. It is frequently difficult to accurately

characterize one or all of these dimensions of market responses and estimate the resulting social costs.

There are three generally practiced approaches

to calculating costs associated with regulation: (i) direct compliance cost, (ii) partial equilibrium modeling, and (iii) general equilibrium modeling. Direct

compliance cost estimates are calculated differently

than the economic welfare impact estimates that result from partial or general equilibrium modeling; a

direct cost estimate is often the most straightforward

of the three approaches. This method estimates compliance expenditures or, in economic terms, how an

industryís or firmís marginal cost curve shifts due to

increased production costs associated with regulatory compliance. As a result, this method does not

account for firm responses and market responses,

such as adjustment of production levels and product

prices. The other two methods measure changes in

producer and consumer welfare, and incorporate

these types of adjustments.

The direct cost approach likely overstates actual

compliance expenditures, but may have an ambiguous relationship to total social costs. There are two

primary reasons for the overstatement of compliance expenditures. First, the direct cost approach

does not account for market responses. As a result,

total direct cost estimates reflect the incremental cost

per unit of output multiplied by the generally higher,

pre-regulation quantity produced. Second, a direct

cost approach tends to make the simplifying assumption that firms rely on static pollution abatement

technology, when in fact the presence of compliance costs provides an incentive to innovate. Several ex post cost analyses suggest that the marginal

cost curve may not necessarily shift by the full

amount of the pollution abatement. For example,

firms may respond by altering production processes

to more efficiently reduce emissions.7 Social cost

estimates, however, may include other costs not reflected in direct cost estimates (discussed below),

thereby offsetting the tendency for direct cost estimates to overstate expenditures.

Measuring net welfare changes due to regulatory

action requires either partial or general equilibrium

modeling. These more complicated approaches estimate social costs by accounting for a wider range

of market consequences associated with compliance

with pollution abatement requirements. The partial equilibrium approach is particularly appropriate when social costs are predominantly incurred in

directly affected markets. It requires modeling both

supply and demand functions in the affected economic sector. Therefore, measures of social cost

reflect behavioral responses by both producers and

consumers in a specific market and do not necessarily reflect how those changes affect related markets.

In cases where the regulatory action is known

to have an impact on many sectors of the US

economy, the general equilibrium model is a more

appropriate approach to estimating social costs. Like

the partial equilibrium model, the general equilibrium model estimates social costs by accounting for

direct compliance costs and producer and consumer

market behavior. The general equilibrium model

can capture first-order effects that occur in multiple

sectors of the economy, and may also provide insight into unanticipated indirect effects in sectors that

might not have been included in the scope of a partial equilibrium analysis.

The relationship of general equilibrium estimates

to estimates from the other two cost approaches is

not always clear. General equilibrium estimates have

a broader basis from which to estimate social costs

and can reflect the net welfare changes across the

full range of economic sectors in the U.S. Partial

equilibrium modeling tends to understate full social

costs because of its restricted scope (i.e., generally

limited to one industry). Total direct cost estimates

are likely to overstate costs in the primary market

because they do not reflect consumer and producer

responses. This is demonstrated in comparisons of

7

Morgenstern et al. (1998) estimate the ratio of incurred

abatement expenditures to estimated direct costs can be as low

as 0.8.
Chapter 3: Direct Costs

29

estimates generated using a direct cost approach and

a partial equilibrium approach. The extent to which

a direct cost estimate will overstate or understate a

social cost estimate from a general equilibrium model

depends on the magnitude of the ìripple effectsî in

economic sectors not targeted by a regulation.8

In the 812 retrospective analysis (EPA, 1997),

we recognized that the Clean Air Act has a pervasive impact on the US economy and opted for the

general equilibrium approach. The retrospective

nature of the analysis, however, provided us with

fairly well-developed historical data sets of goods and

service flows throughout the economy. These data

sets facilitated the development of detailed, year-byyear expenditures in all sectors of the economy, from

which we modeled producer and consumer behavior and estimated net social costs. In the retrospective, our central estimate of total annualized direct

costs, from 1970 to 1990, was $523 billion. In comparison, we estimated the aggregate welfare effects

to be between $493 and $621 billion.9

For the prospective analysis, however, we adopt

a direct compliance cost approach. Although the

general equilibrium approach may represent a more

theoretically preferable method for measuring social costs, we use the simpler direct cost modeling

method for three reasons:

ï First, we believe that the direct cost approach

provides a good first approximation of the

CAAAís economic impacts on various sectors the U.S. economy. Comparison of the

direct cost approach to the partial equilibrium modeling suggests that the direct cost

approach likely overstates costs to the entity that incurs the pollution control cost expenditure. As discussed earlier, the direct

cost approach does not reflect adjustments

to prices and quantities that might mitigate

the effects of regulation. Recent research

analyzing ex ante and ex post cost estimates

of regulations suggests that ex ante analyses

are far more likely to overstate than understate costs.10 However, direct cost estimates

may also understate the effects of long-term

changes in productivity and the ripple effects

of regulation on other economic sectors that

are captured by a general equilibrium approach. The magnitude of those other effects, including potential magnification of

social costs by existing tax distortions, may

be substantial.

ï Second, we believe that the closer approximation of social costs that might be gained

through a general equilibrium approach

could be compromised by the difficulty and

uncertainty associated with projecting future

economic and technological changes. The

general equilibrium approach could provide

many insights that the direct cost approach

cannot, but also introduces a significant level

of additional uncertainty.

ï Third, the focus of the present analysis is a

comparison of direct costs and direct benefits. To provide a balanced treatment of

costs and benefits in a general equilibrium

framework, the social cost model must be

designed and configured to reflect the indirect economic consequences of both costly

and beneficial economic effects. None of

the general equilibrium models available in

the timeframe of this study could be configured to support effective analysis of the full

range of specific direct costs and, especially,

direct benefits of the 1990 Clean Air Act

Amendments.

8

 Current regulatory analyses that apply partial equilibrium modeling or general equilibrium modeling tend to measure costs with the assumption that markets are currently operating under optimally efficient conditions. Emerging literature

suggests that a full accounting of the social costs and efficiency

impacts of environmental regulations could also include an assessment of the incremental costs that reflect existing market

distortions, such as those imposed by the current tax code. The

distortions introduced by existing taxes, in combination with

new regulatory requirements, are collectively referred to as the

tax-interaction effect. One of the major conclusions of this

emerging literature is that the social cost of environmental policy

changes can be substantially higher when pre-existing tax distortions are taken into account. Our direct cost estimates do not

reflect quantification of this effect, in part because of the emerging nature of this literature and in part because existing estimates of the magnitude of the tax-interaction effect are calculated as increments to social costs and are not necessarily applicable adjustments to direct cost estimates.

9

 Estimates are in 1990 dollars. The retrospective states,

ìIn general the estimated second order macroeconomic effects

were small relative to the size of the U.S. economy.î The rate

of long term GNP growth between the control and no-control

scenarios amounted to roughly one-twentieth of one percent

less growth.

10 See, for example, Harrington et al (1999), referenced in

Appendix B, for a comparative analysis of ex ante and ex post

regulatory cost estimates.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

30

ï Fourth, undertaking a general equilibrium

modeling exercise remains a very resourceintensive task. For the purposes of comparing costs to benefits we concluded that more

detailed modeling would not be the most

cost-effective use of the project resources.

Uncertainty in the Cost

Estimates

Overview

As we note at the beginning of this chapter, explicit and implicit assumptions regarding changes in

consumption patterns, input costs, and technological innovation are crucial to framing the question of

the CAAAís cost impact. Given the nature of this

prospective study, there is no way to verify the accuracy of the assumptions applied to future scenarios.

We can envision other plausible analyses with estimates that differ from results in this chapter. Moreover, for many of the factors contributing to uncertainty, the degree or even direction of the bias is

unknown or cannot be determined. Nevertheless,

uncertainties and/or sensitivities can be identified

and in many cases the potential measurement errors

can be quantitatively characterized. In this section

of the chapter, we first discuss several quantitative

sensitivity analyses undertaken to characterize the

impact of key assumptions on the ultimate cost analysis. We conclude the chapter with a qualitative discussion of the impact of both quantified and

unquantified sources of uncertainty.

Quantitative Sensitivity Tests

In order to characterize the uncertainty in the

cost estimates, we conducted sensitivity analyses on

the key parameters and analytic assumptions of six

major provisions. The provisions are the following:

ï Progress Requirements,

ï California Reformulated Gasoline,

ï PM NAAQS Controls,

ï LEV program (the National and California

programs combined),

ï Non-utility Stationary Source NOx

 Controls, and

ï NOx

 Tailpipe/Extended Useful Life Standard.

We selected these provisions because they are

among the most significant sources of CAAA costs,

yet cost estimates for each of the provisions incorporate significant uncertainties. Collectively, these

provisions account for nearly 50 percent of total direct compliance cost estimates for 2010. Table 3-2

summarizes the methods we used to conduct the cost

sensitivity analyses and the results.

For each test, we developed three estimates for

one or more components of costs affecting the total

cost estimate for a given provision: (1) a central estimate, equal to the 2010 primary cost estimate reported in this chapter11 , (2) a low estimate; and (3) a

high estimate. The low and high estimates assess

the potential magnitude of the effect of the

component(s) on the provisionís costs and consequently, total CAAA costs, using reasonable alternative assumptions for each cost component. For

progress requirements, PM NAAQS controls, and

stationary source NOx

 controls, the cost projections

are based on models of future emissions controls.

Accurately identifying the set of adopted controls is

a key source of uncertainty. For example, cost-effective control measures for complying with progress

requirements have not yet been identified and the

sensitivity test suggests the potential for substantial

variability in progress requirement compliance costs.

In the case of motor vehicle provisions, there are

two significant sources of uncertainty, projecting

future car sales and forecasting accurate per vehicle

costs.

The results indicate that the sensitivity of our

primary cost estimates (central estimates) is not uniform across provisions. In addition, low and high

estimates may vary by as much as a factor of two.

These sensitivity analyses demonstrate the potential

effect of altering selected assumptions and data. We

do not assign probabilities to the likelihood of the

alternative. In other words, it would be inappropriate simply to add up the array of low and high estimates to arrive at an overall range of uncertainty

around the central estimates, because it is unlikely

that a plausible scenario could be constructed where

all the estimates are concurrently either at the high

11 The one exception is the central estimate of progress

requirements. Our sensitivity analysis which is based on more

recent cost information indicates that our primary estimate is

more reflective of a high estimate. See Appendix B for more

details.
Chapter 3: Direct Costs

31

or low end of their individual plausible ranges. A

better interpretation of these results is that uncertainty in key input parameters can have a significant

effect on the overall uncertainty of our estimates of

direct compliance costs and ultimately the net benefits calculation.

In addition to examining specific provisions, we

conducted a sensitivity analysis of the cost of capital

used throughout the analysis. Cost estimates presented earlier in this chapter reflect application of a

cost of capital (for the purposes of annualizing total

capital costs) of five percent. We also examined the

effect on cost estimates for those provisions which

involve significant capital expenditures and where

we could recalculate annualized costs from the available information. These provisions include non-utility and area source estimates for VOC, NOx

, and

PM control. The alternative estimates use three and

seven percent for the cost of capital. Results indicate that cost estimates are only moderately sensitive to the discount rate. The provisions evaluated

have a total annualized capital cost of approximately

$3 billion in 2010. Varying the cost of capital generated alternative estimates of $2.8 billion (three percent) and $3.1 billion (seven percent).12

Qualitative Analysis of Key Factors

Contributing to Uncertainty

There are a wide range of other factors which

contribute to uncertainty in the overall cost estimates. In most cases, the effect of these other factors cannot be quantitified, though some may have

significant influences on our overall net benefits estimate. We present a summary of these factors in

Table 3-3 below, and provide a characterization of

the potential effect of each uncertainty on the primary estimate of the net benefits (i.e., if costs are

overestimated, net benefits are underestimated). The

two most important factors are the potential impact

of innovation on the ultimate control costs incurred

and the conservative assumptions we made to estimate RFP costs.

The regulatory documents which provide cost

inputs to ERCAM and the IPM contain the most

recent data available, given existing technological

development. Between 2000 and 2010, however,

advancements in control technologies will allow

sources to comply with CAAA requirements at

lower costs. For example, we anticipate technological improvements for complying with the multiple

tiers of proposed emission standards during the

phase-in of nonroad engine controls will likely lead

to reduced costs. In addition, the costs for certain

control equipment may decrease over time as demand

increases and technology innovation and competition exert downward pressure on equipment prices.

For instance, selective catalytic reduction (SCR )

costs have decreased over the past three years as more

facilities begin to apply the technology. We also

believe that even in the absence of new emission standards, manufacturers will eventually upgrade engines

to improve performance or to control emissions

more cost-effectively; firms will institute technologies such as turbocharging, aftercooling, and variable-valve timing, all of which improve engine performance.

There is considerable uncertainty surrounding

the development of Statesí control plans for meeting ozone NAAQS attainment requirements. We

base the RFP cost estimate on the assumption that

ozone nonattainment areas (NAAs) will take credit

for NOx

 reductions for meeting progress requirements. Additional area-specific analysis would be

necessary to determine the extent to which areas find

NOx

 reductions beneficial in meeting attainment and

progress requirement targets. Trading of NOx

 for

VOC to meet RFP requirements may result in distributions of VOC and NOx

 emission reductions

which differ from those used in this analysis. In

response to these uncertainties, we adopted a conservative strategy for estimating the costs of RFP

reductions in the primary analysis. We use a relatively high cost per ton reduced estimate of $10,000

for all required reductions. Since the time we conducted our primary cost analysis more information

has emerged suggesting controls could cost much less,

perhaps as little as $3,500 (see Table 3-2 and Appendix B for more details). In our sensitivity analysis of

this variable, we incorporate the more recent cost

per ton estimates. The analysis suggests that the

$10,000 per ton reduced may in fact be more repre12 Note that these calculations reflect the use of alternative

discount rates to estimate annual costs. The use of alternative

rates to calculate the total net present value of costs incurred

through the full 1990 to 2010 study period is examined separately in Chapter 8, where we compare total costs to total benefits.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

32

sentative of an upper bound cost estimate, rather than

a central estimate as our primary cost analysis reflects. The result of our conservative approach indicates that we may overstate RFP costs by a factor of

two in 2010.

One other factor is also worth noting, although

its impact is likely to be less important than the previous two factors. Under the 1990 CAAA, EPA

created economic incentive provisions in several rules

to provide flexibility for affected facilities that comply with the rules. These provisions include banking, trading, and emissions-averaging provisions.

Flexible compliance provisions tend to lower the cost

of compliance. For example, the emissions-averaging program grants flexibility to facilities affected

by the marine vessels rule, the petroleum refinery

National Emission Standard for Hazardous Air Pollutants (NESHAP), and the gasoline distribution

NESHAP. These facilities can choose which sources

to control, as long as they achieve the required overall emissions reduction. In many of the cost analyses, EPA does not attempt to quantify the effect that

economic incentive provisions will have on the overall costs of a particular rule. In these cases, to the

extent that affected sources use economic incentive

provisions to minimize compliance costs, costs may

be overstated. The major trading programs authorized under the Amendments, however, governing

sulfur and nitrogen oxide emissions reductions from

utilities and major non-utility point sources, are reflected in the cost estimates presented here.

Table 3-2

Results of Quantitative Sensitivity Tests

Provision

Primary Cost

Estimate in 20101

(billions 1990 $) Strategy for Sensitivity Analysis

Range of Estimates

from Sensitivity Test

(billions 1990 $)

Progress

Requirements $2.46 Vary unit costs for unidentifiedmeasures

$1.07 - $2.46

(central, $1.15)

California

Reformulated

Gasoline

$2.45

Vary incremental fuel costs and

gasoline sales estimates $1.4 - $3.5

PM NAAQS Controls

$2.22

Vary model attainment plan

assumptions and cost per ton

estimates

$0.09 to $3.35

LEV costs (California

and National

Combined)

$2.16

Vary per vehicle costs and

projections of vehicle sales $1.08 - $2.48

Non-Utility Stationary

Source NOx Costs $2.15 Vary unit-level cost per ton $1.1 - $3.2

NOx Tailpipe/Useful

Life Standards $1.65 Vary per vehicle costs and vehicle

sales data $0.83 - $2.48

Note:

1

 In all cases, except progress requirements, the Post-CAAA 2010 primary cost estimates is equal to the central

estimate in the sensitivity analysis. For more details on the sensitivity analysis of progress requirements and other

provisions, see Appendix B.
Chapter 3: Direct Costs

33

Table 3-3

Key Uncertainties Associated with Cost Estimation

Potential Source of Error

Direction of

Potential Bias

for Net Benefits

Likely Significance Relative to Key Uncertainties

on Net Benefits Estimate1

Costs are based on today's

technologies. Innovations

in future emission control

technology and

competition among

equipment suppliers tend

to reduce costs over time.

Underestimate Probably minor. Available evidence suggests that estimates

of pollution control costs based on current engineering can

substantially overestimate the ultimate cost incurred,

resulting in understating net benefits.2

Uncertainty of final State

strategies for meeting

Reasonable Further

Progress (RFP)

requirements.

Underestimate Probably minor. We apply a conservative estimate for costs

of RFP measures. Available evidence for identified RFP

measures suggests costs could be as much as 70 percent

lower than this value. The bias most likely results in

significantly understating net benefits.

Errors in emission

projections that form the

basis of selecting control

strategies and costs in

both the IPM and ERCAM

models.

Unable to

determine based

on current

information

Probably minor. In many cases, emissions reductions are

specified in the regulations, suggesting that errors in the

estimation of absolute levels of emissions under Pre- and

Post-CAAA scenarios may have only a small impact on cost

estimates. The effect on net benefits is unknown.

Exclusion of the impact of

economic incentive

provisions, including

banking, trading, and

emissions averaging

provisions.

Underestimate Probably minor. Economic incentive provisions can

substantially reduce costs, but the major economic programs

for trading of sulfur and nitrogen dioxide emissions are

reflected in the analysis.

Incomplete

characterization of certain

indirect costs, including

vehicle owner opportunity

costs associated with

Inspection and

Maintenance Programs

and performance

degradation issues

associated with the

incorporation of emission

control technology.

Overestimate Probably minor. Preliminary evidence suggests that the

opportunity costs of vehicle owners is most likely small

relative to other cost inputs.3

 In addition, it is will vary from

State to State and is subject to a variety of influencing

factors. The potential magnitude of indirect costs associated

with performance degradation is more uncertain, because

few data currently exist to quantify this effect.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

34

Table 3-3 (continued)

Key Uncertainties Associated with Cost Estimation

Potential Source of Error

Direction of

Potential Bias

for Net Benefits

Likely Significance Relative to Key Uncertainties

on Net Benefits Estimate1

Choice to model direct

costs rather than social

costs

Unable to

determine based

on current

information

Probably minor. The relationship of social cost to direct cost

estimates is influenced by multiple factors that operate in

opposite directions, suggesting the magnitude of the net

effect is reduced. Social cost estimates can reflect the net

welfare changes across the full range of economic sectors in

the U.S, and so may yield higher estimates of costs than a

direct cost approach. In addition, social cost estimates can

be constructed to reflect the potentially substantial costmagnifying effect of existing tax distortions. Direct cost

estimates, however, are likely to overstate costs in the

primary market because they do not reflect consumer and

producer responses. The extent to which a direct cost

estimate will overstate or understate a social cost estimate

depends on the magnitude of the "ripple effects" in economic

sectors not targeted by a regulation. In addition, assessment

of the effect on net benefit estimates must also account for

any economy-wide effects of direct benefits (e.g., the broader

implications of improving health status, and improving

environmental quality).

Use of costs for rules that

are currently in draft form

(i.e., not yet finalized).

Unable to

determine based

on current

information

Probably minor. Rules that are most important to the overall

cost estimate are largely finalized. For example, there is

some uncertainty as to how the cap-and-trade program

through the SIP process will lower NOx emissions in an

efficient manner. The expected effect on net benefits is

minimal.

Exclusion of costs of 7-

year and 10-year MACT

standards and the

residential risk standards

for the 2- and 4-year

MACT standards.

Unable to

determine based

on current

information

Probably minor. Costs for the 7- and 10-year MACT

standards are likely to be less than for the 2- and 4-year

standards included in the analysis and the need for, and

potential scope and stringency of, future Title III residual risk

standards remain highly uncertain. For consistency, benefits

of the 7- and 10-year standards and the residual risk

standards are also excluded.

Note: 1

 The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The

Project Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could influence

the overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or approach is

likely to change the total benefit estimate by less than five percent, the Project Team assigns a classification of "probably

minor."

2

 For more detail, see Harrington et al (1999), referenced in Appendix B.

3

 Preliminary evidence based on Arizona's Enhanced I/M program indicates that major components of the programs costs

are associated with test and repair costs rather than the costs of waiting and travel for vehicle owners. (Harrington and

McConnell, 1999.) To date, Enhanced I/M programs have been implemented in only four States.
Chapter 4: Air Quality Modeling

35

Air Quality

Modeling

Air quality modeling links changes in emissions

Chapter

4

to changes in the atmospheric concentrations of pollutants that may affect human health and the environment. A crucial analytical step, air quality modeling is one of the more complex and resource-intensive components of the prospective analysis. This

chapter outlines how we estimated future-year pollutant concentrations under both the Pre- and PostCAAA scenarios using air quality modeling results

and ambient monitor data. The first section of the

chapter begins with a discussion of some of the challenges faced by air quality modelers and a brief description of the models we used in this analysis. The

following section provides an overview of the general methodology we used to estimate future-year

ambient concentrations. This methodology section

includes a description of how we used modeling results to adjust monitor concentration data and estimate ambient concentrations for the years 2000 and

2010. The third section of this chapter summarizes

the results of the air quality modeling and presents

the expected effects of the CAAA on future-year

pollutant concentrations. A discussion of the key

uncertainties associated with air quality modeling

concludes the chapter.

Overview of Air Quality

Models

Air quality modelers face two key challenges in

attempting to translate emission inventories into pollutant concentrations. First, they must model the

dispersion and transport of pollutants through the

atmosphere. Second, they must model pertinent atmospheric chemistry and other pollutant transformation processes. These challenges are particularly

acute for those pollutants that are not emitted directly, but instead form through secondary processes.

Ozone is the best example; it forms in the atmosphere through a series of complex, non-linear chemical interactions of precursor pollutants, particularly

certain classes of volatile organic compounds (VOCs)

and nitrogen oxides (NOx

). We faced similar challenges when estimating PM concentrations. Atmospheric transformation of gaseous sulfur dioxide and

nitrogen oxides to particulate sulfates and nitrates,

respectively, contributes significantly to ambient

concentrations of fine particulate matter. In addition to recognizing the complex atmospheric chemistry relevant for some pollutants, air quality modelers also must deal with uncertainties associated with

variable meteorology and the spatial and temporal

distribution of emissions.

Air quality modelers and researchers have responded to the need for scientifically valid and reliable estimates of air quality changes by developing a

number of sophisticated atmospheric dispersion and

transformation models. Some of these models have

been employed in support of the development of

federal clean air programs, national assessment studies, State Implementation Plans (SIPs), and individual

air toxic source risk assessments. In this analysis,

we used several of these well-established models to

develop a picture of future changes in air quality resulting from the implementation of the 1990 CAAA.

We focused our air quality modeling efforts on

estimating the impact of Pre- and Post-CAAA emissions on future-year ambient concentrations of

ozone, PM10, PM2.5, SO2

, NOx, and CO and on future-year acid deposition and visibility. The ideal

model for this analysis would be a single integrated

air quality model capable of estimating ambient concentrations for all criteria pollutants throughout the

U.S. Although EPA is working to develop such a

model, at the time of this analysis the model was not

sufficiently developed and tested. In the absence of

a single integrated model, we employed the Urban

Airshed Model (UAM) in our analysis of ozone and

used both the Regional Acid Deposition Model/Regional Particulate Model (RADM/RPM) and the

Regulatory Modeling System for Aerosols and Acid
The Benefits and Costs of the Clean Air Act, 1990 to 2010

36

Deposition (REMSAD) model to assess PM10, PM2.5,

acid deposition and visibility. All three of these models are three-dimensional grid models which require

emissions and meteorological data as input. Each of

these models calculate pollutant concentrations by

simulating the physical and chemical pollution formation processes that occur in the atmosphere.

We conducted separate UAM, RADM/RPM,

and REMSAD model runs for the 1990 base-year

and each future-year projection scenario. The primary model input used for each run consisted of

emissions estimates corresponding to the year and

scenario being modeled (as described in Chapter 2

and Appendix A) and historical meteorological data

corresponding to a past time period, referred to as a

simulation period. We selected previous ozone episodes, i.e., multi-day events characterized by weather

conditions conducive to ozone formation and transport (and as a result, characterized by multi-day spans

with higher than average ozone concentrations), to

serve as the simulation periods for UAM model runs.

Although ozone concentrations during these simulation periods exceed the seasonal average, because

the simulation periods for both the eastern and western U.S. cover roughly a two week span, ozone concentration peaks are largely offset by the surrounding lows. Overall, the selected simulation periods

reasonably represent summertime ozone forming

meteorological conditions and ozone concentrations.

RADM/RPM simulation periods used to model PM,

acid deposition, and visibility were chosen using a

random selection process, while separate simulation

periods at the beginning of each of the four seasons

were chosen for REMSAD.

Table 4-1 provides an overview of the air quality models used in this analysis. We modeled concentrations of all pollutants across the 48 contiguous states; however due to the lack of an integrated

model, separate air quality models were used to estimate ozone and PM for the eastern and western U.S.

Table 4-1 shows the domain for each model and the

simulation periods selected for use with each model

and provides an overview of the spatial resolution

of the models used as part of this analysis. The finer

the resolution (i.e., the smaller the grid cells) the

better the model can capture the effects of localized

changes in emissions and weather conditions on

ambient air quality. Recognizing the relationship

between grid cell resolution and the certainty of results, we endeavored to estimate pollutant concentrations in more populated areas using higher resolution models. For this reason, we used the fine grid

UAM-IV, an urban-scale model, to estimate ambient ozone levels in selected western cities. Similarly,

we used an intermediate resolution grid (12 km x 12

km) to model ozone in ìinner OTAGî states where

population density is high and ozone transport is a

major problem.1

Using the three-dimensional grid cell models,

UAM, RADM/RPM, and REMSAD, we estimated

grid-cell specific, hourly ozone and daily PM10, and

PM2.5 concentrations for each day of the relevant

simulation periods. We conducted separate model

runs for the 1990 base-year and 2000 and 2010 future-year Pre- and Post-CAAA scenarios. Using

these results, we ultimately projected the impact of

the CAAA on ozone and PM ambient levels.

We relied on the same models used to predict

PM concentrations to estimate changes in future-year

acid deposition and visibility. For each model gridcell we predicted daily acid deposition levels and visibility. Estimates for each day of the simulation

period were generated for the base-year and both

projection years under the Pre- and Post-CAAA scenarios.

We estimated future-year Pre- and Post-CAAA

ambient SO2

, NO, NO2, and CO concentrations by

adjusting 1990 concentrations using future-year to

base-year emissions ratios. This technique assumes

a linear relationship between changes in emissions

in an area and changes in that areaís ambient concentration of the emitted pollutant.2 Although this

technique does not take into account pollutant transport or atmospheric chemistry, we believe linear scaling generates reasonable approximations of ambient

concentrations of gaseous pollutants such as SO2

,

NOx

, and CO.

1 The Ozone Transport Assessment Group (OTAG) consists of the 37 easternmost states and the District of Columbia.

The ìinner OTAGî region is comprised of the more eastern

(and more populated) states within the OTAG domain.

2 It is important to emphasize that the correlation expected

is between changes in emissions and changes in air quality. Direct correlations between the absolute emissions estimates and

empirical air quality measurements used in the present analysis

may not be strong due to expected inconsistencies between the

geographically local, monitor proximate emissions densities affecting air quality data.
Chapter 4: Air Quality Modeling

37

Table 4-1

Overview of Air Quality Models

Air Quality

Measure Region Model Spatial Resolution Simulation Period

Ozone Eastern

U.S.

UAM-V a) 12 km x 12 km grid for "Inner

OTAG Region"

b) 36 km x 36 km grid for remainder

of 37-state OTAG region

July 20-30, 1993 and July 7-

18, 1995

Ozone Western

U.S.

UAM-V 56 km x 56 km grid (regional scale)

covering the 11 westernmost states

(states west of North and South

Dakota, including western Texas)

July 1-10, 1990

Ozone San

Francisco

Bay Area

UAM-IV 4 km x 4 km (urban scale) grid

covering the San Francisco Bay

Area, the Monterrey Bay Area,

Sacramento, and a portion of the

San Joaquin Valley

Aug. 3-6, 1990

Ozone Los

Angeles

Area

UAM-IV 5 km x 5 km grid covering the

South Coast Air Basin from Los

Angeles to beyond Riverside and

including part of the Mojave Desert

June 23-25, 1987 and Aug.

26-28, 1987

Ozone Maricopa

County

(Phoenix)

Area

UAM-IV 4 km x 4 km grid covering

urbanized portion of Maricopa

County

Aug. 9-10, 1992 and June 13-

14, 1993

Particulate

Matter

Eastern

U.S.

RADM/RPM 80 km x 80 km grid (coarse

resolution) covering eastern North

America from the Rocky Mountains

eastward to Newfoundland,

Canada and the Florida Keys (see

Fig. C-14 in Appendix C)

30 randomly selected 5-day

periods spanning a four-year

period

Particulate

Matter

Western

U.S.

REMSAD 56 km x 56 km grid covering the 11

westernmost states

ten-day period for each of four

seasons:

May 1-10,

July 1-10,

Oct. 1-10, and

Dec. 1-10

Visibility Eastern

U.S.

RADM/RPM (same as PM) (same as PM)

Visibility Western

U.S.

REMSAD (same as PM) (same as PM)

Acid

Deposition

Eastern

U.S.

RADM (same as RADM/RPM) (same as RADM/RPM)

Sulfur

Dioxide

U.S. linear scaling 56 km x 56 km REMSAD grid

covering 48 contiguous states

not applicable

Oxides of

Nitrogen

U.S. linear scaling 56 km x 56 km REMSAD grid

covering 48 contiguous states

not applicable

Carbon

Monoxide

U.S. linear scaling 56 km x 56 km REMSAD grid

covering 48 contiguous states

not applicable
The Benefits and Costs of the Clean Air Act, 1990 to 2010

38

General Methodology

The air quality modeling component of the 812

prospective analysis involved the application of a

variety of complex, sophisticated air quality modeling tools and techniques. Overall, however, the

method we used to estimate the impact of changes

in emissions on air quality was relatively straight

forward. We began by gathering 1990 air quality

monitor data for the six criteria pollutants analyzed

in this study. These observational data served as the

air quality baseline for both the Pre- and Post-CAAA

scenarios. We then estimated 2000 and 2010 concentrations of each pollutant under each emissions

scenario by applying adjustment factors to the 1990

monitor data. The adjustment factors for each future-year projection scenario were based on the relative change in pollutant concentration between 1990

and the desired future-year, as predicted by air quality simulation modeling. This section presents an

overview of the methodology we used to estimate

future-year ambient concentrations. For a more

detailed description, please refer to Appendix†C.

The diagram in Figure 4-1 illustrates the methodology used to estimate ozone and PM concentrations. First, we compiled distributions of observed

pollutant concentrations recorded at each air quality monitor in 1990. We obtained these data from

EPAís Aerometric Information Retrieval System

(AIRS), a publicly accessible database of air quality

information. Separately, we then developed distributions of estimated concentrations for each pollutant in 1990 using 1990 emissions data and the appropriate air quality model. Unlike the 1990 observed

concentrations that were measured at monitoring

sites, the 1990 estimated concentrations were calculated at the center of each cell of a grid covering the

domain of the applicable air quality model. Using

future-year emission inventory estimates for the PreCAAA and Post-CAAA scenarios (developed as described in Chapter 2 and Appendix A) and the appropriate air quality models, we next developed distributions of model-estimated concentrations at each

grid cell for each of four future-year projection scenarios: 2000 Pre-CAAA, 2010 Pre-CAAA, 2000 PostCAAA, and 2010 Post-CAAA. These results were

used to derive adjustment factors for each air quality monitor, based on the simulation results for the

grid cell in which the monitor is located. The future-year/scenario adjustment factor for each pollutant represents the ratio of the simulated futureyear/scenario concentration to the 1990 model-estimated concentration. These factors were calculated

by matching future-year and 1990 concentrations at

regular intervals in each distribution. Finally, four

sets of model-derived adjustment factors were applied

to the distribution of observed 1990 concentrations

at each monitor to forecast distributions of concentrations for each of the four future-year projection

scenarios. It is these concentrations that serve as

inputs into the CAAA benefits modeling.

An illustrative example follows. Assume the

median observed concentration of Pollutant A at

Monitor X in 1990 was 0.24 ppm. Air quality modeling for the grid cell in which Monitor X is located

predicts a median Pollutant A concentration of 0.30

ppm in 1990 and 0.15 ppm in 2010 under the postCAAA scenario. The 2010 Post-CAAA adjustment

factor for the median Pollutant A concentration

would be 0.5, and the predicted 2010 Post-CAAA

median concentration at Monitor X would be 0.5

(=0.15/0.30) times the 1990 monitor value of 0.24

ppm, or 0.12 ppm.

Our approach for forecasting concentrations of

SO2

, NOx, and CO involved the same basic approach

described above. However, instead of applying

model-derived adjustment factors to the 1990 observed distribution of concentrations, we adjusted

the 1990 distribution using the ratio of future-year

emissions to 1990 emissions in the vicinity of the

monitor for each of the four future-year projection

scenarios. For more information about this approach, please refer to Appendix C.
Chapter 4: Air Quality Modeling

39

Air Quality Model Results

This section presents a summary representation

of the air quality modeling results. We discuss the

model-simulated concentration estimates and the

adjusted future-year concentration predictions with

a focus on the change in air quality resulting from

the implementation of the 1990 CAAA.

Ozone

We modeled ozone concentrations separately for

the eastern U.S., western U.S., San Francisco Bay

area, Los Angeles area, and Maricopa County (Phoenix, AZ) area. Examination of base-year and futureyear model concentration estimates shows expected

increases in Pre-CAAA ozone concentrations and

expected decreases in Post-CAAA ozone concentrations in the eastern U.S. In this part of the country,

UAM-V predicts Pre-CAAA ozone concentration

increases will occur primarily over the states of Virginia, North Carolina, Kentucky, Tennessee, Georgia, and Alabama; while Post-CAAA decreases will

be more widespread. Comparison of Pre- and PostCAAA model estimates shows that, with the exception of a few isolated areas, ambient ozone levels

throughout the East will be reduced in the year 2010

as a result of the CAAA. These lower levels are

largely due to significant reductions in area source

and motor vehicle VOC emissions and utility, point

source, and motor vehicle NOx

 emissions.

Regional-scale model results for the western U.S.

indicate that ozone concentrations in this portion

of the country, just as in the eastern U.S., will generally increase from the 1990 base-year under the

Pre-CAAA scenario and decrease from 1990 levels

under the Post-CAAA scenario. In the West, we

anticipate widespread changes under both scenarios;

however, we project that the increases in Pre-CAAA

ozone concentrations and decreases in Post-CAAA

model concentrations will be smaller than the preFigure 4-1

Schematic diagram of the future-year concentration estimation methodology

NOTE: Figure illustrates how model results and observations are used to produce the air quality profiles (concentration distributions) for the

benefits analysis. The figure shows model runs at the top; four sets of "ratios" of model results in space in the middle; and frequency

distributions of pollutant monitor concentrations and the space-dependent scaling of these by the ratios of the model predictions on the bottom. freq

conc

city2

freq

conc

city1 freq

conc

city3 freq

conc

county1

freq

conc

city2

freq

conc

city1 freq

conc

city3 freq

conc

county1

freq

conc

city2

freq

conc

city1 freq

conc

city3 freq

conc

county1

AQ Model

Predictions

AQ

Observations

AQ

Results

Adjustments

Factors

Concentration

distributions

Concentration

distributions

Concentration

distributions

Ratio to Base Case Ratio to Base Case

Conc

Conc

Conc

Conc

Conc

1990

(Base Case)

2000 2010

city1

city2

city3

county1

pre-CAAA

post-CAAA

pre-CAAA

post-CAAA

pre-CAAA pre-CAAA

post-CAAA post-CAAA
The Benefits and Costs of the Clean Air Act, 1990 to 2010

40

dicted changes in ambient ozone levels in the eastern U.S . Furthermore,

comparison of 2010 Pre- and PostCAAA model estimates shows that

future-year western ozone concentrations will be lower as a result of the

1990 Amendments, but UAM-V results indicate that the reductions in the

West will likely be about half the size

of the reductions in the eastern portion of the country. The difference

between the change in western ozone

concentrations and the change in eastern ozone concentrations is largely

due to the more aggressive NOx

 controls expected in the East. Specifically, the Post-CAAA scenario incorporates the effects of a NOx

 cap-and-trade system for the eastern

U.S. (OTAG region). Another reason for the difference between the modeled change in eastern and

western ozone concentrations is that we estimated

ozone levels in the East and West using different

model grid resolutions. The coarser the resolution,

the less responsive the model concentration estimates

are to localized changes in emissions. Thus, the

smaller estimated change in western ozone concentrations than in eastern ozone concentrations may,

in part, be attributable to the fact that UAM-V gridcells covering the western U.S. are larger than those

covering the eastern U.S.

Western urban-area modeling results differ from

the regional scale results described above. Examination of Pre- and Post-CAAA modeling estimates

shows that, in some portions of the urban centers of

San Francisco and Los Angeles, future-year PostCAAA ozone concentrations are expected to be

higher than Pre-CAAA estimates. This ozone

ìdisbenefitî is the result of inhibiting a complex

chemical reaction termed ìNOx

 scavenging,î during

which a reduction in NOx

, an ozone precursor, leads

to an increase in ozone production instead of the

typical decrease.3 In the area immediately surrounding the two cities, however, and in Maricopa County,

model results show that scavenging is not expected

to be influential, if it occurs at all, and future-year

Post-CAAA ozone concentration estimates are predicted to be lower than Pre-CAAA estimates.

As described above, we used the UAM-V model

results to calculate adjustment factors for each of the

four future-year projection scenarios. We estimated

future-year monitor-level ozone concentrations by

applying these factors to 1990 observed concentrations. Examination of the distribution of adjusted

monitor concentration ratios for 95th percentile

ozone concentrations is one means of analyzing the

impact of the CAAA on air pollution. The distribution of ratios of 2010 Pre-CAAA to 1990 baseyear ozone concentrations reveals that the majority

of future year Pre-CAAA ozone concentration estimates are between zero and 10 percent greater than

1990 levels, with most concentrations falling in the

middle of this range. The distribution of ratios of

2010 Post-CAAA to 1990 base-year shows that in

nearly all areas of the U.S. ozone concentrations will

be lower in 2010 than in the base-year; in the majority of the country, future-year concentrations will

be five to 20 percent lower than in the base-year.4

The histogram in Figure 4-2 depicts the distribution

of ratios of 2010 Post-CAAA ozone estimates to 2010

Pre-CAAA ozone estimates. Most of the ratios in

the distribution are less than one, with a median of

0.883. This indicates that the 95th percentile level

Post-CAAA concentrations, with few exceptions, are

lower than the corresponding Pre-CAAA values.

The smaller the ratio, the greater the difference between future-year scenarios.

3 Scavenging occurs in areas, typically cities, with limited

VOC and abundant NOx

. In VOC-limited areas where there is

a relatively high NOx

 concentration (regions where the concentration of VOC, not NOx

, dictates the amount of ozone that

can be formed), these two ozone precursors (VOC and NOx

)

compete to react with a particular gaseous compound. To produce ozone, this compound must combine with VOC. As a

result, if the compound joins with NOx

, ozone production is

impeded; thus, a decrease in NOx

 leads to an increase in ozone

concentrations.

4 See Appendix C for histograms illustrating the change in

ozone concentrations from the base-year.

Figure 4-2

Distribution of Monitor Level Ratios for 95th Percentile Ozone

Concentrations: 2010 Post-CAAA/Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 0.883
Chapter 4: Air Quality Modeling

41

Particulate Matter

To model Pre- and Post-CAAA particulate matter (PM10 and PM2.5) concentrations, we used

RADM/RPM for the eastern U.S. and REMSAD

for the western U.S. Results from both models show

PM concentrations are expected to be lower under

the Post-CAAA scenario than under the Pre-CAAA

scenario. This projected improvement in air quality

is widespread throughout the eastern U.S., with 2010

Post-CAAA PM estimates in some parts of the East

up to 15 to 30 percent lower than 2010 Pre-CAAA

estimates. In the West, projected reductions in future-year PM concentrations (Pre-CAAA minus

Post-CAAA) are largely restricted to urban areas.5

The broad scale improvement in eastern PM concentrations is driven largely by reductions in utility

source sulfur dioxide emissions throughout this portion of the country.6 In the West, however, sulfur

dioxide emissions have a much smaller impact on

overall PM concentrations. Western PM concentrations are more significantly influenced by area,

motor vehicle, and nonroad source emissions of nitrogen oxides and directly emitted PM. These

sources are more concentrated in urban areas. As a

result, the impact of the CAAA on PM concentrations in the West is primarily restricted to urban

areas.

Examination of the distribution of adjusted

monitor-level concentration ratios for annual average PM concentrations reveals that 2010 Pre-CAAA

PM10 and PM2.5 estimates are both higher than 1990

base-year estimates in almost all areas of the country. Pre-CAAA 2010 PM10 and PM2.5 estimates are

generally zero to 10 percent greater than 1990 baseyear estimates. The average estimated increase in

PM2.5 concentrations, however, is slightly larger than

the average estimated increase in PM10.

7 The estimated change in PM concentrations from the baseyear to 2010 under the Post-CAAA scenarios is less

uniform. While the majority of areas experience a

reduction in annual average PM10 and PM2.5 concentrations, in a number of areas ambient PM levels,

more frequently PM2.5, increase from the base-year

under the Post-CAAA scenario. On average, however, 2010 Post-CAAA PM10 and PM2.5 concentrations are between zero and five percent and zero

and 10 percent, respectively, lower than 1990 baseyear concentrations.8

As shown in Figures 4-3 and 4-4, the percentage

reduction in PM2.5 concentrations across the U.S.

between the Pre- and Post-CAAA scenarios vary

more widely than the percentage reduction in PM10.

In the emissions analysis we focus on the impact of

the CAAA on anthropogenic emissions and, so, hold

natural source PM emissions constant at 1990 levels.

Natural source emissions make up a much larger

portion of PM10 concentrations than PM2.5 concentrations and dampen the influence of changes in anthropogenic emissions on ambient PM10 concentrations.

Comparison of the two distributions in Figures

4-3 and 4-4 shows that, despite the greater variation

of PM2.5 reductions, the percentage reduction in PM2.5

concentrations are larger on average than the percentage reduction in PM10 concentrations. The reason for this difference is two fold. First, as described

above, PM2.5 concentrations are more susceptible to

the influence of changes in anthropogenic emissions,

which are regulated by the CAAA. Second, the

CAAA provisions that influence PM emissions (regulations that focus on secondary PM precursors such

as NOx

, and SO2, and primary PM sources such as

diesel engine exhaust standards) affect the fine particulate (PM2.5) subset of PM10 to a much greater extent than the coarser fraction that makes up the rest

of PM10. As a result of these two factors, the projected difference in ambient concentrations between

the Pre-CAAA and Post-CAAA scenarios reflect a

larger percentage reduction in PM2.5 than PM10.

5 Outside the larger urban areas in the West, REMSAD

results show little or no change in PM concentrations between

Pre- and Post-CAAA estimates.

6 Sulfur dioxide is a secondary PM precursor.

7 In some of the figures in this chapter the Pre-CAAA and

Post-CAAA scenarios are referred to as Pre-CAAA90 and PostCAAA90, respectfully.

8 See Appendix C for histograms illustrating the change in

PM concentrations from the 1990 base-year to each of the PreCAAA and Post-CAAA future year scenarios.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

42

Visibility

We also relied on RADM/RPM and REMSAD

to estimate the impact of the CAAA on future-year

visibility. Tables 4-2 and 4-3 compare the mean annual visibility (expressed in deciviews)9 in selected

eastern urban areas and National Parks, respectively,

as estimated by RADM/RPM under the 1990 baseyear and 2010 Pre- and Post-CAAA

scenarios. Comparison of these values reveals that, in the eastern U.S.,

we anticipate that future-year visibility in both urban and rural areas is

projected to improve under the PostCAAA scenario. RADM/RPM predicts that Post-CAAA visibility in

2010 will not only be better than PreCAAA visibility, but also, in many

areas, it will be better than the visibility in the 1990 base-year. This

improvement in visibility is attributable to reductions in the concentration of gaseous and suspended particles, such as PM, that scatter and

absorb light, and thus influence visibility.

Visibility in the West is also significantly better under the PostCAAA scenario than under the PreCAAA scenario (see Tables 4-4 and

4-5). Base-year model runs show that

visibility in the western U.S. is the

poorest in larger metropolitan areas

such as Los Angeles, CA; San Francisco, CA; Denver, CO; and Phoenix, AZ. Under the 2010 Pre-CAAA

scenario, REMSAD estimates that,

throughout much of the West, visibility will remain relatively unchanged from the base-year, and in

some cases will even improve. In the

metropolitan areas, however, the

model predicts visibility degradation.

Under the Post-CAAA scenario, however,

REMSAD estimates widespread improvement in

future-year visibility in the West. In both metropolitan and non-urban areas, deciview levels estimated for 2010 are lower under the Post-CAAA scenario than under the Pre-CAAA scenario. The

model suggests Los Angeles and Las Vegas will experience the greatest improvement.

9 The deciview is a measure of visibility which captures the

relationship between air pollution and human perception of visibility. When air is free of the particles that cause visibility degradation, the DeciView Haze Index is zero. The higher the

deciview level, the poorer the visibility; a one to two deciview

change translates to a just noticeable change in visibility for most

individuals.

Figure 4-3

Distribution of Combined RADM/RPM and REMSAD Derived

Monitor Level Ratios for Annual Average PM10 Concentrations:

2010 Post-CAAA/Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.946

Figure 4-4

Distribution of Combined RADM/RPM and REMSAD Derived

Monitor Level Ratios for Annual Average PM2.5 Concentrations:

2010 Post-CAAA/Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.919
Chapter 4: Air Quality Modeling

43

Table 4-2

Comparison of Visibility in Selected Eastern Urban Areas

Mean Annual Deciview*

Area Name State

1990

Base-Year

2010

Pre-CAAA

2010

Post-CAAA

Atlanta Metro Area GA 20.9 22.8 20.0

Boston Metro Area MA 13.2 14.0 11.9

Chicago Metro Area IL 17.5 19.1 17.0

Columbus OH 16.5 17.7 15.1

Detroit Metro Area MI 16.0 18.5 15.3

Indianapolis IN 20.1 21.1 19.0

Little Rock AR 15.0 17.2 15.1

Milwaukee Metro Area WI 15.6 18.4 15.3

Minn.-St. Paul Metro Area MN 10.1 12.4 10.3

Nashville TN 20.4 21.5 19.0

New York City Metro Area NY/NJ 15.2 18.0 13.9

Pittsburgh Metro Area PA 15.8 16.9 14.2

St. Louis Metro Area MO 16.5 17.8 16.0

Syracuse NY 12.4 13.2 11.5

Washington, DC Metro Area DC/VA/MD 17.5 19.2 16.3

*For cities or metropolitan areas not contained by a single RADM/RPM grid cell, the visibility measure

presented in this table is a weighted average of the mean annual deciview level from each of the grid

cells that together completely contain the selected area. Weighting is based upon the spatial

distribution of an area over the various grid cells.

Table 4-3

Comparison of Visibility in Selected Eastern National Parks

Mean Annual Deciview*

Area Name State

1990

Base-Year

2010

Pre-CAAA

2010

Post-CAAA

Acadia NP ME 11.1 12.0 10.4

Everglades NP FL 7.6 9.2 6.9

Great Smoky Mtns. NP TN 20.4 22.3 19.6

Shenandoah NP VA 16.5 17.8 15.2

*For national parks not contained by a single RADM/RPM grid cell, the visibility measure presented in

this table is a weighted average of the mean annual deciview level from each of the grid cells that

together completely contain the selected area. Weighting is based upon the spatial distribution of an

area over the various grid cells.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

44

Table 4-4

Comparison of Visibility in Selected Western Urban Areas

Mean Annual Deciview*

Area Name State

1990

Base-Year

2010

Pre-CAAA

2010

Post-CAAA

Denver CO 19.4 22.6 21.0

Las Vegas NV 14.6 17.9 15.2

Los Angeles CA 22.7 24.6 22.0

Phoenix AZ 15.4 17.1 15.3

Salt Lake City UT 12.5 14.8 13.4

San Francisco CA 24.4 26.1 24.6

Seattle WA 20.5 22.2 21.0

*For cities not contained by a single REMSAD grid cell, the visibility measure presented in this table is a

weighted average of the mean annual deciview level from each of the grid cells that together completely

contain the selected area. Weighting is based upon the spatial distribution of an area over the various grid

cells.

Table 4-5

Comparison of Visibility in Selected Western National Parks

Mean Annual Deciview*

Area Name State

1990

Base-Year

2010

Pre-CAAA

2010

Post-CAAA

Glacier NP MT 11.2 11.9 11.5

Grand Canyon NP AZ 8.3 8.8 8.3

Olympic NP WA 11.1 11.8 11.7

Yellowstone NP WY 9.0 9.7 9.5

Yosemite NP CA 11.5 13.2 12.2

Zion NP UT 8.0 9.0 8.4

*For national parks not contained by a single REMSAD grid cell, the visibility measure presented in this

table is a weighted average of the mean annual deciview level from each of the grid cells that together

completely contain the selected area. Weighting is based upon the spatial distribution of an area over the

various grid cells.
Chapter 4: Air Quality Modeling

45

Acid Deposition

We estimated nitrogen and sulfur

deposition for the 1990 base-year and

each of the future-year emissions scenarios. Using RADM, we focused on

acid deposition in the eastern U.S.

where the acidification problem is the

most acute. Under the Pre-CAAA

scenario, model results show an increase in both nitrogen and sulfur

deposition between 1990 and 2010.

However, under the Post-CAAA scenario, 2010 deposition projections are

not only lower than 2010 Pre-CAAA

projections, but also below 1990 baseyear levels as well. Average annual

acid deposition is expected to decrease

as a result of the CAAA. Motor vehicle tailpipe emissions standards and

Title IV Acid Rain provisions are expected to significantly reduce both

NOx

 and SO2 emissions thus contributing to significant reductions in

downwind deposition of acidic nitrogen and sulfur compounds. The differences between the Pre-CAAA and

Post-CAAA projections, however,

imply that the 1990 Amendments will

have a larger impact on the percentage reduction in nitrogen deposition

than on the percentage reduction in

sulfur deposition. One reason for the greater change

in nitrogen deposition is the region-wide NOx emissions cap-and-trade program that is part of the PostCAAA scenario.

SO2, NO, NO2, and CO

To estimate future-year SO2, NO, NO2, and CO

concentrations we relied on linear emissions scaling,

adjusting 1990 base-year concentrations using ratios of future-year to base-year emissions. Ratios

greater than one indicate an increase in ambient concentrations relative to the base-year, while ratios less

than one indicate a decrease.10

Our results indicate that compared to the baseyear, future-year concentrations of SO2

, NO, NO2,

and CO tend to increase under the Pre-CAAA scenario, while Post-CAAA concentrations for all four

pollutants except SO2

 tend to decrease. For example,

the median 2010 Pre-CAAA emission-based ratio for

SO2

 is roughly 1.35, indicating an increase in median 2010 Pre-CAAA SO2

 concentration of approximately 35 percent from the 1990 base-year. The

median ratios for NO, NO2

, and CO are roughly

1.13, 1.17, and 1.05 respectively. Under the PostCAAA scenario we estimate that in 2010 NO, NO2

,

and CO concentrations will tend to be approximately 25 and 30 percent below base-year levels. The

median 2010 Post-CAAA emission-based ratios for

these three pollutants are roughly 0.74, 0.70, and 0.76

respectively.

10 The values in this section represent ratios for actual monitoring site locations. Interpolated data are not included in these

figures. We believe, however, that the values presented in this

section accurately reflect the impact of the 1990 Amendments

on SOx

, NO, NO2, and CO ambient concentrations.

Figure 4-5

Distribution of Monitor - Level Ratios of SO2 Emissions

Figure 4-6

Distribution of Monitor - Level Ratios of NO Emissions

Note: 2.4 percent of the distribution of ratios is less than 0.40.

Note: 3.3 percent of the distribution of ratios is less than 0.40.

0

10

20

30

40

50

60

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Ratio

Relative Frequency (percent)

median: 0.666

0

10

20

30

40

50

60

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Ratio

Relative Frequency (percent)

median: 0.892
The Benefits and Costs of the Clean Air Act, 1990 to 2010

46

Comparison of Pre- and Post-CAAA emissionbased adjustment factors also helps illustrate the effect of the 1990 Amendments on ambient pollution

concentrations. The ratio of 2010 Post-CAAA adjustment factors to 2010 Pre-CAAA adjustment factors shows the impact of the 1990 Amendments on

ambient concentrations relative to the baseline scenario. Ratios less than one indicate

that we estimate that future-year concentrations of SO2

, NO, NO2, and

CO are lower under the Post-CAAA

scenario than under the Pre-CAAA

scenario.

Figures 4-5 through 4-8 show the

distribution of 2010 Post-CAAA to

2010 Pre-CAAA ratios for summertime SO2

, NO, NO2, and CO respectively. These figures illustrate the regional variation in the influence of the

1990 Amendments on ambient concentrations of these pollutants. Although we estimate concentrations in

some areas will increase under the

Post-CAAA scenario relative to PreCAAA estimates, the median summertime 2010 Post- to Pre-CAAA ratios for SO2

, NO, NO2, and CO are

0.90, 0.67, 0.58, and 0.72 respectively.

These values, each less than one, indicate that the central tendency for summertime 2010 Post-CAAA concentration estimates of these four pollutants

is to be lower than 2010 Pre-CAAA

estimates.

Table 4-6 displays the median values of the distribution of Post- to PreCAAA ratios for the summer months

described above and the remaining three seasons. Just

as for the summer; spring, autumn, and winter median values are less than one. Averaged over all four

seasons, we estimate a median reduction in SO2

,

NO, NO2

, and CO concentrations of approximately

9, 33, 40, and 25 percent respectively. RACT requirements, tailpipe emissions standards, and NOx

emissions trading account for the bulk of the reducTable 4-6

Median Values of the Distribution of Ratios of 2010 Post-CAAA/

Pre-CAAA Adjustment Factors

SO2 NO NO2 CO

Spring 0.904 0.669 0.598 0.790

Summer 0.892 0.666 0.575 0.720

Autumn 0.916 0.677 0.614 0.756

Winter 0.924 0.686 0.626 0.692

Figure 4-7

Distribution of Monitor - Level Ratios of NO2 Emissions

Figure 4-8

Distribution of Monitor - Level Ratios of CO Emissions

Note: 2.7 percent of the distribution of ratios is less than 0.40.

Note: 15.7 percent of the distribution of ratios is less than 0.40.

0

10

20

30

40

50

60

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Ratio

Relative Frequency (percent)

median: 0.575

0

10

20

30

40

50

60

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Ratio

Relative Frequency (percent)

median: 0.720
Chapter 4: Air Quality Modeling

47

tion in NO and NO2 concentrations. Title I

nonattainment area controls and Title II motor vehicle provisions are responsible for much of the

change in CO concentrations, while regulation of

utility and motor vehicle emissions account for majority of the decrease in SO2

 concentrations.

Uncertainty in the Air Quality

Estimates

Many sources of uncertainty affect the precision

and accuracy of the projected changes in air quality

presented in this study. These uncertainties arise

largely from potential inaccuracies in the emissions

inventories used as air quality modeling inputs and

potential errors in the structure and parameterization of the air quality models themselves. For example, we estimated changes in PM concentrations

in the eastern U.S. based exclusively on changes in

the concentrations of sulfate and nitrate particles.

By not accounting for changes in organic and primary particulate fractions, we likely underestimate

the impact of the CAAA on PM concentrations.

Also, by using separate air quality models for individual pollutants and different geographic regions,

as opposed to a single integrated model, we were

unable to fully capture the interaction among air

pollutants or reflect transport of pollutants or precursors across the boundaries of the models covering the western and eastern states. The direction

and magnitude of bias these limitations impose on

net benefits estimate presented in this analysis can

not be determined based on current information.

Some model-related uncertainties, however, may

be mitigated because this analysis uses the air quality modeling results in a relative, not absolute, sense.

We focus on the change in air quality between the

Pre- and Post-CAAA scenarios and not on the ambient concentrations projected by the individual

models themselves. Therefore, uncertainties that

affect a modelís ability to accurately predict the relative change in concentration of a pollutant from one

scenario to another are more important in the context of this study than those that affect only the absolute model results.

The relatively coarse grid cells used to model

ozone in most areas of the U.S. represents a potential source of uncertainty affecting a modelís sensitivity to changes in emissions. Grid size affects chemistry, transport, and diffusion processes that in turn

determine the response of pollutant concentrations

to changes in emissions. The less accurately a model

can predict the impact of changes in emissions on

ambient levels, the greater the uncertainty associated with predicted differences between Pre- and

Post-CAAA concentration estimates.

Table 4-7 presents the most important specific

sources of uncertainty and Appendix C further describes the uncertainties associated with air quality

modeling. While the list of potential errors presented

in Table 4-7 is not exhaustive, it includes discussion

of those factors with the greatest likelihood of contributing to any potential bias in the primary net

benefit estimates.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

48

Table 4-7

Key Uncertainties Associated with Air Quality Modeling

Potential Source of Error

Direction of

Potential Bias for

Net Benefits

Estimate

Likely Significance Relative to Key

Uncertainties in

Net Benefit Estimate*

PM10 and PM2.5 concentrations in

the East (RADM domain) are

based exclusively on changes in

the concentrations of sulfate and

nitrate particles, omitting the

effect of anticipated reductions in

organic or primary particulate

fractions.

Underestimate. Potentially major. Nitrates and sulfates

constitute major components of PM, especially

PM2.5, in most of the RADM domain and

changes in nitrates and sulfates may serve as a

reasonable approximation to changes in total

PM10 and total PM2.5. Of the other components,

primary crustal particulate emissions are not

expected to change between scenarios;

primary organic carbon particulate emissions

are expected to change, but an important

unknown fraction of the organic PM is from

biogenic emissions, and biogenic emissions are

not expected to change between scenarios. If

the underestimation is major, it is likely the

result of not capturing reductions in motor

vehicle primary elemental carbon and organic

carbon particulate emissions.

The number of PM2.5 ambient

concentration monitors

throughout the U.S. is limited. As

a result, cross estimation of PM2.5

concentrations from PM10 (or

TSP) data was necessary in

order to complete the "monitorlevel" observational dataset

used in the calculation of air

quality profiles.

Unable to

determine based on

the current

information.

Potentially major. PM2.5 exposure is linked to

mortality, and avoided mortality constitutes a

large portion of overall CAAA benefits. Cross

estimation of PM2.5, however, is based on

studies that account for seasonal and

geographic variability in size and species

composition of particulate matter. Also, results

are aggregated to the annual level, improving

the accuracy of cross estimation.

Use of separate air quality

models for individual pollutants

and for different geographic

regions does not allow for a fully

integrated analysis of pollutants

and their interactions.

Unable to

determine based on

current information.

Potentially major. There are uncertainties

introduced by different air quality models

operating at different scales for different

pollutants. Interaction is expected to be most

significant for PM estimates. However,

important oxidant interactions are represented

in all PM models and the models are being

used as designed. The greatest likelihood of

error in this case is for the summer period in

areas with NOx inhibition of ambient ozone

(e.g., Los Angeles).

Future-year adjustment factors

for seasonal or annual monitoring

data are based on model results

for a limited number of simulation

days.

Overall, unable to

determine based on

current information.

Probably minor. RADM/RPM and REMSAD

PM modeling simulation periods represent all

four seasons and characterize the full seasonal

distribution. Potential overestimation of ozone,

due to reliance on summertime episodes

characterized by high ozone levels and applied

to the May-September ozone season, is

mitigated by longer simulation periods, which

contain both high and low ozone days. Also,

underestimation of UAM-V western and UAMIV Los Angeles ozone concentrations (see

below) may help offset the potential bias

associated with this uncertainty.
Chapter 4: Air Quality Modeling

49

Comparison of modeled and

observed concentrations

indicates that ozone

concentrations in the western

states were somewhat underpredicted by the UAM-V model,

and ozone concentrations in

the Los Angeles area were

underestimated by the UAM-IV

model.

Unable to

determine based

on current

information.

Probably minor. Because model results are

used in a relative sense (i.e., to develop

adjustment factors for monitor data) the

tendency for UAM-V or UAM to underestimate

absolute ozone concentrations would be unlikely

to affect overall results. To the extent that the

model is not accurately estimating the relative

changes in ozone concentrations across

regulatory scenarios, the effect could be greater.

Ozone modeling in the eastern

U.S. relies on a relatively

coarse 12 km grid, suggesting

NOx inhibition of ambient ozone

levels may be under

represented in some eastern

urban areas. Coarse grid may

affect both model performance

and response to emissions

changes.

Unable to

determine based

on current

information.

Probably minor. Though potentially major for

eastern ozone results in those cities with known

NOx inhibition, ozone benefits contribute only

minimally to net benefit projections in this study.

Grid size affects chemistry, transport, and

diffusion processes which in turn determine the

response to changes in emissions, and may also

affect the relative benefits of low-elevation

versus high-stack controls. However, the

approach is consistent with current state-of-theart for regional-scale ozone modeling.

UAM-V modeling of ozone in

the western U.S. uses a

coarser grid than the eastern

UAM-V (OTAG) or UAM-IV

models, limiting the resolution

of ozone predictions in the

West.

Unable to

determine based

on current

information.

Probably minor. Also, probably minor for ozone

results. Grid cell-specific adjustment factors for

monitors are less precise for the west and may

not capture local fluctuations. However,

exposure tends to be lower in the predominantly

non-urban west, and models with finer grids

have been applied to three key population

centers with significant ozone concentrations.

May result in underestimation of benefits in the

large urban areas not specifically modeled (e.g.,

Denver, Seattle) with finer grid.

Emissions estimated at the

county level (e.g., area source

and motor vehicle NOx and

VOC emissions) are spatially

and temporally allocated based

on land use, population, and

other surrogate indicators of

emissions activity. Uncertainty

and error are introduced to the

extent that area source

emissions are not perfectly

spatially or temporally

correlated with these indicators.

Unable to

determine based

on current

information.

Probably minor. Potentially major for estimation

of ozone, which depends largely on VOC and

NOx emissions; however, ozone benefits

contribute only minimally to net benefit

projections in this study.

The REMSAD model underpredicted western PM

concentrations during fall and

winter simulation periods.

Unable to

determine based

on current

information.

Probably minor. Because model results are

used in a relative sense (i.e., to develop

adjustment factors for monitor data) REMSAD's

underestimation of absolute PM concentrations

would be unlikely to significantly affect overall

results. To the extent that the model is not

accurately estimating the relative changes in PM

concentrations across regulatory scenarios, or

the individual PM components (e.g., sulfates,

primary emissions) do not vary uniformly across

seasons, the effect could be greater.

Table 4-7

Key Uncertainties Associated with Air Quality Modeling

Potential Source of Error

Direction of

Potential Bias for

Net Benefits

Estimate

Likely Significance Relative to Key

Uncertainties in

Net Benefit Estimate*

(continued)
The Benefits and Costs of the Clean Air Act, 1990 to 2010

50

Lack of model coverage for acid

deposition in Western states.

Underestimate Probably minor. Because acid deposition tends

to be a more significant problem in the eastern

U.S. and acid deposition reduction contributes

only minimally to net monetized benefits, the

monetized benefits of reduced acid deposition

in the western states would be unlikely to

significantly alter the total estimate of

monetized benefits.

Uncertainties in biogenic

emissions inputs increase

uncertainty in the AQM

estimates.

Unable to

determine based on

current information.

Probably minor. Potentially major impacts for

ozone outputs, but ozone benefits contribute

only minimally to net benefit projections in this

study. Uncertainties in biogenics may be as

large as a factor of 2 to 3. These biogenic

inputs affect the emissions-based VOC/NOx

ratio and, therefore, potentially affect the

response of the modeling system to emissions

changes.

* The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The

Project Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could

influence the overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or

approach is likely to change the total benefit estimate by less than five percent, the Project Team assigns a classification

of "probably minor."

Table 4-7

Key Uncertainties Associated with Air Quality Modeling

Potential Source of Error

Direction of

Potential Bias for

Net Benefits

Estimate

Likely Significance Relative to Key

Uncertainties in

Net Benefit Estimate*

(continued)
Chapter 5: Human Health Effects of Criteria Pollutants

51

Human Health

Effects of

Criteria Pollutants

Health benefits resulting from improved air qualChapter

5

ity constitute a significant portion of the overall benefits of the Clean Air Act Amendments of 1990. As

part of the prospective analysis of these amendments,

we have identified and, where possible, estimated

the magnitude of the health benefits that Americans

are likely to enjoy in future years as a result of the

CAAA. These health benefits are expressed as

avoided cases of air-pollution related health effects

such as premature mortality, heart disease, and respiratory illness. This chapter presents an overview

of our approach to modeling these changes in adverse health effects, discusses key assumptions associated with this approach, and summarizes modeling results for major health effect categories. Although this chapter focuses predominantly on the

human health effects associated with exposure to

criteria pollutants, the final section of this chapter

presents a discussion of the effects associated with

air toxics and stratospheric ozone.

In general, this analysis finds that the CAAA

will result in significant reductions in mortality, respiratory illness, heart disease, and other adverse

health effects, with much of these reductions resulting from decreases in ambient particulate matter concentrations.

Analytical Approach

We estimate the impact of the CAAA on human health by analyzing the difference in the expected incidence of adverse health effects between

the Pre-CAAA and Post-CAAA regulatory scenarios. As described in Chapter 2, the Pre-CAAA

scenario assumes no further controls on criteria pollutant emissions besides those already in place in

1990, while the Post-CAAA scenario assumes full

implementation of the 1990 CAAA. For each regulatory scenario, we use the Criteria Air Pollutant

Modeling System (CAPMS) to estimate the incidence

of health effects for 1990 (base-year), 2000, and 2010.

Modeling the incidence of adverse health effects resulting from exposure to criteria air pollutants requires three types of inputs: (1) estimates of the

changes in air quality for the Pre- and Post-CAAA

scenarios in 2000 and 2010; (2) estimates of the number of people exposed to air pollutants at a given

location; and (3) concentration-response (C-R) functions that link changes in air pollutant concentrations with changes in adverse health effects. We discuss each of these inputs in greater detail below.

Air Quality

The development of criteria pollutant concentration estimates for use in the CAPMS model consists of two steps. First, air quality modeling and

1990 base-year monitoring data are used to project

ambient pollution levels at monitors throughout the

48 contiguous states. Second, because air quality

monitors are neither uniformly nor pervasively distributed across the country, concentration data at

monitors are extrapolated to non-monitored areas

in order to generate a more comprehensive air quality data set covering the 48 contiguous states and the

District of Columbia.

The projections of criteria pollutant concentrations at air pollution monitors are developed as summarized in Chapter 4 and described in detail in Appendix C. Briefly, baseline 1990 concentrations at

each monitor are adjusted using monitor- and pollutant-specific adjustment factors to produce estimates of concentrations in 2000 and 2010 for each

regulatory scenario. Each adjustment factor reflects

the relative change in the concentration of a pollutant in a specific geographic area between 1990 and

the target year, as predicted by air quality modeling.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

52

To develop pollutant concentration estimates for

the entire continental U.S. we extrapolate the 1990

monitor data and the future-year estimates to the

eight kilometer by eight kilometer CAPMS grid cells

that cover the 48 contiguous states. Within each of

these cells, we calculate an estimated pollutant concentration using data from nearby monitors according to a distance-weighted averaging method described in Appendix D. We then use these grid cell

pollutant concentration estimates to predict changes

in health effects among the population residing

within each cell.

Population

Health benefits resulting from the CAAA are

related to the change in air pollutant exposure experienced by individuals. Because the expected changes

in pollutant concentrations vary from location to

location, individuals in different parts of the country may not experience the same level of health benefits. This analysis apportions benefits among individuals by matching the change in air pollutant concentration in a CAPMS grid cell with the size of the

population that experiences that change.

As a result, we require an estimate of the distribution of the U.S. population among CAPMS grid

cells. The grid-cell-specific population counts for

1990 are derived from U.S. Census Bureau block level

population data. Grid cell population estimates for

future years are extrapolated from 1990 levels using

the ratio of future-year and 1990 state-level population estimates provided by the U.S. Bureau of Economic Analysis.1

Concentration-Response Functions

We calculate the benefits attributable to the

CAAA as the avoided incidence of adverse health

effects. Such benefits can be measured using C-R

functions specific to each health effect. C-R functions are equations that relate the change in the number of individuals in a population exhibiting a ìresponseî (in this case an adverse health effect such as

respiratory disease) to a change in pollutant concentration experienced by that population. The C-R

functions used in CAPMS generate changes in the

incidence of an adverse health effect using three values: the grid-cell-specific change in pollutant concentration, the grid-cell-specific population, and an estimate of the change in the number of individuals that

suffer an adverse health effect per unit change in air

quality.2 As described in Appendix D, we derive

this last factor, as well as the specific form of the CR equation, from the published scientific literature

for each pollutant/health effect relationship of interest.

Using the appropriate C-R functions, CAPMS

generates estimates for each grid-cell of the change

in incidence of a set of adverse health effects resulting from the incremental change in exposure between

the Pre- and Post-CAAA scenarios in 2000 and 2010.

For each health effect, CAPMS then generates national health benefits estimates by summing the annual incidence change across all grid cells.

Each criteria pollutant evaluated in the 812 prospective analysis has been associated with multiple

adverse health effects. The published scientific literature contains information that supports the estimation of some, but not all, of these effects. Thus,

it is not possible currently to estimate all of the human health benefits attributable to the CAAA. In

addition, for some of the health effects we do quantify, the current economic literature does not support the estimation of the economic value of these

effects. For each of the criteria pollutants we evaluate in this analysis, Table 5-1 presents the health effects that are quantitatively estimated and those that

can not currently be quantified. The sixth criteria

pollutant, lead (Pb), is not included in this analysis

since airborne emissions of lead were virtually eliminated by pre-1990 Clean Air Act programs.

Key Analytical Assumptions

The modeling of health benefits attributable to

the CAAA involves numerous judgments and assumptions to address data limitations and other constraints. Each of these analytical assumptions affects

both the accuracy and precision with which we can

estimate health benefits of the CAAA, but some as1

 U.S. Bureau of Economic Analysis. 1995. BEA Regional

Projections to 2045: Volume 1, States. U.S. Department of Commerce. Washington, DC. July.

2

 An estimate of the baseline incidence of the adverse health

effect may also be required for certain C-R functions.
Chapter 5: Human Health Effects of Criteria Pollutants

53

Table 5-1

Human Health Effects of Criteria Pollutants

Pollutant Quantified Health Effects Unquantified Health Effects†

Ozone Respiratory symptoms

Minor restricted activity days

Respiratory restricted activity days

Hospital admissionsAll Respiratory and

All Cardiovascular

Emergency room visits for asthma

Asthma attacks

Mortality‡

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

Particulate

Matter

(PM10,

PM2.5)

Mortality*

Bronchitis - Chronic and Acute

New asthma cases

Hospital admissions -

All Respiratory and

All Cardiovascular

Emergency room visits for asthma

Lower respiratory illness

Upper respiratory illness

Shortness of breath

Respiratory symptoms

Minor restricted activity days

All restricted activity days

Days of work loss

Moderate or worse asthma status

 (asthmatics)

Neonatal mortality‡

Changes in pulmonary function

Chronic respiratory diseases

other than chronic bronchitis

Morphological changes

Altered host defense mechanisms

Cancer

Non-asthma respiratory emergency room visits

Carbon

Monoxide

Hospital Admissions -

All Respiratory and

All Cardiovascular

Behavioral effects

Other hospital admissions

Other cardiovascular effects

Developmental effects

Decreased time to onset of angina

Non-asthma respiratory emergency room visits

Nitrogen

Oxides

Respiratory illness

Hospital Admissions -

All Respiratory and

All Cardiovascular

Increased airway responsiveness to stimuli

Chronic respiratory damage / Premature aging of the lungs

Inflammation of the lung

Increased susceptibility to respiratory infection

Acute inflammation and respiratory cell damage

Non-asthma respiratory emergency room visits

Sulfur

Dioxide

Hospital Admissions -

All Respiratory and

All Cardiovascular

In exercising asthmatics:

Chest tightness,

Shortness of breath, or

Wheezing

Changes in pulmonary function

Respiratory symptoms in non-asthmatics

Non-asthma respiratory emergency room visits

† Some of the unquantified adverse health effects of air pollution may be associated with adverse health endpoints that we

have quantitatively evaluated (e.g., chronic respiratory damage and premature mortality). However, it is likely that the value

assigned to the quantified endpoint may not fully capture the value of the associated health effect (e.g., chronic respiratory

damage may result in significant pain and suffering prior to mortality). As a result, we include such effects separately in the

unquantified health effects column.

‡Appendix D includes detailed discussion of the scientific evidence for these potential health effects and includes illustrative

benefit calculations for them. Current uncertainties in our understanding of these effects do not support including these

quantitative estimates in the overall CAAA benefits estimate. However, ozone-related mortality may be implicitly quantified in

the overall analysis as part of the PM mortality estimate because of the significant correlation between ozone and PM

concentrations.

* This analysis estimates avoided mortality using PM as an indicator of the criteria air pollutant mix to which individuals were

exposed.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

54

sumptions introduce greater uncertainty into the

results than others. This section characterizes these

key assumptions and the associated uncertainties to

allow the reader to gain a better understanding of

the potential for misestimation of avoided health

effects. In addition, health benefits are presented as

ranges to reflect the aggregate effect of the uncertainty in key variables (see Results section below).

This section discusses the most important analytical

assumptions of this modeling effort, grouped into

the following categories: (1) exposure analysis, (2)

selection and application of C-R functions, and (3)

estimation of changes in PM-related mortality.

Exposure Analysis

The key analytical assumptions involved in estimating exposure to criteria air pollutants relate to

two steps: the extrapolation of air quality data from

monitors and the mapping of population data to air

quality data.

As discussed above, actual ambient air pollution

data are available only for a limited number of monitor sites that are not uniformly distributed across

the U.S. Thus, to estimate the impact of air pollution changes on the health of the U.S. population,

data from monitors are extrapolated to the cells of a

grid that covers the 48 contiguous states and are

matched with population data for each grid-cell.

Essentially, the extrapolation method uses data from

the closest set of monitors surrounding a grid-cell to

compute a weighted average concentration for that

cell. Monitors closer to the grid cell are assumed to

yield a more accurate estimate of air quality in the

cell; thus data from these monitors receive more

weight than data from more distant monitors when

calculating an air quality estimate for the cell.3 The

resulting estimates are uncertain because the geography, weather, land use, and other factors influencing air pollution may differ significantly between a

grid cell and the monitor or monitors used to generate estimates of air quality, especially as the monitor-to-grid-cell distance grows.4 As a result, they may

not sufficiently capture local variation in air pollution levels (e.g., hot spots).

However, since the uncertainty in these extrapolated values is inversely proportional to the density

of monitors in a given area, and since air quality

monitors are more prevalent in high pollution areas

than in low pollution areas, this extrapolation

method estimates the air quality in high pollution

areas (where the potential benefits of the CAAA are

greatest) with greater certainty than in low pollution areas. Thus, grid-cell ozone estimates in the

eastern U.S., where ozone levels and ozone monitor density are higher, are likely to be more accurate

than those in the west, where monitor coverage is

more sparse. Also, estimates of concentrations of

criteria pollutants, which are measured by a greater

number of monitors nationwide (PM, ozone, SO2

),

are expected to be less uncertain than estimates for

CO and NOx

, which are measured by considerably

fewer monitors.

Air pollutant concentration changes are mapped

to grid-cell population data derived from U.S. Census bureau data, and extrapolated to future years

using population growth estimates from the U.S.

Bureau of Economic Analysis. There are two key

assumptions associated with this population mapping. First, we assume the population in each grid

cell grows at the same rate as the state population as

a whole. As a result, exposures (and potential benefits) in individual grid cells may be either under- or

over-estimated if population growth varies from the

state average during the 1990 to 2010 period. This

uncertainty is likely to be more significant in larger

states such as California and Texas, which may have

more geographic variability in growth patterns.

Also, the effect of this assumption may be less significant for large population centers because their

growth rate better approximates the growth rate of

the state as a whole. Second, we assume in the exposure analysis that the population in the grid cell is

similar in terms of its activity patterns and demographic characteristics to the populations in the studies from which the C-R functions are derived. This

is a potentially significant uncertainty which is discussed further in the next section and in Appendix†D.

3

 Specifically, monitor data are weighted based on the

inverse of the distance between the monitor and the grid-cell

center. Additional information on the extrapolation method is

provided in Appendix D.

4

 In order to address this issue for long-distance extrapolation (i.e., grid cells greater than 50 kilometers from a monitor),

the method is modified to also incorporate air quality modeling

predictions for the source and target locations. See Appendix D

for details.
Chapter 5: Human Health Effects of Criteria Pollutants

55

Selection and Application

of C-R Functions

We rely on the most recent available, published

scientific literature to ascertain the relationship between air pollution and adverse human health effects. The uncertainties underlying those published

studies and our method for selecting studies that

could be used to derive C-R functions likely contributes to the uncertainty of the health effects results. For example, the uncertainty associated with

the current state of the published scientific literature could potentially have two contradictory influences on the results of this analysis. First, to the

extent that the published literature may collectively

overstate the effects of pollution, our analysis will

overstate the benefits of CAAA-related pollution

reduction. This overestimation is possible because

scientific journals tend to publish research reporting significant associations between pollution and

disease more often than research that fails to find

such associations. On the other hand, our analysis

may underestimate overall health benefits of the

CAAA because, as the state of the science evolves,

current pollutant/health effect associations may be

found to be stronger than previously thought, and

new associations may be identified. For example, in

recent years, studies have shown the potential health

benefits from reductions in ambient PM to be much

greater than previously believed. To the extent that

the present analysis does not include health effects

whose link to air pollution has not been subject to

adequate scientific inquiry, this analysis may understate CAAA-related health benefits.

Our method of identifying appropriate C-R functions for use in the benefits analysis may also introduce uncertainty. We evaluate studies using the nine

selection criteria summarized in Table 5-2 and described in detail in Appendix D. These criteria include consideration of whether the study was peerreviewed, the study design and location, and characteristics of the study population, among others. The

selection of C-R functions for the benefits analysis

is guided by the goal of achieving a balance between

comprehensiveness and scientific defensibility. However, to the extent that this selection process may

lead to the exclusion of valid studies, the process introduces uncertainty into the analysis. The overall

effect of this uncertainty is expected to be minor,

given the emphasis of the selection process on scientific validity. Appendix D lists the studies selected

for each category of health effects, and presents the

associated C-R functions for each criteria pollutant.

Once the C-R functions have been selected, uncertainty may also enter the analysis due to both

within-study and across-study variation in C-R functions for individual health effects. Within-study

variation refers to the uncertainty and error that may

surround a given studyís estimate of a C-R function.

Health effects studies provide both ìbest estimatesî

of the relationship between air quality changes and

health effects and a measure of the statistical uncertainty of the relationship. We use statistical simulation modeling techniques to evaluate the overall

uncertainty of the results given the uncertainties associated with individual studies. 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, activity patterns). Alternatively,

study results may differ because they 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.5 In either case,

where we identify multiple studies that are appro5

 PM is a mix of particles of varying size and chemical

properties. The composition of PM can vary considerably from

one region to another depending on the sources of particulate

emissions in each region.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

56

priate for estimating a given health effect, we use

the multiple C-R estimates, applied to the entire U.S.,

to derive a range of possible results for that health

effect.

Whether this analysis estimates the C-R relationship between a pollutant and a given health endpoint

using a single function from a single study or using

multiple C-R functions from several studies, each

C-R relationship is applied throughout the U.S. to

generate health benefit estimates. However, to the

extent that pollutant/health effect relationships are

region-specific, applying a location-specific C-R function at all locations in the U.S. 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

C-R function to the entire U.S. This may be a sigTable 5-2

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 (a.k.a. "ecological studies")

because they control for important confounding variables that cannot be controlled for in

cross-sectional studies. If the chronic effects of a pollutant are considered more important

than its acute effects, prospective cohort studies may also be preferable to longitudinal time

series studies because the latter type of study is typically designed to detect the effects of

short-term (e.g. daily) exposures, rather than chronic exposures.

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.

Study population Studies examining a relatively large sample are preferred. Studies of narrow population

groups are generally disfavored, although this does not exclude the possibility of studying

populations that are potentially more sensitive to pollutants (e.g., asthmatics, children,

elderly). However, there are tradeoffs to comprehensiveness of study population.

Selecting a C-R function from a study that considered all ages will avoid omitting the

benefits associated with any population age category. However, if the age distribution of a

study population from an “all population” study is different from the age distribution in the

assessment population, and if pollutant effects vary by age, then bias can be introduced

into the benefits analysis.

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, and life style.

Pollutants

included in

model

Models with more pollutants are generally preferred to models with fewer pollutants, though

careful attention must be paid to potential collinearity between pollutants. Because PM has

been acknowledged to be an important and pervasive pollutant, models that include some

measure of PM are highly preferred to those that do not.

Measure of PM PM2.5 and PM10 are preferred to other measures of particulate matter, such as total

suspended particulate matter (TSP), coefficient of haze (COH), or black smoke (BS) based

on evidence that PM2.5 and PM10 are more directly correlated with adverse health effects

than are these other measures of PM.

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.

Non-overlapping

endpoints

Although the benefits associated with each individual health endpoint may be analyzed

separately, care must be exercised in selecting health endpoints to include in the overall

benefits analysis because of the possibility of double counting of benefits. Including

emergency room visits in a benefits analysis that already considers hospital admissions, for

example, will result in double counting of some benefits if the category “hospital

admissions” includes emergency room visits.
Chapter 5: Human Health Effects of Criteria Pollutants

57

nificant uncertainty in the analysis, but the current

state of the scientific literature does not allow for a

region-specific estimation of health benefits.

PM-Related Mortality

This section discusses the estimation of one of

the most serious health impacts of air pollution: premature mortality associated with PM exposure. This

section consists of three parts. It begins with a discussion of the uncertainties surrounding the PM/

mortality relationship. Then, it presents specific

factors to consider when selecting a PM mortality

C-R function. It ends with a brief discussion of the

advantages and disadvantages of the study we selected

for the PM mortality analysis: Pope et al., 1995.

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,

there is much about this relationship that is still uncertain.6 These uncertainties include:

ï Causality. For this analysis, we assume a

causal relationship between exposure to elevated PM and premature mortality, based

on the evidence of a correlation between PM

and mortality reported in the scientific literature. This assumption is necessary because the epidemiological studies on which

this analysis relies, by design, can not definitively prove causation.

ï 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 pollutant

may influence elevated mortality rates. Recent studies have explored whether ozone

and CO may have mortality effects independent of PM, but we do not view the evidence

as sufficient to include such effects in the

overall CAAA-related health benefits estimate.7 As a result, we use the reported PM/

mortality relationship as a proxy for the

mortality effects of the air pollutant mixture.

ï 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.8 If this is not the correct form of the C-R function, or if certain

scenarios (e.g., 2010 Pre-CAAA) predict concentrations well above the range of values

for which the C-R function was fitted,

avoided mortality may be mis-estimated.

ï Regional Differences. As discussed earlier,

significant variability exists in the results of

different PM studies. This variability may

reflect regionally-specific C-R functions resulting from regional differences in factors

such as the physical and chemical composition of PM. If true regional differences exist, applying these C-R functions to regions

other than the study location would result

in mis-estimation of effects in these regions.

ï Exposure/Mortality Lags. It is currently

unknown whether there is a time lag ó a

delay between changes in PM exposures and

changes in mortality rates ó in the chronic

PM/mortality relationship. The existence

of such a lag could be important for the valuation of benefits, if one were to assume that

lagged incidences of premature mortality

should be discounted over the period between when the fatal increment of exposure

is experienced and premature mortality actually occurs. Although there is no specific

scientific evidence of the existence or structure of a PM effects lag, current scientific

literature on adverse health effects such as

those associated with PM (e.g., smoking-related disease) leaves us skeptical that all inci6

 The morbidity studies used in this analysis may also be

subject to many of the uncertainties listed in this section.

7

 Appendix D discusses the evidence linking both ozone

and CO with mortality. It also describes and presents the results of an illustrative analysis estimating CAAA-related reductions in ozone-related mortality using currently available studies.

8

 C-R functions for other health effects may be assumed to

be linear or log-linear. See Appendix D for more details.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

58

dences of premature mortality associated

with a given incremental change in PM exposure would occur in the same year as the

exposure reduction. This same literature

implies that lags of up to a few years are plausible, and we chose to assume a five-year lag

structure, with 25 percent of deaths occurring in the first year, another 25 percent in

the second year, and 16.7 percent in each of

the remaining three years.

ï Cumulative Effects. We attribute the PM/

mortality relationship used in this study

(Pope et al., 1995) primarily to PM-associated cumulative damage to the cardiopulmonary system, since the short-term mortality

estimates reported in time-series studies account for only a minor fraction of total excess mortality. However, the relative roles

of exposure duration and exposure level remain unknown at this time.

Selection of a PM Mortality C-R

Function

In addition to the study selection criteria listed

in Table 5-2, we consider three additional factors

when selecting a PM mortality function. The first

focuses on the PM indicator (i.e., PM10 or PM2.5), the

second focuses on whether the study measured shortterm or long-term PM exposure, and the third focuses on whether the study used a cohort or ecologic design.

Current research suggests that particle size matters when estimating the health impacts of PM. Particulate matter is a heterogeneous mixture that includes particles of varying sizes. Fine PM is generally viewed as having a more harmful impact than

coarse PM, especially for coarse particles larger than

10μm in aerodynamic diameter, although it is not

clear to what extent this may differ by the type of

health effect or the exposed population. While one

cannot necessarily assume that coarse PM has no

adverse impact on health, we prefer the use of PM2.5

as the best currently available measure of the impact

of PM on mortality.9

Two types of exposure studies (short-term and

long-term) have been used to estimate a PM/mortality relationship. Short-term exposure studies attempt

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 exposure studies examine

the potential relationship between longer-term (e.g.,

annual) changes in exposure to PM and annual mortality rates. Researchers have found significant correlations using both types of studies; however, for

this analysis, we rely exclusively on long-term studies to quantify PM mortality effects, though the

short-term studies provide additional scientific evidence supporting the PM/mortality relationship.

Because short-term studies focus only on the

acute effects associated with daily peak exposures,

they are unable to evaluate the degree to which observed excess mortality is premature,10 and they may

underestimate the C-R coefficient because they do

not account for the cumulative mortality effects of

long-term exposures (i.e., exposures over many years

rather than a few days). Long-term studies, on the

other hand, are able to discern changes in mortality

rates due to long-term exposure to elevated air pollution concentrations, and are not limited to measuring mortalities that occur within a few days of a

high-pollution event (though they may not predict

cases of premature mortality that were only hastened by a few days). Consequently, the use of C-R

functions derived from long-term studies is likely to

result in a more complete assessment of the effect of

air pollution on mortality risk. However, to the

extent that long-term studies fail to capture acute

mortality effects related to peak exposures, the use

of long-term mortality studies may underestimate

CAAA-related avoided mortality benefits.

Among long-term PM studies, we prefer studies

using a prospective cohort design to those using an

ecologic or population-level design. Prospective

9

 Due to the relative abundance of studies using PM10, however, and the reasonably good correlation between PM2.5 and

PM10, the 812 prospective analysis also uses PM10 studies to estimate the impact of PM on non-mortality health effects.

10 This can be important in cost-benefit analysis if benefits

are estimated in terms of life-years lost. In short-term studies

evaluating peak pollution events, it is likely that many of the

ìexcess mortalityî cases represented individuals who were already suffering impaired health, and for whom the high-pollution event represented an exacerbation of an already serious

condition. Based on the episodic studies only, however, it is

unknown how many of the victims would have otherwise lived

only a few more days or weeks, or how many would have recovered to enjoy many years of a healthy life in the absence of

the high-pollution event.
Chapter 5: Human Health Effects of Criteria Pollutants

59

cohort studies follow individuals forward in time for

a specified period, periodically evaluating each

individualís exposure and health status. Populationlevel ecological studies assess the relationship between

population-wide health information (such as counts

for daily mortality) and ambient levels of air pollution. Prospective cohort studies are preferred because they are better at controlling a source of uncertainty known as ìconfounding.î Confounding

is the mis-estimation of an association that results if

a study does not control for factors that are correlated with both the outcome of interest (e.g., mortality) and the exposure of interest (e.g., PM exposure). For example, smoking is associated with mortality. If populations in high PM areas tend to smoke

more than populations in low PM areas, and a PM

exposure study does not include smoking as a factor

in its model, then the mortality effects of smoking

may be erroneously attributed to PM, leading to an

overestimate of the risk from PM. Prospective cohort studies are better at controlling for confounding than ecologic studies because the former follow

a group of individuals forward in time and can gather

individual-specific information on important risk

factors such as smoking. However, it is always possible, even in well-designed studies, that a relevant

risk factor (e.g., climate, the presence of other pollutants) may not have been adequately considered

or controlled for. As a result, it is possible that differences in mortality rates ascribed to differences in

average PM levels may be due, in part, to some other

factor or factors (e.g., differences among communities in diet, exercise, ethnicity, climate, industrial

effluents, etc.) that have not been adequately addressed in the exposure models.

The Pope Study

Three recent studies have examined the relationship between mortality and long-term exposure to

PM: Pope et al. (1995), Dockery et al. (1993), and

Abbey et al. (1991). Of these three studies, we prefer using the Pope et al. study as the basis for developing the primary PM mortality estimates in this

analysis. Pope et al. studied the largest cohort, had

the broadest geographic scope, and effectively controlled for potentially significant sources of confounding.

Pope et al. examined a much larger population

(over 295,000) and many more locations (50 metropolitan areas) than either the Dockery study or the

Abbey study. The Dockery study covered a cohort

of over 8,000 individuals in six U.S. cities, and the

Abbey study covered a cohort of 6,000 people in

California. In particular, the cohort in the Abbey

study was considered substantially too small and too

young to enable the detection of small increases in

mortality risk. The study was therefore omitted

from consideration in this analysis. Even though

Pope et al. (1995) reports a smaller premature mortality response to elevated PM than Dockery et al.

(1993), the results of the Pope study are nevertheless

consistent with those of the Dockery study.

Pope et al., (1995) is unique in that it followed a

largely white and middle class population. The use

of this study population reduces the potential for

confounding because it decreases the likelihood that

differences in premature mortality across locations

were attributable to differences in socioeconomic

status or related factors rather than PM. However,

the demographics of the study population may also

produce a downward bias in the PM mortality coefficient, because short-term studies indicate that the

effects of PM tend to be significantly greater among

groups of lower socioeconomic status.

Although it is the strongest of the PM cohort

studies, Pope et al. does have some limitations. For

example, Pope et al. did not consider the migration

of cohort members across study cities, which would

cause exposures to be more similar across individuals than those indicated by assigning city-specific

annual average pollution levels to each member of

the cohort. As intercity migration increases among

cohort members, the exposure experienced by migrating individuals will tend toward an intercity

mean. If this migration is significant and is ignored,

approximating true differences in exposure levels

by differences in city-specific annual average PM levels will exaggerate changes in exposure, resulting in

a downward bias of the PM coefficient. This occurs

because a given difference in mortality rates is being

associated with a larger difference in PM levels than

that actually experienced by individuals in the study

cohort. When the relationship between elevated PM

exposure and premature mortality derived from the
The Benefits and Costs of the Clean Air Act, 1990 to 2010

60

Pope et al. study is applied in the present analysis,

the effect of the potential mis-specification of exposure due to migration in the underlying study is to

underestimate PM-related mortality reduction benefits attributable to the CAAA.

Also, Pope et al. only included PM when estimating a C-R function. Because PM concentrations

are correlated with the concentrations of other criteria air pollutants (e.g., ozone), and because these

other pollutants may be correlated with premature

mortality (see Appendix D), the PM risk estimate

may be overestimated because it includes the mortality impacts of these confounders. However, in

an effort to avoid overstating benefits, and because

the evidence associating mortality with PM exposure is stronger than for other pollutants, the 812

Prospective analysis uses PM as a surrogate for PM

and related criteria pollutants.

Although we use the Pope study exclusively to

derive our primary estimates of avoided mortality,

the C-R function based on Dockery et al. (1993) may

provide a reasonable alternative estimate. While the

Dockery et al. study used a smaller sample of individuals from fewer cities than the study by Pope et

al., it features improved exposure estimates, a slightly

broader study population (adults aged 25 and older),

and a follow-up period nearly twice as long as that

of Pope et al. We present an alternative estimate of

the premature adult mortality associated with longterm PM exposure based on Dockery et al. (1993) in

Chapter 8 and in Appendix D. We emphasize, however, that the estimate based on Pope et al. (1995) is

our primary estimate of the effect of the 1990 Amendments on this important health effect.

Health Effects Modeling

Results

This section presents a summary of the differences in health effects resulting from improvements

in air quality between the Pre-CAAA and PostCAAA scenarios. Table 5-3 summarizes the CAAArelated avoided health effects in 2010 for each study

included in the analysis. The mean estimate is presented as the Primary Central estimate, the 5th percentile observation from the statistical uncertainty

modeling is presented as the Primary Low estimate,

and the 95th percentile observation is presented as

the Primary High estimate of the number of avoided

cases of each endpoint.11 To provide context for these

results, Table 5-3 also expresses the mean reduction

in incidence for each adverse health effect as a percentage of the baseline incidence of that effect (extrapolated to the appropriate future year) for the

population considered (e.g., adults over 30 years of

age). In general, because the differences in air quality between the Pre- and Post-CAAA scenarios are

expected to increase from 1990 to 2010 and because

population is also expected to increase during that

time, the health benefits attributable to the CAAA

are expected to increase consistently from 1990 to

2010. More detailed results are presented in Appendix D.

Avoided Premature Mortality Estimates

Table 5-3 summarizes the avoided mortality due

to reductions in PM exposure in 2010 between the

Pre- and Post-CAAA scenarios. As this table shows,

our Primary Central estimate implies that PM reductions due to the CAAA in 2010 will result in

23,000 avoided deaths, with a Primary Low and Primary High bound on this estimate of 14,000 and

32,000 avoided deaths, respectively. The Primary

Central estimate of 23,000 avoided deaths represents

roughly one percent of the projected annual nonaccidental mortality of adults aged 30 and older in

the year 2010. Additionally, Table 5-4 summarizes

the distribution of avoided mortality for 2010 by

age cohort, along with the expected remaining lifespan (i.e., the life years lost) for the average person

in each age cohort. The majority of the estimated

deaths occur in people over the age of 65 (due to

their higher baseline mortality rates), and this group

has a shorter life expectancy relative to other age

groups. The life years lost estimates might be higher

if data were available for PM-related mortality in

the under 30 age group.

11 The Primary Low, Primary Central and Primary High

health benefit estimates represent points on a distribution of

estimated incidence changes for each health effect. This distribution reflects the uncertainty associated with the coefficient of

the C-R function for each health endpoint. More information

about C-R function uncertainty and the uncertainty modeling

that generates the results distributions is presented in Appendix

D.
Chapter 5: Human Health Effects of Criteria Pollutants

61

Table 5-3

Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants in 2010

(Pre-CAAA minus Post-CAAA) – 48 State U.S. Population (avoided cases per year)

2010

% of Baseline

Incidences for

the mean

estimates a

Endpoint Pollutant 5th % mean 95th % 2010

Mortality

ages 30 and older PM 14,000 23,000 32,000 1.00%

Chronic Illness

chronic bronchitis PM 5,000 20,000 34,000 3.14%

chronic asthma O3 1,800 7,200 12,000 3.83%

Hospitalization

respiratory

admissions

PM, CO, NO2,

SO2, O3

13,000 22,000 34,000 0.62%

cardiovascular

admissions

PM, CO, NO2,

SO2, O3

10,000 42,000 100,000 0.86%

emergency room

visits for asthma

PM, O3 430 4,800 14,000 0.55%

Minor Illness

acute bronchitis PM 0 47,000 94,000 5.06%

upper respiratory

symptoms

PM 280,000 950,000 1,600,000 0.86%

lower respiratory

symptoms

PM 240,000 520,000 770,000 3.57%

respiratory illness NO2 76,000 330,000 550,000 10.44%

moderate or worse

asthmac PM 80,000 400,000 720,000 0.24%

asthma attacksc O3, PM 920,000 1,700,000 2,500,000 1.04%

chest tightness,

shortness of breath,

or wheeze

SO2 290 110,000 520,000 0.003%

shortness of breath PM 26,000 91,000 150,000 1.69%

work loss days PM 3,600,000 4,100,000 4,600,000 0.94%

minor restricted

activity days / any of

19 respiratory

symptomsd

O3, PM 25,000,000 31,000,000 37,000,000 2.15%

restricted activity

daysc PM 10,000,000 12,000,000 13,000,000 1.00%

a

 The baseline incidence generally is the same as that used in the C-R function for a particular health effect. However, there are a few

exceptions. To calculate the baseline incidence rate for respiratory-related hospital admissions, we used admissions for persons of all

ages for International Classification of Disease (ICD) codes 460-519; for cardiovascular admissions, we used admissions for persons of

all ages for ICD codes 390-429; for emergency room visits for asthma, we used the estimated ER visit rate for persons of all ages; for

chronic bronchitis we used the incidence rate for individuals 27 and older; for the pooled estimate of minor restricted activity days and

any-of-19 respiratory symptoms, we used the incidence rate for minor restricted activity days.

b

 Percentage is calculated as the ratio of avoided mortality to the projected baseline annual non-accidental mortality for adults aged 30

and over. Non-accidental mortality was approximately 95% of total mortality for this subpopulation in 2010.

c

 These health endpoints overlap with the "any-of-19 respiratory symptoms" category. As a result, although we present estimates for

each endpoint individually, these results are not aggregated into the total benefits estimates.

d

 Minor restricted activity days and any-of-19 respiratory symptoms have overlapping definitions and are pooled.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

62

Non-Fatal Health Impacts

We report non-fatal health effects estimates in a

similar manner to estimates of premature mortalities: as a range of estimates for each quantified health

endpoint, with the range dependent on the quantified uncertainties in the underlying concentrationresponse functions. The range of results for 2010

only is characterized in Table 5-3 with 5th percentile, mean, and 95th percentile estimates which correspond to the Primary Low, Primary Central, and

Primary High estimates, respectively. All estimates

are expressed as new cases avoided in 2010, with the

following exceptions. Hospital admissions reflect

admissions for a range of respiratory and cardiovascular diseases, and these results, along with emergency room visits for asthma, do not necessarily represent the avoidance of new cases of disease (i.e., air

pollution may simply exacerbate an existing condition, resulting in an emergency room visit or hospital admission). Further, each admission is only

counted once, regardless of the length of stay in the

hospital. ìShortness of breathî is expressed in terms

of symptom days: that is, one ìcaseî represents one

child experiencing shortness of breath for one day.

Likewise, ìRestricted Activity Daysî and ìWork

Loss Daysî are expressed in person-days.

Table 5-4

Mortality Distribution by Age in Primary Analysis (2010 only), Based on Pope et al. (1995)a

Age Group Proportion of Premature Mortality by Age b Life Expectancy (years)

Infants not estimated --

1-29 not estimated --

30-34 1% 48

35-44 4% 38

45-54 6% 29

55-64 12% 21

65-74 24% 14

75-84 30% 9

85+ 24% 6 a

 Results based on PM-related mortality incidence estimates for the 48 state U.S. population.

b

 Percentages may not sum to 100 percent due to rounding.

Avoided Health Effects of

Other Pollutants

This section discusses the health effects associated with non-criteria air pollutants regulated by the

Clean Air Act Amendments of 1990. It first discusses the effects of pollutants known as ìair toxicsî, and then summarizes the effects associated with

stratospheric ozone depleting substances.

Avoided Effects of Air Toxics

In addition to addressing the control of criteria

pollutants, the Clean Air Act Amendments revamped regulations for air toxics ó defined as noncriteria pollutants which can cause adverse effects to

human health and to ecological resources ó under

section 112 of the Act. Among other changes, the

1990 Amendments establish a list of air toxics to be

regulated, require EPA to establish air toxic emissions standards based on maximum achievable control technology (MACT standards), and include a

provision that requires EPA to establish more stringent air toxic standards if MACT controls do not

sufficiently protect the public health against residual

risks. Control of air toxics is expected to result both

from these changes and from incidental control due

to changes in criteria pollutant programs.
Chapter 5: Human Health Effects of Criteria Pollutants

63

For several decades, the primary focus of risk

assessments and control programs designed to reduce

air toxics has been cancer. According to present EPA

criteria, over 100 air toxics are known or suspected

carcinogens. EPAís 1990 Cancer Risk study indicated that as many as 1,000 to 3,000 cancers annually may be attributable to the air toxics for which

assessments were available (virtually all of this estimate came from assessments of about a dozen wellstudied pollutants).12 We note, however, that the

results of this analysis are based, in part, on conservative, upper-bound estimates of chemical specific

risk factors.

In addition to cancer, inhalation of air toxics

compounds can cause a wide variety of health effects, including neurotoxicity, respiratory problems,

and adverse reproductive and developmental effects.

However, there has been considerably less work

done to assess the magnitude of non-cancer effects

from air toxics.

Air toxics can also cause adverse health effects

via non-inhalation exposure routes. Persistent

bioaccumulating pollutants, such as mercury and

dioxins, can be deposited into water or soil and subsequently taken up by living organisms. The pollutants can biomagnify through the food chain and

exist in high concentrations when consumed by

humans in foods such as fish or beef. The resulting

exposures can cause adverse effects in humans.

Finally, there are a host of other potential ecological and welfare effects associated with air toxics,

for which very little exists in the way of quantitative analysis. Toxic effects of these pollutants have

the potential to disrupt both terrestrial and aquatic

ecosystems and contribute to adverse welfare effects

such as fish consumption advisories in the Great

Lakes.13

Unfortunately, the effects of air toxics emissions

reductions could not be quantified for the present

study. Unlike criteria pollutants, monitoring data

for air toxics are relatively scarce, and the data that

do exist cover only a handful of pollutants. Emissions inventories are very limited and inconsistent,

and air quality modeling has only been performed

for a few source categories. In addition, the scientific literature on the effects of air toxics is generally

much weaker than that available for criteria pollutants. Appendix I presents a list of research needs

identified by the Project Team which, if met, would

enable at least a partial assessment of air toxics benefits in future section 812 prospective studies.

Avoided Health Effects for Provisions to

Protect Stratospheric Ozone

We estimate benefits of stratospheric ozone protection programs by relying on analyses conducted

to support a series of regulatory support documents

for these provisions. The series of basic steps to arrive at physical effects estimates ó from emissions

estimation, atmospheric modeling, exposure assessment, and dose-response characterization ó is similar to that used to estimate effects of criteria pollutants, but the details of each modeling step are vastly

different. The emissions and atmospheric modeling

yields estimates of changes in ultraviolet-b (UV-b)

radiation, and the exposure and dose-response analyses then yield estimates of the effects of changes in

UV-b radiation, including human health, welfare,

and ecological effects. Appendix G provides a detailed description of the methodology and sources

used to generate these estimates. Several of the benefits can be identified but cannot yet be reliably quantified, and so are described qualitatively.

The quantified physical effects estimates of sections 604 and 606 of Title VI, the provisions that

provide the primary controls on production and release of CFCs and HCFCs generate about 98 percent of the monetized quantified benefits estimate.

The quantified health benefits include the following: reduced incidences of mortality and morbidity

associated with skin cancer (melanoma and

nonmelanoma); and reduced incidences of cataracts

12 These pollutants included PIC (products of incomplete

combustion), 1,3-butadiene, hexavalent chromium, benzene,

formaldehyde, chloroform, asbestos, arsenic, ethylene

dibromide, dioxin, gasoline vapors, and ethylene dichloride. See

U.S. EPA, Cancer Risk from Outdoor Exposure to Air Toxics.

EPA-450/1-90-004f. Prepared by EPA/OAR/OAQPS.

13 U.S. EPA, Office of Air Quality Planning and Standards.

ìDeposition of Air Pollutants to the Great Waters, First Report

to Congress,î May 1994. EPA-453/R-93-055.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

64

and their associated pain and suffering.14 Using the

change in UV radiation dose, we estimate the number of additional cases of skin cancer (melanoma and

nonmelanoma) and cataracts. Because the baseline

levels of all of these UV-related health effects tend

to be higher for older people and for those with

lighter skins, EPAís method for projecting future

incremental skin cancers and cataracts incorporates

these factors in its benefits estimates.15 We present

a brief summary of these benefits in Table 5-5, and

the analysis is described in detail in Appendix G.

To calculate the number of deaths from melanoma, the model uses a dose response function simiTable 5-5

Major Health Benefits of Provisions to Protect Stratospheric Ozone

(CAAA Sections 604, 606, And 609)

Health Effects- Quantified Estimate Basis for Estimate

Melanoma and nonmelanoma

skin cancer

(fatal)

6.3 million lives saved from skin

cancer in the U.S. between 1990

and 2165

Dose-response function based on UV

exposure and demographics of

exposed populations.1

Melanoma and nonmelanoma

skin cancer

(non-fatal)

299 million avoided cases of nonfatal skin cancers in the U.S.

between 1990 and 2165

Dose-response function based on UV

exposure and demographics of

exposed populations.1

Cataracts 27.5 million avoided cases in the

U.S. between 1990 and 2165

Dose-response function uses a

multivariate logistic risk function based

on demographic characteristics and

medical history. 1

Health Effects- Unquantified

Skin cancer: reduced pain and suffering

Reduced morbidity effects of increased UV. For example,

• reduced actinic keratosis (pre-cancerous lesions resulting from excessive sun exposure)

• reduced immune system suppression.

Notes: 1

 For more detail see EPA’s Regulatory Impact Analysis: Protection of Stratospheric Ozone (1988). 2

 Note that the ecological effects, unlike the health effects, do not reflect the accelerated reduction and

phaseout schedule of section 606.

3

 Benefits due to the section 606 methyl bromide phaseout are not included in the benefits total because

annual incidence estimates are not currently available.

16 Scotto, Fears, and Fraumeni, U.S. Department of Health

and Human Services, NIH, ìIncidence of Nonmelanoma Skin

Cancer in the United States,î 1981, pages 2, 7, and 13.

14 Quantitative estimates presented in Appendix G also

include reduced crop damage associated with UV-b radiation

and tropospheric ozone; reduced damage to fish harvests associated with UV-b radiation; and reduced polymer degradation

from UV-b radiation. The derivation of these effects is described

in more detail in Chapter 7.

15 The dose-response equation is (fractional change in incidence) = (fractional change in UV-b dose + 1)b

 -1, where b (the

biological amplification factor) equals the percent change in incidence associated with a one percent change in dose. More

information about the origins of the models can be found in

Appendix G.

lar to the C-R functions for criteria pollutants. For

nonmelanoma, the model estimates the number of

deaths by assuming that a fixed percentage of the

total nonmelanoma cases will result in death.16 We

estimate that from 1990 to 2165 sections 604 and

606 will result in 6.3 million avoided deaths from

skin cancer, 27.5 million avoided cataract cases, and

299.0 million cases of non-fatal skin cancers (melanoma and nonmelanoma). The unquantified effects

of sections 604 and 606 include avoided pain and

suffering from skin cancer and human health and

environmental benefits outside the United States.
Chapter 5: Human Health Effects of Criteria Pollutants

65

Uncertainty in the Health

Effects Analysis

As discussed above, and in greater detail in Appendix D, a number of important assumptions and

uncertainties in the physical effects analysis may influence the estimate of monetary benefits presented

in this study. Several of these key uncertainties, their

potential directional bias (i.e., overestimation or

underestimation), and the potential significance of

each of these uncertainties for the overall net benefit results of the analysis are summarized in Table

5-6. As shown in this table, the decisions made to

overcome the problems of limited data, the inadequacy of the currently available scientific literature,

and other constraints do not clearly bias the overall

results of this analysis in one particular direction.

Table 5-6

Key Uncertainties Associated with Human Health Effects Modeling (continued)

Potential Source of Error

Direction of Potential

Bias for Net Benefits

Estimate

Likely Significance Relative to

Key Uncertainties in Net Benefit Estimate*

Application of C-R

relationships only to those

subpopulations matching the

original study population.

Underestimate. Potentially major. The C-R functions for several

health endpoints (including PM-related premature

mortality) were applied only to subgroups of the U.S.

population (e.g., adults over 30) and thus may

underestimate the whole population benefits of

reductions in pollutant exposures. In addition, the

demographics of the study population in the Pope et

al. study (largely white and middle class) may result

in an underestimate of PM-related mortality, because

the effects of PM tend to be significantly greater

among groups of lower socioeconomic status.

No quantification of health

effects associated with

exposure to air toxics.

Underestimate Potentially major. According to EPA criteria, over

100 air toxics are known or suspected carcinogens,

and many air toxics are also associated with adverse

health effects such as neurotoxicity, reproductive

toxicity, and developmental toxicity. Unfortunately,

current data and methods are insufficient to develop

(and value) quantitative estimates of the health

effects of these pollutants.

Use of long-term global

warming estimates in Title VI

analysis that show more

severe warming than is now

generally anticipated.

Overestimate (for Title

VI estimate only)

Potentially major. Global warming can accelerate

the pace of stratospheric ozone recovery; if warming

is less severe than anticipated at the time the Title VI

analyses were conducted, the modeled pace of

ozone recovery may be overestimated, suggesting

benefits of the program could be delayed, perhaps

by many years. The magnitude of estimated Title VI

benefits suggests that the impact of delaying

benefits could be major.

The quantitative analysis of

Title VI (see next section)

does not account for

potential increases in

averting behavior (i.e.,

people's efforts to protect

themselves from UV-b

radiation).

Unable to determine

based on current

information.

Potentially major. Murdoch and Thayer (1990)

estimate that the cost-of-illness estimates for

nonmelanoma skin cancer cases between 2000 and

2050 may be almost twice the estimated cost of

averting behavior (application of sunscreen). Our

Title VI analysis relies on epidemiological studies,

which incorporate averting behavior as currently

practiced. Omission of future increases in averting

behavior, however, may overstate the benefits of

reduced emissions of ozone-depleting chemicals.

Benefits could be understated if individuals alter their

behaviors in ways that could increase exposure or

risk (e.g., sunbathing more frequently). A recent

European study by Autier et al. (1999) found that the

use of high sun protection factor (SPF) sun screen is

associated with increased frequency and duration of

sun exposure.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

66

Table 5-6

Key Uncertainties Associated with Human Health Effects Modeling (continued)

Potential Source of Error

Direction of Potential

Bias for Net Benefits

Estimate

Likely Significance Relative to

Key Uncertainties in Net Benefit Estimate*

Analysis assumes a causal

relationship between PM

exposure and premature

mortality based on strong

epidemiological evidence of

a PM/mortality association.

However, epidemiological

evidence alone cannot

establish this causal link.

Unable to determine

based on current

information.

Potentially major. A basic underpinning of this

analysis, this assumption is critical to the estimation

of health benefits. However, the assumption of

causality is suggested by the epidemiologic evidence

and is consistent with current practice in the

development of a best estimate of air pollutionrelated health benefits. At this time, we can identify

no basis to support a conclusion that such an

assumption results in a known or suspected

overestimation bias.

Across-study variance /

application of regionally

derived C-R estimates to

entire U.S.

Unable to determine

based on current

information.

Potentially major. The differences in the expected

changes in health effects calculated using different

underlying studies can be large. If differences reflect

real regional variation in the PM/mortality

relationship, applying individual C-R functions

throughout the U.S. could result in considerable

uncertainty in health effect estimates.

Estimate of non-melanoma

skin cancer mortality

resulting from reductions in

stratospheric ozone is

calculated indirectly, by

assuming the mortality rate

is a fixed percentage of nonmelanoma incidence.

Unable to determine

based on current

information.

Potentially major. New data on the death rate for

non-melanoma skin cancer may significantly

influence the Title VI mortality estimate. Some

preliminary estimates suggest that this estimate may

need to be adjusted downward.

The baseline incidence

estimate of chronic bronchitis

based on Abbey et al. (1995)

excluded 47 percent of the

cases reported in that study

because those reported

"cases" experienced a

reversal of symptoms during

the study period. These

"reversals" may constitute

acute bronchitis cases that

are not included in the acute

bronchitis analysis (based on

Dockery et al., 1996).

Underestimate. Probably minor. The relative contribution of acute

bronchitis cases to the overall benefits estimate is

small compared to other health benefits such as

avoided mortality and avoided chronic bronchitis.

CAAA fugitive dust controls

implemented in PM nonattainment areas would

reduce lead exposures by

reducing the re-entrainment

of lead particles emitted prior

to 1990. This analysis does

not estimate these benefits.

Underestimate Probably minor. While the health and economic

benefits of reducing lead exposure can be

substantial (e.g., see section 812 Retrospective

Study Report to Congress), most additional fugitive

dust controls implemented under the Post-CAAA

scenario (e.g., unpaved road dust suppression,

agricultural tilling controls, etc) tend to be applied in

relatively low population areas.

Exclusion of C-R functions

from short-term exposure

studies in PM mortality

calculations.

Underestimate Probably minor. Long-term PM exposure studies

may be able to capture some of the impact of shortterm peak exposure on mortality; however the extent

of overlap between the two study types is unclear.
Chapter 5: Human Health Effects of Criteria Pollutants

67

Table 5-6

Key Uncertainties Associated with Human Health Effects Modeling (continued)

Potential Source of Error

Direction of Potential

Bias for Net Benefits

Estimate

Likely Significance Relative to

Key Uncertainties in Net Benefit Estimate*

Age-specific C-R functions

for PM related premature

mortality not reported by

Pope et al. (1995).

Estimation of the degree of

life-shortening associated

with PM-related mortality

used a single C-R function

for all applicable age groups.

Unable to determine

based on current

information.

Unknown, possibly major when using a value of life

years approach. Varying the estimate of degree of

prematurity has no effect on the aggregate benefit

estimate when a value of statistical life approach is

used, since all incidences of premature mortality are

valued equally. Under the alternative approach

based on valuing individual life-years, the influence

of alternative values for numbers of average lifeyears lost may be significant.

Assumption that PM-related

mortality occurs over a

period of five-years following

the critical PM exposure.

Analysis assumes that 25

percent of deaths occur in

year one, 25 percent in year

two, and 16.7 percent in

each of the remaining three

years.

Unable to determine

based on current

information.

Probably minor. If the analysis underestimates the

lag period, benefits will be overestimated, and viceversa. However, available epidemiological studies

do not provide evidence of the existence or potential

magnitude of a lag between exposure and incidence.

Thus, an underestimate of the lag seems unlikely. If

the assumed lag structure is an overestimate, even if

benefits are fully discounted from the future year of

death, application of reasonable discount rates over

this period would not significantly alter the monetized

benefit estimate.

Extrapolation of criteria

pollutant concentrations to

populations distant from

monitors.

Unable to determine

based on current

information.

Probably minor. Extrapolation method is most

accurate in areas where monitor density is high.

Monitor density tends to be highest in areas with

high criteria pollutant exposures; thus most of this

uncertainty affects low exposure areas where

benefits are likely to be low. In addition, an

enhanced extrapolation method incorporating

modeling results is used for areas far (> 50 km) from

a monitor.

Exposure analysis in areas

beyond 50 km is based on a

new technique that relies on

the direct use of air quality

modeling results in

combination with adjusted

monitor data.

Unable to determine

based on current

information.

Probably minor. The new technique is used for less

than 10 percent of the country for PM exposure, and

less than 15 percent for ozone. The approach we

use should be more accurate than the alternative

approach of linear interpolation over long distances.

The new method nonetheless requires further testing

against monitor data to access its accuracy.

Pope et al. (1995) study did

not include pollutants other

than PM.

Unable to determine

based on current

information.

Probably minor. If ozone and other criteria pollutants

correlated with PM contribute to mortality, that effect

may be captured in the PM estimate. Thus, PM is

essentially used as a surrogate for a mix of

pollutants. This uncertainty does make it difficult to

disaggregate avoided mortality benefits by pollutant,

however other studies (besides Pope) suggest that

PM is the dominant factor in premature mortality.

* The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The

Project Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could influence

the overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or approach is

likely to change the total benefit estimate by less than five percent, the Project Team assigns a classification of "probably

minor."
The Benefits and Costs of the Clean Air Act, 1990 to 2010

68

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Chapter 6: Economic Valuation of Human Health Effects

69

Economic Valuation

of Human Health

Effects

The reduced incidence of physical effects is a

Chapter

6

valuable measure of health benefits for individual

endpoints; however, to compare or aggregate benefits across endpoints, the benefits must be monetized. Assigning a dollar value to avoided incidences

of each effect permits us to sum monetized benefits

realized as a result of the CAAA, and compare them

with the associated costs.

In the 812 prospective analysis, we have two

broad categories of benefits, health and welfare benefits. Human health effects include mortality and

morbidity endpoints, which are presented in this

chapter. Welfare effects include agricultural and ecological benefits, visibility, and worker productivity,

which are covered in the following chapter. We

obtain valuation estimates from the economic literature, and report them in ìdollars per case reduced

for health effectsî and ìdollars per unit of avoided

damage for welfare effectsî.1 Similar to estimates of

physical effects provided by health studies, we report each of the monetary values of benefits applied

in this analysis in terms of a central estimate and a

probability distribution around that value. The statistical form of the probability distribution varies

by endpoint. For example, we use a Weibull distribution to describe the estimated dollar value of an

avoided premature mortality, while we assume the

estimate for the value of a reduced case of acute bronchitis is uniformly distributed between a minimum

and maximum value.

Although human health benefits of the 1990

Amendments are attributed to reduced emissions of

criteria pollutants (Titles I through V) and reduced

emission of ozone depleting substances (Title VI),

this chapter focuses only on the valuation of human

health effects attributed to the reduction of criteria

pollutants. The chapter begins with an brief review

of the economic concepts behind valuing human

health effects in a cost-benefit context and a summary of the unit values applied to health endpoints.

We follow with a discussion of how we derive valuation estimates for specific health effects. We then

present the results of this analysis. We conclude the

chapter with a review of the uncertainties associated

with benefits valuation.

Our analysis indicates that the benefit of avoided

premature mortality risk reduction dominates the

overall net benefit estimate. This is, in part, due to

the high monetary value assigned to the avoidance

of premature mortality relative to the unit value of

other health endpoints. Because of the critical importance of this endpoint in the studyís results, this

chapter pays particular attention to the major challenges to valuing mortality risk reductions and the

limitations of the estimate we apply in this analysis.

There are also significant reductions in short term

and chronic health effects and a substantial number

of health (and welfare) benefits that we could not

quantify or monetize.

Valuation of Benefit

Estimates

In an environmental benefit-cost analysis, the

dollar value of an environmental benefit (e.g., a

health-related improvement due to environmental

quality) enjoyed by an individual is the dollar amount

such that the person would be indifferent between

experiencing the benefit and possessing the money.

In general, the dollar amount required to compensate a person for exposure to an adverse effect is

roughly the same as the dollar amount a person is

willing to pay to avoid the effect. Thus, economists

speak of ìwillingness-to-payî (WTP) as the appropriate measure of the value of avoiding an adverse

1

 The literature reviews and process for developing valuation estimates are described in detail in Appendix I and in referenced supporting reports.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

70

effect. For example, the value of an avoided respiratory symptom would be a personís WTP to avoid

that symptom.

For most goods, WTP can be observed by examining actual market transactions. For example, if

a gallon of bottled drinking water sells for one dollar, it can be observed that at least some persons are

willing to pay one dollar for such water. For goods

that are not exchanged in the market, such as most

environmental ìgoods,î valuation is not so straightforward. Nevertheless, a value may be inferred from

observed behavior, such as through estimation of

the WTP for mortality risk reductions based on

observed sales and prices of products that result in

similar effects or risk reductions, (e.g., non-toxic

cleaners or bike helmets). Alternatively, surveys may

be used in an attempt to directly elicit WTP for an

environmental improvement.

Wherever possible in this analysis, we use estimates of mean WTP. In cases where WTP estimates

are not available, we use the cost of treating or mitigating the effect as an alternative 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 costs of illness (COI) estimates generally understate the true value of avoiding a health

effect. They tend to reflect the direct expenditures

related to treatment and not the utility an individual

derives from improved health status or avoided

health effect. As noted above, we use a range of

values for most environmental effects, to support

the primary central estimate of net benefits. Table

6-1 summaries the mean unit value estimates that

we use in this analysis. We present the full range of

values in Appendix H, including those used to derive the primary low and primary high estimates, as

well as values used to generate an alternative value

for avoiding premature mortality.

Valuation of Premature Mortality

Some forms of air pollution increase the probability that individuals will die prematurely. We use

concentration-response functions for mortality that

express the increase in mortality risk as cases of ìexTable 6-1

Health Effects Unit Valuation (1990 dollars)

Endpoint Pollutant Valuation (mean est.)

Mortality PM10 $4,800,000 per case

Chronic Bronchitis PM10 $260,000 per case

Chronic Asthma O3 $25,000 per case

Hospital Admissions

 All Respiratory SO2, NO2, PM10 & O3 $6,900 per case

 All Cardiovasular SO2, NO2, & CO PM10 &

O3

$9,500 per case

Emergency Room Visits for Asthma PM10 & O3 $194 per case

Respiratory Illness and Symptoms

 Acute Bronchitis PM10 $45 per case

 Asthma Attack or Moderate or

 Worse Asthma Day

PM10 & O3 $32 per case

 Acute Respiratory Symptoms SO2, NO2, PM1, & O3 $18 per case

 Upper Respiratory Symptoms PM1 $19 per case

 Lower Respiratory Symptoms PM10 $12 per case

 Shortness of Breath, Chest

 Tightness, or Wheeze

PM10 & SO2 $5.30 per day

Work Loss Days PM10 $83 per day

Mild Restricted Activity Days PM10 & O3 $38 per day
Chapter 6: Economic Valuation of Human Health Effects

71

cess premature mortalityî per time period (e.g., per

year). The benefit, however, is the avoidance of small

increases in the risk of mortality. By summing individualsí WTP to avoid small increases in risk over

enough individuals, we can infer the value of a statistical premature death avoided.2 For expository

purposes, we express this valuation as ìdollars per

mortality avoided,î or ìvalue of a statistical lifeî

(VSL), even though the actual valuation is of small

changes in mortality risk experience by a large number of people. The economic benefits associated with

avoiding premature mortality were the largest category of monetized benefits in the section 812 CAA

retrospective analysis (U.S. EPA 1997) and continue

to be the largest source of monetized benefits for

this prospective analysis. Mortality benefits, however, are also the largest contributor to the range of

uncertainty in monetized benefits. For a more detailed discussion of the factors affecting the valuation of premature mortality see Appendix H.

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. At risk individuals include

those who have suffered strokes or are suffering from

cardiovascular disease and angina (Rowlatt, et al.

1998). An ideal economic benefits estimate of mortality risk reduction would reflect these human characteristics, in addition to an individualís willingness

to pay (WTP) to improve oneís own chances of survival plus WTP to improve other individualsí survival rates.3 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 individualís 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 individualís survival. This

probability shift will differ across individuals because

survival curves are dependent 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 economic benefits of reduced risk of premature mortality associated with reducing air 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 value of

statistical life approach, supplemented by an alternative valuation based on a value of statistical life

years lost approach. We provide a review of the

relevant literature and a more detailed discussion of

our selected approach in Appendix H.

As in the retrospective, we use a mortality risk

valuation estimate which is based on an analysis of

26 policy-relevant value-of-life studies (see Table 6-

2). Five of the 26 studies are contingent valuation

(CV) studies, which directly solicit WTP information from subjects; the rest are wage-risk studies,

which base WTP estimates on estimates of the additional compensation demanded in the labor market

for riskier jobs. We used the best estimate from each

of the 26 studies to construct a distribution of mortality risk valuation estimates for the section 812

study. A Weibull distribution, with a mean of $4.8

million and standard deviation of $3.24 million, provided the best fit to the 26 estimates. There is considerable uncertainty associated with this approach.

We discuss this issue in detail later in this chapter

and in Appendix H.

In addition, we developed alternative calculations

based on a life-years lost approach. To employ the

value of statistical life-year (VSLY) approach, we first

estimated the age distribution of those lives projected

to be saved by reducing air pollution. Based on life

expectancy tables, we calculate the life-years saved

2 Because people are valuing small decreases in the risk of

premature mortality, it is expected deaths that are inferred. For

example, suppose that a given reduction in pollution confers on

each exposed individual a decrease in mortal risk of 1/100,000.

Then among 100,000 such individuals, one fewer individual can

be expected to die prematurely . If each individualís WTP for

that risk reduction is $50, then the implied value of a statistical

premature death avoided is $50 x 100,000 = $5 million.

3

 For a more detailed discussion of altruistic values related

to the value of life, see Jones-Lee (1992).
The Benefits and Costs of the Clean Air Act, 1990 to 2010

72

from each statistical life saved within each age and

gender cohort. To value these statistical life-years,

we hypothesized a conceptual model which depicted

the relationship between the value of life and the

value of life-years. As noted in Chapter 5, the average number of life-years saved across all age groups

for which data were available is 14 for PM-related

mortality. The average for PM, in particular, differs

from the 35-year expected remaining lifespan derived

from existing wage-risk studies.4 Using the same

distribution of value of life estimates used above (i.e.

the Weibull distribution with a

mean estimate of $4.8 million),

we estimated a distribution for the

value of a life-year and combined

it with the total number of estimated life-years lost. The details

of these calculations are presented

in Appendix H.

Valuation of Specific

Health Effects

Chronic Bronchitis

The best available estimate of

WTP to avoid a case of chronic

bronchitis (CB) comes from

Viscusi et al. (1991). The Viscusi

study, however, describes to the

respondents a severe case of CB.

We employ an estimate of WTP

to avoid a pollution-related case

of CB that is based on adjusting

the WTP to avoid a severe case,

as estimated by Viscusi et al.

(1991), to account for the likelihood that an average case of pollution-related CB is not as severe.

We use the mean of a distribution of WTP estimates as the

central tendency estimate of WTP

to avoid a pollution-related case

of chronic bronchitis in this

analysis. The distribution incorporates uncertainty from three

sources: (1) the WTP to avoid a

case of severe CB, as described by

Viscusi et al., 1991; (2) the severity level of an average pollution-related case of CB (relative to that of

the case described by Viscusi et al., 1991); and (3) the

elasticity of WTP with respect to severity of the illness. Based on assumptions about the distributions

of each of these three uncertain components, we

derive a distribution of WTP to avoid a pollutionrelated case of CB by statistical uncertainty analysis

techniques.5 The expected value of this distribution,

Table 6-2

Summary of Mortality Valuation Estimates (millions of $1990)

Study

Type of

Estimate

Valuation

(millions 1990$)

Kneisner and Leeth (1991) (US) Labor Market 0.6

Smith and Gilbert (1984) Labor Market 0.7

Dillingham (1985) Labor Market 0.9

Butler (1983) Labor Market 1.1

Miller and Guria (1991) Cont. Value 1.2

Moore and Viscusi (1988a) Labor Market 2.5

Viscusi, Magat, and Huber (1991b) Cont. Value 2.7

Gegax et al. (1985) Cont. Value 3.3

Marin and Psacharopoulos (1982) Labor Market 2.8

Kneisner and Leeth (1991)

(Australia)

Labor Market 3.3

Gerking, de Haan, and Schulze

(1988)

Cont. Value 3.4

Cousineau, Lacroix, and Girard

(1988)

Labor Market 3.6

Jones-Lee (1989) Cont. Value 3.8

Dillingham (1985) Labor Market 3.9

Viscusi (1978, 1979) Labor Market 4.1

R.S. Smith (1976) Labor Market 4.6

V.K. Smith (1976) Labor Market 4.7

Olson (1981) Labor Market 5.2

Viscusi (1981) Labor Market 6.5

R.S. Smith (1974) Labor Market 7.2

Moore and Viscusi (1988a) Labor Market 7.3

Kneisner and Leeth (1991) (Japan) Labor Market 7.6

Herzog and Schlottman (1987) Labor Market 9.1

Leigh and Folson (1984) Labor Market 9.7

Leigh (1987) Labor Market 10.4

Garen (1988) Labor Market 13.5

Source: Viscusi, 1992

4 See, for example, Moore and Viscusi (1988) or Viscusi

(1992).

5

 The statistical uncertainty analysis technique we used,

which is also known as simulation modeling, is a probabilistic

approach to characterizing the uncertainty or the distribution

of potential values around a central estimate.

and EPA analysis.
Chapter 6: Economic Valuation of Human Health Effects

73

which is about $260,000, is taken as the central tendency estimate of WTP to avoid a pollution-related

case of CB. We describe the three underlying distributions, and the generation of the resulting distribution of WTP, in Appendix H.

Chronic Asthma

The valuation of this health endpoint requires

an estimate which reflects an individualís desire to

avoid the effects of chronic asthma throughout his

or her lifetime. We derive this valuation estimate

from two studies that solicit values from individuals

diagnosed as asthmatics. Blumenschein and

Johannesson (1998) generate an estimate of monthly

WTP, while OíConor and Blomquist (1997) generate an annual WTP estimate. In order to extend

monthly and annual WTP estimates over an

individualís lifetime, we adjusted the reported estimates to reflect the average life-years remaining and

age distribution of the adult U.S. population, given

that chronic asthma is not expected to affect the average life expectancy. The mean estimate of WTP

to avoid a case of chronic asthma resulting from this

method is approximately $25,000.

Respiratory-Related Ailments

In general, the values we assign to the respiratory-related ailments in Table 6-1 are a combination

of WTP estimates for individual symptoms comprising each ailment. For example, a willingness to pay

estimate to avoid the combination of specific upper

respiratory symptoms defined in the concentrationresponse relationship measured by Pope et al. (1991)

is not available. While that study defines upper respiratory symptoms as one suite of ailments (runny

or stuffy nose; wet cough; and burning, aching, or

red eyes), the valuation literature reports individual

WTP estimates for three closely matching symptoms

(head/sinus congestion, cough, and eye irritation).

We therefore use these available WTP estimates and

a benefits transfer procedure to estimate the value

of avoiding those symptoms defined in the concentration-response study.

To capture the uncertainty associated with the

valuation of respiratory-related ailments, we incorporated a range of values reflecting the fact that an

ailment, as defined in the concentration-response

relationship, could be comprised of just one symptom or several. At the high end of the range, the

valuation represents an aggregate of WTP estimates

for several individual symptoms. The low end represents the value of avoiding a single mild symptom.

Minor Restricted Activity Days

An individual suffering from a single severe pollution-related symptom or combination of symptoms may experience a Minor Restricted Activity

Day (MRAD). Krupnick and Kopp (1988) argue that

mild symptoms will not be sufficient to result in a

MRAD, so that WTP to avoid a MRAD should exceed WTP to avoid any single mild symptom. On

the other hand, WTP to avoid a MRAD should not

exceed the WTP to avoid a work loss day (which

results when the individual experiences more severe

symptoms). No studies report an estimate for WTP

to avoid a day of minor restricted activity. Therefore, we derive for this analysis a value from WTP

estimates for avoiding combinations of symptoms

which may result in a day of minor restricted activity ($38 per day). The uncertainty range associated

with this value extends from the highest value for a

single symptom to the value for a work loss day.

Furthermore, a distributional form is used which

reflects our expectations that the actual value is likely

to be closer to the central estimate than either extreme.

Hospital Admissions, Cardiovascular

and Respiratory

The valuation of this benefits category reflects

the value of reduced incidences of hospital admissions due to respiratory or cardiovascular conditions.

We use avoided hospital admissions as a measure as

opposed to the number of avoided cases of respiratory or cardiovascular conditions, because of the

availability of C-R relationships for the hospital admissions endpoint. Hospital admissions reflect a class

of health effects linked to air pollution which are

acute in nature but more severe than the symptomday measures discussed above.

As described in Chapter 5, our approach to estimating the number of incidences for this category

involves reliance on several concentration-response

(C-R) functions. Each concentration response func-
The Benefits and Costs of the Clean Air Act, 1990 to 2010

74

tion provides an alternative definition of either respiratory effects or cardiovascular effects, and may

be based on different pollutants. For valuation of

the incidences, the current literature provides welldeveloped and detailed cost estimates of hospitalization by health effect or illness. Using illness-specific

estimates of avoided medical costs and avoided costs

of lost work-time, developed by Elixhauser (1993),

we construct cost of illness (COI) estimates that are

specific to the suite of health effects defined by each

C-R function. For example, we use twelve distinct

C-R functions to quantify the expected change in

respiratory admissions.6 Consequently in this analysis, we develop twelve separate COI estimates, each

reflecting the unique composition of health effects

considered in the individual studies.

The use of COI estimates suggests we likely understand the WTP to avoid these effects. The valuation of any given health effect, such as hospitalization, should reflect the value of avoiding associated

pain and suffering and lost leisure time, in addition

to medical costs and lost work time. While the probability distributions in this analysis characterize a

range of potential costs associated with hospitalization, they do not account for the omission of factors from the COI estimates such as pain and suffering. Consequently, the valuations for these endpoints most likely understate the true social values

for avoiding hospital admissions due to respiratory

or cardiovascular conditions.

Stratospheric Ozone Provisions

We develop monetary estimates of the health

benefits due to stratospheric ozone provisions based

on estimated incidences presented in a series of existing regulatory support analyses. To ensure consistency with the valuation strategy of this analysis,

however, we adjust certain parameters used in the

existing regulatory analyses of Title VI provisions.

Specifically, we re-evaluate the physical effects change

projected in the RIAs using the discount rate and

the value of statistical life adopted throughout the

rest of our present study. The net effect of these

changes is to reduce the estimates of benefits from

those found in the regulatory source support documents. The most important change is the discount

rate. Because the benefits of stratospheric ozone

protection accrue over several hundred years, the

discount rate chosen can have an especially large influence on the benefits estimate. The central estimate employed in this analysis is five percent; the

rate used in the source documents is two percent.

The value of statistical life (VSL) estimate is also

an important factor in the calculations, because the

vast majority of benefits of stratospheric ozone protection result from avoided fatal skin cancer cases.

To reflect the uncertainty of the VSL estimates, we

employ the same statistical uncertainty aggregation

approach used in the criteria pollutant analysis, using a Weibull distribution of VSL estimates as an

input. Appendix G describes the details of these and

other changes made to ensure consistency between

our stratospheric ozone provision benefits analysis

and our criteria pollutant analyses.

Results of Benefits Valuation

We combine the number of reduced incidences

of our health endpoints with our estimated values

of avoiding the health effect to generate total annual

monetized human health benefits in 2000 and 2010.

We attribute to Titles I through V of the CAAA

total annual human health benefits of $68 billion in

2000 and $110 billion in 2010. We summarize the

Post-CAAA 2010 monetized benefit in Table 6-3.

The table provides our central estimate, in addition

to the 5th and 95th percentile estimates for each benefit category.

There are two aspects of our results that warrant discussion. The first is the valuation of premature mortality due to PM exposure. The second is

our strategy to avoid double-counting when aggregating health benefits. As discussed in Chapter 5,

premature mortality is attributed to PM exposure

and our primary estimate reflects a lag between PM

exposure and premature mortality. While this lag

does not alter the number of estimated incidences, it

does alter the monetization of benefits. Because we

value the ìeventî rather than the present risk, in this

analysis we assume that the value of avoided future

premature mortality should be discounted. Therefore, the type of lag structure employed plays a direct role in the valuation of this endpoint.

6 For more detailed discussion of the various health effects

considered by each C-R function and methodology for estimating the number of avoided hospital admissions, see Appendix

D.
Chapter 6: Economic Valuation of Human Health Effects

75

The primary analysis reflects a five-year lag structure. Under this scenario, 50 percent of the estimated cases of avoided mortality occur within the

first two years. The remaining 50 percent are then

distributed across the next three years. Our valuation of avoided premature mortality applies a five

percent discount rate to the lagged estimates over

the periods 2000 to 2005 and 2010 to 2015. We discount over the period between the initial PM exposure change (either 2000 or 2010) and timing of the

incidence.

Many of the monetized health

benefit categories include overlapping

health endpoints, creating the potential for double-counting. In an effort

to avoid overstating the benefits, we

do not aggregate all of the quantified

health effects. For example, asthma

attacks and moderate to worse asthma

are considered components of the

endpoint, ìAny of 19 Respiratory

Symptomsî. Consequently, we

present the results but do not include

them in our reported total benefits

figures. In other cases, there are endpoints included in our aggregation of

benefit that appear to have overlapping health effects. For those benefit categories that describe similar

health effects, it is important to keep

in mind that estimated incidences are

based on unique portions of the population.

Valuation

Uncertainties

We addressed many valuation

uncertainties explicitly and quantitatively by expressing values as distributions (see Appendix H for a complete description of distributions

employed), using a computerized statistical technique to apply the valuations to physical effects (see Chapters

5 and 8) with the mean of each valuation distribution providing the foundation for the primary central estimate of total net benefits. This approach does not,

of course, guarantee that all uncertainties have been

adequately characterized, nor that the valuation estimates are unbiased. It is possible that the actual

WTP to avoid an air pollution-related impact is outside of the range of estimates used in this analysis.

Nevertheless, we assume that the distributions employed are reasonable approximations of the ranges

of uncertainty, and that there is no compelling reason to believe that the mean values employed are

systematically biased (except for the cost of illness

values, which probably underestimate WTP). There

are, however, a limited number of health endpoints

Table 6-3

Results of Human Health Benefits Valuation, 2010

Monetary Benefits

(in millions 1990$)

5th %ile Mean 95th %ile

Mortality

Ages 30+ $ 14,000 $ 100,000 $ 250,000

Chronic Illness

Chronic Bronchitis $ 360 $ 5,600 $ 18,000

Chronic Asthma 40 180 300

Hospitalization

All Respiratory $ 75 $ 130 $ 200

Total Cardiovascular 93 390 960

Asthma-Related ER Visits 0.1 1 3

Minor Illness

Acute Bronchitis $ 0 $ 2 $ 5

URS 4 19 39

LRS 2 6 12

Respiratory Illness 1 6 15

Mod/Worse Asthma1 2 13 29

Asthma Attacks1 20 55 100

Chest tightness, Shortness of

Breath, or Wheeze 0 0.6 3

Shortness of Breath 0 0.5 1.2

Work Loss Days 300 340 380

MRAD/Any-of-19 680 1,200 1,800

Total Benefits in 20102 - $ 110,000 -

Note:

1

 Moderate to worse asthma and asthma attacks are endpoints included in the

definition of MRAD/Any-of-19 respiratory effects. Although valuation estimates are

presented for these categories, the values are not included in total benefits to avoid

the potential for double-counting.

2

 Summing 5th and 95th percentile values would yield a misleading estimate of the

5th and 95th percentile estimate of total health benefits. For example, the likelihood

that the 5th percentile estimates for each endpoint would simultaneously be drawn

during the statistical uncertainty analysis is much less than 5 percent. As a result,

we present only the total mean.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

76

for which a different valuation approach may yield

results significantly different from out primary central benefit estimate. For example, using a value of

statistical life year approach in lieu of the value of

statistical life method for valuing avoided premature

mortality yields a mean estimate for this benefit

which is approximately 45 percent lower than our

primary central estimate. For those few endpoints

where reasonable alternative valuation paradigms

yield significantly different results from our preferred

approach, see our discussion in Chapter 8.

The potential for biases as introduced by benefits transfer methodology is applicable to all benefits categories and, as noted in Table 6-4, the direction of its bias is unknown. Because changes in

mortality risk are the single most important component of aggregate benefits, mortality risk valuation

is also the dominant component of the quantified

uncertainty. This category accounts for over 90

percent of total annual estimates under the PostCAAA scenario. The second largest benefits category, reduced risk of chronic bronchitis, valued at

approximately $5.6 billion per year in 2010, accounts

for roughly five percent of the total estimated benefits. Consequently, any uncertainty concerning

mortality risk valuation beyond that addressed by

the quantitative uncertainty assessment (i.e., that

related to the Weibull distribution with a mean value

of $4.8 million) deserves note.

Mortality Risk Benefits Transfer

One issue that merits special attention is the

uncertainties and possible biases related to the ìbenefits transferî from the 26 valuation source studies

to valuation of reductions in PM-related mortality

rates. Given the limitations of the current literature, we address this source of uncertainty qualitatively in this section. Although each of the mortality risk valuation source studies (see Table 6-2) estimate the average WTP for a given reduction in mortality risk, the degree of reduction in risk being valued varies across studies and is not necessarily the

same as the degree of mortality risk reduction estimated in this analysis. The transferability of estimates of the value of a statistical life from the 26

studies to the section 812 benefit 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 ten

times the risk reduction valued in the study). Under the assumption of linearity, the estimate of the

value of a statistical life 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).

Although the particular amount of mortality risk

reduction being valued in a study may not affect the

transferability of the WTP estimate from the study

Table 6-4

Valuation of CAAA Benefits: Potential Sources and Likely Direction of Bias

Benefits Category Factor

Likely Direction of Bias in WTP

Estimates Used in this Study

Premature Mortality Age Uncertain, perhaps overestimate

Degree of Risk Aversion Underestimate

Income Uncertain

Voluntary vs. Involuntary Underestimate

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

Discounting over a latency period Uncertain, perhaps underestimate

Chronic Bronchitis Severity-level Uncertain

Elasticity of WTP with respect to

severity

Uncertain

All other benefit endpoints Benefits Transfer Uncertain
Chapter 6: Economic Valuation of Human Health Effects

77

8 See, for example, Violette and Chestnut, 1983.

7

 See Schwartz and Dockery (1992), Ostro et al. (1995),

and Chestnut (1995).

to the benefit analysis, the characteristics of the study

subjects and the nature of the mortality risk being

valued in the study could be important. Certain characteristics of both the population affected and the

mortality risk facing that population are believed to

affect the average WTP to reduce risk. The appropriateness of the mean of the WTP estimates from

the 26 studies for valuing the mortality-related benefits of reductions in pollutant 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 (1) the extent to which the

subjects in the studies are similar to the population

affected by changes in air pollution and (2) the extent to which the risks being valued are similar.

The substantial majority of the 26 studies relied

upon are wage-risk (or labor market) studies. Compared with the subjects in these wage-risk studies,

the population most affected by air pollution-related

mortality risk changes is likely to be, on average,

older and probably more risk averse. Some evidence

suggests that approximately 85 percent of those identified in short-term (ìepisodicî) studies who die prematurely from PM-related causes are over 65.7

 The

average age of subjects in wage-risk studies, in contrast, would be well under 65, and probably closer

to 40 years of age.

The direction of bias resulting from the age difference is unclear. We could argue that, because an

older person has fewer expected years left to lose,

his or her WTP to reduce mortality risk would be

less than that of a younger person. This hypothesis

is supported by one empirical study, Jones-Lee et al.

(1985), which found WTP to avoid mortality risk at

age 65 to be about 90 percent of what it is at age 40.

On the other hand, there is reason to believe that

those over 65 are, in general, more risk averse than

the general population. This would imply that older

populations are likely to select occupations that are

relatively less risky than workers represented in

wage-risk studies or the general population. Although the list of 26 studies used here excludes studies that consider only much-higher-than-average occupational risks, there is nevertheless likely to be

some selection bias in the remaining studies, because

these studies are likely to be based on samples of

workers who are, on average, more risk-loving than

the general population. In contrast, older people as

a group exhibit more risk-averse behavior.

There is substantial evidence that the income

elasticity of WTP for health risk reductions is positive (although there is uncertainty about the exact

value of this elasticity). This implies that individuals with higher incomes and/or greater wealth should

be willing to pay more to reduce risk, all else equal,

than individuals with lower incomes or wealth. The

comparison between the income, both actual and

potential, or wealth of the workers in the wage-risk

studies versus that of the population of individuals

most likely to be affected by changes in pollution

concentrations, however, is unclear. One could argue that because the elderly are relatively wealthy,

the affected population is also wealthier, on average, than are the wage-risk study subjects, who tend

to be middle-aged (on average) blue-collar workers.

On the other hand, the workers in the wage-risk

studies will have potentially more years remaining

in which to acquire streams of income from future

earnings. On net, the potential income comparison

is unclear.

Although there may be several ways in which

job-related mortality risks differ from air pollutionrelated 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.

There is some evidence8 that people will pay more

to reduce involuntarily incurred risks than risks incurred voluntarily. If this is the case, WTP estimates

based on wage-risk studies may understate WTP to

reduce involuntarily incurred air pollution-related

mortality risks.

Another 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
The Benefits and Costs of the Clean Air Act, 1990 to 2010

78

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.

Economic assessment of WTP for lagged mortality effects also introduces uncertainty. For lack

of a more refined technique, our analysis relies on

the simplifying assumption that lagged mortality

risks can be valued at the time of the occurrence of

death, rather than at the time of exposure. In subsequent development of the annual and present value

estimates, we therefore discount the dollar benefits

estimate as if the full benefit accrues only in the year

of death. There are several reasons to believe that

this approach underestimates willingness to pay.

Most importantly, while death may occur after a lag

period, morbidity effects may appear at any time

prior to death, including immediately upon exposure. It is not clear that other dose-response assessments capture the full range of morbidity effects,

direct and indirect, that might be associated with a

latent fatal exposure. Other potentially important

factors include the use of a financial discount rate,

which may or may not accurately represent the rate

at which individuals might discount delayed health

benefits and the effect of knowledge of a fatal exposure on valuation of a delayed effect, in other words

whether the valuation is affected by a prior diagnosis of a fatal condition.

We summarize the potential sources of bias introduced by relying on wage-risk studies to derive

an estimate of the WTP to reduce air pollution-related mortality risk in Table 6-4; the overall effect of

these multiple biases is addressed in Table 6-5.

Among these potential biases, it is disparities in age

and income between the subjects of the wage-risk

studies and those affected by air pollution which have

thus far motivated specific suggestions for quantitative adjustment;9 however, the appropriateness and

the proper magnitude of such potential adjustments

remain unclear given presently available information.

These uncertainties are particularly acute given the

possibility that age and income biases might offset

each other in the case of pollution-related mortality

risk aversion. Furthermore, the other potential bi9 Chestnut, 1995; IEc, 1992.

ases discussed above, and summarized in Table 6-4,

add additional uncertainty regarding the transferability of WTP estimates from wage-risk studies to environmental policy and program assessments.
Chapter 6: Economic Valuation of Human Health Effects

79

Table 6-5

Key Uncertainties Associated with Valuation of Health Benefits

Potential Source of Error

Direction of Potential

Bias for Net Benefits

Likely Significance Relative to Key

Uncertainties on Net Benefits Estimate1

Benefits transfer for mortality

risk valuation, including

differences in age, income,

degree of risk aversion, the

nature of the risk, and

treatment of latency between

mortality risks presented by PM

and the risks evaluated in the

available economic studies.

Unable to determine based

on currently available

information

Potentially major. The mortality valuation

step is clearly a critical element in the net

benefits estimate, so any uncertainties can

have a large effect. As discussed in the text,

however, information on the combined effect

of these known biases is relatively sparse,

and it is therefore difficult to assess the

overall effect of multiple biases that work in

opposite directions.

Benefits transfer for chronic

bronchitis, including

adjustments made to better

match the severity of the risks

modeled in the available

economic studies.

Unable to determine based

on currently available

information

Probably minor. Benefits of avoided chronic

bronchitis account for about five percent of

total benefits, limiting the effect on net

benefits to a maximum of about seven

percent. Steps taken in the study to adjust

for severity using the best available empirical

information likely limit the effect to much less

than this maximum value.

Inability to value some

quantifiable morbidity

endpoints, such as impaired

lung function.

Underestimate Probably minor. Reductions in lung function

are a well-established effect, based on

clinical evaluations of the impact of air

pollutants on human health, and the effect

would be pervasive, affecting virtually every

exposed individual. There is therefore a

potential for a major impact on benefits

estimates. The lack of a clear symptomatic

presentation of the effect, however, could

limit individual WTP to avoid lung function

decrements.

Note: 1 The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The

Project Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could influence

the overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or approach is likely

to change the total benefit estimate by less than five percent, the Project Team assigns a classification of "probably minor."
The Benefits and Costs of the Clean Air Act, 1990 to 2010

80

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Chapter 7: Ecological and Other Welfare Effects

81

Ecological and

Other Welfare

Effects

EPAís traditional focus in environmental benChapter

7

efits assessment has been on quantifying beneficial

impacts of environmental regulation on human

health. As we have learned more about the effects

of anthropogenic stressors on ecological systems,

however, pursuit of environmental programs targeted on reductions of damage to the environment

have become more common. The CAAA Title IV

provisions, collectively referred to as the Acid Rain

Program, are a good example. These provisions are

in place largely as the result of a major research effort to better understand and quantify the effects of

sulfur and nitrogen oxides on natural systems susceptible to acid rain. Although the benefits of this

program include improvements in human health, the

initial impetus was protection of ecological resources.

We have designed this first section 812 prospective analysis to be responsive to the increased focus

on the importance of ecological resources by devoting a great deal of effort to characterizing and, where

possible, quantifying and monetizing the impacts of

air pollutants on natural systems. This increased focus is also partly a result of the outcome of EPAís

retrospective analysis, in which we identified an increased understanding of and focus on ecological effects as one of the important research directions for

the first prospective and subsequent analyses. This

chapter presents the results of these efforts.

This chapter consists of four sections. First, we

provide an overview of our approach to estimating

the effects of air pollution on ecological systems.

Second, we provide a characterization of these effects in qualitative terms. The second section concludes with a summary of the process for selecting

specific impacts which can be quantified and monetized using currently available methods. Third, we

present the results of our quantitative and economic

analyses. Finally, we discuss major uncertainties of

the ecological and other welfare effects analyses.

Overview of Approach

Our analysis of ecological effects involves three

major steps:

ï First, we identify and characterize ecological effects from air pollution.

ï Second, we develop and implement selection

criteria for more in-depth assessment of ecological impacts.

ï Third, we perform quantitative and qualitative analyses to characterize a portion of the

benefits of the 1990 CAAA provisions.

The first step involves taking a broad view of

pollutants controlled under the CAAA and their

documented effects on ecological systems, both as

individual pollutants and, to the extent possible, as

one component in multiple-stressor effects on ecosystems and their components. We organize our

analysis in terms of major pollutant classes and by

the level of biological organization at which impacts

are measured (e.g., regional ecosystem, local ecosystem, community, population, individual, etc.).

After completing the first step on a broad level,

the second step involves narrowing the scope of subsequent analyses. While it is desirable to focus effort on those impacts that are of greatest importance,

in practice the state of the science in ecological assessment largely dictates the subsequent focus of the

analysis. There exist only a handful of comprehensive ecological assessments from which to draw conclusions about those effects that are most important

either ecologically or in economic terms, and those

studies are potentially controversial in their methods and conclusions, in part because of the incomplete understanding of many of these effects. As a

result, the categories of effects ultimately chosen for

assessment here are necessarily limited by available
The Benefits and Costs of the Clean Air Act, 1990 to 2010

82

methods and data. As scientific understanding and

impact assessment methods grow more comprehensive, however, we expect that the focus of subsequent

analyses will be on those effects whose avoidance

would have the greatest potential ecological and/or

economic value.

The third step involves implementing a wide

range of analyses to more exhaustively characterize

specific effects of air pollution on ecological systems.

We provide quantitative estimates of the benefits of

the 1990 CAAA for the following effects:

ï eutrophication of estuaries associated with

airborne nitrogen deposition;

ï acidification of freshwater bodies associated

with airborne nitrogen and sulfur deposition;

and

ï reduced forest growth associated with ozone

exposure.

In addition, in this chapter we present the methods and results for quantitative analysis of other

welfare effects, including reduced agricultural yields

associated with ozone exposure, the impact of ambient particulate matter on visibility, the effects of

ozone on farm worker productivity, and the effects

of stratospheric ozone on crop and fisheries yields.

These effects have been identified as important categories of benefits in many previous analyses, including the section 812 retrospective analysis. As a result, these effects were not considered in the same

three step process used for other service flows.

We attempted to conduct quantitative analyses

of two other benefits categories: the accumulation

of toxics in freshwater fisheries associated with airborne toxics deposition; and aesthetic degradation

of forests associated with ozone and airborne toxics

exposure. However, we found that, while some

quantitative methods exist to evaluate these benefits,

key links are missing in the analytic process. This in

turn prevents development of defensible benefits estimates which can be reasonably associated with the

air quality and air pollutant deposition patterns developed from our Post-CAAA and Pre-CAAA scenarios. See Appendix E for more detailed discussion of these service flows. In addition, in assessing

nitrogen deposition impacts to estuarine systems, we

relied on a displaced cost approach with results that

we chose to omit from the primary benefits estimate

because of uncertainties in the methodology. These

results are nonetheless reported in this chapter, but

are used for the purposes of sensitivity testing only.

Because the breadth and complexity of air pollutant-ecosystem interactions do not allow for comprehensive quantitative analysis of all the ecological

benefits of the CAAA, we stress the importance of

continued consideration of those impacts not valued in this report in policy decision-making and in

further technical research. Judging from the geographic breadth and magnitude of the relatively

modest subset of impacts that we find sufficiently

well-understood to quantify and monetize, it is apparent that the economic benefits of the CAAAís

reduction of air pollution impacts on ecosystems are

substantial.

Characterization of Impacts

of Air Pollution on Ecological

Systems

The purpose of this section is to provide an overview of potential interactions between air pollutants

and the natural environment. We identify major

single pollutant-environment interactions, as well as

the synergistic impacts of ecosystem exposure to

multiple air pollutants. Although a wide variety of

complex air pollution-environment interactions are

described or hypothesized in the literature, for the

purposes of this analysis we focus on major aspects

of ecosystem-pollutant interactions. We do this by

limiting our review according to the following criteria:

ï Pollutants regulated by the CAAA.

ï Known interactions between pollutants and

natural systems as documented in

peer-reviewed literature.

ï Pollutants present in the atmosphere in sufficient amounts after 1990 to cause significant damages to natural systems.

Our understanding of air pollution effects on

ecosystems has progressed considerably during recent decades. Previously, air pollution was regarded

primarily as a local phenomenon and concern was

associated with the vicinity of industrial facilities,
Chapter 7: Ecological and Other Welfare Effects

83

power plants or urban areas. The pollutants of concern were gaseous (e.g., sulfur dioxide and ozone) or

heavy metals (e.g., lead) and the observed effects were

visible stress- specific symptoms of injury (e.g., foliar chlorosis). The most typical approach to documenting the effects of specific pollutants was a

dose-response experiment, where the objective was

to develop a regression equation describing the relationship between exposure and some easily measured

effect (e.g., growth, yield or mortality). As analytic

methods improved and ecology progressed, a broader

range of effects of air pollutants was identified and

understanding of the mechanisms of effect improved.

Observations made on various temporal scales (e.g.,

long-term studies) and spatial scales (e.g., watershed

studies) led to the recognition that air pollution can

affect all organizational levels of biological systems.

Our current understanding of ecosystem impacts

can be organized by the pollutants of concern and

by the level of biological organization at which impacts are directly measured. We attempt to address

both dimensions of categorization in this overview.

In Table 7-1 we summarize the major pollutants of

concern, and the documented acute and long-term

ecological impacts associated with them.

The summary in Table 7-1 is a highly condensed

version of the results of our characterization of ecological impacts. In addition to the pollutant-specific

effects outlined in the table, it is important to identify the level of biological organization and types of

ecosystems that are susceptible to these types of effects. Tables 7-2 through 7-4 provide more detail on

pollutant-specific impacts at a range of levels of biological organization. It is important to note that the

interactions listed are intended to illustrate the range

of possible adverse effects. For a more complete review of air-pollutant-induced effects on ecosystems,

see Appendix E.

Effects of Mercury and Ozone

Table 7-2 summarizes the effects of mercury and

ozone on ecological systems. To illustrate the nature of our review of effects, consider the second

row in Table 7-2. This row summarizes the effects

of the air pollutants mercury and ozone at the ìindividualî level of biological organization. As indicated

in the table, in a general sense air pollutants can induce a direct physiological response in individuals

(analogous to that experienced by humans exposed

to pollutants), or an indirect effect either through

impacts on the individualís surroundings or by weakening the individual and making it more susceptible

to other stressors. Mercury has several direct effects

to fauna, including effects to the central nervous

system and the liver, while the documented direct

effects of ozone tend to be to a variety of plant functions. Indirect effects of mercury are not well understood, but the indirect effects of ozone may serve

to compound the direct effects to plants by also

making the plants more susceptible to drought or

heat stress, for example. This type of cataloging of

Table 7-1

Classes of Pollutants and Ecological Effects

Pollutant

Class

Major Pollutants and

Precursor Emissions Acute Effects Long-term Effects

Acidic

Deposition

Sulfuric acid, nitric acid

Precursor emissions: Sulfur

dioxide, nitrogen oxides

Direct toxic effects to

plant leaves and

aquatic organisms.

Progressive deterioration of soil

quality. Chronic acidification of

surface waters.

Nitrogen

Deposition

Nitrogen compounds (e.g.,

nitrogen oxides)

Saturation of terrestrial ecosystems

with nitrogen. Progressive nitrogen

enrichment of coastal estuaries.

Hazardous Air

Pollutants

(HAPs)

Mercury, dioxins Direct toxic effects to

animals.

Conservation of mercury and dioxins

in biogeochemical cycles and

accumulation in the food chain.

Ozone Tropospheric ozone

Precursor emissions: Nitrogen

Oxides and Volatile Organic

Compounds (VOCs)

Direct toxic effects to

plant leaves.

Alterations of ecosystem wide

patterns of energy flow and nutrient

cycling.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

84

effects, while limited in its direct usefulness in a costbenefit framework, nonetheless does convey the wide

range of documented effects of air pollutants on ecological resources. These tables and the accompanying text, found in Appendix E, also provide a framework for determining the extent to which important factors may not be well characterized by quantitative analysis, setting the stage for prioritization

of research needs.

Effects of Nitrogen Deposition

Table 7-3 provides a summary of the effects of

nitrogen deposition on natural systems. These impacts are manifest in both terrestial and coastal estuarine systems. In both types of systems, nitrogen

can be a growth-enhancing nutrient. As shown in

the rows characterizing individual and population

level impacts, the effects on many varieties of plants

are beneficial. This growth can have other harmful

effects, however. For example, excessive growth of

Table 7-2

Interactions of Mercury and Ozone with Natural Systems At Various Levels of Organization

Examples of Interactions

Spatial Scale Type of Interaction

Mercury in

streams and lakes Ozone

Molecular and

cellular

Chemical and

biochemical processes

Mercury enters the body of

vertebrates and binds to

sulfhydril groups (i.e.

proteins).

Oxidation of enzymes of plants.

Disruption of the membrane

potential.

Direct physiological

response.

Neurological effects in

vertebrates. Behavioral

abnormalities. Damages to

the liver.

Direct injuries include visible

foliar damage, premature needle

senescence, reduced

photosynthesis, altered carbon

allocation, and reduction of

growth rates and reproductive

success.

Individual

Indirect effects:

Response to altered

environmental factors or

alterations of the

individual's ability to cope

with other kinds of stress.

Few interactions known.

Damages through

increased sensitivity to

other environmental stress

factors could occur, for

example, through

impairment of immune

response.

Increased sensitivity to biotic

and abiotic stress factors like

pathogens and frost. Disruption

of plant-symbiont relationship

(mychorrhiza), and symbionts.

Population Change of population

characteristics like

productivity or mortality

rates.

Reduced reproductive

success of fish and bird

species. Increased

mortality rates, especially in

earlier life stages.

Reduced biological productivity.

Selection for less sensitive

individuals. Possibly

microevolution for ozone

resistance.

Community Changes of community

structure and competitive

patterns

Loss of species diversity of

benthic invertebrates.

Alteration of competitive

patterns. Selective advantage

for ozone-resistant species.

Loss of ozone sensitive species

and individuals. Reduction in

productivity.

Local Ecosystem

(e.g.,landscape

element)

Changes in nutrient

cycle, hydrological cycle,

and energy flow of lakes,

wetlands, forests,

grasslands, etc.

Not well understood. Alterations of ecosystem-wide

patterns of energy flow and

nutrient cycling.

Regional Ecosystem

(e.g., watershed)

Biogeochemical cycles

within a watershed.

Region-wide alterations

of biodiversity.

Not well understood. Region-wide loss of sensitive

species.
Chapter 7: Ecological and Other Welfare Effects

85

marine organisms can lead to eutrophy, a state where

the enhanced surface growth of plants shields bottom growing plants from sunlight, causing them to

die and, in extreme cases, lead to low dissolved oxygen, or anoxic, conditions that impair a wide range

of species and ecological functions. These effects

are described in the table in the rows characterizing

effects at the community and ecosystem levels. For

this reason, isolated analysis of the effects of nitrogen on individuals or populations may provide misleading results; by the same token, analyses which

ignore the beneficial effects of nitrogen in certain

types of systems may lead to similarly misleading

results. These complex linkages across biological

levels of organization suggest that, when feasible, a

systems level approach to ecological assessments is

preferable to isolated analyses of effects at lower orders of organization.

Effects of Acid Deposition

Table 7-4 provides a summary of the effects of

acid deposition on forest and freshwater systems.

The direct effects of acid deposition in lakes and

streams include effects on fish species, as charaterized

in the row describing individual-level effects. These

Table 7-3

Interactions Between Nitrogen Deposition and Natural Systems

At Various Levels of Organization

Examples of Interactions

Spatial Scale Type of Interaction

Eutrophication and

Nitrogen Saturation of

Terrestrial Landscapes

Eutrophication of Coastal

Estuaries

Molecular and

cellular

Chemical and

biochemical processes

Assimilation of nitrogen by

plants and microorganisms

Assimilation of nitrogen by

plants and microorganisms.

Direct physiological

response.

Increases in leaf- size of

terrestrial plants.

Increase in growth of marine

plants.

Individual

Indirect effects:

Response to altered

environmental factors or

alterations of the

individual's ability to

cope with other kinds of

stress.

Decreased resistance to

biotic and abiotic stress

factors like pathogens and

frost. Disruption of plantsymbiont relationships with

mycorrhiza fungi.

Injuries to marine fauna through

oxygen depletion of the

environment. Loss of physical

habitat due to loss of sea-grass

beds. Injury through increased

shading. Toxic blooms of

plankton.

Population Change of population

characteristics like

productivity or mortality

rates.

Increase in biological

productivity and growth

rates of some species.

Increase in biological

productivity. Increase of growth

rates (esp. of algae and marine

plants).

Community Changes of community

structure and

competitive patterns

Alteration of competitive

patterns. Selective

advantage for fast growing

species and individuals

that efficiently use

additional nitrogen. Loss

of species adapted to

nitrogen-poor

environments.

Excessive algal growth.

Changes in species

composition. Decrease in seagrass beds.

Local Ecosystem

(e.g., landscape

element)

Changes in nutrient

cycle, hydrological cycle,

and energy flow of lakes,

wetlands, forests,

grasslands, etc.

Magnification of the

biogeochemical nitrogen

cycle. Progressive

saturation of

microorganisms, soils, and

plants with nitrogen.

Magnification of the nitrogen

cycle. Depletion of oxygen,

increased shading through

algal growth.

Regional Ecosystem

(e.g., watershed)

Biogeochemical cycles

within a watershed.

Region-wide alterations

of biodiversity.

Leaching of nitrogen from

terrestrial sites to streams

and lakes. Acidification of

aquatic bodies.

Eutrophication of estuaries.

Additional input of nitrogen

from nitrogen-saturated

terrestrial sites within the

watershed.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

86

effects are not as straightforward as they might appear, however, because it is not only the acidity (pH)

of the water itself that causes the effect but the increased leaching of metals, particularly aluminum,

which takes place in acidic (low pH) environments

that contributes substantially to the effects on fish.

These effects will vary widely from place to place

according to the mineral content of the soil near the

lake and the lakebed sediment, as well as the natural

resistance of the lake in absorbing acid deposition

(i.e., its buffering capacity). Other important effects

characterized in the table include the ability of acid

deposition to deplete cation concentrations in terrestrial ecosystems; increase the concentration of

aluminum in soils; and leach nutrients, sulfates, and

metals to surrounding streams and lakes. Effects of

note at the individual level include foliar damage to

trees.

Table 7-4

Interactions Between Acid Deposition and Natural Systems At Various Levels of Organization

Examples of Interactions

Spatial Scale Type of Interaction Acidification of Forests

Acidification of Streams

and Lakes

Molecular and

cellular

Chemical and

biochemical processes

Damages to epidermal

layers and cells of plants

through deposition of

acids.

Impairment of ion interactions

of fish at the cellular level.

Direct physiological

response

Increased loss of nutrients

via foliar leaching.

Decreases in pH and increase

in aluminum ions causes

pathological changes in gill

structure of fish.

Individual

Indirect effects:

Response to altered

environmental factors or

alterations of the

individual's ability to

cope with other kinds of

stress.

Cation depletion in the soil

causes nutrient

deficiencies in plants.

Concentrations of

aluminum ions in soils can

reach phytotoxic levels.

Increased sensitivity to

other stress factors like

pathogens and frost.

Aluminum ions in the water

column can be toxic to many

aquatic organisms through

impairment of gill regulation.

Acidification can indirectly

affect submerged plant species,

because it reduces the

availability of dissolved carbon

dioxide (CO2).

Population Change of population

characteristics like

productivity or mortality

rates.

Decrease of biological

productivity of sensitive

organisms. Selection for

less sensitive individuals.

Microevolution of

resistance.

Decrease of biological

productivity of sensitive

organisms. Selection for less

sensitive individuals.

Microevolution of resistance.

Community Changes of community

structure and

competitive patterns

Alteration of competitive

patterns. Selective

advantage for acidresistant species. Loss of

acid sensitive species and

individuals. Decrease in

productivity. Decrease of

species richness and

diversity.

Alteration of competitive

patterns. Selective advantage

for acid-resistant species. Loss

of acid sensitive species and

individuals. Decrease in

productivity. Decrease of

species richness and diversity.

Local Ecosystem

(e.g., landscape

element)

Changes in nutrient

cycle, hydrological cycle,

and energy flow of lakes,

wetlands, forests,

grasslands, etc.

Progressive depletion of

nutrient cations in the soil.

Increase in the

concentration of mobile

aluminum ions in the soil.

Measurable declines of

decomposition of some forms

of organic matter, potentially

resulting in decreased rates of

nutrient cycling.

Regional Ecosystem

(e.g., watershed)

Biogeochemical cycles

within a watershed.

Region-wide alterations

of biodiversity.

Leaching of sulfate, nitrate,

aluminum, and calcium to

streams and lakes.

Acidification of aquatic

bodies.

Additional acidification of

aquatic systems through

processes in terrestrial sites

within the watershed.
Chapter 7: Ecological and Other Welfare Effects

87

A few general points emerge from our review of

ecological effects:

ï Air pollutants have indirect effects that are

at least as important as direct toxic effects

on living organisms. Indirect effects include

those in which the pollutant alters the physical or chemical environment (e.g., soil properties), the plantís ability to compete for limited resources (e.g., water, light), or the

plantís ability to withstand pests or pathogens. Examples are excessive availability of

nitrogen, depletion of nutrient cations in the

soil by acid deposition, mobilization of toxic

elements such as aluminum, and changes in

winter hardiness. As is true for other complex interactions, indirect effects are more

difficult to observe than direct toxic relationships between air pollutants and biota, and

there may be a variety of interactions that

have not yet been detected.

ï There is a group of pollutants that tend to

be conserved in the landscape after they have

been deposited to ecosystems. These conserved pollutants are transformed through

biotic and abiotic processes within ecosystems, and accumulate in biogeochemical

cycles. These pollutants include, but are not

limited to, hydrogen ions (H+), sulfur (S)

and nitrogen (N) containing substances, and

mercury (Hg). Chronic deposition of these

pollutants can result in progressive increases

in concentrations and cause injuries due to

cumulative effects. Indirect, cumulative

damages caused by chronic exposure (i.e.,

long-term, moderate concentrations) to these

pollutants may increase in magnitude over

time frames of decades or centuries with very

subtle annual increments of change. Examples are N-saturation of terrestrial ecosystems, cation depletion of terrestrial ecosystems, acidification of streams and lakes, and

accumulation of mercury in aquatic food

webs.

ï Damages to ecosystems are most likely

caused by a combination of environmental

stress factors. These include anthropogenic

factors such as air pollution and other environmental stress factors such as low temperature, excess or limited water, and limited

availability of nutrients. The specific combinations of factors differ among regions and

ecosystems where declines have been observed. Accurately predicting the impacts

of multiple stress factors is an extremely difficult task, but this is an area of very active

research among ecologists.

ï Pollutant-environment interactions are complicated by the fact that biotic and abiotic

factors in ecosystems change dramatically

over time. Besides oscillations on a daily

basis, and changes in a seasonal rhythm, there

are long-range successional developments

over time periods of years, decades, or even

centuries. These temporal variations occur

in polluted and pristine ecosystems, and no

single point in time or space can be defined

as representative of the entire system.

Selection of Service Flows

Potentially Amenable to

Economic Analysis

Based on this broad overview of effects, we identify a set of pollutant-environment interactions

which are amenable to more detailed quantification

and monetization. We evaluate the long list of effects and seek categories where a defensible link exists between changes in air pollution emissions and

the quality or quantity of the ecological service flow,

and where economic models are available to monetize these changes. The use of these criteria greatly

constrains the range of impacts that can be treated

quantitatively. While the previous section identifies many pollutant-ecosystem interactions, only a

handful are understood and have been modeled to

an extent sufficient to reliably quantify their impact.

The theoretical basis of economic benefits assessment is that ecosystems provide services to mankind, and that those services have economic value.

The application of this theory requires the isolation

of service flows that have market values or are otherwise amenable to available methods for determining value in the absence of formal markets. Available methods do not exist to comprehensively value

all service flows for any particular ecosystem or aggregation of ecosystems. Generally, we are limited
The Benefits and Costs of the Clean Air Act, 1990 to 2010

88

to those service flows that are either sources of material inputs or associated with natural amenities that

involve active recreation. Impacts to these service

flows that can be valued tend to manifest themselves

immediately and can be readily measured and assessed

in terms of the established cause and effect relationships.

Based on the constraints of economic valuation

methods and data, we select from the host of ecosystem impacts identified in the previous section a set

of service flows as candidate endpoints for analysis.

The list of service flows establishes the potential

scope of economic analysis for ecological effects feasible in the context of the present study. Table 7-5

presents the service flow impacts that we quantitatively estimate in this analysis plus those effects that

currently cannot be quantified for each of the four

ecological pollutant categories discussed in Table 7-1.

From the list of effects in Table 7-5, we further

limited the quantitative and qualitative analyses conducted to reflect the available model coverage. The

results are summarized in Table 7-6. The relatively

short list of effects in Tables 7-5 and 7-6 demonstrates

that, of the great number of known impacts of air

pollution, only a subset can be assessed quantitatively. Note that for one category of effects, nitrogen deposition impacts to estuarine systems, we relied on a displaced cost approach (described below)

Table 7-5

Ecological Effects of Air Pollutants

Pollutant Quantified Effects Unquantified Effects

Acidic Deposition Impacts to recreational

freshwater fishing

Impacts to commercial forests

(e.g., timber, non-timber forest products)

Impacts to commercial freshwater fishing

Watershed damages (water filtration

flood control)

Impacts to recreation in terrestrial

ecosystems (e.g. forest aesthetics,

nature study)

Reduced existence value and option

values for nonacidified ecosystems (e.g.

biodiversity values)

Nitrogen

Deposition

Additional costs of alternative or

displaced nitrogen input controls

for eastern estuaries

Impacts to commercial fishing,

agriculture, and forests

Watershed damages (water filteration,

flood control)

Impacts to recreation in estuarine

ecosystems (e.g. Recreational fishing,

aesthetics, nature study)

Reduced existence value and option

values for non-eutrophied ecosystems

(e.g. biodiversity values)

Tropospheric

Ozone Exposure

Reduced commercial timber

yields and reduced tons of carbon

sequestered

Impacts to recreation in terrestrial

ecosystems (e.g. forest aesthetics,

nature study)

Reduced existence value and option

values for ozone-impacted ecosystems

Hazardous Air

Pollutant (HAPS)

Deposition

No service flows quantified Impacts to commercial and recreational

fishing from toxification of fisheries

Reduced existence value and option

values for non-toxified ecosystems (e.g.

biodiversity values)
Chapter 7: Ecological and Other Welfare Effects

89

that we chose to omit from the primary benefits estimate because of uncertainties in the methodology.

These results are nonetheless reported in this chapter, but are used for the purposes of sensitivity testing only. In the next section we discuss the methods, results, and caveats of the analyses of these selected endpoints.

Results

In this section we summarize the methods used

for, and results obtained from, our quantitative and

economic analyses of selected service flows. We first

review the methods for each analysis, and then

present a summary of key quantitative results. For

a more detailed description of methods and results,

see Appendix†E.

Estuarine Eutrophication Associated

with Airborne Nitrogen Deposition

Atmospherically derived nitrogen makes up a

sizable fraction of total nitrogen inputs in estuaries

in the eastern United States. Airborne nitrogen deposition accounts for a significant fraction of the total

nitrogen loads to coastal estuaries, particularly on

the East and Gulf coasts. For example, the most

recent estimates for the Chesapeake Bay indicate airborne deposition accounts for over 40 percent of the

total nitrogen load to the estuary; in Galveston Bay,

the share is almost 50 percent. When nitrogen enters estuaries it can cause eutrophication, or an increased nutrient load that, in excess, changes the

ecosystemís structure and function and affects ecological service flows. Many state governments and

multi-state regional authorities have expressed increasing concern about the control of airborne nitrogen deposition as an important source of nitrogen loading.

Our analysis of the effects of nitrogen deposition followed two tracks. We first attempted to quantify the service flows affected by and the damages

associated with eutrophication, and derive dose-response relationships and valuation strategies for each

of the key service flow categories (for example, recreational fishing). The derivation of dose-response

relationships between atmospheric nitrogen loading

and ecological effects, however, is complicated by

the dynamic nature of ecological systems. In addition to being characterized by non-linear, ìthresholdî type responses, estuarine ecosystems are simultaneously influenced by a variety of stressors (both

anthropogenic and natural). This makes it difficult

to quantify the nature and magnitude of ecological

changes expected to result from a change in a single

stressor such as nutrient loading. Further, if the state

of the ecosystem has changed (as from oligotrophic1

to eutrophic) the removal of the initial stressor does

not necessarily mean a rapid return to the prior state.

This complicates the quantitative benefits assessment

of controlling nitrogen deposition through the

CAAA.

Table 7-6

Summary of Endpoints Selected for Quantitative Analysis

Endpoint Analysis Geographic Scope

Lake acidification impacts on

recreational fishing

Quantification of improved fishing

with monetization of recreational

value

Case study of New York State

Estuarine eutrophication

impacts on recreational and

commercial fishing

Quantification of improved fishing

with monetization of displaced

costs of alternative

eutrophication control methods

Case studies of Chesapeake Bay,

Long Island Sound, and Tampa

Bay (with illustrative extensions to

East Coast estuaries)

Ozone impacts on commercial

timber sales

Quantification of improved timber

growth with monetization of

commercial timber revenues

National assessment

Ozone impacts on carbon

sequestration in commercial

timber

Quantification of improved

carbon sequestration

National assessment

1

 Oligotrophy refers to a state of relatively low nutrient

enrichment and low productivity of aquatic ecosystems. In contrast, eutrophy refers to a state of relatively high nutrient loading and higher productivity, sometimes leading to

overenrichment and reduction in ecological service flows due

to water quality decline.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

90

Our second track relies on a displaced cost approach to benefit estimation. To reduce excess nutrient loads (including nitrogen) to local estuaries,

many coastal communities are pursuing a range of

abatement options. These options include wastewater and stormwater discharge point source controls as well as urban non-point and agricultural

non-point source controls for runoff from the land.

If atmospheric nitrogen depostion is reduced, the

need for these types of expenditure to control other

sources of nitrogen loading is also lessened, and the

displaced control expenditures represent a benefit

to society.

Displaced or avoided cost approaches are not

always justified. In order to establish that the costs

would truly be avoided, and to ensure that the avoidance of that cost represents a real benefit to society,

we need to show that realistic and enforceable nitrogen reduction goals exist for each evaluated estuary.

Without specific targets or reduction goals, it is not

possible to suggest that there are specific control

expenditures to be displaced. Therefore, we choose

case study estuaries that most closely meet this criterion: Chesapeake Bay, Long Island Sound, and

Tampa Bay. These areas have established nitrogen

reduction programs that rely primarily on reductions

of effluent from point sources as well as reductions

in non-point source discharges. Information on the

reduction goal and potential abatement options for

meeting those goals allows us to estimate the portion of the goal that can be met by the CAAA, as

well as the associated cost savings.2

The benefits valuation derived using the displaced-costs approach should be interpreted cautiously for two reasons. First, it is an estimation of

capital costs that serve more purposes than mitigating nitrogen inputs into the estuaries of concern.

Water treatment works are intended to provide waste

water treatment for a variety of pollutants and may

be required even in the absence of deposition of airborne nitrogen. Second, the nitrogen loading targets for the estuaries are not concrete, strictly enforced limits, based on certain knowledge of the capacity of the estuaries to accept nitrogen inputs.

Instead, the targets may change over time as knowledge of the effects of nitrogen to these estuaries

change. For these reasons, and because of the uncertainty about the ability of local and regional entities to enforce the nitrogen reduction targets, we

calculate estimates of displaced costs for these three

estuaries but do not include them in the primary

benefits estimate for the CAAA.

Our approach involves three basic steps. First,

we estimate the total loading of nitrogen to each of

the three target estuaries. We use nitrogen deposition estimates from the RADM model, generated

for each 80 km x 80 km grid cell in the eastern U.S.

We then estimate the ultimate fate of deposited nitrogen through a GIS-based model of nitrogen ìpassthrough.î The pass-through is the share of nitrogen

deposited that is ultimately transported to the estuarine waters rather than retained by the land. Passthrough factors vary by land use, from about 20 percent (for forests and wetlands) to 100 percent (for

open water). We estimate the nitrogen loading for

each scenario, and the within-year, cross-scenario

differences are the reduced nitrogen deposition attributed to the CAAA. We present these estimates

in the second column of Table 7-7.

Second, we estimate the marginal costs of alternative abatement actions which could be implemented in the three case study estuaries. We develop

our displaced-cost estimate by assuming that decision makers will choose to forego the most costly

nitrogen abatement projects first. That is, we assume that reduced deposition and the resulting loadings reduction will eliminate the need for additional

point or non-point source controls at the high end

of the marginal cost curve. We summarize those results in the third and fourth columns of Table 7-7.

Third, we multiply the reduced nitrogen loading attributed to the CAAA by the marginal cost

estimate to arrive at a range of estimates of displaced

cost, ensuring that the reduction in airborne nitrogen is less than or equal to the potential tonnage

reduction achieved by the displaced, high marginal

cost abatement strategies. We present our results in

the last column of Table 7-7. Our estimates suggest

that the displaced cost is substantial for the large

Chesapeake Bay and Long Island Sound estuaries,

and more modest for Tampa Bay. The Chesapeake

2

 With increasing populations, controls of alternative

sources (e.g., automobile and utility emissions) may be needed

simply to meet the original target or goal. If the CAA amendments are necessary just to achieve the target reductions, then

we are actually measuring alternative costs and not avoided costs.
Chapter 7: Ecological and Other Welfare Effects

91

Bay and Long Island Sound watersheds together account for about 40 percent of the total estuarine

watershed area on the East (Atlantic) coast that is

sensitive to nitrogen deposition, while Tampa Bay

accounts for about two percent of the sensitive watershed area for the Gulf coast.

Acidification of Freshwater Fisheries

During the 1970s and 1980s, ìacid rainî came to

be known to the public as a phenomenon that injures trees, forests, and water bodies throughout

Europe and in some areas of the United States and

Canada. One of the goals of the CAAA was to address the problem of acidification of terrestrial and

aquatic ecosystems caused by acidic deposition. To

assess this effect we conducted a quantitative analysis of benefits derived from a reduction in acidification of aquatic bodies as they relate to recreational

fishing in the Adirondacks region of New York State.

As discussed earlier in this chapter, acidification

of water bodies is a complex process. Airborne acids, in the form of sulfur and nitrogen compounds,

are deposited to water bodies and surrounding drainage areas, with the potential to change the pH of the

water body. Many water bodies are relatively resistant and can absorb a great deal of deposition before

pH changes substantially. This buffering capacity is

referred to as acid neutralizing capacity (ANC).

Once pH begins to be affected, a series of interactions occur, the most important of which is the leaching of aluminum from sediments and surrounding

soil and the suspension of this metal in the water

column. While acidic pH presents a direct stress to

aquatic organisms, it is the combined effect of pH

and aluminum exposure that presents the greatest

risk. Lakes in the Adirondacks region of New York

State are particularly susceptible to acidification because they have low baseline ANC, relative to water bodies in other areas of the country.

Because of these complex physical and chemical

interactions, acidification stress is typically evaluated

by application of a model that simulates these processes, and requires data on individual lake chemistry and sediment composition. We relied on the

scenario-specific atmospheric deposition data (both

sulfur and nitrogen) from the RADM air quality

model (see Chapter 4 and Appendix C) as an input

to EPAís Model of Acidification of Groundwater in

Catchments (MAGIC). MAGIC generates several

measures of the impact of sulfur and nitrogen deposition on lake acidity, including ANC and pH.3 We

used the pH outputs to classify lakes where recreational fishing might be impaired, and those estimates

were used in an economic model of recreational fishing behavior in New York State to develop economic

estimates of the impact of acid rain on recreational

fishing resources in that state.

We summarize the results of our analysis of economic benefits of avoided Adirondacks acidification

attributable to the CAAA in 2010 in Table 7-8. The

range of annual benefits from the CAAA are $12

million to $49 million using the low-end assumptions on the threshold of effect (pH 5.0), and $82 to

$88 million for the high-end assumptions on the effects threshold (pH 5.4). Higher pH (or, less acidic)

threshold assumptions lead to greater damage estimates, because more lakes cross the less acidic threshold. We calculate our benefits results by comparing

Table 7-7

Estimated Displaced Costs for Three Estuaries

Estuary

Reduced N Deposition in

2010(millions of pounds)

Low Marginal

Cost($/lb/yr.)

High Marginal Cost

($/lb/yr.)

Estimated Annual

Displaced Costs in

2010 ($millions)

Long Island

Sound

12.8 $2 $8 $26-$100

Central Estimate: $63

Chesapeake

Bay

58.1 $6 $22 $350-$1,300

Central Estimate: $820

Tampa Bay 1.8 $6 $38 $11 - $68

Central Estimate: $40

3 For more information on EPAís MAGIC model see Cosby

et al. (1985a), as referenced in Appendix E.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

92

the suite of Post-CAAA 2010 estimates of total damages to the corresponding suite of estimates using

Pre-CAAA deposition. The impact of nitrogen saturation in the surrounding terrestrial environment is

reflected in the range of estimates presented in Table

7-8. If surrounding soils are saturated, less deposited

nitrogen will remain on the land and more nitrogen

will enter the water bodies, increasing the stress on

the aquatic ecosystem. This phenomenon is reflected

by the higher damage estimates for saturated versus

non-saturated scenarios, other factors equal, although

our model shows no effect of saturation in the 2010

Pre-CAAA low estimate. The results we present

are in line with those generated from previous analyses that find annual benefits to the Adirondacks of

halving utility emissions to be approximately in the

millions to tens of millions of dollars.4

Reduced Timber Growth Associated

with Ozone Exposure

The third category of effects we quantify is improved commercial timber growth through the reduction of tropospheric ozone concentrations attributable to the CAAA. There is substantial scientific

evidence to suggest that elevated ozone concentrations in the troposphere disrupt ecosystems by damaging and slowing the growth of vegetation. In this

analysis, we examine one aspect of these impacts,

reduced commercial timber growth. Much of the literature on the effects of ozone on tree growth is

based on laboratory exposures of seedlings or leafscale experiments in the field. Estimates from those

studies have been used in previous analyses, making

use of professional judgment as an interpretive tool,

but always with strong caveats about the potential

applicability of the seedling and leaf-scale results to

tree growth and, in particular, the rate of accumulation of wood mass that is important for commercial

timber production.5 In an attempt to overcome these

issues, we sought to find a concentration-response

relationship that would provide a more defensible

and broadly applicable basis for estimating effects

on tree growth.

Our analysis makes use of the Net Photosynthesis and Evapo-Transpiration model II (PnET II),

a biological model of timber stand productivity to

estimate the impacts of ozone on timber yields. The

PnET II model was designed to estimate the combined effects of several stressors on the rate of net

primary productivity (NPP), a measure of the rate

of photosynthesis. NPP in a tree does not necessarily all go towards accumulation of wood mass; some

may be allocated to root growth, leaf growth, or

other tree functions. The PnET II model provides a

means to measure both NPP and wood mass growth,

as well as the effect on trees of several stressors combined. One important stressor to acknowledge in

an analysis of the effects of ozone on trees is drought

stress. Ozone has the effect of reducing water loss in

trees by stimulating the closing of stomata through

which water is transpired. As a result, in drought

stress conditions, ozone can have beneficial effects

on tree growth. The PnET II model reflects the

impact of this factor in combination with other direct effects of ozone on tree function.

We used the PnET II model to provide estimates

of timber stand responses to ozone exposure under

each of the scenarios examined in this analysis. We

aggregated tree growth results by region, with separate estimates for hardwoods and softwoods, and used

them as inputs to the Timber Assessment Market

4 For alternative estimates see, for example, Englin et al.

(1991), Mullen and Menz (1985), and Morey and Shaw (1990), as

referenced in Appendix†E.

5 See de Steiger et al. (1990) for an example of the generation of tree growth dose-response estimates based on professional

judgement.

Table 7-8

Annual Economic Impact of Acidification in 2010 (Millions of 1990 Dollars)

Range of Economic Impact

Year Scenario Low Estimate Central Estimate High Estimate

1990 Base Year $61 $320

2010 Post-CAAA $24 to $61 $261 to $281

Pre-CAAA $73 $349 to $363

Range of CAAA Benefits in 2010 $12 to $49 $50 $82 to $88
Chapter 7: Ecological and Other Welfare Effects

93

Model (TAMM), an economic model of the forest sector maintained by the United States Forest Service. There are three stages to the economic estimation. First, forest growth rate information generated by PnET II is provided to

the Aggregate Timber Land Assessment System

(ATLAS), the forest inventory tracking component of TAMM. Growth rate information is

provided for each of the forest production regions defined by TAMM.6 Second, ATLAS

generates an estimate of forest inventories in

each major region, which in turn serves as input to the market component of TAMM.

Third, TAMM estimates the future harvests and

market responses in each region.

Our analysis suggests that there is a significant and measurable difference in timber harvests attributable to ozone exposure under the PostCAAA and Pre-CAAA scenarios. At the outset of

our modeling period, the early 1990s, virtually no

change is measured in forest harvest volumes. This

result occurs because increases in growth rates do

not substantively affect timber volume over a short

period of time. By the end of our modeling period,

nearing 2010, increased growth rates over the previous decade(s) begin to affect overall forest yields of

harvestable timber. This is observed in Figure 7-1 as

an increasing annual benefit estimate over the modeling period. The shape of the benefits time-series

reveals a production spike in the 2007 to 2008 period. This spike is due to a large anticipated harvest

of Southeast U.S. timber due to forest maturity during this period. The spike would occur even in the

absence of the CAAA, but is elevated by the CAAA

due to increased growth rates projected under the

Post-CAAA scenario. Although this change is small

in percentage terms relative to total economic surplus generated by the timber sector, it contributes

to a large portion of the commercial timber benefits

estimate over the 1990-2010 period.

We calculate the cumulative value of annual benefits based on the discounted stream of the annual

differences in consumer and producer surplus from

commercial timber harvests under the Post-CAAA

and Pre-CAAA ozone exposure scenarios from 1990

to 2010. Discounting annual benefits to 1990 using

a five percent discount rate, the total cumulative

benefits estimate is approximately $1.9 billion. These

estimates are incorporated into the primary central

estimate by developing a range of annual estimates

for the year 2000, based on model results for the

period 1998 to 2002, and the year 2010, based on

model results for the period 2005 to 2010. The averaging of results across several years to generate our

target year results avoids the potential problem of a

particular yearís results (such as for 2010)

mischaracterizing the full time series of estimates

when we later calculate the net present value of effects.

Reduced Carbon Sequestration

Associated with Reduced Timber

Growth

Forest ecosystems help mitigate increasing atmospheric concentrations of carbon dioxide by sequestering carbon from the atmosphere. These ecosystems convert atmospheric carbon into biological

structures (e.g., wood) or substances needed in the

treeís physiological processes. As described above,

however, ozone reduces the growth of forests,

thereby limiting the amount of carbon that is sequestered. Sequestered carbon can help mitigate global climate change that has been linked to anthropogenic emissions of carbon and other greenhouse

gases.

6

 TAMM includes Canadian as well as U.S. timber production regions because of the important influence of Canadian timber supply on the U.S. market. This analysis reflects modeling

of Canadian timber regions and their impact on U.S. production, but we did not simulate changes in ozone in Canadian regions.

Figure 7-1

Annual Economic Welfare Benefit of Mitigating Ozone

Impacts on Commercial Timber: Difference Between

the Pre-CAAA and Post-CAAA Scenarios

$(50)

$150

$350

$550

$750

$950

$1,150

1990

1992

1994

1996

1998

200020022004200620082010

Year

Millions of 1990 Dollars
The Benefits and Costs of the Clean Air Act, 1990 to 2010

94

We used the timber inventory output of the

TAMM/ATLAS modeling system (described above),

in combination with a forest carbon model

(FORCARB), to estimate changes in carbon storage

in each of four ecosystem components: trees, forest

understory, forest floor, and soil. The estimates from

FORCARB, however, do not account for ìleakagesî

of carbon back to the atmosphere as wood or wood

products decay and decompose over time. To estimate the amount of carbon that is sequestered over

the long-term, we used a second model,

HARVCARB, to estimate the life-cycle of harvested

forest timber and thereby adjust the forest carbon

sequestration estimates of FORCARB.

The results of these calculations yield estimates

of long-term increases in carbon storage as a result

of the CAAA provisions of 8 million metric tons of

carbon per year by the year 2000, and 29 million

metric tons of carbon per year by the year 2010.

Because of the great uncertainties in assessing the

mitigating effect of carbon sequestration on global

climate change, and the economic value of avoiding

climate change, we do not attempt to monetize this

category of benefit.

Other Categories of Ecological Benefits

There were two additional categories of ecological effects for which we considered developing economic estimates; however, we abandoned the exercise when key portions of the analysis proved to be

excessively problematic. Aesthetic degradation of

forests, the first of these additional categories, was

supported by a benefits transfer of contingent valuation studies of individual willingness to pay to avoid

foliar damage. This category of effects, however,

proved too difficult to link to the specific air quality

scenarios we evaluated. In other words, available

scientific methods and data on the visual appearance

of forest stands and their impact on perceived forest

aesthetics make it difficult to precisely describe

changes in forest aesthetics. Evaluation of the second additional effect category, toxification of freshwater fisheries, was limited by the lack of toxic deposition and exposure data as well as by the limitations

of available economic estimates of the impacts of

toxics on recreational and commercial fishery resources. (See Appendix E for a more detailed discussion of these service flows). These and many other

ecological benefit categories could not be quantified

given current data and methods and are thus not reflected in our overall benefits estimates.

Valuation of Other Effects

Agricultural Benefits

As discussed earlier in this chapter, tropospheric

ozone affects the growth of a wide range of plant

species, including agricultural crops. Our agricultural benefits analysis relies on crop-yield loss C-R

functions derived from the National Crop Loss Assessment Network (NCLAN) research and a national

economic model of the agricultural sector (AGSIM).

The NCLAN-derived relationships use a sum of

hourly ozone concentration at or above 0.06 ppm

(SUM06) as a measure of ozone exposure for the May

to September ozone season; these exposure estimates

are derived from the ozone air quality modeling results discussed in Chapter 4. Where the C-R functions require a longer time period of ozone concentrations, for example, for winter crops or when the

growing or harvest season for summer crops extends

beyond the end of September, we rely on 1990 monitor data to estimate ozone exposure, conservatively

using the same estimates for both Pre-CAAA and

Post-CAAA scenarios. The NCLAN functions

cover the following crops: corn, cotton, peanuts,

sorghum, soybeans, and winter wheat.

The AGSIM agricultural sector model takes the

yield loss information, incorporates agricultural

price, farm policy, and other data for each year, and

then estimates production levels for each crop and

the economic benefits to consumers and producers

associated with these production levels. The crop

coverage in the AGSIM model includes a wider range

of crops than the NCLAN data inputs, adding barley, oats, hay, rice, and cottonseed. The broader

crop coverage ensures that the model addresses price

and production quantity effects on potential substitute crops that might be related to the effects in the

six NCLAN crops. We estimate economic effects

using a range of C-R outcomes for several crops, to

reflect the variation in ozone sensitivity among the

various crop cultivars. Our central estimate is the

expected value of the range of results that emerge

from the economic model.
Chapter 7: Ecological and Other Welfare Effects

95

Our results indicate significant beneficial effects

of ozone reductions in the agricultural sector. Our

Primary Central estimate of the benefit in 2000 is

$450 million; the annual benefit rises to $550 million in 2010. Our estimated uncertainty around the

Primary Central estimates, however, is very broad.

For example, in 2010, the Primary Low estimate is

$7.1 million, and the Primary High is $1,100 million. The uncertainty range reflects variation in the

ozone response of crop cultivars and uncertainty

about the suitability of alternative crop cultivars for

the soil types and climate conditions in various agricultural regions. See Appendix F for more details

on the methods and results of the C-R functions and

economic modeling for agricultural effects.

Visibility

As outlined in Chapter 4, air pollution impairs

visibility in both residential and recreational settings.

An individualís willingness to pay to avoid reductions in visibility differs in these two settings. Impairments in residential visibility are experienced

throughout an individualís daily life and activities.

Visibility in recreational settings, on the other hand,

is experienced by visitors to areas with notable vistas. For the purposes of this report, we interpret

recreational settings applicable for this category of

effects to include National Parks throughout the

nation. Other recreational settings may also be applicable, for example National Forests, state parks,

or even hiking trails or roadside areas, but a lack of

suitable economic valuation literature to identify

these other areas, as well as a lack of visitation data

in some cases, prevents us from generating estimates

for those recreational vista areas.

We derive a residential visibility valuation function from the Chestnut and Dennis (1997) published

estimates for the Eastern U.S. These estimates are

based on original research conducted by McClelland

et al. (1990) in two Eastern cities (Atlanta and Chicago). Because of technical concerns about the

studyís methodology, however, we calculate a benefits estimate but omit the results from the primary

benefits estimates.7 For recreational visibility, we

derive values from the the Chestnut and Rowe (1989)

study of WTP for visibility in three park regions in

the Western, Southwestern, and Eastern U.S.8 In

both cases, the valuation function takes the following form:

HHWTP = B * ln(VR1/VR2)

where:

HHWTP = annual WTP per household for

visibility changes

VR1 = the starting annual average visual

range

VR2 = the annual average visual range after

the change in air quality

B = the estimated visibility coefficient.

The form of this valuation function is designed

to reflect the way individuals perceive and express

value for changes in visibility. In general terms, expressed WTP for visibility changes varies with the

percentage change in visual range, a measure that is

closely related to, though not exactly analogous to,

the Deciview index used in Chapter 4. We use a

central B coefficient for residential visibility of 141,

as reported in Chestnut and Dennis (1997). For recreational visibility, the coefficients vary based on the

region of study and whether the household is within

or outside of the National Park region studied. Inregion coefficients are higher than those for out-ofregion households. The in-region estimates for California, the Southwest, and Southeast are $105, $137,

and $65, respectively; the corresponding out-of-region estimates are $73, $110, and $40, respectively.

The derivation and application of these valuation

functions are described in more detail in Appendix

H. The results of this procedure suggest visibility is

an important category of CAAA benefits; the Primary Central estimate for 2010, for example, indicates annual recreational visibility benefits of $2.9

billion.

Worker Productivity

We base the valuation of worker productivity

on a study that measures the decline in worker pro8 The visibility valuation function, and the sources of estimates for the coefficients for the functions, were originally developed as part of the National Acid Precipitation Assessment

Program (NAPAP), and were subjected to peer-review as part

of that program.

7 The two technical concerns involve the method of adjusting the contingent valuation survey results for non-response, and

the failure to include adjustments for the ìwarm glowî effect,

or the tendency of respondents to indicate higher willingness to

pay for an environmental good because of a strong desire to

improve the environment in general.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

96

ductivity among outdoor farm workers exposed to

ozone (Crocker and Horst, 1981). In our analysis,

we estimate the value of reduced productivity at $1

per 10 percent increase in ozone concentration. This

estimate reflects valuing reduced productivity in

terms of the reduction in percentage of daily income

incurred by the average worker engaged in strenuous outdoor labor.

Stratospheric Ozone Provisions

The quantified benefits of stratospheric ozone

protection provisions are dominated by the reduced

health effects expected from reductions in UV-b radiation; the derivation of health benefits of these

provisions is discussed in Chapter 5. We summarize

other categories of benefits associated with reduced

UV-b radiation in Table 7-9. The quantified benefits include: reduced crop damage; and reduced polymer degradation. To estimate crop damage, we apply the results of existing studies on the relationship

between crops and UV-b radiation to the changes in

UV-b radiation predicted by the emissions and atmospheric models.9 The polymer damage function

is based on a study by Horst (1986). The estimated

total cumulative benefits associated with these ecological and other welfare effects are about 2 percent

of the total cumulative benefits of the Title VI provisions.

9

 Sources of dose-response relationship for crops and UVb: Teramura and Murali (1986) and Rowe and Adams (1987).

Source of dose-response relationship for crops and tropospheric

ozone: Rowe and Adams (1987).

Table 7-9

Quantified and Unquantified Ecological and Welfare Effects of Title VI Provisions

Ecological Effects- Quantified Estimate Basis for Estimate

American crop harvests Avoided 7.5 percent decrease

from UV-b radiation by 2075

Dose-response sources: Teramura and Murali

(1986), Rowe and Adams (1987)

American crop harvests Avoided decrease from

tropospheric ozone

Estimate of increase in tropospheric ozone:

Whitten and Gery (1986). Dose-response

source: Rowe and Adams (1987)

Polymers Avoided damage to materials

from UV-b radiation

Source of UV-b/stabilizer relationship: Horst

(1986)

Ecological Effects- Unquantified

Ecological effects of UV. For example, benefits relating to the following:

• recreational fishing

• forests

• marine ecosystem and fish harvests

• avoided sea level rise, including avoided beach erosion, loss of coastal wetlands, salinity of estuaries

and aquifers

• other crops

• other plant species

• fish harvests

Ecological benefits of reduced tropospheric ozone relating to the overall marine ecosystem, forests, man-made

materials, crops, other plant species, and fish harvests

Benefits to people and the environment outside the U.S.

Notes:

1) For more detail see EPA’s Regulatory Impact Analysis: Protection of Stratospheric Ozone (1988).

2) Note that the ecological effects, unlike the health effects, do not reflect the accelerated reduction and phaseout schedule

of section 606.

3) Benefits due to the section 606 methyl bromide phaseout are not included in the benefits total because the EPA provides

neither annual incidence estimates nor a monetary value.
Chapter 7: Ecological and Other Welfare Effects

97

Summary of Quantitative

Results

Although the effects of air pollutants on ecological systems are likely to be widespread, many

effects may be poorly understood and lack quantitative effects characterization methods and supporting data. In addition, many of our quantitative results reflect an incomplete geographic scope of analysis; for example, we generated monetized acidification results only for the Adirondacks region of New

York State. As a result, the quantitative results we

generate for the purposes of estimating the benefits

of the CAAA reflect only a small portion of the overall impacts of air pollution on ecological systems or

ecological service flows.

Despite these limitations, it is important to recognize the magnitude of the monetized ecological

benefits that we could estimate and reflect those results in the overall estimates of benefits generated

in the larger analysis. Table 7-10 provides a tabular

summary of the results documented earlier in this

chapter. It is not possible to indicate the degree to

which ecological benefits are underestimated, but

considering the magnitude of benefits estimated for

the select endpoints considered in our analysis, it is

reasonable to conclude that a comprehensive benefits assessment would yield substantially greater total

benefits estimates.

In Table 7-11 we provide a summary of benefits

estimates for other welfare effects, including reduced

agricultural yields, impaired visibility, and decreased

Table 7-10

Summary of Evaluated Ecological Benefits (millions 1990$)

Description

of Effect

Air

Pollutant

Geographic

Scale of

Economic

Estimate

Range of

Annual Impact

Estimates in

2010

Primary

Central

Estimate

for 2010

Primary Central

Cumulative

Impact Estimate

1990-2010 Key Limitations

Freshwater

acidification

Sulfur and

nitrogen

oxides

Regional

(Adirondacks)

$12 to $88 $50 $260 - Captures only

recreational fishing

impact

- Incomplete

geographic coverage

leads to underestimate

of benefits

Reduced tree

growth - Lost

commercial

timber

Ozone National $190 to $1000 $600 $1,900 - Uncertainties in

stand-level response to

ozone exposure

 - Uncertainty in future

timber markets

TOTAL MONETIZED

ECONOMIC BENEFIT

$200 to $1,100 $650 $2,200 - Partial estimate that

omits major

unquantifiable benefits

categories; see text

Note: Estimates reflect only those benefits categories for which quantitative economic analysis was supported. A

comprehensive total economic benefit estimate would likely greatly exceed the estimates in the table. Range of

estimates for timber assessment is based on variation in annual point estimates for 2005 through 2010.

Table 7-11

Summary of Other Welfare Benefits (millions 1990$)

Description

of Effect

Air

Pollutant

Geographic

Scale of

Economic

Estimate

Primary Central

Annual Estimate

2000 2010

Primary Central

Cumulative

Estimate

1990-2010 Key Limitations

Reduced

Agricultural

Yields

Ozone National $450 $550 $3,900 - Covers only major grain crops

- Omits effects on fruits and

vegetables

Impaired

Recreational

Visibility

Particulate

Matter

National $2,000 $2,900 $19,000 - National Parks only

- Omits residential visibility

benefits

Reduced

Worker

Productivity

Ozone National $460 $710 $4,400 - Reflects effects on workers

engaged in strenuous outdoor

employment

Note: Estimates reflect only those benefits categories for which quantitative economic analysis was supported.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

98

worker productivity. These estimates add substantially to the total non-health benefits of the CAAA.

In particular, our estimates for the annual value of

avoiding visibility impairments is $2,900 million by

2010, even through this estimate does not reflect the

value of residential visibility improvements.

Uncertainty

Because of the limitations in the available methods and data, the benefits assessment in this report

does not represent a comprehensive estimate of the

economic benefits of the CAAA. Moreover, the

potential magnitude of long-term economic impacts

of ecological damages mitigated by the CAAA suggests that great care must be taken to consider those

ecosystem impacts that are not quantified here. Significant future analytical work and basic ecological

and economic research must be performed to build

a sufficient base of knowledge and data to support

an adequate assessment of ecological benefits. For

the current analysis, this incomplete coverage of

effects represents the greatest source of uncertainty

in the ecological assessment. This and other key

uncertainties are summarized in Table 7-12.

Because the chronic ecological effects of air pollutants may be poorly understood, difficult to observe, or difficult to discern from other influences

on dynamic ecosystems, our analysis focuses on acute

or readily observable impacts. Disruptions that may

seem inconsequential in the short-term, however, can

have hidden, long-term effects through a series of

interrelationships that can be difficult or impossible

to observe, quantify, and model. This factor suggests that many of our qualitative and quantitative

results may underestimate the overall, long-term effects of pollutants on ecological systems and resources.

Table 7-12

Key Uncertainties Associated with Ecological Effects Estimation

Potential Source of Error

Direction of

Potential

Bias for Net

Benefits

Estimate

Likely Significance Relative to Key Uncertainties in Net

Benefit Estimate*

Incomplete coverage of

ecological effects

identified in existing

literature, including the

inability to adequately

discern the role of air

pollution in multiple

stressor effects on

ecosystems.

Underestimate Potentially major. The extent of unquantified and unmonetized

benefits is largely unknown, but the available evidence suggests

the impact of air pollutants on ecological systems may be

widespread and significant. At the same time, it is possible that a

complete quantification of effects might yield economic valuation

results that remain small in comparison to the total magnitude of

health benefits.

Omission of the effects of

nitrogen deposition as a

nutrient with beneficial

effects.

Overestimate Probably minor. Although nitrogen does have beneficial effects

as a nutrient in a wide range of ecological systems, nitrogen in

excess also has significant and in some cases persistent

detrimental effects that are also not adequately reflected in the

analysis.

Incomplete assessment of

long-term bioaccumulative

and persistent effects of

air pollutants.

Underestimate Potentially major. Little is currently known about the longer-term

effects associated with the accumulation of toxins in ecosystems,

but what is known suggests the potential for major impacts.

Future research into the potential for threshold effects is

necessary to establish the ultimate significance of this factor.

The PnET II modeling of

the effects of ozone on

timber yields relies on a

simplified mechanism of

response (i.e., changes in

net primary productivity).

Overestimate Probably minor. Existing evidence suggests that the growth

changes PnET II projects are relatively large, however none of

the currently available points of conparison fully address such

issues as the impact of stand-level competition, and the net

primary productivity results are within the range of results of other

studies of environmental and anthropogenic stressors.

*The classification of each potential source of error reflects the best judgement of the section 812 Project Team. The Project

Team assigns a classification of "potentially major" if a plausible alternative assumption or approach could influence the

overall monetary benefit estimate by approximately five percent or more; if an alternative assumption or approach is likely to

change the total benefit estimate by less than five percent, the Project Team assigns a classification of "probably minor."
Chapter 8: Comparison of Costs and Benefits

99

Comparison of

Costs and Benefits

In this chapter we present our summary of the

Chapter

8

primary estimates of monetized benefits of the

CAAA from 1990 to 2010, compare the benefits estimates with the corresponding costs, and explore

some of the major sources of uncertainty in the benefits estimates. We also present the results of our

calculations using alternative assumptions for several key input variables.

Monetized Benefits of the

CAAA

In this section we provide an overview of the

three types of analyses conducted to estimate benefits, present the annual estimates of monetized benefits for the human health, ecological, and welfare

analyses, and then present an aggregate measure of

benefits from all titles of the CAAA for the full study

period.

Overview of Benefits Analyses

Our primary estimates of the monetized economic benefits for the 1990 to 2010 period derive

from three distinct analyses: (1) the analysis of

changes in human health effects associated with reduced exposures to criteria pollutants and the subsequent valuation of these changes, summarized and

described in Chapters 5 and 6; (2) the analysis of

monetized ecological and other welfare benefits (e.g.,

visibility), described in Chapter 7; and (3) the analysis of the benefits of stratospheric ozone protection

provisions, summarized briefly in Chapters 5, 6, and

7 and described in detail in Appendix G.

We measure the benefits and present the results

from each of these analyses in slightly different ways.

For the first two analyses, we generate annual estimates of benefits that result from changes in exposures in two target years of the study, 2000 and 2010.

These estimates can be directly compared to the estimates of costs incurred in the target years, because

for the most part the annual benefits accrue in the

same year as the costs are incurred. There is one

exception, however: we model the effect of particulate matter on premature mortality to occur over a

period of five years from the time of exposure. In

this case, we have accounted for the incidence of

premature mortality over the assumed lag period,

and discounted the valuation of this effect back to

the target year.

The annual estimates provide an indication of

the trend in benefits accrued over the 20-year study

period. To generate a cumulative measure of benefits over the full 20-year period, we must make an

assumption about the level of benefits that would

be realized in the years between the target years. We

interpolate these values, assuming a linear trend in

both costs and benefits over the 1990 to 2000 and

2000 to 2010 periods (assuming benefits and costs in

the starting year, 1990, are zero). In one portion of

the ecological benefits analysis, acidification, we generate only a single annual estimate for the target year

2010. In that case, we assume a linear trend in annual benefits over the full 20-year study period.

The third analysis, assessing changes in stratospheric ozone and the resulting health effects, is different from the criteria pollutant analyses. The longterm nature of the program, and the significant lag

effects associated with the processes of ozone depletion over decades-long time scales, make it difficult

to generate a meaningful estimate for any single target year. As a result, we could not generate an annual benefit estimate that could be reliably linked to

emissions reductions in a single year and, by extension, compared to the costs incurred to achieve that

yearís allocation of reductions in stratospheric ozone

depleting substances. Instead, we generate an annualized equivalent of the cumulative present value of
The Benefits and Costs of the Clean Air Act, 1990 to 2010

100

benefits and costs of the Title VI program. These

annualized equivalents cannot be ascribed to any

particular target year.

These fundamental differences in the measurement of benefits affect our presentation of benefits

estimates in this chapter. Although we generate and

report an annual estimate of costs and benefits of

Title VI provisions, we encourage the reader to interpret aggregations of these annual estimates with

those from other titles of the CAAA with caution.

In particular, we discourage the use of these CAAA

Title-specific benefit-cost ratios as the sole, or even

primary, basis for comparing the relative economic

value of Title VI versus other CAAA titles. The

comparative benefit-cost ratios are too sensitive to

important, highly uncertain analytical assumptions

such as the discount rate.

Summary of Monetized Benefits for

Human Health and Welfare Effects

As discussed above, we generate annual estimates

for the human health and welfare effects based on

exposure analysis conducted for each of the two target years of the analysis, 2000 and 2010. The range

of estimates we generate for the monetized benefits

of human health effects incorporates both the quantified uncertainty associated with each of the health

effect estimates and the quantified uncertainty associated with the corresponding economic valuation

strategy. Quantitative estimates of uncertainties in

earlier steps of the analysis (i.e., emissions and air

quality changes) could not be developed adequately

and are therefore not applied in the present study.

As a result, the range of estimates for monetized benefits presented in this chapter is more narrow than

would be expected with a complete accounting of

the uncertainties in all analytical components. The

characterization of the uncertainty surrounding economic valuation is discussed in detail in Appendix

H. The characterization of the uncertainty surrounding specific health effect estimates is discussed in

Appendix D. Below, we discuss the combined effect of these two categories of uncertainty and our

techniques for aggregating uncertainty across endpoints and analyses.

We assume that for each endpoint-pollutant combination there are distributions for both the concentration-response function and the valuation coefficients. We combine these distributions by using

a computerized, statistical aggregation technique to

estimate the mean of the monetized benefit estimate

for each endpoint-pollutant combination and to characterize the uncertainty surrounding each estimate.1

In the first step of our procedure, we employ

statistical analysis to generate mean estimates and

quantified uncertainty measures for the C-R function for each endpoint-pollutant combination. For

many health and welfare effects, only a single study

is available to use as the basis for the C-R function.

In this case, the best estimate of the mean of the

distribution of C-R coefficients is the reported estimate in the study. The uncertainty surrounding the

estimate of the mean C-R coefficient is characterized by the standard error of the reported estimate.

This yields a normal distribution, centered at the

reported estimate of the mean. If multiple studies

are considered for a given C-R function, a normal

distribution is derived for each study, centered at

the mean estimate reported in the study. On each

iteration of the aggregation procedure, a C-R coefficient is selected from an aggregate distribution of CR estimates for that endpoint. The aggregate distribution of C-R coefficients is determined by a variance-weighted aggregate distribution of values.

In the second step, we estimate incidence for each

exposure analysis unit (i.e., 8 km by 8 km cell in a

grid pattern) in the 48 contiguous states, and aggregate the results into an estimate of the change in

national incidence of the health or welfare effects.

Through repeated iterations from the distribution

of mean C-R coefficients, we generate a distribution

of the estimated change in incidence for each health

and welfare effect due to the change in air quality

between the Post-CAAA and Pre-CAAA scenarios.

Finally, in the third step we use computerized

statistical aggregation methods once again to charac1 The statistical aggregation technique applied is commonly

referred to as simulation modeling. The technique involves many

re-calculations of results, using different combinations of input

parameters each time. For each calculation, values from each

input parameterís statistical distribution are selected at random

to ensure that the calculation does not always result in extreme

values, or rely solely on low end or solely on high end input

parameters. The aggregate distribution more accurately reflects

a reasonable likelihood of the joint occurrence of multiple input parameters.
Chapter 8: Comparison of Costs and Benefits

101

terize the overall uncertainty surrounding monetized

benefits. For each distinct health and welfare effect,

the aggregation procedure selects an estimated incidence change from the distribution of changes for

that endpoint, selects a unit value from the corresponding distribution of economic valuation unit

values, and multiplies the two to generate a monetized benefit estimate. We then repeat the process

many times to generate a distribution of estimated

monetized benefits for each endpoint-pollutant combination. Combining the results for the individual

endpoints using the aggregation procedure yields a

distribution of total estimated monetized benefits for

each target year (2000 and 2010).2 We present the

results of this analysis of health effects in Table 6-3

in Chapter 6.

The ecological and welfare results are not currently amenable to the same type of uncertainty

analysis. The modeling procedures for estimating

the effects of sulfur and nitrogen deposition in acidifying lakes, the effects of ozone in reducing timber

and agricultural production, and the effects of particular matter on visibility are all subject to uncertainty and require substantial resources simply to

develop single estimates. We describe key uncertainties in Chapter 7 and they are reflected in the

ranges of values we present at the end of that chapter. The sources of uncertainty in these estimates,

however, cannot as easily be disaggregated among physical effects modeling and valuation

components. The endpoints of

the ranges we present reflect reasonable alternative choices in

key input variables, but the

ranges cannot currently be interpreted as points on a statistical

distribution of results. For these

ecological effects, the central estimate is the midpoint of the

ranges of values. We then interpret the endpoints of the range

of estimates as the upper and

lower bounds of a uniform distribution of values. The uniform distribution is used when we aggregate the ecological and other welfare effects analyses with the

analyses of human health.

Annual Benefits Estimates

We present the results of our aggregation of primary annual benefits estimates for Titles I through

V in Figure 8-1 below. The figure provides a characterization of both the primary central estimate and

the range of values generated by the aggregation

procedure described above, for each of the two target years of the analysis (2000 and 2010). The Primary High estimate corresponds to the 95th percentile value from the aggregation, and the Primary Low

estimate corresponds to the 5th percentile value. The

total benefits estimates are substantial; the Primary

Central estimate in 2010 is $110 billion.

Table 8-1 shows the detailed breakdown of benefits estimates for one of the two target years, 2010.

As shown in the table, $100 billion of the $110 billion total benefit estimate in 2010, or roughly 90

percent, is attributable to reductions in premature

mortality associated with reductions in ambient particulate matter and associated criteria pollutants. The

remaining benefits are divided among two broad

categories of benefits: avoided morbidity, the largest component of which is avoided chronic bron2 This procedure implicitly assumes independence between

the specific aggregation simulation draws from the distribution

of health and economic valuation estimates.

Figure 8-1

Central, Low, and High Primary Benefits Results for

Target Years (in billions of 1990 dollars) - Titles I through V

0

50

100

150

200

250

300

2000 2010

High 160

Central 71

Low 16

High 270

Central 110

Low 26

Total Benefits ($Billions)

Benefits

Benefits
The Benefits and Costs of the Clean Air Act, 1990 to 2010

102

Table 8-1

Criteria Pollutant Health and Welfare Benefits in 2010

Monetary Benefits (in millions 1990$)*

Benefits Category Primary Low

Primary

Central Primary High

Mortality

Ages 30+ 14,000 100,000 250,000

Chronic Illness

Chronic Bronchitis 360 5,600 18,000

Chronic Asthma 40 180 300

Hospitalization

All Respiratory 76 130 200

Total Cardiovascular 93 390 960

Asthma-Related ER Visits 0.1 1.0 2.8

Minor Illness

Acute Bronchitis 0.0 2.1 5.2

URS 4.2 19 39

LRS 2.2 6.2 12

Respiratory Illness 0.9 6.3 15

Mod/Worse Asthma1 1.9 13 29

Asthma Attacks1 20 55 100

Chest Tightness, Shortness of

Breath, or Wheeze

0.0 0.6 3.1

Shortness of Breath 0.0 0.5 1.2

Work Loss Days 300 340 380

MRAD/Any-of-19 680 1,200 1,800

Welfare

Decreased Worker

Productivity

710 710 710

Visibility - Recreational 2,500 2,900 3,300

Agriculture (Net Surplus) 7.1 550 1,100

Acidification 12 50 76

Commercial Timber 180 600 1,000

Aggregate Range of Benefits2 26,000 110,000 270,000

Note:

* The estimates reflect air quality results for the entire population in the US.

1

 Moderate to worse asthma, asthma attacks, and shortness of breath are endpoints

included in the definition of MRAD/Any of 19 respiratory effects. Although valuation

estimates are presented for these categories, the values are not included in total benefits to

avoid the potential for double-counting.

2

 The Aggregate Range reflects the 5th, mean, and 95th percentile of the estimated credible

range of monetary benefits based on quantified uncertainty, as discussed in the text.
Chapter 8: Comparison of Costs and Benefits

103

chitis, comprises about 60 percent of the non-mortality benefits; and avoided ecological and other welfare effects, the largest component of which is improved recreational visibility, comprises about 40

percent. Note that, because of the aggregation procedure used, and because we round all intermediate

results to two significant digits for presentation purposes, the columns of Table 8-1 may not sum to the

total estimate presented in the last row.3

Aggregate Monetized Benefits

As discussed earlier in this chapter, we linearly

interpolate benefit estimates between 1990 and 2000

and between 2000 and 2010 and then aggregate the

resulting annual estimates across the entire 1990 to

2010 period of the study to yield a present discounted

value of total aggregate benefits for the period. In

this section we discuss issues involved in each stage

of aggregation, as well as the results of the aggregation.

As noted earlier, air quality modeling was carried out only for the two target years (2000 and 2010).

The resulting annual benefit estimates provide a temporal trend of monetized benefits across the period

resulting from the annual changes in air quality.

They do not, however, characterize the uncertainty

associated with the yearly estimates for intervening

years. In an attempt to capture uncertainty associated with these estimates, we relied on the ratios of

the 5th percentile to the mean and the 95th percentile to the mean in the two target years. In general,

these ratios were fairly constant across the target

years, for a given endpoint. The ratios were interpolated between the target years, yielding ratios for

the intervening years. Multiplying the ratios for each

intervening year by the central estimate generated

for that year provided estimates of the 5th and 95th

percentiles, which we use to characterize uncertainty

about the Primary Central estimate.

In Table 8-2 we present the cumulative monetized benefits aggregated from 1990 to 2010. We

present the mean estimate from the aggregation procedure, along with the Primary Low (i.e., 5th percentile of the distribution) and Primary High (i.e.,

95th percentile of the distribution) estimates, for all

provisions of Titles I through V and, then, separately

for Title VI. Aggregating the stream of monetized

benefits across years involved discounting the stream

of monetized benefits estimated for each year to the

1990 present value (using a five percent discount rate).

Aggregate Benefits of Title VI Provisions

As described in summary form in Chapters 5, 6,

and 7 and in detail in Appendix G, expected human

health benefits from Title VI provisions are substantial. The analysis we conducted is based largely on

existing results from EPA Regulatory Impact Analyses for individual rules promulgated under Title VI.

To the extent possible, we adjusted existing estimates

to reflect both the central estimates and uncertainty

characterizations used in the criteria pollutant analysis. We made major adjustments for both the value

of statistical life (VSL) and the discount rate. We

adjusted the VSL estimate to reflect the Weibull distribution of VSL used in our analysis for other provisions. As discussed in the appendix, the choice of

the discount rate for estimated benefits which accrue over decades to century-long time spans presents special problems. Although we argue that a

two percent discount rate is more appropriate for

such long-term discounting, for consistency in this

chapter we present estimates using the five percent

discount rate used throughout the rest of this study.

3

 The sum of benefits across endpoints at a given percentile

level does not result in the total monetized benefits estimate at

the same percentile level in Table 8-1. For example, if the fifth

percentile benefits of the endpoints shown in Table 8-1 were

added, the resulting total would be substantially less than $30

billion, the fifth percentile value of the distribution of aggregate

monetized benefits reported in Table 8-1. This is because the

various health and welfare effects are treated as stochastically

independent, so that the probability that the aggregate monetized

benefit is less than or equal to the sum of the separate five percentile values is substantially less than five percent.

Table 8-2

Present Value of Monetized Benefits for 48 State Population

Present Value (millions 1990$, discounted to 1990 at 5 percent)

Primary Low Primary Central Primary High

Titles I through V (1990 through 2010) $160,000 $690,000 $1,600,000

Title VI (1990 through 2165) $100,000 $530,000 $900,000
The Benefits and Costs of the Clean Air Act, 1990 to 2010

104

Table 8-3

Summary of Quantified Primary Central Estimate Benefits and Costs

(Estimates in million 1990$)

Cost or Benefit Annual Estimates

Category 2000 2010 Present Value

Costs:

Title I $8,600 $14,500 $85,000

Title II $7,400 $9,000 $65,000

Title III $780 $840 $6,600

Title IV $2,300 $2,000 $18,000

Title V $300 $300 $2,500

Total Costs, Title I-V $19,000 $27,000 $180,000

Title VI $1,400* $27,000*

Monetized Benefits:

Avoided Mortality $63,000 $100,000 $610,000

Avoided Morbidity $5,100 $7,900 $49,000

Ecological and

Welfare Effects

$3,000 $4,800 $29,000

Total Benefits, Title I-V $71,000 $110,000 $690,000

Stratospheric Ozone $25,000* $530,000*

* Annual estimates for Title VI stratospheric ozone protection provisions are annualized

equivalents of the net present value of costs over 1990 to 2075 (for costs) or 1990 to 2165

(for benefits). The difference in time scales for costs and benefits reflects the persistence of

ozone depleting substances in the atmosphere, the slow processes of ozone formation and

depletion, and the accumulation of physical effects in response to elevated UV-b radiation

levels.

The results of the benefits calculations in Appendix G indicate a cumulative central benefit estimate of $530 billion for Title VI (see Appendix G

for details). Using the same aggregation techniques

for the valuation analysis described above, but only

for the mortality valuation step, we generate a 90

percent confidence interval around this central estimate to derive Primary Low and Primary High estimates of $100 billion to $900 billion, respectively.

We present these estimates in Table 8-2 above. The

annual human health benefits from Title VI provisions steadily increase until about 2045, then decrease

until 2165, the last year in the analysis. About 93

percent of the benefits accrue from 2015 to 2165.

These benefit estimates only partially reflect potential averting behaviors, such as remaining indoors

or increasing use of sun screens or hats, which may

mitigate the effects of the UV-b exposure increases

estimated under the Pre-CAAA scenario.

Comparison of Monetized

Benefits and Costs

Table 8-3 presents summary quantitative results

for the prospective assessment, with costs disaggregated by Title and benefits disaggregated by major

category. We present annual,

primary estimate results for

each of the two target years of

the analysis, with all dollar figures expressed as inflation-adjusted 1990 dollars. The final

columns provide net present

value estimates for costs and

benefits from 1990 to 2010 or,

in the case of stratospheric

ozone protection provisions,

1990 to 2165, discounted to

1990 at five percent. The results indicate that the Primary

Central estimate of benefits

clearly exceeds the costs of the

CAAA, for each of the two

target years and for the cumulative estimates of present

value over the 1990 to 2010 period.

The estimates in Table 8-3

reflect the difficulty we encountered in reliably disaggregating benefits by CAAA Title or even by pollutant. As the table indicates, a very high percentage

of the benefits is attributable to reduced premature

mortality associated with reductions in ambient particulate matter and associated criteria pollutants. The

CAAA achieves ambient PM reductions through a

wide range of provisions controlling emissions of

both gaseous precursors of PM that form particles

in the atmosphere (sulfur and nitrogen oxides as well

as, to a lesser extent, organic constituents) and directly emitted PM (i.e., dust particles). Because the

effects of these constituents on ambient PM are nonlinear, and because some precursor pollutants interact with each other in ways which influence the total concentration of particulates in the atmosphere,

separating the effects of individual pollutants on the

change in ambient PM would require many iterations of our air quality modeling system. These difficulties in separating the effects of individual emissions reductions on the benefits estimates also highlight the need for an integrated air quality modeling

system that can more readily analyze multiple scenarios within reasonable time and resource constraints. A tool of this nature could allow us to more

reliably and cost-effectively estimate incremental

contributions to ambient PM and ozone concentration reductions.
Chapter 8: Comparison of Costs and Benefits

105

Table 8-4

Summary Comparison of Benefits and Costs (Estimates in millions 1990$)

Titles I through V Title VI All Titles

Annual Estimates

Present Value

Estimate

2000 2010 1990-2010

Present Value

Estimate

1990-2165

Total Present

Value

Monetized Direct Costs:

Low a Not Estimated

Central $19,000 $27,000 $180,000 $27,000 $210,000

High a Not Estimated

Monetized Direct Benefits:

Lowb $16,000 $26,000 $160,000 $100,000 $260,000

Central $71,000 $110,000 $690,000 $530,000 $1,200,000

Highb $160,000 $270,000 $1,600,000 $900,000 $2,500,000

Net Benefits:

Low ($3,000) ($1,000) ($20,000) $73,000 $50,000

Central $52,000 $93,000 $510,000 $500,000 $1,000,000

High $140,000 $240,000 $1,400,000 $870,000 $2,300,000

Benefit/Cost Ratio:

Lowc less than 1/1 less than 1/1 less than 1/1 less than 4/1 1/1

Central 4/1 4/1 4/1 20/1 6/1

Highc more than 8/1 more than 10/1 more than 9/1 more than 33/1 12/1

a The cost estimates for this analysis are based on assumptions about future changes in factors such as

consumption patterns, input costs, and technological innovation. We recognize that these assumptions introduce

significant uncertainty into the cost results; however the degree of uncertainty or bias associated with many of the

key factors cannot be reliably quantified. Thus, we are unable to present specific low and high cost estimates.

b Low and high benefits estimates are based on primary results and correspond to 5th and 95th percentile results

from statistical uncertainty analysis, incorporating uncertainties in physical effects and valuation steps of benefits

analysis. Other significant sources of uncertainty not reflected include the value of unquantified or unmonetized

benefits that are not captured in the primary estimates and uncertainties in emissions and air quality modeling.

c

 The low benefit/cost ratio reflects the ratio of the low benefits estimate to the central costs estimate, while the high

ratio reflects the ratio of the high benefits estimate to the central costs estimate. Because we were unable to reliably

quantify the uncertainty in cost estimates, we present the low estimate as "less than X," and the high estimate as

"more than Y", where X and Y are the low and high benefit/cost ratios, respectively.

Table 8-4 provides the results of our comparison of primary benefits estimates to primary cost

estimates. In the top half of the table we show both

annual and present value estimates for Titles I

through V, present value estimates for Title VI, and

a total present value for all titles. The ìmonetized

benefitsî indicate both the Primary Central estimate

(the mean) from our statistical aggregation modeling analysis and the Primary Low and Primary High

estimates (5th and 95th percentile values, respectively). In the bottom half of the table we present

two alternative methods for comparing benefits to

costs. ìNet benefitsî are the Primary Central estimates of monetized benefits less the Primary Central estimates of costs. The table also notes the benefit/cost ratios implied by the benefit ranges.

The conclusion we draw from Table 8-4 is that,

given the particular data, models and assumptions

we believe are most appropriate at this time, our

analysis indicates that the benefits of the CAAA substantially exceed its costs. Furthermore, the results

of the uncertainty analysis imply that it is extremely

unlikely that the monetized benefits of the CAAA

over the 1990 to 2010 period could be less than its

costs. Looking at Titles I through V, the central

benefits estimate exceeds costs by a factor of four to

one, whether we are looking at annual or present

value measures, and the high estimate exceeds costs

by more than twice that factor (a ratio of nine or ten

to one). Using the Primary Low estimate of benefits, the annual estimates of benefits in 2000 and

2010 are slightly less than the annual costs for that

year. The data also suggest that costs for criteria
The Benefits and Costs of the Clean Air Act, 1990 to 2010

106

pollutant programs grow somewhat more rapidly

than benefits from 1990 to 2000, but that benefits

grow more rapidly from 2000 to 2010.

The estimates for Title VI indicate that benefits

well exceed costs, even at the low benefits estimate.

This conclusion holds despite the relatively high discount rate used for the estimates in Table 8-4 (5 percent) a value that most analysts would consider too

high for the long time period over which benefits of

this program are discounted (175 years).4 The total

estimates for all titles of the CAAA also indicate benefits in excess of costs for the full range of primary

benefits.

Cost-Effectiveness Evaluation

The approach to premature mortality valuation

used in our primary estimates is a method that allows us to aggregate the benefits of reducing mortality risks with other monetized benefits of the CAAA.

One of the great advantages of the benefit-cost paradigm is that a wide range of quantifiable benefits can

be compared to costs to evaluate the economic efficiency of particular actions. Some analysts suggest,

however, that presentation of the results of a costbenefit analysis may mask the key assumptions that

are made to quantify all benefits in monetary terms.

Another evaluative paradigm, cost-effectiveness

analysis, is sometimes suggested as further evidence

of whether the benefits of a regulatory program justify its costs. Cost-effectiveness analysis involves estimation of the costs per unit of benefit (e.g., lives

saved). This type of analysis is most useful for comparing programs that have similar goals, for example,

alternative medical interventions or treatments that

can save a life or cure a disease. They are less readily

applicable to programs with multiple categories of

benefits, such as the CAAA, because the cost-effectiveness calculation is based on quantity of a single

benefit category. In other words, we cannot readily

convert reductions in new cases of chronic bronchitis, reduced hospital admissions, improvements in

visibility, and increased commercial timber and crop

yields to a single metric such as ìlives saved.î For

these reasons, we prefer to present our results in

terms of monetary benefits.

Despite the risks of oversimplification of benefits, cautiously interpreted cost-effectiveness calculations may provide further evidence of whether the

costs incurred to implement the CAAA are a reasonable investment for the nation. The most common cost-effectiveness metric, costs per life saved,

can be readily calculated from the information presented in this report. For example, we estimate the

total annual direct costs of implementation of Titles

I through V in 2010 to be approximately $27 billion. In exchange for this expenditure, in the year

2010 we avoid 23,000 cases of premature mortality

and gain estimated non-mortality benefits of about

$20 billion. We can generate a net cost per life saved

by subtracting from costs the total non-mortality

benefits, and then dividing by lives saved. For Titles

I through V, we estimate a net cost per life saved of

approximately $300,000 ($27 billion minus $20 billion divided by 23,000).5 Although we are also concerned about many of the uncertain assumptions

required to generate cost per life-year saved estimates,

we include an estimate for illustrative purposes. For

the year 2010, the net cost per life-year saved estimate implied by the primary central case results is

$23,000 per life-year ($7 billion divided by 310,000

life-years saved).6

Major Sources of Uncertainty

We can obtain additional insights into key assumptions and findings of the present study through

further analysis of potentially important variables

and inputs. The estimated uncertainty ranges for

each endpoint category summarized in Table 8-1

reflect the measured uncertainty associated with two

aspects of the analysis: avoided physical effects (both

health and welfare benefits) and economic valuation

of benefits. In addition, in Chapter 3 we conduct

quantitative sensitivity analyses of key components

of the direct cost estimates. For many other aspects

of our analysis, however, including emissions esti4

 The primary central benefit-cost ratio for Title VI using

a 3 percent discount rate is 44 to 1, higher than any of those

presented in Table 8-4 (see Table 8-6 below). In addition, the

ratio using a 2 percent discount rate, the rate used in the underlying RIAs, is 75 to 1. See Appendix G for more detail on the

sensitivity of Title VI benefits to the choice of discount rate.

5

 The illustrative calculations presented here do not reflect

discounting of the physical incidence of mortality.

6

 Because of Agency concerns regarding discounting of

physical effects, the ratio presented here reflects undiscounted

life-years saved. If future years were discounted, the implicit

cost per life-year saved would be significantly higher.
Chapter 8: Comparison of Costs and Benefits

107

mates, air quality modeling, and unquantified categories of benefits, we are unable to conduct quantitative analysis of uncertainty. Instead, we have attempted throughout this report to identify and characterize major sources of uncertainty ó we present

the results of these efforts at the end of Chapters 2

through 7. In this section, we provide a summary

evaluation of the relative importance of key sources

of uncertainty.

Table 8-5 below provides a summary of both

quantified and unquantified sources of uncertainty

and our estimates of the impact of these sources of

uncertainty on the primary central estimates of benefits and costs. The table covers seven major categories of uncertainties: measurement uncertainties in

physical effects and valuation components of the

benefits analysis; measurement uncertainties in estimation of direct costs; alternative assumptions for

PM-related mortality valuation; alternative assumptions for PM-related mortality risk; unquantified

sources of error in emissions and air quality modeling; and omissions of key benefits categories. The

table entries cover quantitative analyses of uncertainty, characterization of unquantified uncertainty,

and the potential effect of alternative modeling paradigms for costs and benefits. Additional treatment

of alternative paradigms is necessary because reasonable people may disagree with our methodological

choices regarding these issues, and these choices

might be considered to significantly influence the

results of the study.

Quantitative Analysis of Physical

Effects and Valuation Uncertainties

As discussed previously in this chapter, we have

conducted quantitative uncertainty analysis of our

benefits estimates to reflect measurement error in

two key steps of the analysis: estimation of physical

effects and economic valuation. We present the results of our analysis in Figure 8-1 and Table 8-1 above.

The procedure used to generate these estimates is

well-suited to analysis of uncertainties where the

probability of alternative outcomes can be quantitatively characterized in an objective manner. For

example, most studies that estimate concentrationresponse relationships report an estimate of the statistical uncertainty around the central estimate. Because many estimates are available for the value of

statistical life, we can use the discrete distribution of

the best available estimates as a basis for quantitatively characterizing the probability of alternative

values. It is important to recognize, however, that

this procedure reflects only a portion of the range of

possible sources of uncertainty in our benefits estimates. Other, nonquantified sources of uncertainty

must also be factored into conclusions about the ratio of benefits to costs.

As part of our analysis of key contributors to

uncertainty in benefits estimates, we also conducted

a sensitivity analysis to determine the physical effects estimation and economic valuation variables

with the greatest contribution to the quantified measurement uncertainty range. We present the results

of this sensitivity analysis in Figure 8-2. In this sen-

$0

$50

$100

$150

$200

$250

$300

All

Components

Mortality

Valuation

Mortality

Incidence

Chronic

Bronchitis

Valuation

Chronic

Bronchitis

Incidence

MRAD/ Any

of 19

Valuation

Visibility

Valuation

MRAD/Any

of 19

Incidence

Total 2010 Benefits (Billions 1990$)

95th %ile

Mean

5th %ile

Figure 8-2

Analysis of Contribution of Key Parameters to Quantified Uncertainty
The Benefits and Costs of the Clean Air Act, 1990 to 2010

108

Table 8-5

Summary of Key Sources of Uncertainty and Their Impact on Costs and Benefits

Impact on Annual Estimates in 2010 Source of

Uncertainty

Description of Alternative Parameter

Inputs Costs Benefits

Measurement

error and

uncertainty in the

physical effects

and economic

valuation steps

Use a range of input assumptions to

reflect statistical measurement

uncertainty in concentration-response

functions, modeling of physical effects,

and estimation of economic values.

Most important input parameters are

value of statistical life and estimated

relationship between particulate matter

and premature mortality (see Chapters

5, 6, and 7).

None For Titles I through V,

effect of the use of

alternative input

assumptions ranges

from a $84 billion

decrease (5th

percentile) to a $160

billion increase (95th

percentile).

Measurement

error and

uncertainty in

direct cost inputs

Use alternative assumptions for key

input parameters for six of the highest

cost provisions. Conduct sensitivity

tests for each provision separately (see

Chapter 3, pages 30 to 32). As

discussed in Chapter 3 and in this

chapter, aggregation of provisionspecific results would be inappropriate.

High estimates for

some provisions are

$1 billion higher

than primary

estimate. Low

estimates are as

much as $2 billion

below primary

estimate

None

Value of

statistical lifebased estimates

do not reflect

age at death

Use estimates of the incremental

number of life-years lost from exposure

to ambient PM and a value of statistical

life-year as opposed to measuring

number of lives lost and a value of

statistical life (see Chapters 5 and 6).

None Decrease by $47

billion

Basis of estimate

of avoided

mortality from

PM exposure

The Dockery et al. study provides an

alternative estimate of the long-term

relationship between chronic PM

exposure and mortality (see Chapter 5).

None Increase by $100 to

$150 billion

Uncertainties in

Title VI health

benefits analysis

Major uncertainties include: estimating

fatal cancer cases resulting from UV-b

exposure; not accounting for future

averting behavior; and not accounting

for future improvements in the early

detection and treatment of melanoma

(see Table 5-6).

None Not quantified, but net

effect is probably that

benefits estimates are

too high.

Uncertainties in

emissions and

air quality steps

Major uncertainties include:

underestimation of PM2.5 emissions;

omission of changes in primary and

organic PM in eastern U.S.; emissions

estimation uncertainties in the western

U.S.; scarcity of PM2.5 monitors; and

lack of a fully integrated air quality and

emissions modeling system (see

Tables 2-5 and 4-7).

Uncertainties in

emissions estimates

affects some costs,

but net effect is

minor.

Not quantified, but net

effect is probably that

benefits estimates are

too low.

Omission of

potentially

important

benefits

categories from

primary estimate

Non-quantified categories of impacts

summarized in Chapters 5 and 7.

Quantified but omitted categories

include household soiling, nitrogen

deposition, and residential visibility (see

Chapter 7).

None Increase by at least $8

billion, (does not

reflect unquantified

categories)
Chapter 8: Comparison of Costs and Benefits

109

sitivity analysis, we hold constant all inputs to the

probabilistic uncertainty analysis except one -- for

example, the economic valuation of mortality. We

allow that one variable to vary across the estimated

range of that variableís uncertainty. The sensitivity

analysis isolates the effect of this single source of uncertainty on the total measured uncertainty in estimated aggregate benefits. The first uncertainty bar

represents the range associated with the total monetized benefits of the Clean Air Act, based on analysis of quantifiable components of uncertainty, as

reported above. This range captures the multiple

measurement uncertainties in the quantified benefits

estimation. The rest of the uncertainty bars represent the quantified measurement uncertainty ranges

generated by single variables. As shown in Figure 8-

2, the most important contributors to aggregate quantified measurement uncertainty are mortality valuation and incidence, followed by chronic bronchitis

valuation and incidence.

Measurement Error and Uncertainty in

Direct Cost Inputs

As noted in Chapter 3, explicit and implicit assumptions about changes in consumption patterns,

input costs, and technological innovation are crucial to estimating the direct compliance costs of the

CAAA. For many of the factors contributing to

uncertainty, the degree and, in some cases, the direction of the bias are unknown or cannot be determined. Uncertainties and sensitivities can be identified, however, and in many cases the potential measurement errors can be quantitatively characterized.

We designed our sensitivity analyses of key input

parameters to provide a sense of the relative importance of various input parameters and assumptions

necessary to generate estimates of direct costs. The

sensitivity tests use ranges of input parameters that

include all reasonable alternative estimates that we

could identify.

The results indicate that the sensitivity of our

primary central cost estimates is not uniform across

provisions. Low and high estimates may vary by as

much as a factor of two. Unlike our quantitative

analysis of benefits, we do not assign probabilities

to the likelihood of alternative input parameters. In

our judgement, assignment of probabilities to these

alternative outcomes would be a largely subjective

task; we know of no objective means to develop these

probabilities. As a result, it would be inappropriate

simply to add up the array of low and the array of

high estimates to arrive at an overall range of uncertainty around the central estimates, because it is unlikely that a plausible scenario could be constructed

where all the estimates are concurrently either at the

high or low end of their individual plausible ranges.

A better interpretation of these results is that uncertainty in key input parameters can have a significant

effect on the overall uncertainty of our estimates of

direct compliance costs and ultimately the net benefits calculation.7

PM Mortality Valuation Based

on Life-Years Lost

The primary analytical results we present earlier in this chapter assign the same economic value

to incidences of premature mortality regardless of

the age and health status of those affected. Although

this has been the traditional practice for benefit-cost

studies conducted within EPA, some argue this may

not be the most appropriate method for valuation

of premature mortality caused by PM exposure.

Some short-term PM exposure studies suggest that a

significantly disproportionate share of PM-related

premature mortality occurs among persons 65 years

of age or older. Combining standard life expectancy

tables with the limited available data on age-specific

incidence allows rough approximations of the number of life-years lost by those who die prematurely

as a result of exposure to PM or, alternatively, the

changes in life expectancy of those who are exposed

to PM.

The ability to estimate, however roughly,

changes in age-specific life expectancy raises the issue of whether available measures of the economic

value of mortality risk reduction can, and should,

be adapted to measure the value of specific numbers

7

 Although the analysis conducted here is a direct cost analysis, other sources of uncertainty would also need to be considered for a social cost analysis. For example, forecasts of key

economic variables (e.g., interest rates), specification of production functions, and the reliability of key supply and demand

elasticities are all important factors in social cost modeling that

contribute to measurement uncertainty. In addition, most current social cost analyses assume that markets are currently operating under optimally efficient conditions. Emerging literature

suggests that a full accounting of the social costs and efficiency

impacts of environmental regulations could also include an assessment of the incremental costs that reflect existing market

distortions, such as those imposed by the current tax code. Our

assessment of uncertainties in direct cost estimates do not reflect these considerations.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

110

of life-years saved.8 As stated in our retrospective

analysis, we have on occasion performed sensitivity

calculations that adjust mortality values for those

over age 65. Nonetheless, as discussed in Appendix

H, the current state of knowledge and available analytical tools do not conclusively support using a lifeyears lost approach or any other approach which

assigns different risk reduction values to people of

different ages or circumstances. While we prefer an

approach which makes no valuation distinctions

based on age or other characteristics of the affected

population, we present alternative results based on

a VSLY approach below. The method used to develop life years lost estimates is described briefly in

Chapter 5 and Appendix D. The method used to

develop VSLY estimates is described in Appendix

H .

The fourth row of Table 8-5 summarizes the effect of using a VSLY approach on results for 2010.

The results indicate that the choice of valuation methodology significantly affects the estimate of the monetized value of reductions in air pollution-related premature mortality. However, the downward adjustment which would result from applying a VSLY approach in lieu of a VSL approach does not change

the basic conclusion of this study, since the central

estimate of monetized benefits of the CAAA still

substantially exceeds the costs of compliance.

We emphasize that the results of the VSLY approach to valuing avoided mortality benefits represent a crude estimate of the value of changes in agespecific life expectancy. These results should be interpreted cautiously, due to the several significant

assumptions required to generate a monetized estimate of life years lost from the relative risks reported

in the Pope et al., 1995 study and the available economic literature. These assumptions include, but

are not limited to: extrapolation of the age distribution of the U.S. population in future years; assumptions about the age-specificity of the relative risk

reported by Pope et al., 1995; assumptions about the

life expectancy of different age groups, adjustment

of the life years lost estimates by an appropriate lag

period (if any); assumptions about the age-specificity of the lag period (if any); derivation of VSLY

estimates from VSL estimates; assumptions about the

variation in VSLY with age; and selection of an appropriate rate at which to discount the lagged estimates of life years lost. Changes in any of these

assumptions could significantly affect the VSLY benefit estimate. For example, if we were to assume no

lag period for PM-related mortality effects instead

of the five-year lag structure described in Chapter 5,

VSLY benefit estimates would increase from $53

billion to $61 billion. The specific assumptions we

used in generating these results are discussed in Appendix H.

PM Mortality Incidence Using

the Dockery Study

As described in Chapter 5, we chose to use the

results of the Pope et al. (1995) study to estimate the

magnitude of the effect of ambient PM exposure on

the incidence of premature mortality. Alternative

estimates do exist in the literature, however. Although we chose the Pope study because of its coverage of the largest number of cities and other technical advantages, the Dockery et al. (1993) study

provides a credible and reasonable alternative to the

Pope study. The Dockery study used a smaller

sample of individuals in fewer U.S. cities than the

Pope study, but it features improved exposure estimates, a slightly broader study population (including adults aged 25 to 30), and a follow-up period

nearly twice as long as that used in the Pope study.

Use of the Dockery study in place of the Pope

study would substantially increase the benefits estimate. As shown in the fifth row of Table 8-5, we

estimate that using the Dockery study estimates

would increase the annual central benefits estimate

by $100 to $150 billion, more than doubling the total annual benefits for Titles I through V and, in

turn, doubling the estimated benefit-cost ratio.

Uncertainties in Title VI

Health Benefits Analysis

As discussed in Chapter 5 and Appendix G,

health benefits such as avoided mortality from melanoma and non-melanoma skin cancers constitute the

majority of monetized benefits resulting from Title

8

 This issue was extensively discussed during the Science

Advisory Board Council review of drafts of the retrospective

study. The Council suggested it would be reasonable and appropriate to show PM mortality benefit estimates based on value

of statistical life-years (VSLY) saved as well as the value of statistical life (VSL) approach traditionally applied by the Agency to

all incidences of premature mortality. Consistent with SAB

Council review advice for the present study, we apply the same

approach in this analysis.
Chapter 8: Comparison of Costs and Benefits

111

VI regulations on stratospheric ozone-depleting

chemicals. Estimates of avoided mortality from skin

cancer due to reduced UV-b exposure between 1990

and 2165 represent over 90 percent of the total health

benefits of Title VI. As a result, uncertainties related to avoided mortality estimation under Title VI

represent key uncertainties for our overall CAAA

benefits estimate. Three main areas of uncertainty

are important for our avoided mortality estimates

for Title VI: dose-response relationships; predicting

averting behavior; and predicting future medical

advancements.

Because the literature on the relationship between exposure to ultraviolet rays and melanoma

and non-melanoma mortality is not as well developed as that for other health effects, the dose-response

functions for both of these endpoints are characterized by significant uncertainty. The association of

UV-b exposure with melanoma is controversial, although studies suggest that sunlight exposure is a

major environmental risk factor for melanoma. If

one assumes that a causal relationship exists between

UV-b rays and melanoma, uncertainty still remains

about three aspects of the nature of the dose-response

relationship. Specifically, the relative contribution

of different wavelengths of light to melanoma development, the critical exposure period (e.g., acute,

intermittent, or chronic), and the existence (and

length) of a latency period between UV exposure

and disease are all unclear. The effect of the first

two uncertainties on our results cannot be determined from available information. If a significant

latency period exists, then the third uncertainty may

indicate that our analysis, which does not include a

latency period, overestimates avoided melanoma

mortality benefits. Because limited data on nonmelanoma mortality precluded the development of

a dose-response function for this endpoint in the

current analysis, our estimate of non-melanoma skin

cancer mortality resulting from UV-b exposure is

calculated indirectly, by assuming the mortality rate

is a fixed percentage of non-melanoma incidence.

New data on the death rate for non-melanoma skin

cancer may significantly influence this mortality estimate.

Our analysis of avoided mortality also does not

incorporate adjustments for future increases in averting behavior (i.e., efforts by individuals to protect

themselves from UV-b radiation ). Our estimates

rely on epidemiological studies that incorporate

averting behavior as currently practiced. However,

if people would react to increased skin cancer risk in

the future by applying sun screen more frequently,

spending more time indoors or otherwise reducing

their UV-b exposure, then our estimate of avoided

mortality would significantly overestimate Title VI

benefits. It is not certain, though, that individuals

will pursue such behavior, and studies show that

those engaging in averting behavior may also alter

their behavior in ways that may increase exposure

or risk, counteracting the benefits of averting behavior. For example, a recent study of young Europeans by Autier et al. (1999) found that the use of

high sun protection factor (SPF) sun screen is associated with increased frequency and duration of sun

exposure.

Finally, our analysis does not adjust estimates of

future mortality for possible advances in medical

technology that could lead to earlier detection and

more effective treatment of melanomas. Such advancements could significantly reduce the expected

future melanoma mortality, and by not adjusting for

such developments, we may be overestimating

avoided melanoma mortality. However, future research may also identify additional adverse human

health outcomes associated with UV exposure that

we have not considered in this analysis, resulting in

an underestimate of Title VI benefits.

Uncertainties in Emissions

and Air Quality Steps

The emissions estimates presented in this analysis are a critical component of the overall analysis.

As the starting point for both costs and benefits, they

provide a consistent basis for evaluating the economic

efficiency of the CAAA. Characterizing emissions

can be very difficult, however, particularly for those

source categories where emissions monitoring data

are sparse or nonexistent. In general, all our emissions estimates are affected by three major sources

of uncertainty: estimation of the base-year inventory, prediction of the growth in pollution-generating activity, and assumptions about future-year controls.

Base-year emissions were estimated using emissions factors that express the relationship between a

particular human/industrial activity and the level of
The Benefits and Costs of the Clean Air Act, 1990 to 2010

112

emissions. The accuracy of base-year emissions estimates varies from pollutant to pollutant, depending

largely on how directly the selected activity and

emissions correlate. We likely estimated 1990 SO2

emissions with the greatest precision. Sulfur dioxide emissions are generated during combustion of

sulfur-containing fuel and are directly related to fuel

sulfur content. In addition, we were able to verify

these estimates through comparison with Continuous Emission Monitoring (CEM) data. As a result,

we were able to accurately estimate SO2

 emissions

using emissions factors based on data on fuel usage

and fuel sulfur content. Nitrogen oxides are also a

product of fuel combustion, allowing us to estimate

emissions of this pollutant using the same general

technique used to estimate SO2

 emissions. However,

the processes involved in the formation of NOx

during combustion are more complicated than those

involved in the formation of SO2

; thus, our NOx

emissions estimates are more variable and less certain than SO2

 estimates.

Volatile organic compounds, like SO2

 and NOx,

are products of fuel combustion; however, these

compounds are also a product of evaporation. To

estimate evaporative emissions of this pollutant we

used emissions factors that relate changes in emissions to changes in temperature. Because future

meteorological conditions are difficult to predict,

the uncertainty associated with forecasting temperature influences the uncertainty in our VOC emissions estimates. The likely significance of this uncertainty, in terms of its impact on the overall monetary benefit present in this analysis, is probably

minor.

Of particular importance, however, are uncertainties that affect the estimation of future year emissions of particulate matter and secondarily formed

PM precursors. In this analysis we estimated primary

PM2.5 emissions based on unit emissions that may

not accurately reflect the composition and mobility

of particles. The ratio of crustal to carbonaceous

particulate material, for example, likely is high as a

result of overestimation of the fraction of crustal

material, primarily composed of fugitive dust, and

underestimation of the fraction of carbonaceous

material. Because the CAAA have a greater impact

on emissions sources that generate carbonaceous particles (mobile sources) than on sources that mainly

emit crustal material (area sources), we likely underestimate the impact of the CAAA on reducing PM2.5,

thereby reducing monetary benefits estimates. The

uncertainty associated with estimating the partition

of PM2.5 emissions components could conceivably

have a major impact on the net benefit estimate.

Compared to secondary PM2.5 precursor emissions,

however, changes in primary PM2.5 emissions have a

relatively small impact on PM2.5 related benefits..

Our future-year control assumptions are also a

source of uncertainty. Despite our efforts to minimize this uncertainty, whether each of the PostCAAA controls will be adopted, whether PostCAAA control programs will be more or less effective than estimated, and whether unanticipated technological shifts will reduce future-year emissions are

all unknown. For example, the Post-CAAA scenario

includes implementation of a region-wide NOx

 control strategy designed to regulate the regional transport of ozone. However, the control program assumed under the Post-CAAA scenario may not reflect the NOx

 controls that are actually implemented

in a regional ozone transport rule.

In addition to potential inaccuracies in the emissions inventories used as air quality modeling inputs,

there are at least three sources of air quality modeling uncertainty that may have a major effect on the

precision and accuracy of our projected changes in

air quality. First, we estimate changes in PM concentrations in the eastern U.S. based exclusively on

changes in the concentrations of sulfate and nitrate

particles. By not accounting for changes in organic

and primary particulate fractions, we likely underestimate the impact of the CAAA on PM concentrations. Second, by using separate air quality models for individual pollutants and different geographic

regions, as opposed to a single integrated model, we

were unable to fully capture the interaction among

air pollutants or reflect transport of pollutants or

precursors across the boundaries of the models covering the western and eastern states. Third, the lack

of a well-developed modeling network for PM2.5

means we must estimate monitored concentrations

of this pollutant based on PM10 monitor estimates.

The direction and magnitude of bias these limitations impose on net benefits estimate presented in

this analysis can not be determined based on current

information.

Some model-related uncertainties, however, may

be mitigated because this analysis uses the air qual-
Chapter 8: Comparison of Costs and Benefits

113

ity modeling results in a relative, not absolute, sense.

We focus on the change in air quality between the

Pre- and Post-CAAA scenarios and not on the ambient concentrations projected by the individual

models themselves. Therefore, uncertainties that

affect a modelís ability to accurately predict the relative change in concentration of a pollutant from one

scenario to another are more important in the context of this study than those that affect only the absolute model results. In addition, as summarized in

the previous chapters, most of the uncertainties in

emissions estimation and air quality modeling contribute to a conservative bias in our benefits results.

When faced with alternative approaches to emissions

and air quality modeling, we made explicit attempts

to choose parameters, assumptions and modeling

strategies that would tend to understate benefits.

Omission of Potentially Important

Benefits Categories

As described in Chapters 5 through 7 above, and

in more detail in Appendix H, the primary estimate

reflects application of a strict set of criteria for inclusion of monetized benefits categories. For example,

estimates of the value of improved visibility in U.S.

residential areas indicate a positive value for this service flow, but the best available residential visibility

estimates rely on an unpublished study of values in

the eastern U.S. Although our physical effects analysis indicates significant visibility improvements in

all regions of the U.S., our application of the results

of the economic valuation literature reflect a conservative approach to valuation of improved visibility in the U.S. While we believe our conservative

inclusion criteria for the primary benefits reflects

the greater uncertainty in measuring some economic

values, we also believe that the statutory language of

section 812 clearly warns against the practice of assuming a default value of zero for demonstrated categories of benefits. Therefore, the last row of Table

8-5 presents the effect of using a somewhat more

inclusive set of criteria for accepting benefits transfer-based economic values. In this alternative case,

we included estimates for improved residential visibility, displaced costs from reduced airborne nitrogen loadings to estuaries, and reduced expenditures

for household soiling (which are not included in any

form in the primary estimate).

In addition to these quantified but omitted categories of benefits, there is a wide range of benefits

of the CAAA that we can identify but cannot quantify. We present summaries of unquantified health

effects in Chapter 5 (Tables 5-1 and 5-5) and

unquantified ecological and welfare effects in Chapter 7 (Tables 7-5 and 7-9). Two of the most important omissions, in our judgement, are the lack of any

quantified estimates for the health benefits of air

toxics control and the omission of the systemic and

long-term ecological effects of mercury and other

persistent air pollutants. The importance of these

two categories of effects are discussed in Chapters 5

and 7, respectively.

Alternative Discount Rates

In some instances, the choice of discount rate

can have an important effect on the results of a benefit-cost analysis; for example, when the distribution

of costs and benefits throughout the time period are

very different from one another. In this assessment,

the discount rate affects annualized costs (i.e., amortized capital expenditures), and the discounting of

all costs and benefits to 1990. Table 8-6 summarizes

the effect of alternative discount rates on the Primary Central estimate results of this analysis. The

estimates we present show that altering the discount

rate has only a small effect on annual cost and benefit estimates. In part, this is due to limitations in

our ability to conclusively identify costs as annualized capital expenditures or annual operating costs

in the underlying estimates. As described in Chapter 3, about $3 billion (or roughly 10 percent) of the

2010 estimate is annualized capital costs. Varying

the discount rate, which we also use to represent the

cost of capital, affects only this component of costs.

The benefits estimates that employ a discount rate

include the mortality estimate, where it is used as

part of our valuation of the lag effect of PM mortality, and the chronic asthma value, where we use a

discount rate to develop a lump-sum value for avoidance of incidence from an annual payment value in

the underlying literature.

Not surprisingly, the effect of discount rates on

the net present value benefit calculations is greater.

Nonetheless, the estimates we present in Table 8-6

show that varying the discount rate assumption also

does not change our overall conclusion that the benefits of the CAAA exceed its costs.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

114

Table 8-6

Effect of Alternative Discount Rates on Primary Central Estimates

(Estimates in million 1990$)

Discount Rate Assumption

3% 5% 7%

Annual Costs in 2010:

Titles I through V $26,600 $26,800 $26,900

Annual Benefits:

Titles I through V $110,000 $110,000 $107,000

Present Value of Costs:

Titles I through V $230,000 $180,000 $140,000

Title VI $43,000 $27,000 $20,000

Present Value of Benefits:

Titles I through V $890,000 $690,000 $520,000

Title VI $1,900,000 $530,000 $240,000

Cumulative Net Benefits:

Titles I through V $650,000 $510,000 $380,000

Title VI $1,860,000 $500,000 $220,000

Benefit/Cost Ratio:

Titles I through V 4/1 4/1 4/1

Title VI 44/1 20/1 12/1
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A-3

Table A-1

Base Year Inventory - Summary of Approach

Sector Analysis Approach/Data Sources

Industrial Point 1985 National Acid Precipitation Assessment Program (NAPAP) emissions

Sources inventory grown to 1990 based on historical Bureau of Economic Analysis (BEA)

earnings data.

PM emissions based on total suspended particulate (TSP) emissions and 10

particle-size multipliers.

Utilities The 1990 utility emission estimates are from the 1990 NPI.

Nonroad Nonroad Engines/Vehicles (VOC, NO , CO, PM ): 1991 Office of Mobile Sources x 10

(OMS) Nonroad Inventory.

Nonroad Engines/Vehicles (SO ) and Aircraft, Commercial Marine Vessels, 2

Railroads: 1985 NAPAP grown to 1990 based on historical BEA earnings data.

Motor Vehicles Federal Highway Administration (FHWA) travel data, MOBIL5a/PART5 emission

factors.

Area Sources 1985 NAPAP inventory grown to 1990 based on historical BEA earnings data and

State Energy Data Systems (SEDS) fuel use data; emission factor changes for

selected categories.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-4

Table A-2

Analysis Approach by Major Sector

Sector Growth Forecast Analysis Approach

Industrial 1995 BEA Gross State Product (GSP) VOC, NO — Emission Reduction and Cost

Point Projections by State/Industry. Analysis Model (ERCAM): applies BEA x

growth projection to base year emissions and

applies future year controls as selected by the

user.

PM , SO , CO — While no formal model 10 2

exists, the same basic approach applied in

ERCAM was used for these pollutants. 

Utilities Projections of heat input by unit based SO , NO — Integrated Planning Model (IPM).

on National Electric Reliability Council VOC, PM , CO — Base year emissions rates

(NERC) data, price and demand or AP-42 emission factors applied to IPM

forecasts, and technology projected heat input by unit.

assumptions.

2 x

10

Nonroad 1995 BEA GSP and Population VOC, NO — ERCAM.

Projections by State/Industry. PM , SO , CO — ERCAM approach (no x

10 2

formal model).

Motor MOBILE Fuel Consumption Model NO , VOC, CO — ERCAM: applies MOBIL5a

Vehicles (FCM) National Vehicle Miles emission factors to projected VMT by month

Traveled (VMT) Projection Scaled to and county/vehicle type/roadway

Metropolitan/REST-of-State Areas by classification.

Population. PM , SO — PART5 emission factors applied

x

10 2

to projected VMT.

Area 1995 BEA GSP and Population VOC, NO — ERCAM.

Projections by State/Industry, and PM , SO , CO — ERCAM approach (no

USDA Agricultural Projections. formal model).

x

10 2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-5

Table A-3

Projection Scenario Summary by Major Sector

Sector Pre-CAAA Post-CAAA*

Industrial RACT held at 1990 levels. VOC and NO RACT for all NAAs (except NO waivers).

Point New control technique guidelines (CTGs) (VOC). x x

0.15 pounds per million British thermal unit (lbs/MMBtu)

OTAG-wide NOx cap on fuel combustors of 250 MMBtu

per hour or above(NO ). x

OTAG Level 2 NO controls across OTAG States (NO ). x x

2- and 4- year MACT standards (VOC).

Ozone Rate-of-Progress (3 percent per year)

requirements (further reductions in VOC).

Utilities 250 ton prevention of Title IV SO emission allowance program (SO ).

Significant Deterioration Title IV Phase I and Phase II emission limits for all

(PSD) and New Source boiler types (NO ).

Performance Standards 250 ton PSD and NSPS.

(NSPS) held at 1990 levels. RACT and NSR for all non-waived (NO waiver) NAAs

RACT and New Source (NO ).

Review (NSR) held at 1990 Phase II of the Ozone Transport Commission (OTC)

levels. NOx memorandum of understanding (MOU) (NO ).

2 2

x

x

x

x

0.15 lbs/MMBtu OTAG-wide seasonal NO cap with x

banking/trading (NO ). x

Nonroad Controls (engine standards) Federal Phase I and II compression ignition (CI) engine

held at 1990 levels. standards (NO , PM). x

Federal Phase I and II spark ignition (SI) engine

standards (VOC, CO, NO ). x

Federal locomotive standards (NO , PM). x

Federal commercial marine vessel standards (NO ). x

Federal recreational marine vessel standards (VOC,

NO ). x

Motor Federal Motor Vehicle Control Tier 1 tailpipe standards (Title II) (VOC, NO ).

Vehicles Program (FMVCP) — engine 49-State LEV program (Title I) (VOC, NO , CO).

standards set prior to 1990. Phase 2 RVP limits (Title II) (VOC).

Phase 1 Reid vapor pressure I/M programs for ozone and CO NAAs (Title I) (VOC,

(RVP) limits. NO , CO).

I/M programs in place by Federal reformulated gasoline for ozone NAAs (Title I)

1990. (VOC, NO , CO).

x

x

x

x

California LEV (California only) (Title I) (VOC, NO ,x

CO).

California reformulated gasoline (California only) (Title

I) (VOC, NO , CO). x

Diesel fuel sulfur content limits (Title II) (SO , PM). 2

Oxygenated fuel in CO NAAs (Title I) (CO).
Table A-3

Projection Scenario Summary by Major Sector

Sector Pre-CAAA Post-CAAA*

A-6

Area Controls held at 1990 levels. VOC and NO RACT requirements. x

New CTGs (VOC).

2- and 4- year MACT standards (VOC).

Ozone Rate-of-Progress (3 percent per year)

requirements (further reductions in VOC).

PM NAA controls (PM).

Onboard vapor recovery (vehicle refueling) (VOC).

Stage II vapor recovery systems (VOC).

*Also includes all Pre-CAAA measures.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-7

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A-10
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A-11
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-12

Prospective Analysis and GCVTC

Emissions Estimates

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A-13

Prospective Analysis PM2.5 Emissions

Estimates and Observed Data

The 1997 National Air Quality and Emissions

Trends Report provides a summary of PM2.5

concentration speciation data. This report shows

the relative contribution of the major PM emissions

source components (crustal material, carbonaceous

particles, nitrate, and sulfate) to ambient PM2.5

concentrations in urban and nonurban areas

throughout the U.S. Comparison of primary PM2.5

emissions estimates generated for this analysis with

the observed concentration data presented in the 1997

report indicates that the ratio in the prospective study

of crustal material to primary carbonaceous particles

is high. At least part of this apparent overestimation

Figure A-6

1990 Primary PM2.5 Emissions by EPA Region (tons/year)

Table A-4

Comparison of Emissions: Prospective Analysis and GCVTC Stud

1990 2000 2010

Post-CAA GCVTC Post-CAA GCVTC Post-CAA GCVTC

NOx 3,303,536 3,058,221 2,938,833 2,596,409 2,784,580 2,532,855

SO2 1,245,439 1,094,928 1,326,546 944,689 1,434,470 970,762

PM2.5 1,701,869 2,412,177 1,759,434 2,535,829 1,864,656 2,730,304

NOTE: The figures in this table represent the total annual emissions (tons per year) for the Western States: Arizona, California,

Colorado, Idaho, Nevada, New Mexico, Oregon, Utah, and Wyoming.
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Table A-5

Industrial Point Source Control Assumptions For The Post-CAAA Scenario

Pollutant Point Source Control Measures

VOC Point source control measures for VOC include RACT, new CTGs, and Title III MACT

controls. Title III MACT controls are generally as stringent, or more stringent, than RACT

controls and are thus the dominant control option for many source categories. An 80

percent RE is assumed for all control measures.

NO Industrial point source NO controls inc x xx x lude NO RACT, OTAG level 2 NO controls, and a

0.15 lbs/MMBtu cap on fuel combustors of 250 MMBtu per hour and above across the

OTAG 37-State region. Major stationary source NO emitters in mar x ginal and above NAAs

and in the northeast Ozone Transport Region (OTR) are required to install RACT-level

controls under the ozone nonattainment-related provisions of Title I. RACT control levels

are specified by each State and are based on an assumed rule effectiveness (RE) of 80

percent. 

CO No new CO controls were modeled for the Post-CAAA scenario, although some CO NAAs

may have adopted controls for specific point sources within NAAs. 

SO SO nonattainment provis 2 2 ions of the CAAA do not specify any mandatory controls for SO2

emitters, though an emission cap of 5.6 million tons of SO per year was set by the CAAA 2

for industrial sources. 

PM and Possible control initiatives for particulates under the CAA would result from the Title I 10

PM provisions related to PM nonattainment. Because the controls are specific to each area, 2.5 10

the CAAA PM emissions for industrial point sources were assumed to be equivalent to the 10

Pre-CAAA emissions. Point source PM emissions were also assumed to be unaffected 2.5

by CAAA provisions.

NH Point source NH emissions were assumed to be unaffected by CAAA provisions. 3 3
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-18

Emission Summary

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-19

Table A-6

Industrial Point Source Emission Summaries by Pollutant For 1990, 2000, and 2010* 

(thousand tons per year)

Pollutant/Source Category 1990 CAAA CAAA Pre-CAAA Post-CAAA

2000 2000

Pre- Post- 2010 2010

VOC

Fuel Comb. Industrial 126.1 135.9 134.9 153.7 157.7

Fuel Comb. Other 10.3 12.3 12.3 14.4 14.9

Chemical & Allied Product Mfg 1,066.2 1,101.4 878.9 1,266.6 1,007.4

Metals Processing 72.5 85.8 83.1 90.3 87.2

Petroleum & Related Industries 238.2 243.1 160.2 269.5 166.7

Other Industrial Processes 327.0 372.5 365.3 418.9 417.3

Solvent Utilization 1,126.2 1,256.7 1,036.0 1,394.1 1,120.0

Storage & Transport 490.1 540.9 405.7 617.0 467.5

Waste Disposal & Recycling 9.7 9.9 9.9 11.1 11.7

Miscellaneous 0.3 0.4 0.4 0.6 0.6

TOTAL 3,466.6 3,758.9 3,086.7 4,236.2 3,450.9

NOx

Fuel Comb. Industrial 1,955.8 2,181.8 1,213.5 2,464.5 1,255.9

Fuel Comb. Other 103.9 122.9 77.4 141.1 84.1

Chemical & Allied Product Mfg 275.4 281.5 263.0 322.2 300.0

Metals Processing 81.0 103.0 98.9 111.3 104.7

Petroleum & Related Industries 99.9 104.1 104.1 111.1 108.1

Other Industrial Processes 308.0 350.9 277.6 394.6 302.8

Solvent Utilization 2.5 2.9 2.9 3.2 3.1

Storage & Transport 2.4 2.6 2.6 3.0 3.0

Waste Disposal & Recycling 20.7 23.5 20.4 26.9 22.9

TOTAL 2,849.7 3,173.3 2,060.4 3,577.9 2,184.5

CO

Fuel Comb. Industrial 529.1 586.8 586.8 656.7 656.7

Fuel Comb. Other 96.8 118.4 118.4 140.1 140.1

Chemical & Allied Product Mfg 1,923.4 1,957.2 1,957.2 2,233.0 2,233.0

Metals Processing 2,106.3 2,418.1 2,418.1 2,486.1 2,486.1

Petroleum & Related Industries 436.3 475.1 475.1 545.9 545.9

Other Industrial Processes 754.0 947.1 947.1 1,134.1 1,134.1

Solvent Utilization 2.5 2.8 2.8 3.2 3.2

Storage & Transport 54.8 51.5 51.5 58.7 58.7

Waste Disposal & Recycling 96.7 106.8 106.8 118.7 118.7

TOTAL 5,999.7 6,663.9 6,663.9 7,376.6 7,376.6
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Pollutant/Source Category 1990 CAAA CAAA Pre-CAAA Post-CAAA

2000 2000

Pre- Post- 2010 2010

A-20

SO2

Fuel Comb. Industrial 2,482.2 2,861.9 2,861.9 3,262.0 3,262.0

Fuel Comb. Other 202.4 243.7 243.7 282.9 282.9

Chemical & Allied Product Mfg 440.1 486.6 486.6 546.4 546.4

Metals Processing 664.7 808.9 808.9 857.1 857.1

Petroleum & Related Industries 434.8 449.5 449.5 489.3 489.3

Other Industrial Processes 393.6 456.4 456.4 522.6 522.6

Solvent Utilization 0.8 0.9 0.9 1.0 1.0

Storage & Transport 4.6 5.4 5.4 6.4 6.4

Waste Disposal & Recycling 21.0 22.8 22.8 26.1 26.1

TOTAL 4,644.2 5,336.2 5,336.2 5,993.9 5,993.9

PM10

Fuel Comb. Industrial 221.1 245.2 245.2 275.8 275.8

Fuel Comb. Other 16.6 19.8 19.8 23.0 23.0

Chemical & Allied Product Mfg 62.5 65.4 65.4 74.2 74.2

Metals Processing 137.9 167.2 167.2 179.4 179.4

Petroleum & Related Industries 28.9 31.7 31.7 36.0 36.0

Other Industrial Processes 374.3 427.3 427.3 485.5 485.5

Solvent Utilization 2.1 2.6 2.6 3.0 3.0

Storage & Transport 64.4 73.8 73.8 83.6 83.6

Waste Disposal & Recycling 8.0 8.7 8.7 9.7 9.7

Miscellaneous 10.7 13.7 13.7 16.9 16.9

TOTAL 926.4 1,055.3 1,055.3 1,186.9 1,186.9

PM2.5

Fuel Comb. Industrial 162.0 178.1 178.1 200.0 200.0

Fuel Comb. Other 8.2 9.7 9.7 11.1 11.1

Chemical & Allied Product Mfg 42.7 45.7 45.7 52.0 52.0

Metals Processing 96.3 116.7 116.7 124.4 124.4

Petroleum & Related Industries 19.5 21.3 21.3 24.1 24.1

Other Industrial Processes 224.3 257.9 257.9 294.7 294.7

Solvent Utilization 1.8 2.2 2.2 2.5 2.5

Storage & Transport 26.5 30.5 30.5 34.4 34.4

Waste Disposal & Recycling 6.7 7.2 7.2 8.0 8.0

Miscellaneous 1.6 2.1 2.1 2.6 2.6

TOTAL 589.5 671.5 671.5 754.0 754.0
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Pollutant/Source Category 1990 CAAA CAAA Pre-CAAA Post-CAAA

2000 2000

Pre- Post- 2010 2010

A-21

NH3

Fuel Comb. Industrial 10.0 10.3 10.3 11.3 11.3

Fuel Comb. Other 0.3 0.3 0.3 0.3 0.3

Chemical & Allied Product Mfg 182.6 202.6 202.6 231.6 231.6

Metals Processing 5.9 6.8 6.8 7.0 7.0

Petroleum & Related Industries 42.8 48.7 48.7 55.1 55.1

Other Industrial Processes 2.1 2.1 2.1 2.1 2.1

TOTAL 243.6 270.8 270.8 307.5 307.5

* The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii. Totals may not add due to rounding.

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Control Scenarios

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A-23

Table A-7

Utility Emission Summary*

(thousand tons per year)

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2000 2000 2010 Post-CAAA

2010

VOC

Coal 27.1 23.6 23.1 26.3 24.7

Gas/Oil/Other 7.8 1.8 1.9 1.5 1.7

Internal Combustion 1.9 5.6 6.1 21.2 23.5

TOTAL 36.8 31.0 31.1 49.0 49.9

NOx

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Gas/Oil/Other 679.1 324.1 216.0 220.0 72.6

Internal Combustion 57.1 97.3 82.1 134.4 83.7

TOTAL 7,425.7 8,317.1 4,077.1 9,054.7 3,766.3

CO

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Gas/Oil/Other 81.9 48.0 49.3 44.8 45.9

Internal Combustion 14.7 50.7 55.4 193.6 214.8

TOTAL 329.2 290.5 293.4 453.7 463.2

SO2

Coal 15,221.9 16,111.3 10,315.0 17,696.0 9,776.6

Gas/Oil/Other 611.9 44.1 175.5 0.0 84.2

Internal Combustion 30.7 0.0 0.0 0.0 0.0

TOTAL 15,897.5 16,155.4 10,490.5 17,696.0 9,860.8

PM10

Coal 268.4 244.7 245.1 281.1 249.0

Gas/Oil/Other 10.6 1.5 2.5 1.0 1.6

Internal Combustion 4.1 6.1 6.6 23.4 26.0

TOTAL 283.1 252.3 254.2 305.5 276.6

PM2.5

Coal 99.2 82.9 82.3 97.0 83.7

Gas/Oil/Other 5.9 1.3 2.4 1.0 1.6

Internal Combustion 3.7 6.0 6.6 23.3 25.8

TOTAL 108.8 90.2 91.3 121.3 111.1

NH3

Coal 0.0 0.0 33.3 0.0 221.9

Gas/Oil/Other 0.0 0.0 0.0 0.0 0.0

Internal Combustion 0.0 0.0 0.0 0.0 0.0

TOTAL 0.0 0.0 33.3 0.0 221.9

* The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii.
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The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-24

Nonroad Engines/Vehicles

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A-26

Table A-8BEA Growth Forecasts by Major Source Category:Nonroad Engines/Vehicles

 Annual Grow

Major Category BEA Growth Category* 1990-2000 Nonroad Internal Combustion Engines and Vehicles: 

Airport Service Equipment Transportation by air (SIC 45) 5.8% Construction Equipment Construction (SIC 15, 16, and 17) 0.8% Farm Equipment Farm (SIC 01) 2.4% Industrial Equipment Total Manufacturing 1.9% Lawn & Garden Equipment Population 1.1% Light Commercial Equipment Total Manufacturing 1.9% Logging Equipment Agricultural Services, Forestry, Fisheries (SIC 07, 08, 09) 7.8% Recreational Marine Vessels Population 1.1% Recreational Vehicles Population 1.1% 

Aircraft:

Military Federal, military -1.2% Commercial Transportation by air (SIC 45) 5.8% Civil Transportation by air (SIC 45) 5.8% 

Railroads Railroad Transportation (SIC 40): Earnings -1.5% Commercial Marine Vessels Water Transportation (SIC 44) -0.5% 

*BEA growth category refers to GSP projections for each industry, unless otherwise specified.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-27

Post-CAAA Scenario

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-28

Table A-9

Nonroad National Emission Projections by Source Category*

(thousand tons per year)

Pollutant/Source Category 1990 Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

2000 2000 2010 2010

VOC

Nonroad Gasoline 1,596.8 1,810.8 1,549.3 2,004.0 1,257.4

Nonroad Diesel 185.0 225.3 225.3 261.6 261.6

Aircraft 191.9 242.6 242.6 300.3 300.3

Marine Vessels 36.0 33.6 33.6 34.4 34.4

Railroads 44.2 37.2 37.2 35.5 35.4

TOTAL 2,053.9 2,349.5 2,088.0 2,635.8 1,889.2

NOx

Nonroad Gasoline 176.0 205.4 220.5 237.0 269.8

Nonroad Diesel 1,438.4 1,751.4 1,603.0 2,032.5 1,546.9

Aircraft 139.7 194.8 194.8 249.2 249.2

Marine Vessels 183.7 169.8 169.8 173.2 161.5

Railroads 898.0 759.4 759.4 725.9 513.6

TOTAL 2,835.8 3,080.9 2,947.5 3,417.8 2,740.9

CO

Nonroad Gasoline 12,047.2 13,973.8 13,417.5 15,735.4 15,020.6

Nonroad Diesel 781.9 948.6 948.6 1,097.3 1,097.3

Aircraft 960.9 1,423.6 1,423.7 1,855.9 1,855.9

Marine Vessels 58.0 54.1 54.1 55.5 55.5

Railroads 121.8 102.8 102.8 98.2 98.2

TOTAL 13,969.8 16,503.0 15,946.6 18,842.3 18,127.5

SO2

Nonroad Gasoline 3.2 3.6 3.6 4.1 4.1

Nonroad Diesel 16.7 19.0 19.0 22.4 22.4

Aircraft 8.0 10.9 10.9 13.8 13.8

Marine Vessels 147.5 139.8 139.8 142.3 142.3

Railroads 66.6 56.3 56.3 53.8 53.8

TOTAL 242.1 229.6 229.6 236.5 236.5

PM10

Nonroad Gasoline 42.1 47.0 47.0 51.6 51.6

Nonroad Diesel 185.6 227.3 227.3 262.6 187.1

Aircraft 40.4 38.1 38.1 41.3 41.3

Marine Vessels 24.2 22.7 22.7 23.3 23.3

Railroads 44.0 37.3 37.3 35.7 33.1

TOTAL 336.3 372.5 372.5 414.4 336.3
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Pollutant/Source Category 1990 Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

2000 2000 2010 2010

A-29

PM2.5

Nonroad Gasoline 35.0 39.2 39.2 42.9 42.9

Nonroad Diesel 170.8 209.1 209.1 241.6 172.1

Aircraft 28.5 26.9 26.9 29.1 29.1

Marine Vessels 17.8 16.6 16.6 17.0 17.0

Railroads 40.5 34.3 34.3 32.8 30.4

TOTAL 292.6 326.1 326.1 363.5 291.7

NH3

Nonroad Gasoline 0.0 0.0 0.0 0.0 0.0

Nonroad Diesel 0.0 0.0 0.0 0.0 0.0

Aircraft 0.0 0.0 0.0 0.0 0.0

Marine Vessels 1.1 1.1 1.1 1.1 1.1

Railroads 1.8 1.5 1.5 1.4 1.4

TOTAL 2.9 2.6 2.6 2.5 2.5

* The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii.

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A-31

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-32

Table A-10

Applicability of Mobile Source Control Programs

Control Measure Applicability

Pre-CAAA Scenario

FMVCP National

Phase I RVP National (standard varies by region)

I/M Programs in place in 1990

Post-CAAA Scenario

Phase II RVP National (standard varies by region)

CAA Tailpipe Standards National

CAA Evaporative Controls National

Heavy Duty NO Standard National x

Federal Reformulated Gasoline Nine areas required to adopt this program under the CAA plus areas

Oxygenated Fuel All CO NAAs

Basic I/M All moderate ozone NAAs, moderate CO NAAs, and areas with I/M in

Low Enhanced I/M All areas previously required to implement high enhanced I/M who are

High Enhanced I/M Serious and above ozone NAAs, in metropolitan areas in the OTR with

National LEV Nationally, with the exception of California

California LEV California
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-33

Table A-11

National Highway Vehicle Emissions by Vehicle Type*

(thousand tons)

Vehicle 1990 2000 2000 2010 2010 

Type Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

VOC

LDGV 4,207.2 3,823.6 2,692.9 4,311.9 2,263.6

LDGT1 954.0 1,089.0 793.5 1,341.0 760.6

LDGT2 803.5 711.4 550.4 867.1 583.0

HDGV 466.9 291.1 228.8 279.8 142.2

LDDV 11.6 1.4 1.4 <0.1 <0.1

LDDT 2.8 1.0 1.0 0.4 0.4

HDDV 313.9 367.5 227.2 469.6 151.8

MC 50.5 42.5 37.8 49.9 44.3

Total 6,810.5 6,327.6 4,533.1 7,319.7 3,945.9

NOx

LDGV 3,406.1 3,633.6 2,873.8 4,161.6 2,402.1

LDGT1 775.2 1,045.5 815.8 1,326.1 783.8

LDGT2 557.8 646.6 565.1 821.6 631.6

HDGV 333.1 333.2 324.3 392.6 296.3

LDDV 35.9 4.0 4.0 <0.1 <0.1

LDDT 7.4 2.5 2.5 1.0 1.0

HDDV 2,318.4 2,135.1 2,051.7 2,359.1 1,443.0

MC 11.6 14.1 14.1 16.5 16.5

Total 7,445.6 7,814.6 6,651.3 9,078.6 5,574.4

CO

LDGV 40,073.4 36,759.0 26,920.2 40,304.6 23,711.9

LDGT1 8,458.6 10,566.9 7,947.5 13,054.8 7,986.4

LDGT2 6,533.4 6,867.5 5,349.4 8,457.5 6,102.3

HDGV 5,895.0 2,921.9 2,766.0 1,807.1 1,639.3

LDDV 29.1 3.8 3.8 <0.1 <0.1

LDDT 5.7 2.3 2.3 1.0 1.0

HDDV 1,300.1 1,827.2 1,826.8 2,397.1 2,208.3

MC 185.6 220.8 209.5 259.3 245.9

Total 62,480.9 59,169.6 45,025.5 66,281.6 41,895.1

SO2

LDGV 143.0 151.6 151.6 178.0 178.0

LDGT1 37.6 50.1 50.1 64.0 64.0

LDGT2 20.5 25.6 25.6 32.6 32.6

HDGV 10.8 11.7 11.7 14.1 14.1

LDDV 12.7 1.1 0.3 <0.1 <0.1

LDDT 2.8 0.8 0.2 0.3 0.1

HDDV 340.1 390.3 97.6 480.3 120.1

MC 0.3 0.4 0.4 0.5 0.5

Total 567.7 631.6 337.4 769.6 409.2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Vehicle 1990 2000 2000 2010 2010 

Type Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

A-34

PM10

LDGV 63.1 66.1 66.1 75.3 75.3

LDGT1 15.2 18.4 18.4 23.0 23.0

LDGT2 16.9 10.7 10.7 11.5 11.5

HDGV 10.6 8.0 8.0 6.6 6.6

LDDV 8.8 0.6 0.6 <0.1 <0.1

LDDT 1.7 0.6 0.4 0.2 0.1

HDDV 238.2 186.9 152.9 179.8 88.4

MC 0.4 0.4 0.4 0.5 0.5

Total 354.7 291.7 257.6 297.0 205.4

PM2.5

LDGV 38.1 36.9 36.9 43.4 43.4

LDGT1 9.8 11.2 11.2 13.8 13.8

LDGT2 11.1 6.7 6.7 7.0 7.0

HDGV 7.0 5.3 5.3 4.3 4.3

LDDV 7.8 0.5 0.5 <0.1 <0.1

LDDT 1.5 0.5 0.3 0.2 0.1

HDDV 215.7 164.4 134.4 164.7 73.9

MC 0.2 0.2 0.2 0.3 0.2

Total 291.0 225.7 195.6 233.7 142.7

NH3

LDGV 165.6 262.8 262.8 314.0 314.0

LDGT1 23.6 58.5 58.5 80.6 80.6

LDGT2 8.6 25.8 25.8 36.8 36.8

HDGV 0.4 1.6 1.6 2.9 2.9

LDDV <0.1 <0.1 <0.1 <0.1 <0.1

LDDT <0.1 <0.1 <0.1 <0.1 <0.1

HDDV 0.2 0.4 0.4 0.5 0.5

MC <0.1 <0.1 <0.1 0.1 0.1

Total 198.5 349.2 349.2 434.9 434.9

*The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii. Totals may not add due to

rounding.
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The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-35

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Table A-12

Area Source Emission Summary by Pollutant For 1990, 2000, and 2010*

(thousand tons)

Pollutant/Source Category 1990 Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

2000 2000 2010 2010

VOC

Fuel Comb. Industrial 17.8 22.4 22.4 26.6 26.6

Fuel Comb. Other 686.0 623.6 623.6 518.2 518.2

Chemical & Allied Product Mfg 449.2 517.0 366.3 578.5 408.5

Petroleum & Related Industries 450.2 454.8 198.5 494.5 207.7

Other Industrial Processes 84.4 94.8 93.1 108.0 106.2

Solvent Utilization 4,701.0 5,459.2 4,290.7 6,146.6 4,780.6

Storage & Transport 1,220.3 1,537.8 1,328.8 1,744.6 1,298.1

Waste Disposal & Recycling 2,154.6 2,596.5 471.3 3,030.2 524.2

Natural Sources 13.8 13.8 13.8 13.8 13.8

Miscellaneous 568.6 621.6 584.9 656.8 656.8

TOTAL 10,345.9 11,941.7 7,993.5 13,317.7 8,540.6

NOx

Fuel Comb. Industrial 1,269.7 1,615.6 1,600.2 1,917.8 1,900.1

Fuel Comb. Other 611.2 702.7 702.7 797.7 797.7

Petroleum & Related Industries 19.4 15.8 15.8 15.0 15.0

Other Industrial Processes 4.3 5.2 5.2 6.0 6.0

Waste Disposal & Recycling 60.0 67.3 67.3 74.5 74.5

Miscellaneous 224.1 228.1 228.1 230.7 230.7

TOTAL 2,188.8 2,634.8 2,619.4 3,041.7 3,023.9

CO

Fuel Comb. Industrial 192.6 244.3 244.3 289.5 289.5

Fuel Comb. Other 3,759.1 4,674.4 4,674.4 5,485.9 5,485.9

Petroleum & Related Industries 3.9 3.2 3.2 3.0 3.0

Other Industrial Processes 2.0 2.7 2.7 3.1 3.1

Waste Disposal & Recycling 1,401.9 1,542.9 1,542.9 1,672.8 1,672.8

Miscellaneous 6,246.0 6,477.6 6,477.6 6,625.6 6,625.6

TOTAL 11,605.5 12,945.0 12,945.0 14,080.0 14,080.0

SO2

Fuel Comb. Industrial 626.9 803.6 803.6 948.4 948.4

Fuel Comb. Other 390.6 463.8 463.8 541.5 541.5

Petroleum & Related Industries 1.4 1.2 1.2 1.1 1.1

Other Industrial Processes 1.7 2.2 2.2 2.5 2.5

Waste Disposal & Recycling 14.8 17.1 17.1 19.6 19.6

Miscellaneous 6.2 6.3 6.3 6.3 6.3

TOTAL 1,041.5 1,294.0 1,294.0 1,519.4 1,519.4
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Pollutant/Source Category 1990 Pre-CAAA Post-CAAA Pre-CAAA Post-CAAA

2000 2000 2010 2010

A-40

PM10

Fuel Comb. Industrial 29.1 37.1 37.1 43.8 43.8

Fuel Comb. Other 510.7 470.1 469.6 398.8 398.3

Metals Processing <0.1 <0.1 <0.1 <0.1 <0.1

Petroleum & Related Industries 1.6 1.3 1.3 1.2 1.2

Other Industrial Processes 34.6 37.9 37.9 44.2 44.2

Waste Disposal & Recycling 218.2 240.7 240.7 261.6 261.6

Natural Sources 2,092.4 2,092.4 2,092.4 2,092.4 2,092.4

Miscellaneous 23,501.7 23,915.2 23,262.0 23,947.9 23,189.6

TOTAL 26,388.4 26,794.7 26,141.0 26,790.0 26,031.3

PM2.5

Fuel Comb. Industrial 14.8 18.8 18.8 22.2 22.2

Fuel Comb. Other 495.8 453.0 452.2 379.3 378.3

Metals Processing <0.1 <0.1 <0.1 <0.1 <0.1

Petroleum & Related Industries 1.6 1.3 1.3 1.2 1.2

Other Industrial Processes 22.6 25.1 25.1 29.3 29.3

Waste Disposal & Recycling 190.7 210.1 210.1 228.1 228.1

Natural Sources 313.9 313.9 313.9 313.9 313.9

Miscellaneous 4,769.0 5,016.9 4,911.3 5,296.1 5,173.0

TOTAL 5,808.0 6,038.7 5,932.7 6,270.2 6,146.0

NH3

Fuel Comb. Industrial 7.3 9.1 9.1 10.7 10.7

Fuel Comb. Other 7.7 8.8 8.8 10.0 10.0

Other Industrial Processes 35.5 42.5 42.5 49.7 49.7

Waste Disposal & Recycling 81.8 100.7 100.7 119.2 119.2

Miscellaneous 3,593.8 4,650.6 4,650.6 5,542.2 5,542.2

TOTAL 3,726.1 4,811.6 4,811.6 5,731.8 5,731.8

* The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii. Totals may not add due to rounding.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-41

Reasonable Further Progress

Requirements

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deadlines, as outlined in the CAAA, are reached.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-42

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A-43

Table A-132000 Rate of Progress Analysis Att. Att. 2000 OSD (tons per day) NO VO

1 x

Class. Date Ozone Nonattainment Area NO VOC Target Selection Target Targ

x 2

Ser. 1999 Atlanta 420.93 518.94 1%NO /8%VOC 426.28 476.

x

Mod. 1996 Atlantic City 46.11 37.97 ROP - 15%VOC 0.00 45.Sev. 2005 Baltimore 335.42 318.33 18%NO 381.53 376.

x

Ser. 1999 Baton Rouge 449.70 203.77 NO waiver 0.00 415.

x

Ser. 1999 Beaumont-Port Arthur 245.20 340.66 NO waiver 0.00 450.

x

Ser. 1999 Boston-Lawrence-Worcester-E.MA 560.20 822.65 9% NO 602.06 918.

x

Sev. 2007 Chicago-Gary-Lake County 1,056.70 1,240.89 NO waiver 0.00 1,202.

x

Mod. 1996 Cincinnati-Hamilton 378.70 305.34 ROP - 15%VOC 0.00 341.Mod. 1996 Cleveland-Akron-Lorain 369.12 521.14 ROP - 15%VOC 0.00 573.Mod. 1996 Dallas-Fort Worth 528.83 694.53 ROP - 15%VOC 0.00 673.Ser. 1999 El Paso 113.35 85.38 0%NO /9%VOC 0.00 69.

x

Mod. 1996 Grand Rapids 129.61 182.72 ROP - 15%VOC 0.00 175.Ser. 1999 Greater Connecticut 211.67 316.27 9%NO 224.49 370.

x

Sev. 2007 Houston-Galveston-Brazoria 1,094.58 1,426.65 NO waiver 0.00 2,268

x

Mod. 1996 Kewaunee Co Wi 3.23 4.86 ROP - 15 VOC 0.00 4.Mod. 1996 Knox & Lincoln Cos ME 9.26 9.95 ROP - 15%VOC 0.00 10.Mod. 1996 Lewiston-Auburn ME 23.56 34.08 ROP - 15%VOC 0.00 35.Ext. 2010 Los Angeles-South Coast 1,010.32 972.91 1%NO /17%VOC 1,019.06 939.

x

Mod. 1996 Louisville 264.00 219.66 ROP - 15%VOC 0.00 215.Mod. 1996 Manitowoc Co WI 13.69 19.72 ROP - 15%VOC 0.00 17.Sev. 2007 Milwaukee-Racine 272.22 327.09 NO waiver 0.00 293.

x

Mod. 1996 Monterey Bay 78.60 64.16 ROP - 15%VOC 0.00 79.Mod. 1996 Muskegon 34.25 46.67 ROP - 15%VOC 0.00 44.Mod. 1996 Nashville 167.12 231.71 ROP - 15%VOC 0.00 205.Sev. 2007 New York-N New Jersey-Long Is 1,280.26 1,994.96 18%NO 1,553.41 2,407.

x

Sev. 2005 Philadelphia-Wilmingtn-Trenton 678.53 1,090.49 18%NO 690.79 1,376

x

Mod. 1996 Phoenix 404.47 377.43 ROP - 15%VOC 0.00 347.Mod. 1996 Pittsburgh-Beaver Valley 534.53 407.05 ROP - 15%VOC 0.00 399.Mod. 1996 Portland ME 53.72 70.05 ROP - 15%VOC 0.00 73.Ser. 1999 Portsmouth-Dover-Rochester 37.55 53.54 9%NO 44.26 58.

x

Ser. 1999 Providence 92.51 173.78 5%NO /4%VOC 91.77 180.

x

Mod. 1996 Reading PA 48.66 60.53 ROP - 15%VOC 0.00 61.
Att. Att. 2000 OSD (tons per day) NO VO

1

x

Class. Date Ozone Nonattainment Area NO VOC Target Selection Target Targ

x

2

A-44

Mod. 1996 Richmond-Petersburg 141.62 179.97 ROP - 15%VOC 0.00 201.Sev. 2005 Sacramento Metro 164.60 158.01 0%NO /18%VOC 0.00 155.

x

Mod. 1996 Salt Lake City 178.25 182.75 ROP - 15%VOC 0.00 150.Ser. 1999 San Diego 228.55 192.90 0%NO /9%VOC 0.00 189.

x

Ser. 1999 San Joaquin Valley 499.25 470.50 5%NO /4%VOC 505.63 532.

x

Mod. 1996 Santa Barbara-Santa Maria-Lomp 63.77 82.75 ROP - 15%VOC 0.00 83.Mod. 1996 Sheyboygan 38.02 24.49 ROP - 15%VOC 0.00 22.Sev. 2007 Southeast Desert Modified 355.88 227.71 3%NO /15%VOC 358.76 219.

x

Ser. 1999 Springfield/Pittsfield-W. MA 112.38 155.51 0%NO /9%VOC 0.00 152.

x

Mod. 1996 St. Louis 476.98 465.64 ROP - 15%VOC 0.00 549.Sev. 2005 Ventura Co CA 80.45 65.69 13%NO /5%VOC 79.55 70.

x

Ser. 1999 Washington DC 449.80 402.76 9%NO 499.12 477.

x

NOTES: OSD = ozone season daily 1

The target selection column indicates the percentage reduction of NO NAA's are credited towards RFP and what pe

2

x

in VOC is then needed to satisfy Title I progress requirements.
A-45

Table A-142010 Rate of Progress AnalysisAtt. Att. 2010 OSD (tons per day) NO VO

1 x

Class. Date Ozone Nonattainment Area NO VOC Target Selection Target Targ

x 2

Ser. 1999 Atlanta 336.10 492.40 9%NO 391.84 541

x

Mod. 1996 Atlantic City 39.46 33.21 ROP - 15%VOC 0.00 45Sev. 2005 Baltimore 278.64 293.56 27%NO 311.86 376

x

Ser. 1999 Baton Rouge 407.49 206.65 NO waiver 0.00 415

x

Ser. 1999 Beaumont-Port Arthur 232.32 377.63 NO waiver 0.00 450

x

Ser. 1999 Boston-Lawrence-Worcester-E.MA 478.53 775.66 9%NO 602.06 918

x

Sev. 2007 Chicago-Gary-Lake County 990.61 1,236.73 NO waiver 0.00 840

x

Mod. 1996 Cincinnati-Hamilton 288.20 283.74 ROP - 15%VOC 0.00 341Mod. 1996 Cleveland-Akron-Lorain 306.32 485.90 ROP - 15%VOC 0.00 573Mod. 1996 Dallas-Fort Worth 472.66 687.15 ROP - 15%VOC 0.00 673Ser. 1999 El Paso 113.85 84.33 0%NO /9%VOC 0.00 69

x

Mod. 1996 Grand Rapids 92.07 183.11 ROP - 15%VOC 0.00 175Ser. 1999 Greater Connecticut 192.62 292.53 9%NO 224.49 370

x

Sev. 2007 Houston-Galveston-Brazoria 1,002.13 1,530.07 NO waiver 0.00 1,606

x

Mod. 1996 Kewaunee Co Wi 2.62 4.77 ROP - 15%VOC 0.00 4Mod. 1996 Knox & Lincoln Cos ME 8.51 8.98 ROP - 15%VOC 0.00 10Mod. 1996 Lewiston-Auburn ME 22.20 32.03 ROP - 15%VOC 0.00 35Ext. 2010 Los Angeles-South Coast 950.39 847.66 5%NO /31%VOC 964.60 670

x

Mod. 1996 Louisville 273.40 216.86 ROP - 15%VOC 0.00 215Mod. 1996 Manitowoc Co WI 12.10 19.37 ROP - 15%VOC 0.00 17Sev. 2007 Milwaukee-Racine 246.74 321.89 NO waiver 0.00 204

x

Mod. 1996 Monterey Bay 71.93 61.76 ROP - 15%VOC 0.00 79Mod. 1996 Muskegon 26.99 46.86 ROP - 15%VOC 0.00 44Mod. 1996 Nashville 143.58 230.00 ROP - 15%VOC 0.00 205Sev. 2007 New York-N New Jersey-Long Is 1,148.95 1,842.53 36%NO 1,166.20 2,407

x

Sev. 2005 Philadelphia-Wilmingtn-Trenton 631.39 1,070.05 18%NO /9%VOC 632.97 1,194

x
Att. Att. 2010 OSD (tons per day) NO VO

1

x

Class. Date Ozone Nonattainment Area NO VOC Target Selection Target Targ

x

2

A-46

Mod. 1996 Phoenix 395.64 347.52 ROP - 15%VOC 0.00 347Mod. 1996 Pittsburgh-Beaver Valley 368.47 358.67 ROP - 15%VOC 0.00 399Mod. 1996 Portland ME 51.54 66.88 ROP - 15%VOC 0.00 73Ser. 1999 Portsmouth-Dover-Rochester 33.08 52.49 9%NO 44.26 58

x

Ser. 1999 Providence 75.73 166.61 9%NO 87.91 193

x

Mod. 1996 Reading PA 46.61 55.33 ROP - 15%VOC 0.00 61Mod. 1996 Richmond-Petersburg 137.12 179.35 ROP - 15%VOC 0.00 201Sev. 2005 Sacramento Metro 152.59 135.99 1%NO /26%VOC 152.62 120

x

Mod. 1996 Salt Lake City 179.22 189.83 ROP - 15%VOC 0.00 150Ser. 1999 San Diego 213.59 174.04 5%NO /4%VOC 213.42 202

x

Ser. 1999 San Joaquin Valley 466.55 448.37 9%NO 484.34 566

x

Mod. 1996 Santa Barbara-Santa Maria-Lomp 59.33 81.53 ROP - 15%VOC 0.00 83Mod. 1996 Sheyboygan 34.84 24.69 ROP - 15%VOC 0.00 22Sev. 2007 Southeast Desert Modified 329.75 213.87 9%NO /27%VOC 332.55 172

x

Ser. 1999 Springfield/Pittsfield-W. MA 97.63 147.45 7%NO /2%VOC 97.87 166

x

Mod. 1996 St. Louis 381.48 439.47 ROP - 15%VOC 0.00 549Sev. 2005 Ventura Co CA 75.89 62.33 13%NO /14%VO 76.34 63

x

Ser. 1999 Washington DC 393.57 355.35 9%NO 499.12 477

x

Notes: OSD = ozone season daily 1

The target selection column indicates the percentage reduction of NO NAA's are credited towards RFP and what percen

2 x VOC is then needed to satisfy Title I progress requirements.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-47

Table A-15

Discretionary Control Measures Modeled For ROP/RFP

Source Category Measure Reduction

Area Source

Adhesives - industrial Reformulation 63%

Metal product surface VOC content limits & improved transfer efficiency 30% (50% in 2010 for difficult

coating areas)

Cutback asphalt Switch to emulsified asphalts (100% RE) 100%

Wood product surface Reformulation 43%

coating

Wood furniture surface Reformulation 43%

coating

Degreasing Solvent Limits 63% (80% in difficult areas)

Open burning Seasonal ban 80%

Automobile refinishing CARB Best Available Retrofit Control Technology 47%

(BARCT) limits

Bulk Terminals Leak Detection and Repair (LDAR) 90% (80% RE)

POTWs Covers/Adsorption 50%

Bakeries Afterburner 33%

Petroleum dry cleaning Recovery dryers 44%

Perchloroethylene dry Recovery dryers 70% (80%RE)

cleaning

Livestock Recovery system 50%

Miscellaneous surface Reformulation 30%

coating

Aerosols South Coast Air Quality Management District 50% (60% in 2010)

(SCAQMD) Standards - Reformulation

Incineration Seasonal ban 80%

Synthetic fiber manufacture Adsorber 78% (80%RE)

Misc. industrial processes Process change/incineration 50%

Consumer solvents Additional reformulation 40% (50% in 2010)

AIM coatings Additional reformulation 40% (50% in 2010)

Lawn & garden Episodic Ban 50%

Recreational vehicles Episodic Ban 50%

Industrial equipment Episodic Ban 50%

Recreational marine Episodic Ban 50%

Point Source

Open burning Seasonal Ban 80%

Industrial surface coating Add-on Control Levels 90%

Metal product surface Reformulation 88%

coating

Wood product surface Reformulation 85%

coating

Point sources Rule effectiveness improvements (80% RE to --

90% RE)

*“Episodic” and “seasonal ban” controls are not creditable toward the ROP shortfall.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-48

Mercury Emission Estimates

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A-51

Table A-16

Airborne Mercury Emission Estimates

2000 Emissions (tons) 2010 Emissions (tons)

Source Category (tons) CAAA CAAA Diff. CAAA CAAA Diff.

1990 Emissions Pre- Post- Pre- PostMedical Waste Incin. 50 17.9 1.3 16.6 22.6 1.6 21.0

Municipal Waste Comb. 54 31.2 5.5 25.7 33.8 6.0 27.8

Electric Utility Generation 51.3 63.0 61.1 1.9 68.5 65.4 3.1

Hazardous Waste Comb. 6.6 6.6 6.6 0 6.6 3.0 3.6

Chlor-Alkali Plants 9.8 6.0 6.0 0 2.0 1.3 0.7

Total CAAA Benefits (Reductions) 44.2 56.2

Uncertainties in the Emission

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Table B-1

Summary of Cost Estimates by Emissions Source

Annual Cost (million 1990 dollars)

Sector/Pollutant Post-CAAA 2000 Post-CAAA 2010

Total Non-utility Point $ 2,900 $ 3,400

Non-utility Point/VOC 900 960

Non-utility Point/NO 1,700 2,100 x

Non-utility Point/Non- 310 320

VOC MACT1

Utility/SO and NO $ 3,100 $ 4,600 2 x

Non-Road Engines/Vehicles $ 100 $ 400

Motor Vehicles $ 9,100 $ 12,300

Total Area Sources $ 2,900 $ 3,300

Area/VOC 920 1,000

Area/NO 16 18 x

Area/PM 1,900 2,200

Progress Requirements $ 1,200 $ 2,500

Permits $ 300 $ 300 2

TOTAL $ 19,400 $ 26,800

Notes:

Costs reflect estimates of annualized costs from final rules. Source categories are not modeled in ERCAM-VOC 1

because the National Emission Standards for Hazardous Air Pollutants (NESHAPs) are associated with non-VOC HAP

emission reductions, and are therefore not included in the Post-CAAA 2000 and 2010 inventories.

These costs include costs only for State-implemented permitting programs. We exclude the costs of Federally- 2

implemented programs since all Title V permit programs will be State-run in 2005.

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Table B-2

Point Source VOC Cost Summary

Annual Costs (million 1990 dollars)1

Source Category Post-CAAA 2000 Post-CAAA 2010

 National Rules

Marine vessel loading: petroleum liquids $ 20 $ 30

TSDFs Less than 0.1 Less than 0.1

 New CTGs (moderate)

Printing - lithographic (0.7) (0.7)

SOCMI distillation 0.1 0.1

SOCMI reactor 1.9 2.2

 Non-CTG and Group III CTG RACT (moderate and above)

Automobile surface coating 210 220

Bakeries 0.9 1.1

Beverage can surface coating 47 47

Carbon black manufacture 1.2 1.3

Charcoal manufacturing 0.0 0.0

Cold cleaning 17 18

Fabric printing 22 23

Flatwood surface coating 20 21

Leather products 1 1.1

Metal surface coating 51 57

Organic acids manufacture 1.7 2.0

Paint and varnish manufacture 2.5 2.8

Paper surface coating 5.5 5.5

Plastic parts surface coating 5.1 5.3

Rubber tire manufacture 1.4 1.4

SOCMI processes - pharmaceutical 3.7 4.1

Whiskey fermentation - aging 0.2 0.2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

Annual Costs (million 1990 dollars)1

Source Category Post-CAAA 2000 Post-CAAA 2010

B-6

 CTG RACT (marginal and above)

Cellulose acetate manufacture 1.5 1.6

Dry cleaning - Stoddard solvants 0.1 0.1

In-line degreasing (0.3) (0.3)

Open top degreasing (1.0) (1.2)

Printing - letterpress 0.5 0.5

Terephthalic acid manufacture 2.3 2.5

Vegetable oil manufacture Less than 0.1 Less than 0.1

 Total Title I Costs $ 420 $ 440

 NESHAP

Benzene NESHAP $ 0.2 $ 0.2

 2-Year MACT (national):

Dry Cleaning - PCE 2.2 2.7

SOCMI HON:

Chemical manufacture 12.0 13.0

SOCMI - process vents 2.1 2.4

SOCMI fugitives (3.9) (4.5)

SOCMI processes 22 26

VOL storage 1.5 1.7

 4-Year MACT (national)

Aerospace industry 3.5 4.7

Coke Oven Batteries 21 21

Gasoline distribution - Stage I 12 13

Halogenated solvent cleaning (8.5) (9.1)

Marine vessel loading: petroleum liquids 17 20 2

Petroleum refineries: other sources not distinctly listed 40 45

Polymers and Resins Group I 110 130

Polymers and Resins Group II 4.3 5.0

Polymers and Resins Group IV 5.3 6.7

Printing and Publishing 200 210

Shipbuilding and ship repair 0.4 0.5

Wood furniture surface coating 37 38

Total Title III Costs $ 480 $ 520

 Total Point Source VOC Control Costs (Title I and Title III) $ 900 $ 960

Notes:

Control costs reflect growth projections and CAAA control assumptions relative to a 1990 baseline. 1

The costs for the joint MACT/RACT rule for marine vessel loading are allocated between Title I and Title III based on the 58 2

percent/42 percent distribution in the addendum to the final rule (EPA, 1995b).
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-7

Table B-3

Point Source NO Summary x

Annual Costs (million 1990 dollars)

Source Category Post-CAAA 2000 Post-CAAA 2010

RACT (outside of OTAG Region)

Adipic and Nitric Acid Manufacturing $ <0.1 $ <0.1

Cement Manufacturing 1.7 1.7

Gas Turbines 0.7 0.7

Glass Manufacturing 3.1 3.2

ICI Boilers 14 15

Internal Combustion Engines 0.8 0.8

Iron & Steel Mills <0.1 <0.1

Waste Combustors 0.1 0.1

Process Heaters 0.8 0.8

Subtotal (RACT outside of OTAG Region) $ 21 $ 22

RACT/OTAG Level 2 (OTAG Region)

Adipic and Nitric Acid Manufacturing $ 31 $ 35

Cement Manufacturing 97 110

Gas Turbines 18 28

Glass Manufacturing 38 41

ICI Boilers 1,200 1,700

Internal Combustion Engines 190 190

Iron & Steel Mills 2.5 2.4

Waste Combustors 10 12

Process Heaters 21 23

Subtotal (RACT+OTAG Level 2+0.15 Cap) $ 1,600 $ 2,100 1

Total Point Source NO Control Costs $ 1,700 $ 2,100 x

Notes:

The 0.15 lbs/MMBtu cap on fuel combustors of 250 MMBtu per hour and above is only applied under the 2010 Post-CAAA 1

scenario.

* Totals may not add due to rounding.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-8

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B-9

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Table B-4

Differences in the Control of Utility NO and SO for the X X

Pre-CAAA and the Post-CAAA Regulatory Scenarios

Pollutant Pre-CAAA Post-CAAA 

SOx Existing units: Comply with State Existing units: Comply with the Acid Rain

Implementation Plan (SIP) requirements Allowance Program under Title IV of the CAAA

prevailing in 1990 to ensure compliance 1990 with phased-in requirements. Phase I

with the National Ambient Air Quality covers the largest 110 coal-fired power plants in

Standard. 1995. All other units above 25 megawatts are

covered in Phase II beginning in 2000.

New units: Comply with New Source New units: Comply with the NSPS set in 1978,

Performance Standards (NSPS) set in BACT/LAER (Lowest Achievable Emission

1978 and BACT fixed at 1990 levels Requirements), and the Acid Rain Allowance

applied through the New Source Review Trading Program under Title IV of the CAAA

(NSR) process. 1990.

NOx Existing Units: No federal standards, Existing units: Meet Reasonably Available Control

except NSPS for new units built after Technology (RACT) in 1995 in the OTR and all

passage of the law. non-attainment areas per Title I. States can file

waivers from RACT requirements. Coal-fired

units comply with Title IV NO requirements that x

are phased in over time, or RACT, whichever is

more stringent. Group 1/Phase I coal-fired units

comply in 1996. Group 1/Phase II and Group 2

coal-fired units comply in 2000. Collective action

by the 37 eastern States in the Ozone Transport

Assessment Group (OTAG) will lead to additional

requirements (known as "Level 2 controls") under

Title I for reducing NO emissions during the x

summer months (May - September). 

New units: Units using fossil fuels comply New units: Comply with Title I NSPS and

with the NSPS for each generation BACT/LAER and Title IV standards for coal-fired

technology and fuel. Application of BACT units, whichever is more stringent. Units subject

in the NSR process at levels existing in to OTAG Level 2 controls for reducing NO during

1990. summer months. x
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-10

Table B-5

Electric Power Industry Costs from PostCAAA Controls for SO and NO X X

Annual Control Costs

(millions 1990 dollars)

Pollutant Post-CAAA

2000

Post-CAAA

2010

SOx $1,900 $1,600

NOx $1,200 $2,900

Total $3,100 $4,600

Compliance Actions

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-12

Table B-6

Cost Estimates for Nonroad Engine/Vehicle CAAA Programs

Annual Cost (million 1990 dollars)

Engine/Vehicle Category Post-CAAA 2000 Post-CAAA 2010

SI Engines:

Construction Equipment $ 1.7 $ 3.1

Industrial Equipment 0.5 1.2

Lawn and Garden Equipment 41 74

Farm Equipment 0.2 0.4

Commercial Equipment 12 23

Logging Equipment 0.9 2.1

CI Engines:

Construction Equipment $ 12 $ 17

Industrial Equipment 3.6 5.2

Farm Equipment 2.9 4.4

Logging Equipment 0.1 0.2

Airport Service Equipment 2.8 4.6

Other 0.2 0.2

Locomotives $ 0 $ 35 1

Marine Vessels:

Recreational $ 27 $ 230

Commercial 0 1 1

Total Nonroad Engine/Vehicle Control Costs $ 104 $ 400

Note:

Costs in 2000 are zero because program emissions reductions are not realized until after 2000. See text and 1

Pechan (1998 and 1997a) for further explanation. 

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B-16

Table B-7Motor Vehicle Unit Costs by Provision

Year Cost Estimate by Vehicle 

Provision Dollars Cost Unit LDGV LDGT1 LDGT2 MC HDGV 

Emission Standards: Tier 1 Tailpipe Standards: VOC 1990 Sales 36.8 33.7 11.7 Tier 1 Tailpipe Standards: NO 1990 Sales 115.0 80.6 45.3 16.0 

X

 Cold Temperature CO Standard 1989 Sales 15-23 15-48 42-55 Evaporative Controls (New Evaporative Emissions 1993 Sales 1.0 8.0 8.0 (13.0) Test Procedure) On-Board Vapor Recovery System 1992 Sales 4.54 4.48 4.48 On-Board Diagnostics 1993 Sales 65.0 65.0 65.0 Heavy Duty Engine Standard (2 gram equivalent) 1995 Sales  Low Emission Vehicles: TLEV 1996 53.0 53.0 LEV 95.0 95.0 ULEV 145.0 145.0 ZEV 5,000.0 5,000.0Fuels: Phase II RVP Limits 1990 Cents/gallon 0.2 0.2 0.2 0.2 0.2 Federal Reformulated Gasoline: Phase I 1993 Cents/gallon 3.9 3.9 3.9 3.9 3.9

Phase II 1993 Cents/gallon 1.2 1.2 1.2 1.2 1.2

 Oxygenated Fuels 1993 Cents/gallon 3.8 3.8 3.8 3.8 3.8 Low-Sulfur Diesel Fuel Requirements (0.05% sulfur) 1990 Cents/gallon  California Phase II Reformulated Gasoline 1991 Cents/gallon 12.3 12.3 12.3 12.3 12.3 California Reformulated Diesel Inspection/Maintenance Programs: Basic 1992 Registrations 5.7 5.7 5.7 Low Enhanced 1992 Registrations 5.7 5.7 5.7 High Enhanced 1992 Registrations 15.7 15.7 15.7Notes:

LDGV = light duty gasoline vehicle; LDGT = light duty gasoline truck; MC = motorcycle; HDGV = heavy duty gasoline vehicle; HDDV = heavy duty 

1

LDDV = light duty diesel vehicle; LDDT = light duty diesel truck
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-17

Cost Results

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Table B-8

Cost Estimates of Motor Vehicle Program

Annual Cost (million 1990 dollars)

Program Post-CAAA 2000 Post-CAAA 2010

Title I

California LEV $ 320 $ 1,100

National LEV 180 1,060

Basic I/M 57 69

Low/OTR Enhanced I/M 82 99

High Enhanced I/M 1,100 1,400

Title II

Onboard Vapor Recovery* $ 63 $ 69

Stage II Vapor Recovery* 71 86

Phase II RVP 280 340

Tailpipe/Extended Useful Life - VOC 504 550

Tailpipe/Extended Useful Life - NO 1,500 1,700 x

Evaporative/Running Losses 42 46

Onboard Diagnostics 880 960

Cold Temperature CO Standard 380 410

Federal Reformulated Gasoline 720 860

California Reformulated Gasoline 2,000 2,400

Oxygenated Fuels 160 204

2 gram NO Heavy Duty Standard 0 69 x

Low Sulfur Diesel Fuel 570 740

California Reformulated Diesel** 170 230

Total Motor Vehicle Control Costs $ 9,070 $ 12,300

Notes:

* The benefits of onboard vapor recovery and stage II vapor recovery are accounted for under area sources. The cost

for onboard vapor recovery systems is estimated assuming phase-in for light duty gasoline vehicles and light duty

trucks. Heavy duty trucks are not affected.

** The analysis does not account for the benefits (emission reductions).
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-18

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B-19

Table B-9

Cost Summary of Area Source NO and PM Controls X

Annual Costs (million 1990 dollars)

Source Category Post-CAAA 2000 Post-CAAA 2010

Area Source NO Controls x

Industrial Fuel Combustion - Coal $ 6.6 $ 7.5

Industrial Fuel Combustion - Oil 0.8 0.8

Industrial Fuel Combustion - Natural Gas 8.5 9.9

Total Area Source NO $ 16 $ 18 x

Area Source PM Controls

Agricultural Burning $ 37 $ 39

Agricultural Tilling 4.1 3.6

Beef Cattle Feedlots 1.4 1.7

Construction 1,500 1,800

Paved Roads 350 440

Unpaved Roads 1.1 0.8

Total Area Source PM $ 1,900 $ 2,200

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B-21

Table B-10

2000 Rate of Progress Analysis

Ozone Nonattainment Area VOC OSD VOC Target Shortfall 1 2

Atlantic City 37.97 45.71 0.00

Baltimore 318.33 376.47 0.00

Baton Rouge 203.77 415.65 0.00

Beaumont-Port Arthur 340.66 450.66 0.00

Chicago-Gary-Lake County 1,240.89 1,202.25 38.64

Cincinnati-Hamilton 305.34 341.18 0.00

Cleveland-Akron-Lorain 521.14 573.07 0.00

Dallas-Fort Worth 694.53 673.97 20.56

El Paso 85.38 69.02 16.36

Grand Rapids 182.72 175.66 7.06

Houston-Galveston-Brazoria 1,426.65 2,268.31 0.00

Lewiston-Auburn ME 34.08 35.98 0.00

Los Angeles-South Coast 972.91 939.08 33.83

Louisville 219.66 215.97 3.69

Milwaukee-Racine 327.09 293.24 33.85

Muskegon 46.67 44.32 2.35

Nashville 231.71 205.60 26.11

New York-N New Jersey-Long Is 1,994.96 2,407.97 0.00

Philadelphia-Wilmington-Trenton 1,090.49 1,376.55 0.00

Phoenix 377.43 347.91 29.52

Pittsburgh-Beaver Valley 407.05 399.80 7.25

Portland ME 70.05 73.33 0.00

Portsmouth-Dover-Rochester 53.54 58.70 0.00

Providence 173.78 180.51 0.00

Reading PA 60.53 61.14 0.00

Richmond-Petersburg 179.97 201.70 0.00

Sacramento Metro 158.01 155.08 2.93

St. Louis 465.64 549.14 0.00

Monterey Bay 64.16 79.63 0.00

Salt Lake City 182.75 150.80 31.95

San Diego 192.90 189.71 3.19

Santa Barbara-Santa Maria-Lomp 82.75 83.10 0.00

Sheyboygan 24.49 22.44 2.05

Washington DC 402.76 477.03 0.00

Knox & Lincoln Cos ME 9.95 10.37 0.00

Kewaunee Co Wi 4.86 4.56 0.30

Manitowoc Co WI 19.72 17.20 2.52

San Joaquin Valley 470.50 532.41 0.00

Ventura Co CA 65.69 70.52 0.00

Southeast Desert Modified 227.71 219.32 8.39

Boston-Lawrence-Worcester-E.MA 822.65 918.01 0.00

Springfield/Pittsfield-W. MA 155.51 152.42 3.09

Greater Connecticut 316.27 370.11 0.00

Notes:

The VOC OSD (ozone season daily) values are the estimated daily emissions in the absence of 1

ROP/RFP requirements. These estimates do, however, incorporate the effect of the VOC

reductions that are credited towards Title I progress requirements.

The VOC target represents the maximum allowable daily VOC emission for NAAs to comply with 2

ROP/RFP requirements. The VOC target is calculated based upon the assumption that all available

NOx cuts are credited towards ROP/RFP requirements and that all necessary remaining reductions

come from VOC.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-22

Table B-11

2010 Rate of Progress Analysis

Ozone Nonattainment Area VOC OSD Target Shortfall 1

 VOC 2

Atlanta 492.40 541.99 0.00

Atlantic City 33.21 45.71 0.00

Baltimore 293.56 376.47 0.00

Baton Rouge 206.65 415.65 0.00

Beaumont-Port Arthur 377.63 450.66 0.00

Chicago-Gary-Lake County 1,236.73 840.15 396.58

Cincinnati-Hamilton 283.74 341.18 0.00

Cleveland-Akron-Lorain 485.90 573.07 0.00

Dallas-Fort Worth 687.15 673.97 13.18

El Paso 84.33 69.02 15.31

Grand Rapids 183.11 175.66 7.45

Houston-Galveston-Brazoria 1,530.07 1,606.75 0.00

Lewiston-Auburn ME 32.03 35.98 0.00

Los Angeles-South Coast 847.66 670.95 176.71

Louisville 216.86 215.97 0.89

Milwaukee-Racine 321.89 204.72 117.17

Muskegon 46.86 44.32 2.54

Nashville 230.00 205.60 24.40

New York-N New Jersey-Long Is 1,842.53 2,407.97 0.00

Philadelphia-Wilmington-Trenton 1,070.05 1,194.41 0.00

Phoenix 347.52 347.91 0.00

Pittsburgh-Beaver Valley 358.67 399.80 0.00

Portland ME 66.88 73.33 0.00

Portsmouth-Dover-Rochester 52.49 58.70 0.00

Providence 166.61 193.15 0.00

Reading PA 55.33 61.14 0.00

Richmond-Petersburg 179.35 201.70 0.00

Sacramento Metro 135.99 120.24 15.75

St. Louis 439.47 549.14 0.00

Monterey Bay 61.76 79.63 0.00

Salt Lake City 189.83 150.80 39.03

San Diego 174.04 202.24 0.00

Santa Barbara-Santa Maria-Lomp 81.53 83.10 0.00

Sheyboygan 24.69 22.44 2.25

Washington DC 355.35 477.03 0.00

Knox & Lincoln Cos ME 8.98 10.37 0.00

Kewaunee Co Wi 4.77 4.56 0.21

Manitowoc Co WI 19.37 17.20 2.17

San Joaquin Valley 448.37 566.96 0.00

Ventura Co CA 62.33 63.11 0.00

Southeast Desert Modified 213.87 172.34 41.53

Boston-Lawrence-Worcester-E.MA 775.66 918.01 0.00

Springfield/Pittsfield-W. MA 147.45 166.51 0.00

Greater Connecticut 292.53 370.11 0.00

Notes:

The VOC OSD (ozone season daily) values are the estimated daily emissions in the absence of 1

ROP/RFP requirements. These estimates do, however, incorporate the effect of the VOC reductions

that are credited towards Title I progress requirements.

The VOC target represents the maximum allowable daily VOC emission for NAAs to comply with 2

ROP/RFP requirements. The VOC target is calculated based upon the assumption that all available

NOx cuts are credited towards ROP/RFP requirements and that all necessary remaining reductions

come from VOC.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-23

Cost Results

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-24

Table B-12

Summary of Cost Estimates by CAAA Title

Annual Cost (million 1990 dollars)

Title Sector/Pollutant Post-CAAA 2000 Post-CAAA 2010

Title I - Provisions for Attainment and Maintenance of NAAQS

Non-utility Point/VOC $ 420 $ 440

Non-utility Point/NO 1,700 2,200 x

Utility/SO and NO 790 2,500 2 x

Area/VOC 920 1,040

Area/NO 16 18 x

Area/PM 1,900 2,200

Motor Vehicle 1,800 3,700

Progress Requirements 1,200 2,500

Title II - Provisions Relating to Mobile Sources

Motor Vehicle $ 7,300 $ 8,700

Nonroad 104 400

Title III - Hazardous Air Pollutants

Point/VOC $ 480 $ 520

Area/VOC 130 150

Non-VOC MACT 170 170 1

Title IV - Acid Deposition Control

Utility/SO and NO $ 2,300 $ 2,040 2 x

Title V - Permits $ 300 $ 300 2 2

TOTAL $ 19,400 $ 26,800

Notes:

Costs reflect estimate of annualized costs from final rules. We do not use ERCAM-VOC to model source categories, 1

because the National Emission Standards for Hazardous Air Pollutants (NESHAPs) are associated with non-VOC HAP

emission reductions. Consequently, they are not included in the Post-CAAA 2000 and 2010 inventories.

Includes costs only for State-implemented permitting programs, excluding the costs of Federally-implemented programs, 2

since all Title V permit programs will be State-run in 2005.

Joint Rules

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-25

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-26

Table B-13

Title I National Rules, Point and Area Source VOC Control Costs

Annual Costs by Sector (million 1990 dollars) 

Post-CAAA 2000 Post-CAAA 2010

Title I National Rules Point Area Total Point Area Total

Consumer Products $ 0.0 $ 81 $ 81 $ 0.0 $ 88 $ 88

AIM Coatings 0.0 24 24 0.0 27 27

Automobile Refinish Coatings 0.0 6 6 0.0 7 7

Hazardous Waste TSDFs <0.1 300 300 <0.1 350 350

Municipal Landfills 0.0 160 160 0.0 170 170

Marine Vessel Loading 24 0 24 28 0 28

TOTAL $ 24 $ 570 $ 590 $ 28 $ 650 $ 680

Notes:

Costs reflect estimates of annualized costs from final rules. We do not use ERCAM-VOC to model source categories, 1

because the NESHAPs are associated with non-VOC HAP emission reductions.

The Off-site Waste Treatment NESHAP was not modeled in this analysis. We assume that the Title I modeling of the 2

RCRA Phase I and Phase 2 rules for hazardous waste TSDFs will capture any future MACT reductions and costs.

EPA estimated that the Medical Waste Incineration guideline would cost between $59 million per year to $120 million per 3

year, depending on the extent to which affected facilities switch to less expensive methods of treatment and disposal. The

cost above represents the midpoint of this range. 

Table B-14

Summary of Costs for Title I

Annual Cost (million 1990 dollars)

Provision Post-CAAA 2000 Post-CAAA 2010

Area Specific:

California LEV $ 320 $ 1,100

National LEV 180 1,060

Basic I/M 57 69

Low/OTR Enhanced I/M 82 99

High Enhanced I/M 1,100 1,400

RACT:

VOC RACT 620 660

Non-utility NO RACT 37 40 x

Utility NO RACT/Best Available Control 140 530 x

 Technology (BACT)

New CTG 130 150

OTR:

Utility Cap-and-Trade Program 640 1,200

NO Stationary (Non-utility) 1,600 2,100 x

PM NAA Controls 1,900 2,200

Progress Requirements $ 1,200 $ 2,500

TOTAL $ 8,050 $ 13,900
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-27

Title II

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Table B-15

Summary of Title II Motor Vehicle and Nonroad 

Engine/Vehicle Program Costs

Annual Cost (million 1990 dollars)

Provision Post-CAAA 2000 Post-CAAA 2010

Motor Vehicles/Fuels:

Motor Vehicle Emission Standards:

Tailpipe/Extended Useful Life - VOC $ 504 $ 550

Tailpipe/Extended Useful Life - NO 1,500 1,700 x

2 gram NO Heavy Duty Standard 0 69 x

1

Onboard Vapor Recovery 63 69

Cold Temperature CO Standard 370 410

Onboard Diagnostics 880 960

Evaporative/Running Losses 42 46

Fuels: 

Phase II RVP 280 330

Federal Reformulated Gasoline 720 860

California Reformulated Gasoline 2,000 2,400

Oxygenated Fuels 170 204

California Reformulated Diesel 170 230

Low Sulfur Diesel Fuel 570 740

Stage II Vapor Recovery 71 86

Motor Vehicle Total $ 7,300 $ 8,700

Nonroad Engines/Vehicles:

Phase I CI engine standards $ 22 $ 32

Phase I and II SI engine standards 56 104

Federal locomotive standards 0 35 1

Federal commercial marine vessel standards 0 1 1

Federal recreational marine vessel standards 27 230

Nonroad Engine Vehicle Total $ 104 $ 400

Total Title II Costs $ 7,400 $ 9,100

Notes:

Columns may not sum to totals due to rounding.

Costs under the 2000 Post-CAAA scenario are zero because emission reductions are not realized until after 2000. 1
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-28

Table B-16

Title III, MACT Standards, Point and Area Source VOC Control Costs

Annual Costs by Sector 

(in million 1990 dollars)

Source Category Post-CAAA 2000 Post-CAAA 2010

Benzene NESHAP $ 0.2 $ 0.2

2-Year MACT:

Dry Cleaning-Perchloroethylene 28 31

SOCMI HON 26 29

4-Year MACT:

Aerospace Industry (surface coating) 4 5.3

Chromium Electroplating 17 17 1

Coke Ovens 21 21

Commercial Sterilizers 7 7 1

Gasoline Distribution-Stage I 12 13

Halogenated Solvent Degreasing (37) (42)

Industrial Process Cooling Towers 14 14 1

Magnetic Tape 0.8 0.8 1

Marine Vessels 17 20

Medical Waste Incineration 89 89 1,3

Municipal Waste Combustors 43 43 1

Off-Site Waste Treatment - - 2

Petroleum Refineries-Other 160 180

 Sources Not Distinctly Listed

Printing/Publishing 200 207

Polymers & Resins Group I 110 128

Polymers & Resins Group II 4.3 5.0

Polymers & Resins Group IV 5.3 6.7

Secondary Lead Smelters 2.0 2.0 1

Shipbuilding and Ship Repair 8.5 11

Wood Furniture (surface coating) 49 50

TOTAL $ 780 $ 840

Notes:

Costs reflect estimates of annualized costs from final rules. Source categories are not modeled in ERCAM-VOC because 1

the NESHAPs are associated with non-VOC HAP emission reductions.

The Off-site Waste Treatment NESHAP was not modeled in this analysis. We assume that the Title I modeling of the 2

RCRA Phase I and Phase 2 rules for hazardous waste TSDFs will capture any future MACT reductions and costs.

EPA estimated that the Medical Waste Incineration guideline would cost between $59 million per year to $120 million per 3

year, depending on the extent to which affected facilities switch to less expensive methods of treatment and disposal. The

cost above represents the midpoint of this range. 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-29

Title III

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Title IV

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Table B-17

Annual Costs of Title IV

Annual Costs (million 1990 dollars)

 Post-CAAA 2000 Post-CAAA 2010

Title

Title IV $ 2,300 $ 2,000

Note: These estimates reflect the base case used in the

Clean Air Power Initiative. See EPA, "Analyzing Electric

Power Generation Under the CAA" (1996) for detail on

scenario development. For more information on its

application to the cost analysis, see EPA, 1997a.

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Table B-18

Potential Effects of Uncertainty on Cost Estimates

Description

Potential Effect on Cost

Estimates

Innovations in future emission control technology Decrease

Emission projections:

Growth factors/activity indicators Unknown

RACT controls for individual States Unknown

Inclusion of economic incentive provisions Decrease 1

Use of costs for rules that are currently in draft form (not yet finalized) Unknown

Uncertainty of final State strategies for meeting Reasonable Further Unknown

 Progress (RFP) RFP requirements

Inclusion of 7- and 10-year MACT standards Increase

Revisions to Title V cost estimate to reflect current State permit Decrease

 program costs

Note:

 Examples include banking, trading, and emissions averaging provisions. 1

Emission Projections

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B-37

Table B-19Factors Affecting Cost of Major CAAA Provisions

Provision Cost Analysis? Strategy for Sensitivity Analysis 

Factors Affecting Sensitivity 

Conduct 

Progress Cost for unidentified Yes Continue to examine costs of identified measures in other specific areas. Requirements measures is most 

uncertain. Lower Bound: Assume average per ton cost of identified measures (e.g.,

$3,500 in Chicago) for all reductions, including unidentified measures. Central Estimate: Use cost figure for identified measures for that fraction of reductions (e.g., $3,500 for 60 percent in Chicago) and assume $10,000 per toncost for unidentified measures. This central estimate reflects more recent costper ton information than was applied to our primary cost estimate. Upper Bound: Assume $10,000 per ton cost for all reductions, includingidentified measures. Our cost analysis adopts a conservative approach andapplies this cost per ton value to our primary cost analysis. 

Impact of revised ozone No Emissions projections in the 812 Prospective do not include revisions to the standard. ozone NAAQS. 

California Incremental fuel costs Yes Lower Bound: Assume 7.3 cents per gallon cost from CARB.Reformulated show wide range and Central Estimate: Current analysis assumes 12.3 cents per gallon cost from Gasoline are most uncertain. CARB. 

Upper Bound: Assume 17.3 cents per gallon cost from CARB.

Gasoline sale Yes Gasoline sales are a function of vehicle miles traveled (VMT). quantities are Apply alternative VMT projection for California: California Motor Vehicle Stock,

important, but less Travel, and Fuel Forecast, California Department of Transportation, Novemberuncertain. 1997.

Alternative VMT would impact emission scenario.
Provision Cost Analysis? Strategy for Sensitivity Analysis 

Factors Affecting Sensitivity 

Conduct 

B-38

PM NAA Controls Base year emissions No Emissions projections and growth estimates are underlying assumptions of the 

and growth. cost analysis.2

Area specific plans may Yes Strategy for sensitivity analysis includes:differ from the "model 1) Apply area-specific control measures where available.plan" applied. 2) Use "model plan" when area plans are unknown. Cost per ton estimates Yes Apply upper and lower bound cost estimates for model plan controls based onand effectiveness of the SCAQMP, the MRI study of agricultural operations, and the PM NAAQSindividual measures. study:

Agricultural Tilling: Low $154/ton (1997 SCAQMP)

High $5,900/ton (midpoint of range from MRI study)

Construction: Low $1,900/ton (50% below value used)

High $5,700/ton (50% above value used)

Paved Roads: Low $50/ton (1997 SCAQMP)

High $1,350/ton (50% above value used)

Unpaved Roads: Low $560/ton (1997 SCAQMP)

High $2,700/ton for rural roads (PM NAAQS)
Provision Cost Analysis? Strategy for Sensitivity Analysis 

Factors Affecting Sensitivity 

Conduct 

B-39

LEV Costs Will 49-State LEV No Recently agreed to by the 23 automobile manufacturers that sell cars in the US 

occur? and are regulated by EPA. Four States in the Northeast (MA, ME, NY, VT) have

opted not to join the NLEV program.

Per vehicle costs. Yes Current analysis uses CARB's per vehicle cost estimates. These estimates are

the lowest and most fully documented, and differ from other industry estimatesby a factor of ten. Lower Bound: 50% below study per vehicle cost estimates.Central Estimate: Use current study (CARB adjusted for national sales volume)per vehicle cost estimates. Upper Bound: Use unadjusted CARB per vehicle cost estimates. 

Projected vehicle sales. Yes Vehicle sales data were obtained from EPA's onboard vapor recovery RIA. 

Apply alternative sales projection: DOE's Annual Energy Outlook 1998 - NEMS

Transportation Demand Model.
Provision Cost Analysis? Strategy for Sensitivity Analysis 

Factors Affecting Sensitivity 

Conduct 

B-40

Non-Utility Unit-level cost Yes ICI boilers account for 79 percent of the total point source NOx control cost Stationary Source equations and cost per estimate in 2010. Apply +50 percent range. Other available data are 3-4 years 

NOx Costs ton. old and would not reflect the fact that the control technology is being

manufactured and applied by more sources. 

Inventory data No Inventory data elements are well-defined for each point source category.elements (e.g., capacity, operatingrate) used in costcalculations.Cap applied to 37 No Current estimates overstate costs for fuel combustors in the 15 States notStates, proposed NOx affected by the NOx cap. NOx SIP call RIA provides estimates for 22-state budgets affect only 22 program. States.Banking not accounted No None for.

NOx Per vehicle costs date Yes Lower Bound: No alternative estimates. Scale down medium estimate by 50 Tailpipe/Useful to 1991 FR Notice. percent. Life Standards Central Estimate: Use current $115 estimate from EPA.

Upper Bound: No alternative estimates. Scale up medium estimate by 50 percent. 

Vehicle Sales Yes Same as LEV projected vehicle sales. See above.

High Enhanced Per vehicle costs are No Alternative per vehicle cost estimates are similar to the costs currently used inI/M most uncertain. the model. 
Provision Cost Analysis? Strategy for Sensitivity Analysis 

Factors Affecting Sensitivity 

Conduct 

B-41

Vehicle registrations No 1990 vehicle registrations are well-documented, and the method used to projectare important, but less future registrations based on population projections is sound. 

uncertain.

Discount Rate Vary the discount rate. Yes Current cost estimates use a five percent discount rate. Vary the cost estimates 

using two alternative discount rates, three percent and seven percent. 

Economic Macroeconomic growth No The current methodology for calculating PM emissions relies on activity level Growth Case projections may affect projections more than macroeconomic growth rates. For example, the modelStudy cost drivers. uses the USDA agricultural baseline projections of farm acres planted to

calculate PM emissions from agricultural production, the largest source of PM. The PM sources influenced by macroeconomic growth rates contribute onlyabout five percent of total PM emissions.

Notes: Estimates are in billion 1990 dollars. 1
The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-42

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Table B-20

Rate-of-Progress Cost Sensitivity Summary

Ozone Nonattainment

Area Low Estimate Central Estimate High Estimate

Annual Post-CAAA 2010 Costs (million 1990 dollars)

Chicago-Gary-Lake County $ 430 $ 810 $ 1,400

Dallas-Fort Worth 0.4 0.4 61

El Paso 8.5 8.5 76

Grand Rapids 0.2 0.2 27

Los Angeles-South Coast 0.8 0.8 44

Louisville 0.1 0.1 30

Milwaukee-Racine 120 210 430

Muskegon 0.6 0.6 9.3

Nashville 15 15 130

Salt Lake City 28 28 180

Sheyboygan 0.5 0.5 8.2

Kewaunee Co. WI 0.2 0.2 1.7

Monitowoc Co. WI 1.1 1.1 11.3

Total $ 607 $ 1,080 $ 2,500

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Table B-21

Area Source PM Control Cost Sensitivity Analysis, Year 2000

(in million 1990 dollars)

Source Type Estimate Analysis Estimate Estimate

Central Model Plan Sensitivity Low High

Agricultural Burning $ 39 $ 23 $ 20 $ 58

Agricultural Tilling 3.9 1.5 0 20

Beef Cattle Feedlots 1.7 0.3 0.9 2.6

Construction Activity 1,700 1,070 870 2,600

Paved Roads 440 380 13 650

Unpaved Roads 0.8 0.5 0.2 1.2

Total $2,200 $1,500 $907 $3,300

Note:

Examples include banking, trading, and emissions averaging provisions. 1 

* Costs are in millions of 1990 dollars.

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Table B-22

Results of Sensitivity Analysis of LEV Costs

Post-CAAA 2010 Annual Cost (million dollars)

Program Central Low High Alternative VMT

California LEV $ 1,100 $ 550 $ 1,100 $ 870

49-State LEV 1,060 530 1,300 820

TOTAL $ 2,200 $ 1,080 $ 2,500 $ 1,700

Note: Columns may not sum to totals due to rounding

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-46

Table B-23

Discount Rate Sensitivity Analysis for 2010 Cost Estimates

Capital Cost (million 1990 dollars)

Discount Rate Percent Difference

Between 3% and 7%

 Sector Pollutant 3% 5% 7% Rate

Non-Utility VOC $ 480 $ 501 $ 530 11%

NO 1,400 1,500 1,600 12% x

Area VOC 508 540 570 11%

NO 17 18 20 15% x

PM 430 440 440 2%

Total $ 2,800 $ 3,000 $ 3,100 11%

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References

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Figure C-1

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-9

Air Quality, Meteorological, and

Land-Use Inputs

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C-10

Table C-1 

Emission Totals by Component for each Scenario for the OTAG Domain (tpd)

VOC

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 33,417 38,517 27,982 43,113 8,638 

Onroad Mobile 17,518 15,102 10,074 17,400 8,552

Point 8,247 9,027 7,317 10,194 8,204

Utility 87 81 82 111 113

 Total 59,270 62,727 45,454 70,818 45,507

NOx

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 12,109 13,858 13,351 15,770 13,741

Onroad Mobile 17,915 17,463 14,923 20,222 12,616

Point 6,647 7,345 4,444 8,365 4,681

Utility 17,637 20,668 8,254 22,670 5,182

 Total 54,307 59,335 40,972 67,026 36,220

CO

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 46,606 53,087 51,544 58,952 57,100

Onroad Mobile 147,842 112,656 84,569 124,385 78,396

Point 13,766 15,463 15,463 17,192 17,192

Utility 710 784 811 1,225 1,327

 Total 208,924 181,990 152,388 201,753 154,015 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-11

UAM-V Simulation Results for the

Eastern U.S.

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C-12

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C-13

Table C-2

Emission Totals by Component for each Scenario for the Entire U.S. (tpd).

VOC

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 33,972 39,154 27,620 43,708 28,575 

Onroad Mobile 18,659 16,454 10,683 18,776 8,804 

Point 9,503 10,298 8,457 11,606 9,454 

Utility 96 85 85 134 137 

Total 62,229 65,991 46,845 74,224 46,970

NOx

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 13,766 15,659 15,252 17,697 15,794 

Onroad Mobile 20,399 20,660 17,421 24,142 14,696 

Point 7,964 8,694 5,645 9,803 5,985 

Utility 20,188 22,787 11,170 24,808 10,319 

Total 62,316 67,800 49,487 76,450 46,793

CO

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 70,069 80,679 79,155 90,198 88,240 

Onroad Mobile 171,181 142,346 103,332 153,706 92,058 

Point 16,478 18,257 18,257 20,210 20,210 

Utility 861 796 804 1,243 1,269 

Total 258,589 242,078 201,547 265,357 201,777 

UAM-V Simulation Results for the

Western U.S.

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-14

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Table C-3

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Base 1990 CAAA CAAA CAAA CAAA

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Base 1990 CAAA CAAA CAAA CAAA

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Base 1990 CAAA CAAA CAAA CAAA

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C-19

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Table C-4

Emission Totals by Component for each Scenario for Los Angeles (tpd)

VOC

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 758 770 607 871 700 

Onroad Mobile 1,179 999 410 1,168 213 

Point

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NOx

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 450 467 453 529 463 

Onroad Mobile 993 1,280 879 1,573 626 

Point

 Low Level 216 186 139 186 139

 Elevated 19 19 18 12 8

Total 1,678 1,953 1,489 2,300 1,236 

CO

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 1,142 1,302 1,286 1,515 1,495 

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Point

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C-22

Table C-5

Emission Totals by Component for each Scenario for Phoenix (tpd)

VOC

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 241 310 201 369 250 

Onroad Mobile 184 215 141 231 85 

Point

 Low Level 2 2 1 2 2

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 Total 426 527 344 602 337 

NOx

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 213 270 259 326 290 

Onroad Mobile 151 214 174 264 147 

Point

 Low Level 1 1 1 2 2

 Elevated 0 0 0 0 0

 Total 371 485 434 592 438 

CO

Base 1990 CAAA CAAA CAAA CAAA

2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 684 820 795 941 907 

Onroad Mobile 1,186 2,001 1,538 1,883 1,002 

Point

 Low Level 0 0 0 0 0

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 Total 1,871 2,822 2,334 2,825 1,910 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-23

Calculation of Ozone Air Quality

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Figure C-2: Difference in daily maximum simulated ozone concentration (ppb) for

the 15 July 1995 OTAG episode day: 2010 pre-CAAA90 minus base 1990.

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-26
Figure C-3. Difference in daily maximum simulated ozone concentration (ppb)

for the 15 July 1995 OTAG episode day: 2010 post-CAAA90 minus base 1990. 

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-27
Figure C-4. Difference in daily maximum simulated ozone concentration (ppb)

for the 15 July 1995 OTAG episode day: 2010 post-CAAA90 minus pre-CAAA90.

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-28
Figure C-5. Difference in daily maximum simulated ozone concentration (ppb)

for the 8 July 1990 western U.S. simulation day: 2010 pre-CAAA90 minus base 199

C-29
Figure C-6. Difference in daily maximum simulated ozone concentration (ppb)

for the 8 July 1990 western U.S. simulation day: 2010 post-CAAA90 minus base 1990

C-30
Figure C-7. Difference in daily maximum simulated ozone concentration (ppb) 

for the 8 July 1990 western U.S. simulation day: 2010 post-CAAA90 minus pre-CAAA90.

C-31
Figure C-8. UAM-V modeling domain for western U.S. analysis with the high-resolution modeling dom

the San Francisco Bay Area, Los Angeles, and Phoenix.

C-32
0

0

10 20 30 40 50 60 70 80 90 100

10

20

30

40

50

60

70

80

90

100

LEVEL 1 Ozone (ppb)

Time: 0-1800 August 6, 1990

+ MAXIMUM = 24.6 ppb

- MINIMUM = -16.5 ppb

0 40 80

Km

Figure C-9. Differences in daily maximum simulated ozone concentration

(ppb) for the 6 August 1990 simulation day for northern California: 2010 postCAAA90 minus pre-CAAA90.

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-33
LEVEL 1 Ozone (ppb)

Time: 0-2400 August 28, 1987

+ MAX- MINI

0 10 20 30 40 50 

0

10

20

30

40

0 25 50

Km

Figure C-10. Difference in daily maximum simulated ozone concentration (ppb)

for the 28 July 1987 simulated day for Los Angeles: 2010 post-CAAA90 minus pre-CAAA90.

C-34
Figure C-11a. Distribution of Monitor-Level Ratios 

for 95th Percentiles Ozone Concentration: 

2000 Pre-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 1.013

Figure C-11b. Distribution of Monitor-Level Ratios 

for 95th Percentiles Ozone Concentration: 

2000 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 0.910

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-35
Figure C-12a. Distribution of Monitor-Level Ratios 

for 95th Percentiles Ozone Concentration: 

2010 Pre-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 1.051

Figure C-12b. Distribution of Monitor-Level Ratios 

for 95th Percentile Ozone Concentration: 

2010 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 0.883

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-36
Figure C-13a. Distribution of Monitor-Level Ratios 

for 95th Percentiles Ozone Concentration: 

2000 Post-CAAA / 2000 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 0.900

Figure C-13b. Distribution of Monitor-Level Ratios 

for 95th Percentiles Ozone Concentration: 

2010 Post-CAAA / 2010 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency (percent)

median: 0.838

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-37
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-38

Estimating the Effects of the CAAA

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RADM/RPM Simulation Results

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Table C-6

Comparison of CASTNet and RPM

Average Concentration of SO 4

Season ()g/m ) ()g/m )

CASTNet RPM

SO SO 4

3 43

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Cold 3.7 3.6

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Table C-7

Comparison of CASTNet and RPM

Average Concentrations and Fractions of NO3

Season ()g/m ) ()g/m ) (ratio) (ratio)

CASTNet RPM CASTNet RPM

NO NO NO /t-NO NO /t-NO 3

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Autumn 1.39 1.25 0.42 0.42

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Table C-8

REMSAD Output File Species

REMSAD Species Gas/Aerosol Description 1

NO G Nitric oxide

NO G Nitrogen dioxide 2

SO G Sulfur dioxide 2

CO G Carbon monoxide

NH G Ammonia 3

VOC G Volatile organic compounds

HNO G Nitric acid 3

PNO A Particulate nitrate 3

GSO4 A Particulate sulfate (gas phase production)

ASO4 A Particulate sulfate (aqueous phase production)

NH4N A Ammonium nitrate

NH4S A Ammonium sulfate

SOA A Secondary organic aerosols

POA A Primary organic aerosols

PEC A Primary elemental carbon

Pmfine A Primary fine PM (<2.5 microns)

Pmcoarse A Primary coarse PM (2.5 to 10 microns) 2

Sulfate=GSO4+ASO4+NH4S

Nitrate=PNO3+NH4N

Total PM2.5 surrogate=sulfate+nitrate+SOA+POA +Pmfine

 These are names used in the model and, for the aerosols, are not necessarily the correct molecular formula (the 1

integers are subscripted only when the formula correctly reflects the species).

 Note that (for consistency with the REMSAD User’s Guide) we are using the terminology “coarse PM” to mean PM in 2

the size range of 2.5 to 10 microns, which is not in agreement with general use, which defines coarse PM to be

particles with size greater than 2.5 microns. 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Chemical Speciation Schemes Applied for

REMSAD

VOC: VOC

NH3: NH3

NOx: NO , NO, NO X 2

PMC: POA, PEC, GSO , PNO , PMcoars 4 3

PM: POA, PEC, GSO , PNO , Pmfine 4 3

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C-46

Table C-10

Emission Totals by Component for each Scenario for the Entire U.S. (tpd)

VOC

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 33,972 39,154 27,620 43,708 28,575 

Onroad Mobile 18,659 16,454 10,683 18,776 8,804 

Point 9,503 10,298 8,457 11,606 9,454 

Utility 96 85 85 134 137 

 Total 62,229 65,991 46,845 74,224 46,970 

NOx

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 13,766 15,659 15,252 17,697 15,794 

Onroad Mobile 20,399 20,660 17,421 24,142 14,696 

Point 7,964 8,694 5,645 9,803 5,985 

Utility 20,188 22,787 11,170 24,808 10,319 

 Total 62,316 67,800 49,487 76,450 46,793 

SO2

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 3,517 4,174 4,174 4,811 4,811 

Onroad Mobile 1,555 1,730 924 2,109 1,121 

Point 12,808 14,620 14,620 16,422 16,422 

Utility 43,380 44,261 28,742 48,482 27,016 

 Total 61,260 64,786 48,460 71,823 49,369 

NH3

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 10,230 13,189 13,189 15,710 15,710 

Onroad Mobile 544 957 957 1,191 1,194 

Point 667 742 742 842 1,015 

Utility - - 91 - 608 

 Total 11,441 14,888 14,979 17,744 18,527 

PM10

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 73,221 74,431 72,640 74,532 72,240 

Onroad Mobile 972 799 706 814 563 

Point 2,549 2,891 2,891 3,252 3,252 

Utility 764 691 697 837 758 

 Total 77,507 78,812 76,933 79,435 76,813 

PM2.5

Base 1990

2000 Pre- 2000 Post- 2010 Pre- 2010 Post-CAAA

CAAA CAAA CAAA

Area 16,717 17,438 17,147 18,174 17,637 

Onroad Mobile 797 618 536 640 391 

Point 1,625 1,840 1,840 2,066 2,066 

Utility 288 247 250 332 305 

 Total 19,427 20,143 19,773 21,212 20,398 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-47

Air Quality, Meteorological, and

Land-Use Inputs

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Table C-11

Background Species Concentration used

for REMSAD Initial and Boundary

Conditions1##

Species (ppd)

Concentration

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NO2 0.1

SO2 0.7

NH3 0.5

VOC 20.0

NHO3 0.01

PNO3 0.01

GSO4 0.1

ASO4 0.0

NH4N 0.01

NH4S 0.1

SOA 1

POA 1

PEC 5

PMFINE 1

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-50

Table C-12

Geographical Regions of the U.S.

Central East West

Oklahoma

Missouri Kentucky Utah

Kansas Ohio Colorado

Nebraska Michigan New Mexico

Iowa Virginia Arizona

South Dakota West Virginia Texas

North Dakota Pennsylvania California

Minnesota New York Oregon

Wisconsin Maryland Washington

Illinois New Jersey Idaho

Indiana

Connecticut Wyoming

Rhode Island Montana

Massachusetts

Vermont

New Hampshire

Maine

Delaware

Washington, DC

Florida

Georgia

Alabama

Mississippi

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Arkansas

Tennessee

North Carolina

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-53

Calculation of PM Profiles

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-54

Figure C-14

80-km RADM Domain

Note: Nested 20-km grid estimates were not used to generate final results, but were used in evaluating

the reasonableness of results.
Figure C-15. Comparison of simulated and

observed seasonal PM10 concentration (ug/m3) for

REMSAD for the western U.S.: spring 1990

Figure C-16. Comparison of simulated and

observed seasonal PM10 concentration (ug/m3) for

REMSAD for the western U.S.: summer 1990

Figure C-17. Comparison of simulated and

observed seasonal PM10 concentration (ug/m3) for

REMSAD for the western U.S.: fall 1990

Figure C-18. Comparison of simulated and

observed seasonal PM10 concentration (ug/m3) for

REMSAD for the western U.S.: winter 1990

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-55
Figure C-19. Difference in seasonal average PM10 concentration (ug/m3)

for the summer REMSAD simulation period (1-10 July 1990) for 2010:

post-CAAA90 minus pre-CAAA90

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-56
Figure C-20. Difference in seasonal average PM25 concentration

(ug/m3) for the summer REMSAD simulation period (1-10 July 1990) for

2010: post-CAAA90 minus pre-CAAA90

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-57
Figure C-21b. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2000 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.976

Figure C-21a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2000 Pre-CAAA / 1990 Base-Year 

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 1.017

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-58
Figure C-22a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2010 Pre-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 1.040

Figure C-22b. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2010 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.981

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-59
Figure C-23a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM2.5 Concentration: 2000 Pre-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 1.033

Figure C-23b. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM2.5 Concentration: 2000 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.968

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-60
Figure C-24a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM2.5 Concentration: 2010 Pre-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 1.070

Figure C-24b. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM2.5 Concentration: 2010 Post-CAAA / 1990 Base-Year

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.976

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-61
Figure C-25a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2000 Post-CAAA / 2000 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.964

Figure C-25b. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM10 Concentration: 2010 Post-CAAA / 2010 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.946

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-62
Figure C-26b. Description of Combined RADM/RPM- and REMSADDerived Monitor-Level Ratios for Annual Average PM2.5 

Concentration: 2010 Post-CAAA / 2010 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.919

Figure C-26a. Distribution of Combined RADM/RPM- and 

REMSAD-Derived Monitor-Level Ratios for Annual Average 

PM2.5 Concentration: 2000 Post-CAAA / 2000 Pre-CAAA

0

10

20

30

40

50

60

0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30

Ratio

Relative Frequency 

(percent)

median: 0.946

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-63
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-64

Estimating the Effects of the

CAAA on Visibility

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-65

Table C-13

Comparison of Visibility in Selected Eastern Cities, Metropolitan Areas, and

National Parks

Mean Annual Deciview

Area Name State Base Year Pre-CAAA Post-CAAA

1990 2010 2010 

Acadia NP ME 11.1 12.0 10.4

Atlanta Metro Area GA 20.9 22.8 20.0

Boston Metro Area MA 13.2 14.0 11.9

Chicago Metro Area IL 17.5 19.1 17.0

Columbus OH 16.5 17.7 15.1

Detroit Metro Area MI 16.0 18.5 15.3

Everglades NP FL 7.6 9.2 6.9

Great Smoky Mtns. NP TN 20.4 22.3 19.6

Indianapolis IN 20.1 21.1 19.0

Little Rock AR 15.0 17.2 15.1

Milwaukee Metro Area WI 15.6 18.4 15.3

Minn.-St. Paul Metro Area MN 10.1 12.4 10.3

Nashville TN 20.4 21.5 19.0

New York City Metro Area NY/NJ 15.2 18.0 13.9

Pittsburgh Metro Area PA 15.8 16.9 14.2

St. Louis Metro Area MO 16.5 17.8 16.0

Shenandoah NP VA 16.5 17.8 15.2

Syracuse NY 12.4 13.2 11.5

Washington, DC Metro Area DC/VA/MD 17.5 19.2 16.3

*For cities, metro areas, or national parks not contained by a single RADM/RPM grid cell, the visibility measure

presented in this table is a weighted average of the mean annual deciview level from each of the grid cells that

together completely contain the selected area. Weighting is based upon the spatial distribution of an area over the

various grid cells.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-66

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-67
Figure C-28. Difference in seasonal average Deciview for the summer REMSAD

simulation period (1-10 July 1990): 2010 pre-CAAA90 minus base 1990 (western

United States only)

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-68
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-69

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C-71

Figure C-29

Annual Sulfur Deposition

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10 - 15 kg/ha

15 - 20 kg/ha

20 - 100 kg/ha

The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-72

Figure C-30

Annual Nitrogen Deposition

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0 - 5 kg/ha

5 - 10 kg/ha

10 - 15 kg/ha

15 - 20 kg/ha

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-73

Figure C-31

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-74

Figure C-32

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-75

Figure C-33

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-76

Figure C-34

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2010 Post CAAA Scenario
The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-77

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CAAA on Sulfur Dioxide, Oxides

of Nitrogen, and Carbon

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50

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0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-78

Figure C-35. Distribution of Monitor-Level Ratios of Summer 

SO Emissions: 2010 Post-C 2 AAA / 2010 Pre-CAAA

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Figure C-36. Distribution of Monitor-Level Ratios of Summer 

NO Emissions: 2010 Post-CAAA / 2010 Pre-CAAA

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0

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50

60

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50

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0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-79

Figure C-37. Distribution of Monitor-Level Ratios of Summer 

NO Emissions: 2010 Post-C 2 AAA / 2010 Pre-CAAA

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Figure C-38. Distribution of Monitor-Level Ratios of Summer 

CO Emissions: 2010 Post-CAAA / 2010 Pre-CAAA

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-80

Emission-Based Ratios for SO , NO, 2

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Table C-14

Median Values of the Distribution of Ratios of 2010 Post-CAAA/Pre-CAAA

Adjustment Factors

SO NO NO CO 2 2

Spring 0.904 0.669 0.598 0.790

Summer 0.892 0.666 0.575 0.720

Autumn 0.916 0.677 0.614 0.756

Winter 0.924 0.686 0.626 0.692

Table C-15

Background Concentrations used to Prepare the SO , NO, NO , and CO Profiles 2 2

Pollutant Background Concentration

SO 0 2

NO 0

NO 0 2

CO 0.2 ppm

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of Criteria Pollutants

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D-4
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D-13

Table D-1 

Summary of Considerations Used in Selecting C-R Functions

Consideration Comments

Peer reviewed Peer reviewed research is preferred to research that has not undergone the peer review

research 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 (a.k.a. "ecological studies")

because they control for important confounding variables that cannot be controlled for in 

cross-sectional studies. If the chronic effects of a pollutant are considered more

important than its acute effects, prospective cohort studies may also be preferable to

longitudinal time series studies because the latter type of study is typically designed to

detect the effects of short-term (e.g. daily) exposures, rather than chronic exposures.

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.

Study population Studies examining a relatively large sample are preferred. Studies of narrow population

groups are generally disfavored, although this does not exclude the possibility of

studying populations that are potentially more sensitive to pollutants (e.g., asthmatics,

children, elderly). However, there are tradeoffs to comprehensiveness of study

population. Selecting a C-R function from a study that considered all ages will avoid

omitting the benefits associated with any population age category. However, if the age

distribution of a study population from an “all population” study is different from the age

distribution in the assessment population, and if pollutant effects vary by age, then bias

can be introduced into the benefits analysis.

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, and life style.

Pollutants included in Models with more pollutants are generally preferred to models with fewer pollutants,

model though careful attention must be paid to potential collinearity between pollutants. 

Because PM has been acknowledged to be an important and pervasive pollutant,

models that include some measure of PM are highly preferred to those that do not.

Measure of PM PM and PM are preferred to other measures of particulate matter, such as total 2.5 10

suspended particulate matter (TSP), coefficient of haze (COH), or black smoke (BS)

based on evidence that PM and PM are more directly correlated with adverse health 2.5 10

effects than are these more general measures of PM. 

Economically Some health effects, such as forced expiratory volume and other technical

valuable health measurements of lung functioning, are difficult to value in monetary terms. These health

effects effects are not quantified in this analysis.

Non-overlapping Although the benefits associated with each individual health endpoint may be analyzed

endpoints separately, care must be exercised in selecting health endpoints to include in the overall

benefits analysis because of the possibility of double counting of benefits. Including

emergency room visits in a benefits analysis that already considers hospital admissions,

for example, will result in double counting of some benefits if the category “hospital

admissions” includes emergency room visits.
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Table D-2a

 Studies and Results Selected for Meta-Analysis of the Relationship between Daily Mortality

and Exposure to Ambient Ozone in the United States

Study Duration in model (ppb) Increase in O

Study Location/ Copollutants Measure 95% CI for a 25 ppb

O Concentration Relative Risk and 3

3

Ito and Thurston Cook County, PM average of same day 1.016

(1996) Illinois and previous day 1-hr (1.004 — 1.029) a

1985-1990 maxima

10

Kinney et al. (1995) Los Angeles PM daily 1-hr max 1.000

County (0.989 — 1.010)

1985-1990

10

The following studies were used to generate a single distribution for Philadelphia:

Moolgavkar et al. Philadelphia TSP, SO daily avg 1.015

(1995) 1973-1988 (1.004 — 1.026) 2

Samet et al. (1997) Philadelphia TSP, SO , NO , 2-day avg 1.024

1974-1988 Lagged CO (1.008 — 1.039) 2 2

 Relative risks derived from the ozone coefficient and standard error from the copollutant model were provided by personal a

communication with Dr. Kazuhiko Ito.

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D-23

Table D-2bSelected Studies and Results for Carbon Monoxide and Mortality

Study Location and Population Endpoint Pollutants Main Findings 

Period

Burnett et Toronto, Canada All ages, non- CO,NO , Significant CO effect found in all two As

al. (1998) 1980-1994 metropolita accidental SO , O , pollutant models. Controlling for CO, rela

n Toronto mortality SO , TSP, significant effect found for SO , TSP, COH, but 

2

2 3

4

COH, PM , and PM . r

PM , mo

10

PM est

2.5

4

10 2.5

Kinney et al. Los Angeles All ages non- CO, O , In single pollutant models, CO significant, Mag

(1995) County accidental PM and PM and O are marginally significant. C

1985-1990 mortality In model with CO and PM , both CO and mo

3

10 10 3

10

PM are not significant. 

10 

Saldiva et Sao Paulo, Brazil Elderly mortality CO, O , CO significant in single pollutant model. 

al. (1995) 1990 to 1991 (+65 years) from PM , SO , CO not significant in model with all other

natural NO pollutants.

causes

3

10 2

x

Touloumi et Athens, Greece All ages total CO, SO , CO, SO , and black smoke significant in Dea

al. (1996) 1987-1991 mortality black single pollutant models. sum

2

smoke e

2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-24

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-25

Table D-3

Studies and Results Selected for Adverse Effects in Fetuses, Infants, and Young Children

Study Period Population Endpoint Pollutants Main Findings

Location and

Bobak and 45 of 86 neonates (0- all-cause TSP, SO , Controlling for SO and NO , TSP

Leon administrative 1 month); mortality; NO linked to all-cause and respiratory

(1992) districts in the post- respiratory post-neonatal mortality; weaker,

Czech Republic neonates (1- mortality insignificant effect found for

1986-1988 12 months) neonatal. Controlling for TSP and

2

x

2 x

SO , NO marginally significant for 2 x

all-cause and respiratory postneonatal mortality; no effect for

neonatal mortality. No effect found

for SO .2

Loomis et southwestern infants <1 all cause PM , O , PM and NO significant in single

al. (1999) Mexico City year old mortality NO , SO pollutant models. PM and NO

1/93-7/95 both not significant in two pollutant

2.5 3

2 2

2.5 2

2.5 2

model; PM coefficient changed 2.5

little from single pollutant; NO2

coefficient dropped substantially. O3

not significant. SO not analyzed 2

since ambient levels were negligible. 

Pereira et Sao Paulo, fetuses over intrauterine PM , O , In single pollutant models, NO , SO ,

al. (1998) Brazil 28 weeks of mortality NO , SO , and CO significantly related to

1/91-12/92 pregnancy CO intrauterine mortality. PM and O

age not significant. Considering all

10 3

2 2

2 2

10 3

pollutants simultaneously, NO is the 2

only significant pollutant.

Ritz and Los Angeles, gestational low birth CO Average CO exposure in the last

Yu (1999) CA age 37-44 weight trimester associated with low birth

1989-1993 weeks weight.

Saldiva et Sao Paulo, children <5 respiratory PM , O , NO significantly related to

al. (1994) Brazil mortality NO , SO , respiratory mortality. No effect found

5/90-4/91 CO for the other pollutants.

10 3

x 2

x

Wang et al. Beijing, China gestational low birth TSP, SO TSP and SO exposure in the final

(1997) 1988-1991 age 37-44 weight trimester significantly related to low

weeks birth weight. Both pollutants highly

2 2

correlated (r=0.92).

Woodruff 86 metropolitan post- all-cause PM PM exposure in the first two months

et al. areas in the neonates (1- mortality; of life significant for all-cause

(1997) U.S. 12 months) respiratory mortality. PM significant for

1989-1991 mortality respiratory mortality in average birth10 10

10

weight infants, but not low birthweight infants.

Xu et al. Beijing, China 25,370 pre-term TSP, SO TSP and SO exposure significant for

(1995a) 1988 pregnant delivery pre-term delivery.

women

2 2
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-26

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D-28

Table D-4Summary of Selected Studies for Chronic Illness

Study Period Population Endpoint Pollutants Main Findings Comm

Location and

Abbey et al. California 3,914 AOD; chronic TSP, O , TSP linked to new cases of AOD and Emph(1993) initial survey: 1977 Seventh bronchitis; SO chronic bronchitis, but not to asthma or bronc

final survey: 1987 Day asthma the severity of asthma. O not linked to comp

Adventists the incidence of new cases of any

3

2

3

endpoint, but O was linked only to the 3severity of asthma. No effect found forSO .2

Abbey et al. California 1,868 AOD; chronic PM PM related to new cases of chronic PM (1995) initial survey: 1977 Seventh bronchitis; bronchitis, but not to new cases of data.

final survey: 1987 Day asthma AOD or asthma.

Adventists

2.5 2.5 2.5

Chapman et 4 Utah 5,623 persistent SO , SO , Persistent cough and phlegm is higheral. (1985) communities young cough and NO , TSP in the community with higher SO , SO ,

1976 adults phlegm and TSP concentrations.

2 4

3 24

McDonnell California 3,091 asthma O , PM ,, Single pollutant models: O Averaet al. (1999) initial survey: 1977 Seventh SO , SO , significantly linked to new asthma 1973-

final survey: 1992 Day NO cases in males, but not in females; 1987, 

Adventists other pollutants not significantly linked TSP.

3 10

4 2

2

3

to new asthma cases in males orfemales. Two pollutant modelsestimated for ozone with anotherpollutant; little impact found on size ofozone coefficient.

Portney and Nationwide sample 1,318 sinusitis, hay O , TSP Controlling for TSP, O significantlyMullahy from the 1979 U.S. persons fever, AOD related to the initiation (or(1990) National Health age 17-93 exacerbation) of sinusitis and hay

Interview Survey fever; no effect on AOD. TSP not

3 3

significantly related to any endpoint,although it is marginally significant forAOD.
Study Period Population Endpoint Pollutants Main Findings Comm

Location and

D-29

Schwartz Nationwide sample 6,138 chronic TSP TSP significantly related to the Resp(1993) from the National individuals bronchitis; prevalence of chronic bronchitis, and a sign

Health and ages 30-74 asthma; marginally significant for respiratory by an Nutrition shortness of illness. No effect on asthma or ICD8 Examination breath dyspnea.Survey (dyspnea);1974-1975 respiratory

illness

Xu et al. Beijing, China 1,576 chronic TSP; SO Chronic bronchitis significantly higher(1993) Survey conducted never bronchitis; in the community with the highest TSP

August-September smokers asthma level. TSP not linked to the prevalence1986 of asthma.

2

Zemp et al. Eight sites in 9,651 chronic TSP, PM , Single pollutant models: PM and NO(1999) Switzerland individuals phlegm, NO , O significantly associated with chronic

1991 ages 18-60 chronic cough, phlegm, chronic cough or phlegm,

breathlessness breathlessness and dyspnea. Similar, asthma, though less significant associationsdyspnea on found for TSP. No significant effectexertion found for O .

10

2 3

10 2

3
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D-30

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D-32

Table D-5Studies Used to Develop Respiratory Admissions Estimates

Location Study (ICD code) Pollutants Used in Final Model 

Endpoints Estimated 

a

Toronto, Canada Burnett et al. (1997b) all respiratory (464-466, 480-486, PM , O , NO , SO 

490-494, 496)

2.5-10 3 2 2

Toronto, Canada Burnett et al. (1999) asthma (493); respiratory infection O , CO, PM (asthma); O , NO , PM 

(464, 466, 480-487, 494); non- (respiratory infection); O , CO, PMasthma COPD (490-492, 496) (COPD).

3 2.5-10 3 2 2

3 2.5-10

Toronto, Canada Thurston et al. (1994) all respiratory (466, 480-482, 485, O , PM 

490-493)

3 2.5

Minneapolis-St. Moolgavkar et al. (1997) pneumonia (480-487); COPD O , SO , NO , PM (pneumonia); O , COPaul, MN (490-496) PM (COPD)

3 2 2 10 3

10

Minneapolis-St. Schwartz (1994c) pneumonia (480-486); COPD (490- O , PM (pneumonia); PM (COPD) Paul, MN 496)

3 10 10

Birmingham, AL Schwartz (1994a) pneumonia (480-487); COPD PM 

(490-496)

10

Detroit, MI Schwartz (1994b) pneumonia (480-486); non-asthma O , PM 

COPD (491-492, 494-496)

3 10

Spokane, WA Schwartz (1996) all respiratory (460-519) PM 

10

New Haven, CT Schwartz (1995) all respiratory (460-519) O , PM 

3 10

Tacoma, WA Schwartz (1995) all respiratory (460-519) O , PM 

3 10

Seattle, WA Sheppard et al. (1999) asthma (493) CO, PM 

2.5

 Monetized benefits of non-overlapping endpoints within each study are aggregated. Monetized benefits for asthma among people age <65 (Sheppard et 

athe benefits in studies of people age >64.
D-33

Table D-6Summary of Hospital Admissions Studies – Respiratory Illnesses

Study Period Population Endpoint Pollutants Main Findings Co

Location and

a

Anderson et Barcelona, Paris all ages; COPD NO , BS COPD: Single pollutant models: meta-analysis Stal. (1997) Amsterdam, >64 (490-492, (black of city specific results found significant effect inc

Rotterdam, 496) smoke), for BS, NO , O , and SO in the all age group; U.Milano TSP, SO , similar results reported for ages >64.Period varies by O Strongest effect found for O . TSP notcity from 5-13 significant in meta-analysis. For a givenyears pollutant, results varied considerably by city. 

2

2

3

23 2

3

Burnett et southern <65; >64 all SO , O All respiratory: SO significantly related to Ma

al. (1995) Ontario, Canada respiratory respiratory admissions in ages <65 and >64. als

1/83-12/88 (466, 480- O significant impact from May-September; no et 

486, 490- effect the rest of the year. us494, 496) no 

43 4

3

co

be

PM 

Burnett et Toronto, all ages all O , CO, NO , All respiratory: COH and O linked to Foal. (1997b) Canada respiratory SO , COH respiratory admissions; other PM measures (P

summers in (464-466, (coefficient less strongly linked. Two pollutant models: us1992-1994 480-486, of haze), H , CO, NO , and SO not significant, controlling res

490-494, SO , PM , for COH; O significant, controlling for COH. 496) PM , Four pollutant models: COH and O

3 2

2

+

4 2.5

2.5-10

PM , significant; no effect for NO and SO ; other 10

3

2 2

3

3

2 2

PM measures not significant, controlling forO , NO , and SO . 32 2

Burnett et 16 Canadian <65; >64 all O , CO, SO , All respiratory: Multiple pollutant models: O Stal. (1997a) cities respiratory NO , COH significantly related to admissions, controlling inc

 3/81-12/91 (466, 480- for CO and COH; significant effect also co

486, 490- reported for CO and COH; no significant effect be494, 496) found for NO and SO after controlling for O PM 

3 2

2

3

22 3

and CO. Montreal and Vancouver decreasedthe size of the effect of O substantially. O 3 3significant with and without these cites in themodel.
Study Period Population Endpoint Pollutants Main Findings Co

Location and a

D-34

Burnett et Toronto, all ages asthma O , CO, NO , Multiple pollutant models estimated, where PM al. (1999) Canada (493); SO , PM , pollutants for best fitting model chosen using es

1980-1994 respiratory PM , stepwise regression based on AIC criterion. an

infection PM , Asthma: O , CO, PM significantly related po(464, 466, to asthma admissions; other pollutants not es480-487, chosen in stepwise regression. Respiratory as494); infection: O , NO , and PM chosen in res

COPD stepwise regression COPD: O and PM NO 

(490-492, chosen in stepwise regression. (O 496)

3 2

2 2.5

2.5-1010 3 2.5-10

3 2 2.5

3 2.5-10

Delfino et Montreal, all ages asthma O , SO , Asthma: Two pollutant model: marginally SO 

al. (1994) Canada (493); all PM significant effect for PM , controlling for O . es

May-October in respiratory No effect for O and SO . All respiratory and oth

1984-1988. (462-466, all respiratory non-asthma: PM suggestive usJuly-August 480-487, but not significant, after controlling forsubset used to 490-494, temperature. Significant link between allexamine all 496); all respiratory non-asthma and SO . No effect forrespiratory respiratory O .admissions. non-asthma

3 4

10 10 3

3 4

10

4

3

Lipfert and Southern all ages all O , SO , All respiratory: SO , SO , and O found to be StHammerstro Ontario, Canada respiratory NO , SO , significant predictors of respiratory admissions incm (1992) January- (466, 480- COH, TSP in July-August. co

February and 482, 485,July-August in 490-493)4/79-3/85

3 4

4 4

24 3
Study Period Population Endpoint Pollutants Main Findings Co

Location and a

D-35

Moolgavkar Minneapolis-St. >64 pneumonia O , CO, SO , Pneumonia: Four pollutant model: O Muet al. (1997) Paul, MN; (480-487); NO , PM significant (NO , SO , and PM not significant) us

Birmingham, AL COPD in Minneapolis-St. Paul; no significant effect pn1/86-12/91 (490-496); found for any pollutant in Birmingham. COPD: SO 

all No significant effect found in Birmingham or COrespiratory Minneapolis-St. Paul for any pollutant. All PM (480-487, respiratory: Single pollutant models: O , NO , Pa490-496) and PM significant in Minneapolis-St.Paul. for 

3 2

2 10

3

2 2 10

3 2

10

Multiple pollutant models (results presented in cograph): O significant, controlling for other er

3

pollutants; PM significant controlling for O , 10 3but not significant controlling for O , SO , and 3 2NO together. No significant effect found in 2Birmingham for admissions with O , CO, or 3PM ; NO and SO data not available for 10 2 2Birmingham.

Morgan et Sydney, 1-14; 15- asthma; O , NO , Asthma: Single pollutant models: NO Sal. (1998) Australia 64; >64 COPD bscat significant for ages 1-14 but not other age es

1/90-12/94 (490- (measure of groups. O and bscat not significant for any ou

492,494 light age group. Three pollutant model: NO496) scattering) remains significantly related to asthma

3 2 2

3

2

admission in ages 1-14. COPD: No pollutant

significantly related to COPD admissions.

Pantazopol Athens, Greece all ages all BS, CO, NO All respiratory: Single-pollutant models: BS, St

ou et al. 1988 respiratory CO, NO significantly related to respiratory inc(1995) (not defined admissions in the winter time. No significant U.

by ICD effect found any pollutant in the summer.code)

2

2

Ponce de London, 0-14; 15- all BS, SO , O , All respiratory: O significantly related to StLeon et al. England 64; >64 respiratory NO admissions in ages >14. No significant effect inc(1996) 4/87-2/92 (460-519) found for SO , O , and NO . U.

2 3

2

3

23 2

Ponka and Helsinki, Finland <65; >64 COPD NO , SO , COPD: Single pollutant models: SO linked to SVirtanen 1/87-12/89 (491-492) O , TSP (491-492) admissions in ages <65; NO linked es(1994) to admissions in ages >64; no significant ou

2 2

3

2

2

effect seen for O and TSP. 3
Study Period Population Endpoint Pollutants Main Findings Co

Location and a

D-36

Schwartz Birmingham, AL >64 pneumonia PM , O Pneumonia: PM significant and O not Si(1994a) 1/86-12/89 (480-487); significant in single pollutant models. COPD: (P

COPD PM significant and O not significant in single pn(490-496) pollutant models. CO

10 3 10 3

10 3

Schwartz Detroit, MI >64 asthma PM , O Asthma: admissions not associated with either Tw(1994b) 1/86-12/89 (493); pollutant; coefficients and standard errors not an

pneumonia reported. Pneumonia: Two pollutant model: pn(480-486); PM and O both significant for pneumonia. nonon-asthma Non-asthma COPD: Two pollutant model: incCOPD PM and O both significant.(491-492,494-496)

10 3

10 310 3

Schwartz Spokane, WA >64 pneumonia PM , O Pneumonia: PM marginally significant and Si(1996) 1/88-12/90 (480-487); O not significant for pneumonia in single (P

COPD pollutant models. COPD: PM significant and res(490-496); O not significant in single pollutant models. all All respiratory: Single pollutant models: PM

respiratory and O both significant. Two pollutant model(460-519) not estimated because of limited overlap

10 3 10

3

10

3

10

3

between PM and O data. 10 3

Schwartz Minneapolis-St. >64 pneumonia PM , O Pneumonia: Two pollutant model: PM Tw

(1994c) Paul, MN (480-486); significantly related to pneumonia; O weakly O 

1/86-12/89 COPD linked to pneumonia. COPD: Single pollutant pn

(490-496) models: PM significant and O not sin

10 3 10

3

10 3

significant. (P

3

CO

Schwartz New Haven, CT; >64 all PM , O , All respiratory: Single pollutant models: PM , Tw

(1995) Tacoma, WA respiratory SO O , SO significant, except O in New Haven. O 

1/88-12/90 (460-519) Two pollutant model results varied by city: O res

10 3

2

10

32 3

3

significant (3 of 4 models) and stablecoefficient estimates PM significant (3 of 4 10models), but less stable estimates. SO 2significant (1of 4 models).

3
Study Period Population Endpoint Pollutants Main Findings Co

Location and a

D-37

Sheppard et Seattle, WA <65 asthma CO, SO , O , Asthma: Single pollutant models: each In 

al. (1999) 1/87-12/94 (493) PM , PM pollutant significantly related to asthma, av

2 3

2.5 2.5-

10 10 2 2.5 , PM except SO . Multiple pollutant models: PM thr

and CO reported to have best fit in models rewithout O . Both PM and CO are significant fit, 

3 2.5

when included together in a model. co

beTw

PM 

as

Spix et al. London, 15-64; >64 all NO , SO , All respiratory: Single pollutant models: O St(1998) Amsterdam, respiratory O , TSP, BS significantly related to admissions in ages 15- inc

Rotterdam, (460-519) 64 and >64. BS significantly related to U.Paris, Milano admissions in ages 15-64. SO significantlyPeriod varies by related to admissions in ages >64.city from 5-13years

2 2

3

3

 2

Sunyer et Barcelona, 0-14; 15-64 asthma NO , SO , Asthma: Two pollutant models: NO significant Stal. (1997) Helsinki, O , BS in ages 15-64, controlling for BS; NO had no inc

London, Paris effect on ages 0-14. SO significant in ages 0- U.Period varies by 14, controlling for either BS or NO ; SO hadcity from 3-6 no effect on ages 15-64.years

2 2

3

2

2

2

2 2

Tenias et al. Valencia, Spain >14 asthma NO , SO , Asthma: Two pollutant models: O and NO St(1998) 1/93-12/95 O , BS both significant. No significant effect found for inc

2 2

3

3 2

SO and BS. U.

2

Thurston et Toronto, all ages asthma H , SO , O , Asthma: Single pollutant models: O , H , SO , Twal. (1994) Canada (493); all PM , PM O , and TSP linked to all respiratory PM 

, PM , TSP admissions; PM , PM , PM not res

and August (466, 480- significant. Two pollutant models: O1986-1988 482, 485, significant, but PM measures no longer

490-493) significant. Best fitting PM measure is H . All

+

4 3

2.5 2.5-

six weeks in July respiratory 10 10

3 4

+

3

2.5 2.5-10 10

3

+

respiratory: Single pollutant models: O and 3various measures of PM linked to allrespiratory admissions. Two pollutant models:with O and PM together, O still significant, 3 3but PM often not significant (only H+significant).
Study Period Population Endpoint Pollutants Main Findings Co

Location and a

D-38

Thurston et Buffalo, NY; all ages all H , SO , O All respiratory: Three pollutant model: H , SO , Stal. (1992) New York City respiratory and O are all significant. This result is found inc

June-August in (466, 480- in both Buffalo and New York City. co1988-1989 486, 490- be

493) (H 

+

4 3

+

4

3

PM 

Vigotti et al. Milan, Italy 15-64; >64 all TSP, SO All respiratory: Single pollutant models: TSP St(1996) 1/89-12/89 respiratory and SO linked to admissions. inc

(460-519) U.

2

2

 Not all pollutants considered in a study are necessarily included in the model used to develop C-R functions.

a
D-39

Table D-7Studies Used to Develop Cardiovascular Admissions Estimates

Location Study (ICD code) Pollutants Used in Final Model 

Endpoints Estimated 

Toronto, Canada Burnett et al. (1997b) cardiac (410-414, 427-428) O , PM 

3 2.5-10

Toronto, Canada Burnett et al. (1999) ischemic heart disease (410-414); NO , SO (ischemic heart disease); PM 

dysrhythmias (427); congestive heart O (dysrhythmias); CO, NO (heart failufailure (428) incidence)

2 2 

3 2

Detroit, MI Schwartz and Morris ischemic heart disease (410-414); CO, PM 

(1995) congestive heart failure (428)

10

Eight U.S. counties Schwartz (1999) cardiovascular disease (390-429) CO, PM 1/88-12/90

10

Tucson, AZ Schwartz (1999) cardiovascular disease (390-429) CO, PM 1/88-12/90

10
D-40

Table D-8Summary of Hospital Admissions Studies – Cardiovascular Illnesses

Study Period Population (ICD code) Pollutants Main Findings 

Location and Endpoint

Burnett et al. southern and all ages cardiac SO , O Cardiac: Two pollutant model: SO 

(1995) central (410, 413, significantly related to cardiac admissions; e

Ontario, 427-428) O not significant, in any season or over stCanada the whole year. a

1/83--12/88 a

43 4

3

Burnett et al. Toronto, all ages cardiac O , CO, Cardiac: COH and O linked to cardiac T(1997b) Canada (410-414, 427- NO , SO , admissions; other PM measures less P

summers 428) COH strongly linked. Two pollutant models: CO, ca1992-1994 (coefficient NO , and SO not significant, controlling for

3

2 2

of haze), COH. O significant, controlling for COH. H , SO , Four pollutant models: COH and O + 4PM , significant; no effect for NO and SO . 2.5PM , Other PM measures not significant, 2.5-10PM , controlling for O , NO , and SO . 10

3

2 2

3

3

2 2

32 2

Burnett et al. 10 Canadian >64 congestive O , CO, Congestive heart failure: Single pollutant S(1997c) cities heart failure NO , SO , models: CO, NO , SO , COH are e

1/81-12/91 (428) COH significant; no effect for O . CO and NO lim

3

22 22

3 2

have the best fit. Two pollutant models:Controlling for NO , CO significant, with 2only small reduction in coefficient size;NO insignificant in this model. 2 

Burnett et al. Toronto, all ages ischemic O , CO, Multiple pollutant model, where pollutants P(1999) Canada heart disease NO , SO , for best fitting model chosen using e

1980-1994 (410-414); PM , stepwise regression based on AIC C

dysrhythmias PM , criterion. Ischemic heart disease: NO and d

(427); PM , SO , chosen by stepwise regression. mcongestive Other pollutants not chosen. isheart failure Dysrhythmias: polluO , CO, and PM (N(428) chosen by stepwise regression. Other (P

3

2 2

2.52.5-1010

2

2

3 2.5

pollutants not chosen. Congestive heart cofailure: NO and CO chosen by stepwise in

2

regression procedure. other pollutants notchosen in stepwise regression.
Study Period Population (ICD code) Pollutants Main Findings 

Location and Endpoint

D-41

Morgan et al. Sydney, 0-64; >64 heart disease O , NO , Single pollutant models: bscat significant R(1998) Australia (410,413, 427- bscat for ages >64; NO significant in ages 0-64 p

1/90-12/94 428) (measure and >64. Three pollutant model: NO 0

3 2

of light significant in ages >64; O and bscat not nscattering) significant. in

2

2

3

U

Morris et al. seven U.S. >64 congestive O , CO, Single pollutant models: CO, NO , and SO S(1995) cities heart failure NO , SO significant in single pollutant models. Four e

1/86-12/89 (428) pollutant model: CO is significant in five of m

3

2 2

2 2

the seven cities; NO is significant in one 2 stcity; SO and O are not significant in any e

2 3

cities.

Morris and Chicago, IL >64 congestive O , CO, Single pollutant models: CO, NO , SO , 

Naumova (1998) 1/86-12/89 heart failure NO , SO , and PM significant. Five pollutant model: e

(428) PM CO significant (CI for RR=1.03-1.12); PM lim

3

2 2

10

2 2

10

10

borderline significant (CI for RR=0.99-1.06); other pollutants not significant.

Pantazopolou et Athens, all ages cardiac BS (black Single pollutant models: BS, CO, NO S

al. (1995) Greece (not defined by smoke), significantly related to cardiac admissions e

1988 ICD code) CO, NO in the winter. No significant effect found o

2

2

for any pollutant in the summer.

Schwartz and Detroit, MI >64 ischemic heart O , CO, Ischemic heart disease: Two pollutant T

Morris (1995) 1/86-12/89 disease (410- SO , PM models: PM and CO both significant; no (P

414); effect seen for SO and O . Dysrhythmias: edysrhythmias Air pollutants did not have a significant d(427); effect. Congestive heart failure: Single hcongestive pollutant models: PM and CO significant;heart failure SO and O not significant. Two pollutant(428) models: PM significant, controlling for CO

3

2 10 10

2 3

10

2 3

10

and SO . Controlling for PM , CO 2 10significant.
Study Period Population (ICD code) Pollutants Main Findings 

Location and Endpoint

D-42

Schwartz (1999) Eight U.S. >64 cardiovascular CO, PM Two pollutant model: CO and PM both T

counties disease (390- significant. (P1/88-12/90 429) e

10 10

ca

a

Schwartz (1997) Tucson, AZ >64 cardiovascular O , CO, In a model with the two pollutants, CO and T

1/88-12/90 disease (390- SO , NO , PM were both significant. No effect seen (P

429) PM for O , SO , and NO . e

3

2 2

10

10

32 2

ca

a

Yang et al. Reno/Sparks, all ages cardiovascular CO Reported significant relationship between S(1998) NV illness (390- CO and admissions. e

1/89-12/94 459) m

st

Table D-9Studies Used to Develop Asthma Emergency Room Visits

Location Study Endpoints Estimated Model 

Pollutants Used in Final

central and northern NJ Cody et al. (1992) asthma O 

3

central and northern NJ Weisel et al. (1995) asthma O 

3

Seattle, WA Schwartz et al. (1993) asthma PM 

10

St. John, New Stieb et al. (1996) asthma O Brunswick, Canada 3
D-43

Table D-10Summary of Selected Studies for Emergency Room Visits -- Asthma and Acute Wheezing

Study Period Population Endpoint Pollutants Main Findings 

Location and

Atkinson et London, England 0-14; 15- asthma NO , BS Single pollutant models: PM and NO S

al. (1999) 1/92-12/94 64; >64; all (black significantly related to asthma visits in all age e

ages smoke), groups. SO significant in ages 0-14. BS is o

2

PM , SO , significant for ages 15-64. No effect seen for 10 2CO, O O . Two pollutant results only for ages 0-14: 3

10 2

2

3

NO and SO significant; other pollutants not 2 2significant.

Bates et al. Vancouver, 1-14; 15- asthma NO , SO , SO correlated with asthma in all age groups S

(1990) Canada 60; >60 SO , O with some variation by season. SO e

7/84-10/86 correlated with asthma visits in ages 15 and c

2 2

4 3

4

2

up. No effect found for NO and O . p

2 3

Buchdahl et London, England <17 acute NO , SO , SO significantly related to acute wheezing. Sal. (1996) 3/92-2/93 wheezing O O has a significant, U-shaped result, e

2 2

3

2

3

suggesting that the optimal level of ozone is onot zero. NO not significant. 2

Castellsagu Barcelona, Spain >14 asthma NO , BS, Single pollutant models: NO significant in Se et al. January-March and SO , O both July-September and January-March. BS e(1995) July-September in linked to asthma ER visits in July-September. o

1985-1989 No significant effect found for SO and O .

2

2 3

2

2 3

Cody et al. central and all ages asthma PM , SO , Two pollutant model: O linked to asthma S(1992) northern NJ O visits; SO not significant. No significant effect (O 

May-August in seen for PM ; PM considered in separate in1988-1989 analysis, because of limited (every sixth day) v

10 2

3

3

2

10 10

sampling.

Goldstein New York City all ages asthma SO No significant correlation found between SO Sand 1/69-2/72 and asthma ER visits. eWeinstein S(1986)

2 2
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-44

Lipsett et al. Santa Clara all ages asthma PM , COH Single pollutant models: NO , PM , and COH P(1997) County, CA (coefficient significant; O not significant. Two pollutant o

November-January of haze), models: PM and COH linked to ER visits uin 1988-1992 O , NO controlling for NO ; NO not significant. PM in

10

3 2

2 10

3

10

2 2 10

reported to provide a slightly better fit than oCOH in

m

Richards et Los Angeles, CA children asthma and COH, HC COH, HC, NO, and NO have positive and 1al. (1981) 8/79-1/80 (median bronchiolitis (hydrocar- significant correlation with ER visits; O and s

age =6) (92% bons), NO, SO have negative significant correlation; SO th

asthma NO , O , and TSP have insignificant correlation. uonly) SO , SO , in

2 3

2 4

TSP o

2

3

2 4

Romieu et Mexico City, <16 asthma SO , O Two pollutant model: O significant and SO Sal. (1995) Mexico marginally significant. e

1/90-6/90 o

23 3 2

Rosas et al. Mexico City, <15; 16-59; asthma O , SO , Little effect found for air pollutants. Strong S(1998) Mexico >59 NO , PM , effect found for aeroallergens, such as grass e

1991 TSP pollen. o

3 2

2 10

Schwartz et Seattle, WA <65; >64 asthma SO , PM , Single pollutant models: PM linked to ER O al. (1993) 9/89-9/90 O visits in ages <65, with no effect in ages >64. S

2 10

3

10

No effect for SO and O on ER visits in either p

2 3

age group. u

in

v

Stieb et al. St. John, New 0-15; >15; asthma NO , TSP, O linked to ER visits in ages >15, especially T(1996) Brunswick, Canada all ages SO , SO , when O levels exceed 75 ppb; O not e

May-September in O significant in ages 0-15. No significant effect p1984-1992 seen for the other pollutants. to 

2

2 4

3

3

3 3

a

Weisel et al. central and all ages asthma O O linked to ER visits. S(1995) northern NJ u

May-August in1986-1990

3 3
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-45

White et al. Atlanta, GA 1-16 asthma and O , SO , O linked to ER visits when O levels P(1994) 6/90-8/90 restrictive PM exceeded 110 ppb. No significant effect v

airway reported for SO or PM . udisease in

3 2

10

3 3

2 10

d
D-46

Table D-11Summary of Selected Studies for Emergency Room Visits -- All-Cause, All-Respiratory, Chronic Obstructive Pulm(COPD), and Bronchitis

Study Period Population (ICD code) Pollutants Main Findings 

Location and Endpoint

Atkinson et al. London, 0-14; 15-64; all NO , BS Single pollutant models: PM significant in Stud

(1999) England >64; all respiratory (black ages 0-14 and 15-64. BS and SO incid

1/92-12/94 ages (not defined smoke), significant in ages 0-14. CO and NO and 

by ICD code) PM , SO , significant in ages >64. O not significant.

2

10 2

CO, O3

10

2

2

3

Bates et al. Vancouver, 1-14; 15-60; all NO , SO , SO correlated with respiratory visits in all Resu(1990) Canada >60 respiratory SO , O age groups. SO correlated in ages >14. seas

7/84-10/86 (466, 480- NO correlated in ages 15-60. O not estim

486, 491- significant. corre493, 496)

2 2

4 3

2

4

2 3

Cody et al. central and all ages bronchitis PM , No significant effect seen for PM , O , or PM (1992) northern New (466, 490, SO , O SO on bronchitis admissions. day, 

Jersey 491, 496) consMay-August analy

1988-1989 estim

10

2 3

10 3

2

10

Delfino et al. Montreal, <2; 2-64; all O , PM , Single pollutant models: H and SO Limit(1997) Canada >64 respiratory PM , significant in ages <2; no effect in ages 2- pres

June- (not defined SO , H 64 for any pollutants; O , PM , PM , and mod

September by ICD code) SO significant in ages >64. Two pollutant estim1992-1993 model: O significant and PM not resp

3 10

2.5

4

+

+

4

3 10 2.5

4

3 2.5

significant in ages >64. ICD 

Delfino et al. Montreal, >64 all PM , O Two pollutant model: O significant; PM PM (1998) Canada respiratory has consistent link but not significant. day, 

June-August (not defined obse1989-1990 by ICD code) visib

2.5 3 3 2.5 2.

Stud

incid

defin

Samet et al. Steubenville, all ages all NO , TSP, Single pollutant models: TSP and SO Stu(1981) Ohio respiratory SO , CO, significant; NO ,, CO, or O were not incid

March-April (not defined O significant. definand October- by ICD code)November1974-1977

2

2

3

2

2 3
Study Period Population (ICD code) Pollutants Main Findings 

Location and Endpoint

D-47

Pantazopolou Athens, all ages all outpatient BS, CO, Single-pollutant models: NO significant in Studet al. (1995) Greece visits NO the winter. No effects found for any incid

1988 pollutant in the summer. and 

2

2

Sunyer et al. Barcelona, >14 COPD (not SO , BS SO correlated with ER visits in the summer Stud(1993) Spain defined by and winter. BS significant in the winter only incid

1985-1989 ICD code) and 

2 2

Xu et al. Beijing, China all ages all causes SO , TSP SO and TSP both linked to ER visits. Stud(1995b) 1990 incid

2 2

and 
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D-48

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D-50

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D-51

Table D-12Studies Used to Develop Minor Illness Estimates

Endpoints Estimated Study Population Age Study 

Pol

acute bronchitis 8-12 Dockery et al. (1996) PM2.5

upper respiratory symptoms 9-11 Pope et al. (1991) PM10lower respiratory symptoms 7-14 Schwartz et al. (1994) PM2.5respiratory illness 6-7 Hasselblad et al. (1992) NO2any of 19 respiratory symptoms 18-65 Krupnick et al. (1990) O , PM 3 moderate or worse asthma all ages (asthmatics) Ostro et al. (1991) PM2.5asthma attacks all ages (asthmatics) Whittemore and Korn (1980) O , PM 3 chest tightness, shortness of breath, or wheeze all ages (asthmatics) Linn et al. (1987; 1988; 1990) SO

and Roger et al. (1985)

2

shortness of breath 7-12 (African-American asthmatics) Ostro et al. (1995) PM10work loss days 18-65 Ostro (1987) PM2.5minor restricted activity days 18-65 Ostro and Rothschild (1989b) PM , 

2.5 

restricted activity days 18-65 Ostro (1987) PM2.5
D-52

Table D-13Summary of Selected Studies for Minor Illness

Study Period Population Endpoint Pollutants Main Findings 

Location and

Dockery et 24 communities 13,369 asthma, persistent particle Annual level of sulfates and particle Stal. (1996) in U.S. and children wheeze, chronic acidity, acidity related to bronchitis. HNO po

Canada ages 8-12 cough, bronchitis SO , PM , and HNO linked to asthma. SO the 1988-1991 PM , linked to chronic phlegm. tha

4 2.1

10

HNO , ex

2

HNO , O to 

3 3

2

3 2

oth

Dockery et Six U.S. cities 5,422 bronchitis, chest SO , NO , Annual level of PM significantlyal. (1989) 1980-1981 children illness, cough, O , PM , related to bronchitis and chronic

ages 10-12 wheeze, asthma PM , TSP, cough. Annual O significantly

2 2

3 2.5

15

SO related to asthma. 4

15

3

Hasselblad Meta-analysis of children lower respiratory tract NO Annual NO change of 30 μg/met al. (1992) 11 studies from ages 5-12 illness associated with lower respiratory

the U.S. and tract illness.Europe

2 2

3

Hoek and Two rural towns 300 children symptoms including: SO , NO , Daily pollutant levels not associatedBrunekreef in the ages 7-11 cough, phlegm, O , PM , with any of the symptoms studied.(1995) Netherlands. wheeze, runny nose, PM ,

Spring-Summer throat pain, PM ,1989 headache, eye SO , NO

irritation, physicianvisit

2 2

3 2.5

102.5-10

4 3

Krupnick et Three 570 adults any of 19 respiratory O , COH In single pollutant models, daily O ,al. (1990) communities in and 756 symptoms including (coefficient COH, and SO related to respiratory

Los Angeles children cough with phlegm of haze), symptoms in adults. O significantCounty, SO , NO controlling for other pollutants. California Results more variable for COH and9/78-3/79 SO , perhaps due to collinearity. 

3

2 2

3

2

3

2

NO had no significant effect. No 2effect seen in children for anypollutant.

Ostro Nationwide Adults ages work-loss days PM Two-week average PM levels PM (1987) sample from U.S. 18-65 restricted activity significantly linked to work-loss days, vis

Health Interview days (RADs), RADs, and respiratory-related RADs. Survey respiratory-related Some year-to-year variability in1976-1981 RADs results.

2.5 2.5 
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-53

Ostro et al. Three 321 non- lower respiratory O , COH, In single pollutant model, daily O(1993) communities in smoking symptoms, upper SO , SO , linked to lower and upper respiratory

Los Angeles adults respiratory NO symptoms. SO linked to lowerCounty, symptoms, eye respiratory symptoms. No significantCalifornia irritation effects seen for COH, SO , and NO9/78-3/79

3

4 2

2

3

4

2 2.

Ostro and Nationwide Adults ages respiratory-related O , PM Controlling for PM , two-week PM Rothschild sample from U.S. 18-65 RADs, minor RADs. average O has highly variable vis(1989b) Health Interview association with respiratory-related

Survey and minor RADs. Controlling for O ,1976-1981 two-week average PM significantly

3 2.5 2.5

3

3

2.5

linked to both health endpoints inmost years.

Peters et al. Twelve 3,676 fourth, asthma, wheeze, SO , NO , Wheeze in males, linked to annual(1999) communities in seventh, bronchitis, cough O , PM , average NO and acid in 1994

southern tenth grade PM , (similar link for exposure averagedCalifornia students PM , over 1986-1990). Peak ozone1994 SO , NO , reported associated with decreased

2 2

3 2.5

102.5-10

4 3

NH , asthma prevalence in females. No 4gaseous other reported effects.acids

2

Pope and Utah Valley 79 children upper respiratory PM PM linked to daily reported Of Dockery 12/90-3/91 ages 10-12 symptoms, lower incidences of upper and lower sa(1992) respiratory respiratory symptoms and cough. sy

symptoms, cough Effect seen in symptomatic sample. 40 

10 10

Only cough in symptomatic samplelinked to PM . 10

Pope et al. Utah Valley 34 children upper respiratory PM PM significantly linked to upper and(1991) 12/89-3/90 ages 9-11, symptoms, lower lower respiratory symptoms in

and 21 respiratory sample of 34 children. PM linkedasthmatics symptoms, took only to increased asthma medicationages 8-72 asthma medication use in the asthmatic sample.

10 10

10
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-54

Schwartz et Six U.S. cities 1,844 upper respiratory SO , NO , In single pollutant models SO , NO ,al. (1994) April-August in children symptoms, lower O , PM , PM , and PM significantly linked to

one year respiratory PM , SO , cough. In two-pollutant models, PMbetween 1984 symptoms, cough H has most consistent effect; otherand 1988 (year pollutants not significant, controllingvaries by city) for PM . In single pollutant models,

2 2

3 2.5

10 4 +

2 2

2.5 10

10

10

SO , O , PM , PM , SO , and H 2 3 2.5 10 4

+

linked to lower respiratory symptoms.No effect seen for upper respiratorysymptoms.

Schwartz Los Angeles, CA 110 student cough, phlegm, sore CO, SO , NO linked to sore throat, phlegm, Reand Zeger 1961-1964 nurses throat, headache, NO , O and eye irritation. Oxidants (O ) of (1990) chest discomfort, eye linked to chest discomfort and eye po

irritation irritation. CO linked to headache. se

2

2 x

2

x

sig

von Mutius Leipzig, 1,500 upper respiratory SO , NO , In single pollutant models, SO , NO , PM et al. (1995) Germany children symptoms PM and PM linked to upper respiratory ab

10/91-7/92 ages 9-11 symptoms in winter (high pollution mo

2x 2x

season). In the summer, only NO for 

x

linked to respiratory symptoms. on

to 
D-55

Table D-14Summary of Selected Studies for Asthmatics

Study Period Population Endpoint Pollutants Main Findings 

Location and

Forsberg et Pitea, Sweden 31persons shortness of BS (black Controlling for other pollutants, daily levels Blal. (1993) about sixty days ages 9-71 breath, smoke), of BS linked to shortness of breath. No link me

wheeze, cough, SO , NO between pollutants and wheeze, cough, andphlegm phlegm.

2 2

Gielen et Amsterdam, 61 children upper O , PM , In single pollutant model, daily levels of BS Real. (1997) Netherlands ages 7-13 respiratory BS significantly linked to lower and upper de

summer symptoms, respiratory symptoms and medication use. len1995 lower PM linked to lower respiratory symptoms me

respiratory and medication use. O linked to upper PM symptoms, respiratory symptoms. resmedication use an

3 10

10

3 

Cu

Hiltermann Leiden 60 adults symptoms O , PM , In single pollutant models, daily levels of O ,et al. University, ages 18-55 include: BS, NO , PM , BS, and NO linked to shortness of(1998) Netherlands shortness of SO breath. Some significant negative

7/3/95-10/6/95 breath, cough, associations reported for nasal symptoms

phlegm, and levels of PM , BS, and NO . Nowheeze, runny significant effect reported for SO .nose, throatpain,headache, eyeirritation,physician visit

3 10

2

2

3

10 2

10 2

2

Linn et al. Chamber Exercising, chest tightness, SO SO exposure linked to moderate symptoms(1987; studies. young shortness of in these studies of moderately exercising1988; asthmatics breath, or young asthmatics.1990) and wheezeRoger et al.(1985)

2 2
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-56

Neukirch et Paris, France 40 persons asthma, PM , BS, In single pollutant models, daily levels of PM al. (1998) 11/92-5/93 (mean age wheeze, NO , SO PM , BS, NO , and SO were each mo

of sample shortness of significantly associated with asthmawas 46) breath, cough, attacks, wheeze, cough, respiratory

respiratory infections, and shortness of breath.infection

13

2 2 13 2 2

Ostro et al. Denver, CO 207 severity of SO , PM , Daily levels of H linked to cough, asthma, So(1991) 12/87-2/88 persons asthma SO , NO , and shortness of breath. PM linked to Ex

ages 18-70 symptoms, H , nitric asthma. SO linked to shortness of breath. da

cough, wheeze, acid No effects seen for other pollutants. linshortness of pobreath, chesttightness

2 2.54 3 +

+

2.5

4

Ostro et al. Los Angeles, CA 83 children cough, O , NO , In single pollutant models, daily levels of O(1995) 8/92-11/92 ages 7-12 shortness of SO , PM and PM linked only to shortness of breath. 

breath, wheeze No effect seen for NO and SO .

3 2

2 10

3

10

2 2

Peters et Three cities in 155 children symptom score TSP, SO , Daily SO linked to the respiratory symptomal. (1996) East Germany ages 7-15 based on a SO , score. No link between the other pollutants

and the Czech and 102 variety of particle and the symptom score.Republic adults ages respiratory acidity9/90-6/92 32-80 symptoms

2

4

2

Roemer et 28 locations in 2,010 symptoms PM , BS, Daily pollutant levels not related to adverseal. (1998) Europe children include: NO , SO health symptoms.

winter 1993- ages 6-12 shortness of1994 breath, cough,

phlegm,wheeze, runnynose, sorethroat,headache, eyeirritation

10

2 2

Romieu et Mexico City 71 children cough, phlegm, PM , Controlling for PM , daily levels of O PM al. (1996) ages 5-7 difficulty PM , O , linked to cough and lower respiratory sy

breathing, NO , SO illness. Controlling for O , PM linked to reswheezing, cough, phlegm, and lower respiratory PM lower symptoms. SO respiratoryillness

102.5 3

2 2

2.5 3

3 2.5
Study Period Population Endpoint Pollutants Main Findings 

Location and

D-57

Whittemore Six communities 443 children asthma O , TSP In a two pollutant model, daily levels of both Reand Korn in southern CA and adults TSP and O were significantly related to we(1980) Three 34-week reported asthma attacks. TS

periods 1972- an

1975

x

x
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-58

C-R Functions Linking Air

Pollution and Adverse Health

Effects

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∆ [ ] ∆ asthma adm issions y e pop C O =− ⋅ − ⋅ − ⋅

0 ( ), 1 β

∆obs lung disease admissions y e pop [ ] CO . ( ), =− ⋅ − ⋅ − ⋅∆

0 1 β

∆COPD admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆asthma admissions y e pop [ ] C O =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆dysrhythmias admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-59

Table D-15

Summary of C-R Functions for Carbon Monoxide

Health C-R Function Source of C-R Function

Endpoint

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

asthma where: Other pollutants in model:

y = daily hospital admission rate for asthma per person = 4.75 E-6 PM , O 0

 ̆ = CO coefficient = 0.0332

ÙCO = change in daily average CO concentration (ppm)

pop = population of all ages

) ̆ = standard error of  ̆ = 0.00861

2.5-10 3

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

obstructive where: Other pollutants in model:

lung disease y = daily hospital admission rate for obstructive lung disease per PM , O 0

person = 5.76 E-6

 ̆ = CO coefficient = 0.0250

ÙCO = change in daily average CO concentration (ppm)

pop = population of all ages

) ̆ = standard error of  ̆ = 0.0165

2.5-10 3

hospital Study: Moolgavkar (1997)

admissions – Location: Minneapolis-St. Paul

COPD where: Other pollutants in model: O ,

y = daily hospital admission rate for COPD per person = 3.75 E-5 PM 0

 ̆ = CO coefficient = 0.0573

Ù CO = change in daily average CO concentration (ppm)

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.0329

3

10

hospital Study: Sheppard (1999)

admissions – Location: Seattle, WA

asthma where: Other pollutants in model:

y = daily hospital admission rate for asthma per person = 4.52 E-6 PM 0

 ̆ = CO coefficient = 0.0528

Ù CO = change in daily average CO concentration (ppm)

pop = population of ages < 65

) ̆ = standard error of  ̆ = 0.0185

2.5

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

dysrhythmias Other pollutants in model:

where: PM , O

y = daily hospital admission rate for dysrhythmias per person = 6.46 0

E-6

 ̆ = CO coefficient = 0.0573

ÙCO = change in daily average CO concentration (ppm)

pop = population of all ages

) ̆ = standard error of  ̆ = 0.0229

2.5 3
∆congestive heart failure admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆ischemic heart disease admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆congestiveheart failure admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆cardiovascular admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

∆cardiovascular admissions y e pop [ ] CO =− ⋅ − ⋅ − ⋅∆

0 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

Health C-R Function Source of C-R Function

Endpoint

D-60

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

congestive where: Other pollutants in model: NO

heart failure y = daily hospital admission rate for congestive heart failure per 0

person = 9.33 E-6

 ̆ = CO coefficient = 0.0340

ÙCO = change in daily average CO concentration (ppm)

pop = population of all ages

) ̆ = standard error of  ̆ = 0.0163

2

hospital Study: Schwartz and Morris

admissions – (1995)

ischemic heart where: Location: Detroit, MI

disease y = daily hospital admission rate for ischemic heart disease per Other pollutants in model: 0

person 65 and older = 9.96 E-5 PM

 ̆ = CO coefficient= 0.000467

ÙCO = change in daily one-hour maximum CO concentration (ppm)

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.000435

10

hospital Study: Schwartz and Morris

admissions – (1995)

congestive where: Location: Detroit, MI

heart failure y = daily hospital admission rate for congestive heart failure per Other pollutants in model: 0

person 65 and older = 5.82 E-5 PM

 ̆ = CO coefficient = 0.0170

ÙCO = change in daily one-hour maximum CO concentration (ppm)

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00468

10

hospital Study: Schwartz (1999)

admissions – Location: eight U.S. counties

cardiovascular where: Other pollutants in model:

y = daily hospital admission rate for cardiovascular disease per PM 0

person 65 and older = 2.23 E-4

 ̆ = CO coefficient = 0.0127

ÙCO = change in daily one-hour maximum CO concentration

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00255

10

hospital Study: Schwartz (1997)

admissions – Location: Tucson, AZ

cardiovascular where: Other pollutants in model:

y = daily hospital admission rate for cardiovascular disease per PM 0

person 65 and older = 2.23 E-4

 ̆ = CO coefficient = 0.0139

ÙCO = change in daily one-hour maximum CO concentration

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00715

10
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-61

Nitrogen Dioxide

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∆ [ ] ∆ All respiratory y e pop N O =− ⋅ − ⋅ − ⋅ 0 2 ( ), 1 β

∆ Re ( ) , spiratory Infection Admissions y e pop [ ] N O =− ⋅ − ⋅ − ⋅∆ 0 2 1 β

∆pneumonia admissions y e pop [ ] N O =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

∆Congestive Heart Failure Admissions y e pop [ ] N O =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

D-62

Table D-16Summary of C-R Functions for Nitrogen Dioxide

Health Endpoint C-R Function Source 

hospital admissions – Study: Burnett et al

all respiratory Location: Toronto, C

where: Other pollutants in y = daily hospital admission rate for all respiratory per person = 2.58 E-5 0

 ̆ = NO coefficient = 0.00378 2ÙNO = change in daily 12-hour average NO concentration (ppb) 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.00221

hospital admissions – Study: Burnett et al

respiratory infection Location: Toronto, C

where: Other pollutants in y = daily hospital admission rate for respiratory infection per person = 1.56 E-5 0

 ̆ = NO coefficient = 0.00172 2ÙNO = change in daily average NO concentration (ppb) 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.000521

hospital admissions – Study: Moolgavkar 

pneumonia Location: Minneapo

where: Other pollutants in y = daily hospital admission rate for pneumonia per person = 5.30 E-5 0

 ̆ = NO coefficient = 0.00169 2Ù NO = change in daily average NO concentration (ppb) 2 2pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00125

hospital admissions – Study: Burnett et al

congestive heart failure Location: Toronto, C

where: Other pollutants in y = daily hospital admission rate for congestive heart failure per person = 9.33 E-6 0

 ̆ = NO coefficient = 0.00264 2ÙNO = change in daily average NO concentration (ppb) 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.000769
∆Ischemic Heart Disease Admissions y e pop [ ] N O =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

∆resp symptoms

e e NO gender NO gender pop pre CAAA post CAAA . , , , = + − +

  ⋅ − − ⋅− ⋅ − − ⋅− ⋅ − − 1 1 1 1 α βγ α βγ 2 2

Health Endpoint C-R Function Source 

D-63

hospital admissions – Study: Burnett et al

ischemic heart disease Location: Toronto, C

where: Other pollutants in y = daily hospital admission rate for ischemic heart disease per person = 2.23 E-5 0

 ̆ = NO coefficient = 0.00318 2ÙNO = change in daily average NO concentration (ppb) 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.000521

respiratory symptoms Study: Hasselblad 

where: by Hasselblad et al ̊ = constant = -0.536 gender coefficient w ̆ = NO coefficient = 0.0275 communication with 

2

ı = gender coefficient (used for males only) = -0.0295 Abt Associates. ThÙNO = change in annual NO concentration (ppb) study results by Me

2 2

pop = children ages 6-7) ̆ = standard error of  ̆ = 0.0132

Location: MiddlesboOther pollutants in Comments: The NO 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-64

Ozone

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∆Mortality y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆Mortality y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆ Mortality y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆Mortality y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆Chronic Asthma ∆

y

ye y = − O y pop −⋅ + − 



 



 ⋅ ⋅

0

0 0

0 1 3 ( ) , β

∆All respiratory y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-65

Table D-17

Summary of C-R Functions for Ozone

Health C-R Function Source of C-R Function

Endpoint

mortality Study: Ito and Thurston (1996)

where: Other pollutants in model: PM

y = non-accidental deaths per person of any age 0

 ̆ = ozone coefficient = 0.000634

ÙO = change in daily one-hour maximum ozone concentration (ppb) 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.000251

Location: Chicago, IL

10

mortality Study: Kinney et al. (1995)

where: Other pollutants in model: PM

y = non-accidental deaths per person of any age 0

 ̆ = ozone coefficient = 0 

ÙO = change in daily 1-hour maximum ozone concentration (ppb) 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.000214

Location: Los Angeles, CA

10

mortality Study: Moolgavkar et al. (1995)

where: Other pollutants in model: SO ,

y = non-accidental deaths per person of any age TSP 0

 ̆ = ozone coefficient = 0.000611 

ÙO = change in daily average ozone concentration (ppb) 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.000216

Location: Philadelphia, PA

2

mortality Study: Samet et al. (1997)

where: Other pollutants in model: CO,

y = non-accidental deaths per person of any age NO , SO , TSP 0

 ̆ = ozone coefficient = 0.000936 

ÙO = change in daily average ozone concentration (ppb) 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.000312

Location: Philadelphia, PA

2 2

adult onset Study: McDonnell et al. (1999)

asthma Location: California

where:

y = annual asthma incidence rate per person = 0.00219 0

 ̆ = estimated O coefficient = 0.0277 3

ÙO = change in annual average 8-hour O concentration 3 3

pop = population of non-asthmatic males ages 27 and older

) ̆ = standard error of  ̆ = 0.0135

Other pollutants in model: none

hospital Study: Burnett et al. (1997b)

admissions – Location: Toronto, Canada

all respiratory where: Other pollutants in model:

y = daily hospital admission rate for all respiratory per person = 2.58 PM , NO , SO 0

E-5

 ̆ = O coefficient = 0.00498 3

ÙO = change in daily 12-hour average O concentration (ppb) 3 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.00106

2.5-10 2 2
∆Asthma Admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆Obstructive Lung Disease Admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆ Re ( ) , spiratory Infection Admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 1 β

∆ ∆ all respiratory admissions O pop =⋅ ⋅ β 3 ,

∆pneumonia admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆COPD admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

Health C-R Function Source of C-R Function

Endpoint

D-66

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

asthma where: Other pollutants in model: CO,

y = daily hospital admission rate for asthma per person = 4.75 E-6 PM 0

 ̆ = ozone coefficient = 0.00250

ÙO = change in daily average ozone concentration (ppb) 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.000718

2.5-10

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

obstructive where: Other pollutants in model: CO,

lung disease y = daily hospital admission rate for obstructive lung disease per PM 0

person = 5.76 E-6

 ̆ = ozone coefficient = 0.00303

ÙO = change in daily average ozone concentration (ppb) 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.00110

2.5-10

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

respiratory where: Other pollutants in model:

infection y = daily hospital admission rate for respiratory infection per person = PM , NO 0

1.56 E-5

 ̆ = ozone coefficient = 0.00198

ÙO = change in daily average ozone concentration (ppb) 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.000520

2.5 2

hospital Study: Thurston et al. (1994)

admissions – Location: Toronto, Canada

all respiratory where: Other pollutants in model: PM

 ̆ = ozone coefficient = 1.68 E-8

ÙO = change in daily one-hour maximum ozone concentration (ppb) 3

pop = population all ages

) = standard error of  ̆ = 9.71 E-9 .  ̆

2.5

hospital Study: Moolgavkar et al. (1997)

admissions – Location: Minneapolis, MN

pneumonia where: Other pollutants in model: SO ,

y = daily hospital admission rate for pneumonia per person = 5.30 E-5 NO , PM 0

 ̆ = O coefficient = 0.00370 3

ÙO = change in daily average O concentration (ppb) 3 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00103

2

2 10

hospital Study: Moolgavkar et al. (1997)

admissions – Location: Minneapolis, MN

COPD where: Other pollutants in model: CO,

y = daily hospital admission rate for COPD per person = 3.75 E-5 PM 0

 ̆ = O coefficient = 0.00274 3

ÙO = change in daily average O concentration (ppb) 3 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00170

10
∆pneumonia admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆pneumonia admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆COPD admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆all respiratory admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆all respiratory related admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆cardiac y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

Health C-R Function Source of C-R Function

Endpoint

D-67

hospital Study: Schwartz (1994c)

admissions – Location: Minneapolis, MN

pneumonia where: Other pollutants in model: PM

y = daily hospital admission rate for pneumonia per person = 5.30 E-5 0

 ̆ = O coefficient = 0.00280 3

ÙO = change in daily average O concentration (ppb) 3 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00172

10

hospital Study: Schwartz (1994b)

admissions – Location: Detroit, MI

pneumonia where: Other pollutants in model: PM

y = daily hospital admission rate for pneumonia per person = 5.18 E-5 0

 ̆ = O coefficient = 0.00521 3

ÙO = change in daily average O concentration (ppb) 3 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.0013

10

hospital Study: Schwartz (1994b)

admissions – Location: Detroit, MI

COPD where: Other pollutants in model: PM

y = daily hospital admission rate for COPD per person = 3.05 E-5 0

 ̆ = O coefficient = 0.00549 3

ÙO = change in daily average O concentration 3 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00205

10

hospital Study: Schwartz (1995)

admissions – Location: New Haven, CT

all respiratory where: Other pollutants in model: PM

y = daily hospital admissions for all respiratory per person 65 and 0

older = 1.187 E-4

 ̆ = ozone coefficient = 0.00265

ÙO = change in daily average ozone concentration (ppb) 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00140

10

hospital Study: Schwartz (1995)

admissions – Location: Tacoma, WA

all respiratory where: Other pollutants in model: PM

y = daily hospital admissions for all respiratory conditions per person 0

65 and older = 1.187 E-4

 ̆ = ozone coefficient = 0.00715

ÙO = change in daily average ozone concentration (ppb) 3

pop = population age 65 and older

) ̆ = standard error of  ̆ = 0.00257

10

hospital Study: Burnett et al. (1997b)

admissions – Location: Toronto, Canada

cardiac where: Other pollutants in model:

y = daily hospital admission rate for cardiac per person = 3.81 E-5 PM 0

 ̆ = O coefficient = 0.00531 3

ÙO = change in daily 12-hour average O concentration (ppb) 3 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.00142

2.5-10
∆Dysrhythmias Admissions y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

∆ ∆ asthm a related ER visits BasePop = ⋅⋅ O pop β 3 ,

∆ ∆ asthma related ER visits

BasePop = ⋅⋅ O pop β 3 ,

∆ ∆ asthm a related ER visits BasePop = ⋅⋅ O pop β 3 ,

∆ ∆ ARD O pop 2 ≅ ⋅⋅ βPM 10 3

* ,

∆asthma attacks ∆

y

ye y = − O y pop −⋅ + − 



 



 ⋅ ⋅

0

0 0

0 1 3 ( ) , β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

Health C-R Function Source of C-R Function

Endpoint

D-68

hospital Study: Burnett et al. (1999)

admissions – Location: Toronto, Canada

dysrhythmias where: Other pollutants in model:

y = daily hospital admission rate for dysrhythmias per person = 6.46 PM , CO 0

E-6

 ̆ = ozone coefficient = 0.00168

ÙO = change in daily average ozone concentration (ppb) 3

pop = population of all ages

) ̆ = standard error of  ̆ = 0.00103

2.5

emergency Study: Cody et al. (1992)

room visits - Location: Northern NJ

asthma Other pollutants in model: none

where: Comment: 63 % of estimate

 ̆ = ozone coefficient = 0.0203 used to avoid double-counting

BasePop = baseline population in northern New Jersey = 4,436,976 hospital admissions for

ÙO = change in daily five-hour average ozone concentration (ppb) asthma. 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.00717

emergency Study: Weisel et al. (1995)

room visits - Location: Northern, NJ

asthma Other pollutants in model: none

where: Comment: 63 % of estimate

 ̆ = ozone coefficient = 0.0443 used to avoid double-counting

BasePop = baseline population in northern New Jersey = 4,436,976 hospital admissions for

ÙO = change in daily five-hour average ozone concentration (ppb) asthma. 3

pop = population all ages

) ̆ = standard error of  ̆ = 0.00723

emergency Study: Stieb et al. (1996)

room visits - Location: New Brunswick,

asthma Canada

where: Other pollutants in model: none

 ̆ = ozone coefficient = 0.0035 Comment: 63 % of estimate

BasePop = baseline population in Saint John, New Brunswick = 125,000 used to avoid double-counting

ÙO = change in the daily one-hour maximum ozone concentration hospital admissions for 3

(ppb) asthma. 

pop = population all ages

) ̆ = standard error of  ̆ = 0.0018

presence of Study: Krupnick et al. (1990)

any of 19 Location: Glendora-Covinaacute where: Azusa, CA

respiratory  ̆ = first derivative of the stationary probability = 0.000137 Other pollutants in model: SO ,

symptoms ÙO = change in daily one-hour maximum ozone concentration (ppb) COH

*

3

pop = population aged 18-65 years old

) ̆ = standard error of  ̆ = 0.0000697 *

2

self-reported Study: Whittemore and Korn

asthma (1980)

attacks Location: Los Angeles, CA

where: Other pollutants in model: TSP

y = daily incidence of asthma attacks = 0.027 0

 ̆ = ozone coefficient = 0.00184

ÙO = change in daily one-hour maximum ozone concentration (ppb) 3

pop = population of asthmatics of all ages = 5.61% of the population

of all ages

) ̆ = standard error of  ̆ = 0.000714
∆MRAD y e pop [ ] O =− ⋅ − ⋅ − ⋅∆

0 3 ( ), 1 β

The Benefits and Costs of the Clean Air Act, 1990 to 2010

Health C-R Function Source of C-R Function

Endpoint

D-69

respiratory Study: Ostro and Rothschild

and (1989b)

nonrespiratory where: Location: U.S.

conditions y = daily MRAD incidence per person = 0.02137 Other pollutants in model: PM

resulting in a  ̆ = inverse-variance weighted PM coefficient = 0.00220 Comments: An inverseminor ÙO = change in two-week average of the daily one-hour maximum variance weighting used to

restricted ozone concentrations (ppb) estimate the coefficient, based

activity day pop = adult population aged 18 to 65 on Ostro and Rothschild.

(MRAD) ) = standard error of  ̆ = 0.000658

0

2.5

3

 ̆

2.5
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-70

Particulate Matter

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∆Nonaccidental Mortality y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆Nonaccidental Mortality y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆ ∆ Infant Mortality y ye y = − P M y pop −⋅ + −   ⋅ ⋅ 0 0 0 0 1 1 0 ( ) , β

∆Chronic Bronchitis y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

D-71

Table D-18Summary of C-R Functions for Particulate Matter

Health C-R Function Source 

Endpoint

mortality Study: Pope et al. (

where: Other pollutants in my = county-level annual non-accidental deaths of persons ages 30+ per person 0

 ̆ = PM coefficient = 0.006408 2.5ÙPM = change in annual median 2.5 2.5 PM concentrationpop = population ages 30 and older) ̆ = standard error of  ̆ = 0.001509

Location: 50 U.S. c

mortality Study: Dockery et a

where: Other pollutants in my = county-level annual non-accidental deaths of persons ages 25+ per person 0

 ̆ = PM coefficient = 0.0124 2.5ÙPM = change in annual mean PM concentration 2.5 2.5pop = population ages 25 and older) ̆ = standard error of  ̆ = 0.00423

Location: six U.S. c

neonatal Study: Woodruff et 

mortality Location: 86 U.S. m

where:y = county annual postneonatal infant deaths per infant 0-1 years old 0

 ̆ = PM coefficient = 0.00392 10ÙPM = change in annual average PM concentration 10 10pop = population infants ages 0-1) ̆ = standard error of  ̆ = 0.00122

Other pollutants in m

chronic Study: Abbey et al. 

bronchitis Location: California

where: Other pollutants in my = annual bronchitis incidence rate per person = 0.00378 Comments: Abbey 

0

 ̆ = estimated PM logistic regression coefficient = 0.00932 PM. The TSP coeff

10

ÙPM = change in annual average PM concentration in PM . 10 10pop = population ages 27 and older "without chronic bronchitis") ̆ = standard error of  ̆ = 0.00475

10
∆Chronic Bronchitis y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆Chronic Bronchitis ∆y ye y y zy = − P M pop −⋅ + −   ⋅   ⋅ ⋅ 0 0 0 0 0 1 0 1 0 ( ) , β

∆ [ ] ∆ All respiratory y e pop P M =− ⋅ − ⋅ − ⋅ − 0 2 5 10 ( ), 1 β .

∆ [ ] ∆ Asthma Admissions y e pop P M =− ⋅ − ⋅ − ⋅ − 0 2 5 10 ( ), 1 β .

Health C-R Function Source 

Endpoint

D-72

chronic Study: Abbey et al. 

bronchitis Location: California

where: Other pollutants in my = annual bronchitis incidence rate per person = 0.00378 0

 ̆ = estimated PM logistic regression coefficient = 0.09132 2.5ÙPM = change in annual average PM concentration 2.5 2.5pop = population ages 27 and older "without chronic bronchitis") ̆ = standard error of  ̆ = 0.00680

chronic Study: Schwartz (19

bronchitis Location: 53 U.S. u

where:y = national chronic bronchitis prevalence rate for individuals 18 and older= 0.0535 0

z = annual bronchitis incidence rate per person = 0.00378 0 ̆ = estimated PM logistic regression coefficient = 0.0123 10ÙPM = change in annual average PM concentration 10 10pop = population ages 30 and older "without chronic bronchitis") ̆ = standard error of  ̆ = 0.00434

Other pollutants in m

hospital Study: Burnett et al

admissions – all Location: Toronto, C

respiratory where: Other pollutants in m

y = daily hospital admission rate for all respiratory per person = 2.58 E-5 0

 ̆ = PM coefficient = 0.00147 2.5-10ÙPM = change in daily average PM concentration 2.5-10 2.5-10pop = population of all ages) ̆ = standard error of  ̆ = 0.00179

hospital Study: Burnett et al

admissions – Location: Toronto, C

asthma where: Other pollutants in m

y = daily hospital admission rate for asthma per person = 4.75 E-6 0

 ̆ = PM coefficient = 0.00321 2.5-10ÙPM = change in daily average PM concentration 2.5-10 2.5-10pop = population of all ages) ̆ = standard error of  ̆ = 0.00106
∆ [ ] ∆ O LD Adm issions y e pop P M =− ⋅ − ⋅ − ⋅ − 0 2 5 10 ( ), 1 β .

∆ Re ( ) , [ ] . spiratory Infection Admissions y e pop PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 1 β

∆ ∆ all respiratory admissions PM pop =⋅ ⋅ β 2 5. ,

∆pneumonia admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆COPD admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

Health C-R Function Source 

Endpoint

D-73

hospital Study: Burnett et al

admissions – Location: Toronto, C

obstructive lung where: Other pollutants in m

disease y = daily hospital admission rate for obstructive lung disease per person = 5.76 E-6 0

 ̆ = PM coefficient = 0.00310 2.5-10Ù PM = change in daily average PM concentration 2.5-10 2.5-10pop = population of all ages) ̆ = standard error of  ̆ = 0.00163

hospital Study: Burnett et al

admissions – Location: Toronto, C

respiratory where: Other pollutants in m

infection y = daily hospital admission rate for respiratory infection per person = 1.56 E-5 0

 ̆ = PM coefficient = 0.00328 2.5ÙPM = change in daily average PM concentration 2.5 2.5pop = population of all ages) ̆ = standard error of  ̆ = 0.000735

hospital Study: Thurston et a

admissions – all Location: Toronto, C

respiratory where: Other pollutants in m

 ̆ = ozone coefficient = 1.81 E-8ÙPM = change in daily average PM 2.5 2.5

pop = population all ages) = standard error of  ̆ = 1.79 E-8 .  ̆

hospital Study: Moolgavkar 

admissions – Location: Minneapo

pneumonia where: Other pollutants in m

y = daily hospital admission rate for pneumonia per person = 5.30 E-5 0

 ̆ = PM coefficient = 0.000498 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000505

hospital Study: Moolgavkar 

admissions – Location: Minneapo

COPD where: Other pollutants in m

y = daily hospital admission rate for COPD per person = 3.75 E-5 0

 ̆ = PM coefficient = 0.000877 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000777
∆pneumonia admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆COPD admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆pneumonia admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆COPD admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆pneumonia admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

Health C-R Function Source 

Endpoint

D-74

hospital Study: Schwartz (19

admissions – Location: Minneapo

pneumonia where: Other pollutants in m

y = daily hospital admission rate for pneumonia per person = 5.18 E-5 0

 ̆ = PM coefficient = 0.00157 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000677

hospital Study: Schwartz (19

admissions – Location: Minneapo

COPD where: Other pollutants in m

y = daily hospital admission rate for COPD per person = 3.75 E-5 0

 ̆ = PM coefficient = 0.00451 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00138

hospital Study: Schwartz (19

admissions – Location: Birmingha

pneumonia where: Other pollutants in m

y = daily hospital admission rate for pneumonia per person = 5.30 E-5 0

 ̆ = PM coefficient = 0.00174 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000536

hospital Study: Schwartz (19

admissions – Location: Birmingha

COPD where: Other pollutants in m

y = daily hospital admission rate for COPD per person = 3.75 E-5 0

 ̆ = PM coefficient = 0.00239 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000838

hospital Study: Schwartz (19

admissions – Location: Detroit, M

pneumonia where: Other pollutants in m

y = daily hospital admission rate for pneumonia per person = 5.18 E-5 0

 ̆ = PM coefficient = 0.00115 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00039
∆COPD admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆ all respiratory admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β∆all respiratory admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆all respiratory related admissions y e pop [ ] P M =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆Asthma Admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

Health C-R Function Source 

Endpoint

D-75

hospital Study: Schwartz (19

admissions – Location: Detroit, M

COPD where: Other pollutants in m

y = daily hospital admission rate for COPD per person = 3.05 E-5 0

 ̆ = PM coefficient = 0.00202 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00059

hospital Study: Schwartz (19

admissions – all Location: Spokane, 

respiratory where: Other pollutants in m

y = daily hospital admission rate for all respiratory per person 65 or older = 1.187 E-4 0

 ̆ = PM coefficient = 0.00163 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000470

hospital Study: Schwartz (19

admissions – all Location: New Have

respiratory where: Other pollutants in m

y = daily hospital admissions for all respiratory per person 65 and older = 1.187 E-4 0

 ̆ = PM coefficient = 0.00172 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000930

hospital Study: Schwartz (19

admissions – all Location: Tacoma, 

respiratory where: Other pollutants in m

y = daily hospital admissions for all respiratory conditions per person 65 and older = 1.187 E-4 0

 ̆ = PM coefficient = 0.00227 10Ù PM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00145

hospital Study: Sheppard et 

admissions – Location: Seattle, W

asthma where: Other pollutants in m

y = daily hospital admission rate for asthma per person = 4.52 E-6 0

 ̆ = PM coefficient = 0.0027 2.5ÙPM = change in daily average PM concentration 2.5 2.5pop = population of ages < 65) ̆ = standard error of  ̆ = 0.000948
∆ [ ] ∆ cardiac y e pop P M =− ⋅ − ⋅ − ⋅ − 0 2 5 10 ( ), 1 β .

∆Dysrhythmias Admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆Ischemic Heart Disease Admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆CongestiveHeart Failure Admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆cardiovascular admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

Health C-R Function Source 

Endpoint

D-76

hospital Study: Burnett et al

admissions – Location: Toronto, C

cardiac where: Other pollutants in m

y = daily hospital admission rate for cardiac per person = 3.81 E-5 0

 ̆ = PM coefficient = 0.00704 2.5-10ÙPM = change in daily average PM concentration 2.5-10 2.5-10pop = population of all ages) ̆ = standard error of  ̆ = 0.00215

hospital Study: Burnett et al

admissions – Location: Toronto, C

dysrhythmias where: Other pollutants in m

y = daily hospital admission rate for dysrhythmias per person = 6.46 E-6 0

 ̆ = PM coefficient = 0.00136 2.5ÙPM = change in daily average PM concentration 2.5 2.5pop = population of all ages) ̆ = standard error of  ̆ = 0.000910

hospital Study: Schwartz an

admissions – Location: Detroit, M

ischemic heart where: Other pollutants in m

disease y = daily hospital admission rate for ischemic heart disease per person 65 and older = 9.96 E-5 0

 ̆ = PM coefficient= 0.000496 10ÙPM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000220

hospital Study: Schwartz an

admissions – Location: Detroit, M

congestive heart where: Other pollutants in m

failure y = daily hospital admission rate for congestive heart failure per person 65 and older = 5.82 E-5 0

 ̆ = PM coefficient = 0.000741 10ÙPM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000311

hospital Study: Schwartz (19

admissions – Location: eight U.S

cardiovascular where: Other pollutants in m

y = daily hospital admission rate for cardiovascular disease per person 65 and older = 2.23 E-4 0

 ̆ = PM coefficient = 0.000737 10ÙPM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000170
∆cardiovascular admissions y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆ asthma visits y e pop [ ] PM =− ⋅ − ⋅ − ⋅∆ 0 10 ( ), 1 β

∆Acute Bronchitis ∆y ye y = − P M y pop −⋅ + −   ⋅ ⋅ 0 0 0 0 1 2 5 ( ) , . β

( ) ∆ ∆ Lower spiratory Symptom s y ye y y pop Re . P M . = − −⋅ + −   ⋅ ⋅ 0 0 0 0 1 2 5 β

Health C-R Function Source 

Endpoint

D-77

hospital Study: Schwartz (19

admissions – Location: Tucson, A

cardiovascular where: Other pollutants in m

y = daily hospital admission rate for cardiovascular disease per person 65 and older = 2.23 E-4 0

 ̆ = PM coefficient = 0.00102 10ÙPM = change in daily average PM concentration 10 10pop = population age 65 and older) ̆ = standard error of  ̆ = 0.000423

emergency room Study: Schwartz (19

visits Location: Seattle, W

where: Other pollutants in my = daily ER visits for asthma per person under 65 years old = 7.69 E-6 0 ̆ = PM coefficient (Schwartz et al., 1993, p. 829) = 0.00367 10ÙPM = change in daily average PM concentration 10 10pop = population ages 0-64) ̆ = standard error of  ̆ (Schwartz et al., 1993, p. 829) = 0.00126

acute bronchitis Study: Dockery et a

where:y = annual bronchitis incidence rate per person = 0.044 0

 ̆ = estimated PM logistic regression coefficient = 0.0272 2.5ÙPM = change in annual average PM concentration 2.5 2.5pop = population ages 8-12) ̆ = standard error of  ̆ = 0.0171

Location: 24 U.S. aOther pollutants in m

lower respiratory Study: Schwartz, et 

symptoms (LRS) Location: six U.S. c

defined as Other pollutants in m

cough, chest where:

pain, phlegm, y = daily lower respiratory symptom incidence rate per person = 0.0012

and wheeze  ̆ = estimated PM logistic regression coefficient = 0.01823

0

2.5

ÙPM = change in daily average PM concentration 2.5 2.5pop = population ages 7-14) ̆ = standard error of  ̆ = 0.00586
∆Shortness of Breath ∆y ye y = − P M y pop −⋅ + −   ⋅ ⋅ 0 0 0 0 1 1 0 ( ) , β

∆Upper spiratory Symptom s ∆y ye y R e P M y pop ( ) = − , −⋅ + −   ⋅ ⋅ 0 0 0 0 1 1 0 β

∆ ∆ ARD PM pop 2 ≅⋅ ⋅ βPM 10 10 * ,

∆Days Moderate Worse Asthma

P M

P M pop

after

before

/ ln , . ,

. ,

=− ⋅

  β ⋅ 2 5 2 5

Health C-R Function Source 

Endpoint

D-78

Shortness of Study: Ostro et al. (

breath, days Location: Los Ange

where:y = daily shortness of breath incidence rate per person = 0.056 0 ̆ = estimated PM logistic regression coefficient = 0.00841 10ÙPM = change in daily average PM concentration 10 10pop = asthmatic African-American population ages 7 to 12) ̆ = standard error of  ̆ = 0.00363

Other pollutants in m

URS, defined as Study: Pope et al. (

runny or stuffy Location: Utah Valle

nose, wet Other pollutants in m

cough, burning, where:

aching, or red y = daily upper respiratory symptom incidence rate per person = 0.3419

eyes  ̆ = estimated PM logistic regression coefficient = 0.0036

0

10

ÙPM = change in daily average PM concentration 10 10pop = asthmatic population ages 9 to 11 = 6.91% of population ages 9 to 11) ̆ = standard error of  ̆ = 0.0015

presence of any Study: Krupnick et a

of 19 acute Location: Glendora-

respiratory where: Other pollutants in m

symptoms  ̆ = first derivative of the stationary probability = 0.000461 Comments: COH u

*

ÙPM = change in daily average PM concentration The estimated COH 

10 10

pop = population ages 18-65 PM data.) ̆ = standard error of  ̆ = 0.000239 *

10

moderate or Study: Ostro et al. (

worse asthma Location: Denver

status Other pollutants in m

where: Comments: The est ̆ = estimated PM coefficient for year i = 0.0006 to populations of all 

2.5

PM = change in daily average PM concentration 2.5 2.5pop = asthmatic population of all ages = 5.61% of the population of all ages) ̆ = standard error of  ̆ = 0.0003
∆asthma attacks ∆y ye y = − P M y pop −⋅ + −   ⋅ ⋅ 0 0 0 0 1 1 0 ( ) , β

∆ ∆ RAD y pop y e pop [ ] PM = ⋅ =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆ ∆ MRAD y pop y e pop [ ] PM = ⋅ =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

∆ ∆ WLD y pop y e pop [ ] PM = ⋅ =− ⋅ − ⋅ − ⋅∆ 0 2 5 ( ), 1 β .

Health C-R Function Source 

Endpoint

D-79

asthma attacks Study: Whittemore 

where:y = daily incidence of asthma atta 0 cks = 0.027

 ̆ = PM coefficient = 0.00144 10ÙPM = change in daily PM concentration 10 10pop = population of asthmatics of all ages = 5.61% of the population of all ages ) ̆ = standard error of  ̆ = 0.000556

Location: Los AngeOther pollutants in m

Restricted Study: Ostro (1987)

Activity Days Location: U.S. metr

(RADs) where: Other pollutants in m

y = daily RAD incidence rate per person = 0.0177 Comments: An inve

0

 ̆ = inverse-variance weighted PM coefficient = 0.00475 to estimate the coef

2.5

ÙPM = change in daily average PM concentration (1987, Table III)]. 2.5 2.5pop = adult population ages 18 to 65 ) ̆ = standard error of  ̆ = 0.00029

respiratory and Study: Ostro and R

nonrespiratory Location: U.S.

conditions where: Other pollutants in m

resulting in a y = daily MRAD daily incidence rate per person = 0.02137 Comments: An inve

minor restricted  ̆ = inverse-variance weighted PM coefficient = 0.00741 to estimate the coef

activity day ÙPM = change in daily average PM concentration Rothschild (1989b, 

(MRAD) pop = adult population ages 18 to 65 

0

2.5

2.5 2.5

) ̆ = standard error of  ̆ = 0.0007

work loss days Study: Ostro (1987)

(WLDs) Location: U.S. metr

where: Other pollutants in my = daily work-loss-day incidence rate per person = 0.00648 Comments: An inve

0

 ̆ = inverse-variance weighted PM coefficient = 0.0046 to estimate the coef

2.5

ÙPM = change in daily average PM concentration (1987, Table III). 2.5 2.5pop = population ages 18 to 65 ) ̆ = standard error of  ̆ = 0.00036
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-80

Sulfur Dioxide

7KH &05 IXQFWLRQV XVHG WR HVWLPDWH WKH LPSDFW RI

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∆All respiratory y e pop [ ] SO =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

∆pneumonia admissions y e pop [ ] SO =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

∆Ischemic Heart Disease Admissions y e pop [ ] SO =− ⋅ − ⋅ − ⋅∆ 0 2 ( ), 1 β

∆symptoms

e e

SO SO pop pre CAAA post CAAA = + − +

  ⋅ −− ⋅− − − ⋅− − − 1 1 1 1 α βγ α βγ 2 2 , , ,

D-81

Table D-19Summary of C-R Functions for Sulfur Dioxide

Health Endpoint Concentration-Response Function Source of C-R 

hospital admissions Study: Burnett et al. (1997b)

– all respiratory Location: Toronto, Canada

where: Other pollutants in model: PM , y = daily hospital admission rate for all respiratory per person = 2.58 E-5 0

 ̆ = SO coefficient = 0.00446 2ÙSO = change in daily one-hour maximum SO concentration (ppb) 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.00293

2.5-10 

hospital admissions Study: Moolgavkar et al. (1997)

– pneumonia Location: Minneapolis, MN

where: Other pollutants in model: O , NO y = daily hospital admission rate for pneumonia per person = 5.30 E-5 0

 ̆ = SO coefficient = 0.00143 2Ù SO = change in daily average SO concentration (ppb) 2 2pop = population age 65 and older) ̆ = standard error of  ̆ = 0.00290

3 2 

hospital admissions Study: Burnett et al. (1999)

– ischemic heart Location: Toronto, Canada

disease where: Other pollutants in model: NO

y = daily hospital admission rate for ischemic heart disease per person = 2.23 E-5 0

 ̆ = SO coefficient = 0.00177 2ÙSO = change in daily average SO concentration 2 2pop = population of all ages) ̆ = standard error of  ̆ = 0.000854

2

chest tightness, Study: Linn et al. (1987; 1988; 199

shortness of breath, Location: Chamber study

or wheeze Other pollutants in model: none

.where: Comments: The results of four cha ̊ = constant = -5.65 combined to develop this C-R funct ̆ = SO coefficient = 0.00589 compose one third of exercising as

2

ı = status coefficient (used for moderate asthmatics only) = 1.10 compose the other two thirds.SO = peak five minute SO concentration (ppb) in an hour = hourly SO 2 2 2

concentration (ppb) multiplied by 2.5 peak to mean ratio of 2.5

pop = exercising asthmatics = population of asthmatics of all ages (5.61% of the

population of all ages (Adams et al., 1995 Table 57)) of whom 1.7% areexercising. Moderate asthmatics compose one third of exercising asthmatics;mild asthmatics compose the other two thirds (U.S. EPA, 1997, p. D-39).

) ̆ = standard error of  ̆ = 0.00247
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-82

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D-85

Table D-20Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants (Pre-CAAA minus Post-CAAA) 

Population within 50 km of a Monitor (avoided cases per year)

2000 2010 % of Ba

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 

th th th th

Mortality

ages 30 and older PM 7,900 13,000 18,000 13,000 20,000 28,000 0.67% 

Chronic Illness

chronic bronchitis PM 2,300 11,000 20,000 4,300 18,000 30,000 2.05% chronic asthma O 910 4,900 8,300 1,100 5,500 8,700 3.46% 

3

Hospitalization

respiratory PM, CO, NO , 7,300 12,000 17,000 12,000 19,000 29,000 0.52% admissions SO , O

2

2 3

cardiovascular PM, CO, NO , 5,400 19,000 41,000 9,400 38,000 90,000 0.59% admissions SO , O

2

2 3

emergency room PM, O 200 1,300 6,600 310 2,000 10,000 0.19% visits for asthma

3

Minor Illness

acute bronchitis PM 0 25,000 49,000 0 40,000 79,000 3.19% 

c c

upper respiratory PM 170,000 570,000 970,000 260,000 870,000 1,500,000 0.61% symptomslower respiratory PM 130,000 270,000 420,000 210,000 440,000 670,000 2.19% symptomsrespiratory illness NO 24,000 110,000 180,000 63,000 270,000 450,000 7.62% 

2

moderate or PM 32,000 210,000 370,000 48,000 310,000 570,000 0.15% worse asthmadasthma attacks O , PM 520,000 960,000 1,400,000 800,000 1,500,000 2,100,000 0.74% 

d 3
2000 2010 % of Ba

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 

th th th th

D-86

chest tightness, SO 200 80,000 370,000 270 100,000 470,000 0.003% shortness ofbreath, or wheeze

2

shortness of PM 16,000 57,000 95,000 25,000 88,000 150,000 1.25% breathwork loss days PM 1,900,000 2,200,000 2,500,000 3,100,000 3,500,000 4,000,000 0.60% minor restricted O , PM 14,000,000 17,000,000 20,000,000 22,000,000 27,000,000 32,000,000 1.47% activity days / anyof 19 respiratorysymptomse

3

restricted activity PM 5,500,000 6,200,000 6,800,000 9,000,000 10,000,000 11,000,000 0.61% daysd

The baseline incidence generally is the same as that used in the C-R function for a particular health effect. However, there are a few exceptions. To ca

a rate for respiratory-related hospital admissions, we used admissions for persons of all ages for ICD codes 460-519; for cardiovascular admissions, we uall ages for ICD codes 390-429; for emergency room visits for asthma, we used the estimated ER visit rate for persons of all ages; for chronic bronchitis individuals 27 and older; for the pooled estimate of minor restricted activity days and any-of-19 respiratory symptoms, we used the incidence rate for mi

 Calculated as the ratio of avoided mortality to the projected baseline annual non-accidental mortality for adults aged 30 and over. Non-accidental morta

btotal mortality for this subpopulation in 2010.

 Monte Carlo modeling returned a negative value for the fifth percentile estimate of this endpoint. However, we believe the negative result represents an art

cemployed in the uncertainty analysis, since none of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations oin this document. 

 These health endpoints overlap with the "any-of-19 respiratory symptoms" category. As a result, although we present estimates for each endpoint ind

daggregated into the total benefits estimates. 

 Minor restricted activity days and any-of-19 respiratory symptoms have overlapping definitions and are pooled.

e
D-87

Table D-21Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants (Pre-CAAA minus Post-CAAA) 

Population (avoided cases per year)

2000 2010 % o

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 

th th th th

Mortality

ages 30 and older PM 8,800 14,000 19,000 14,000 23,000 32,000 0

Chronic Illness

chronic bronchitis PM 3,100 13,000 22,000 5,000 20,000 34,000 2chronic asthma O 1,300 5,600 9,600 1,800 7,200 12,000 3

3

Hospitalization

respiratory PM, CO, NO , 8,100 13,000 20,000 13,000 22,000 34,000 0admissions SO , O

2

2 3

cardiovascular PM, CO, NO , 5,800 22,000 48,000 10,000 42,000 100,000 0admissions SO , O

2

2 3

emergency room PM, O 260 3,100 8,900 430 4,800 14,000 0visits for asthma

3

Minor Illness

acute bronchitis PM 0 29,000 59,000 0 47,000 94,000 3

c c

upper respiratory PM 180,000 620,000 1,000,000 280,000 950,000 1,600,000 0symptomslower respiratory PM 150,000 320,000 480,000 240,000 520,000 770,000 2symptomsrespiratory illness NO 31,000 130,000 220,000 76,000 330,000 550,000 4

2

moderate or PM 52,000 260,000 460,000 80,000 400,000 720,000 0worse asthmadasthma attacks O , PM 590,000 1,100,000 1,600,000 920,000 1,700,000 2,500,000 0

d 3
2000 2010 % o

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 

th th th th

D-88

chest tightness, SO 220 88,000 410,000 290 110,000 520,000 0shortness ofbreath, or wheeze 

2

shortness of PM 16,000 59,000 98,000 26,000 91,000 150,000 1breathwork loss days PM 2,200,000 2,500,000 2,900,000 3,600,000 4,100,000 4,600,000 0minor restricted O , PM 16,000,000 19,000,000 23,000,000 25,000,000 31,000,000 37,000,000 1activity days / anyof 19 respiratorysymptomse

3

restricted activity PM 6,400,000 7,200,000 7,900,000 10,000,000 12,000,000 13,000,000 0daysd

The baseline incidence generally is the same as that used in the C-R function for a particular health effect. However, there are a few exceptions. To ca

a rate for respiratory-related hospital admissions, we used admissions for persons of all ages for ICD codes 460-519; for cardiovascular admissions, we uall ages for ICD codes 390-429; for emergency room visits for asthma, we used the estimated ER visit rate for persons of all ages; for chronic bronchitis individuals 27 and older; for the pooled estimate of minor restricted activity days and any-of-19 respiratory symptoms, we used the incidence rate for mi

 Calculated as the ratio of avoided mortality to the projected baseline annual non-accidental mortality for adults aged 30 and over. Non-accidental morta

btotal mortality for this subpopulation in 2010.

 Monte Carlo modeling returned a negative value for the fifth percentile estimate of this endpoint. However, we believe the negative result represents an art

cemployed in the uncertainty analysis, since none of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations oin this document.

 These health endpoints overlap with the "any-of-19 respiratory symptoms" category. As a result, although we present estimates for each endpoint ind

daggregated into the total benefits estimates. 

 Minor restricted activity days and any-of-19 respiratory symptoms have overlapping definitions and are pooled.

e
D-89

Table D-22Mortality Distribution by Age in Primary Analysis, Based on Pope et al. (1995)

Age Group Proportion of Premature Mortality by Age Life E

a

Infants not estimated 

1-29 not estimated 

30-34 1% 35-44 4% 45-54 6% 55-64 12% 65-74 24% 75-84 30% 

85+ 24% 

 Percentages sum to 101 percent due to rounding. a
D-90

Table D-23Illustrative Estimates of the Impact of Criteria Pollutants on Mortality – 48 State U.S. Population within 50 km of a year)

2000 2010 % 

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 

th th th th

ages 30 and older PM 7,900 13,000 18,000 13,000 20,000 28,000 

(Pope et al., 1995)

ages 25 and older PM 12,000 29,000 46,000 20,000 47,000 73,000 

(Dockery et al., 1993)*

all ages * O 81 1,100 2,200 130 1,600 3,400 

3

post-neonatal * PM 39 81 120 59 120 180 

*The Dockery et al. (1993), ozone mortality, and post-neonatal mortality estimates are not aggregated into total benefits estimates.

Table D-24Illustrative Estimates of the Impact of Criteria Pollutants on Mortality – 48 State U.S. Population (cases per year)

2000 2010 % 

Endpoint Pollutant 5 % mean 95 % 5 % mean 95 % 

th th th th

ages 30 and older PM 8,800 14,000 19,000 14,000 23,000 32,000 

(Pope et al., 1995)

ages 25 and older PM 15,987 34,860 54,677 26,000 56,000 88,000 

(Dockery et al., 1993)*

all ages * O 0 1,400 2,800 0 2,200 4,600 

3 † †

post-neonatal * PM 45 88 130 69 130 200 

*The Dockery et al. (1993), ozone mortality, and post-neonatal mortality estimates are not aggregated into total benefits estimates.

†Monte Carlo modeling returned a negative value for the fifth percentile estimate of this endpoint. However, we believe the negative result represents an art

employed in the uncertainty analysis, since none of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations oin this document.
D-91

Table D-25Comparison of Alternative Lag Assumptions for Premature Mortality Associated with PM ExposureYear

Percent of Avoided Mortality By Year

1 2 3 4 5 6 7 8 9 10 11 12 

Tier II SA Lag 25 25 16.67 16.67 16.67 0 0 00000(Primary Estimate)No Lag 100 0 0 0 0 0 0 0 0 0 0 0 Lag Distributed 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 0000Evenly Over thePeriod Covered byPope et al., 1995Lag Distributed 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 Evenly Over thePeriod Covered byDockery et al., 1993

Totals may not sum to 100 percent due to rounding.
0

5,000

10,000

15,000

20,000

25,000

05

1015202530354045

Assumed Effect Threshold (Annual Mean PM2.5 (ug/m3))

Avoided Incidence (2010)

The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-92

Figure D-2 

Long-Term Mortality Based on Pope (1995): National Avoided Incidence Estimates (2010)

at Different Assumed Effect Thresholds, Based on a 50 Km Maximum Distance
The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-93

References

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Classes of Pollutants And Ecological Effects

Pollutant Class Major Pollutants and Acute Effects Long-term Effects

Precursors

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Precursors: Sulfur dioxide, organisms. surface waters.

nitrogen oxides

leaves and aquatic quality. Chronic acidification of

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enrichment of coastal estuaries.

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Pollutants (HAPs) animals. in biogeochemical cycles and 

accumulation in the food chain.

Ozone Tropospheric ozone Direct toxic effects to plant Alterations of ecosystem wide

Precursors: Nitrogen oxides cycling.

and Volatile Organic

Compounds (VOCs)

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E-10

Table E-2

Interactions Between Acid Deposition and Natural Systems

At Various Levels of Organization

Examples of Interactions

Spatial Acidification of Streams

Scale Type of Interaction Acidification of Forests and Lakes

Molecular Chemical and Damages to epidermal layers and Decreases in pH and increases

and cellular biochemical processes cells of plants through deposition in aluminum ions cause

of acids. pathological changes in

structure of gill tissue in fish.

Individual Direct physiological Increased loss of nutrients via Hydrogen and aluminum ions in

response foliar leaching. the water column impair

regulation of body ions.

Indirect effects: Death Cation depletion in the soil Aluminum ions in the water

due to ionoregulatory causes nutrient deficiencies in column can be toxic to many

failure. Acidification can plants. Concentrations of aquatic organisms through

indirectly affect response aluminum ions in soils can reach impairment of gill regulation. 

to altered environmental phytotoxic levels. Increased Acidification can indirectly

factors or alterations of sensitivity to other stress factors affect submerged plant species,

the individual's ability to like pathogens and frost. because it reduces the

cope with other kinds of availability of dissolved carbon

stress. dioxide (CO ). 2

Population Change of population Decrease of biological Decrease of biological

characteristics like productivity of sensitive productivity of sensitive

productivity or mortality organisms. Selection for less organisms. Selection for less

rates. sensitive individuals. sensitive individuals. 

Microevolution of resistance. Microevolution of resistance.

Community Changes of community Alteration of competitive patterns. Alteration of competitive

structure and competitive Selective advantage for acid- patterns. Selective advantage

patterns resistant species. Loss of acid for acid-resistant species. Loss

sensitive species and individuals. of acid sensitive species and

Decrease in productivity. individuals. Decrease in

Decrease of species richness and productivity. Decrease of

diversity. species richness and diversity.

Local Changes in nutrient Progressive depletion of nutrient Measurable declines of

Ecosystem cycle, hydrological cycle, cations in the soil. Increase in the decomposition of some forms

(e.g., and energy flow of lakes, concentration of mobile aluminum of organic matter, potentially

landscape wetlands, forests, ions in the soil. resulting in decreased rates of

element) grasslands, etc. nutrient cycling.

Regional Biogeochemical cycles Leaching of sulfate, nitrate, Additional acidification of

Ecosystem within a watershed. aluminum, and calcium to aquatic systems through

(e.g., Region-wide alterations streams and lakes. Acidification processes in terrestrial sites

watershed) of biodiversity. of aquatic bodies. within the watershed. 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-11

Table E-3

Interactions Between Nitrogen Deposition and Natural Systems

At Various Levels of Organization

Examples of Interactions

Spatial

Scale Type of Interaction

Eutrophication and Nitrogen

Saturation of Terrestrial Eutrophication of Coastal

Landscapes Estuaries

Molecular Chemical and biochemical Assimilation of nitrogen by Assimilation of nitrogen by

and cellular processes plants and microorganisms plants and microorganisms. 

Individual Direct physiological Increases in leaf- size of Increase in growth of marine

response. terrestrial plants. plants.

Indirect effects: Response Decreased resistance to biotic Injuries to marine fauna through

to altered environmental and abiotic stress factors like oxygen depletion of the

factors or alterations of the pathogens and frost. Disruption environment. Loss of physical

individual's ability to cope of plant-symbiont relationships habitat due to loss of sea-grass

with other kinds of stress. with mycorrhiza fungi. beds. Injury through increased

shading. Toxic blooms of

plankton. 

Population Change of population Increase in biological Increase in biological

characteristics like productivity and growth rates of productivity. Increase of growth

productivity or mortality some species. rates (esp. of algae and marine

rates. plants). 

Community Changes of community Alteration of competitive Excessive algal growth. 

structure and competitive patterns. Selective advantage Changes in species

patterns for fast growing species and composition. Decrease in seaindividuals that efficiently use grass beds.

additional nitrogen. Loss of

species adapted to nitrogenpoor environments.

Local Changes in nutrient cycle, Magnification of the Magnification of the nitrogen

Ecosystem hydrological cycle, and biogeochemical nitrogen cycle. cycle. Depletion of oxygen,

(e.g., energy flow of lakes, Progressive saturation of increased shading through

landscape wetlands, forests, microorganisms, soils, and algal growth. 

element) grasslands, etc. plants with nitrogen.

Regional Biogeochemical cycles Leaching of nitrogen from Additional input of nitrogen

Ecosystem within a watershed. Region- terrestrial sites to streams and from nitrogen-saturated

(e.g., wide alterations of lakes. Acidification of aquatic terrestrial sites within the

watershed) biodiversity. bodies. Eutrophication of watershed. 

estuaries.

Note: See Overview of Ecological Impacts of Air Pollutants Regulated by the 1990 Clean Air Act Amendments (IEc 1998) for

sources.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-12

Table E-4

Interactions of Mercury and Ozone with Natural Systems

At Various Levels of Organization

Examples of Interactions

Spatial Mercury in streams

Scale and lakes Ozone Type of Interaction

Molecular Chemical and Mercury enters the body of Oxidation of enzymes of

and cellular biochemical processes vertebrates and binds to sulfhydril plants. Disruption of the

groups (i.e. proteins). membrane potential.

Individual Direct physiological Neurological effects in vertebrates. Direct injuries include visible

response. Behavioral abnormalities. Damages foliar damage, premature

to the liver. needle senescence, reduced

photosynthesis, altered

carbon allocation, and

reduction of growth rates and

reproductive success. 

Indirect effects: Few interactions known. Increased sensitivity to biotic

Response to altered Damages through increased and abiotic stress factors like 

environmental factors or sensitivity to other environmental pathogens and frost. 

alterations of the stress factors could occur, for Disruption of plant-symbiont

individual's ability to example, through impairment of relationship (mychorrhizae),

cope with other kinds of immune response. and symbionts.

stress.

Population Change of population Reduced reproductive success of fish Reduced biological

characteristics like and bird species. Increased mortality productivity. Selection for

productivity or mortality rates, especially in earlier life stages. less sensitive individuals. 

rates. Possibly microevolution for

ozone resistance. 

Community Changes of community Loss of species diversity of benthic Alteration of competitive

structure and invertebrates. patterns. Selective

competitive patterns advantage for ozoneresistant species. Loss of

ozone sensitive species and

individuals. Reduction in

productivity. 

Local Changes in nutrient Not well understood. Alterations of ecosystemEcosystem cycle, hydrological wide patterns of energy flow

(e.g.,landsc cycle, and energy flow of and nutrient cycling. 

ape lakes, wetlands, forests,

element) grasslands, etc.

Regional Biogeochemical cycles Not well understood. Region-wide loss of sensitive

Ecosystem within a watershed. species. 

(e.g., Region-wide alterations

watershed) of biodiversity.

Note: See Overview of Ecological Impacts of Air Pollutants Regulated by the 1990 Clean Air Act Amendments (IEc 1998) for

sources.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

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E-16

Table E-5

Ecological Impacts with Identifiable Human Service Flows

Pollutant Class Ecosystem Effect Service Flow Impacted

Acidification (H SO , High-elevation forest acidification resulting in Forest aesthetics 2 4

HNO ) dieback 3

Freshwater acidification resulting in aquatic

organism (e.g. fish) population decline

Changes in biological diversity and species mix in

terrestrial and aquatic systems

Recreational fishing

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biological diversity

Nitrogen Saturation Freshwater acidification resulting in aquatic Recreational fishing

and Eutrophication organism (e.g. fish) population decline

(NO )x

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and changes in nutrient cycling

Changes in biological diversity and species mix in

terrestrial and aquatic systems

Recreational and commercial

fishing

Existence value for maintenance of

biological diversity

Toxics Deposition Terrestrial bioaccumulation of mercury and dioxin Hunting, wildlife aesthetics

(Mercury, Dioxin)

Aquatic bioaccumulation of mercury and dioxin Recreational and commercial

Changes in biological diversity and species mix in Existence value for maintenance of

terrestrial and aquatic systems biological diversity

fishing

Tropospheric Ozone Terrestrial plant foliar damage causing lower Commercial timber productivity,

(O ) productivity and reduced competitiveness forest aesthetics, existence value 3

Multiple Pollutant Ecosystem deterioration resulting in visual effects, Ecosystem aesthetics, ecosystem

Stress habitat loss, and changes in biological diversity and existence value 

species mix caused by synergistic action of several

pollutants

Defensible Links and Quantitative

Modeling Requirements

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E-18

Table E-6

Model Coverage for Candidate Endpoints for Quantitative Assessment

Pollutant Endpoint Model Deposition Model Functions Model

Emissions and Exposure response Economic

Transport DoseAcidification Forest NEI, RADM Not Multiple Only site-

(H SO , aesthetics ERCAM Required available specific 2 4

HNO )3 models

available

Recreational NEI, RADM MAGIC Multiple Only sitefishing ERCAM (region available specific

specific) models

available

Biological NEI, RADM MAGIC Multiple Only sitediversity ERCAM (region available specific

existence value specific) models

available

Nitrogen Recreational NEI, RADM Estuary- None Only siteSaturation and and commercial ERCAM specific Available specific

Eutrophication fisheries models models

(NO ) available available x

Biological NEI, RADM Estuary- Multiple None Available

diversity ERCAM specific available

existence value models

available

Toxics Forest None RELMAP, None Multiple Only siteDeposition aesthetics Available ISC3 Available available specific

(Hg, Dioxin) models

available

Hunting, wildlife None RELMAP, None Multiple Only siteaesthetics Available ISC3 Available available specific

models

available

Recreational None RELMAP, IEM-2M (site Multiple Only siteand commercial Available ISC3 specific) available, or specific

fishing consumption models

advisory limits available

can be used

Biological None RELMAP None Multiple None Available

diversity Available Available available

existence value

Multiple Ecosystem NEI, None None None Available

Pollutant aesthetics, ERCAM Available Available

Stress ecosystem

existence

value.

NEI: National Emissions Inventory; ERCAM: Emissions Reduction and Cost Analysis Model; RADM: Regional Acid Deposition

Model;REMSAD: Regulatory Modeling System for Aerosols and Deposition; RELMAP: Regional Lagrangian Model of Air Pollution;

UAM: Urban Airshed Model; TAMM: Timber Assessment Market Model, developed and maintained by the U.S. Forest Service.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-19

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Table E-7

Summary of Endpoints Selected for Quantitative Analysis

Endpoint Analysis Geographic Scope

Lake acidification impacts on Quantification of improved fisheries Case study of New York State

recreational fisheries with monetization of recreational

value

Estuarine eutrophication impacts on Quantification of improved fisheries Illustrative calculations for case

recreational and commercial fisheries with monetization of avoided costs studies of Chesapeake Bay,

of alternative eutrophication control Long Island Sound, and Tampa

methods Bay (with extensions to East

Coast estuaries) 

Ozone impacts on commercial timber Quantification of improved timber National assessment

sales growth with monetization of

commercial timber revenues

Ozone impacts on carbon sequestration Quantification of improved carbon National assessment

in commercial timber sequestration

Toxicity impacts on recreational fishing Qualitative analysis of improved Qualitative regional case studies

recreational fisheries of New York and Tennessee
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-20

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-21

Table E-8

Service Flows Affected by Changes in Estuarine Ecosystems

Ecosystem Changes Service Flow Impacts

Deterioration of breeding grounds for fisheries Commercial fishing yields, species mix

Recreational fishing catch rate, species mix

Loss of habitat for aquatic and avian biota Existence value of a healthy estuarine ecosystem

Wildlife viewing

Aesthetics

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E-22

Table E-9

Total Nitrogen Deposition Based on GIS Analysis

(millions of lbs.)

Watershed Scenario 1: 1990 2010 without CAAA 2010 with CAAA Difference

Scenario 2: Scenario 3: 

Chesapeake Bay 345.1 452.4 258.1 194.3 

Long Island Sound 78.3 93.7 56.8 36.9 

Tampa Bay 8.1 11.3 7.0 4.3 

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-23

Table E-10

Land Use Prevalence and Pass-Through Figures

Watershed Forest Agricultural Urban Water Wetlands Other*

Chesapeake Bay

Land Use 53% 32% 6% 7% 1% 1%

N Pass-Through 20% 30% 50% 100% 20% 30%

Long Island Sound

Land Use 67% 11% 11% 9% 2% 0%

N Pass-Through 20% 30% 50% 100% 20% 30%

Tampa Bay

Land Use 5% 33% 15% 15% 10% 22%

N Pass-Through 20% 30% 50% 100% 20% 30%

* “Other” areas in Tampa Bay include rangeland (19%) and barren land (3%).

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Table E-11

Nitrogen Loadings from Atmospheric Deposition 

(millions of lbs.)

Watershed 1990 2010 without CAAA W/CAAA Difference

Scenario 1: Scenario 2: Scenario 3: 2010

Chesapeake Bay 105.2 137.5 79.4 58.1

Long Island Sound 26.7 31.9 19.1 12.8

Tampa Bay 3.4 4.7 2.9 1.8

Displaced Costs from Reducing

Atmospheric Deposition to Estuaries

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E-24

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0

1

2

3

4

5

6

7

8

9

10

N Level (t/yr)

Marginal Cost of N Reductions ($/t/yr)

Target (allowed)

N levels at 2010

Projected N levels

at 2010 

Required reductions 

from waterborne 

sources

Reductions from CAAA

Figure E-1. Nitrogen Reduction Without CAAA

0

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2

3

4

5

6

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8

9

10

N Level (t/yr)

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Target (allowed)

N levels at 2010

Projected N levels

at 2010 

Required reductions from waterborne sources

The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-25
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-26

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-27

Table E-12

Estimated Avoided Costs For Three Estuaries

Estuary Reduced N Deposition in Lower-BoundMarginal Upper-Bound Estimated Annual

2010(millions of pounds) Cost($/lb/yr.) Marginal Cost Avoided Costs in 2010

($/lb/yr.) ($millions)

Long Island 12.8 $2 $8 $25.6-$102.4

Sound

Chesapeake 58.1 $6 $22 $349-$1,278

Bay

Tampa Bay 1.8 $6 $38 $11 - $68

Results for Case Study Estuaries

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Table E-13

Avoided Cost for Atlantic Coast

Watershed Pounds (2010) Source ($1000/yr.) Difference ($1000/yr.)

Reduction through Avoided Cost, 100 Nonpoint Source First,

CAAA, Millions of Percent from Point then Point Source on

Avoided Cost, from

North Atlantic

Cape Cod Bay 0.51 1,282 7,915

Great Bay 0.57 1,446 19,130

Massachusetts Bay 1.19 3,015 66,887

Sheepscot Bay 0.13 340 2,397

 Sub-Total 13.02 6,083 96,329

Mid Atlantic

Barnegat Bay 0.48 1,202 19,688

Buzzards Bay 0.78 1,976 14,429

Chincoteaque Bay 0.33 824 3,690

Delaware Bay 12.11 30,640 384,002

Delaware Inland Bays 0.18 459 4,592 1

Gardiners Bay 0.64 1,624 13,929

Great South Bay 1.30 3,294 78,682

Hudson River/Raritan Bay 11.15 28,197 460,659

Narragansett Bay 1.65 4,176 65,841

New Jersey Inland Bays 1.12 2,825 28,314

 Sub-Total 29.74 75,219 1,073,826 2

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Albemarle Sound 16.29 41,223 272,772

Altamaha River 7.24 18,315 182,591

Biscayne Bay 0.78 1,973 12,136 3

Bogue Sound 0.30 756 9,426

Broad River 0.46 1,160 7,137 3

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Charleston Harbor 10.58 26,764 306,698

Indian River 0.76 1,926 36,045

North/South Santee Rivers 0.32 821 3,671

Pamlico Sounds 9.58 24,225 191,289

St. Andrew/St. Simons Sounds 1.18 2,974 18,297 3

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St. Johns River 4.72 11,940 152,909

St. Marys River/Cumberland Sound 0.53 1,338 8,886

Winyah Bay 11.63 29,415 215,612 1

 Sub-Total 81.07 180,225 1,595,799

Total East Coast 123.82 261,527 2,765,954

 CAAA N reductions met with agriculture, forestry, and urban BMPs (point source reductions not required). 1

 Excluding Long Island Sound and Chesapeake Bay. 2

 CAAA N reductions met with agriculture BMPs (further reductions not required). 3
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Table E-14

Summary of pH-Based Effects Threshold 

pH Effects Threshold pH Effects Threshold

(Low End) (High End)

Range for All Fish Species 4.2 5.8

Range of Mean Values for All Species 4.8 5.3

Range of recreationally important 4.6 5.4

species (weighted average)
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-39

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E-40

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Table E-15

Acidification Results - 2010

Year Status of CAAA Level of N Percentage of Lakes Acidified at

Saturation Selected pH Levels

pH 4.6 pH 5.0 pH 5.4

1990 No CAAA 0% 5% 20%

(Base Year) Regulations

Promulgated

2010 With CAAA No Saturation 0% 2% 18%

Saturated 0% 5% 17%

Without CAAA No Saturation 0% 6% 23%

Saturated 0% 6% 22%

Range of Benefits from CAAA in 0% 1%-4% 5%

2010

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-41

Table E-16

Annual Economic Impact of Acidification in 2010

(Millions of 1990 Dollars)

Year Status of Level of N Economic Impact of Acidification

CAAA Saturation at Each pH Threshold

pH 5.0 pH 5.4

1990 No CAAA $61 $320

(Base Year) Regulations

Promulgated

2010 With CAAA No Saturation $24 $281

Saturated $61 $261

Without CAAA No Saturation $73 $363

Saturation $73 $349

Range of CAAA Benefits in 2010 $12-$49 $82-$88

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-42

Table E-17

Cumulative Economic Benefits of Acidification from 1990 to 2010

(Millions of 1990 Dollars)

Economic Impact of Acidification at Each pH Threshold

pH 5.0 pH 5.4

CAAA Benefit Minimum $67 $433

CAAA Benefit Maximum $271 $465

Avoided Cost of Liming

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E-43

Table E-18

Cumulative Cost of Ph Stabilization from 1990 to 2010

(Millions of 1990 Dollars)

Number Cost of Cost of

of Lakes Liming Cost of Monitoring Stocking Total Cost

Program Remains 

Constant

32 $0.11 $0.07 $0.23 $0.40

Program Grows by

Two Lakes per Year

72 $0.16 $0.10 $0.35 $0.61

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E-48

Table E-19

Difference in Commercial Timber Growth Rates With and Without The CAAA

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PN W-W 1.17% 0.42% -0.56% 1.13%

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S. Rocky 4.77% 2.25% 4.14% 3.88%

S. Central 4.54% 4.80% 7.93% 8.41%

S. East 5.40% 5.65% 10.38% 10.91%

N. Central 1.80% 5.74% 4.36% 9.22%

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-49

Figure E-8

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E-52

Table E-20

Differences in Carbon Flux (millions of metric tons/year)

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Forest Flux 8 28

Land Use Change > -1 > -1

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The Benefits and Costs of the C

E-54

Table E-21Typical Impacts of Specific Pollutants on the Visual Quality of Forests

Geographic Extent Direct/Indirect Injuries Major Types of Visual 

Ozone Area or regional effects Direct injuries Foliar injuries (e.g., pigmented stipple), increased 

senescence of leaves. Pattern, size, location, and species can be specific for ozone. 

Indirect Injuries Increased susceptibility to visual injuries that may r

environmental factors, such as insect attacks. For abscission, elevated mortality rates, and/or change

Acidic Deposition Area or regional effects Indirect Injuries Increased susceptibility to visual injuries that may r

environmental factors, such as climatic factors. Foabscission, elevated mortality rates, and/or changeAcidic deposition can also cause direct foliar injurieto indirectly affect the visual appearance of forest tvery high.

Sulfur Dioxide Point source pollution Direct Injuries Foliar injuries including leaf/needle discoloration an

and shape of foliar injuries to indicator species can high concentrations, elevated mortality rates of senspecies composition may occur.Sulfur dioxide may also cause indirect injuries. Inddocumented.

Hydrogen Fluoride Point source pollution Direct Injuries Foliar injuries including leaf/needle discoloration an

and shape of foliar injuries to indicator species can high concentrations, elevated mortality rates of senspecies composition may occur.Hydrogen fluoride may also cause indirect injuries. well documented.
The Benefits and Costs of the C

E-55

Table E-22Forests Affected by Regional Pollution

Affected Forest Type Region Major Air Documented Visual Suspected Mechanisms of In

/ Species Pollutants Injuries

Mixed Conifer Forest / San Bernardino Ozone and nitrogen Foliar injuries include Direct ozone-induced foliar injuries. He

Ponderosa and Jeffrey Mountains, containing chlorotic mottle, tip beetle attacks facilitated by drought, ozPines California substances necrosis, premature nitrogen containing air pollutants. 

senescence of needles, and Ponderosa and Jeffrey pine have showincreased needle pollution-related symptoms of decline pabscission. Elevated since the mid 1950s.mortality rates andchanges in speciescomposition haveoccurred.

Sierra Nevada, Ozone Foliar injuries include Direct ozone-induced foliar injuries. California chlorotic mottle, tip 

necrosis, premature The Sierra Nevada contains the largest senescence of in the world with documented damage fneedles, and point source pollutant but ozone exposuincreased needle injuries are not as severe as in the San abscission. Mountains. Visible ozone-induced foliar 

were first documented in the early 1970
The Benefits and Costs of the C

E-56

Affected Forest Type Region Major Air Documented Visual Suspected Mechanisms of In

/ Species Pollutants Injuries

Spruce-Fir Forest / High elevation Acidic deposition Foliar dieback, bud Acidic deposition increases the suscep

Red Spruce areas in the (esp. acidic cloud injury, foliar loss, spruce to winter injury (freezing). 

northern water), and ozone Elevated mortality A dramatic increase in the frequency of Appalachians. rates. in red spruce stands occurred in the lat

1960s, coincident with a significant incremissions of precursors of acidic depos

High elevation Acidic deposition Crown thinning and Acidic deposition leads to nutrient imbaareas in the (esp. acidic cloud pockets of high red through accelerated foliar leaching and southern water) and ozone spruce mortality have acidification. Soil acidification is characAppalachians. been detected on a loss of soil nutrient cations and occurre

few mountain sites. aluminum levels. Also: direct foliar injurOzone-induced foliar by ozone. injury. 

Eastern Hardwood Northeastern US Acidic deposition Crown thinning, Acidic deposition leads to nutrient imbaForest / Sugar Maple and Canada and ozone branch dieback, through accelerated foliar leaching and 

elevated mortality acidification. Soil acidification is characrates loss of soil nutrient cations and occurre

aluminum levels.During 1980s sugar maple declined in min the northeastern US and Canada. Inacidic deposition in sugar maple decline been demonstrated but cannot be ruled 

The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-58

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E-60

Table E-23

Summary of Monetized Estimates of the Annual Value of Forest Quality Changes

Study Change Valued Dollars) (1990 Dollars) (Current Dollars) Dollars)

Aesthetic (Current Household Change for Region Region (1990

Value of Total Annual

Change per Value of Value of

Household Change per Total Annual Value of Change for

i i

Peterson et Ozone damage to $6.31-$32.70 $7.26-$37.62 $27-$140 million $31-$161million

al. (1987) San Bernardino

and Angeles

National Forests

ii

Walsh et al. Visual damage to $47 $61.68 $55.7 million $73.09 million

(1990) Colorado’s Front

Range

Holmes and Visual damage to $10.81 $10.37 NA NA

Kramer spruce-fir forests nonusers nonusers

(1996) in southern

Appalachia $36.22 users $34.76 users

Note: i.) Values adjusted using all item Consumer Price Index, Economic Report of the President, 1998. Years for current dollar

estimates: Peterson et al, 1987; Walsh et al, 1983; Holmes et al, 1991.

ii) Based on 4.3 million households in Los Angeles, Orange, and San Bernardino counties.

iii) Assumes 2.5 million households in North Carolina and 1.8 million in Tennessee.

Table E-24

Illustrative Value of Avoiding Forest Damage in the United States

(1990 Dollars)

Affected States Value per Estimated Total Value

System Included Household Households Annual Value (1990-2010) i ii

Cumulative

iii

Sierra Nevada CA $7.26-$37.62 10.4 million $75.5 million -$391.2 $1.02 billion -

and Los million $5.27 billion

Angeles Basin

Eastern Spruce ME, VT, NH, $7.26-$37.62 23.2 million $168 million - $872.8 $2.27 billion -

Fir and MA, NY, PA, million $11.75 billion

Selected WV, TN,

Eastern KY, NC, VA

Hardwood

Notes: i.) Household data from 1990 Census; ii) Total Value = Households x Value per Household; iii)Assumes a 5 percent real

discount rate.

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Table E-25

Summary of National Data on Toxicity Sampling for Fishing Advisories

Water Body

Percentage of Water

Bodies Assessed for

Contaminants

Percentage of Assessed Water Bodies

Under Advisory

Lakes

(acres)

11.36 78.61

Rivers and Streams

(miles)

2.41 29.58

Source: EPA 1997a

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Table E-26

Estimates of the Welfare Cost of Toxification in New York State 

(1990 Dollars)

Level of Toxicity Variation per Trip per Day per Season

Compensating Variation per Capita Variation per Capita

Compensating Compensating

Toxic Contamination $1.23 $0.37 $51.51

Site Closed Due to Toxic $1.69 $0.50 $70.92

Contamination

Source: Montgomery and Needelman 1997
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-64

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-66

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E-67

Table E-27

Summary of Monetized Ecological Benefits (millions 1990$)

Description Air Economic Estimates in Estimate Estimate

of Effect Pollutant Estimate 2010 for 2010 1990-2010 Key Limitations

Geographic Range of Primary Cumulative

Scale of Annual Impact Central Impact

Primary

Central

Freshwater Sulfur and Regional $12 to $88 $50 $260 - Captures only

acidification nitrogen (Adirondacks) recreational

oxides fishing impact

- Incomplete

geographic

coverage leads to

underestimate of

benefits

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growth - Lost stand-level

commercial response to

timber ozone exposure

 - Uncertainty in

future timber

markets

TOTAL MONETIZED ECONOMIC BENEFIT $200 to $1,100 $650 $2,200 - Partial estimate

that omits major

unquantifiable

benefits

categories; see

text

Note: Estimates reflect only those benefits categories for which quantitative economic analysis was supported. A comprehensive

total economic benefit estimate would likely greatly exceed the estimates in the table. Range of estimates for timber assessment is

based on variation in annual point estimates for 2005 through 2010.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-68

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Appendix

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Ozone Exposure-Response Functions for Selected Crops (SUM06)

Ozone Index Quantity Crop Function Duration (Days) (Months)

Median Median

Experimental Duration

SUM06 Max Cotton 1-exp(-(index/78)^1.311) 119 4

SUM06 Max Field Corn 1-exp(-(index/92.4)^2.816) 83 3

SUM06 Max Grain Sorghum 1-exp(-(index/177.8)^2.329) 85 3

SUM06 Max Peanut 1-exp(-(index/99.8)^2.219) 112 4

SUM06 Max Soybean 1-exp(-(index/131.4)^1) 104 3

SUM06 Max Winter Wheat 1-exp(-(index/27.2)^1.0) 58 2

SUM06 Min Cotton 1-exp(-(index/116.8)^1.523) 119 4

SUM06 Min Field Corn 1-exp(-(index/94.2)^4.307) 83 3

SUM06 Min Grain Sorghum same as max (see above) 85 3

SUM06 Min Peanut same as max (see above) 112 4

SUM06 Min Soybean 1-exp(-(index/299.7)^1.547) 104 3

SUM06 Min Winter Wheat 1-exp(-(index/72.1)^2.353) 58 2

Source: Lee and Hogsett (1996)
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Table F-2

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Table F-3

Change in Net Crop Income, Consumer Surplus and Net Surplus 

Under the Post-CAAA Scenario (millions of 1990$)

Change in Net Crop Income Change in Consumer Surplus Change in Net Surplus

Minimum Maximum Minimum Maximum Minimum Maximum

1990 $0 $0 $0 $0 $0 $0

2000 -$320 -$1,901 $367 $2,763 $46 $862

2010 -$736 -$4,555 $743 $5,643 $7.5 $1,088

Conclusions

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References

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Appendix

G Stratospheric Ozone

Assessment

Introduction

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-3

Table G-1

Six Major Sections of Title VI

Section Description

Section 604 - Class I Phaseout Institutes the reduction and phaseout schedules for Class I substances

(methyl chloroform, halons, chlorofluorocarbons, carbon tetrachloride).

Section 605 - Class II Phaseout Institutes the reduction and phaseout schedules for Class II substances

(hydrochlorofluorocarbons).

Section 606 - Accelerated Schedule Permits Congress to accelerate the phaseout schedule of Class I and II

substances if necessary for the protection of human health and the

environment.

Also, if countries modify the Montreal Protocol to accelerate phaseout

schedules of Class I and II chemicals, Congress can amend the Clean Air

Act to reflect these modifications.

Section 608 - National Recycling and Requires the EPA to establish standards regarding the use and disposal of

Emission Reduction Program Class I and II substances during the service, repair, or disposal of

appliances and industrial refrigeration units.

Section 609 - Servicing of Motor Requires the EPA to regulate the servicing of motor vehicle air

Vehicle Air Conditioners conditioners.

Section 611 - Labeling Stipulates that the EPA establish labeling requirements for containers of

Class I and II substances and for products containing these substances.

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Table G-2Phaseout Scenario in Clean Air Act Section 604 and Phaseout Scenario in Amendments Added under Clean Air Act Section 606Year Section 604 (Original Schedule) Section 606 (Revisions to Original Schedule)

1991 & Methyl chloroform (MCF) production decreases to 1989 levels1993 & MCF production decreases 10% from 1989 levels

1994 & MCF production decreases 15% from 1989 levels ù & MCF production decreases from 1989 levels by 

1995 & MCF production decreases 30% from 1989 levels ù & MCF production decreases from 1989 levels by 

1996 & MCF production decreases 50% from 1989 levels ù & 100% phaseout of MCF 

2000 & MCF production decreases 80% from 1989 levels

2002 & 100% phaseout of MCF 

1989 & Chlorofluorocarbon (CFC) production decreases to 1986 levels 1991 & CFC production decreases 15% from 1986 levels1992 & CFC production decreases 20% from 1986 levels1993 & CFC production decreases 25% from 1986 levels1994 & CFC production decreases 35% from 1986 levels ù & CFC production decreases to 75% from 1986 le

1995 & CFC production decreases 50% from 1986 levels ù & CFC production decreases to 75% from 1986 le

1996 & CFC production decreases 60% from 1986 levels ù & 100% phaseout of CFC production 

1997 & CFC production decreases 85% from 1986 levels2000 & 100% phaseout of CFC production 
The Benefits and Costs of the C

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Year Section 604 (Original Schedule) Section 606 (Revisions to Orig1991 & Halon production decreases 15% from 1986 levels

1992 & Halon production decreases 20% from 1986 levels

1993 & Halon production decreases 25% from 1986 levels

1994 & Halon production decreases 35% from 1986 levels ù & 100% phaseout of halons

1995 & Halon production decreases 50% from 1986 levels

1996 & Halon production decreases 60% from 1986 levels

1997 & Halon production decreases 85% from 1986 levels

2000 & 100% phaseout of halon production

1991 & Carbon tetrachloride production decreases to 1989 levels

1992 & Carbon tetrachloride production decreases 10% from 1989 levels

1993 & Carbon tetrachloride production decreases 20% from 1989 levels

1994 & Carbon tetrachloride production decreases 30% from 1989 levels ù & Carbon tetrachloride production decreases 5

1995 & Carbon tetrachloride production decreases 85% from 1989 levels ù & Carbon tetrachloride production decreases 81996 ù & Carbon tetrachloride production decreases 1

2000 & 100% phaseout of carbon tetrachloride

tetrachloride 
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Year Section 604 (Original Schedule) Section 606 (Revisions to Original Schedule)

1994 & Freeze on production and consumption of methyl bromide at 1991 lev

1999 & 25% phaseout of methyl bromide 2001 & 50% phaseout of methyl bromide 2003 & 75% phaseout of methyl bromide 2005 & 100% phaseout of methyl bromide 

(quarantine and preshipment uses exempt; critical agriculture uses alloc

1996 & 100% phaseout of hydrobromofluorocarbons (HBFCs)2003 ù & Production and consumption of HCFC-141b banned

& Production and consumption of HCFC-142b and HCFC-22 decrease& Production and consumption of HCFC-142b and HCFC-22 permissib

equipment manufactured prior to 2010

& Production and consumption of the remaining HCFCs decreases to & 100% phasout of HCFC-142b and HCFC-22& 100% phaseout of the final category of HCFCs

2010 ù(2010-2020)

ù

2015 & Freeze on HCFC production ù2020 ù2030 & Prohibition of HCFC production after ù

January 1, 2030
The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Cost and Benefit Approaches

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Table G-3

Scope of Title VI Cost Estimates

Section Costs

604 & 606 & Capital investment and variable costs associated with switching to alternative technologies

and non-ozone-depleting substances;

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unable to develop cost-effective manufacturing alternatives;

& Costs of recycling ODSs;

& Additional costs of switching from methyl bromide to other substances (e.g. purchases of

more costly substitutes, incremental labor needs, and crop and throughput issues).

608 & Purchase of ODS recovery equipment;

& Training and certification of technicians;

& Filtration of refrigerants to remove impurities;

& Leak repair requirements;

& Storage of unused chlorofluorocarbons (CFCs);

& Paperwork.

609 & Training and certification of mobile air conditioner (MAC) service technicians;

& Recycling costs, including fees for off-site recycling or labor and capital costs for on-site

recycling.

611 & Development and application of new labels;

& Administrative activities associated with compliance;

& Possible accelerated substitution of less harmful substances relative to the mandates of the

rule codifying the phaseout of ODSs (thereby resulting in additional costs beyond those

resulting from the phaseout rule).

Costs: Sections 604 and 606

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Figure G -1

SCHEM ATIC OF COST AND

BENEFIT ANALYSES O F TITLE VI

Cost estim ates Stratospheric

ozone m odeling

G lobal w arm ing

estim ates*

UV-b

radiation

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effects

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qualitatively

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 potential relative to carbon dioxide. W e do not assess the im pact of global w arm ing in this analysis. 

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68
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-16

Table G-4

Benefits of Section 604, 606, and 609

Health Effects- Quantified Estimate Basis for Estimate

& Melanoma and nonmelanoma 6.3 million lives saved from skin Dose-response function based on UV

skin cancer cancer in the U.S. between 1990 exposure and demographics of exposed

(fatal) and 2165 populations.1

& Melanoma and nonmelanoma 299 million avoided cases of non- Dose-response function based on UV

skin cancer fatal skin cancers in the U.S. exposure and demographics of exposed

(non-fatal) between 1990 and 2165 populations.1

& Cataracts 27.5 million avoided cases in the Dose-response function uses a multivariate

U.S. between 1990 and 2165 logistic risk function based on demographic

characteristics and medical history. 1

Ecological Effects- Quantified Estimate Basis for Estimate

& American crop harvests Avoided 7.5 percent decrease from Dose-response sources: Teramura and Murali

UV-b radiation by 2075 (1986), Rowe and Adams (1987)

& American crops Avoided decrease from Estimate of increase in tropospheric ozone:

tropospheric ozone Whitten and Gery (1986). Dose-response

source: Rowe and Adams (1987)

& Polymers Avoided damage to materials from Source of UV-b/stabilizer relationship: Horst

UV-b radiation (1986)

Health Effects- Unquantified

Skin cancer: reduced pain and suffering

Reduced morbidity effects of increased UV. For example,

& reduced actinic keratosis (pre-cancerous lesions resulting from excessive sun exposure)

& reduced immune system suppression.

Ecological Effects- Unquantified

Ecological effects of UV. For example, benefits relating to the following:

& recreational fishing

& forests

& overall marine ecosystem

& avoided sea level rise, including avoided beach erosion, loss of coastal wetlands, salinity of estuaries and aquifers

& other crops

& other plant species

& fish harvests

Ecological benefits of reduced tropospheric ozone relating to the overall marine ecosystem, forests, man-made materials,

crops, other plant species, and fish harvests

Benefits to people and the environment outside the U.S.

Effects, both ecological and human health, associated with global warming.

Notes: 

1) For more detail see EPA’s Regulatory Impact Analysis: Protection of Stratospheric Ozone (1988).

2) Note that the ecological effects, unlike the health effects, do not reflect the accelerated reduction and phaseout 

 schedule of section 606.

3) Benefits due to the section 606 methyl bromide phaseout are not included in the benefits total because the EPA 

 provides neither annual incidence estimates nor a monetary value. The EPA does provide, however, a total 

 estimate of 2,800 avoided skin cancer fatalities in the U.S.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Table G-5

Benefits of Section 608

Quantified Health Effects

Skin cancer: fatal and nonfatal

Health Effects- Unquantified

Skin cancer: reduced pain and suffering

Cataracts: reduced morbidity, pain and suffering

Reduced morbidity effects of increased UV. For example,

& reduced actinic keratosis (pre-cancerous lesions resulting from excessive sun exposure)

& reduced immune system suppression.

Ecological Effects- Unquantified

Ecological effects of UV. For example, benefits relating to the following:

& recreational fishing

& forests

& overall marine ecosystem

& avoided sea level rise - which, in turn, leads to: 

decreased beach erosion

decreased loss of coastal wetlands

decreases in the salinity of estuaries and aquifers

& other crops

& other plant species

Other ecological benefits of reduced tropospheric ozone relating to

& the overall marine ecosystem

& forests

& man-made materials (e.g., degradation of elastomers, textile fibers and dyes, certain paints)

& other crops

& other plant species

Benefits to people and the environment outside the U.S.

Effects, both ecological and human health, associated with global warming.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-21

Table G-6

Benefits of Section 611

Health Effects- Quantified 

Skin cancer: fatal and nonfatal 

Cataracts: reduced morbidity, pain and suffering 

Ecological Effects- Quantified 

Crops: reduced damage associated with increased UV radiation

Crops: reduced damage associated with increased tropospheric ozone

Polymers: reduced degradation from UV-b radiation

Health Effects- Unquantified

Skin cancer: reduced pain and suffering

Reduced morbidity effects of increased UV. For example,

& reduced actinic keratosis (pre-cancerous lesions resulting from excessive sun exposure)

& reduced immune system suppression

Ecological Effects- Unquantified

Ecological effects of UV. For example, benefits relating to the following:

& recreational fishing

& forests

& overall marine ecosystem

& avoided sea level rise - which, in turn, leads to:

decreased beach erosion

decreased loss of coastal wetlands

decreases in the salinity of estuaries and aquifers

& crops in general

& other plant species

& fish harvests

Ecological benefits of reduced tropospheric ozone relating to

& the overall marine ecosystem

& forests

& man-made materials (e.g., degradation of elastomers, textile fibers and dyes, certain paints)

& crops in general

& other plant species

& fish harvests

Benefits to people and the environment outside the U.S.

Enhanced implementation and enforcement of EPA's refrigerant recycling program

Increase in available information regarding ozone-depleting substances (ODSs); consumers who wish to

buy products that do not contain ODSs will be better able to express their preferences through their

purchasing power.

Effects, both ecological and human health, associated with global warming.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-22

Valuation

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G-23

Table G-7Sections 604 and 606: Valuation of Total Benefits from 1990 to 2165, With a Two Percent Discount RateQuantified Effects Valuation Source Data

(1990 dollars)

Health EffectsMortalities from skin cancer (melanoma and $3,900 billion Value of statistical life: $4.8 million (1990 dollars). (See Appendinonmelanoma) in the U.S. (1990-2165) source data.)Cataract cases in the U.S. (1990-2165) $72 billion Avoided cost of cataracts: $15,000 (dollar year not provided) Co

medical costs, increased work loss, increased costs for chores, economic costs, and willingness to pay to avoid cataracts. Data contacts with health providers, and cataract patient survey. (Sou

Nonfatal skin cancer cases (melanoma and $220 billion Cost of melanoma skin cancer: $15,000 per case (dollar year nononmelanoma) in the U.S. (1990-2165) nonmelanoma skin cancer: $5,000 per case (dollar year not prov

increased medical costs and decreased productivity but do not iand chores performed by others. Data from Skin Cancer Focus 1988 RIA for details.)

Total Health Benefits $4,200 billionEcological Effects& Decrease in American crop harvests from UV-b $49 billion Crop values from Department of Agriculture. 

radiation by 2075

& Decrease in American crops from tropospheric $28 billion Crop values from Department of Agriculture. 

ozone by 2075

& Damage to polymers from UV-b radiation by 2075 $6 billion Costs are proportional to the increase in polymer prices followin

stabilizers. Price increase of 1.86% expected for a 25% increase 

Total Environmental Benefits $84 billionTotal Benefits $4,300 billion

Note : 1) The RIAs do not provide specific crop values 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-24

Adjustments to Estimates From

Existing Analyses

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-25

Table G-8

Adjustments to Key Parameters of Existing Analyses That Support the 812 Title VI Estimates

Parameter Assumption for Adjustments to Title VI Analyses

Section 812

Prospective Sections 604 and Section 608 Section 609 Section 611

606

Discount Rate Central Case: 5% Used Section 812 2% (No 2% (No 2% (No

(Used for Sensitivity tests: parameters, plus adjustment adjustment adjustment

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Benefits) costs.)

Value of Distribution of Used Section 812 $3 to $12 million Used Section $3 to $12 million

Statistical Life values from $0.6 parameters. 812

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expected value of

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Value of Statistical Life""

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G-26

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G-27

Table G-9

Costs and Benefits of Sections 604 and 6061

Discount Benefits (Trillions) Costs (Trillions) Benefits/Cost Ratio

Rate 1990 Dollars 1990 Dollars

2% $4.24 $0.06 76

3% $1.81 $0.04 44

5% $0.44 $0.03 17

7% $0.14 $0.02 8

Notes:

1. The cost and benefits estimates associated with a two percent discount rate are the estimates for sections 604,

606, 608, 609, and 611. For the other discount rates the estimates represent the costs and the human health

benefits for sections 604 and 606. These two sections generate the majority of the Title VI costs and benefits

(approximately 98 percent of the benefits and 97 percent of the costs in the two percent discount rate

calculations).

2. We do not include the costs of the methyl bromide phaseout of section 606 because existing RIAs do not

provide benefit estimates for this phaseout.

3. In general, the costs occur from 1990 to 2075, while the benefits occur from 1990 to 2165. (Tables G-11 and G12 provide the specific time frame for each section of Title VI.) 

Three Percent and Seven Percent

Sensitivity Tests

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Probability

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-28

Table G-10

Human Health Benefits for Sections 604 and 606

Billions of 1990 dollars

Range: $40 to $2,600

Mean: $400

Median: $400

Standard Deviation: $300

Percentiles:

5% $100

50% $400

95% $900

Percent of Benefits from 1990 to 2014 3.64%

Percent of Benefits from 2015 to 2165 93.36%

Note: Estimates calculated with a five percent discount rate.
Figure G-2

Annual Human Health Benefits From Sections 604 and 606 (Discounted at 5%)

$0

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G-29
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-30

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G-31

Table G-11Summary of Benefits of Title VI with a Two Percent Discount Rate and $4.8 Million VSL

Section 1990 Dollars Notes 

Benefits 

(Millions) 

604 & 606 Class I Phaseout $4,338,000

- Reduced Mortality, Cataracts, and $4,243,000 

 Non-Fatal Cancers- Methyl Bromide Reductions No information currently available. - Ecological Benefits $84,000 

605 Class II Phaseout No information currently available.608 National Recycling and $670 1) Only health effects are monetized. 

Emission Reduction Program 2) The RIA for section 608 does not include benefits of HCFC 

because this phaseout is too complex and predicting baselinis too difficult. 

3) The benefits estimate listed is the weighted average of the 

calculated with $3 million and $12 million for the VSL. (The 

 million estimate has a weight of 0.8 and the $12 million est

weight of 0.2)

4) Benefits reflect the accelerated phaseout schedule.

609 Servicing of Motor Vehicle Air $300 The benefits of section 609 are a subset of the benefits of sectio

Conditioners 606; we calculate section 609 benefits as 0.00682% of the ben

and 606 combined. (This percentage is the total benefits of 6dollars divided by the total 604 benefits in 1989 dollars.)

611 Labeling $830 1) The benefits do not reflect the accelerated phase-out sched

2) The benefits estimate listed is the weighted average of the  calculated with $3 million and $12 million for the value of a 

life. (The $3 million estimate has a weight of 0.8 and the $1

 has a weight of 0.2)

TOTAL $4,339,000

Notes: 1) All benefits expressed in 1990 dollars using the implicit GDP deflator from the 1998 Economic Report of the President.

2) The benefits of section 611 and the ecological benefits of section 604 do not reflect the accelerated reduction and phaseout schedule of se
G-32

Table G-12Summary of Costs for Title VI by Section with a Two Percent Discount Rate

Section 1990 Dollars) Notes 

Cost 

Estimate 

(Millions, 

604 & Class I Phaseout $56,000 Does not include cost of methyl bromide reductions. 

606 

605 Class II Phaseout No information currently available.608 National Recycling and $1,200 1) We convert costs from 1991 to 1990 dollars using the GDP deflator from the 

Emission Reduction Report of the President. 

Program 2) The RIA for Sect. 608 does not include costs of HCFC phaseout.

3) At a 4% discount rate the costs are $1,074.38 million, and at a 7% discount ra

$853.91.

609 Servicing of Motor $14 1) We convert from 1991 to 1990 dollars using the GDP deflator from the 1998 E

Vehicle Air the President. 

Conditioners 2) To avoid counting the same costs for both section 604 and 609, we include o

training and equipment certification costs of section 609 here. 

611 Labeling $250 1) The costs are probably in 1990 dollars, but this is unclear. 

2) Most costs are one-time costs. 3) The cost estimate is an average of the lower bound, which assumes that only 

as solvents will label products, and an upper bound, which assumes that 10 twill need to label their products because they incorporate solvent-cleaned pro

TOTAL $57,000

Notes: 1) The costs listed above for sections 604 & 606 do not include methyl bromide costs, which equal $1.7 billion with a 3% discount rate, beca

corresponding benefits. 

2) The costs of sections 609 and 611 do not reflect the accelerated reduction and phaseout schedules of section 606.
Figure G-3

ANNUAL UNDISCOUNTED HUMAN HEALTH BENEFITS OF SECTIONS 604 AND 606

$0

$100,000$200,000$300,000$400,000$500,000$600,000

199019962002200820142020202620322038204420502056206220682074208020862092209821042110211621222128213421

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G-33
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-34

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-36

Table G-13

Major Limitations of Existing Cost Analyses for Title VI

Sections 604*, 606, and 609

Limitations Effects on Cost Estimates

The assumption of little or no technical innovation in ODS- Inclusion of innovation may decrease the cost estimate.

related industries over the time span of the analysis may be

inaccurate.

The model does not quantify transition costs, such as Inclusion of transition costs may increase the cost

temporary layoffs, administrative costs, and the costs of estimate.

unknown environmental hazards created by the use of

alternatives to CFCs.

Section 608

The assumptions of the capital and operating costs of A better understanding of the capital and operating costs,

recovery devices and the time necessary to perform as well as the time necessary for recycling, may either

recycling operations are largely hypothetical. increase or decrease cost estimates.

The extent of compliance with recycling rates mandated by A better ability to forecast recycling rates may either

the Venting Prohibition is very uncertain. Therefore, the decrease or increase cost estimates. 1

baseline assumptions regarding the percentages of CFCs

that are actually recycled are also very uncertain.

The baseline recycling value assumes no innovation in Including innovation in recycling technologies may

recycling technologies through 2017, which may be decrease the cost estimates.

inaccurate.

Section 611

Manufacturers' responses to the labeling requirement may A better ability to forecast manufacturers' responses may

include labeling, reformulating products, ceasing production, either decrease or increase cost estimates.

or petitioning for an exemption to the labeling requirement.

Predicting the frequency of these responses is difficult.

Note: *We do not include the costs of the methyl bromide phaseout in the total cost estimate because the RIAs do not provide

the benefits of this phaseout. The cost estimates for the methyl bromide phaseout are uncertain, because the model

assumes that the demand for output manufactured with methyl bromide is perfectly inelastic and that the methyl bromide

production industry is perfectly competitive. While these assumptions may be unrealistic, they allowed the analysis to

focus on consumer impacts and ignore effects on output markets.

The Venting Prohibition is essentially a recovery and recycling requirement. For more detail see ICF (1993). 1 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-37

Benefits

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9:
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-38

Table G-14

Major Limitations of Existing Benefits Analyses for Title VI

Limitations Effects on Benefits Estimates

Scientists have an incomplete understanding of A better understanding may either increase or decrease

& the chemical and physical processes that cause ozone the benefits estimate.

depletion, 

& the relationship between ozone depletion and exposure to

ultraviolet radiation (UV-b), and

& the dose-response coefficients relating UV-b exposure to

melanoma skin cancer and cataracts.

Scientists have not yet developed quantified dose-response Additional dose-response relationships will increase the

relationships for some benefits, such as reduced damage to benefits estimate.

the immune system from UV-b radiation. 

The long term nature of the studies introduces a significant A better ability to forecast future events may either

degree of uncertainty. For example, predicting innovation, decrease or increase benefits estimates.

research and development, producer and consumer responses

to price changes, technological change, and major natural

events for the next 100 to 150 years is difficult.

Although truncation of benefits and cost streams is necessary The current method of truncating benefit and costs

for the analysis, it does influence the size of the benefit and streams results in a greater underestimation of benefits

cost estimates. than costs.

The RIAs do not account for averting behavior (i.e., people's Inclusion of averting behavior may decrease the benefits

efforts to protect themselves from UV-b radiation) or behavior estimate, while inclusion of enhancing behavior may

increasing exposure or risk (e.g., increased sunbathing). increase the benefits estimate.

Not all RIAs for Title VI include comprehensively monetized More comprehensive monetization will increase the

benefits, due, in part, to key data gaps (e.g., accepted benefits estimate.

concentration response functions for ozone effects on forests).

Section 608

The extent of compliance with recycling rates mandated by the A better ability to forecast recycling rates may either

Venting Prohibition is very uncertain. Therefore, the baseline decrease or increase benefits estimates.

assumptions regarding the percentages of CFCs that are

actually recycled are also very uncertain.

The baseline recycling value assumes no innovation in Including innovation in recycling technologies may

recycling technologies through 2017, which may be inaccurate. increase the benefits estimate.

Section 611

Manufacturers' and consumers' responses to labeling rules are An enhanced ability to forecast their responses may either

difficult to predict. increase or decrease the benefits estimate.

Benefits attributed to labeling regulations may actually result Consequently, the benefits resulting from this rule may be

from other circumstances as well. less than the estimate included in the RIA.

Although some sectors may reduce the use of MCF as a result A better ability to predict people's actions may decrease

of the labeling rule, other sectors may increase their use of this the benefits estimate.

substance.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-39

References

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Study Type of

Estimate Valuation (millions 1990$)

Kneisner and Leeth (1991) (US) Labor Market 0.6

Smith and Gilbert (1984) Labor Market 0.7

Dillingham (1985) Labor Market 0.9

Butler (1983) Labor Market 1.1

Miller and Guria (1991) Cont. Value 1.2

Moore and Viscusi (1988a) Labor Market 2.5

Viscusi, Magat, and Huber (1991b) Cont. Value 2.7

Gegax et al. (1985) Cont. Value 3.3

Marin and Psacharopoulos (1982) Labor Market 2.8

Kneisner and Leeth (1991) (Australia) Labor Market 3.3

Gerking, de Haan, and Schulze (1988) Cont. Value 3.4

Cousineau, Lacroix, and Girard (1988) Labor Market 3.6

Jones-Lee (1989) Cont. Value 3.8

Dillingham (1985) Labor Market 3.9

Viscusi (1978, 1979) Labor Market 4.1

R.S. Smith (1976) Labor Market 4.6

V.K. Smith (1976) Labor Market 4.7

Olson (1981) Labor Market 5.2

Viscusi (1981) Labor Market 6.5

R.S. Smith (1974) Labor Market 7.2

Moore and Viscusi (1988a) Labor Market 7.3

Kneisner and Leeth (1991) (Japan) Labor Market 7.6

Herzog and Schlottman (1987) Labor Market 9.1

Leigh and Folson (1984) Labor Market 9.7

Leigh (1987) Labor Market 10.4

Garen (1988) Labor Market 13.5

SOURCE: Viscusi, 1992 and EPA analysis.

Table H-1

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Exhibit H-2Summary of Alternative Methods for Assessing the Value of Reduced Mortality Risk

Method Description Strengths Weaknesses 

Value of Statistical Uses wage and risk data to - Revealed preference - Workplace risk context; workingLife (VSL) - hedonic estimate WTP to avoid risk in the - Well-established approach: more subjects and voluntary risk 

wage studies workplace than 60 primary studies - VSL may imply ex post risk VSL - contingent Uses survey responses to - Flexible approach; some studies - Risk information not well-undersvaluation studies estimate WTP to avoid risks use environmental risk context by subjects; questions may be un

- Good data on WTP by - VSL may imply ex post riskrespondent

VSL - consumer Uses consumer expense and risk - Revealed preference - Major difficulties estimating both market studies data (e.g., smoke detectors) to - Flexible approach and expense variables 

estimate WTP to avoid risks - VSL may imply ex post risk

Value of Statistical Annual equivalent of VSL - Provides financially accurate - Adjustment may not reflect how Life Year (VSLY) estimates adjustment for age at death individuals consider life-years; as

they have equal value for all remalife-years

Quality Adjusted Life Applies quality adjustment to life- - Widely used in public health - Lack of data on health state indiYear (QALY) extension data, uses cost- literature that assess different and life quality adjustments that a

effectiveness data to value private medical interventions applicable to an air pollution conte

WTP for change in Reflects WTP for change in risk, - Theoretically preferred approach - Almost no current literature 

survival curve potentially incorporates age- that most accurately reflects - Lack of available data due to the 

specific nature of risk reduction nature of risk reductions from air severe methodological difficulties 

pollution control presenting complex risk data to su

and eliciting reliable values

WTP for change in Uses stated preference approach - Life expectancy is familiar term to - Life expectancy is a simplifed telongevity to generate WTP for longevity or most individuals does not incorporate age-specific 

longer life expectancy information 

-Methodological and data problemattempting to adapt to air pollutioncontext

Cost-Effectiveness Develops a standard of - Widely used in public health - Public health context may be for 

comparison to measure the contexts private goods (i.e., treatment) efficiency of various treatments in - Dollar values do not necessarily achieving a given health outcome patient preferences

Note: WTP = willingness to pay
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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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H-21

Table H-3Unit Values Used for Economic Valuation of Health and Welfare Endpoints

Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

Mortality $4.8 million per Weibull distribution, Central Estimate: Value is the mean of value-o

statistical life mean = $4.8 million from 26 studies (5 contingent valuation and 21 --------------------- --------------------------------- Central Estimate: Value is the mean of the distr$293,000 per Weibull distribution, statistical life-year, derived from the distribution statistical life-year mean = $293,000 statistical life (see below). 

std. dev. = 3,240,000 std. dev. = 198,000

Uncertainty: Best-fit distribution to the 26 sampdistribution prevents selection of negative WTP ---------------------------------------------------------------Uncertainty: Assuming the discount rate is five an expected 35 years remaining to the average studies (see above), the value of a statistical lif0.061, multiplied by the value of a statistical lifevalue of a life-year is derived from the distributistatistical life. Because the VSL is expressed aas indicated above, the value of a statistical lifeas a Weibull distribution, with mean equal to 0.mean of the original Weibull distribution (0.061 $293,000) and standard deviation equal to 0.06standard deviation of the original distribution (0$198,000).
H-22

Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

Chronic Bronchitis (CB) $260,000 A Monte Carlo-generated Central Estimate: Value is the mean of a Monte 

distribution, based on three WTP to avoid a case of pollution-related CB. Wunderlying distributions, as pollution-related CB is derived by adjusting WTdescribed more fully under Viscusi et al., 1991) to avoid a severe case of C“Derivation of Estimates” severity and taking into account the elasticity of and in the text. severity of CB. The mean of the resulting distri

Uncertainty: The distribution of WTP to avoid a CB was generated by Monte Carlo methods, drdistributions: (1) WTP to avoid a severe case oprobability of being each of the first nine deciles WTP responses in Viscusi et al., 1991; (2) the srelated case of CB (relative to the case describis assumed to have a triangular distribution, ce6.5 with endpoints at 1.0 and 12.0 (see text for (3) the constant in the elasticity of WTP with resnormally distributed with mean = 0.18 and stan(from Krupnick and Cropper, 1992). See text fo

Chronic Asthma $25,000 Triangular distribution, Central Estimate: Based on results reported in 

centered at $25,000 on the (Blumenschein and Johannesson, 1998 and O'interval [$19,000, $30,000] 1997). Assumes a 5% discount rate and reflec

distribution among adults (ages 27 and older) aremaining. Uncertainty: Reflects the range in central estim

the two studies. 
Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

H-23

Hospital Admissions 1. All Respiratory variable— See Derivation of Estimates Central Estimate: Central estimate is the result 

- ICD codes: 460-519 analysis for each. The COI estimates are based on ICD

function of the analysis uses 12 distinct C-R functions. A COI 

(e.g., average hospital care costs, average lengweighted share of total respiratory illnesses) re(1993). Uncertainty: Probability distribution is a result o

reflects: (1) uncertainty range of C-R function oin study-specific COI estimates. 

 2. All Cardiovascular variable— See Derivation of Estimates Central Estimate: Central estimate is the result 

- ICD codes: 390-429 function of the analysis uses five distinct C-R functions. A CO

analysis for each. The COI estimates are based on ICD

(e.g., average hospital care costs, average lengweighted share of total respiratory illnesses) re(1993). Uncertainty: Probability distribution is a result o

reflects: (1) uncertainty range of C-R function oin study-specific COI estimates. 

 3. Emergency room visits for $194 Triangular distribution, Central Estimate: COI estimate based on data 

asthma centered at $194 on the (1997). 

interval [$144, $269]

Uncertainty: Based on reported 95% confidencestimates of the number and costs of ER visits
Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

H-24

Respiratory Ailments Not Requiring Hospitalization 1. Upper Resp. Symptoms $19 Continuous uniform Central Estimate: Combinations of the 3 sympt

 (URS) distribution over the interval estimates are available that closely match thos

(defined as one or more URS. A dollar value was derived for each type of the range estimates of WTP to avoid each symptom following: assuming additivity of WTPs. The dollar value runny or of the dollar values for the 7 different types of Ustuffy nose,wet cough, Uncertainty: Assumed to be a continuous unifo

burning, the range of values described by the 7 URS typaching, orred eyes)

[$7, $33] result in 7 different “symptom clusters,” each de

 2. Lower Resp. Symptoms $12 Continuous uniform Central Estimate: Combinations of the 4 sympt

 (LRS) distribution over the interval estimates are available that closely match thos

(defined in the study as LRS. A $ value was derived for each type of LRtwo or more of the estimates of WTP to avoid each symptom in thfollowing: cough, chest additivity of WTPs. The $ value for LRS is the apain, phlegm, and for the 11 different types of LRS.wheeze.)

[$5, $19] al. result in 11 different “symptom clusters,” eac

Uncertainty: Taken to be a continuous uniform 

range of values described by the 11 LRS types

 3. Acute Bronchitis $45 Continuous uniform Central Estimate: Average of low and high valu

distribution over the interval IEC for use in section 812 analysis (Neumann e[$13, $77]

Uncertainty: Continuous distribution between lo(Neumann et al., 1994) assigns equal likelihood value within the range.
Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

H-25

 4. Acute Respiratory $18 1. URS, probability = 40% Central Estimate: Assuming that respiratory illn

Symptoms and Illnesses LRS, probability = 40% be characterized as some combination of URS - Presence of any of 19 with 20% probability. The $ value for these endacute respiratory 2. If URS, use URS $ dist. average (using the weights 0.40, 0.40, and 0.20symptoms If LRS, use LRS $ dist. for URS, LRS, and URS + LRS.- Any Resp. Symptom select one value each from Uncertainty: Based on variability assumed for c

- Respiratory Illness distributions; sum the two

 URS+LRS, prob. = 20% with 40% probability, LRS with 40% probability,  If URS+LRS, randomlyURS and LRS $ URS and LRS uncertainty distributions present

 5. Asthma Attack $32 Continuous uniform Central Estimate: Mean of average WTP estim

distribution over the interval definitions of a “bad asthma day.” Source: Row[$12, $54] study which surveyed asthmatics to estimate W

"bad asthma day,” as defined by the subjects.Uncertainty: Based on the range of values estifour severity definitions.

 6. Moderate or worse $32 Continuous uniform Central Estimate: Reflects the mean WTP to av

asthma distribution over the interval as reported by Rowe and Chestnut (1986). 

[12, 54]

Uncertainty: Taken to be a continuous uniform 

range of values obtained from the study.

 7. Shortness of breath, $5.30 Continuous uniform Central Estimate: From Ostro et al., 1995. This 

chest tightness or distribution over the interval median estimates from two studies of WTP to awheeze [$0, $10.60] of breath: Dickie et al., 1991 ($0.00), and Loehm

($10.60). Uncertainty: Taken to be a continuous uniform 

range of values obtained from the two studies.
Health or Welfare

Endpoint

Estimated Value Per Incidence (1990$)

Central Estimate Uncertainty Distribution Derivation of Estimate

H-26

Restricted Activity and Work Loss Days 1. WLDs $83 none available Central Estimate: Median weekly wage for 199

Department of Commerce, 1992)Uncertainty: Insufficient information to derive a

 2. MRADs $38 Triangular distribution Central Estimate: Median WTP estimate to avo

centered at $38 on the respiratory restricted activity day -- from Tolley interval [$16, $61] (recommended by IEc as the mid-range estima

Uncertainty: Range is based on assumption thWTP for a single mild symptom (the highest essymptom--for eye irritation--is $16.00) and be leThe triangular distribution acknowledges that thbe closer to the point estimate than either extre

Welfare Effects 1. Visibility Valuation function: Central Estimate: Estimated WTP for valuation 

Residential Visibility HHWTP= B * ln(VR1/VR2) change in visual range. Visibility coefficients ap

"in-region" analysis vary by category of visibility change an"out-of-region" where: Recreational visibility valuation is based on Che

B = estimated visibility coefficient $65, for California, the Southwest, and the SouHHWTP = annual WTP per household For "in region" recreational visibility, the coefficVR1 = starting annual average visual range "out-of-region" recreational visibility, the coefficVR2 = the annual average visual range after the for California, the Southwest, and the Southeas

change in air quality

depend upon two factors: (i) visibility coefficien

 2. Worker Productivity Change in daily none available Central Estimate: Based on elasticity of income 

wages: $1 per concentration derived from study of California cworker per 10% and Horst, 1981 and U.S. EPA, 1994). Elasticitchange in O daily income for workers engaged in strenuous 

3

1990 Census). 

Note: All WTP estimates converted to 1990 dollars using the Consumer Price Index (CPI); COI estimates converted using the CPI-Medical.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-27

Results of Valuation of Health

and Welfare Effects

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H-29

Table H-4

Primary Estimates of Health and Welfare Benefits Due to Criteria Pollutants in 2000

 Monetary Benefits (in millions 1990$)

Benefits Category 5th %ile Mean 95th %ile 

Mortality

Ages 30+ $ 8,600 $ 63,000 $ 150,000 

Chronic Illness

Chronic Bronchitis $ 220 $ 3,600 $ 11,000 

Chronic Asthma 29 140 240 

Hospitalization

All Respiratory $ 46 $ 78 $ 120 

Total Cardiovascular 53 200 430 

Asthma-Related ER Visits 0.1 0.6 1.8 

Minor Illness

Acute Bronchitis $ 0 $ 1.3 $ 3.3 

URS 2.8 12 26 

LRS 1.4 3.9 7.2 

Respiratory Illness 0.4 2.5 6.1 

Mod/Worse Asthma 1.2 8.5 19 1

Asthma Attacks 13 35 66 1

Chest tightness, Shortness of

 Breath, or Wheeze 0 0.5 2.4 

Shortness of Breath 0 0.3 0.7 

Work Loss Days 180 210 240 

MRAD/Any-of-19 420 760 1,100 

Welfare

Decreased Worker Productivity $ 460 $ 460 $ 460 

Visibility 

Recreational 1,700 2,000 2,300 

Agriculture 46 450 860 

Total Benefits $ 71,000 2

Note: 

 Moderate to worse asthma and asthma attacks are endpoints included in the definition of MRAD/Any of 19 respiratory effects. 1

Although valuation estimates are presented for these categories, the values are not included in total benefits to avoid the potential

for double-counting. 

 Summing 5th and 95th percentile values would yield a misleading estimate of the 5th and 95th percentile estimate of total health 2

benefits. For example, the likelihood that the 5th percentile estimates for each endpoint would simultaneously be drawn from a

Monte Carlo procedure is much less than 5 percent. As a result, we present only the total mean. 
The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-30

Table H-5

Primary Estimates of Health and Welfare Benefits Due to Criteria Pollutants in 2010

Monetary Benefits (in millions 1990$)

Benefits Category 5th %ile Mean 95th %ile 

Mortality

Ages 30+ $ 14,000 $ 100,000 $ 250,000 

Chronic Illness

Chronic Bronchitis $ 360 $ 5,600 $ 18,000 

Chronic Asthma 40 180 300 

Hospitalization

All Respiratory $ 76 $ 130 $ 200 

Total Cardiovascular 93 390 960 

Asthma-Related ER Visits 0.1 1 2.8 

Minor Illness

Acute Bronchitis $ 0 $ 2.1 $ 5.2 

URS 4.2 19 39 

LRS 2.2 6.2 12 

Respiratory Illness 0.9 6.3 15 

Mod/Worse Asthma 1.9 13 29 1

Asthma Attacks 20 55 100 1

Chest tightness, Shortness of

 Breath, or Wheeze 0 0.6 3.1 

Shortness of Breath 0 0.5 1.2 

Work Loss Days 300 340 380 

MRAD/Any-of-19 680 1,200 1,800 

Welfare

Decreased Worker Productivity $ 710 $ 710 $710 

Visibility 

Recreational 2,500 2,900 3,300 

Agriculture 7.1 550 1,100 

Total Benefits $ 110,000 2

Note: 

 Moderate to worse asthma, asthma attacks, and shortness of breath are endpoints included in the definition of MRAD/Any of 19 1

respiratory effects. Although valuation estimates are presented for these categories, the values are not included in total

benefits to avoid the potential for double-counting. 

 Summing 5th and 95th percentile values would yield a misleading estimate of the 5th and 95th percentile estimate of total health 2

benefits. For example, the likelihood that the 5th percentile estimates for each endpoint would simultaneously be drawn from a

Monte Carlo procedure is much less than 5 percent. As a result, we present only the total mean. 
Figure H-4

Analysis of Contribution of Key Parameters to 

Quantified Uncertainty

$0

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All

Components

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ValuationMortality

Incidence

Chronic

Bronchitis

Valuation

Chronic

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Incidence

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of 19

Valuation

Visibility

Valuation

MRAD/Any

of 19

Incidence

Total 2010 Benefits (Billions 1990$)

95th %ile

Mean

5th %ile

Contribution to Variance from the Mean

The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-31

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-37

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Table H-6

Sensitivity Analysis of Alternative Discount Rates on the Valuation of Reduced Premature

Mortality

Benefit Category & 2000 (in millions, 1990$) 2010 (in millions, 1990$)

Discount Rate 5th %ile Central 95th %ile 5th %ile Central 95th %ile

VSL Approach

3% Discount Rate $ 8,900 $ 65,000 $ 150,000 $ 14,000 $ 100,000 $ 250,000

5% Discount Rate 8,600 63,000 150,000 14,000 100,000 250,000

7% Discount Rate 8,300 61,000 150,000 14,000 97,000 240,000

VSLY Approach

3% Discount Rate $ 4,600 $ 30,000 $ 68,000 $ 7,400 $ 48,000 $ 110,000

5% Discount Rate 5,000 33,000 74,000 8,100 53,000 120,000

7% Discount Rate 5,400 35,000 80,000 8,800 57,000 130,000

Note: The discount rate affects the benefits estimates of VSL and VSLY approach differently. With the VSL approach, higher

discount rates lead to lower estimates because of the lag structure. With the VSLY approach, the higher discount rates lead to

higher estimates because of its affect on the annualized values.
The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-38

Sensitivity Test for Impact of Income

Changes Over Time

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Table H-7

Elasticity Values for Conducting Sensitivity Analysis

Health Endpoint Lower Estimate Central Estimate Upper Estimate

Minor Health Effect 0.04 0.14 0.30

Severe and Chronic

Health Effects 0.25 0.45 0.60

Premature Mortality 0.08 0.40 1.00

Note: Sources for the derivation of these values can be found in Industrial Economics 1999.

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The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-40

Illustrative Calculations —

Morbidity Benefits Estimates

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Table H-8

Illustrative Adjustment to Estimates of WTP to Avoid Morbidity

Year (in millions) (in millions) Income Estimate Estimate Estimate

US Population Real GDP Lower Central Upper

WTP Estimate (1990 Dollars)1

Minor Health Effect- Asthma E =0.04 E =0.14 E =0.30 yyy

1990 249,440 5,744 23,026 $32 $32 $32

2000 274,634 7,123 25,936 $32.20 $32.50 $33.20

2010 297,716 8,959 30,092 $32.30 $33.20 $34.70

Severe Health Effect- Chronic Bronchitis E =0.25 E =0.45 E =0.60 yyy

1990 249,440 5,744 23,026 $260,000 $260,000 $260,000

2000 274,634 7,123 25,936 $267,850 $274,300 $279,240

2010 297,716 8,959 30,092 $277,990 $293,280 $305,290

Note:

 WTP estimates are reported in undiscounted 1990 dollars and represent value per case. 1
The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-41

Illustrative Calculations —

VSL Estimate

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Illustrative Adjustment to Estimates of The Value of Statistical Life

Year (in millions) (in millions) Income E =0.08 E =0.40 E =1.0

US Population Real GDP Estimate Estimate Estimate

Value of Life Estimate (in thousands)1

Lower Central Upper

yy y

1990 249,440 5,744 23,026 $4,800 $4,800 $4,800

2000 274,634 7,123 25,936 $4,848 $5,036 $5,410

2010 297,716 8,959 30,092 $4,905 $5,345 $6,271

Note:

 Value of life estimates reported in undiscounted 1990 dollars. 1
The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Erratum

Text of the November 19, 1999 Final

Advisory by the Advisory Council on Clean

Air Compliance Analysis on the 1999

Prospective Study of Costs and Benefits of

Implementation of the Clean Air Act

Amendments (CAAA)