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

xxxii 

## [This page left blank intentionally.]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 CleanAir Act on the ìpublic health, economy, and environment of the United States,î and to report thefindings 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 informationon 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 theClean 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 thenationís investment in clean air was more than justified by the substantial benefits that were gained inthe 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 thestringency of some requirements, revamp the hazardous air pollutant regulatory program, refine and streamline permitting requirements, and introducenew 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 reportand 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 focusedour emissions analysis on six major pollutants: volatile organic compounds (VOCs), nitrogen oxidesThe Benefits and Costs of the Clean Air Act, 1990 to 2010

ii (NO

x ), sulfur dioxide (SO

2 ), carbon monoxide (CO), coarse particulate matter (PM
10 ), and fine particulate matter (PM
2.5 ). For each of these pollutants weforecast 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 inpopulation, 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 achievedfor the two major precursors of ambient groundlevel ozone: volatile organic compounds (VOCs) and oxides of nitrogen (NO

x ). Relative to the Pre-CAAAscenario, estimated VOC emissions under the PostCAAA case are 35 percent lower by 2010. Thischange in emissions is due largely to VOC reductions from motor vehicles and area sources (e.g., drycleaners, commercial bakeries, and other widely dispersed sources).

The NO

x emission reduction under the PostCAAA scenario represents the greatest proportional emissions change estimated in our analysis. For the year 2010, the Post-CAAA NO

x 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 NO

xemissions 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 (PM
2.5 ). Sulfur dioxide (SO

2) is an important precursor of PM. By 2010, SO
2 emissions are 31 percent

lower under the Post-CAAA scenario. Of the 8.2

million ton difference between Pre- and Post-CAAA

SO
2 estimates, 96 percent is attributable to additionalcontrol of utility emissions through a national capand-trade program involving marketable SO

2 emission allowances. Oxides of nitrogen, discussed above, are also important fine PM precursors.

We project the 1990 Clean Air Act Amendmentsto have more modest effects on emissions of particulate material which is emitted in solid form (i.e., ìprimaryî or ìdirectî PM
10 and PM
2.5 emissions). Overall, emissions of primary PM
10 and PM
2.5 areeach 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 PMemissions 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 SO

2 , NO

x , and organic compounds. These PMspecies, 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 PM
10 and PM
2.5 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 costsinclude 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 plusExecutive 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 billionannually 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 thesecosts. 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 asan 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:

Low b $16,000 $26,000

Central $71,000 $110,000

High b $160,000 $270,000

Net Benefits:

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

Central $52,000 $83,000
High $140,000 $240,000

Benefit/Cost Ratio:

Low c less than 1/1 less than 1/1

Central 4/1 4/1

High c 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 emissionsanalysis served as the principal input to a linked series of models. We used these models to estimatechanges 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 onestimating the impact of Pre- and Post-CAAA emissions on ambient concentrations of ozone, PM
10 , PM
2.5 , SO
2 , NO

x , 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 ofpremature mortality. We estimate that 2010 PostCAAA PM
10 and PM
2.5 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 greaterreductions of up to 30 percent. The air quality modeling also indicates a substantial overall reduction in future-year PM
10 and PM
2.5 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 year2010 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 andenvironmental 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 ourprimary 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 otherkey 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 importantresearch direction for the first prospective and subsequent analyses. Quantified benefits of CAAA programs reflected in the overall monetized benefitsinclude: increased agricultural and timber yields; reduced effects of acid rain on aquatic ecosystems; andreduced effects of nitrogen deposited to coastal estuaries. Many ecological benefits, however, remain difficult or impossible to quantify, or can only bequantified for a limited geographic area. The magnitude of quantified benefits and the wide range of unquantified benefits nonetheless suggest that as welearn 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 TitleVI 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 costsby 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 ofnet benefits. This range, reflecting a 90 percent probability range around the mean, or central estimate, is negative $20 billion (implying a small probabilitythat 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 PrimaryLow 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 onair 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 outsidethe 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 modelchoices 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 toolsbased on an open, consistent model architecture ï Development of tools and data to assess thesignificance of wetland, aquatic, and terrestrial ecosystem changes associated with air pollution ï Increased basic and targeted research on thehealth effects of air pollution, especially particulate matterï 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 ofEPAís Science Advisory Board (SAB) as the Advisory Council on Clean Air Act Compliance Analysis (hereafter, the Council). Organizing the reviewcommittee 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 metmany 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 concerningphysical 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 providedits 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 andmodeling 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 treatmentof uncertainty in the relationship between particulate matter exposure and mortality; the valuation ofpremature mortality; the treatment of tax interaction effects; the assessment of stratospheric ozonerecovery; and the treatment of ecological and welfare effects. These issues could not be resolved withinthe 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 .......................................................................................................................... 38The 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 .............................................................................................................. 96Table 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 -29The 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-5 1

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-35Table 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-5The 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-43Table 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-25The 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- 6List 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 ......................................................................... 8 3 Table 7-2 Interactions of Mercury and Ozone with Natural Systems at Various Levels of Organization

............................................................................................................................... . 84 

# TablesThe 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-16List 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-23The 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-22List 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-3The 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 ...................................................................................... 46List 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-34The 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-60List 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-32The 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-31Acronyms 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

b

ext 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

FEV
1 forced expiratory volume in one
secondThe 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

HNO
3 nitric acid

HPMS Highway Performance Monitoring System

HS
2 O
4 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

m 3 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 PollutantsAcronyms and Abbreviations

xxvii NET National Emission Trend

NH
3 ammonia

NHANES National Health and Nutrition
Examination

NIH National Institutes of Health

NMOC non-methane organic compound
N O nitrogen oxide

NO
2 nitrogen dioxide

NO

x 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

O
3 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 PM
10 and

PM
2.5 ) PM
10 particulate matter less than or equal
to 10 microns in diameter

PM
2.5 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

RO
2 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

SO
2 sulfur dioxideThe 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

XO
2 halogenated peroxy radical

yr year

ZEV zero emission vehicleAcknowledgments

xxix 

# Acknowledgments

This project is managed under the direction ofRobert 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, EPAOffice of Policy/Office of Economy and Environment, directed the ecological assessment; and SamNapolitano, 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/NationalExposure 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, formerAssistant 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 ofICF 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 SABreviews 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, PaulLioy of the Robert Wood Johnson School of Medicine, Paulette Middleton of the RAND Center forEnvironmental Sciences and Policy, Donald Fullerton of the University of Texas ñ Austin, LawrenceGoulder 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 duringthe 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 theUniversity 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 alsoprovided 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 providedvaluable 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 byPaulette 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 ChapelHill, 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 

# 1Chapter



## 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 andconveyed 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 formof 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 produceevery 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 toCongress 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 inconducting 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 requirementsto 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. Ourintent 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 toconduct 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 recentThe 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 revisionof 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 astheir 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 forecastthe 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 ofthe 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 notassess 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 leademissions. 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 maintenanceof 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; andestablishes 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-effectivenessassumption. 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 of1990 requires the EPA to analyze the costs and benefits to human health and the environment that areattributable to the Clean Air Act. In addition, section 812 directs EPA to measure the effects of thisstatute 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 tothe 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 byrecognizing 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 concentrationsof air pollutants. However, pollutant emissions reductions achieved contribute to changes in ambientconcentrations 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 basedon 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 historicaltrends, see Chapter 2 and Appendix A of this document.



### Key Assumptions

Similar to the retrospective analysis, we madetwo key assumptions during the scenario design process to avoid miring the analytical process in endlessspeculation. 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

4particularly since some states, most notably California, have in the past done so. If one were to assume that state and local regulations would have beenequivalent 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 nothave 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 to2010 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 localgovernments 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 resultfrom partnerships among all levels of government and with the active participationand 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 actual1990 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 mostheavily 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 AnalysisChapter 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 threestep 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 butwith 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 modelsdeveloped and used in various offices of EPAís Office of Air and Radiation. These emissions modelsprovide estimates of emissions of six criteria air pollutants 2 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 thefirst 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 developsan 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 somegaseous 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 andresources 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 foundationbased 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 forthe 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 advantageof 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 profilesfor 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 PM
10 and PM
2.5 ), sulfur dioxide (SO

2 ), nitrogen oxides (NO

x), carbon monoxide (CO), volatile organic compounds (VOCs), and ammonia (NH
3 ). One of the CAA criteria pollutants, ozone (O
3 ), is formed in the atmosphere through the interaction of sunlight and ozone precursor pollutants such as
NO

x and VOCs. Ammonia is not a criteria pollutant, but is animportant 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 effectsamenable 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 valuationsteps. 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, orassumptions 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, weconduct sensitivity analyses for a number of variables that may influence the primary net benefit estimate. The primary estimate and the range aroundthis 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 ofthe 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 andinterpretation of the 812 studies. This panel of experts was organized in 1991 under the auspices ofEPAís Science Advisory Board (SAB) as the Advisory Council on Clean Air Act Compliance Analysis (hereafter, the Council). Organizing the reviewcommittee 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 metmany 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 concerningphysical 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 providedChapter 1: Introduction

7its 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 andmodeling 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 treatmentof uncertainty in the relationship between particulate matter exposure and mortality; the valuation ofpremature mortality; the treatment of tax interaction effects; the assessment of stratospheric ozonerecovery; and the treatment of ecological and welfare effects. These issues could not be resolved withinthe 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 andprincipal results obtained through the human health effects estimation process.

ï Chapter 6 describes the human health effectseconomic valuation methodology and results. ï Chapter 7 summarizes the ecological andother 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 whichmay 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 

## [This page left blank intentionally.]Chapter 2: Emissions

9 

# Emissions 

# 2Chapter

Estimation of pollutant emissions, a key component 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 (NO

x ), sulfur dioxide (SO

2), carbon monoxide (CO), particulate matter with an aerodynamic diameter of 10 microns or less (PM
10), and fine particulate matter (PM
2.5 ). 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 fordeveloping 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 2010and presents our estimates of changes in future emissions resulting from the implementation of the 1990Amendments. 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 2000and 2010 in the absence of additional CAAA requirements, and modifying these projections to reflectfuture-year control assumptions. The resulting estimates depend largely upon three factors: the method for selecting the base-year inventory, the indicatorsused 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 datacompiled primarily by the National Acid Precipitation Assessment Program (NAPAP), EPAís Office of Mobile Sources (OMS), and the Federal Highway Administration (FHWA). For both PM
2.5 and PM
10 , however, we updated NPI estimates to incorporatechanges 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 (NH
3 ) emissions. NH
3 influences the formation of secondary PM (PM formed as a result of atmospheric chemical processes). We used NH
3 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 NH
3 . In addition to NH
3 , we also estimated mercury (Hg) emissions. Wequalitatively 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) PM
2.5 and PM
10 inventory, leads to lower estimates of fugitive dust emissions and therefore of overall primary PM. 3

Once we established the base-year inventory, weprojected 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 airquality. 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 recreationalmarine vessels, recreational vehicles, and lawn/garden equipment as well as an indicator of the increasein 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 successfuloperation 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 NO

x 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 asfrom 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 producerswhich 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 aswood 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 isbased 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, wildfiresChapter 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 usedIPM 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 sourcecategories 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 differencein 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 maintainsthe 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 NO

x 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 SO
2 and NO

x 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 NO

x 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 designedtrading 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, NO

x , SO
2 , CO, PM
10 , and PM
2.5 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, NO

x , SO
2 , 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 SO

2 emissions. IPM accounts for the influence of the future cost of low-sulfur coal prices expected to occur asa 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 NO

x control program incorporated in the PostCAAA scenario may not reflect the NO

x 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

12similar 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 rateover the last ten years of the 20 year projection period.

Post-CAAA estimates of VOC, NO

x , SO
2 , and CO emissions for the modeled regulatory scenarios decrease significantly from 1990 to 2000 and then plateau, remaining relatively constant from 2000 to2010. 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 aremade, 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 to2010, 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 emissionsestimates 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 is8.2 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 NO

xemissions will be reduced by the greatest percentage. Comparison of projections for the year 2010

indicates the Post-CAAA NO

x 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%

NO
x 22,747 25,021 18,414 -26% 28,172 17,290 -39%

SO 2 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
PM 10 28,289 28,768 28,082 -2% 28,993 28,035 -3%

Primary
PM 2.5 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 SO

2 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 betweenPre- 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) ofthe 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 PM
10 and PM
2.5 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 PM
10 and PM
2.5 , however, follow different paths. While we estimate implementation of the CAAA will cause primary

PM
10 levels to slowly decrease from 1990 to 2010, Post-CAAA projections indicate primary PM
2.5 emissions will actually rise despite the influence of the CAAA. Overall, however, emissions of primary

PM
10 and PM
2.5 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 usedin this analysis, area sources account for over 90 percent of primary PM
10 emissions and over 80 percent of primary PM
2.5 emissions. 12 As a result, even the

successful reduction of motor vehicle and nonroademissions have only a slight impact on overall primary PM
10 and PM
2.5 estimates developed for this study. 13 Furthermore, the CAAAís most significant primary PM area source controls target emissions incounties 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 nationalstandards. 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 tominor 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, NO

x , and SO

2 , all pollutantsregulated by the CAA, are secondary PM precursors. The reduction in the emissions of these threepollutants also leads to lower overall PM concentrations in the atmosphere. The complete impact ofthe 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 NH
3 emissions

. The 1990 Amendments, however, do not include provisions designed

10 EPA projected PM
10 and PM
2.5 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. NO

s and SO

2 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 usedin this analysis may underestimate the contribution from motor vehicle carbonaceous emissions.

13 The difference between 2010 Pre- and Post-CAAA estimates for PM
10 and PM
2.5 motor vehicle emissions is 31 percentand 39 percent respectively. The difference between 2010 Preand Post CAAA estimates for PM
10 and PM
2.5 nonroad emissions is 19 percent and 20 percent respectively.

14 The PM NAAQS referred to here is the 50 ug/m 3 (annual mean) 150 ug/m 3 (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 %

NO
x 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 %

SO 2 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
PM 10 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
PM 2.5 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 NH
3. As a result, the Pre- and PostCAAA estimates follow a similar upward trend. We

estimate NH
3 emissions will increase roughly 55 percent from 1990 to 2010. Although we do not

estimate the costs and benefits associated with NH
3 controls and changes in NH
3 ambient concentrations as part of this analysis, estimation of NH
3 emissions is an important part of the prospective study. NH
3is a secondary PM precursor, and we relied on future-year NH
3 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 accountfor 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-CAAAestimates for each projection year. Overall, the results of this analysis indicate that the 1990 Amendments will provide a reduction in Hg emissions of44.2 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 emissionsthat 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) PreCAAA PostCAAA Diff. PreCAAA PostCAAA 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 56.2The 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 NO
X Emissions

Estimates

Figure 2-3

Pre- and Post-CAAA Scenario SO
2 Emissions

Estimates 0 5 10 15 20 25 30

1980 1990 2000

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAA

0 5 10 15 20 25 30

1980 1990 2000 2010

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAA

0 5 10 15 20 25 30

1990 2000 2010

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAAChapter 2: Emissions

17 Figure 2-4

Pre- and Post-CAAA Scenario CO Emissions

Estimates

Figure 2-5 Pre- and Post-CAAA Scenario Primary PM
10 Emissions Estimates

Figure 2-6 Pre- and Post-CAAA Scenario Primary PM
2.5 Emissions Estimates 0 20 40 60 80 100 120

1980 1990 2000 2010

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAA

0 5 10 15 20 25 30

1980 1990 2000 2010

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAA

0 5 10 15 20 25 30

1980 1990 2000

YearEmissions in Short Tons(Millions)

Pre-CAAA Post-CAAAThe 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 ofthe Grand Canyon Visibility Transport Commission (GCVTC) study of western regional haze provides an initial test of the sensitivity of emissionsprojections 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, NO

x , SO
2 , CO, and PM
10 , 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 captureyearly fluctuations in emissions and the exact timing of emissions cuts. Only for NO

x 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 NO

x emission reductions that have occurred since 1990. For example, significant

reductions attributable to reasonable available control technology (RACT) requirements for major stationary source NO

x 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 NO

x , SO
2 , and PM
2.5 , 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 to2010 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 PM
2.5 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 PM
2.5 concentrations in urban and nonurban areas throughout the U.S. 16 Comparison of primary PM
2.5 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 particlesis 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 onambient air quality. In this analysis, we apply a factor of 0.2 to crustal emissions to estimate the fraction of crustal PM
2.5 that makes its way into theìmixed layerî of the atmosphere and influences pollutant concentrations. Figure 2-7 displays the breakout of primary PM
2.5 into its adjusted crustal and carbonaceous (elemental carbon and organic carbon) components. The figure divides crustalmaterial into two subcategories, fugitive dust or industrial sources, based on the source of the material and also shows the fraction of primary PM
2.5 that is

16 Crustal material is directly emitted from fugitive dust sources such as agricultural operations, construction, paved andunpaved 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 PM
2.5 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, theexpected 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 sourcesof 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 SO

2emissions 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 verifythese estimates through comparison with Continuous Emission Monitoring (CEM) data. As a result, we were able to accurately estimate SO

2 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 SO

2 emissions. However, the processes involved in the formation of NO

x during combustion are more complicated than those

involved in the formation of SO

2 ; thus, our NO

xemissions estimates are more variable and less certain than SO

2 estimates.

Volatile organic compounds, like SO

2 and NO

x , 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 PM
2.5 Emissions by EPA Region (tons/year)The Benefits and Costs of the Clean Air Act, 1990 to 2010

20used 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 PM
2.5 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 emitcrustal material (area sources), we likely underestimate the impact of the CAAA on reducing PM
2.5 , thereby reducing monetary benefits estimates. The uncertainty associated with estimating the partition

of PM
2.5 emissions components could conceivably have a major impact on the net benefit estimate; compared to secondary PM
2.5 precursor emissions, however, changes in primary PM
2.5 emissions have a relatively small impact on PM
2.5 related benefits.

.

We estimated future-year emissions levels basedon 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 mostdirectly 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 goodportion 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 asource 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 NO

x control strategy designed to regulate the regional transport of ozone. However, the control program assumed under the Post-CAAA scenario may not reflect the NO

x 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*

PM 2.5 emissions are largely based on scaling of PM 10 emissions. Overall, unable to determine based on current information,
but current emission factors are likely to underestimate PM 2.5 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 PM 2.5 constitute a large portion of overall CAAA-related
benefits.

Primary PM 2.5 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 PM 2.5 emissions by underestimating
PM 2.5 emissions reductions
associated with mobile source tailpipe controls. Potentially major. Mobile source primary carbonaceous particles are a significant contributor to public exposure to PM 2.5. Overall, however, compared to secondary PM 2.5 precursor emissions, changes in primary PM 2.5 emissions have only a small impact on PM 2.5 related benefits.

The Post-CAAA scenario includes implementation of a region-wide NO x emissions reduction strategy to control regional transport of ozone that may not reflect the NO x 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, NO x 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 couldbe more or less stringent, widereaching (e.g., NO x 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 

# 3Chapter

The costs of complying with the requirements of the Clean Air Act Amendments (CAAA) of 1990will affect all levels of the U.S. economy. The impact, initially experienced through the direct costs imposed by regulations promulgated under theamendments, will also be seen in patterns of industrial production, research and development, capitalinvestment, 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 firstsection summarizes our approach to estimating direct compliance costs. In the second section wepresent 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 Amendmentsto characterize the potential welfare loss not captured in the direct cost approach. We conclude thechapter 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 firststep 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 baselinethat represents projected emission levels and control costs in the absence of the 1990 Amendments. Under the Post-CAAA scenario, costs are based oncompliance with selected CAAA provisions. Together these two scenarios form the foundation uponwhich the incremental costs and benefits of complying with the 1990 Amendments are estimated. Formore 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 emissionsprojections 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 (NO

x ). 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

24taining 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 thisanalysis, 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 NO

x and sulfur dioxide (SO

2) emissions, we use the Integrated Planning Model (IPM). IPM allows us to estimate the control costs of several pollutants whilemaintaining consistent control scenarios and economic forecasts of the electric power industry. Itassesses 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 SO

2 emission control costs forpoint 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 attainmentcosts, 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 estimatecompliance 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 2000will 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 recurringexpenditures) 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, theStates can determine how best to meet progress requirements and are responsible for creating permit programs (under Title V). As a result, a significantportion 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,

NO

x , and PM emissions) implemented primarily bypoint 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 additional34 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 millionby 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 costsdo include some costs of final MACT rules that regulate non-VOC HAP emissions.

In order to estimate the costs associated withTitle IV, we considered the implications of pollution abatement controls (for SO

2 and NO

x ) on the electric power industryís operation of generationunits 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. Thisestimate decreases to $2.0 billion by 2010. This decrease reflects, in part, the future compliance cost savings resulting from the SO
2 allowance trading

program.

Title V costs are associated with new operatingpermit 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. Theestimate 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, Stateand 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 yearrequires assumptions that result in potentially misleading figures. The difficulty is due to the differing time horizons and the complexity of the process bywhich ozone-depleting substances (ODSs) cause adverse effects on human health and the environment. Title VI provisions incur costs over significantlyvarying 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 involvesspreading the costs of capital equipment uniformly over the useful life of the equipment, by using a discount rate to account forthe 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 titlebased 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 ofthese 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 NO x 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%

NO x 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 no t 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 controls 6

,ï Electric power industry compliance (stationary NO

x 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 NO

x Tailpipe/Extended Useful Life Standard.

The 1990 CAAA regulates stationary sourceemissions primarily under Title I. Among the relevant provisions, PM NAAQS, utility industry compliance with NO

x 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 forthe emissions analysis, the utility industryís compliance with NO

x emission standards affects all electric generation units using fossil fuels. Existing oil andgas units face Reasonable Available Control Technology (RACT) requirements and all new units mustcomply with more stringent New Source Performance Standards (NSPS) and New Source Review (NSR) requirements. By 2010, estimated costs for stationary NO

x 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 componentof total costs in 2010 is attributed to progress requirements. Annual compliance costs with progressrequirements 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 ofthe same magnitude as seen with the costs of regulating stationary sources under Title I. The combined cost of national and California LEV programsis $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. By2010, however, we expect that the program will affect areas outside of the OTR. As a result, 2010national 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 andfive 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 coststo the California reformulated gasoline (RFG) program and NO

x 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 NO

x 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 setof 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 oncompliance 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 fromthe 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 approachesto calculating costs associated with regulation: (i) direct compliance cost, (ii) partial equilibrium modeling, and (iii) general equilibrium modeling. Direct compliance cost estimates are calculated differentlythan the economic welfare impact estimates that result from partial or general equilibrium modeling; a direct cost estimate is often the most straightforwardof the three approaches. This method estimates compliance expenditures or, in economic terms, how an industryís or firmís marginal cost curve shifts due toincreased 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 actualcompliance expenditures, but may have an ambiguous relationship to total social costs. There are twoprimary 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 directcost approach tends to make the simplifying assumption that firms rely on static pollution abatementtechnology, 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 costestimates, 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 equilibriummodeling. These more complicated approaches estimate social costs by accounting for a wider range of market consequences associated with compliancewith pollution abatement requirements. The partial equilibrium approach is particularly appropriate when social costs are predominantly incurred in directly affected markets. It requires modeling bothsupply and demand functions in the affected economic sector. Therefore, measures of social cost reflect behavioral responses by both producers andconsumers 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. Likethe 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 multiplesectors of the economy, and may also provide insight into unanticipated indirect effects in sectors thatmight 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 datasets facilitated the development of detailed, year-byyear expenditures in all sectors of the economy, fromwhich we modeled producer and consumer behavior and estimated net social costs. In the retrospective, our central estimate of total annualized directcosts, 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 moretheoretically 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 theCAAAís economic impacts on various sectors the U.S. economy. Comparison of thedirect cost approach to the partial equilibrium modeling suggests that the direct costapproach 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 analysesare 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 thatare 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 acomparison 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 bedesigned and configured to reflect the indirect economic consequences of both costly

and beneficial economic effects. None of the general equilibrium models available inthe 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 efficiencyimpacts 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 policychanges can be substantially higher when pre-existing tax distortions are taken into account. Our direct cost estimates do notreflect 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 equilibriummodeling 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 inconsumption patterns, input costs, and technological innovation are crucial to framing the question of the CAAAís cost impact. Given the nature of thisprospective 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 theimpact 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 NO

x Controls, and

ï NO

x 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, theseprovisions 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 totalcost estimate for a given provision: (1) a central estimate, equal to the 2010 primary cost estimate reported in this chapter 11 , (2) a low estimate; and (3) a high estimate. The low and high estimates assess the potential magnitude of the effect of thecomponent(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 NO

x controls, the cost projections

are based on models of future emissions controls. Accurately identifying the set of adopted controls isa 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 ourprimary 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 thealternative. 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. Abetter interpretation of these results is that uncertainty in key input parameters can have a significant effect on the overall uncertainty of our estimates ofdirect compliance costs and ultimately the net benefits calculation.

In addition to examining specific provisions, we conducted a sensitivity analysis of the cost of capitalused 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, NO

x , and

PM control. The alternative estimates use three andseven 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 whichcontribute to uncertainty in the overall cost estimates. In most cases, the effect of these other factors cannot be quantitified, though some may havesignificant influences on our overall net benefits estimate. We present a summary of these factors in Table 3-3 below, and provide a characterization ofthe 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 incurredand 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 atlower 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 demandincreases 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 emissionsmore cost-effectively; firms will institute technologies such as turbocharging, aftercooling, and variable-valve timing, all of which improve engine performance.

There is considerable uncertainty surroundingthe 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 NO

x reductions for meeting progress requirements. Additional area-specific analysis would be necessary to determine the extent to which areas find

NO

x reductions beneficial in meeting attainment and progress requirement targets. Trading of NO

x forVOC to meet RFP requirements may result in distributions of VOC and NO

x emission reductions which differ from those used in this analysis. Inresponse to these uncertainties, we adopted a conservative strategy for estimating the costs of RFPreductions in the primary analysis. We use a relatively high cost per ton reduced estimate of $10,000for 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 incurredthrough 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 thana 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, althoughits impact is likely to be less important than the previous two factors. Under the 1990 CAAA, EPA created economic incentive provisions in several rulesto 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 costof 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 sourcesto 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 thateconomic 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 maybe overstated. The major trading programs authorized under the Amendments, however, governing sulfur and nitrogen oxide emissions reductions fromutilities 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 2010 1 (billions 1990 $) Strategy for Sensitivity Analysis Range of Estimates from Sensitivity Test (billions 1990 $)

Progress Requirements $2.46 Vary unit costs for unidentified

measures $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 NO x Costs $2.15 Vary unit-level cost per ton $1.1 - $3.2

NO x 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 Estimate 1

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 Estimate 1

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 canbe 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 

# 4Chapter

Air quality modeling links changes in emissionsto 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. Thischapter 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 thechapter 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. Thefollowing section provides an overview of the general methodology we used to estimate future-year ambient concentrations. This methodology sectionincludes 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 inattempting to translate emission inventories into pollutant concentrations. First, they must model the dispersion and transport of pollutants through theatmosphere. Second, they must model pertinent atmospheric chemistry and other pollutant transformation processes. These challenges are particularlyacute 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 (NO

x). 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 ambientconcentrations 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 offederal 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 todevelop a picture of future changes in air quality resulting from the implementation of the 1990 CAAA.

We focused our air quality modeling efforts onestimating the impact of Pre- and Post-CAAA emissions on future-year ambient concentrations of
ozone, PM
10 , PM
2.5 , SO
2 , NO

x , and CO and on future-year acid deposition and visibility. The ideal model for this analysis would be a single integratedair 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 andused both the Regional Acid Deposition Model/Regional Particulate Model (RADM/RPM) and the Regulatory Modeling System for Aerosols and AcidThe Benefits and Costs of the Clean Air Act, 1990 to 2010

36 Deposition (REMSAD) model to assess PM
10 , PM
2.5 ,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 bysimulating 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-yearand 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 asimulation 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, becausethe 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 integratedmodel, 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 relationshipbetween 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 gridUAM-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 PM
10 , and

PM
2.5 concentrations for each day of the relevant simulation periods. We conducted separate modelruns 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-yearacid 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 bothprojection years under the Pre- and Post-CAAA scenarios.

We estimated future-year Pre- and Post-CAAA

ambient SO

2 , NO, NO
2 , and CO concentrations by adjusting 1990 concentrations using future-year to base-year emissions ratios. This technique assumes a linear relationship between changes in emissionsin an area and changes in that areaís ambient concentration of the emitted pollutant. 2 Although thistechnique 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 SO

2 , NO

x , 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 expectedis 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 thegeographically 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 applicableThe 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 avariety 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 1990monitor data. The adjustment factors for each future-year projection scenario were based on the relative change in pollutant concentration between 1990and 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 observedpollutant 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 qualityinformation. 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 monitoringsites, the 1990 estimated concentrations were calculated at the center of each cell of a grid covering the domain of the applicable air quality model. Usingfuture-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 eachgrid cell for each of four future-year projection scenarios: 2000 Pre-CAAA, 2010 Pre-CAAA, 2000 PostCAAA, and 2010 Post-CAAA. These results wereused to derive adjustment factors for each air quality monitor, based on the simulation results for thegrid 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 atMonitor 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 0.30ppm 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

SO
2 , NO

x , and CO involved the same basic approach described above. However, instead of applyingmodel-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 projectionscenarios. 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 Bayarea, 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 andexpected decreases in Post-CAAA ozone concentrations in the eastern U.S. In this part of the country, UAM-V predicts Pre-CAAA ozone concentrationincreases will occur primarily over the states of Virginia, North Carolina, Kentucky, Tennessee, Georgia, and Alabama; while Post-CAAA decreases willbe 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 NO

x emissions.

Regional-scale model results for the western U.S. indicate that ozone concentrations in this portionof 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-CAAAmodel 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 distri butions) 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 predictio ns 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-CAAAThe Benefits and Costs of the Clean Air Act, 1990 to 2010

40dicted changes in ambient ozone levels in the eastern U.S

. Furthermore,comparison of 2010 Pre- and PostCAAA model estimates shows thatfuture-year western ozone concentrations will be lower as a result of the1990 Amendments, but UAM-V results indicate that the reductions in the West will likely be about half the sizeof the reductions in the eastern portion of the country. The difference between the change in western ozoneconcentrations and the change in eastern ozone concentrations is largely due to the more aggressive NO

x controls expected in the East. Specifically, the Post-CAAA scenario incorporates the effects of a NO

x cap-and-trade system for the easternU.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, thesmaller 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 fromthe regional scale results described above. Examination of Pre- and Post-CAAA modeling estimates shows that, in some portions of the urban centers ofSan 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 ìNO

x scavenging,î during

which a reduction in NO

x , 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-yearPost-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 byapplying 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 theimpact of the CAAA on air pollution. The distribution of ratios of 2010 Pre-CAAA to 1990 baseyear ozone concentrations reveals that the majorityof 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 willbe 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 NO

x . In VOC-limited areas where there is a relatively high NO

x concentration (regions where the concentration of VOC, not NO

x , dictates the amount of ozone that can be formed), these two ozone precursors (VOC and NO

x )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 NO

x , ozone production is impeded; thus, a decrease in NO

x 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

RatioRelative Frequency (percent) median: 0.883Chapter 4: Air Quality Modeling

41 

### Particulate Matter

To model Pre- and Post-CAAA particulate matter (PM
10 and PM
2.5 ) 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-CAAAestimates. In the West, projected reductions in future-year PM concentrations (Pre-CAAA minus Post-CAAA) are largely restricted to urban areas. 5The broad scale improvement in eastern PM concentrations is driven largely by reductions in utilitysource 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 aresult, 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

PM
10 and PM
2.5 estimates are both higher than 1990base-year estimates in almost all areas of the country. Pre-CAAA 2010 PM
10 and PM
2.5 estimates aregenerally zero to 10 percent greater than 1990 baseyear estimates. The average estimated increase in

PM
2.5 concentrations, however, is slightly larger than the average estimated increase in PM
10 . 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 PM
10 and PM
2.5 concentrations, in a number of areas ambient PM levels, more frequently PM
2.5 , increase from the base-yearunder the Post-CAAA scenario. On average, however, 2010 Post-CAAA PM
10 and PM
2.5 concentrations are between zero and five percent and zeroand 10 percent, respectively, lower than 1990 baseyear concentrations. 8

As shown in Figures 4-3 and 4-4, the percentage

reduction in PM
2.5 concentrations across the U.S. between the Pre- and Post-CAAA scenarios vary more widely than the percentage reduction in PM
10 . 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 PM
10 concentrations than PM
2.5 concentrations and dampen the influence of changes in anthropogenic emissions on ambient PM
10 concentrations.

Comparison of the two distributions in Figures 4-3 and 4-4 shows that, despite the greater variation

of PM
2.5 reductions, the percentage reduction in PM
2.5concentrations are larger on average than the percentage reduction in PM
10 concentrations. The reason for this difference is two fold. First, as described above, PM
2.5 concentrations are more susceptible to the influence of changes in anthropogenic emissions, which are regulated by the CAAA. Second, theCAAA provisions that influence PM emissions (regulations that focus on secondary PM precursors such

as NO

x , and SO

2 , and primary PM sources such asdiesel engine exhaust standards) affect the fine particulate (PM
2.5 ) subset of PM
10 to a much greater extent than the coarser fraction that makes up the rest

of PM
10 . 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 PM
2.5 than PM
10 .

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 inPM 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-yearvisibility. 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-CAAAscenarios. Comparison of these values reveals that, in the eastern U.S.,we anticipate that future-year visibility in both urban and rural areas isprojected to improve under the PostCAAA scenario. RADM/RPM predicts that Post-CAAA visibility in2010 will not only be better than PreCAAA visibility, but also, in manyareas, it will be better than the visibility in the 1990 base-year. Thisimprovement in visibility is attributable to reductions in the concentration of gaseous and suspended particles, such as PM, that scatter andabsorb 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 areassuch 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 infuture-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. Themodel suggests Los Angeles and Las Vegas will experience the greatest improvement.

9 The deciview is a measure of visibility which captures therelationship 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 PM
10 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

RatioRelative Frequency(percent) median: 0.946

Figure 4-4

Distribution of Combined RADM/RPM and REMSAD Derived Monitor Level Ratios for Annual Average PM
2.5 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

RatioRelative Frequency(percent) median: 0.919Chapter 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 andeach 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-CAAAscenario, 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-CAAAprojections, 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 andTitle IV Acid Rain provisions are expected to significantly reduce both

NO

x and SO

2 emissions thus contributing to significant reductions indownwind deposition of acidic nitrogen and sulfur compounds. The differences between the Pre-CAAA and Post-CAAA projections, however, imply that the 1990 Amendments willhave 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 NO

x emissions cap-and-trade program that is part of the PostCAAA scenario.



### SO
2 

### , NO, NO
2



### , and CO

To estimate future-year SO

2 , NO, NO
2 , and CO concentrations we relied on linear emissions scaling,adjusting 1990 base-year concentrations using ratios of future-year to base-year emissions. Ratiosgreater 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 SO

2 , NO, NO
2 ,and CO tend to increase under the Pre-CAAA scenario, while Post-CAAA concentrations for all four pollutants except SO
2 tend to decrease. For example,

the median 2010 Pre-CAAA emission-based ratio for

SO
2 is roughly 1.35, indicating an increase in median 2010 Pre-CAAA SO

2 concentration of approximately 35 percent from the 1990 base-year. The median ratios for NO, NO

2 , and CO are roughly1.13, 1.17, and 1.05 respectively. Under the PostCAAA scenario we estimate that in 2010 NO, NO

2 ,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 SO

x , NO, NO
2 , and CO ambient concentrations. Figure 4-5

Distribution of Monitor - Level Ratios of SO
2 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

RatioRelative 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

RatioRelative Frequency (percent) median: 0.892The Benefits and Costs of the Clean Air Act, 1990 to 2010

46Comparison 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 indicatethat we estimate that future-year concentrations of SO
2 , NO, NO
2 , 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 SO
2 , NO, NO
2 , 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 SO
2 , NO, NO
2 , 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. Justas for the summer; spring, autumn, and winter median values are less than one. Averaged over all four seasons, we estimate a median reduction in SO

2 , NO, NO
2 , and CO concentrations of approximately9, 33, 40, and 25 percent respectively. RACT requirements, tailpipe emissions standards, and NO

xemissions 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

SO 2 NO NO 2 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 NO
2 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

RatioRelative 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

RatioRelative Frequency (percent) median: 0.720Chapter 4: Air Quality Modeling

47 tion in NO and NO

2 concentrations. Title I

nonattainment area controls and Title II motor vehicle provisions are responsible for much of the change in CO concentrations, while regulation ofutility and motor vehicle emissions account for majority of the decrease in SO

2 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 andpotential 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 airpollutants 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, maybe 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 thataffect a modelís ability to accurately predict the relative change in concentration of a pollutant from onescenario 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 modelozone 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 onambient levels, the greater the uncertainty associated with predicted differences between Pre- and

Post-CAAA concentration estimates.

Table 4-7 presents the most important specificsources 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 discussionof 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 *

PM 10 and PM 2.5 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
PM 2.5 , in most of the RADM domain and changes in nitrates and sulfates may serve as a reasonable approximation to changes in total
PM 10 and total PM 2.5 . 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 PM 2.5 ambient
concentration monitors throughout the U.S. is limited. As a result, cross estimation of PM 2.5 concentrations from PM 10 (or TSP) data was necessary inorder to complete the "monitorlevel" observational dataset
used in the calculation of air quality profiles. Unable to
determine based on
the current
information. Potentially major. PM 2.5 exposure is linked to mortality, and avoided mortality constitutes a large portion of overall CAAA benefits. Cross
estimation of PM 2.5 , 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 NO x 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 NO x 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, theapproach 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 NO x 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
NO x 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/NO x 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 

# 5Chapter

Health benefits resulting from improved air quality 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 effectssuch as premature mortality, heart disease, and respiratory illness. This chapter presents an overviewof 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 CAAAwill result in significant reductions in mortality, respiratory illness, heart disease, and other adversehealth 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 betweenthe Pre-CAAA and Post-CAAA regulatory scenarios. As described in Chapter 2, the Pre-CAAAscenario assumes no further controls on criteria pollutant emissions besides those already in place in 1990, while the Post-CAAA scenario assumes fullimplementation 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-CAAAscenarios in 2000 and 2010; (2) estimates of the number of people exposed to air pollutants at a givenlocation; 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 qualitymonitors are neither uniformly nor pervasively distributed across the country, concentration data at monitors are extrapolated to non-monitored areasin 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 ateach 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 reflectsthe 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 ofthese 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 arerelated to the change in air pollutant exposure experienced by individuals. Because the expected changes in pollutant concentrations vary from location tolocation, 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 usingthe 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-Rfunctions 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 asrespiratory disease) to a change in pollutant concentration experienced by that population. The C-R functions used in CAPMS generate changes in theincidence 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 derivethis last factor, as well as the specific form of the CR equation, from the published scientific literaturefor 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 multipleadverse 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. Inaddition, for some of the health effects we do quantify, the current economic literature does not support the estimation of the economic value of theseeffects. 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 analysissince airborne emissions of lead were virtually eliminated by pre-1990 Clean Air Act programs.



## Key Analytical Assumptions

The modeling of health benefits attributable tothe 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 canestimate health benefits of the CAAA, but some as1 U.S. Bureau of Economic Analysis. 1995. BEA RegionalProjections 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 daysHospital 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 (PM 10 , PM 2.5 ) 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 th e unquantified health effects column. ‡Appendix D includes detailed discussion of the scientific evidence for these potential health effects and includes illustrativ e 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 wer e 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 asranges 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 pollutiondata are available only for a limited number of monitor sites that are not uniformly distributed acrossthe 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 Theresulting estimates are uncertain because the geography, weather, land use, and other factors influencing air pollution may differ significantly between agrid 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 aregreatest) with greater certainty than in low pollution areas. Thus, grid-cell ozone estimates in theeastern 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, SO

2 ), are expected to be less uncertain than estimates for

CO and NO

x , which are measured by considerably

fewer monitors.

Air pollutant concentration changes are mappedto 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 keyassumptions associated with this population mapping. First, we assume the population in each grid cell grows at the same rate as the state population asa 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 ofthe state as a whole. Second, we assume in the exposure analysis that the population in the grid cell issimilar in terms of its activity patterns and demographic characteristics to the populations in the studies from which the C-R functions are derived. Thisis 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, publishedscientific 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 thatcould be used to derive C-R functions likely contributes to the uncertainty of the health effects results. For example, the uncertainty associated withthe 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 becausescientific 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 toadequate 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 betweencomprehensiveness and scientific defensibility. However, to the extent that this selection process maylead 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 bothwithin-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 overalluncertainty 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 conductedin New York and one conducted in Seattle may report different C-R functions for the relationshipbetween 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. Thismay 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 areregion-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 singleC-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 PM 2.5 and PM 10 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 PM 2.5 and PM 10 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 ofthe most serious health impacts of air pollution: premature mortality associated with PM exposure. Thissection 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 literaturerecognizes 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 acausal relationship between exposure to elevated PM and premature mortality, based

on the evidence of a correlation between PMand mortality reported in the scientific literature. This assumption is necessary because the epidemiological studies on whichthis 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 pollutantmay influence elevated mortality rates. Recent studies have explored whether ozoneand 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 ofthe 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 certainscenarios (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 mayreflect regionally-specific C-R functions resulting from regional differences in factorssuch 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 existenceof such a lag could be important for the valuation of benefits, if one were to assume that lagged incidences of premature mortalityshould be discounted over the period between when the fatal increment of exposureis 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 asthose 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 ozoneand 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 associatedwith a given incremental change in PM exposure would occur in the same year as the exposure reduction. This same literatureimplies that lags of up to a few years are plausible, and we chose to assume a five-year lagstructure, 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 mortalityestimates reported in time-series studies account for only a minor fraction of total excess mortality. However, the relative rolesof 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., PM
10 or PM
2.5 ), thesecond 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 PM
2.5 as the best currently available measure of the impact of PM on mortality. 9 Two types of exposure studies (short-term andlong-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 ratesup 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, forthis analysis, we rely exclusively on long-term studies to quantify PM mortality effects, though theshort-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 mortalityrates 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 predictcases 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 PM
10 , however, and the reasonably good correlation between PM
2.5 and

PM
10 , the 812 prospective analysis also uses PM
10 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 theìexcess 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 livedonly 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 eachindividualís exposure and health status. Populationlevel ecological studies assess the relationship between population-wide health information (such as countsfor 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 ifa 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 anoverestimate 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 riskfactors such as smoking. However, it is always possible, even in well-designed studies, that a relevantrisk factor (e.g., climate, the presence of other pollutants) may not have been adequately consideredor 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 otherfactor or factors (e.g., differences among communities in diet, exercise, ethnicity, climate, industrialeffluents, 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), andAbbey 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, hadthe 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 thoughPope 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 alsoproduce 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 wouldcause 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 amongcohort 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 levelsby 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 theThe 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 tounderestimate 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 estimatemay be overestimated because it includes the mortality impacts of these confounders. However, in an effort to avoid overstating benefits, and becausethe 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 theDockery 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 ofthe premature adult mortality associated with longterm PM exposure based on Dockery et al. (1993) inChapter 8 and in Appendix D. We emphasize, however, that the estimate based on Pope et al. (1995) isour 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 improvementsin air quality between the Pre-CAAA and PostCAAA scenarios. Table 5-3 summarizes the CAAArelated avoided health effects in 2010 for each studyincluded 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 reductionin 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 ofage). 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 to2010. 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 in23,000 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 representsroughly 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 byage 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 ofestimated 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 5 th % mean 95 th % 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 O 3 1,800 7,200 12,000 3.83%

Hospitalization

respiratory
admissions PM, CO, NO 2 , SO 2 , O 3 13,000 22,000 34,000 0.62%

cardiovascular
admissions PM, CO, NO 2 , SO 2 , O 3 10,000 42,000 100,000 0.86%

emergency room visits for asthma PM, O 3 430 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 NO 2 76,000 330,000 550,000 10.44%

moderate or worse
asthma c PM 80,000 400,000 720,000 0.24%

asthma attacks c O 3 , PM 920,000 1,700,000 2,500,000 1.04%

chest tightness, shortness of breath,
or wheeze SO 2 290 110,000 520,000 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 symptoms d O 3 , PM 25,000,000 31,000,000 37,000,000 2.15%

restricted activity days c 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 perso ns of all ages for International Classification of Disease (ICD) codes 460-519; for cardiovascular admissions, we used admissions for per sons 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 ag es; for chronic bronchitis we used the incidence rate for individuals 27 and older; for the pooled estimate of minor restricted activi ty 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 adult s 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 asimilar manner to estimates of premature mortalities: as a range of estimates for each quantified healthendpoint, with the range dependent on the quantified uncertainties in the underlying concentrationresponse functions. The range of results for 2010only 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 reflectadmissions 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., airpollution 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 criteriapollutants, 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 beregulated, require EPA to establish air toxic emissions standards based on maximum achievable control technology (MACT standards), and include aprovision 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 suspectedcarcinogens. 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 whichassessments were available (virtually all of this estimate came from assessments of about a dozen wellstudied pollutants). 12 We note, however, that theresults of this analysis are based, in part, on conservative, upper-bound estimates of chemical specific

risk factors.

In addition to cancer, inhalation of air toxicscompounds 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 anddioxins, 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 thatdo exist cover only a handful of pollutants. Emissions inventories are very limited and inconsistent, and air quality modeling has only been performedfor a few source categories. In addition, the scientific literature on the effects of air toxics is generallymuch weaker than that available for criteria pollutants. Appendix I presents a list of research needs identified by the Project Team which, if met, wouldenable 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 documentsfor these provisions. The series of basic steps to arrive at physical effects estimates ó from emissionsestimation, 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 sourcesused 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 thatprovide 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 thechange 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 radiationand 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 (thebiological 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, and299.0 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 anduncertainties 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 toovercome 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 thedevelopment 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 rateis 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 controlsimplemented 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 studiesmay 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 influenceof 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 thelag 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 

## [This page left blank intentionally.]Chapter 6: Economic Valuation of Human Health Effects

69 

# Economic Valuation
of Human Health
Effects 

# 6Chapter

The reduced incidence of physical effects is a

valuable measure of health benefits for individualendpoints; 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 twobroad categories of benefits, health and welfare benefits. Human health effects include mortality and morbidity endpoints, which are presented in thischapter. 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 ofphysical 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 aprobability distribution around that value. The statistical form of the probability distribution variesby 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 ofother health endpoints. Because of the critical importance of this endpoint in the studyís results, thischapter 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, economistsspeak 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

70effect. 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, ifa 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 mostenvironmental 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 estimatesare 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 thevalue 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 ofvalues 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 thatexpress the increase in mortality risk as cases of ìexTable 6-1 Health Effects Unit Valuation (1990 dollars)

Endpoint Pollutant Valuation (mean est.)

Mortality PM 10 $4,800,000 per case

Chronic Bronchitis PM 10 $260,000 per case

Chronic Asthma O 3 $25,000 per case

Hospital Admissions

All Respiratory SO 2 , NO 2 , PM 10 & O 3 $6,900 per case

All Cardiovasular SO 2 , NO 2 , & CO PM 10 & O 3 $9,500 per case

Emergency Room Visits for Asthma PM 10 & O 3 $194 per case

Respiratory Illness and Symptoms

Acute Bronchitis PM 10 $45 per case

Asthma Attack or Moderate or

Worse Asthma Day PM 10 & O 3 $32 per case

Acute Respiratory Symptoms SO 2 , NO 2 , PM 1 , & O 3 $18 per case

Upper Respiratory Symptoms PM 1 $19 per case

Lower Respiratory Symptoms PM 10 $12 per case

Shortness of Breath, Chest

Tightness, or Wheeze PM 10 & SO 2 $5.30 per day

Work Loss Days PM 10 $83 per day

Mild Restricted Activity Days PM 10 & O 3 $38 per dayChapter 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 smallincreases in the risk of mortality. By summing individualsí WTP to avoid small increases in risk overenough 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 smallchanges in mortality risk experience by a large number of people. The economic benefits associated withavoiding 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 forthis prospective analysis. Mortality benefits, however, are also the largest contributor to the range ofuncertainty 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 moresusceptible 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 willingnessto pay (WTP) to improve oneís own chances of survival plus WTP to improve other individualsí survival rates. 3 The ideal measure would also take intoaccount 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 becausesurvival 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 atheoretically 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 ofstatistical 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 $4.8million 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 thevalue 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 thisanalysis. The distribution incorporates uncertainty from three sources: (1) the WTP to avoid a case of severe CB, as described byViscusi 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) theelasticity of WTP with respect to severity of the illness. Based on assumptions about the distributions of each of these three uncertain components, wederive 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

73which is about $260,000, is taken as the central tendency estimate of WTP to avoid a pollution-relatedcase 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 monthlyWTP, while OíConor and Blomquist (1997) generate an annual WTP estimate. In order to extend monthly and annual WTP estimates over anindividualís lifetime, we adjusted the reported estimates to reflect the average life-years remaining and age distribution of the adult U.S. population, giventhat 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 combinationof WTP estimates for individual symptoms comprising each ailment. For example, a willingness to pay estimate to avoid the combination of specific upperrespiratory 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 valueof avoiding those symptoms defined in the concentration-response study.

To capture the uncertainty associated with thevaluation 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 estimatesfor 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 aMRAD, 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 WTPto avoid a day of minor restricted activity. Therefore, we derive for this analysis a value from WTP estimates for avoiding combinations of symptomswhich 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 reflectsthe value of reduced incidences of hospital admissions due to respiratory or cardiovascular conditions. We use avoided hospital admissions as a measure asopposed to the number of avoided cases of respiratory or cardiovascular conditions, because of theavailability of C-R relationships for the hospital admissions endpoint. Hospital admissions reflect a class of health effects linked to air pollution which areacute 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

74tion provides an alternative definition of either respiratory effects or cardiovascular effects, and may be based on different pollutants. For valuation ofthe 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 additionto medical costs and lost work time. While the probability distributions in this analysis characterize arange 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 basedon 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 fromthose 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, thediscount 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 thevast 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 aggregationapproach 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 additionto 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 isour 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 futurepremature 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 effectsconsidered 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

75The 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 thendistributed across the next three years. Our valuation of avoided premature mortality applies a five percent discount rate to the lagged estimates overthe 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 overlappinghealth 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 benefitsfigures. In other cases, there are endpoints included in our aggregation ofbenefit 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 arebased on unique portions of the population.



## Valuation
Uncertainties

We addressed many valuationuncertainties explicitly and quantitatively by expressing values as distributions (see Appendix H for a complete description of distributionsemployed), using a computerized statistical technique to apply the valuations to physical effects (see Chapters5 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 beenadequately characterized, nor that the valuation estimates are unbiased. It is possible that the actualWTP 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 rangesof 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 Asthma 1 2 13 29

Asthma Attacks 1 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 2010 2 - $ 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 yieldresults 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 inmortality 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 90percent 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, accountsfor 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 theuncertainties 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 thesame 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, WTPfor 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 mortalityrisk 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 tentimes the risk reduction valued in the study). Under the assumption of linearity, the estimate of thevalue of a statistical life does not depend on the particular amount of risk reduction being valued. Thisassumption has been shown to be reasonable provided the change in the risk being valued is withinthe 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 UncertainChapter 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 beingvalued in the study could be important. Certain characteristics of both the population affected and the mortality risk facing that population are believed toaffect the average WTP to reduce risk. The appropriateness of the mean of the WTP estimates fromthe 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 populationaffected by changes in air pollution and (2) the extent to which the risks being valued are similar.

The substantial majority of the 26 studies reliedupon 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 evidencesuggests that approximately 85 percent of those identified in short-term (ìepisodicî) studies who die prematurely from PM-related causes are over 65. 7 Theaverage 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 inwage-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 incomeelasticity of WTP for health risk reductions is positive (although there is uncertainty about the exactvalue 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 pollutionconcentrations, 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 whichjob-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 evidence 8 that people will pay moreto 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 risksThe 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 ofdeath, 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 timeprior 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 healthbenefits and the effect of knowledge of a fatal exposure on valuation of a delayed effect, in other wordswhether 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 derivean 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 havethus 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 mortalityrisk 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 Estimate 1

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 

## [This page left blank intentionally.]Chapter 7: Ecological and Other Welfare Effects

81 

# Ecological and
Other Welfare
Effects 

# 7Chapter

EPAís traditional focus in environmental benefits 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 arein place largely as the result of a major research effort to better understand and quantify the effects ofsulfur 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 focuson the importance of ecological resources by devoting a great deal of effort to characterizing and, where possible, quantifying and monetizing the impacts ofair pollutants on natural systems. This increased focus is also partly a result of the outcome of EPAísretrospective 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 selectingspecific 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 selectioncriteria 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, asone 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 impactsare 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 theanalysis. 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 thosestudies 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 availableThe Benefits and Costs of the Clean Air Act, 1990 to 2010

82 methods and data. As scientific understanding andimpact 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 yieldsassociated 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 inturn prevents development of defensible benefits estimates which can be reasonably associated with theair 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 ofcontinued consideration of those impacts not valued in this report in policy decision-making and infurther technical research. Judging from the geographic breadth and magnitude of the relatively modest subset of impacts that we find sufficientlywell-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 bylimiting 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 onecosystems 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

83power 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 werevisible 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 wasto 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 andby 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-specificeffects 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 onpollutant-specific impacts at a range of levels of biological organization. It is important to note that the interactions listed are intended to illustrate the rangeof 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 andozone on ecological systems. To illustrate the nature of our review of effects, consider the second

row in Table 7-2. This row summarizes the effectsof the air pollutants mercury and ozone at the ìindividualî level of biological organization. As indicatedin 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 throughimpacts 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 directeffects 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

84effects, while limited in its direct usefulness in a costbenefit framework, nonetheless does convey the widerange 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 ofnitrogen 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 wherethe enhanced surface growth of plants shields bottom growing plants from sunlight, causing them todie 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. Forthis 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 ispreferable 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 andfrost. 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 speciescomposition. 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

86effects 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 aciddeposition 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 (CO 2 ).

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. Selectiveadvantage 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 includethose 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 theplantís 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 havebeen deposited to ecosystems. These conserved pollutants are transformed throughbiotic 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 verysubtle 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 anthropogenicfactors such as air pollution and other environmental stress factors such as low temperature, excess or limited water, and limitedavailability of nutrients. The specific combinations of factors differ among regions andecosystems where declines have been observed. Accurately predicting the impactsof 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 quantificationand 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 treatedquantitatively. 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 valueall service flows for any particular ecosystem or aggregation of ecosystems. Generally, we are limitedThe Benefits and Costs of the Clean Air Act, 1990 to 2010

88to 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 valuationmethods 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 potentialscope 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 furtherlimited 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 airpollution, 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

89that 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 estuariesin 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 mostrecent 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 theecosystemís structure and function and affects ecological service flows. Many state governments andmulti-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 damagesassociated with eutrophication, and derive dose-response relationships and valuation strategies for eachof 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 bythe 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 oligotrophic 1 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 nutrientenrichment 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

90Our 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 ofabatement 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 costswould 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 choosecase 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 formeeting 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 ofcapital 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 maybe 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 estuarieschange. 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 ofthe 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 nitrogendeposited 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-scenariodifferences 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 developour displaced-cost estimate by assuming that decision makers will choose to forego the most costlynitrogen 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 endof 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 displacedcost, 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 neededsimply 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

91Bay 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 Bayaccounts 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 tobe known to the public as a phenomenon that injures trees, forests, and water bodies throughout Europe and in some areas of the United States andCanada. One of the goals of the CAAA was to address the problem of acidification of terrestrial and aquatic ecosystems caused by acidic deposition. Toassess 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, acidificationof 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 thewater 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 YorkState 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 evaluatedby 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 severalmeasures of the impact of sulfur and nitrogen deposition on lake acidity, including ANC and pH. 3 Weused 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 $12million 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

92the suite of Post-CAAA 2010 estimates of total damages to the corresponding suite of estimates usingPre-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 presentare 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 scientificevidence 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 isbased 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. ThePnET II model was designed to estimate the combined effects of several stressors on the rate of net primary productivity (NPP), a measure of the rateof 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 theimpact 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. Weaggregated 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 $88Chapter 7: Ecological and Other Welfare Effects

93Model (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 isprovided for each of the forest production regions defined by TAMM. 6 Second, ATLAS generates an estimate of forest inventories ineach 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 asan increasing annual benefit estimate over the modeling period. The shape of the benefits time-seriesreveals a production spike in the 2007 to 2008 period. This spike is due to a large anticipated harvestof 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 smallin 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 onmodel 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 estimateswhen 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 modelingof 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 2000 2002 2004 2006 2008 2010

YearMillions of 1990 DollarsThe 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 woodproducts 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, wassupported 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 describechanges 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 oftoxics 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 quantifiedgiven 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 plantspecies, including agricultural crops. Our agricultural benefits analysis relies on crop-yield loss C-Rfunctions 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 estimatesare 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 extendsbeyond 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 rangeof crops than the NCLAN data inputs, adding barley, oats, hay, rice, and cottonseed. The broader crop coverage ensures that the model addresses priceand 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 forthe 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 thenation. 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 McClellandet al. (1990) in two Eastern cities (Atlanta and Chicago). Because of technical concerns about thestudyís 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 Inboth 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 expressvalue 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 withinor 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 isan 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 productivityon 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 incomeincurred by the average worker engaged in strenuous outdoor labor.



### Stratospheric Ozone Provisions

The quantified benefits of stratospheric ozone protection provisions are dominated by the reducedhealth 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 reducedUV-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 inUV-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 percentof 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, manyeffects 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 benefitsof 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 isreasonable 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

98worker 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 impactsof ecological damages mitigated by the CAAA suggests that great care must be taken to consider thoseecosystem 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 impossibleto observe, quantify, and model. This factor suggests that many of our qualitative and quantitativeresults 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 othe r 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 

# 8Chapter

In this chapter we present our summary of the primary estimates of monetized benefits of theCAAA from 1990 to 2010, compare the benefits estimates with the corresponding costs, and exploresome of the major sources of uncertainty in the benefits estimates. We also present the results of ourcalculations using alternative assumptions for several key input variables.



## Monetized Benefits of the
CAAA

In this section we provide an overview of thethree 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 ofchanges 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 oneexception, 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 studyperiod. 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 ofthe ecological benefits analysis, acidification, we generate only a single annual estimate for the target year2010. 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 lageffects associated with the processes of ozone depletion over decades-long time scales, make it difficultto 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 toemissions reductions in a single year and, by extension, compared to the costs incurred to achieve that yearís allocation of reductions in stratospheric ozonedepleting substances. Instead, we generate an annualized equivalent of the cumulative present value ofThe 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 ofTitle 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 onexposure analysis conducted for each of the two target years of the analysis, 2000 and 2010. The range of estimates we generate for the monetized benefitsof human health effects incorporates both the quantified uncertainty associated with each of the healtheffect 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. Thecharacterization of the uncertainty surrounding economic valuation is discussed in detail in AppendixH. The characterization of the uncertainty surrounding specific health effect estimates is discussed inAppendix D. Below, we discuss the combined effect of these two categories of uncertainty and ourtechniques 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 estimatefor 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 andquantified 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 thedistribution 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 eachiteration 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 agrid 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 computerizedstatistical 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 reflectsa 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 forthat endpoint, selects a unit value from the corresponding distribution of economic valuation unitvalues, and multiplies the two to generate a monetized benefit estimate. We then repeat the process many times to generate a distribution of estimatedmonetized 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 estimatingthe effects of sulfur and nitrogen deposition in acidifying lakes, the effects of ozone in reducing timberand agricultural production, and the effects of particular matter on visibility are all subject to uncertainty and require substantial resources simply todevelop single estimates. We describe key uncertainties in Chapter 7 and they are reflected in theranges 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 ofthe ranges we present reflect reasonable alternative choices in key input variables, but theranges cannot currently be interpreted as points on a statistical

distribution of results. For theseecological effects, the central estimate is the midpoint of theranges 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 throughV in Figure 8-1 below. The figure provides a characterization of both the primary central estimate and the range of values generated by the aggregationprocedure 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 prematuremortality associated with reductions in ambient particulate matter and associated criteria pollutants. The remaining benefits are divided among two broadcategories 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 26Total Benefits ($Billions)

Benefits BenefitsThe 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 Asthma 1 1.9 13 29

Asthma Attacks 1 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 Benefits 2 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

103chitis, 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 40percent. Note that, because of the aggregation procedure used, and because we round all intermediateresults 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 stageof 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 interveningyears. In an attempt to capture uncertainty associated with these estimates, we relied on the ratios ofthe 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. Wepresent 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 humanhealth benefits from Title VI provisions are substantial. The analysis we conducted is based largely onexisting 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 uncertaintycharacterizations used in the criteria pollutant analysis. We made major adjustments for both the value of statistical life (VSL) and the discount rate. Weadjusted 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 $30 billion, the fifth percentile value of the distribution of aggregate monetized benefits reported in Table 8-1. This is because the various health and welfare effects are treated as stochastically independent, so that the probability that the aggregate monetizedbenefit is less than or equal to the sum of the separate five percentile values is substantially less than five percent. Table 8-2 Present Value of Monetized Benefits for 48 State Population
Present Value (millions 1990$, 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 $900,000The 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$)

Annual Estimates Cost or Benefit 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 90percent 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. Theannual 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 resultsfor 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 ofthe 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 to1990 at five percent. The results indicate that the Primary

Central estimate of benefits clearly exceeds the costs of the CAAA, for each of the twotarget years and for the cumulative estimates of presentvalue over the 1990 to 2010 period.

The estimates in Table 8-3reflect 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 prematuremortality 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 wellas, 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 thechange 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 modelingsystem 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:

Low b $16,000 $26,000 $160,000 $100,000 $260,000

Central $71,000 $110,000 $690,000 $530,000 $1,200,000

High b $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:

Low c 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

High c 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 Highestimates (5th and 95th percentile values, respectively). In the bottom half of the table we present two alternative methods for comparing benefits tocosts. 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, ouranalysis 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 tento 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 criteriaThe 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 valuationused 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 canbe 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 evidenceof whether the benefits of a regulatory program justify its costs. Cost-effectiveness analysis involves estimation of the costs per unit of benefit (e.g., livessaved). 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 ofbenefits, 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 thecosts 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 TitlesI 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 ofapproximately $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. Forthe 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 aspectsof 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, theratio 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

107mates, 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 ofuncertainty 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 thebenefits analysis; measurement uncertainties in estimation of direct costs; alternative assumptions forPM-related mortality valuation; alternative assumptions for PM-related mortality risk; unquantifiedsources of error in emissions and air quality modeling; and omissions of key benefits categories. Thetable entries cover quantitative analyses of uncertainty, characterization of unquantified uncertainty,and the potential effect of alternative modeling paradigms for costs and benefits. Additional treatmentof 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 physicaleffects 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 theprobability of alternative outcomes can be quantitatively characterized in an objective manner. Forexample, 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 ofthe 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 ofpossible 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 conducteda sensitivity analysis to determine the physical effects estimation and economic valuation variableswith 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 $300AllComponentsMortalityValuationMortalityIncidenceChronicBronchitisValuationChronicBronchitisIncidenceMRAD/ Anyof 19ValuationVisibilityValuationMRAD/Anyof 19IncidenceTotal 2010 Benefits (Billions 1990$)

95th %ile

Mean

5th %ile Figure 8-2 Analysis of Contribution of Key Parameters to Quantified UncertaintyThe Benefits and Costs of the Clean Air Act, 1990 to 2010

108 Table 8-5 Summary of Key Sources o f U ncertainty and Their Impact o n C osts and Benefits

I m pa c t on Annu a l Esti m at e s in 2 0 1 0 Source of Uncertainty Descriptio n of Alternative Parameter Inp u ts Costs Benefits

M e a s u r e m e n t
error and

u ncerta i nty i n the

p h y s i c a l e ffe c ts

a n d e c o no m i c

v a l ua ti o n s t e ps U s e a ran g e o f in p ut a s s um p tio n s to ref l e c t st a ti s ti c al m e a s u re m e nt

u n c e rt a i n ty i n c o n c e n tr a ti o n- r e s p o n se f u n c ti o n s , m o del i n g of p h y s i c al e ffe c t s

, a n d e s tim a tio n of e c o nomi c v a lu e s. Most imp o rta n t in p ut p aramet e rs a r e

v a l ue o f st a ti s ti c a l l i fe a n d e st i mat e d

r e l a tio n sh i p b e twee n p a rtic u l a te m a tt e r

a n d premat u r e m o rta l ity (s e e Cha p ters 5, 6 , a n d 7 ). N o ne F o r Tit l es I thr o u g h V,

e ffect of t h e u s e of

a l t er na t i v e i n p u t

a ssumpti o ns ran g es f rom a $ 84 b il l i o n d ecrease ( 5 th

p ercent i l e ) to a $1 6 0
b i l li o n i n c r e a s e ( 95 t h

p e rc en t i l e).

M e a s u r e m e n t
error and

u ncerta i nty in
d i re c t c o st i n pu ts Use a l te r n a ti v e a s s u m p t i o n s f o r k e y i np u t p a ram e ters for six of t h e h i g hest

c o s t pr o v i si o n s . Con d u c t s e n s iti v ity

t e s t s f o r e a ch pr o v i s i o n s e p a r a t e l y ( s ee Ch ap t er 3 , p a g e s 30 t o 3 2). As
d i s c u ss ed in C ha p t e r 3 an d i n t h is
ch a pt e r, ag g reg a tio n of p rovis i ons p e c i f i c r e s u l t s w ou ld b e i n a p p r o p r i a t e. H i g h e st i m a tes for

some p rov i s i ons are $1 b il l i o n h i g he r
t h a n pr i mary

e st i m a te. L ow

e st i m a te s ar e as

m u ch as $2 b i l l i o n b e l ow p ri m a ry

e st i m a te None

V a lu e of

st a ti s ti c al l i feb a s e d e s t i m a t e s do n o t re f l e c t

a g e at d e ath Use e st i m a tes of t h e i n cr e me n tal

n u m be r of l if e -y ea rs l o s t from e x p o s ure
t o amb i ent PM a n d a v a lu e of stat i st i c a l
l if e -ye a r as o p posed t o m e asuring

n umber o f l i v e s l o s t an d a v al u e of

stat i st i c a l l if e (se e Cha p ters 5 a n d 6 ). N o ne D e cre a s e b y $47
b i l li o n

B a s i s o f e s timate

o f a v o i ded

m o rta l ity from
PM e x p o s u re T h e D o c k er y et a l. s tu d y p r o v i de s an

a lt e rnat i v e estimate o f th e l o ng-t e rm

r e l a tio n sh i p b e twee n c h ron i c PM

e x p o s ure a nd m o rta li ty ( s ee C h a p ter 5). N o ne I n cr e ase b y $ 1 00 to $ 15 0 b i ll i on

U n c e rtai n ti e s i n T i tl e VI h e a l th
b en e fit s a n al y s i s Ma j or u n c e rta i nti e s i n c l ud e : e s timati n g f a ta l c a n c er c as e s r e su l tin g from UV-b

e x p os u re; n ot a ccou n tin g for f u ture

a v e rtin g be h avior; a n d not a ccou n ti n g f o r fut u re i m p rovem e nts i n th e ear l y d et e ct i on a nd tr e atme n t of me l an o ma (se e Ta b le 5 -6). N o ne N o t q u ant i fie d , b u t n e t

e ffect is pr o b a bly th a t
b en e fit s e s timat e s a re
t o o h ig h

.

U n c e rtai n ti e s i n

e m i s s i o ns a n d

a i r q ua l i ty s te p s Ma j or u n c e rta i nti e s i n c l ud e :

u nd e restimati o n o f PM 2.5 e m i s s i o n s;

o m i s s io n of c ha n g e s i n pr i mar y a n d

or ga n i c PM in e a s t ern U

. S

. ; e m i s s i o ns

e s t i m a t i o n u n c er t a i n t i e s i n t h e w e s t ern U.S.; sc a rc i ty o f PM 2

. 5 m o n i tors; a n d
l a c k o f a f u l l y i nte g rate d a i r qu a lit y a n d

e m i s s i o ns m o d e li ng s y st e m ( s ee T a b l es 2-5 a nd 4 -7). U n c e rtai n ti e s i n

e m i s s i o ns e s t i m a t e s aff e cts s o me c o st s

, b ut n et e ffect is

m i n o r. N o t q u ant i fie d , b u t n e t

e ffect is pr o b a bly th a t b en e fit s e s timat e s a re too low.

O m i s s i on of

p ot e nti a l l y i m p o r t a nt b en e fits

cat e g o r i es from

p rimary estimate N o n-q u ant i fie d c a teg o ri e s o f impa c ts

summ a rized i n Cha p ters 5 a nd 7

. Qu a nt i fie d b u t omitt e d c a teg o ri e s i n c lu d e h o us e ho l d s oi l in g , n i trogen d ep o s i tio n , a n d r e sid e nt i al v i s ib i lit y (s e e C h apt e r 7). No n e I n c re a se by at l e a s t $8
b i l li o n, ( d o e s n ot

r e fl e ct u nq u ant i fi e d

cat e g o r i es)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 sensitivityanalysis isolates the effect of this single source of uncertainty on the total measured uncertainty in estimated aggregate benefits. The first uncertainty barrepresents 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 benefitsestimation. 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 touncertainty, 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 inputparameters 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 ofhigh 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 ofdirect 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-specificincidence 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 keyeconomic 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 thatcontribute 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 efficiencyimpacts 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 AppendixH, 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 ona 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 significantassumptions required to generate a monetized estimate of life years lost from the relative risks reportedin the Pope et al., 1995 study and the available economic literature. These assumptions include, butare 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 lagperiod (if any); assumptions about the age-specificity of the lag period (if any); derivation of VSLY estimates from VSL estimates; assumptions about thevariation 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 theseassumptions 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 weused 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. Alternativeestimates 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 thePope 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 Popestudy 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 estimateby $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 retrospectivestudy. The Council suggested it would be reasonable and appropriate to show PM mortality benefit estimates based on valueof 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 healthbenefits 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 melanomaand non-melanoma mortality is not as well developed as that for other health effects, the dose-responsefunctions for both of these endpoints are characterized by significant uncertainty. The association ofUV-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 contributionof 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 melanomamortality 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 skincancer may significantly influence this mortality estimate.

Our analysis of avoided mortality also does notincorporate 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 exposureor risk, counteracting the benefits of averting behavior. For example, a recent study of young Europeans by Autier et al. (1999) found that the use ofhigh 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 overestimatingavoided 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 dataare sparse or nonexistent. In general, all our emissions estimates are affected by three major sourcesof 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 ofThe Benefits and Costs of the Clean Air Act, 1990 to 2010

112emissions. 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 SO

2emissions 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 verifythese estimates through comparison with Continuous Emission Monitoring (CEM) data. As a result, we were able to accurately estimate SO

2 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 SO

2 emissions. However, the processes involved in the formation of NO

x during combustion are more complicated than those

involved in the formation of SO

2 ; thus, our NO

xemissions estimates are more variable and less certain than SO
2 estimates.

Volatile organic compounds, like SO

2 and NO

x , are products of fuel combustion; however, these compounds are also a product of evaporation. To estimate evaporative emissions of this pollutant weused 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

PM
2.5 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 impacton emissions sources that generate carbonaceous particles (mobile sources) than on sources that mainlyemit crustal material (area sources), we likely underestimate the impact of the CAAA on reducing PM
2.5 , thereby reducing monetary benefits estimates. The uncertainty associated with estimating the partition

of PM
2.5 emissions components could conceivably have a major impact on the net benefit estimate. Compared to secondary PM
2.5 precursor emissions, however, changes in primary PM
2.5 emissions have a relatively small impact on PM
2.5 related benefits.

.
Our future-year control assumptions are also asource 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 NO

x control strategy designed to regulate the regional transport of ozone. However, the control program assumed under the Post-CAAA scenario may not reflect the NO

x 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 inair 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 organicand 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 orprecursors across the boundaries of the models covering the western and eastern states. Third, the lack of a well-developed modeling network for PM
2.5 means we must estimate monitored concentrations of this pollutant based on PM
10 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 thataffect a modelís ability to accurately predict the relative change in concentration of a pollutant from onescenario 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 inemissions 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 estimatereflects 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 inthe 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 ofsection 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 moreinclusive 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 benefitsof 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) andunquantified 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 ratecan 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 discountrate has only a small effect on annual cost and benefit estimates. In part, this is due to limitations inour ability to conclusively identify costs as annualized capital expenditures or annual operating costsin 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 aspart of our valuation of the lag effect of PM mortality, and the chronic asthma value, where we use adiscount 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 alsodoes 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 12/1A-1 

# AAppendix 

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1The Benefits and Costs of the Clean Air Act, 1990 to 2010

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

Sector Analysis Approach/Data Source s

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 2The 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

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

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

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NO x Emissions Estimates

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SO Emissions Estimates

0 5 10 15 20 25 30

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YearEmissions in Short Tons(Millions) Pre-CAAA

Post-CAAA

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

A-10Figure A-5. Comparison of Pre-CAAA, Post-CAAA, and Trends

PM 10 Emissions Estimates

0 5 10 15 20 25 30

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YearEmissions in Short Tons(Millions) Pre-CAAA

Post-CAAA

Trends Figure A-4. Comparison of Pre-CAAA, Post-CAAA, and Trends
CO Emissions Estimates

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A-11The Benefits and Costs of the Clean Air Act, 1990 to 2010

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### Emissions
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The 1997 National Air Quality and Emissions Trends Report provides a summary of PM
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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

NO x 3,303,536 3,058,221 2,938,833 2,596,409 2,784,580 2,532,855

SO 2 1,245,439 1,094,928 1,326,546 944,689 1,434,470 970,762

PM 2.5 1,701,869 2,412,177 1,759,434 2,535,829 1,864,656 2,730,304

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Table A-5 Industrial Point Source Control Assumptions For The Post-C AAA S cenario

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 lude NO RACT, OTAG level 2 NO controls, and a

x xx x 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 ginal and above NAAs

x and in the nort heast 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 ions of the CAAA do not specify any mandatory controls for SO 2 2 2 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 3The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-18 

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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

NO

x 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 7,376.6The 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 SO 2 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

PM 10 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

PM 2.5 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 589.5 671.5 671.5 754.0 754.0The 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 NH 3 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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A-23 Table A-7 Utility Emission Summary* (thousand tons per year)

Pollutant/Source Category 1990 Pre-CAAA Post-CAAA Pre-CAAA 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

NO

x Coal 6,689.5 7,895.7 3,779.0 8,700.3 3,610.0

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

Coal 232.6 191.8 188.7 215.3 202.5

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

SO 2 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

PM 10 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

PM 2.5 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

NH 3 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.EMIS Y ˆ EMIS 90 ı GFAC Y ı 1

 ̃ CE Y ı PE Y The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-24 

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A-27 

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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

NO

x 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

SO 2 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

PM 10 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 336.3 372.5 372.5 414.4 336.3The 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 PM 2.5 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

NH 3 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-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 CaliforniaThe 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

NO

x 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

SO 2 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 567.7 631.6 337.4 769.6 409.2The 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 PM 10 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

PM 2.5 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

NH 3 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.EMIS Y ˆ EMIS 90 ı GFAC Y ı 1

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43The Benefits and Costs of the Clean Air Act, 1990 to 2010

A-39 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

NO

x 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

SO 2 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 1,519.4The 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 PM 10 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

PM 2.5 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

NH 3 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 

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UHGXFWLRQ UHTXLUHPHQWV VROHO\ E\ FXWWLQJ 92&A-43Table A-132000 Rate of Progress AnalysisAtt. Att. 2000 OSD (tons per day)NO VOC1xClass. Date Ozone Nonattainment Area NO VOC Target Selection Target Target Shortfallx2Ser. 1999 Atlanta 420.93 518.94 1%NO /8%VOC 426.28 476.35 42.59xMod. 1996 Atlantic City 46.11 37.97 ROP - 15%VOC 0.00 45.71 0.00Sev. 2005 Baltimore 335.42 318.33 18%NO 381.53 376.47 0.00xSer. 1999 Baton Rouge 449.70 203.77 NO waiver 0.00 415.65 0.00xSer. 1999 Beaumont-Port Arthur 245.20 340.66 NO waiver 0.00 450.66 0.00xSer. 1999 Boston-Lawrence-Worcester-E.MA 560.20 822.65 9% NO 602.06 918.01 0.00xSev. 2007 Chicago-Gary-Lake County 1,056.70 1,240.89 NO waiver 0.00 1,202.25 38.64xMod. 1996 Cincinnati-Hamilton 378.70 305.34 ROP - 15%VOC 0.00 341.18 0.00Mod. 1996 Cleveland-Akron-Lorain 369.12 521.14 ROP - 15%VOC 0.00 573.07 0.00Mod. 1996 Dallas-Fort Worth 528.83 694.53 ROP - 15%VOC 0.00 673.97 20.56Ser. 1999 El Paso 113.35 85.38 0%NO /9%VOC 0.00 69.02 16.36xMod. 1996 Grand Rapids 129.61 182.72 ROP - 15%VOC 0.00 175.66 7.06Ser. 1999 Greater Connecticut 211.67 316.27 9%NO 224.49 370.11 0.00xSev. 2007 Houston-Galveston-Brazoria 1,094.58 1,426.65 NO waiver 0.00 2,268.31 0.00xMod. 1996 Kewaunee Co Wi 3.23 4.86 ROP - 15 VOC 0.00 4.56 0.30Mod. 1996 Knox & Lincoln Cos ME 9.26 9.95 ROP - 15%VOC 0.00 10.37 0.00Mod. 1996 Lewiston-Auburn ME 23.56 34.08 ROP - 15%VOC 0.00 35.98 0.00Ext. 2010 Los Angeles-South Coast 1,010.32 972.91 1%NO /17%VOC 1,019.06 939.08 33.83xMod. 1996 Louisville 264.00 219.66 ROP - 15%VOC 0.00 215.97 3.69Mod. 1996 Manitowoc Co WI 13.69 19.72 ROP - 15%VOC 0.00 17.20 2.52Sev. 2007 Milwaukee-Racine 272.22 327.09 NO waiver 0.00 293.24 33.85xMod. 1996 Monterey Bay 78.60 64.16 ROP - 15%VOC 0.00 79.63 0.00Mod. 1996 Muskegon 34.25 46.67 ROP - 15%VOC 0.00 44.32 2.35Mod. 1996 Nashville 167.12 231.71 ROP - 15%VOC 0.00 205.60 26.11Sev. 2007 New York-N New Jersey-Long Is 1,280.26 1,994.96 18%NO 1,553.41 2,407.97 0.00xSev. 2005 Philadelphia-Wilmingtn-Trenton 678.53 1,090.49 18%NO 690.79 1,376.55 0.00xMod. 1996 Phoenix 404.47 377.43 ROP - 15%VOC 0.00 347.91 29.52Mod. 1996 Pittsburgh-Beaver Valley 534.53 407.05 ROP - 15%VOC 0.00 399.80 7.25Mod. 1996 Portland ME 53.72 70.05 ROP - 15%VOC 0.00 73.33 0.00Ser. 1999 Portsmouth-Dover-Rochester 37.55 53.54 9%NO 44.26 58.70 0.00xSer. 1999 Providence 92.51 173.78 5%NO /4%VOC 91.77 180.51 0.00xMod. 1996 Reading PA 48.66 60.53 ROP - 15%VOC 0.00 61.14 0.00Att. Att. 2000 OSD (tons per day)NO VOC1xClass. Date Ozone Nonattainment Area NO VOC Target Selection Target Target Shortfallx2A-44Mod. 1996 Richmond-Petersburg 141.62 179.97 ROP - 15%VOC 0.00 201.70 0.00Sev. 2005 Sacramento Metro 164.60 158.01 0%NO /18%VOC 0.00 155.08 2.93xMod. 1996 Salt Lake City 178.25 182.75 ROP - 15%VOC 0.00 150.80 31.95Ser. 1999 San Diego 228.55 192.90 0%NO /9%VOC 0.00 189.71 3.19xSer. 1999 San Joaquin Valley 499.25 470.50 5%NO /4%VOC 505.63 532.41 0.00xMod. 1996 Santa Barbara-Santa Maria-Lomp 63.77 82.75 ROP - 15%VOC 0.00 83.10 0.00Mod. 1996 Sheyboygan 38.02 24.49 ROP - 15%VOC 0.00 22.44 2.05Sev. 2007 Southeast Desert Modified 355.88 227.71 3%NO /15%VOC 358.76 219.32 8.39xSer. 1999 Springfield/Pittsfield-W. MA 112.38 155.51 0%NO /9%VOC 0.00 152.42 3.09xMod. 1996 St. Louis 476.98 465.64 ROP - 15%VOC 0.00 549.14 0.00Sev. 2005 Ventura Co CA 80.45 65.69 13%NO /5%VOC 79.55 70.52 0.00xSer. 1999 Washington DC 449.80 402.76 9%NO 499.12 477.03 0.00xNOTES: OSD = ozone season daily1The target selection column indicates the percentage reduction of NO NAA's are credited towards RFP and what percentage reduction2xin VOC is then needed to satisfy Title I progress requirements.A-45Table A-142010 Rate of Progress AnalysisAtt. Att. 2010 OSD (tons per day)NO VOC1xClass. Date Ozone Nonattainment Area NO VOC Target Selection Target Target Shortfallx2Ser. 1999 Atlanta 336.10 492.40 9%NO 391.84 541.99 0.00xMod. 1996 Atlantic City 39.46 33.21 ROP - 15%VOC 0.00 45.71 0.00Sev. 2005 Baltimore 278.64 293.56 27%NO 311.86 376.47 0.00xSer. 1999 Baton Rouge 407.49 206.65 NO waiver 0.00 415.65 0.00xSer. 1999 Beaumont-Port Arthur 232.32 377.63 NO waiver 0.00 450.66 0.00xSer. 1999 Boston-Lawrence-Worcester-E.MA 478.53 775.66 9%NO 602.06 918.01 0.00xSev. 2007 Chicago-Gary-Lake County 990.61 1,236.73 NO waiver 0.00 840.15 396.58xMod. 1996 Cincinnati-Hamilton 288.20 283.74 ROP - 15%VOC 0.00 341.18 0.00Mod. 1996 Cleveland-Akron-Lorain 306.32 485.90 ROP - 15%VOC 0.00 573.07 0.00Mod. 1996 Dallas-Fort Worth 472.66 687.15 ROP - 15%VOC 0.00 673.97 13.18Ser. 1999 El Paso 113.85 84.33 0%NO /9%VOC 0.00 69.02 15.31xMod. 1996 Grand Rapids 92.07 183.11 ROP - 15%VOC 0.00 175.66 7.45Ser. 1999 Greater Connecticut 192.62 292.53 9%NO 224.49 370.11 0.00xSev. 2007 Houston-Galveston-Brazoria 1,002.13 1,530.07 NO waiver 0.00 1,606.75 0.00xMod. 1996 Kewaunee Co Wi 2.62 4.77 ROP - 15%VOC 0.00 4.56 0.21Mod. 1996 Knox & Lincoln Cos ME 8.51 8.98 ROP - 15%VOC 0.00 10.37 0.00Mod. 1996 Lewiston-Auburn ME 22.20 32.03 ROP - 15%VOC 0.00 35.98 0.00Ext. 2010 Los Angeles-South Coast 950.39 847.66 5%NO /31%VOC 964.60 670.95 176.71xMod. 1996 Louisville 273.40 216.86 ROP - 15%VOC 0.00 215.97 0.89Mod. 1996 Manitowoc Co WI 12.10 19.37 ROP - 15%VOC 0.00 17.20 2.17Sev. 2007 Milwaukee-Racine 246.74 321.89 NO waiver 0.00 204.72 117.17xMod. 1996 Monterey Bay 71.93 61.76 ROP - 15%VOC 0.00 79.63 0.00Mod. 1996 Muskegon 26.99 46.86 ROP - 15%VOC 0.00 44.32 2.54Mod. 1996 Nashville 143.58 230.00 ROP - 15%VOC 0.00 205.60 24.40Sev. 2007 New York-N New Jersey-Long Is 1,148.95 1,842.53 36%NO 1,166.20 2,407.97 0.00xSev. 2005 Philadelphia-Wilmingtn-Trenton 631.39 1,070.05 18%NO /9%VOC 632.97 1,194.41 0.00xAtt. Att. 2010 OSD (tons per day)NO VOC1xClass. Date Ozone Nonattainment Area NO VOC Target Selection Target Target Shortfallx2A-46Mod. 1996 Phoenix 395.64 347.52 ROP - 15%VOC 0.00 347.91 0.00Mod. 1996 Pittsburgh-Beaver Valley 368.47 358.67 ROP - 15%VOC 0.00 399.80 0.00Mod. 1996 Portland ME 51.54 66.88 ROP - 15%VOC 0.00 73.33 0.00Ser. 1999 Portsmouth-Dover-Rochester 33.08 52.49 9%NO 44.26 58.70 0.00xSer. 1999 Providence 75.73 166.61 9%NO 87.91 193.15 0.00xMod. 1996 Reading PA 46.61 55.33 ROP - 15%VOC 0.00 61.14 0.00Mod. 1996 Richmond-Petersburg 137.12 179.35 ROP - 15%VOC 0.00 201.70 0.00Sev. 2005 Sacramento Metro 152.59 135.99 1%NO /26%VOC 152.62 120.24 15.75xMod. 1996 Salt Lake City 179.22 189.83 ROP - 15%VOC 0.00 150.80 39.03Ser. 1999 San Diego 213.59 174.04 5%NO /4%VOC 213.42 202.24 0.00xSer. 1999 San Joaquin Valley 466.55 448.37 9%NO 484.34 566.96 0.00xMod. 1996 Santa Barbara-Santa Maria-Lomp 59.33 81.53 ROP - 15%VOC 0.00 83.10 0.00Mod. 1996 Sheyboygan 34.84 24.69 ROP - 15%VOC 0.00 22.44 2.25Sev. 2007 Southeast Desert Modified 329.75 213.87 9%NO /27%VOC 332.55 172.34 41.53xSer. 1999 Springfield/Pittsfield-W. MA 97.63 147.45 7%NO /2%VOC 97.87 166.51 0.00xMod. 1996 St. Louis 381.48 439.47 ROP - 15%VOC 0.00 549.14 0.00Sev. 2005 Ventura Co CA 75.89 62.33 13%NO /14%VO 76.34 63.11 0.00xSer. 1999 Washington DC 393.57 355.35 9%NO 499.12 477.03 0.00xNotes: OSD = ozone season daily1The target selection column indicates the percentage reduction of NO NAA's are credited towards RFP and what percentage reduction in2xVOC 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 

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Table B-1 Summary of Cost Estimates by Emissions Source

Annual Cost (million 1990 dollars)

Sector/Pollutant Post-C AAA 2000 Post-C AAA 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 MACT 1

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

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Table B-2 Point Source VOC Cost Summary
Annual Costs (million 1990 dollars) 1

Source Category Post-CAAA 2000 Post-C AAA 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 0.2The Benefits and Costs of the Clean Air Act, 1990 to 2010

Annual Costs (million 1990 dollars) 1

Source Category Post-CAAA 2000 Post-C AAA 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-C AAA 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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Pollutant Pre-C AAA Post-CAAA

SO

x 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.

NO

x 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. xThe Benefits and Costs of the Clean Air Act, 1990 to 2010

B-10 Table B-5Electric 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

SO

x $1,900 $1,600

NO

x $1,200 $2,900

Total $3,100 $4,600 

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B-12 Table B-6 Cost Estimates for Nonroad Engine/Vehicle C AAA Prog rams
Annual Cost (million 1990 dollars)
Engine/Vehicle Category Post-C AAA 2000 Post-C AAA 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:
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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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1##B-16Table B-7Motor Vehicle Unit Costs by ProvisionYear Cost Estimate by Vehicle Type:1Provision Dollars Cost Unit LDGV LDGT1 LDGT2 MC HDGV HDDV LDDV LDDTEmission Standards:Tier 1 Tailpipe Standards: VOC 1990 Sales 36.8 33.7 11.7Tier 1 Tailpipe Standards: NO 1990 Sales 115.0 80.6 45.3 16.0 78.0XCold Temperature CO Standard 1989 Sales 15-23 15-48 42-55Evaporative 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 4.48On-Board Diagnostics 1993 Sales 65.0 65.0 65.0Heavy Duty Engine Standard (2 gram equivalent) 1995 Sales 140.0Low Emission Vehicles:TLEV 1996 53.0 53.0LEV95.0 95.0ULEV145.0 145.0ZEV5,000.0 5,000.0Fuels:Phase II RVP Limits 1990 Cents/gallon 0.2 0.2 0.2 0.2 0.2Federal Reformulated Gasoline: Phase I 1993 Cents/gallon 3.9 3.9 3.9 3.9 3.9Phase II 1993 Cents/gallon 1.2 1.2 1.2 1.2 1.2Oxygenated Fuels 1993 Cents/gallon 3.8 3.8 3.8 3.8 3.8Low-Sulfur Diesel Fuel Requirements (0.05% sulfur) 1990 Cents/gallon 2.1 2.1 2.1California Phase II Reformulated Gasoline 1991 Cents/gallon 12.3 12.3 12.3 12.3 12.3California Reformulated Diesel6.0 6.0 6.0Inspection/Maintenance Programs:Basic 1992 Registrations 5.7 5.7 5.7Low Enhanced 1992 Registrations 5.7 5.7 5.7High 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 diesel vehicle;1LDDV = light duty diesel vehicle; LDDT = light duty diesel truckThe Benefits and Costs of the Clean Air Act, 1990 to 2010

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Annual Cost (million 1990 dollars)

Program Post-CAAA 2000 Post-C AAA 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

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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-C AAA 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

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

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 

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1The Benefits and Costs of the Clean Air Act, 1990 to 2010

B-24 Table B-12 Summary of Cost Estimates by C AAA Title

Annual Cost (million 1990 dollars)

Title Sector/Pollutant Post-C AAA 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.



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### Title IThe Benefits and Costs of the Clean Air Act, 1990 to 2010

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1The 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-C AAA 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-C AAA 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 $ 13,900The 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-C AAA 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. 1The 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-C AAA 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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1 Table B-17
Annual Costs of Title IV

Annual Costs (million 1990 dollars)

Post-CAAA 2000 Post-C AAA 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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B-33 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

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1##B-37Table B-19Factors Affecting Cost of Major CAAA ProvisionsProvision Cost Analysis? Strategy for Sensitivity Analysis EstimatesFactors Affecting SensitivityCAAA2010 CostConductUncertainty on PostPotential Effect of1Progress Cost for unidentified Yes Continue to examine costs of identified measures in other specific areas. Central Estimate:Requirements measures is most$1.1uncertain. Lower Bound: Assume average per ton cost of identified measures (e.g.,$3,500 in Chicago) for all reductions, including unidentified measures. Range:Central Estimate: Use cost figure for identified measures for that fraction of ($0.06 - $2.5)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 no estimatestandard. 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 Central Estimate:Gasoline are most uncertain. CARB.$2.5Upper Bound: Assume 17.3 cents per gallon cost from CARB.Range:($1.4 - $3.5)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 EstimatesFactors Affecting SensitivityCAAA2010 CostConductUncertainty on PostPotential Effect of1B-38PM NAA Controls Base year emissions No Emissions projections and growth estimates are underlying assumptions of the no estimateand growth. cost analysis.2Area 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. Central Estimate:$2.2Range:($0.9 - $3.3)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 EstimatesFactors Affecting SensitivityCAAA2010 CostConductUncertainty on PostPotential Effect of1B-39LEV Costs Will 49-State LEV No Recently agreed to by the 23 automobile manufacturers that sell cars in the US no estimateoccur? and are regulated by EPA. Four States in the Northeast (MA, ME, NY, VT) haveopted not to join the NLEV program.Per vehicle costs. Yes Current analysis uses CARB's per vehicle cost estimates. These estimates arethe lowest and most fully documented, and differ from other industry estimatesby a factor of ten. Central Estimate:Lower Bound: 50% below study per vehicle cost estimates.Central Estimate: Use current study (CARB adjusted for national sales volume)per vehicle cost estimates. Range:Upper Bound: Use unadjusted CARB per vehicle cost estimates. ($1.08 - $2.5)$2.2(national and CA LEVcombined)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- NEMSTransportation Demand Model.Provision Cost Analysis? Strategy for Sensitivity Analysis EstimatesFactors Affecting SensitivityCAAA2010 CostConductUncertainty on PostPotential Effect of1B-40Non-Utility Unit-level cost Yes ICI boilers account for 79 percent of the total point source NOx control cost Central Estimate:Stationary Source equations and cost per estimate in 2010. Apply +50 percent range. Other available data are 3-4 years $2.2NOx Costs ton. old and would not reflect the fact that the control technology is beingmanufactured and applied by more sources. Range:($1.1 - $3.2)Inventory data No Inventory data elements are well-defined for each point source category.elements (e.g.,no estimatecapacity, 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 no estimatebudgets affect only 22 program.States.Banking not accounted No None no estimatefor.NOx Per vehicle costs date Yes Lower Bound: No alternative estimates. Scale down medium estimate by 50 Central Esimate:Tailpipe/Useful to 1991 FR Notice. percent.$1.7Life Standards Central Estimate: Use current $115 estimate from EPA.Upper Bound: No alternative estimates. Scale up medium estimate by 50 Range:percent.($0.83 - $2.5)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.no estimateProvision Cost Analysis? Strategy for Sensitivity Analysis EstimatesFactors Affecting SensitivityCAAA2010 CostConductUncertainty on PostPotential Effect of1B-41Vehicle 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.no estimateuncertain.Discount Rate Vary the discount rate.Yes Current cost estimates use a five percent discount rate. Vary the cost estimates Central Estimate:using two alternative discount rates, three percent and seven percent.$3.0Range:($2.8 - $3.2)Economic Macroeconomic growth No The current methodology for calculating PM emissions relies on activity level no estimateGrowth 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 tocalculate 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.1The Benefits and Costs of the Clean Air Act, 1990 to 2010

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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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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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B-45 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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43The 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%
Rate Sector Pollutant 3% 5% 7%

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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VOC

Base 1990 CAAA CAAA CAAA CAAA2000 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

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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

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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

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Total 62,316 67,800 49,487 76,450 46,793

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Base 1990 CAAA CAAA CAAA CAAA2000 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



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Onroad Mobile 900 572 223 680 97

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Base 1990 CAAA CAAA CAAA CAAA2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 758 770 607 871 700

Onroad Mobile 1,179 999 410 1,168 213

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Low Level 216 186 139 186 139
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Base 1990 CAAA CAAA CAAA CAAA2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 1,142 1, 302 1,286 1,515 1,495

Onroad Mobile 9,046 10, 043 5,046 11,278 3,728

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Low Level 208 197 197 197 197
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Total 10,398 11,586 6,573 13,024 5,456



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Base 1990 CAAA CAAA CAAA CAAA2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 241 310 201 369 250

Onroad Mobile 184 215 141 231 85

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Base 1990 CAAA CAAA CAAA CAAA2000 Pre- 2000 Post- 2010 Pre- 2010 PostArea 213 270 259 326 290

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C-23 

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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-28Figure 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 1990.C-29Figure 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-30Figure 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-31Figure C-8. UAM-V modeling domain for western U.S. analysis with the high-resolution modeling domains forthe 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

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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-33LEVEL 1 Ozone (ppb)Time: 0-2400 August 28, 1987+ MAXIMUM = 7.4 ppb- MINIMUM = -57.6 ppb010 2030 40506001020304002550KmFigure 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



### RatioRelative 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



### RatioRelative 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



### RatioRelative 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



### RatioRelative 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



### RatioRelative 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



### RatioRelative Frequency (percent)
median: 0.838 The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-37The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-38 

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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

C-44 

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VOC : VOC

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PM : POA, PEC, GSO , PNO , Pmfine 4 3 HPLVVLRQV

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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 20,398The 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
Conditions

1##

Species (ppd) Concentration

NO 0.0

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

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C-53 

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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

PM 10 

### 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



### RatioRelative Frequency(percent) median: 0.976 

### Figure C-21a. Distribution of Combined RADM/RPM- and REMSAD-Derived Monitor-Level Ratios for Annual Average

PM 10 

### 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



### RatioRelative 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

PM 10 

### Concentration: 2010 Pre-C AAA / 1990 B ase-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

RatioRelative Frequency(percent) median: 1.040



### Figure C-22b. Distribution of Combined RADM/RPM- and REMSAD-Derived Monitor-Level Ratios for Annual Average

PM 10 

### Concentration: 2010 Post-C AAA / 1 990 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

RatioRelative 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

PM 2.5 

### 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



### RatioRelative Frequency(percent) median: 1.033



### Figure C-23b. Distribution of Combined RADM/RPM- and REMSAD-Derived Monitor-Level Ratios for Annual Average

PM 2.5 

### 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



### RatioRelative 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

PM 2.5 

### 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

RatioRelative Frequency(percent) median: 1.070



### 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

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



### RatioRelative 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

PM 10 

### 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



### RatioRelative Frequency(percent) median: 0.964



### 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

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



### RatioRelative 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 PM 2.5 

### 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



### RatioRelative Frequency(percent) median: 0.919 

### 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

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



### RatioRelative Frequency(percent) median: 0.946 The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-63The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-64 

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1The 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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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-68The 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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C-72 

## Figure C-30 Annual Nitrogen Deposition

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C-73 

## Figure C-31 Annual Nitrogen Deposition
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C-74 

## Figure C-32 Annual Sulfur Deposition
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C-75 

## Figure C-33 Annual Nitrogen Deposition
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C-76 

## Figure C-34 Annual Nitrogen Deposition

2010 Post C AAA ScenarioThe Benefits and Costs of the Clean Air Act, 1990 to 2010

C-77 

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C-78 Figure C-35. Distribution of Monitor-Level Ratios of Summer
SO Emissions: 2010 Post-C AAA / 2010 Pre-CAAA 2

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Figure C-36. Distribution of Monitor-Level Ratios of Summer
NO Emissions: 2010 Post-C AAA / 2010 Pre-CAAA

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RatioRelative Frequency (percent) median: 0.720 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 AAA / 2010 Pre-CAAA 2

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Figure C-38. Distribution of Monitor-Level Ratios of Summer
CO Emissions: 2010 Post-C AAA / 2010 Pre-CAAA

Note: 15.7 percent of the distribution of ratios is less than 0.40.The Benefits and Costs of the Clean Air Act, 1990 to 2010

C-80 

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C-81 Table C-14

Median Values of the Distribution of Ratios of 2010 Post-C AAA/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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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.The Benefits and Costs of the Clean Air Act, 1990 to 2010

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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 oz one coefficient and standard error from the copollutant model were provided by personal a
communication with Dr. Kazuhiko Ito.



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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 xall-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

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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

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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 for1989-1991 mortality respiratory mortality in average birth10 10

10

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Xu et al. Beijing, China 25,370 pre-term TSP, SO TSP and SO exposure significant for (1995a) 1988 pregnant delivery pre-term delivery.

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1D-28Table D-4Summary of Selected Studies for Chronic IllnessStudy Period Population Endpoint Pollutants Main Findings CommentLocation andAbbey et al. California 3,914 AOD; chronic TSP, O , TSP linked to new cases of AOD and Emphysema, chronic(1993) initial survey: 1977 Seventh bronchitis; SO chronic bronchitis, but not to asthma or bronchitis, and asthmafinal survey: 1987 Day asthma the severity of asthma. O not linked to comprise AOD.Adventists the incidence of new cases of any323endpoint, but O was linked only to the3severity of asthma. No effect found forSO .2Abbey et al. California 1,868 AOD; chronic PM PM related to new cases of chronic PM estimated from visibility(1995) initial survey: 1977 Seventh bronchitis; bronchitis, but not to new cases of data.final survey: 1987 Day asthma AOD or asthma.Adventists2.5 2.5 2.5Chapman 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 43 24McDonnell California 3,091 asthma O , PM ,, Single pollutant models: O Average pollution level fromet al. (1999) initial survey: 1977 Seventh SO , SO , significantly linked to new asthma 1973-1992 used. Prior tofinal survey: 1992 Day NO cases in males, but not in females; 1987, PM estimated fromAdventists other pollutants not significantly linked TSP.3 104 223to new asthma cases in males orfemales. Two pollutant modelsestimated for ozone with anotherpollutant; little impact found on size ofozone coefficient.10Portney 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 hayInterview Survey fever; no effect on AOD. TSP not3 3significantly related to any endpoint,although it is marginally significant forAOD.Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-29Schwartz Nationwide sample 6,138 chronic TSP TSP significantly related to the Respiratory illness defined as(1993) from the National individuals bronchitis; prevalence of chronic bronchitis, and a significant condition, codedHealth and ages 30-74 asthma; marginally significant for respiratory by an examining physician asNutrition shortness of illness. No effect on asthma or ICD8 code (460-519)Examination breath dyspnea.Survey (dyspnea);1974-1975 respiratoryillnessXu et al. Beijing, China 1,576 chronic TSP; SO Chronic bronchitis significantly higher(1993) Survey conducted never bronchitis; in the community with the highest TSPAugust-September smokers asthma level. TSP not linked to the prevalence1986 of asthma.2Zemp 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 chronic1991 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 .102 310 23The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-30 

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44# SURYLGH PRUH GHWDLOHG LQIRUPDWLRQ RQ WKHVH VWXGLHVD-32Table D-5Studies Used to Develop Respiratory Admissions EstimatesLocation Study (ICD code) Pollutants Used in Final Model PopulationEndpoints Estimated StudyaToronto, Canada Burnett et al. (1997b) all respiratory (464-466, 480-486, PM , O , NO , SO all ages490-494, 496)2.5-10 3 2 2Toronto, Canada Burnett et al. (1999) asthma (493); respiratory infection O , CO, PM (asthma); O , NO , PM all ages(464, 466, 480-487, 494); non- (respiratory infection); O , CO, PMasthma COPD (490-492, 496) (COPD).3 2.5-10 3 2 2.53 2.5-10Toronto, Canada Thurston et al. (1994) all respiratory (466, 480-482, 485, O , PM all ages490-493)3 2.5Minneapolis-St. Moolgavkar et al. (1997) pneumonia (480-487); COPD O , SO , NO , PM (pneumonia); O , CO, >64Paul, MN (490-496) PM (COPD)3 2 2 10 310Minneapolis-St. Schwartz (1994c) pneumonia (480-486); COPD (490- O , PM (pneumonia); PM (COPD) >64Paul, MN 496)3 10 10Birmingham, AL Schwartz (1994a) pneumonia (480-487); COPD PM>64(490-496)

10Detroit, MI Schwartz (1994b) pneumonia (480-486); non-asthma O , PM >64COPD (491-492, 494-496)3 10Spokane, WA Schwartz (1996) all respiratory (460-519) PM >6410New Haven, CT Schwartz (1995) all respiratory (460-519) O , PM >643 10Tacoma, WA Schwartz (1995) all respiratory (460-519) O , PM >643 10Seattle, WA Sheppard et al. (1999) asthma (493) CO, PM <652.5Monetized benefits of non-overlapping endpoints within each study areaggregated. Monetized benefits for asthma among people age <65 (Sheppard et al., 1999) are aggregated withathe benefits in studies of people age >64.D-33Table D-6Summary of Hospital Admissions Studies – Respiratory IllnessesStudy Period Population Endpoint Pollutants Main Findings CommentLocation andaAnderson et Barcelona, Paris all ages; COPD NO , BS COPD: Single pollutant models: meta-analysis Study not used to estimateal. (1997) Amsterdam, >64 (490-492, (black of city specific results found significant effect incidence: study outsideRotterdam, 496) smoke), for BS, NO , O , and SO in the all age group; U.S. and Canada.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.22323 23Burnett et southern <65; >64 all SO , O All respiratory: SO significantly related to May-September resultsal. (1995) Ontario, Canada respiratory respiratory admissions in ages <65 and >64. also discussed in Burnett1/83-12/88 (466, 480- O significant impact from May-September; no et al. (1994). Study not486, 490- effect the rest of the year. used to estimate incidence:494, 496) no study specific43 43 conversion available betweenSO and PM or4

2.5

PM . 10Burnett et Toronto, all ages all O , CO, NO , All respiratory: COH and O linked to Four pollutant modelal. (1997b) Canada respiratory SO , COH respiratory admissions; other PM measures (PM , O , NO , and SO )summers in (464-466, (coefficient less strongly linked. Two pollutant models: used to estimate all1992-1994 480-486, of haze), H , CO, NO , and SO not significant, controlling respiratory incidence.490-494, SO , PM , for COH; O significant, controlling for COH.496) PM , Four pollutant models: COH and O3 22+4 2.52.5-10PM , significant; no effect for NO and SO ; other1032 2332 2PM measures not significant, controlling forO , NO , and SO .32 2 2.5-10 3 2 2Burnett et 16 Canadian <65; >64 all O , CO, SO , All respiratory: Multiple pollutant models: O Study not used to estimateal. (1997a) cities respiratory NO , COH significantly related to admissions, controlling incidence: no study specific3/81-12/91 (466, 480- for CO and COH; significant effect also conversion available486, 490- reported for CO and COH; no significant effect between COH and PM or494, 496) found for NO and SO after controlling for O PM .3 22322 3and CO. Montreal and Vancouver decreasedthe size of the effect of O substantially. O3 3significant with and without these cites in themodel. 2.5

10Study Period Population Endpoint Pollutants Main Findings CommentLocation andaD-34Burnett et Toronto, all ages asthma O , CO, NO , Multiple pollutant models estimated, where PM , PM , and PMal. (1999) Canada (493); SO , PM , pollutants for best fitting model chosen using estimated from TSP, COH,1980-1994 respiratory PM , stepwise regression based on AIC criterion. and SO data. Multipleinfection PM , Asthma: O , CO, PM significantly related pollutant models used to(464, 466, to asthma admissions; other pollutants not estimate incidence of:480-487, chosen in stepwise regression. Respiratory asthma (O , CO, PM ),494); infection: O , NO , and PM chosen in respiratory infection (O ,COPD stepwise regression COPD: O and PM NO , PM ), and COPD(490-492, chosen in stepwise regression. (O , CO, PM ).496)3 22 2.52.5-1010 3 2.5-103 2 2.53 2.5-102.5 2.5-10 1043 2.5-1032 2.53 2.5-10Delfino et Montreal, all ages asthma O , SO , Asthma: Two pollutant model: marginally SO and PM were bothal. (1994) Canada (493); all PM significant effect for PM , controlling for O . estimated from COH andMay-October in respiratory No effect for O and SO . All respiratory andother variables. Study not1984-1988. (462-466, all respiratory non-asthma: PM suggestive used to estimate incidenceJuly-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-asthma3 410 10 33 410434 10Lipfert and Southern all ages all O , SO , All respiratory: SO , SO , and O found to be Study not used to estimateHammerstro Ontario, Canada respiratory NO , SO , significant predictors of respiratory admissions incidence: estimatedm (1992) January- (466, 480- COH, TSP in July-August. coefficients not reported.February and 482, 485,July-August in 490-493)4/79-3/853 44 424 3Study Period Population Endpoint Pollutants Main Findings CommentLocation andaD-35Moolgavkar Minneapolis-St. >64 pneumonia O , CO, SO , Pneumonia: Four pollutant model: O Multiple pollutant modelset al. (1997) Paul, MN; (480-487); NO , PM significant (NO , SO , and PM not significant) used to estimateBirmingham, AL COPD in Minneapolis-St. Paul; no significant effect pneumonia incidence (O ,1/86-12/91 (490-496); found for any pollutant in Birmingham. COPD: SO , NO , PM ) andall No significant effect found in Birmingham or COPD incidence (O , CO,respiratory Minneapolis-St. Paul for any pollutant. All PM ) in Minneapolis-St.(480-487, respiratory: Single pollutant models: O , NO , Paul. No model estimated490-496) and PM significant in Minneapolis-St.Paul. for Birmingham:3 22 1032 2 103 210Multiple pollutant models (results presented in coefficients and standardgraph): O significant, controlling for other errors not reported.3pollutants; PM significant controlling for O ,10 3but not significant controlling for O , SO , and3 2NO together. No significant effect found in2Birmingham for admissions with O , CO, or3PM ; NO and SO data not available for10 2 2Birmingham. 3 2 2 10 3

10Morgan et Sydney, 1-14; 15- asthma; O , NO , Asthma: Single pollutant models: NO Study not used toal. (1998) Australia 64; >64 COPD bscat significant for ages 1-14 but not other age estimate incidence: study1/90-12/94 (490- (measure of groups. O and bscat not significant for any outside U.S. and Canada.492,494 light age group. Three pollutant model: NO496) scattering) remains significantly related to asthma3 2232admission in ages 1-14. COPD: No pollutantsignificantly related to COPD admissions.Pantazopol Athens, Greece all ages all BS, CO, NO All respiratory: Single-pollutant models: BS, Study not used to estimateou et al. 1988 respiratory CO, NO significantly related to respiratory incidence: study outside(1995) (not defined admissions in the winter time. No significant U.S. and Canada.by ICD effect found any pollutant in the summer.code)22Ponce de London, 0-14; 15- all BS, SO , O , All respiratory: O significantly related to Study not used to estimateLeon et al. England 64; >64 respiratory NO admissions in ages >14. No significant effect incidence: study outside(1996) 4/87-2/92 (460-519) found for SO , O , and NO . U.S. and Canada.2 32323 2Ponka and Helsinki, Finland <65; >64 COPD NO , SO , COPD: Single pollutant models: SO linked to Study not used toVirtanen 1/87-12/89 (491-492) O , TSP (491-492) admissions in ages <65; NO linked estimate incidence: study(1994)to admissions in ages >64; no significant outside U.S. and Canada.2 2322effect seen for O and TSP.3Study Period Population Endpoint Pollutants Main Findings CommentLocation andaD-36Schwartz Birmingham, AL >64 pneumonia PM , O Pneumonia: PM significant and O not Single pollutant models(1994a) 1/86-12/89 (480-487); significant in single pollutant models. COPD: (PM ) used to estimateCOPD PM significant and O not significant in single pneumonia incidence and(490-496) pollutant models. COPD incidence.10 3 10 310 3 10Schwartz Detroit, MI >64 asthma PM , O Asthma: admissions not associated with either Two pollutant models (PM(1994b) 1/86-12/89 (493); pollutant; coefficients and standard errors not and O ) used to estimatepneumonia reported. Pneumonia: Two pollutant model: pneumonia incidence and(480-486); PM and O both significant for pneumonia. non-asthma COPDnon-asthma Non-asthma COPD: Two pollutant model: incidence.COPD PM and O both significant.(491-492,494-496)10 310 310 3 10

3Schwartz Spokane, WA >64 pneumonia PM , O Pneumonia: PM marginally significant and Single pollutant model(1996) 1/88-12/90 (480-487); O not significant for pneumonia in single(PM ) used to estimate allCOPD pollutant models. COPD: PM significant and respiratory incidence.(490-496); O not significant in single pollutant models.all All respiratory: Single pollutant models: PMrespiratory and O both significant. Two pollutant model(460-519) not estimated because of limited overlap10 3 103103103between PM and O data.10 3 10Schwartz Minneapolis-St. >64 pneumonia PM , O Pneumonia: Two pollutant model: PM Two pollutant model (PM ,(1994c) Paul, MN (480-486); significantly related to pneumonia; O weakly O ) used to estimate1/86-12/89 COPD linked to pneumonia. COPD: Single pollutant pneumonia incidence;(490-496) models: PM significant and O not single pollutant model10 310310 3significant. (PM ) used to estimate10310 COPD incidence.Schwartz New Haven, CT; >64 all PM , O , All respiratory: Single pollutant models: PM , Two pollutant model (PM ,(1995) Tacoma, WA respiratory SO O , SO significant, except O in New Haven. O ) used to estimate all1/88-12/90 (460-519) Two pollutant model results varied by city: O respiratory incidence.10 321032 33significant (3 of 4 models) and stablecoefficient estimates PM significant (3 of 410models), but less stable estimates. SO2significant (1of 4 models).103Study Period Population Endpoint Pollutants Main Findings CommentLocation andaD-37Sheppard et Seattle, WA <65 asthma CO, SO , O , Asthma: Single pollutant models: each In most years, O data wasal. (1999) 1/87-12/94 (493) PM , PM pollutant significantly related to asthma, available only from April2 32.5 2.5-, PM except SO . Multiple pollutant models: PM through October. O10 10 2 2.5and CO reported to have best fit in models reported to have the bestwithout O . Both PM and CO are significant fit, but authors did not3 2.5when included together in a model. consider O further333because of limited data. Two pollutant model (CO, PM ) used to estimate

2.5 asthma incidence.Spix et al. London, 15-64; >64 all NO , SO , All respiratory: Single pollutant models: O Study not used to estimate(1998) Amsterdam, respiratory O , TSP, BS significantly related to admissions in ages 15- incidence: study outsideRotterdam, (460-519) 64 and >64. BS significantly related to U.S. and Canada.Paris, Milano admissions in ages 15-64. SO significantlyPeriod varies by related to admissions in ages >64.city from 5-13years2 2332Sunyer et Barcelona, 0-14; 15-64 asthma NO , SO , Asthma: Two pollutant models: NO significant Study not used to estimateal. (1997) Helsinki, O , BS in ages 15-64, controlling for BS; NO had no incidence: study outsideLondon, Paris effect on ages 0-14. SO significant in ages 0- U.S. and Canada.Period varies by 14, controlling for either BS or NO ; SO hadcity from 3-6 no effect on ages 15-64.years2 232222 2Tenias et al. Valencia, Spain >14 asthma NO , SO , Asthma: Two pollutant models: O and NO Study not used to estimate(1998) 1/93-12/95 O , BS both significant. No significant effect found for incidence: study outside2 233 2SO and BS. U.S. and Canada.2Thurston et Toronto, all ages asthma H , SO , O , Asthma: Single pollutant models: O , H , SO , Two pollutant model (O ,al. (1994) Canada (493); all PM , PM O , and TSP linked to all respiratory PM ) used to estimate all, PM , TSP admissions; PM , PM , PM not respiratory incidence.and August (466, 480- significant. Two pollutant models: O1986-1988 482, 485, significant, but PM measures no longer490-493) significant. Best fitting PM measure is H . All+4 32.5 2.5-six weeks in July respiratory10 103 4+32.5 2.5-10 103+respiratory: Single pollutant models: O and3various 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). 3

2.5Study Period Population Endpoint Pollutants Main Findings CommentLocation andaD-38Thurston et Buffalo, NY; all ages all H , SO , O All respiratory: Three pollutant model: H , SO , Study not used to estimateal. (1992) New York City respiratory and O are all significant. This result is found incidence: no study specificJune-August in (466, 480- in both Buffalo and New York City. conversion available1988-1989 486, 490- between study pollutants493) (H and SO ) and PM or+4 3+43 + 4 2.5

PM . 10Vigotti et al. Milan, Italy 15-64; >64 all TSP, SO All respiratory: Single pollutant models: TSP

Study not used to estimate(1996) 1/89-12/89 respiratory and SO linked to admissions. incidence: study outside(460-519) U.S. and Canada.22Not all pollutants considered in a study are necessarily included in the model used to develop C-R functions.aD-39Table D-7Studies Used to Develop Cardiovascular Admissions EstimatesLocation Study (ICD code) Pollutants Used in Final Model PopulationEndpoints Estimated StudyToronto, Canada Burnett et al. (1997b) cardiac (410-414, 427-428) O , PM all ages3 2.5-10Toronto, Canada Burnett et al. (1999) ischemic heart disease (410-414); NO , SO (ischemic heart disease); PM , CO, all agesdysrhythmias (427); congestive heart O (dysrhythmias); CO, NO (heart failurefailure (428) incidence)2 2 2.53 2Detroit, MI Schwartz and Morris ischemic heart disease (410-414); CO, PM >64(1995) congestive heart failure (428)10Eight U.S. counties Schwartz (1999) cardiovascular disease (390-429) CO, PM >641/88-12/9010Tucson, AZ Schwartz (1999) cardiovascular disease (390-429) CO, PM >641/88-12/9010D-40Table D-8Summary of Hospital Admissions Studies – Cardiovascular IllnessesStudy Period Population (ICD code) Pollutants Main Findings CommentLocation and EndpointBurnett et al. southern and all ages cardiac SO , O Cardiac: Two pollutant model: SO Study not used to(1995) central (410, 413, significantly related to cardiac admissions; estimate incidence: noOntario, 427-428) O not significant, in any season or over study specific conversionCanada the whole year. available betweenSO1/83--12/88 and PM or PM .43 434 2.5 10Burnett et al. Toronto, all ages cardiac O , CO, Cardiac: COH and O linked to cardiac Two pollutant model (O ,(1997b) Canada (410-414, 427- NO , SO , admissions; other PM measures less PM ) used to estimatesummers 428) COH strongly linked. Two pollutant models: CO, cardiac incidence.1992-1994 (coefficient NO , and SO not significant, controlling for32 2of 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 .1032 2332 232 2 32.5-10Burnett et al. 10 Canadian >64 congestive O , CO, Congestive heart failure: Single pollutant Study not used to(1997c) cities heart failure NO , SO , models: CO, NO , SO , COH are estimate incidence:1/81-12/91 (428) COH significant; no effect for O . CO and NO limited endpoint.322 223 2have the best fit. Two pollutant models:Controlling for NO , CO significant, with2only small reduction in coefficient size;NO insignificant in this model.2Burnett et al. Toronto, all ages ischemic O , CO, Multiple pollutant model, where pollutants PM , PM , and PM(1999) Canada heart disease NO , SO , for best fitting model chosen using estimated from TSP,1980-1994 (410-414); PM , stepwise regression based on AIC COH, and sulfate (SO )dysrhythmias PM , criterion. Ischemic heart disease: NO and data. Multiple pollutant(427); PM , SO , chosen by stepwise regression. models used to estimatecongestive Other pollutants not chosen. ischemic heart diseaseheart failure Dysrhythmias:polluO , CO, and PM (NO , SO ), dysrhythmias(428) chosen by stepwise regression. Other (PM , CO, O ), and32 22.52.5-1010223 2.5pollutants not chosen. Congestive heart congestive heart failurefailure: NO and CO chosen by stepwise incidence (CO, NO ).2regression procedure. other pollutants notchosen in stepwise regression.2.5 2.5-10 104

2 2 2.5 3 2Study Period Population (ICD code) Pollutants Main Findings CommentLocation and EndpointD-41Morgan et al. Sydney, 0-64; >64 heart disease O , NO , Single pollutant models: bscat significant Results from three(1998) Australia (410,413, 427- bscat for ages >64; NO significant in ages 0-64 pollutant model for ages1/90-12/94 428) (measure and >64. Three pollutant model: NO 0-64 not presented. Study3 2of light significant in ages >64; O and bscat not not used to estimatescattering) significant. incidence: study outside223 U.S. and Canada.Morris et al. seven U.S. >64 congestive O , CO, Single pollutant models: CO, NO , and SO Study not used to(1995) cities heart failure NO , SO significant in single pollutant models. Four estimate incidence: no PM1/86-12/89 (428) pollutant model: CO is significant in five of measure used in the32 22 2the seven cities; NO is significant in one study, plus limited2city; SO and O are not significant in any endpoint.2 3cities.Morris and Chicago, IL >64 congestive O , CO, Single pollutant models: CO, NO , SO , Study not used toNaumova (1998) 1/86-12/89 heart failure NO , SO , and PM significant. Five pollutant model: estimate incidence:(428) PM CO significant (CI for RR=1.03-1.12); PM limited endpoint.32 2102 21010borderline 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 Study not used toal. (1995) Greece (not defined by smoke), significantly related to cardiac admissions estimate incidence: study1988 ICD code) CO, NO in the winter. No significant effect found outside U.S. and Canada.22for any pollutant in the summer.Schwartz and Detroit, MI >64 ischemic heart O , CO, Ischemic heart disease: Two pollutant Two pollutant modelsMorris (1995) 1/86-12/89 disease (410- SO , PM models: PM and CO both significant; no (PM , CO) used to414); effect seen for SO and O . Dysrhythmias: estimate ischemic heartdysrhythmias Air pollutants did not have a significant disease and congestive(427); effect. Congestive heart failure: Single heart failure incidence.congestive pollutant models: PM and CO significant;heart failure SO and O not significant. Two pollutant(428) models: PM significant, controlling for CO32 10 102 3102 310and SO . Controlling for PM , CO2 10significant. 10Study Period Population (ICD code) Pollutants Main Findings CommentLocation and EndpointD-42Schwartz (1999) Eight U.S. >64 cardiovascular CO, PM Two pollutant model: CO and PM both Two pollutant modelcounties disease (390- significant. (PM , CO) used to1/88-12/90 429) estimate incidence of10 10 10 cardiovascular admissions.Schwartz (1997) Tucson, AZ >64 cardiovascular O , CO, In a model with the two pollutants, CO and Two pollutant model1/88-12/90 disease (390- SO , NO , PM were both significant. No effect seen (PM , CO) used to429) PM for O , SO , and NO . estimate incidence of32 2101032 2 10 cardiovascular admissions.Yang et al. Reno/Sparks, all ages cardiovascular CO Reported significant relationship between Study not used to(1998) NV illness (390- CO and admissions. estimate incidence: no PM1/89-12/94 459) measure used in the

study.Table D-9Studies Used to Develop Asthma Emergency Room VisitsLocation Study Endpoints Estimated Model Study PopulationPollutants Used in Finalcentral and northern NJ Cody et al. (1992) asthma O all ages3central and northern NJ Weisel et al. (1995) asthma O all ages3Seattle, WA Schwartz et al. (1993) asthma PM <6510St. John, New Stieb et al. (1996) asthma O all agesBrunswick, Canada3D-43Table D-10Summary of Selected Studies for Emergency Room Visits -- Asthma and Acute WheezingStudy Period Population Endpoint Pollutants Main Findings CommentLocation andAtkinson et London, England 0-14; 15- asthma NO , BS Single pollutant models: PM and NO Study not used toal. (1999) 1/92-12/94 64; >64; all (black significantly related to asthma visits in all age estimate incidence: studyages smoke), groups. SO significant in ages 0-14. BS is outside U.S. and Canada.2PM , SO , significant for ages 15-64. No effect seen for10 2CO, O O . Two pollutant results only for ages 0-14:310 223NO and SO significant; other pollutants not2 2significant.Bates et al. Vancouver, 1-14; 15- asthma NO , SO , SO correlated with asthma in all age groups Study not used to(1990) Canada 60; >60 SO , O with some variation by season. SO estimate incidence:7/84-10/86 correlated with asthma visits in ages 15 and correlations only2 24 342up. No effect found for NO and O . presented.2 3Buchdahl et London, England <17 acute NO , SO , SO significantly related to acute wheezing. Study not used toal. (1996) 3/92-2/93 wheezing O O has a significant, U-shaped result, estimate incidence: study2 2323suggesting that the optimal level of ozone is outside U.S. and Canada.not zero. NO not significant.2Castellsagu Barcelona, Spain >14 asthma NO , BS, Single pollutant models: NO significant in Study not used toe et al. January-March and SO , O both July-September and January-March. BS estimate incidence: study(1995) July-September in linked to asthma ER visits in July-September. outside U.S. and Canada.1985-1989 No significant effect found for SO and O .22 322 3Cody et al. central and all ages asthma PM , SO , Two pollutant model: O linked to asthma Single pollutant model(1992) northern NJ O visits; SO not significant. No significant effect (O ) used to estimateMay-August in seen for PM ; PM considered in separate incidence of asthma1988-1989 analysis, because of limited (every sixth day) visits.10 233210 10sampling. 3Goldstein New York City all ages asthma SO No significant correlation found between SO Study not used toand 1/69-2/72 and asthma ER visits. estimate incidence: onlyWeinstein SO in the analysis.(1986)22 2Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-44Lipsett et al. Santa Clara all ages asthma PM , COH Single pollutant models: NO , PM , and COH PM estimated from COH(1997) County, CA (coefficient significant; O not significant. Two pollutant observations. Study notNovember-January of haze), models: PM and COH linked to ER visits used to estimatein 1988-1992 O , NO controlling for NO ; NO not significant. PM incidence: results depend103 22 103102 2 10reported to provide a slightly better fit than on temperatureCOH

interaction that we cannot10 model.Richards et Los Angeles, CA children asthma and COH, HC COH, HC, NO, and NO have positive and 13% of reported visitsal. (1981) 8/79-1/80 (median bronchiolitis (hydrocar- significant correlation with ER visits; O and subsequently admitted toage =6) (92% bons), NO, SO have negative significant correlation; SO the hospital. Study notasthma NO , O , and TSP have insignificant correlation. used to estimateonly) SO , SO , incidence: correlations2 32 4TSP only presented.232 4Romieu et Mexico City, <16 asthma SO , O Two pollutant model: O significant and SO Study not used toal. (1995) Mexico marginally significant. estimate incidence: study1/90-6/90 outside U.S. and Canada.23 3 2Rosas et al. Mexico City, <15; 16-59; asthma O , SO , Little effect found for air pollutants. Strong Study not used to(1998) Mexico >59 NO , PM , effect found for aeroallergens, such as grass estimate incidence: study1991 TSP pollen. outside U.S. and Canada.3 22 10Schwartz et Seattle, WA <65; >64 asthma SO , PM , Single pollutant models: PM linked to ER O only available Mayal. (1993) 9/89-9/90 O visits in ages <65, with no effect in ages >64. September. Single2 10310No effect for SO and O on ER visits in either pollutant model (PM )2 3age group. used to estimate3 10 incidence of asthma

visits.Stieb et al. St. John, New 0-15; >15; asthma NO , TSP, O linked to ER visits in ages >15, especially TSP and SO gathered(1996) Brunswick, Canada all ages SO , SO , when O levels exceed 75 ppb; O not every sixth day. SingleMay-September in O significant in ages 0-15. No significant effect pollutant model (O ) used1984-1992 seen for the other pollutants. to estimate incidence of22 4333 34 3 asthma visits.Weisel et al. central and all ages asthma O O linked to ER visits. Single pollutant model(1995) northern NJ used.May-August in1986-19903 3Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-45White et al. Atlanta, GA 1-16 asthma and O , SO , O linked to ER visits when O levels PM estimated from(1994) 6/90-8/90 restrictive PM exceeded 110 ppb. No significant effect visibility levels. Study notairway reported for SO or PM . used to estimatedisease incidence: limited study3 2103 32 1010 data.D-46Table D-11Summary of Selected Studies for Emergency Room Visits -- All-Cause, All-Respiratory, Chronic Obstructive Pulmonary Disease(COPD), and BronchitisStudy Period Population (ICD code) Pollutants Main Findings CommentLocation and EndpointAtkinson et al. London, 0-14; 15-64; all NO , BS Single pollutant models: PM significant in Study not used to estimate(1999) England >64; all respiratory (black ages 0-14 and 15-64. BS and SO incidence: study outside U.S.1/92-12/94 ages (not defined smoke), significant in ages 0-14. CO and NO and Canada.by ICD code) PM , SO , significant in ages >64. O not significant.210 2CO, O310223Bates et al. Vancouver, 1-14; 15-60; all NO , SO , SO correlated with respiratory visits in all Results varied somewhat by(1990) Canada >60 respiratory SO , O age groups. SO correlated in ages >14. season. Study not used to7/84-10/86 (466, 480- NO correlated in ages 15-60. O not estimate incidence:486, 491- significant. correlations only presented.493, 496)2 24 3242 3Cody et al. central and all ages bronchitis PM , No significant effect seen for PM , O , or PM sampled every sixth(1992) northern New (466, 490, SO , O SO on bronchitis admissions. day, so limited dataset. PMJersey 491, 496) considered in separateMay-Augustanalysis. Study not used to1988-1989 estimate incidence.102 310 321010Delfino et al. Montreal, <2; 2-64; all O , PM , Single pollutant models: H and SO Limited number of results(1997) Canada >64 respiratory PM , significant in ages <2; no effect in ages 2- presented for two pollutantJune- (not defined SO , H 64 for any pollutants; O , PM , PM , and models. Study not used toSeptember by ICD code) SO significant in ages >64. Two pollutant estimate incidence: all1992-1993 model: O significant and PM not respiratory not defined by3 102.54++43 10 2.543 2.5significant in ages >64. ICD code.Delfino et al. Montreal, >64 all PM , O Two pollutant model: O significant; PM PM measured every sixth(1998) Canada respiratory has consistent link but not significant. day, with rest of dailyJune-August (not defined observations estimated from1989-1990 by ICD code) visibility and other data.2.5 3 3 2.5 2.5 Study not used to estimate incidence: all respiratory not defined by ICD code.Samet et al. Steubenville, all ages all NO , TSP, Single pollutant models: TSP and SO Study not used to estimate(1981) Ohio respiratory SO , CO, significant; NO ,, CO, or O were not incidence: all respiratory notMarch-April (not defined O significant. defined by ICD code.and October- by ICD code)November1974-197722322 3Study Period Population (ICD code) Pollutants Main Findings CommentLocation and EndpointD-47Pantazopolou Athens, all ages all outpatient BS, CO, Single-pollutant models: NO significant in Study not used to estimateet al. (1995) Greece visits NO the winter. No effects found for any incidence: study outside U.S.1988 pollutant in the summer. and Canada.22Sunyer et al. Barcelona, >14 COPD (not SO , BS SO correlated with ER visits in the summer Study not used to estimate(1993) Spain defined by and winter. BS significant in the winter only incidence: study outside U.S.1985-1989 ICD code) and Canada.2 2Xu et al. Beijing, China all ages all causes SO , TSP SO and TSP both linked to ER visits. Study not used to estimate(1995b) 1990 incidence: study outside U.S.2 2 and Canada.The Benefits and Costs of the Clean Air Act, 1990 to 2010

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1D-51Table D-12Studies Used to Develop Minor Illness EstimatesEndpoints Estimated Study Population Age Study ModelPollutants Used in Finalacute bronchitis 8-12 Dockery et al. (1996) PM2.5upper 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 , PM3 10moderate or worse asthma all ages (asthmatics) Ostro et al. (1991) PM2.5asthma attacks all ages (asthmatics) Whittemore and Korn (1980) O , PM3 10chest tightness, shortness of breath, or wheeze all ages (asthmatics) Linn et al. (1987; 1988; 1990) SOand Roger et al. (1985)2shortness 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 , O2.5 3restricted activity days 18-65 Ostro (1987) PM2.5D-52Table D-13Summary of Selected Studies for Minor IllnessStudy Period Population Endpoint Pollutants Main Findings CommentLocation andDockery et 24 communities 13,369 asthma, persistent particle Annual level of sulfates and particle Study examined annualal. (1996) in U.S. and children wheeze, chronic acidity, acidity related to bronchitis. HNO pollution exposures, andCanada ages 8-12 cough, bronchitis SO , PM , and HNO linked to asthma. SO the authors did not rule out1988-1991 PM , linked to chronic phlegm. that acute (daily)4 2.110HNO , exposures could be related2HNO , O to asthma attacks and3 323 2other acute episodes.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 chronicages 10-12 wheeze, asthma PM , TSP, cough. Annual O significantly2 23 2.515SO related to asthma.4153Hasselblad 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 respiratorythe U.S. and tract illness.Europe2 23Hoek 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 , NOirritation, physicianvisit2 23 2.5102.5-104 3Krupnick 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 respiratoryLos 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.32 23232NO had no significant effect. No2effect seen in children for anypollutant.Ostro Nationwide Adults ages work-loss days PM Two-week average PM levels PM estimated from(1987) sample from U.S. 18-65 restricted activity significantly linked to work-loss days, visibility data.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 2.5Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-53Ostro 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 respiratoryLos Angeles adults respiratory NO symptoms. SO linked to lowerCounty, symptoms, eye respiratory symptoms. No significantCalifornia irritation effects seen for COH, SO , and NO9/78-3/7934 22342 2.Ostro and Nationwide Adults ages respiratory-related O , PM Controlling for PM , two-week PM estimated fromRothschild sample from U.S. 18-65 RADs, minor RADs. average O has highly variable visibility data.(1989b) Health Interview association with respiratory-relatedSurvey and minor RADs. Controlling for O ,1976-1981 two-week average PM significantly3 2.5 2.5332.5linked to both health endpoints inmost years.2.5Peters 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 1994southern tenth grade PM , (similar link for exposure averagedCalifornia students PM , over 1986-1990). Peak ozone1994 SO , NO , reported associated with decreased2 23 2.5102.5-104 3NH , asthma prevalence in females. No4gaseous other reported effects.acids2Pope and Utah Valley 79 children upper respiratory PM PM linked to daily reported Of the 79 children in theDockery 12/90-3/91 ages 10-12 symptoms, lower incidences of upper and lower sample, 39 were(1992) respiratory respiratory symptoms and cough. symptomatic, and the othersymptoms, cough Effect seen in symptomatic sample. 40 were asymptomatic.10 10Only cough in symptomatic samplelinked to PM .10Pope 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 inand 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 1010Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-54Schwartz 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 toone 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 23 2.510 4+2 22.5 101010SO , O , PM , PM , SO , and H2 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, Results presented as a mixand Zeger 1961-1964 nurses throat, headache, NO , O and eye irritation. Oxidants (O ) of single pollutant and dual(1990) chest discomfort, eye linked to chest discomfort and eye pollutant models. Stepwiseirritation irritation. CO linked to headache. selection used to pick22 x2xsignificant covariates.von Mutius Leipzig, 1,500 upper respiratory SO , NO , In single pollutant models, SO , NO , PM measured by betaet al. (1995) Germany children symptoms PM and PM linked to upper respiratory absorption. The limited10/91-7/92 ages 9-11 symptoms in winter (high pollution modeling results presented2x 2xseason). In the summer, only NO for models with more thanxlinked to respiratory symptoms. one pollutant were similarto single pollutant results.D-55Table D-14Summary of Selected Studies for AsthmaticsStudy Period Population Endpoint Pollutants Main Findings CommentLocation andForsberg et Pitea, Sweden 31persons shortness of BS (black Controlling for other pollutants, daily levels Black smoke is an indirectal. (1993) about sixty days ages 9-71 breath, smoke), of BS linked to shortness of breath. No link measure of PM.wheeze, cough, SO , NO between pollutants and wheeze, cough, andphlegm phlegm.2 2Gielen et Amsterdam, 61 children upper O , PM , In single pollutant model, daily levels of BS Results in the model highlyal. (1997) Netherlands ages 7-13 respiratory BS significantly linked to lower and upper dependent on the lagsummer symptoms, respiratory symptoms and medication use. length used. The five-day1995 lower PM linked to lower respiratory symptoms mean black smoke andrespiratory and medication use. O linked to upper PM yielded significantsymptoms, respiratory symptoms. results, but current, onemedication use and two day lags did not.3 10103 10Current day O significant.3Hiltermann 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 negative7/3/95-10/6/95 breath, cough, associations reported for nasal symptomsphlegm, and levels of PM , BS, and NO . Nowheeze, runny significant effect reported for SO .nose, throatpain,headache, eyeirritation,physician visit3 1022310 210 22Linn 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 2Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-56Neukirch et Paris, France 40 persons asthma, PM , BS, In single pollutant models, daily levels of PM used rather than theal. (1998) 11/92-5/93 (mean age wheeze, NO , SO PM , BS, NO , and SO were each more common PM .of sample shortness of significantly associated with asthmawas 46) breath, cough, attacks, wheeze, cough, respiratoryrespiratory infections, and shortness of breath.infection132 2 13 2 21310Ostro et al. Denver, CO 207 severity of SO , PM , Daily levels of H linked to cough, asthma, Some PM estimated.(1991) 12/87-2/88 persons asthma SO , NO , and shortness of breath. PM linked to Exclusion of estimatedages 18-70 symptoms, H , nitric asthma. SO linked to shortness of breath. data removes significantcough, wheeze, acid No effects seen for other pollutants. link to asthma. Only singleshortness of pollutant models reported.breath, chesttightness2 2.54 3++2.542.5Ostro 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 22 103102 2Peters 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 pollutantsand the Czech and 102 variety of particle and the symptom score.Republic adults ages respiratory acidity9/90-6/92 32-80 symptoms242Roemer 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, eyeirritation102 2Romieu et Mexico City 71 children cough, phlegm, PM , Controlling for PM , daily levels of O PM also linked adverseal. (1996) ages 5-7 difficulty PM , O , linked to cough and lower respiratory symptoms. Publishedbreathing, NO , SO illness. Controlling for O , PM linked to results focused on O andwheezing, cough, phlegm, and lower respiratory PM . Results for NO andlower symptoms. SO not reported.respiratoryillness102.5 32 22.5 33 2.51032.5 22Study Period Population Endpoint Pollutants Main Findings CommentLocation andD-57Whittemore Six communities 443 children asthma O , TSP In a two pollutant model, daily levels of both Respirable PM, NO , SOand Korn in southern CA and adults TSP and O were significantly related to were highly correlated with(1980) Three 34-week reported asthma attacks. TSP and excluded from theperiods 1972-analysis.1975xx2 2The Benefits and Costs of the Clean Air Act, 1990 to 2010

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0 1

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## [ ] ∆ 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
Ù CO = 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
Ù CO = 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
Ù CO = 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
Ù CO = 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
Ù CO = 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
Ù CO = 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
Ù CO = 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
Ù CO = change in daily one-hour maximum CO concentration pop = population age 65 and older

) = standard error of  ̆ = 0.00715

 ̆ 10The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-61 

### Nitrogen Dioxide

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EDVLV IRU WKLV DQDO\VLV

1## 7DEOH ' 049# VXPPDUL]HV WKH &

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EHWZHHQ 12 DQG DGYHUVH KHDOWK HIIHFWV

1 5[ ]∆∆ All respiratory y e pop

N O=− ⋅ − ⋅− ⋅021( ),

β[ ]∆Re ( ) , spiratory Infection Admissions y e pop

N O=− ⋅ − ⋅− ⋅∆021

β[ ]∆ pneumonia admissions y e pop

N O=− ⋅ − ⋅− ⋅∆021( ),

β[ ]∆ Congestive Heart Failure Admissions y e pop

N O=− ⋅ − ⋅− ⋅∆021( ),

βD-62Table D-16Summary of C-R Functions for Nitrogen DioxideHealth Endpoint C-R Function Source of C-R Functionhospital admissions –Study: Burnett et al. (1997b)all respiratoryLocation: Toronto, Canadawhere:Other pollutants in model: PM , O , SOy = daily hospital admission rate for all respiratory per person = 2.58 E-50 ̆= NO coefficient = 0.003782ÙNO = change in daily 12-hour average NO concentration (ppb)2 2pop = population of all ages)= standard error of ̆= 0.00221 ̆2.5-10 3 2hospital admissions –Study: Burnett et al. (1999)respiratory infectionLocation: Toronto, Canadawhere:Other pollutants in model: PM , Oy = daily hospital admission rate for respiratory infection per person = 1.56 E-50 ̆= NO coefficient = 0.001722ÙNO = change in daily average NO concentration (ppb)2 2pop = population of all ages)= standard error of ̆= 0.000521 ̆2.5 3hospital admissions –Study: Moolgavkar et al. (1997)pneumoniaLocation: Minneapolis, MNwhere:Other pollutants in model: O , SO , PMy = daily hospital admission rate for pneumonia per person = 5.30 E-50 ̆= NO coefficient = 0.001692ÙNO = change in daily average NO concentration (ppb)2 2pop = population age 65 and older)= standard error of ̆= 0.00125 ̆3 2 10hospital admissions –Study: Burnett et al. (1999)congestive heart failureLocation: Toronto, Canadawhere:Other pollutants in model: COy = daily hospital admission rate for congestive heart failure per person = 9.33 E-60 ̆= NO coefficient = 0.002642ÙNO = 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=− ⋅ − ⋅− ⋅∆021( ),

β∆ resp symptoms e e pop NO gender NO gender pre CAAA post CAAA.,, ,=+−+⋅− − ⋅− ⋅ − − ⋅− ⋅− −11112 2 α βγ α βγHealth Endpoint C-R Function Source of C-R FunctionD-63hospital admissions –Study: Burnett et al. (1999)ischemic heart diseaseLocation: Toronto, Canadawhere:Other pollutants in model: SOy = daily hospital admission rate for ischemic heart disease per person = 2.23 E-50 ̆= NO coefficient = 0.003182ÙNO = change in daily average NO concentration (ppb)2 2pop = population of all ages)= standard error of ̆= 0.000521 ̆2respiratory symptomsStudy: Hasselblad et al. (1992)where:by Hasselblad et al. The constant and the ̊= constant = -0.536gender coefficient were obtained via personal ̆= NO coefficient = 0.0275 communication with V. Hasselblad 2/28/95 by2ı= gender coefficient (used for males only) = -0.0295 Abt Associates. The equation is based onÙNO = change in annual NO concentration (ppb) study results by Melia et al. (1980).2 2pop = children ages 6-7)= standard error of ̆= 0.0132 ̆Location: Middlesborough, EnglandOther pollutants in model: noneComments: The NO coefficient was reported2The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-64 

### Ozone

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WKH UHODWLRQVKLSV EHWZHHQ R]RQH DQG D YDULHW\ RI

KHDOWK HIIHFWV

1## 7DEOH ' 04:# VXPPDUL]HV WKH R]RQH &

0 5
IXQFWLRQV XVHG LQ WKLV DQDO\VLV

1

## [ ] ∆ 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 y pop O = − −⋅ + − 

  

 

⋅

⋅ 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
Ù O = 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
Ù O = 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
Ù O = 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
Ù O = 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 Ù O = 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 Ù O = 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
Ù O = 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
Ù O = 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
Ù O = 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
Ù O = 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 Ù O = 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 Ù O = 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 Ù O = 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 Ù O = 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 Ù O = 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
Ù O = 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
Ù O = 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 Ù O = 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 PM 2

10 3

≅ ⋅⋅

β *

,

∆ ∆

asthma attacks y

ye y y pop O = − −⋅ + − 

  

 

⋅

⋅ 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
Ù O = 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
Ù O = 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
Ù O = 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
Ù O = 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 Ù O = 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 atta cks = 0. 027

0  ̆ = ozone coefficient = 0.00184
Ù O = 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 Ù O = 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

 ̆ 2.5The Benefits and Costs of the Clean Air Act, 1990 to 2010

D-70 

### Particulate Matter

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1[ ]∆ Nonaccidental Mortality y e pop

PM=− ⋅ − ⋅− ⋅∆02 51( ),.

β[ ]∆ Nonaccidental Mortality y e pop

PM=− ⋅ − ⋅− ⋅∆02 51( ),.

β∆∆ Infant Mortality y ye y

y pop

P M= −−⋅ +−⋅⋅00 0011 0( ),

β[ ]∆ Chronic Bronchitis y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

βD-71Table D-18Summary of C-R Functions for Particulate MatterHealth C-R Function Source of C-R FunctionEndpointmortalityStudy: Pope et al. (1995)where:Other pollutants in model: noney = county-level annual non-accidental deaths of persons ages 30+ per person0 ̆= PM coefficient = 0.0064082.5ÙPM = change in annual medianPM concentration2.5 2.5pop = population ages 30 and older)= standard error of ̆= 0.001509 ̆Location: 50 U.S. citiesmortalityStudy: Dockery et al. (1993)where:Other pollutants in model: noney = county-level annual non-accidental deaths of persons ages 25+ per person0 ̆= PM coefficient = 0.01242.5ÙPM = change in annual mean PM concentration2.5 2.5pop = population ages 25 and older)= standard error of ̆= 0.00423 ̆Location: six U.S. citiesneonatalStudy: Woodruff et al. (1997)mortalityLocation: 86 U.S. metropolitan areaswhere:y = county annual postneonatal infant deaths per infant 0-1 years old0 ̆= PM coefficient = 0.0039210ÙPM = change in annual average PM concentration10 10pop = population infants ages 0-1)= standard error of ̆= 0.00122 ̆Other pollutants in model: nonechronicStudy: Abbey et al. (1993)bronchitisLocation: Californiawhere:Other pollutants in model: noney = annual bronchitis incidence rate per person = 0.00378 Comments: Abbey et al. used TSP to measure0 ̆= estimated PM logistic regression coefficient = 0.00932 PM. The TSP coefficient is applied to changes10ÙPM = 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=− ⋅ − ⋅− ⋅∆02 51( ),.

β∆∆ Chronic Bronchitis y ye y

y z y

pop

P M= −−⋅ +−⋅⋅⋅00 000011 0( ),

β[ ]∆∆ All respiratory y e pop

P M=− ⋅ − ⋅− ⋅−02 5 101( ),.

β[ ]∆∆ Asthma Admissions y e pop

P M=− ⋅ − ⋅− ⋅−02 5 101( ),.

βHealth C-R Function Source of C-R FunctionEndpointD-72chronicStudy: Abbey et al. (1995)bronchitisLocation: Californiawhere:Other pollutants in model: noney = annual bronchitis incidence rate per person = 0.003780 ̆= estimated PM logistic regression coefficient = 0.091322.5ÙPM = change in annual average PM concentration2.5 2.5pop = population ages 27 and older "without chronic bronchitis")= standard error of ̆= 0.00680 ̆chronicStudy: Schwartz (1993)bronchitisLocation: 53 U.S. urban areaswhere:y = national chronic bronchitis prevalence rate for individuals 18 and older= 0.05350z = annual bronchitis incidence rate per person = 0.003780 ̆= estimated PM logistic regression coefficient = 0.012310ÙPM = change in annual average PM concentration10 10pop = population ages 30 and older "without chronic bronchitis")= standard error of ̆= 0.00434 ̆Other pollutants in model: nonehospitalStudy: Burnett et al. (1997b)admissions – allLocation: Toronto, Canadarespiratory where:Other pollutants in model: O , NO , SOy = daily hospital admission rate for all respiratory per person = 2.58 E-50 ̆= PM coefficient = 0.001472.5-10ÙPM = change in daily average PM concentration2.5-10 2.5-10pop = population of all ages)= standard error of ̆= 0.00179 ̆322hospitalStudy: Burnett et al. (1999)admissions –Location: Toronto, Canadaasthma where:Other pollutants in model: CO, Oy = daily hospital admission rate for asthma per person = 4.75 E-60 ̆= PM coefficient = 0.003212.5-10ÙPM = change in daily average PM concentration2.5-10 2.5-10pop = population of all ages)= standard error of ̆= 0.00106 ̆3[ ]∆∆ O LD Adm issions y e pop

P M=− ⋅ − ⋅− ⋅−02 5 101( ),.

β[ ]∆Re ( ) ,. spiratory Infection Admissions y e pop

PM=− ⋅ − ⋅− ⋅∆02 51

β∆ ∆ all respiratory admissions PM pop=⋅ ⋅

β2 5.,[ ]∆ pneumonia admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ COPD admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

βHealth C-R Function Source of C-R FunctionEndpointD-73hospitalStudy: Burnett et al. (1999)admissions –Location: Toronto, Canadaobstructive lung where:Other pollutants in model: CO, Odisease y = daily hospital admission rate for obstructive lung disease per person = 5.76 E-60 ̆= PM coefficient = 0.003102.5-10ÙPM = change in daily average PM concentration2.5-10 2.5-10pop = population of all ages)= standard error of ̆= 0.00163 ̆3hospitalStudy: Burnett et al. (1999)admissions –Location: Toronto, Canadarespiratory where:Other pollutants in model: NO , Oinfection y = daily hospital admission rate for respiratory infection per person = 1.56 E-50 ̆= PM coefficient = 0.003282.5ÙPM = change in daily average PM concentration2.5 2.5pop = population of all ages)= standard error of ̆= 0.000735 ̆2 3hospitalStudy: Thurston et al. (1994)admissions – allLocation: Toronto, Canadarespiratory where:Other pollutants in model: O ̆= ozone coefficient = 1.81 E-8ÙPM = change in daily average PM2.5 2.5pop = population all ages)= standard error of ̆= 1.79 E-8 . ̆3hospitalStudy: Moolgavkar et al. (1997)admissions –Location: Minneapolis, MNpneumonia where:Other pollutants in model: O , SO , NOy = daily hospital admission rate for pneumonia per person = 5.30 E-50 ̆= PM coefficient = 0.00049810ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000505 ̆322hospitalStudy: Moolgavkar et al. (1997)admissions –Location: Minneapolis, MNCOPD where:Other pollutants in model: CO, Oy = daily hospital admission rate for COPD per person = 3.75 E-50 ̆= PM coefficient = 0.00087710ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000777 ̆3[ ]∆ pneumonia admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ COPD admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ pneumonia admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ COPD admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ pneumonia admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

βHealth C-R Function Source of C-R FunctionEndpointD-74hospitalStudy: Schwartz (1994c)admissions –Location: Minneapolis, MNpneumonia where:Other pollutants in model: Oy = daily hospital admission rate for pneumonia per person = 5.18 E-50 ̆= PM coefficient = 0.0015710ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000677 ̆3hospitalStudy: Schwartz (1994c)admissions –Location: Minneapolis, MNCOPD where:Other pollutants in model: noney = daily hospital admission rate for COPD per person = 3.75 E-50 ̆= PM coefficient = 0.0045110ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.00138 ̆hospitalStudy: Schwartz (1994a)admissions –Location: Birmingham, ALpneumonia where:Other pollutants in model: noney = daily hospital admission rate for pneumonia per person = 5.30 E-50 ̆= PM coefficient = 0.0017410ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000536 ̆hospitalStudy: Schwartz (1994a)admissions –Location: Birmingham, ALCOPD where:Other pollutants in model: noney = daily hospital admission rate for COPD per person = 3.75 E-50 ̆= PM coefficient = 0.0023910ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000838 ̆hospitalStudy: Schwartz (1994b)admissions –Location: Detroit, MIpneumonia where:Other pollutants in model: Oy = daily hospital admission rate for pneumonia per person = 5.18 E-50 ̆= PM coefficient = 0.0011510ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.00039 ̆3[ ]∆ COPD admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ all respiratory admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ all respiratory admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ all respiratory related admissions y e pop

P M=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ Asthma Admissions y e pop

PM=− ⋅ − ⋅− ⋅∆02 51( ),.

βHealth C-R Function Source of C-R FunctionEndpointD-75hospitalStudy: Schwartz (1994b)admissions –Location: Detroit, MICOPD where:Other pollutants in model: Oy = daily hospital admission rate for COPD per person = 3.05 E-50 ̆= PM coefficient = 0.0020210ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.00059 ̆3hospitalStudy: Schwartz (1996)admissions – allLocation: Spokane, WArespiratory where:Other pollutants in model: noney = daily hospital admission rate for all respiratory per person 65 or older = 1.187 E-40 ̆= PM coefficient = 0.0016310ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000470 ̆hospitalStudy: Schwartz (1995)admissions – allLocation: New Haven, CTrespiratory where:Other pollutants in model: Oy = daily hospital admissions for all respiratory per person 65 and older = 1.187 E-40 ̆= PM coefficient = 0.0017210ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000930 ̆3hospitalStudy: Schwartz (1995)admissions – allLocation: Tacoma, WArespiratory where:Other pollutants in model: Oy = daily hospital admissions for all respiratory conditions per person 65 and older = 1.187 E-40 ̆= PM coefficient = 0.0022710ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.00145 ̆3hospitalStudy: Sheppard et al. (1999)admissions –Location: Seattle, WAasthma where:Other pollutants in model: COy = daily hospital admission rate for asthma per person = 4.52 E-60 ̆= PM coefficient = 0.00272.5ÙPM = change in daily average PM concentration2.5 2.5pop = population of ages < 65)= standard error of ̆= 0.000948 ̆[ ]∆∆ cardiac y e pop

P M=− ⋅ − ⋅− ⋅−02 5 101( ),.

β[ ]∆ Dysrhythmias Admissions y e pop

PM=− ⋅ − ⋅− ⋅∆02 51( ),.

β[ ]∆ Ischemic Heart Disease Admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ CongestiveHeart Failure Admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ cardiovascular admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

βHealth C-R Function Source of C-R FunctionEndpointD-76hospitalStudy: Burnett et al. (1997b)admissions –Location: Toronto, Canadacardiac where:Other pollutants in model: Oy = daily hospital admission rate for cardiac per person = 3.81 E-50 ̆= PM coefficient = 0.007042.5-10ÙPM = change in daily average PM concentration2.5-10 2.5-10pop = population of all ages)= standard error of ̆= 0.00215 ̆3hospitalStudy: Burnett et al. (1999)admissions –Location: Toronto, Canadadysrhythmias where:Other pollutants in model: CO, Oy = daily hospital admission rate for dysrhythmias per person = 6.46 E-60 ̆= PM coefficient = 0.001362.5ÙPM = change in daily average PM concentration2.5 2.5pop = population of all ages)= standard error of ̆= 0.000910 ̆3hospitalStudy: Schwartz and Morris (1995)admissions –Location: Detroit, MIischemic heart where:Other pollutants in model: COdisease y = daily hospital admission rate for ischemic heart disease per person 65 and older = 9.96 E-50 ̆= PM coefficient= 0.00049610ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000220 ̆hospitalStudy: Schwartz and Morris (1995)admissions –Location: Detroit, MIcongestive heart where:Other pollutants in model: COfailure y = daily hospital admission rate for congestive heart failure per person 65 and older = 5.82 E-50 ̆= PM coefficient = 0.00074110ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000311 ̆hospitalStudy: Schwartz (1999)admissions –Location: eight U.S. countiescardiovascular where:Other pollutants in model: COy = daily hospital admission rate for cardiovascular disease per person 65 and older = 2.23 E-40 ̆= PM coefficient = 0.00073710ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000170 ̆[ ]∆ cardiovascular admissions y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β[ ]∆ asthma visits y e pop

PM=− ⋅ − ⋅− ⋅∆0101( ),

β∆∆ Acute Bronchitis y ye y

y pop

P M= −−⋅ +−⋅⋅00 0012 5( ),. β( )∆∆ Lower spiratory Symptom s y ye y

y pop

P MRe ..= −−⋅ +−⋅⋅00 0012 5 βHealth C-R Function Source of C-R FunctionEndpointD-77hospitalStudy: Schwartz (1997)admissions –Location: Tucson, AZcardiovascular where:Other pollutants in model: COy = daily hospital admission rate for cardiovascular disease per person 65 and older = 2.23 E-40 ̆= PM coefficient = 0.0010210ÙPM = change in daily average PM concentration10 10pop = population age 65 and older)= standard error of ̆= 0.000423 ̆emergency roomStudy: Schwartz (1993)visitsLocation: Seattle, WAwhere:Other pollutants in model: noney = daily ER visits for asthma per person under 65 years old = 7.69 E-60 ̆= PM coefficient (Schwartz et al., 1993, p. 829) = 0.0036710ÙPM = change in daily average PM concentration10 10pop = population ages 0-64)= standard error of ̆(Schwartz et al., 1993, p. 829) = 0.00126 ̆acute bronchitisStudy: Dockery et al. (1996)where:y = annual bronchitis incidence rate per person = 0.0440 ̆= estimated PM logistic regression coefficient = 0.02722.5ÙPM = change in annual average PM concentration2.5 2.5pop = population ages 8-12)= standard error of ̆= 0.0171 ̆Location: 24 U.S. and Canadian citiesOther pollutants in model: nonelower respiratoryStudy: Schwartz, et al. (1994)symptoms (LRS)Location: six U.S. citiesdefined asOther pollutants in model: nonecough, chest where:pain, phlegm, y = daily lower respiratory symptom incidence rate per person = 0.0012and wheeze ̆= estimated PM logistic regression coefficient = 0.0182302.5ÙPM = change in daily average PM concentration2.5 2.5pop = population ages 7-14)= standard error of ̆= 0.00586 ̆∆∆ Shortness of Breath y ye y

y pop

P M= −−⋅ +−⋅⋅00 0011 0( ),

β∆∆ Upper spiratory Symptom s y ye y

y pop

P MR e( ),= −−⋅ +−⋅⋅00 0011 0 β∆ ∆ ARD PM pop

PM21010≅⋅ ⋅

β*,∆ Days Moderate Worse Asthma P M P M

pop

after before/ ln ,. ,. ,=− ⋅⋅

β2 52 5Health C-R Function Source of C-R FunctionEndpointD-78Shortness ofStudy: Ostro et al. (1995)breath, daysLocation: Los Angeles, CAwhere:y = daily shortness of breath incidence rate per person = 0.0560 ̆= estimated PM logistic regression coefficient = 0.0084110ÙPM = change in daily average PM concentration10 10pop = asthmatic African-American population ages 7 to 12)= standard error of ̆= 0.00363 ̆Other pollutants in model: noneURS, defined asStudy: Pope et al. (1991)runny or stuffyLocation: Utah Valleynose, wetOther pollutants in model: nonecough, burning, where:aching, or red y = daily upper respiratory symptom incidence rate per person = 0.3419eyes ̆= estimated PM logistic regression coefficient = 0.0036010ÙPM = change in daily average PM concentration10 10pop = asthmatic population ages 9 to 11 = 6.91% of population ages 9 to 11)= standard error of ̆= 0.0015 ̆presence of anyStudy: Krupnick et al. (1990)of 19 acuteLocation: Glendora-Covina-Azusa, CArespiratory where:Other pollutants in model: SO , Osymptoms ̆= first derivative of the stationary probability = 0.000461 Comments: COH used in estimation of model.*ÙPM = change in daily average PM concentration The estimated COH coefficient is used with10 10pop = population ages 18-65PM data.)= standard error of ̆= 0.000239 ̆*2 310moderate orStudy: Ostro et al. (1991)worse asthmaLocation: DenverstatusOther pollutants in model: nonewhere:Comments: The estimated coefficient is applied ̆= estimated PM coefficient for year i = 0.0006 to populations of all ages.2.5PM = change in daily average PM concentration2.5 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

y pop

P M= −−⋅ +−⋅⋅00 0011 0( ),

β[ ]∆ ∆ RAD y pop y e pop

PM= ⋅ =− ⋅ − ⋅− ⋅∆02 51( ),.

β[ ]∆ ∆ MRAD y pop y e pop

PM= ⋅ =− ⋅ − ⋅− ⋅∆02 51( ),.

β[ ]∆ ∆ WLD y pop y e pop

PM= ⋅ =− ⋅ − ⋅− ⋅∆02 51( ),.

βHealth C-R Function Source of C-R FunctionEndpointD-79asthma attacksStudy: Whittemore and Korn (1980)where:y = daily incidence of asthma attacks = 0.0270 ̆= PM coefficient = 0.0014410ÙPM = change in daily PM concentration10 10pop = population of asthmatics of all ages = 5.61% of the population of all ages)= standard error of ̆= 0.000556 ̆Location: Los Angeles, CAOther pollutants in model: O3RestrictedStudy: Ostro (1987)Activity DaysLocation: U.S. metropolitan areas(RADs) where:Other pollutants in model: noney = daily RAD incidence rate per person = 0.0177 Comments: An inverse-variance weighting used0 ̆= inverse-variance weighted PM coefficient = 0.00475 to estimate the coefficient, based on Ostro2.5ÙPM = 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 andStudy: Ostro and Rothschild (1989b)nonrespiratoryLocation: U.S.conditions where:Other pollutants in model: Oresulting in a y = daily MRAD daily incidence rate per person = 0.02137 Comments: An inverse-variance weighting usedminor restricted ̆= inverse-variance weighted PM coefficient = 0.00741 to estimate the coefficient, based on Ostro andactivity dayÙPM = change in daily average PM concentration Rothschild (1989b, Table 4)(MRAD) pop = adult population ages 18 to 6502.52.5 2.5)= standard error of ̆= 0.0007 ̆3work loss daysStudy: Ostro (1987)(WLDs)Location: U.S. metropolitan areaswhere:Other pollutants in model: noney = daily work-loss-day incidence rate per person = 0.00648 Comments: An inverse-variance weighting used0 ̆= inverse-variance weighted PM coefficient = 0.0046 to estimate the coefficient, based on Ostro2.5ÙPM = 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

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VXOIXU GLR[LGH DUH VXPPDUL]HG LQ 7DEOH ' 04<1[ ]∆ All respiratory y e pop

SO=− ⋅ − ⋅− ⋅∆021( ),

β[ ]∆ pneumonia admissions y e pop

SO=− ⋅ − ⋅− ⋅∆021( ),

β[ ]∆ Ischemic Heart Disease Admissions y e pop

SO=− ⋅ − ⋅− ⋅∆021( ),

β∆ symptoms e e pop SO SO pre CAAA post CAAA=+−+⋅−− ⋅− − − ⋅−− −11112 2 α βγ α βγ, ,,D-81Table D-19Summary of C-R Functions for Sulfur DioxideHealth Endpoint Concentration-Response Function Source of C-R Functionhospital admissionsStudy: Burnett et al. (1997b)– all respiratoryLocation: Toronto, Canadawhere:Other pollutants in model: PM , NO , Oy = daily hospital admission rate for all respiratory per person = 2.58 E-50 ̆= SO coefficient = 0.004462ÙSO = change in daily one-hour maximum SO concentration (ppb)2 2pop = population of all ages)= standard error of ̆= 0.00293 ̆2.5-10 2 3hospital admissionsStudy: Moolgavkar et al. (1997)– pneumoniaLocation: Minneapolis, MNwhere:Other pollutants in model: O , NO PMy = daily hospital admission rate for pneumonia per person = 5.30 E-50 ̆= SO coefficient = 0.001432ÙSO = change in daily average SO concentration (ppb)2 2pop = population age 65 and older)= standard error of ̆= 0.00290 ̆3 2 10hospital admissionsStudy: Burnett et al. (1999)– ischemic heartLocation: Toronto, Canadadisease where:Other pollutants in model: NOy = daily hospital admission rate for ischemic heart disease per person = 2.23 E-50 ̆= SO coefficient = 0.001772ÙSO = change in daily average SO concentration2 2pop = population of all ages)= standard error of ̆= 0.000854 ̆2chest tightness,Study: Linn et al. (1987; 1988; 1990) and Roger et al. (1985)shortness of breath,Location: Chamber studyor wheezeOther pollutants in model: none.where:Comments: The results of four chamber studies were ̊= constant = -5.65 combined to develop this C-R function. Moderate asthmatics ̆= SO coefficient = 0.00589 compose one third of exercising asthmatics; mild asthmatics2ı= 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 SO2 22concentration (ppb) multiplied by 2.5 peak to mean ratio of 2.5pop = exercising asthmatics = population of asthmatics of all ages (5.61% of thepopulation 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 

## Modeling Results

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However, there are a few exceptions. To calculate the baseline incidencearate for respiratory-related hospital admissions, we used admissions for persons of all ages for ICD codes 460-519; for cardiovascular admissions, we used admissions for persons ofall 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 forindividuals 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.Calculated as the ratio of avoided mortality to the projected baseline annual non-accidental mortality for adults aged 30 andover. Non-accidental mortality was approximately 95% ofbtotal 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 artifact of the statistical methodscemployed in the uncertainty analysis, sincenone of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollutant exposureand this healthendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations of benefits estimates presentedin this document.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 notdaggregated into the total benefits estimates.Minor restricted activity days and any-of-19 respiratory symptoms have overlapping definitions and are pooled.eD-87Table D-21Change in Incidence of Adverse Health Effects Associated with Criteria Pollutants (Pre-CAAA minus Post-CAAA) – 48 State U.S.Population (avoided cases per year)2000 2010 % of Baseline Incidences forthe mean estimatesaEndpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 2010th th th thMortalityages 30 and older PM 8,800 14,000 19,000 14,000 23,000 32,000 0.66% 1.00%b bChronic Illnesschronic bronchitis PM 3,100 13,000 22,000 5,000 20,000 34,000 2.21% 3.14%chronic asthma O 1,300 5,600 9,600 1,800 7,200 12,000 3.22% 3.83%3Hospitalizationrespiratory PM, CO, NO , 8,100 13,000 20,000 13,000 22,000 34,000 0.40% 0.62%admissions SO , O22 3cardiovascular PM, CO, NO , 5,800 22,000 48,000 10,000 42,000 100,000 0.49% 0.86%admissions SO , O22 3emergency room PM, O 260 3,100 8,900 430 4,800 14,000 0.39% 0.55%visits for asthma3Minor Illnessacute bronchitis PM 0 29,000 59,000 0 47,000 94,000 3.39% 5.06%ccupper respiratory PM 180,000 620,000 1,000,000 280,000 950,000 1,600,000 0.61% 0.86%symptomslower respiratory PM 150,000 320,000 480,000 240,000 520,000 770,000 2.38% 3.57%symptomsrespiratory illness NO 31,000 130,000 220,000 76,000 330,000 550,000 4.46% 10.44%2moderate or PM 52,000 260,000 460,000 80,000 400,000 720,000 0.17% 0.24%worse asthmadasthma attacks O , PM 590,000 1,100,000 1,600,000 920,000 1,700,000 2,500,000 0.73% 1.04%d32000 2010 % of Baseline Incidences forthe mean estimatesaEndpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 2010th th th thD-88chest tightness, SO 220 88,000 410,000 290 110,000 520,000 0.002% 0.003%shortness ofbreath, or wheeze2shortness of PM 16,000 59,000 98,000 26,000 91,000 150,000 1.19% 1.69%breathwork loss days PM 2,200,000 2,500,000 2,900,000 3,600,000 4,100,000 4,600,000 0.62% 0.94%minor restricted O , PM 16,000,000 19,000,000 23,000,000 25,000,000 31,000,000 37,000,000 1.43% 2.15%activity days / anyof 19 respiratorysymptomse3restricted activity PM 6,400,000 7,200,000 7,900,000 10,000,000 12,000,000 13,000,000 0.65% 1.00%daysdThe 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 incidencearate for respiratory-related hospital admissions, we used admissions for persons of all ages for ICD codes 460-519; for cardiovascular admissions, we used admissions for persons ofall 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 forindividuals 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.Calculated as the ratio of avoided mortality to the projected baseline annual non-accidental mortality for adults aged 30 andover. Non-accidental mortality was approximately 95% ofbtotal 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 artifact of the statistical methodscemployed in the uncertainty analysis, sincenone of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollutant exposureand this healthendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations of benefits estimates presentedin this document.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 notdaggregated into the total benefits estimates.Minor restricted activity days and any-of-19 respiratory symptoms have overlapping definitions and are pooled.eD-89Table D-22Mortality Distribution by Age in Primary Analysis, Based on Pope et al. (1995)Age Group Proportion of Premature Mortality by Age Life Expectancy (years)aInfants not estimated --1-29 not estimated --30-34 1% 4835-44 4% 3845-54 6% 2955-64 12% 2165-74 24% 1475-84 30% 985+ 24% 6Percentages sum to 101 percent due to rounding.aD-90Table D-23Illustrative Estimates of the Impact of Criteria Pollutants on Mortality – 48 State U.S. Population within 50 km of a Monitor (cases peryear)2000 2010 % of Baseline Incidences forthe mean estimatesEndpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 2010th th th thages 30 and older PM 7,900 13,000 18,000 13,000 20,000 28,000 0.67% 0.95%(Pope et al., 1995)ages 25 and older PM 12,000 29,000 46,000 20,000 47,000 73,000 1.35% 2.19%(Dockery et al., 1993)*all ages * O 81 1,100 2,200 130 1,600 3,400 0.06% 0.08%3post-neonatal * PM 39 81 120 59 120 180 0.93% 1.39%*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 % of Baseline Incidences forthe mean estimatesEndpoint Pollutant 5 % mean 95 % 5 % mean 95 % 2000 2010th th th thages 30 and older PM 8,800 14,000 19,000 14,000 23,000 32,000 0.66% 1.00%(Pope et al., 1995)ages 25 and older PM 15,987 34,860 54,677 26,000 56,000 88,000 1.60% 2.39%(Dockery et al., 1993)*all ages * O 0 1,400 2,800 0 2,200 4,600 0.07% 0.09%3††post-neonatal * PM 45 88 130 69 130 200 1.02% 1.38%*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 artifact of the statistical methodsemployed in the uncertainty analysis, sincenone of the studies used in the health benefits analysis suggest a negative correlation between criteria air pollutant exposureand this healthendpoint. We therefore truncate this value at zero for presentation. The full distribution of estimates, including negative values, is used in all aggregations of benefits estimates presentedin this document.D-91Table D-25Comparison of Alternative Lag Assumptions for Premature Mortality Associated with PM ExposureYearPercent of Avoided Mortality By Year1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Tier II SA Lag 25 25 16.67 16.67 16.67 0 000000000(Primary Estimate)No Lag 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0Lag Distributed 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.50000000Evenly 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 6.67 6.67 6.67Evenly Over thePeriod Covered byDockery et al., 1993Totals may not sum to 100 percent due to rounding.0 5,000 10,000 15,000 20,000 25,000051015202530354045

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

D-93 

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# Ecological Effects

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Pollutant Class Major Pollutants and Acute Effects Long-term Effects
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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 speciespatterns for fast growing species and composition. Decrease in seaindividuals that efficiently use grass beds. additional nitrogen. Loss ofspecies 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. Selectivecompetitive 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

Existence value for maintenance of biological diversity Nitrogen Saturation Freshwater acidification resulting in aquatic Recreational fishing and Eutrophication organism (e.g. fish) population decline (NO )

x Estuarine eutrophication causing oxygen depletion 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 EconomicTransport DoseAcidification Forest NEI, RADM Not Multiple Only site-

(H SO , aesthetics ERCAM Required available specific

2 4 HNO ) models 3 availableRecreational NEI, RADM MAGIC Multiple Only sitefishing ERCAM (region available specific specific) models
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availableNitrogen 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
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models
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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

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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

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Endpoint Analysis Geographic Scope Lake acidification impacts on Quantification of improved fisheries Case study of New York State
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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 TennesseeThe Benefits and Costs of the Clean Air Act, 1990 to 2010

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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 194.3 345.1 452.4 258.1 Long Island Sound 36.9 78.3 93.7 56.8 Tampa Bay 4.3 8.1 11.3 7.0

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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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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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0 1 2 3 4 5 6 7 8 9 10

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### Figure E-1. Nitrogen Reduction Without CAAA

0 1 2 3 4 5 6 7 8 9 10

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at 2010 Required reductions from waterborne sources The Benefits and Costs of the Clean Air Act, 1990 to 2010

E-25The Benefits and Costs of the Clean Air Act, 1990 to 2010

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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



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Avoided Cost for Atlantic Coast

Watershed Pounds (2010) Source ($1000/yr.) Difference ($1000/yr.) Reduction through Avoided Cost, 100 Nonpoint Source First, CAAA, M illions of Percent from Point then Point Source on Avoided Cost, from

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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 St. Catherines/Sapelo Sounds 0.28 721 6,525 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
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Total East Coast 123.82 261,527 2,765,954

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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%

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2010

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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

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Saturated $61 $261

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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 B enefit Minimum $67 $433

CAAA B enefit Maximum $271 $465



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For example, increased needle/leafabscission, elevated mortality rates, and/or changes in species composition.Acidic deposition can also cause direct foliar injuries. Acids are, however, more likelyto indirectly affect the visual appearance of forest trees, unless exposure levels arevery high.Sulfur Dioxide Point source pollution Direct Injuries Foliar injuries including leaf/needle discoloration and necrosis. Pattern, size, location,and shape of foliar injuries to indicator species can be specific for sulfur dioxide. Athigh concentrations, elevated mortality rates of sensitive species and changes inspecies composition may occur.Sulfur dioxide may also cause indirect injuries. Indirect injuries, however, are not welldocumented.Hydrogen Fluoride Point source pollution Direct Injuries Foliar injuries including leaf/needle discoloration and necrosis. Pattern, size, location,and shape of foliar injuries to indicator species can be specific for sulfur dioxide. Athigh concentrations, elevated mortality rates of sensitive species and changes inspecies composition may occur.Hydrogen fluoride may also cause indirect injuries. Indirect injuries, however, are notwell documented.The Benefits and Costs of the Clean Air Act, 1990 to 2010E-55Table E-22Forests Affected by Regional PollutionAffected Forest Type Region Major Air Documented Visual Suspected Mechanisms of Injury Sources/ Species Pollutants InjuriesMixed Conifer Forest / San Bernardino Ozone and nitrogen Foliar injuries include Direct ozone-induced foliar injuries. Heavy bark EPA 1996a; Miller,Ponderosa and Jeffrey Mountains, containing chlorotic mottle, tip beetle attacks facilitated by drought, ozone, and 1992; Stolte etal.,Pines California substances necrosis, premature nitrogen containing air pollutants. 1992; NAPAP,senescence of 1991; Miller andneedles, and Ponderosa and Jeffrey pine have shown air McBride, 1998increased needle pollution-related symptoms of decline probablyabscission. Elevated since the mid 1950s.mortality rates andchanges in speciescomposition haveoccurred.Sierra Nevada, Ozone Foliar injuries include Direct ozone-induced foliar injuries. EPA, 1996a;California chlorotic mottle, tip Peterson andnecrosis, premature The Sierra Nevada contains the largest forest area Arbaugh, 1992;senescence of in the world with documented damage from a non- NAPAP, 1991;needles, and point source pollutant but ozone exposure and Miller and Millecan,increased needle injuries are not as severe as in the San Bernardino 1971abscission. Mountains. Visible ozone-induced foliar injurieswere first documented in the early 1970s.The Benefits and Costs of the Clean Air Act, 1990 to 2010E-56Affected Forest Type Region Major Air Documented Visual Suspected Mechanisms of Injury Sources/ Species Pollutants InjuriesSpruce-Fir Forest / High elevation Acidic deposition Foliar dieback, bud Acidic deposition increases the susceptibility of red EPA,1995a;Red Spruce areas in the (esp. acidic cloud injury, foliar loss, spruce to winter injury (freezing). Johnson et al.,northern water), and ozone Elevated mortality A dramatic increase in the frequency of winter injury 1992; DeHayes,Appalachians. rates. in red spruce stands occurred in the late 1950s and 1992;1960s, coincident with a significant increase in the NAPAP, 1991emissions of precursors of acidic deposition.High elevation Acidic deposition Crown thinning and Acidic deposition leads to nutrient imbalances EPA, 1995a;areas in the (esp. acidic cloud pockets of high red through accelerated foliar leaching and soil Johnson et al.,southern water) and ozone spruce mortality have acidification. Soil acidification is characterized by a 1992; Johnson andAppalachians. been detected on a loss of soil nutrient cations and occurrence of toxic Fernandez, 1992;few mountain sites. aluminum levels. Also: direct foliar injuries caused Cook and Zedaker,Ozone-induced foliar by ozone. 1992; NAPAP,injury.1991Eastern Hardwood Northeastern US Acidic deposition Crown thinning, Acidic deposition leads to nutrient imbalances USFS, 1995b;Forest / Sugar Maple and Canada and ozone branch dieback, through accelerated foliar leaching and soil EPA, 1995a;elevated mortality acidification. Soil acidification is characterized by a NAPAP, 1991rates loss of soil nutrient cations and occurrence of toxicaluminum levels.During 1980s sugar maple declined in many standsin the northeastern US and Canada. Involvement ofacidic deposition in sugar maple decline has notbeen demonstrated but cannot be ruled out.E-57 Adirondacks (NY) Green Mountains (VT) White Mountains (NH)

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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
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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 1997The Benefits and Costs of the Clean Air Act, 1990 to 2010

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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

Reduced tree Ozone National $190 to $1000 $600 $1,900 - Uncertainties in 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 assessme nt is based on variation in annual point estimates for 2005 through 2010.The Benefits and Costs of the Clean Air Act, 1990 to 2010

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Table F-1 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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F-7 Table F-2 Relative Percent Yield Change

Corn Cotton Peanuts Sorgham Soybeans Winter Wheat

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2010 Minimum Response 0.01% 2.84% 1.36% 0.02% 0.42% 0.39% Maximum Response 0.10% 6.58% 1.36% 0.02% 4.38% 9.11%



## Economic Impact Estimates

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Table F-3 Change in Net Crop Income, Consumer Surplus and Net Surplus Under the Post-CAAA Scenario (m illions 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



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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
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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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Section Costs

604 & 606 & Capital investment and variable costs associated with switching to alternative technologies and non-ozone-depleting substances;

& Possible elimination of products containing ozone-depleting substances (ODSs) if firms are unable to develop cost-effective manufacturing alternatives;

& Costs of recycling ODSs;

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608 & Purchase of ODS recovery equipment;

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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;

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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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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
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& 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)

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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 

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1G-23Table 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 Appendix Hfor a description ofnonmelanoma) 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) Costs include increasedmedical costs, increased work loss, increased costs for chores, other indirect social andeconomic costs, and willingness to pay to avoid cataracts. Data from literature review,contacts with health providers, and cataract patient survey. (Source: Rowe et al.1987)Nonfatal skin cancer cases (melanoma and $220 billion Cost of melanoma skin cancer: $15,000 per case (dollar year not provided);costs ofnonmelanoma) in the U.S. (1990-2165) nonmelanoma skin cancer: $5,000 per case (dollar year not provided). Estimates includeincreased medical costs and decreased productivity but do not include costs of caregivingand chores performed by others. Data from Skin Cancer Focus Group. (See ICF's August1988 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 following theaddition ofstabilizers. Price increase of 1.86% expected for a 25% increase in stabilizer.Total Environmental Benefits $84 billionTotal Benefits $4,300 billionNote : 1) The RIAs do not provide specific crop valuesThe Benefits and Costs of the Clean Air Act, 1990 to 2010

G-24 

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G-25 Table G-8 Adjustments to Key Parameters of Existing Analyses That Support the 812 Title VI Estimates

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Section 812 Prospective Sections 604 and Section 608 Section 609 Section 611

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G-27 Table G-9
Costs and Benefits of Sections 604 and 606 1

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.)



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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-2Annual Human Health Benefits From Sections 604 and 606 (Discounted at 5%)$0$1,000$2,000$3,000$4,000$5,000$6,000$7,000$8,000$9,000 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100 2105 2110 2115 2120 2125 2130 2135 2140 2145 2150 2155 2160 2165Year

Benefits (Millions)G-29The Benefits and Costs of the Clean Air Act, 1990 to 2010

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1G-31Table G-11Summary of Benefits of Title VI with a Two Percent Discount Rate and $4.8 Million VSLSection 1990 Dollars Notes AccrueBenefitsYears During(Millions)Which Benefits604 & 606 Class I Phaseout $4,338,000- Reduced Mortality, Cataracts, and $4,243,0001990 to 2165Non-Fatal Cancers- Methyl Bromide Reductions No information currently available. 1994 to 2160- Ecological Benefits $84,0001989 to 2075605 Class II PhaseoutNo information currently available.608 National Recycling and $6701) Only health effects are monetized. 1994 to 2165Emission Reduction Program2) The RIA for section 608 does not include benefits of HCFC phaseoutbecause this phaseout is too complex and predicting baseline innovationis too difficult.3) The benefits estimate listed is the weighted average of the benefitscalculated with $3 million and $12 million for the VSL. (The $3million estimate has a weight of 0.8 and the $12 million estimate has aweight of 0.2)4) Benefits reflect the accelerated phaseout schedule.609 Servicing of Motor Vehicle Air $300The benefits of section 609 are a subset of the benefits of sections 604 and1991 to 2075Conditioners606; we calculate section609 benefits as 0.00682% of the benefits of 604and 606 combined. (This percentage is the total benefits of 609 in 1989dollars divided by the total 604 benefits in 1989 dollars.)611 Labeling $8301) The benefits do not reflect the accelerated phase-out schedule. 1989 to 20752) The benefits estimate listed is the weighted average of the benefitscalculated with $3 million and $12 million for the value of a statisticallife. (The $3 million estimate has a weight of 0.8 and the $12 estimatehas a weight of 0.2)TOTAL $4,339,000Notes: 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 phaseoutschedule of section 606.G-32Table G-12Summary of Costs for Title VI by Section with a Two Percent Discount RateSection 1990 Dollars) Notes AccrueCostDuringEstimateWhich(Millions,CostsYears604 & Class I Phaseout $56,000Does not include cost of methyl bromide reductions. 1990 to6062075605 Class II PhaseoutNo information currently available.608 National Recycling and $1,2001) We convert costs from 1991 to 1990 dollars using the GDP deflator from the 1998 Economic1994 toEmission Reduction Report of the President.2015Program2) 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 rate the costs are$853.91.609 Servicing of Motor $141) We convert from 1991 to 1990 dollars using the GDP deflator from the 1998 Economic Report of1992 toVehicle Air the President.2008Conditioners2) To avoid counting the same costs for both section 604 and 609, we include only the operatortraining and equipment certification costs of section 609 here.611 Labeling $2501) The costs are probably in 1990 dollars, but this is unclear. 1994 to2) Most costs are one-time costs.20003) The cost estimate is an average of the lower bound, which assumes that only firms using ODSsas solvents will label products, and an upper bound, which assumes that 10 times as many firmswill need to label their products because they incorporate solvent-cleaned products.TOTAL $57,000Notes: 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, because the RIA did not present thecorresponding benefits.2) The costs of sections 609 and 611 do not reflect the accelerated reduction and phaseout schedules of section 606.Figure G-3ANNUAL UNDISCOUNTED HUMAN HEALTH BENEFITS OF SECTIONS 604 AND 606$0$100,000$200,000$300,000$400,000$500,000$600,000 1990 1996 2002 2008 2014 2020 2026 2032 2038 2044 2050 2056 2062 2068 2074 2080 2086 2092 2098 2104 2110 2116 2122 2128 2134 2140 2146 2152 2158 2164Year

Benefits (Millions)G-33The Benefits and Costs of the Clean Air Act, 1990 to 2010

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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). 1The Benefits and Costs of the Clean Air Act, 1990 to 2010

G-37 

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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 

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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 Summary of Mortality Valuation Estimates

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The Weibulldistribution prevents selection of negative WTP values.-------------------------------------------------------------------------------------Uncertainty: Assuming the discount rate is five percent, and assumingan expected 35 years remaining to the average worker in the wage-riskstudies (see above), the value of a statistical life-year is just a constant,0.061, multiplied by the value of a statistical life. The distribution of thevalue of a life-year is derived from the distribution of the value of astatistical life. Because the VSL is expressed as a Weibull distribution,as indicated above, the value of a statistical life-year is also expressedas a Weibull distribution, with mean equal to 0.061 multiplied by themean of the original Weibull distribution (0.061 x $4.8 million =$293,000) and standard deviation equal to 0.061 multiplied by thestandard deviation of the original distribution (0.061 x $3.24 =$198,000).H-22Health or WelfareEndpointEstimated Value Per Incidence (1990$)Derivation of EstimatesCentral Estimate Uncertainty DistributionChronic Bronchitis (CB)$260,000 A Monte Carlo-generated Central Estimate: Value is the mean of a Monte Carlo distribution ofdistribution, based on three WTP to avoid a case of pollution-related CB. WTP to avoid a case ofunderlying distributions, as pollution-related CB is derived by adjusting WTP (as described indescribed more fully under Viscusi et al., 1991) to avoid a severe case of CB for the difference in“Derivation of Estimates” severity and taking into account the elasticity of WTP with respect toand in the text. severity of CB. The mean of the resulting distribution is $260,000.Uncertainty: The distribution of WTP to avoid a case of pollution-relatedCB was generated by Monte Carlo methods, drawing from each of threedistributions: (1) WTP to avoid a severe case of CB is assigned a 1/9probability of being each of the first nine deciles of the distribution ofWTP responses in Viscusi et al., 1991; (2) the severity of a pollutionrelated case of CB (relative to the case described in the Viscusi study)is assumed to have a triangular distribution, centered at severity level6.5 with endpoints at 1.0 and 12.0 (see text for further explanation); and(3) the constant in the elasticity of WTP with respect to severity isnormally distributed with mean = 0.18 and standard deviation = 0.0669(from Krupnick and Cropper, 1992). See text for further explanation.Chronic Asthma$25,000 Triangular distribution, Central Estimate:Based on results reported in two studiescentered at $25,000 on the (Blumenschein and Johannesson, 1998 and O'Conor and Blumquist,interval [$19,000, $30,000] 1997). Assumes a 5% discount rate and reflects adjustments for agedistribution among adults (ages 27 and older) and projected life yearsremaining.Uncertainty:Reflects the range in central estimate values reported inthe two studies.Health or WelfareEndpointEstimated Value Per Incidence (1990$)Derivation of EstimatesCentral Estimate Uncertainty DistributionH-23Hospital Admissions1. All Respiratory variable— See Derivation of Estimates Central Estimate: Central estimate is the result of the analysis. The- ICD codes: 460-519 analysis for each. The COI estimates are based on ICD-9 code level informationfunction of the analysis uses 12 distinct C-R functions. A COI estimate is constructed(e.g., average hospital care costs, average length of hospital stay, andweighted share of total respiratory illnesses) reported in Elixhauser(1993).Uncertainty: Probability distribution is a result of the analysis andreflects: (1) uncertainty range of C-R function outcome; and (2) variationin study-specific COI estimates.2. All Cardiovascular variable— See Derivation of Estimates Central Estimate: Central estimate is the result of the analysis. The- ICD codes: 390-429 function of the analysis uses five distinct C-R functions. A COI estimate is constructedanalysis for each. The COI estimates are based on ICD-9 code level information(e.g., average hospital care costs, average length of hospital stay, andweighted share of total respiratory illnesses) reported in Elixhauser(1993).Uncertainty: Probability distribution is a result of the analysis andreflects: (1) uncertainty range of C-R function outcome; and (2) variationin study-specific COI estimates.3. Emergency room visits for $194 Triangular distribution, Central Estimate: COI estimate based on data reported by Smith et al.asthma centered at $194 on the (1997).interval [$144, $269]Uncertainty: Based on reported 95% confidence intervals for annualestimates of the number and costs of ER visits.Health or WelfareEndpointEstimated Value Per Incidence (1990$)Derivation of EstimatesCentral Estimate Uncertainty DistributionH-24Respiratory Ailments Not Requiring Hospitalization1. Upper Resp. Symptoms $19 Continuous uniform Central Estimate: Combinations of the 3 symptoms for which WTP(URS) distribution over the interval estimates are available that closely match those listed by Pope et al.(defined as one or more URS. A dollar value was derived for each type of URS, using IEc midof therange estimates of WTP to avoid each symptom in the cluster andfollowing: assuming additivity of WTPs. The dollar value for URS is the averagerunny orof the dollar values for the 7 different types of URS.stuffy nose,wet cough, Uncertainty: Assumed to be a continuous uniform distribution acrossburning,the range of values described by the 7 URS types.aching, orred eyes)[$7, $33] result in 7 different “symptom clusters,” each describing a “type” of2. Lower Resp. Symptoms $12 Continuous uniform Central Estimate: Combinations of the 4 symptoms for which WTP(LRS) distribution over the interval estimates are available that closely match those listed by Schwartz et(defined in the study as LRS. A $ value was derived for each type of LRS, using IEc mid-rangetwo or more of the estimates of WTP to avoid each symptom in the cluster and assumingfollowing: cough, chest additivity of WTPs. The $ value for LRS is the average of the $ valuespain, phlegm, and for the 11 different types of LRS.wheeze.)[$5, $19] al. result in 11 different “symptom clusters,” each describing a “type” ofUncertainty: Taken to be a continuous uniform distribution across therange of values described by the 11 LRS types.3. Acute Bronchitis $45 Continuous uniform Central Estimate: Average of low and high values recommended bydistribution over the interval IEC for use in section 812 analysis (Neumann et al., 1994).[$13, $77]Uncertainty: Continuous distribution between low and high values(Neumann et al., 1994) assigns equal likelihood of occurrence of anyvalue within the range.Health or WelfareEndpointEstimated Value Per Incidence (1990$)Derivation of EstimatesCentral Estimate Uncertainty DistributionH-254. Acute Respiratory $18 1. URS, probability = 40% Central Estimate: Assuming that respiratory illness and symptoms canSymptoms and Illnesses LRS, probability = 40% be characterized as some combination of URS and LRS, namely: URS- Presence of any of 19 with 20% probability. The $ value for these endpoints is the weightedacute respiratory 2. If URS, use URS $ dist. average (using the weights 0.40, 0.40, and 0.20) of the $ values derivedsymptoms 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 central estimate, and- Respiratory Illness distributions; sum the twoURS+LRS, prob. = 20% with 40% probability, LRS with 40% probability, and both URS and LRSIf URS+LRS, randomlyURS and LRS $ URS and LRS uncertainty distributions presented previously.5. Asthma Attack $32 Continuous uniform Central Estimate: Mean of average WTP estimates for the four severitydistribution over the interval definitions of a “bad asthma day.” Source: Rowe and Chestnut (1986), a[$12, $54] study which surveyed asthmatics to estimate WTP for avoidance of a"bad asthma day,” as defined by the subjects.Uncertainty: Based on the range of values estimated for each of thefour severity definitions.6. Moderate or worse $32 Continuous uniform Central Estimate: Reflects the mean WTP to avoid a "bad asthma day"asthma distribution over the interval as reported by Rowe and Chestnut (1986).[12, 54]Uncertainty: Taken to be a continuous uniform distribution across therange of values obtained from the study.7. Shortness of breath, $5.30 Continuous uniform Central Estimate: From Ostro et al., 1995. This is the mean of thechest tightness or distribution over the interval median estimates from two studies of WTP to avoid a day of shortnesswheeze [$0, $10.60] of breath: Dickie et al., 1991 ($0.00), and Loehman et al., 1979($10.60).Uncertainty: Taken to be a continuous uniform distribution across therange of values obtained from the two studies.Health or WelfareEndpointEstimated Value Per Incidence (1990$)Derivation of EstimatesCentral Estimate Uncertainty DistributionH-26Restricted Activity and Work Loss Days1. WLDs $83 none available Central Estimate: Median weekly wage for 1990 divided by 5 (U.S.Department of Commerce, 1992)Uncertainty: Insufficient information to derive an uncertainty estimate.2. MRADs $38 Triangular distribution Central Estimate: Median WTP estimate to avoid 1 MRRAD -- minorcentered at $38 on the respiratory restricted activity day -- from Tolley et al. (1986)interval [$16, $61] (recommended by IEc as the mid-range estimate).Uncertainty: Range is based on assumption that value should exceedWTP for a single mild symptom (the highest estimate for a singlesymptom--for eye irritation--is $16.00) and be less than that for a WLD.The triangular distribution acknowledges that the actual value is likely tobe closer to the point estimate than either extreme.Welfare Effects1. Visibility Valuation function: Central Estimate: Estimated WTP for valuation of visibility changesResidential Visibility HHWTP= B * ln(VR1/VR2) change in visual range. Visibility coefficients applied in the primary"in-region" analysis vary by category of visibility change and region."out-of-region" where: Recreational visibility valuation is based on Chestnut and Rowe (1990).B = estimated visibility coefficient $65, for California, the Southwest, and the Southeast, respectively. ForHHWTP = annual WTP per household For "in region" recreational visibility, the coefficients are $105, $137,VR1 = starting annual average visual range "out-of-region" recreational visibility, the coefficients are $73, $110, $40,VR2 = the annual average visual range after the for California, the Southwest, and the Southeast, respectively.change in air qualitydepend upon two factors: (i) visibility coefficient, B, and (ii) incremental2. Worker Productivity Change in daily none available Central Estimate: Based on elasticity of income with respect to O3wages: $1 per concentration derived from study of California citrus workers (Crockerworker per 10% and Horst, 1981 and U.S. EPA, 1994). Elasticity applied to the averagechange in O daily income for workers engaged in strenuous outdoor labor, $73 (U.S.31990 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 

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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 atta cks 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 pot ential for double-counting. Summing 5th and 95th percentile values would yield a misleading estimate of the 5th and 95th percentile estimate of total hea lth 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 atta cks, 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 hea lth 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 $50 $100 $150 $200 $250 $300AllComponentsMortalityValuationMortalityIncidenceChronicBronchitisValuationChronicBronchitisIncidenceMRAD/ Anyof 19ValuationVisibilityValuationMRAD/Anyof 19IncidenceTotal 2010 Benefits (Billions 1990$)

95th %ile

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Contribution to Variance from the Mean The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-31 

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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

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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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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. 1The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-41 

### Illustrative Calculations —
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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

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yy y
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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. 1The Benefits and Costs of the Clean Air Act, 1990 to 2010

H-42 

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# 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)