Final Regulatory Impact Analysis (RIA) for the SO2

National Ambient Air Quality Standards (NAAQS) 

June 2010

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Air Benefit-Cost Group

Research Triangle Park, North Carolina
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Table of Contents

Page

Executive Summary

ES.1 Overview ...................................................................................................... ES-1

ES.2 Summary of Analytic Approach ............................................................ ES-2

ES.3. Results of Analysis ................................................................................ ES-6

ES.4. Caveats and Limitations ........................................................................... ES-10

ES.5. References ................................................................................................ ES-14

Chapter 1: Introduction and Background

1.1 Background................................................................................................... 1-2

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process. 1-2

1.3 Overview and Design of the RIA ................................................................. 1-6

1.4 SO2 Standard Alternatives Considered ...................................................... 1-8

1.5 References ..................................................................................................... 1-8

Chapter 2: SO2 Emissions and Monitoring Data

2.1 Sources of SO2 ............................................................................................... 2-1

2.2 Air Quality Monitoring Data ........................................................................ 2-2

Chapter 3: Air Quality Analysis

3.1 2005-2007 Design Values ..............................................................................3-1

3.2 Calculation of 2020 Projected Design Values ........................................... 3-3

3.3 Results ............................................................................................................ 3-12

3.4 Summary................................................................................................ 3-23

3.5 References .................................................................................................... 3-31

Appendix 3a: 2005-2007 and 2020 Design Values

Chapter 4: Emissions Controls Analysis – Design and Analytical Results

4.1 Developing the Identified Control Strategy Analysis ............................... 4-3

4.2 SO2 Emission Reductions Achieved with Identified Controls Analysis.. 4-7

4.3 Impacts Using Identified Controls ............................................................. 4-11

4.4 Emission Reductions Needed Beyond Identified Controls .................... 4-12

4.5 Key Limitations.............................................................................................. 4-14

4.6 References .................................................................................................... 4-14

Chapter 5: Benefits Analysis Approach and Results

5.1 Introduction ................................................................................................. 5-2

5.2 Primary Benefits Approach ................................................................... 5-2
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5.3 Overview of analytical framework for benefits analysis ....................... 5-3

5.4 Estimating Avoided Health Effects from SO2 Exposure ......................... 5-6

5.5 Valuation of Avoided Health Effects from SO2 Exposure ....................... 5-18

5.6 Health Benefits of Reducing Exposure to SO2 Results ........................... 5-20

5.7 PM2.5 Health Co-Benefits............................................................................... 5-22

5.8 Summary of Total Monetized Benefits (SO2 and PM2.5) ........................ 5-33

5.9 Unquantified Welfare Benefits ................................................................... 5-36

5.10 Limitations and Uncertainties ................................................................... 5-55

5.11 Discussion .................................................................................................... 5-60

5.12 References .................................................................................................. 5-62

Chapter 6: Cost Analysis Approach and Results

6.1 Engineering Cost Estimates ..................................................................... 6-2

6.2 Economic Impacts .................................................................................... 6-21

6.3 Energy Impacts ...................................................................................... 6-22

6.4 Limitations and Uncertainties Associated with Engineering Cost 

Estimates ......................................................................................... 6-23

Chapter 7: Estimates of Costs and Benefits

7.1 Benefits and Costs ................................................................................... 7-1

7.2 Discussion of Uncertainties and Limitations............................................. 7-4

Chapter 8: Statutory and Executive Order Reviews

1.0 Executive Order 12866: Regulatory Planning and Review

2.0 Paperwork Reduction Act

3.0 Regulatory Flexibility Act

4.0 Unfunded Mandates Reform Act

5.0 Executive Order 13132: Federalism

6.0 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments

7.0 Executive Order 13045: Protection of Children from Environmental Health & Safety 

Risks

8.0 Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution 

or Use

9.0 National Technology Transfer and Advancement Act

10.0 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Population
Executive Summary

ES.1 Overview

This Regulatory Impact Analysis (RIA) provides illustrative estimates of the incremental 

costs and monetized human health benefits of attaining a revised short-term Sulfur Dioxide 

(SO2) National Ambient Air Quality Standard (NAAQS) within the current monitoring network of

488 SO2monitors. Because this analysis only considers counties with an SO2 monitor, the 

possibility exists that there may be many more potential nonattainment areas than have been 

analyzed in this RIA. 

This RIA chiefly serves two purposes. First, it provides the public with an estimate of the 

costs and benefits of attaining a new SO2 NAAQS. Second, it fulfills the requirements of

Executive Order 12866 and the guidelines of OMB Circular A-4. 1

 1 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Available at 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.

These documents present 

guidelines for EPA to assess the benefits and costs of the selected regulatory option, as well as 

one less stringent and one more stringent option. The RIA analyzes the new short-term SO2

NAAQS of 75 parts per billion (ppb), based on the 3-year average of the 99th percentile of 1-

hour daily maximum concentrations. This RIA also analyzes alternative primary standards of 50 

and 100 ppb. 

This analysis does not estimate the projected attainment status of areas of the country 

other than those counties currently served by one of the approximately 488 monitors in the 

current network. It is important to note that the final rule requires a monitoring network 

comprised of monitors sited at locations of expected maximum hourly concentrations, and also 

provides for nonattainment designations using air quality modeling near large stationary 

sources. Only about one third of the existing SO2 network may be source-oriented and/or in 

the locations of maximum concentration required by the final rule because the current network 

is focused on population areas and community-wide ambient levels of SO2. Actual monitored 

levels using the new monitoring network and/or air quality modeling results near large 

stationary sources may be higher than levels measured using the existing network. We 

recognize that once the new requirements are put in place, more areas could find themselves 

exceeding the new SO2 NAAQS. However for this RIA analysis, we lack sufficient data to predict 

which counties might exceed the new NAAQS after implementation of the new monitoring 

network and modeling requirements. Therefore we lack a credible analytic path to estimating 

costs and benefits for such a future scenario.
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In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect public health, regardless of the costs of implementing a new 

standard. The Clean Air Act requires EPA, for each criteria pollutant, to set a standard that 

protects public health with “an adequate margin of safety.” As interpreted by the Agency and 

the courts, the Act requires EPA to create standards based on health considerations only. 

The prohibition against the consideration of cost in the setting of the primary air quality 

standard, however, does not mean that costs or other economic considerations are 

unimportant or should be ignored. The Agency believes that consideration of costs and benefits 

is essential to making efficient, cost effective decisions for implementation of these standards. 

The impacts of cost and efficiency are considered by states during this process, as they decide 

what timelines, strategies, and policies are most appropriate. This RIA is intended to inform the 

public about the potential costs and benefits associated with a hypothetical scenario that may 

result when a new SO2 standard is implemented, but is not relevant to establishing the 

standards themselves. 

ES.2 Summary of Analytic Approach 

This RIA includes several key elements, including specification of baseline SO2 emissions 

and concentrations; development of illustrative control strategies to attain the standard in 

2020; and analyses of the control costs and health benefits of reaching the various alternative 

standards. Additional information on the methods employed by the Agency for this RIA is 

presented below.

Overview of Baseline Emissions Forecast and Baseline SO2 Concentrations

The baseline emissions and concentrations for this RIA are emissions data from the 2005

National Emissions Inventory (NEI), and baseline SO2 concentration values from 2005-2007

across the community-wide monitoring network. We used results from community multi-scale 

air quality model (CMAQ) simulations to calculate the expected reduction in ambient SO2

concentrations between the 2005 base year and 2020. More specifically, design values (i.e. air 

quality concentrations at each monitor) were calculated for 2020 using monitored air quality 

concentrations from 2005 and modeled air quality projections for 2020, countywide emissions 

inventory data for 2005 and 2006-8, and emissions inventory projections for 2020. These data 

were used to create ratios between emissions and air quality, and those ratios (relative 

response factors, or RRFs) were used to estimate air quality monitor design values for 2020. 

The 2020 baseline air quality estimates revealed that 27 monitors in 24 counties were projected 

to exceed the 75 ppb NAAQS in 2020.
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Development of Illustrative Control Strategies

 

For each alternative standard, we analyzed the impact that additional emissions 

controls applied to numerous sectors would have on predicted ambient SO2 concentrations, 

incremental to the baseline set of controls. Thus the modeled analysis for a revised standard 

focuses specifically on incremental improvements beyond the current standards, and uses 

control options that might be available to states for application by 2020. The hypothetical 

modeled control strategy presented in this RIA is one illustrative option for achieving emissions 

reductions to move towards a national attainment of a tighter standard. It is not a 

recommendation for how a tighter SO2 standard should be implemented, and states will make 

decisions regarding implementation strategies once a final NAAQS has been set. 

The baseline for this analysis is complicated by the expected issuance of additional air 

quality regulations. The SO2 NAAQS is only one of several regulatory programs that are likely to 

affect EGU emissions nationally in the next several years. We thus expect that EGUs will apply 

controls in the coming years in response to multiple rules. These include the maximum 

achievable control technology (MACT) rule for utility boilers, revisions to the Clean Air 

Interstate Rule, and reconsideration of the Clean Air Mercury Rule. Therefore controls and 

costs attributed solely to the SO2 NAAQS in this analysis will likely be needed for compliance 

with other future rules as well.

The 2020 baseline air quality estimates revealed that 27 monitors in 24 counties were 

projected to exceed the 75 ppb NAAQS in 2020. We then developed hypothetical control 

strategies that could be adopted to bring the current highest emitting monitor in each of those 

counties into attainment with 75 ppb by 2020, as well as hypothetical control strategies for 

counties exceeding the lower bound analytic target of 50 ppb, and the upper bound analytic 

target of 100 ppb. Controls for three emissions sectors were included in the control analysis: 

non-electricity generating unit point sources (nonEGU), area sources (area), and electricity 

generating unit point sources (EGU). Finally, we note it was not possible, in this analysis, to 

bring all areas into attainment with alternative standards in all areas using identified 

engineering controls. For these monitor areas we estimated the cost of unspecified emission 

reductions. 

Analysis of Costs and Benefits

We estimated the benefits and costs for the final NAAQS of 75 ppb, as well as 

alternative SO2 NAAQS levels of 50 ppb and 100 ppb (99th percentile). These costs and benefits 
ES-4

are associated with an incremental difference in ambient concentrations between a baseline 

scenario and a pollution control strategy. As indicated in Chapter 4, several areas of the 

country may not be able to attain some alternative standard using known pollution control 

methods. Because some areas require substantial emission reductions from unknown sources 

to attain the various standards, the results are very sensitive to assumptions about the costs of 

full attainment. For this reason, we provide the full attainment results and the partial 

attainment results for both benefits and costs.

Benefits

Our benefits analysis estimates the human health benefits for each of the alternative 

standard levels including benefits related to reducing SO2 concentrations and the co-benefits of 

reducing concentrations of fine particulate matter (PM2.5). For the SO2 benefits analysis, we use 

the Environmental Benefits Mapping and Analysis Program (BenMAP) to estimate the health 

benefits occurring as a result of implementing alternative SO2 NAAQS levels. BenMAP has been 

used extensively in previous RIAs to estimate the health benefits of reducing exposure to 

various pollutants. 

The primary input to the benefits assessment for SO2 effects is the estimated changes in 

ambient air quality expected to result from a simulated control strategy or attainment of a 

particular standard. CMAQ projects both design values at SO2 monitors and air quality 

concentrations at 12 km by 12 km grid cells nationwide. To estimate the benefits of fully 

attaining the standards in all areas, EPA employed the “monitor rollback” approach to 

approximate the air quality change resulting from just attaining alternative SO2 NAAQS at each 

design value monitor. Under this approach, we use data from the existing SO2 monitoring 

network and the inverse distance-squared variant of the Veronoi Neighborhood Averaging 

(VNA) interpolation method to adjust the air quality modeled concentrations such that each 

area just attains the target NAAQS levels.

We quantified SO2-related health endpoints for which the SO2 ISA provides the strongest 

evidence of an effect. In this analysis, we only estimated the benefits for those endpoints with 

sufficient evidence to support a quantified concentration-response relationship using the 

information presented in the SO2 ISA, which contains an extensive literature review for several 

health endpoints related to SO2 exposure. Based on our review of this information, we 

quantified three short-term morbidity endpoints that the SO2 ISA identified as “sufficient to 

infer a likely causal relationship”: asthma exacerbation, respiratory-related emergency 

department visits, and respiratory-related hospitalizations. We then selected concentrationresponse functions and valuation functions based on criteria detailed in chapter 5. The 
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valuation functions, ambient concentrations, and population data in the monitor areas are 

combined in BenMAP to provide the benefits estimates for this analysis. In this analysis, we 

decided not to quantify the premature mortality from SO2 exposure in this analysis despite 

evidence suggesting a positive association. As the literature continues to evolve, we may revisit 

this decision in future benefits assessment for SO2.

In addition, because SO2 is also a precursor to PM2.5, reducing SO2 emissions in the 

projected non-attainment areas will also reduce PM2.5 formation, human exposure, and the 

incidence of PM2.5-related health effects. In this analysis, we estimated the co-benefits of 

reducing PM2.5 exposure for the alternative standards. Due to analytical limitations, it was not 

possible to provide a comprehensive estimate of PM2.5-related benefits. Instead, we used the 

“benefit-per-ton” method to estimate these benefits. The PM2.5 benefit-per-ton estimates 

provide the total monetized human health benefits (the sum of premature mortality and 

premature morbidity) of reducing one ton of PM2.5 from a specified source. EPA has used these 

estimates in previous RIAs, including the recent NO2 NAAQS RIA. 

These estimates reflect EPA’s most current interpretation of the scientific literature and 

are consistent with the methodology used for the proposal RIA. These benefits are incremental 

to an air quality baseline that reflects attainment with the 2008 ozone and 2006 PM2.5 National 

Ambient Air Quality Standards (NAAQS). More than 99% of the total dollar benefits are 

attributable to reductions in PM2.5 exposure resulting from SO2 emission controls. Higher or 

lower estimates of benefits are possible using other assumptions; examples of this are provided 

in Figure 5.1 for the selected standard of 75 ppb. Methodological limitations prevented EPA 

from quantifying the impacts to, or monetizing the benefits from several important benefit 

categories, including ecosystem effects from sulfur deposition, improvements in visibility, and 

materials damage. Other direct benefits from reduced SO2 exposure have not been quantified, 

including reductions in premature mortality. 

Costs

Consistent with our development of the illustrative control strategies described above, 

our analysis of the costs associated with the range of alternative NAAQS focuses on SO2

emission controls for electric generating units (EGU) and nonEGU stationary and area sources. 

EGU, nonEGU and area source controls largely include measures from the Control Strategy Tool 

(CoST), and the AirControlNET control technology database. For these sources, we estimated 

costs based on the cost equations included in AirControlNET. 
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As indicated in the above discussion on illustrative control strategies, implementation of 

the SO2 control measures identified from AirControlNET and other sources does not result in 

attainment with the selected NAAQS in several areas. In these areas, additional unspecified 

emission reductions might be necessary to reach some alternative standard levels. In order to 

bring these monitor areas into attainment, we calculated controls costs using a fixed cost per 

ton approach similar to that used in the ozone RIA analysis. We recognize that a single fixed 

cost of control of $15,000 per ton of emissions reductions does not account for the significant 

emissions cuts that are necessary in some areas, and so its use provides an estimate that is 

likely to differ from actual future costs. 

ES.3 Results of Analysis

Air Quality

Table ES.1 presents the number of monitors and counties exceeding the various target 

NAAQS levels in 2020 prior to control, out of 229 monitors from which a full set of data were 

available for this analysis.

Table ES.1. Number of monitors and counties projected to exceed 50, 75, and 100

ppb alternative NAAQS target levels in 2020.

Alternative standard (ppb) Number of monitors Number of counties

50 71 56

75 27 24

100 11 9

Table ES.2 presents the emission reductions achieved through applying identical control 

measures, both by sector and in total. As this table reveals, a majority of the emission 

reductions would be achieved through EGU emission controls. 
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Table ES.2: Emission Reductions from Identified Controls in 2020 in Total and by Sector (Tons) a for Each Alternative Standard

50 ppb 75 ppb 100 ppb

Total Emission

Reductions from 

Identified Controls b

800,000 370,000 190,000

EGUs 540,000 260,000 110,000

Non-EGUs 250,000 110,000 79,000

Area Sources 15,000 200 100

a All estimates rounded to two significant figures. As such, totals may not sum down columns. b

These values represent emission reductions for the identified control strategy analysis. There were locations not 

able to attain the alternative standard being analyzed with identified controls only.

Table ES.3 shows the emission reductions needed beyond identified controls for 

counties to attain the alternative standards being analyzed. 

Table ES.3: Total Emission Reductions and those from Extrapolated Controls in 2020 in Total 

and by Sector (Tons) a for Each Alternative Standard

50 ppb 75 ppb 100 ppb

Total Emission

Reductions from 

Identified and 

Unidentified Controls

920,000 350,000 170,000

Total Emission 

Reductions from

Unidentified Controls

110,000 33,000 18,000

Unidentified Reductions

from EGUs 33,000 5,000 -

Unidentified Reductions

from non-EGUs 54,000 22,000 15,000

Unidentified Reductions 

from Area Sources 19,000 6,400 3,000

a All estimates rounded to two significant figures.

Benefit and Cost Estimates

When estimating the SO2- and PM2.5-related human health benefits and compliance 

costs in Table ES.4 below, EPA applied methods and assumptions consistent with the state-ofthe-science for human health impact assessment, economics and air quality analysis. EPA 

applied its best professional judgment in performing this analysis and believes that these 

estimates provide a reasonable indication of the expected benefits and costs to the nation of 

the selected SO2 standard and alternatives considered by the Agency. The Regulatory Impacts 

Analysis (RIA) available in the docket describes in detail the empirical basis for EPA's 
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assumptions and characterizes the various sources of uncertainties affecting the estimates 

below.

EPA's 2009 Integrated Science Assessment for Particulate Matter concluded, based on 

the scientific literature, that a no-threshold log-linear model most adequately portrays the PMmortality concentration-response relationship. Nonetheless, consistent with historical practice 

and our commitment to characterizing the uncertainty in our benefits estimates, EPA has 

included a sensitivity analysis with an assumed threshold in the PM-mortality health impact 

function in the RIA. EPA has included a sensitivity analysis in the RIA to help inform our 

understanding of the health benefits which can be achieved at lower air quality concentration 

levels. While the primary estimate and the sensitivity analysis are not directly comparable, due 

to differences in population data and use of different analysis years, as well as the difference in 

the assumption of a threshold in the sensitivity analysis, comparison of the two results provide 

a rough sense of the proportion of the health benefits that occur at lower PM2.5 air quality 

levels. Using a threshold of 10 μg/m3 is an arbitrary choice (EPA could have assumed 6, 8, or 12 

μg/m3 for the sensitivity analysis). Assuming a threshold of 10 μg/m3, the sensitivity analysis 

shows that roughly one-third of the benefits occur at air quality levels below that threshold. 

Because the primary estimates reflect EPA’s current methods and data, EPA notes that caution 

should be exercised when comparing the results of the primary and sensitivity analyses. EPA 

appreciates the value of sensitivity analyses in highlighting the uncertainty in the benefits 

estimates and will continue to work to refine these analyses, particularly in those instances in 

which air quality modeling data are available. 

Table ES.4 shows the results of the cost and benefits analysis for each standard 

alternative. As indicated above, implementation of the SO2 control measures identified from 

AirControlNET and other sources does not result in attainment with the all target NAAQS levels

in several areas. In these areas, additional unspecified emission reductions might be necessary 

to reach some alternative standard levels. The first part of the table, labeled Partial attainment 

(identified controls), shows only those benefits and costs from control measures we were able 

to identify. The second part of the table, labeled Unidentified Controls, shows only additional 

benefits and costs resulting from unidentified controls. The third part of the table, labeled Full 

attainment, shows total benefits and costs resulting from both identified and unidentified 

controls. It is important to emphasize that we were able to identify control measures for a 

significant portion of attainment for many of those counties that would not fully attain the 

target NAAQS level with identified controls. Note also that in addition to separating full and 

partial attainment, the table also separates the portion of benefits associated with reduced SO2

exposure (i.e., SO2 benefits) from the additional benefits associated with reducing SO2

emissions, which are precursors to PM2.5 formation – (i.e., the PM2.5 co-benefits). For instance, 
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for the selected standard of 75 ppb, $2.2 million in benefits are associated with reduced SO2

exposure while $15 billion to $37 billion are associated with reduced PM2.5 exposure.

Table ES.4: Monetized Benefits and Costs to Attain Alternate Standard Levels in 2020 

(millions of 2006$)a

# Counties

Fully

Controlled

Discount 

Rate

Monetized 

SO2

Benefits

Monetized PM2.5

Co-Benefits c,d Costs Net Benefits

Partial 

Attainment

(identified controls)

50 ppb 40 3%

7% -

b $30,000 to $74,000 $2,600 $27,000 to $71,000

$28,000 to $67,000 $25,000 to $64,000

75 ppb 20 3%

7% -

b $14,000 to $35,000 $960 $13,000 to $34,000

$13,000 to $31,000 $12,000 to $30,000

100 ppb 6 3% -

b $6,900 to $17,000 $470 $6,400 to $17,000

7% $6,200 to $15,000 $5,700 to $15,000

Unidentified 

Controls

50 ppb 16 3% -

b $4,000 to $9,000 $1,800 $2,200 to $7,200

7% $3,000 to $8,000 $1,200 to $6,200

75 ppb 4 3% -

b $1,000 to $3,000 $500 $500 to $1,500

7% $1,000 to $3,000 $500 to $2,500

100 ppb 3 3% -

b $500 to $1,000 $260 $240 to $740

7% $500 to $1,000 $240 to $740

Full Attainment

50 ppb 56 3% $8.50 $34,000 to $83,000 $4,400 $30,000 to $79,000

7% $31,000 to $75,000 $27,000 to $71,000

75 ppb 24 3% $2.20 $15,000 to $37,000 $1,500 $14,000 to $36,000

7% $14,000 to $34,000 $13,000 to $33,000

100 ppb 9 3% $0.60 $7,400 to $18,000 $730 $6,700 to $17,000

7% $6,700 to $16,000 $6,000 to $15,000 a Estimates have been rounded to two significant figures and therefore summation may not match table estimates. b The approach used to simulate air quality changes for SO2 did not provide the data needed to distinguish partial 

attainment benefits from full attainment benefits from reduced SO2 exposure. Therefore, a portion of the SO2

benefits is attributable to the known controls and a portion of the SO2 benefits are attributable to the unidentified

controls. Because all SO2-related benefits are short-term effects, the results are identical for all discount rates. c Benefits are shown as a range from Pope et al (2002) to Laden et al. (2006). Monetized benefits do not include 

unquantified benefits, such as other health effects, reduced sulfur deposition, or improvements in visibility.

d These models assume that all fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality because there is no clear scientific evidence that would support the development of 

differential effects estimates by particle type. Reductions in SO2 emissions from multiple sectors to meet the SO2

NAAQS would primarily reduce the sulfate fraction of PM2.5. Because this rule targets a specific particle precursor 

(i.e., SO2), this introduces some uncertainty into the results of the analysis.
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ES.4. Caveats and Limitations

Air Quality, Emissions, and Control Strategies

The estimates of emission reductions associated with the control strategies described 

above are subject to important limitations and uncertainties. We summarize these limitations 

as follows:

• Actual State Implementation Plans May Differ from our Simulation: In order to reach 

attainment with the proposed NAAQS, each state will develop its own implementation 

plan implementing a combination of emissions controls that may differ from those 

simulated in this analysis. This analysis therefore represents an approximation of the 

emissions reductions that would be required to reach attainment and should not be 

treated as a precise estimate.

• Use of Existing CMAQ Model Runs: This analysis represents a screening level analysis. 

We did not conduct new regional scale modeling specifically targets to SO2; instead we 

relied upon impact ratios developed from model runs used in the analysis underlying 

the PM2.5 NAAQS. 

• Unidentified controls: We have limited information on available controls for some of 

the monitor areas included in this analysis. For a number of small non-EGU and area 

sources, there is little or no information available on SO2 controls. 

Costs

• We do not have sufficient information for all of our known control measures to calculate 

cost estimates that vary with an interest rate. We are able to calculate annualized costs 

at an interest rate other than 7% (e.g., 3% interest rate) where there is sufficient 

information—available capital cost data, and equipment life—to annualize the costs for 

individual control measures. For the vast majority of nonEGU point source control 

measures, we do have sufficient capital cost and equipment life data for individual 

control measures to prepare annualized capital costs using the standard capital recovery 

factor. Hence, we are able to provide annualized cost estimates at different interest 

rates for the point source control measures.
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• There are some unquantified costs that are not adequately captured in this illustrative 

analysis. These costs include the costs of federal and State administration of control 

programs, which we believe are less than the alternative of States developing 

approvable SIPs, securing EPA approval of those SIPs, and Federal/State enforcement. 

Additionally, control measure costs referred to as “no cost” may require limited 

government agency resources for administration and oversight of the program not 

included in this analysis; those costs are generally outweighed by the saving to the 

industrial, commercial, or private sector. The Agency also did not consider transactional 

costs and/or effects on labor supply in the illustrative analysis. 

Benefits

Although we strive to incorporate as many quantitative assessments of uncertainty, there 

are several aspects for which we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the attainment strategies 

for each of the alternative standards: 

• The 12 km CMAQ grid, which is the air quality modeling resolution, may be too coarse to 

accurately estimate the potential near-field health benefits of reducing SO2 emissions. 

These uncertainties may under- or over-estimate benefits. 

• The interpolation techniques used to estimate the full attainment benefits of the 

alternative standards contributed some uncertainty to the analysis. The great majority 

of benefits estimated for the various standard alternatives were derived through 

interpolation. As noted previously in this chapter, these benefits are likely to be more 

uncertain than if we had modeled the air quality scenario for both SO2 and PM2.5. In 

general, the VNA interpolation approach may under-estimate benefits because it does 

not account for the broader spatial distribution of air quality changes that may occur 

due to the implementation of a regional emission control program.

• There are many uncertainties associated with the health impact functions used in this 

modeling effort. These include: within study variability (the precision with which a given 

study estimates the relationship between air quality changes and health effects); across 

study variation (different published studies of the same pollutant/health effect 

relationship typically do not report identical findings and in some instances the 

differences are substantial); the application of C-R functions nationwide (does not 

account for any relationship between region and health effect, to the extent that such a 

relationship exists); extrapolation of impact functions across population (we assumed 

that certain health impact functions applied to age ranges broader than that considered 

in the original epidemiological study); and various uncertainties in the C-R function, 
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including causality and thresholds. These uncertainties may under- or over-estimate 

benefits. 

• Co-pollutants present in the ambient air may have contributed to the health effects 

attributed to SO2 in single pollutant models. Risks attributed to SO2 might be 

overestimated where concentration-response functions are based on single pollutant 

models. If co-pollutants are highly correlated with SO2, their inclusion in an SO2 health 

effects model can lead to misleading conclusions in identifying a specific causal 

pollutant. Because this collinearity exists, many of the studies reported statistically 

insignificant effect estimates for both SO2 and the co-pollutants; this is due in part to the 

loss of statistical power as these models control for co-pollutants. Where available, we 

have selected multipollutant effect estimates to control for the potential confounding 

effects of co-pollutants; these include NYDOH (2006), Schwartz et al. (1994) and 

O’Connor et al. (2008). The remaining studies include single pollutant models. 

• This analysis is for the year 2020, and projecting key variables introduces uncertainty. 

Inherent in any analysis of future regulatory programs are uncertainties in projecting 

atmospheric conditions and source level emissions, as well as population, health 

baselines, incomes, technology, and other factors. 

• This analysis omits certain unquantified effects due to lack of data, time and resources. 

These unquantified endpoints include other health effects, ecosystem effects, and 

visibility. EPA will continue to evaluate new methods and models and select those most 

appropriate for estimating the benefits of reductions in air pollution. Enhanced 

collaboration between air quality modelers, epidemiologists, toxicologists, ecologists, 

and economists should result in a more tightly integrated analytical framework for 

measuring benefits of air pollution policies. 

• PM2.5 co-benefits represent a substantial proportion of total monetized benefits (over 

99% of total monetized benefits), and these estimates are subject to a number of 

assumptions and uncertainties. 

a. PM2.5 co-benefits were derived through benefit per-ton estimates, which do not 

reflect local variability in population density, meteorology, exposure, baseline 

health incidence rates, or other local factors that might lead to an over-estimate 

or under-estimate of the actual benefits of controlling directly emitted fine 

particulates. 

b. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may 

differ significantly from direct PM2.5 released from diesel engines and other 

industrial sources, but no clear scientific grounds exist for supporting differential 

effects estimates by particle type. 
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c. We assume that the health impact function for fine particles is linear down to

the lowest air quality levels modeled in this analysis. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied concentrations 

of PM2.5, including both regions that are in attainment with fine particle standard 

and those that do not meet the standard down to the lowest modeled 

concentrations. 

d. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized benefits), 

we include a set of twelve estimates based on results of the expert elicitation 

study in addition to our core estimates. Even these multiple characterizations 

omit the uncertainty in air quality estimates, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse 

locations. As a result, the reported confidence intervals and range of estimates 

give an incomplete picture about the overall uncertainty in the PM2.5 estimates. 

This information should be interpreted within the context of the larger 

uncertainty surrounding the entire analysis. For more information on the 

uncertainties associated with PM2.5 co-benefits, please consult the PM2.5 NAAQS 

RIA (Table 5.5).

While the monetized benefits of reduced SO2 exposure appear small when compared to the 

monetized benefits of reduced PM2.5 exposure, readers should not necessarily infer that the 

total monetized benefits of attaining a new SO2 standard are minimal. For this rule, the 

monetized PM2.5 co-benefits represent over 99% of the total monetized benefits. This result is 

consistent with other recent RIAs, where the PM2.5 co-benefits represent a large proportion of 

total monetized benefits. This result is amplified in this RIA by the decision not to quantify SO2-

related premature mortality and other morbidity endpoints due to the uncertainties associated 

with estimating those endpoints. Studies have shown that there is a relationship between SO2 

exposure and premature mortality, but that relationship is limited by potential confounding. 

Because premature mortality generally comprises over 90% of the total monetized benefits, 

this decision may substantially underestimate the monetized health benefits of reduced SO2 

exposure.

In addition, we were unable to quantify the benefits from several welfare benefit 

categories. We lacked the necessary air quality data to quantify the benefits from 

improvements in visibility from reducing light-scattering particles. Previous RIAs for ozone (U.S. 

EPA, 2008a) and PM2.5 (U.S. EPA, 2006a) indicate that visibility is an important benefit category, 

and previous efforts to monetize those benefits have only included a subset of visibility 
ES-14

benefits, excluding benefits in urban areas and many national and state parks. Even this subset 

accounted for up to 5% of total monetized benefits in the Ozone NAAQS RIA (U.S. EPA, 2008a). 

We were also unable to quantify the ecosystem benefits of reduced sulfur deposition 

because we lacked the necessary air quality data, and the methodology to estimate ecosystem 

benefits is still being developed. Previous assessments (U.S. EPA, 1999; U.S. EPA, 2005; U.S. 

EPA, 2009e) indicate that ecosystem benefits are also an important benefits category, but those 

efforts were only able to monetize a tiny subset of ecosystem benefits in specific geographic 

locations, such as recreational fishing effects from lake acidification in the Adirondacks. We 

were also unable to quantify the benefits of decreased mercury methylation from sulfate 

deposition. Quantifying the relationship between sulfate and mercury methylation in natural 

settings is difficult, but some studies have shown that decreasing sulfate deposition can also 

decrease methylmercury.

ES.5. References

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air 

Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 

173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, DC: The National Academies Press.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. 

“Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air 

Pollution.” Journal of the American Medical Association 287:1132-1141.

U.S. Environmental Protection Agency (U.S. EPA). 1999a. The Benefits and Costs of the Clean 

Air Act 1990 to 2010: EPA Report to Congress. EPA-410-R-99-001. Office of Air and 

Radiation, Office of Policy, Washington, DC. November. Available on the Internet at < 

http://www.epa.gov/air/sect812/1990-2010/fullrept.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2005. Regulatory Impact Analysis for the

Final Clean Air Interstate Rule. Office of Air and Radiation. March. Available on the 

Internet at < http://www.epa.gov/cair/pdfs/finaltech08.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006a. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. October. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 
ES-15

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2009e. Risk and Exposure Assessment for 

Review of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and 

Oxides of Sulfur (Final). EPA-452/R-09-008a. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/no2so2sec/data/NOxSOxREASep2009MainCont

ent.pdf>. 
1-1

Chapter 1: Introduction and Background

Synopsis

This document estimates the incremental costs and monetized human health benefits of 

attaining a revised primary sulfur dioxide (SO2) National Ambient Air Quality Standard (NAAQS) 

nationwide. This document contains illustrative analyses that consider limited emission control 

scenarios that states, tribes and regional planning organizations might implement to achieve a 

revised SO2 NAAQS. EPA weighed the available empirical data and photochemical modeling to 

make judgments regarding the proposed attainment status of certain urban areas in the future. 

According to the Clean Air Act, EPA must use health-based criteria in setting the NAAQS and 

cannot consider estimates of compliance cost. This Regulatory Impact Analysis (RIA) is intended 

to provide the public a sense of the benefits and costs of meeting new alternative SO2 NAAQS, 

and to meet the requirements of Executive Order 12866 and OMB Circular A-4 (described 

below in Section 1.2.2). 

This RIA provides illustrative estimates of the incremental costs and monetized human 

health benefits of attaining a revised primary SO2 National Ambient Air Quality Standard 

(NAAQS) in 2020 within the current monitoring network1

 1 There are 488 monitors. Currently xx monitors (representing xx counties) exceed the final NAAQS in this analysis 

(75 ppb, 99th percentile daily 1-hour maximum SO2 concentration).

. This proposal would add a new 

short-term (1-hour exposure) standard, in addition to the current annual average standard. 

This analysis does not estimate the projected attainment status of areas of the country 

other than those counties currently served by one of the approximately 488 monitors in the 

current network. It is important to note that the final rule requires a monitoring network 

comprised of monitors sited at locations of expected maximum hourly concentrations, and also 

provides for nonattainment designations using air quality modeling near large stationary 

sources. Only about one third of the existing SO2 network may be source-oriented and/or in 

the locations of maximum concentration required by the final rule because the current network 

is focused on population areas and community-wide ambient levels of SO2. Actual monitored 

levels using the new monitoring network and/or air quality modeling results near large 

stationary sources may be higher than levels measured using the existing network. We 

recognize that once the new requirements are put in place, more areas could find themselves 

exceeding the new SO2 NAAQS. However for this RIA analysis, we lack sufficient data to predict 

which counties might exceed the new NAAQS after implementation of the new monitoring 

network and modeling requirements. Therefore we lack a credible analytic path to estimating 

costs and benefits for such a future scenario. 
1-2

1.1 Background

Two sections of the Clean Air Act (“Act”) govern the establishment and revision of 

NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants which 

“may reasonably be anticipated to endanger public health or welfare,” and to issue air quality 

criteria for them. These air quality criteria are intended to “accurately reflect the latest 

scientific knowledge useful in indicating the kind and extent of all identifiable effects on public 

health or welfare which may be expected from the presence of [a] pollutant in the ambient air.” 

SO2 is one of six pollutants for which EPA has developed air quality criteria. 

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate 

“primary” and “secondary” NAAQS for pollutants identified under section 108. Section 

109(b)(1) defines a primary standard as “the attainment and maintenance of which in the 

judgment of the Administrator, based on [the] criteria and allowing an adequate margin of 

safety, [are] requisite to protect the public health.” A secondary standard, as defined in section 

109(b)(2), must “specify a level of air quality the attainment and maintenance of which in the 

judgment of the Administrator, based on [the] criteria, [are] requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] 

pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] 

include but are not limited to “effects on soils, water, crops, vegetation, manmade materials, 

animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and 

hazards to transportation, as well as effects on economic values and on personal comfort and 

well-being.” 

Section 109(d) of the Act directs the Administrator to review existing criteria and 

standards at 5-year intervals. When warranted by such review, the Administrator is to retain or 

revise the NAAQS. After promulgation or revision of the NAAQS, the standards are 

implemented by the States. 

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process

1.2.1 Legislative Roles

In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect public health, regardless of the costs of implementing a new 

standard. The Clean Air Act requires EPA, for each criteria pollutant, to set a standard that 

protects public health with “an adequate margin of safety.” As interpreted by the Agency and 

the courts, the Act requires EPA to create standards based on health considerations only. 
1-3

The prohibition against the consideration of cost in the setting of the primary air quality 

standard, however, does not mean that costs or other economic considerations are 

unimportant or should be ignored. The Agency believes that consideration of costs and benefits 

are essential to making efficient, cost effective decisions for implementation of these 

standards. The impact of cost and efficiency are considered by states during this process, as 

they decide what timelines, strategies, and policies make the most sense. This RIA is intended 

to inform the public about the potential costs and benefits that may result when a new SO2

standard is implemented, but is not relevant to establishing the standards themselves. 

1.2.2 Role of Statutory and Executive Orders

There are several statutory and executive orders that dictate the manner in which EPA 

considers rulemaking and public documents. This document is separate from the NAAQS 

decision making process, but there are several statutes and executive orders that still apply to 

any public documentation. The analysis required by these statutes and executive orders is 

presented in Chapter 8.

EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB 

Circular A-4.2

1.2.3 Market Failure or Other Social Purpose

These documents present guidelines for EPA to assess the benefits and costs of 

the selected regulatory option, as well as one less stringent and one more stringent option. 

OMB circular A-4 also requires both a benefit-cost, and a cost-effectiveness analysis for rules 

where health is the primary effect. Within this RIA we provide a benefit-cost analysis. 

Methodological and data limitations prevent us from performing a cost-effectiveness analysis 

and a meaningful more formal uncertainty analysis for this RIA.

The proposal would set a new short-term SO2 standard based on the 3-year average of 

the 99th percentile of 1-hour daily maximum concentrations, establishing a new standard within 

the range of 75 parts per billion (ppb). This RIA analyzes alternative primary standards of 50

ppb, and 100 ppb. 

OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may 

be issued is to address market failure. The major types of market failure include: externality, 

market power, and inadequate or asymmetric information. Correcting market failures is one 

reason for regulation, but it is not the only reason. Other possible justifications include 

 2 U.S. Office of Management and Budget. Circular A-4, September 17, 2003, available at 

<http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf>. 
1-4

improving the function of government, removing distributional unfairness, or promoting 

privacy and personal freedom.

An externality occurs when one party’s actions impose uncompensated benefits or costs 

on another party. Environmental problems are a classic case of externality. For example, the 

smoke from a factory may adversely affect the health of local residents while soiling the 

property in nearby neighborhoods. If bargaining was costless and all property rights were well 

defined, people would eliminate externalities through bargaining without the need for 

government regulation. From this perspective, externalities arise from high transaction costs 

and/or poorly defined property rights that prevent people from reaching efficient outcomes 

through market transactions.

Firms exercise market power when they reduce output below what would be offered in 

a competitive industry in order to obtain higher prices. They may exercise market power 

collectively or unilaterally. Government action can be a source of market power, such as when 

regulatory actions exclude low-cost imports. Generally, regulations that increase market power 

for selected entities should be avoided. However, there are some circumstances in which 

government may choose to validate a monopoly. If a market can be served at lowest cost only 

when production is limited to a single producer of local gas and electricity distribution services, 

a natural monopoly is said to exist. In such cases, the government may choose to approve the 

monopoly and to regulate its prices and/or production decisions. Nevertheless, it should be 

noted that technological advances often affect economies of scale. This can, in turn, transform 

what was once considered a natural monopoly into a market where competition can flourish.

Market failures may also result from inadequate or asymmetric information. Because 

information, like other goods, is costly to produce and disseminate, an evaluation will need to

do more than demonstrate the possible existence of incomplete or asymmetric information. 

Even though the market may supply less than the full amount of information, the amount it 

does supply may be reasonably adequate and therefore not require government regulation. 

Sellers have an incentive to provide information through advertising that can increase sales by 

highlighting distinctive characteristics of their products. Buyers may also obtain reasonably 

adequate information about product characteristics through other channels, such as a seller 

offering a warranty or a third party providing information.

There are justifications for regulations in addition to correcting market failures. A 

regulation may be appropriate when there are clearly identified measures that can make 

government operate more efficiently. In addition, Congress establishes some regulatory 

programs to redistribute resources to select groups. Such regulations should be examined to 

ensure that they are both effective and cost-effective. Congress also authorizes some 
1-5

regulations to prohibit discrimination that conflicts with generally accepted norms within our 

society. Rulemaking may also be appropriate to protect privacy, permit more personal freedom 

or promote other democratic aspirations.

From an economics perspective, setting an air quality standard is a straightforward case 

of addressing an externality, in this case where entities are emitting pollutants, which cause 

health and environmental problems without compensation for those suffering the problems. 

Setting a standard with a reasonable margin of safety attempts to place the cost of control on 

those who emit the pollutants and lessens the impact on those who suffer the health and 

environmental problems from higher levels of pollution.

1.2.4 Illustrative Nature of the Analysis

This SO2 NAAQS RIA is an illustrative analysis that provides useful insights into a limited 

number of emissions control scenarios that states might implement to achieve a revised SO2

NAAQS. Because states are ultimately responsible for implementing strategies to meet any 

revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. They 

are not forecasts of expected future outcomes. Important uncertainties and limitations are 

documented in the relevant portions of the analysis. 

The illustrative goals of this RIA are somewhat different from other EPA analyses of 

national rules, or the implementation plans states develop, and the distinctions are worth brief 

mention. This RIA does not assess the regulatory impact of an EPA-prescribed national or 

regional rule, nor does it attempt to model the specific actions that any state would take to 

implement a revised SO2 standard. This analysis attempts to estimate the costs and human and 

welfare benefits of cost-effective implementation strategies which might be undertaken to 

achieve national attainment of new standards. These hypothetical strategies represent a 

scenario where states use one set of cost-effective controls to attain a revised SO2 NAAQS.

Because states—not EPA—will implement any revised NAAQS, they will ultimately determine 

appropriate emissions control scenarios. State implementation plans would likely vary from 

EPA’s estimates due to differences in the data and assumptions that states use to develop these 

plans. 

The illustrative attainment scenarios presented in this RIA were constructed with the 

understanding that there are inherent uncertainties in projecting emissions and controls. 

Furthermore, certain emissions inventory, control, modeling and monitoring limitations and 

uncertainties inhibit EPA’s ability to model full attainment in all areas. Despite these limitations, 

EPA has used the best available data and methods to produce this RIA.
1-6

1.3 Overview and Design of the RIA

This Regulatory Impact Analysis evaluates the costs and benefits of hypothetical 

national strategies to attain several potential revised primary SO2 standards. The document is 

intended to be straightforward and written for the lay person with a minimal background in

chemistry, economics, and/or epidemiology. Figure 1-1 provides an illustration of the process 

used to create this RIA.

Framework of this RIA. 

1.3.1 Baseline and Years of Analysis

The analysis year for this regulatory impact analysis is 2020, which approximates the 

required attainment year under the Clean Air Act. Many areas will reach attainment of any 

alternative SO2 standard before 2020. For purposes of this analysis, we assess attainment by 

2020 for all areas. Some areas for which we assume 2020 attainment may in fact need more 

time to meet one or more of the analyzed standards, while others will need less time. This 

analysis does not prejudge the attainment dates that will ultimately be assigned to individual 

areas under the Clean Air Act. 

The methodology first estimates what baseline SO2 levels might look like in 2020 with 

existing Clean Air Act programs, including application of controls to meet the current SO2

NAAQS, various maximum achievable control technology (MACT) standards, and then predicts 

Use air quality monitoring 

data to determine number 

areas exceeding alternative 

SO2 NAAQS

Determine sources of 

SOx emissions in areas 

exceeding alternative 

SO2 NAAQS

Determine baseline: estimated 

emission reductions to meet 

other federal regulations & the 

current SO2 NAAQS

Determine emission reductions & 

engineering costs incremental to baseline 

to meet alternative SO2 NAAQS using 

known & if appropriate extrapolated 

Determine energy and 

economic impacts 

Estimate SO2 and where 

appropriate particulate 

benefits associated with air 

quality changes from 

application of simulated 

emission reductions 

Present benefit-cost 

results

Identify uncertainties and 

limitations, providing 

appropriate context for the 

RIA results

Figure 1-1: The Process Used to Create this RIA
1-7

the change in SO2 levels following the application of additional controls to reach tighter 

alternative standards. This allows for an analysis of the incremental change between the 

current standard and alternative standards. 

1.3.2 Control Scenarios Considered in this RIA 

In this RIA we analyzed the final NAAQS of 75 ppb, as well as hypothetical target NAAQS 

levels of 50 and 100 ppb. Hypothetical control strategies were developed for each NAAQS 

level. First, we used outputs from CMAQ model runs to estimate air quality changes that would 

result from the application of emissions control options that are known to be available to 

different types of sources in areas with monitoring levels currently exceeding the alternative 

standards. However, given and the amount of improvement in air quality needed to reach the 

some standards in some areas, as well as circumstances specific to those areas, it was also 

expected that applying these known controls would not reduce SO2 concentrations sufficiently 

to allow these two areas to reach some standards. In order to bring these monitor areas into 

attainment, we calculated the cost of unspecified emission reductions by extrapolating from a 

range of fixed costs per ton of emission control that are generally identified nationally. 

1.3.3 Evaluating Costs and Benefits

We applied a two step methodology for estimating emission reductions needed to reach 

full attainment. First, we quantified the costs associated with applying known controls. Second, 

we estimated costs of the additional tons of extrapolated emission reductions estimated which 

were needed to reach full attainment. This methodology enabled us to evaluate nationwide 

costs and benefits of attaining a tighter SO2 standard using hypothetical strategies, albeit with 

substantial additional uncertainty regarding the second step estimates. 3

To streamline this RIA, this document refers to several previously published documents, 

including two technical documents EPA produced to prepare for promulgation of the SO2

NAAQS. The first was the Integrated Science Assessment (ISA) created by EPA’s Office of 

Research and Development (U.S. EPA, 2008), which presented the latest available pertinent 

information on atmospheric science, air quality, exposure, health effects, and environmental 

effects of SO2. The second was the Risk and Exposure Assessment (REA) (U.S. EPA, 2009) for 

various standard levels. The REA also includes staff conclusions and recommendations to the 

Administrator regarding potential revisions to the standards. 

 3 Because the secondary SO2 NAAQS is under development in a separate regulatory process, no additional 

costs and benefits were calculated in this RIA.
1-8

1.4 SO2 Standard Alternatives Considered

EPA has performed an illustrative analysis of the potential costs and human health and 

visibility benefits of nationally attaining SO2 NAAQS of 50, 75, and 100 ppb, assuming a baseline 

of no additional control beyond the controls expected from rules that are already in place 

(including the current PM2.5 NAAQS), and solely within the bounds of the existing monitoring 

network. The benefit and cost estimates below are calculated incremental to a 2020 baseline 

that incorporates air quality improvements achieved through the projected implementation of 

existing regulations and attainment of the existing PM National Ambient Air Quality Standards 

(NAAQS). The baseline also includes the MACT program, the clean air interstate rule (CAIR), and 

implementation of current consent decrees, all of which would help many areas move toward 

attainment of the SO2 standard. 

1.5 References

U.S. Environmental Protection Agency (U.S. EPA). 1970. Clean Air Act. 40 CFR 50.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for 

Sulfur Oxides - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. September. Available on the Internet at <

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Risk and Exposure Assessment to 

Support the Review of the SO2 Primary National Ambient Air Quality Standards: Final 

Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August. 

Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/so2/data/Risk%20and%20Exposure%20Assess

ment%20to%20Support%20the%20Review%20of%20the%20SO2%20Primary%20National%

20Ambient%20Air%20Quality%20Standards-%20Final%20Report.pdf>. 
1

Chapter 2: SO2 Emissions and Monitoring Data

Synopsis

This chapter describes the available SO2 emissions and air quality data used to 

inform and develop the control strategies outlined in this RIA. We first describe data on 

SO2 emission sources contained in available EPA emission inventories. We then provide 

an overview of data sources for air quality measurement. For a more in-depth discussion 

of SO2 emissions and air quality data, see the Integrated Science Assessment for the SO2

NAAQS.1

2.1 Sources of SO2

In order to estimate risks associated with SO2 exposure, principal sources of the 

pollutant must first be characterized because the majority of human exposures are likely 

to result from the release of emissions from these sources. Anthropogenic SO2

emissions originate chiefly from point sources, with fossil fuel combustion at electric 

utilities (~66%) and other industrial facilities (~29%) accounting for the majority of total 

emissions (ISA, section 2.1). Other anthropogenic sources of SO2 include both the 

extraction of metal from ore as well as the burning of high sulfur containing fuels by 

locomotives, large ships, and non-road diesel equipment. Notably, almost the entire 

sulfur content of fuel is released as SO2 or SO3 during combustion. Thus, based on the 

sulfur content in fuel stocks, oxides of sulfur emissions can be calculated to a higher 

degree of accuracy than can emissions for other pollutants such as PM and NO2 (ISA, 

section 2.1). 

The largest natural sources of SO2 are volcanoes and wildfires. Although SO2

constitutes a relatively minor fraction (0.005% by volume) of total volcanic emissions, 

concentrations in volcanic plumes can be in the range of several to tens of ppm 

(thousands of ppb). Volcanic sources of SO2 in the U.S. are limited to the Pacific 

Northwest, Alaska, and Hawaii. Emissions of SO2 can also result from burning 

vegetation. The amount of SO2 released from burning vegetation is generally in the 

range of 1 to 2% of the biomass burned and is the result of sulfur from amino acids 

being released as SO2 during combustion. 

 1 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality 

Standards for SO2: Policy Assessment of Scientific and Technical Information, Integrated Science 

Assessment, Chapter 2, EPA-452/R-08-xxx, Office of Air Quality Planning and Standards, RTP, NC.
2

Emissions inventory inputs representing the year 2005 for the sources above 

were developed to provide a base year for the air quality analysis presented in Chapter 

3. The 2005 National Emissions Inventory (NEI), version 2 from October 6, 2008 was the 

starting point for the U.S. inventories used for the air quality analysis. This inventory 

includes 2005-specific data for most point and mobile sources, while most nonpoint and 

other data were carried forward from version of the 2002 NEI. For more information on 

the 2005 NEI, upon which significant portions of the 2005 modeling platform are based, 

see http://www.epa.gov/ttn/chief/net/2005inventory.html. 

2.2 Air Quality Monitoring Data

2.2.1 Background on SO2 monitoring network

The following section provides general background on the SO2 monitoring 

network. A more detailed description of this network can be found in Watkins (2009). 

The SO2 monitoring network was originally deployed to support implementation of the 

SO2 NAAQS established in 1971. Despite the establishment of an SO2 standard, uniform 

minimum monitoring requirements for SO2 monitoring did not appear until May 1979. 

From the time of the implementation of the 1979 monitoring rule through 2008, the SO2

network has steadily decreased in size from approximately 1496 sites in 1980 to the 

approximately 488 sites operating in 2008. 

The 1979 monitoring rule established two categories of SO2 monitoring sites: 

State and Local Ambient Monitoring Stations (SLAMS) and the smaller set of National 

Ambient Monitoring Stations (NAMS). No minimum requirements were established for 

SLAMS. Minimum requirements (described below) were established for NAMS. The 

1979 rule also required that SO2 only be monitored using Federal Reference Methods 

(FRMs) or Federal Equivalent Methods (FEMs). The 1979 monitoring rule called for a 

range of number of sites in a metropolitan statistical area (MSA) based both on 

population size and known concentrations relative to the NAAQS (at that point in time; 

see Watkins, 2009).

In October 2006, EPA revised the monitoring requirements for SO2 in light of the 

fact that there was not an SO2 non-attainment problem (Watkins, 2009). The 2006 rule 

eliminated the minimum requirements for the number of SO2 monitoring sites. The 

current SO2 monitoring rule, 40 CFR Part 58, Appendix D, section 4.4 states:
3

Sulfur Dioxide (SO2) Design Criteria:

(a) There are no minimum requirements for the number of SO2 monitoring sites. 

Continued operation of existing SLAMS SO2 sites using FRM or FEM is required 

until discontinuation is approved by the EPA Regional Administrator. Where 

SLAMS SO2 monitoring is ongoing, at least one of the SLAMS SO2 sites must be a 

maximum concentration site for that specific area. 

(b) The appropriate spatial scales for SO2 SLAMS monitoring are the microscale, 

middle, and possibly neighborhood scales. The multi-pollutant NCore sites can 

provide for metropolitan area trends analyses and general control strategy 

progress tracking. Other SLAMS sties are expected to provide data that are 

useful in specific compliance actions, for maintenance plan agreements, or for 

measuring near specific stationary sources of SO2.

(1) Micro and middle scale – Some data uses associated with microscale 

and middle scale measurements for SO2 include assessing the effects of control 

strategies to reduce concentrations (especially for the 3-hour and 24-hour 

averaging times) and monitoring air pollution episodes.

(2) Neighborhood scale – This scale applies where there is a need to 

collect air quality data as part of an ongoing SO2 stationary source impact 

investigation. Typical locations might include suburban areas adjacent to SO2

stationary sources for example, or for determining background concentrations as 

part of these studies of population responses to exposure to SO2. 

(c) Technical guidance in reference 1 of this appendix should be used to evaluate 

the adequacy of each existing SO2 site, to relocate an existing site, or to locate 

new sites. 

To ascertain what the current SO2 network is addressing or characterizing, and in 

light of the relatively recent removal of a specific SO2 monitoring requirement, EPA 

reviewed some of the SO2 network meta-data (Watkins, 2009). The data reviewed are 

those available from AQS for calendar year 2008, for any monitors reporting data at any 

point during the year. In 2008, there were 488 SO2 monitors reporting data to AQS at 

some point during the year. 

2.2.2 Ambient concentrations of SO2

Since the integrated exposure to a pollutant is the sum of the exposures over all 

time intervals for all environments in which the individual spends time, understanding 

the temporal and spatial patterns of SO2 levels across the U.S is an important 

component of conducting air quality, exposure, and risk analyses. SO2 emissions and 
4

ambient concentrations follow a strong east to west gradient due to the large numbers 

of coal-fired electric generating units in the Ohio River Valley and upper Southeast 

regions. In the 12 CMSAs that had at least 4 SO2 regulatory monitors from 2003-2005, 

24-hour average concentrations in the continental U.S. ranged from a reported low of 

~1 ppb in Riverside, CA and San Francisco, CA to a high of ~12 ppb in Pittsburgh, PA and 

Steubenville, OH (ISA, section 2.4.4). In addition, inside CMSAs from 2003-2005, the 

annual average SO2 concentration was 4 ppb (ISA, Table 2-8). However, spikes in hourly 

concentrations occurred; the mean 1-hour maximum concentration was 130 ppb, with a 

maximum value of greater than 700 ppb (ISA, Table 2-8).

In addition to considering 1-hour, 24-hour, and annual SO2 levels, examining the 

temporal and spatial patterns of 5-minute peaks of SO2 is also important given that 

human clinical studies have demonstrated exposure to these peaks can result in adverse 

respiratory effects in exercising asthmatics (see REA, Chapter 4). Although the total 

number of SO2 monitors across the continuous U.S. can vary from year to year, in 2006 

there were approximately 500 SO2 monitors in the NAAQS monitoring network (ISA, 

section 2.5.2). State and local agencies responsible for these monitors are required to 

report 1-hour average SO2 concentrations to the EPA Air Quality System (AQS). 

However, a small number of sites, only 98 total from 1997 to 2007, and not the same 

sites in all years, voluntarily reported 5-minute block average data to AQS (ISA, section 

2.5.2). Of these, 16 reported all twelve 5-minute averages in each hour for at least part 

of the time between 1997 and 2007. The remainder reported only the maximum 5-

minute average in each hour. When maximum 5-minute concentrations were reported, 

the absolute highest concentration over the ten-year period exceeded 4000 ppb, but for 

all individual monitors, the 99th percentile was below 200 ppb (ISA, section 2.5.2). 

Medians from these monitors reporting data ranged from 1 ppb to 8 ppb, and the 

average for each maximum 5-minute level ranged from 3 ppb to 17 ppb. Delaware, 

Pennsylvania, Louisiana, and West Virginia had mean values for maximum 5-minute 

data exceeding 10 ppb (ISA, section 2.5.2). Among aggregated within-state data for the 

16 monitors from which all 5-minute average intervals were reported, the median 

values ranged from 1 ppb to 5 ppb, and the means ranged from 3 ppb to 11 ppb (ISA, 

section 2.5.2). The highest reported concentration was 921 ppb, but the 99th percentile 

values for aggregated within-state data were all below 90 ppb (ISA, section 2.5.2). 
3-1

Chapter 3: Air Quality Analysis

Synopsis

This chapter describes the approach used to calculate 2020 baseline SO2 design values 

and the amount of emissions reductions needed to attain the alternative 1-hour SO2 NAAQS. 

The NAAQS being analyzed are 50, 75, and 100 ppb based on design values calculated using the 

3-year average of the 99th percentile 1-hour daily maximum concentrations based on the 

monitoring network described in Chapter 2. The projected 2020 baseline SO2 design values are 

used to identify 2020 nonattainment counties and to calculate, for each such county, the 

amount of reduction in SO2 concentration necessary to attain the alternative NAAQS. This 

chapter also describes the approach for calculating “ppb SO2 concentration per ton SO2

emissions” ratios that are used to estimate the amount of SO2 emissions reductions that may 

be needed to provide for attainment of the alternative SO2 standards. As described below, the 

air quality analysis relies on SO2 emissions from simulations of the Community Multiscale Air 

Quality (CMAQ) model coupled with ambient 2005-2007 design values and emissions data to 

project 2020 SO2 design value concentrations and the “ppb per ton” ratios. A description of 

CMAQ is provided in the Ozone NAAQS RIA Air Quality Modeling Platform Document (EPA, 

2008). 

3.1 2005-2007 Design Values 

The proposed standard is based on the 3-year average of the 99th percentile 

concentration of the daily 1-hour maximum concentration for a year. The design value for each 

percentile is calculated as:

• Identify daily 1-hour maximum concentration for each day for each year

• Calculate 99th percentile values of the daily 1-hour maximum concentrations for each 

year

• Average the 99th percentile values for the three years. 

Monitors that had valid measurements for at least 75% of the day, 75% of the days in a 

quarter and all 4 quarters for all three years were included in the analysis1

 1 Email from Rhonda Thompson to James Thurman, January 22, 2009.

. The resulting 3-year 

averaged 99th percentile daily 1-hour maximum concentrations are shown in Figure 3.1 for 229

monitored counties. Counties in blue, green, and dark red would exceed the lowest alternative 

standard considered in the RIA, 50 ppb. Monitors with design values of 50.0 to 50.4 ppb would 

not exceed the standard 50 ppb as those concentrations would round to 50 ppb. 
3-2

Concentrations 50.5 ppb and higher are considered exceeding the lowest alternative standard. 

Similar rounding is done for the 75, and 100 ppb alternative standards (75.4 and 100.4 are the 

cut-offs for nonattainment). A summary of the number of counties exceeding the alternative 

standards for 2005-2007 is shown in Table 3.1. Appendix 3 contains the complete list of 2005-

2007 design values used in calculation of the 2020 design values. Table 3.2 lists the top ten 

counties for the 99th percentile design values for 2005-2007.

Figure 3.1. 2005-2007 3-year averaged design values (ppb) for 99th percentile daily 1-hour 

maximum SO2 concentrations. Values shown are county maxima.
3-3

Table 3.1. Number of monitors and counties exceeding 50, 75, and 100 ppb alternative 

standards for the 99th percentile design values for 2005-07.

Alternative standard 

(ppb)

Number of monitors Number of counties

50 169 119

75 95 70

100 59 46

Table 3.2. Top 10 2005-07 counties 99th percentile design values.

State County Design value (ppb)

MO Jefferson 350.6

AZ Gila 286.0

IL Tazewell 222.3

PA Warren 214.0

TN Blount 196.3

PA Northampton 187.0

IN Fountain 183.0

OH Lake 180.3

WI Oneida 179.0

IN Floyd 176.3

3.2 Calculation of 2020 Projected Design Values

The 2020 baseline design values were determined using CMAQ gridded emissions for 

2005 and 2020. Gridded emissions were utilized instead of county emissions because of the 

influence of stationary sources on SO2 concentrations. For monitors near county boundaries, 

stationary sources in a neighboring county may have more influence over the monitor than a 

stationary source in the monitor’s home county. The SO2 emissions in the CMAQ runs reflect 

reductions from the following controls and programs shown in Table 3.3.
3-4

Table 3.3. Controls in the 2020 SO2 inventory.

Control Strategies

Approach or 

Reference:

Non-EGU Point Controls

Consent decrees apportioned to several plants

DOJ Settlements: plant SCC controls

Alcoa, TX 

Premcor (formerly MOTIVA), DE

1

Refinery Consent Decrees: plant/SCC controls 2

Closures, pre-2007: plant control of 100%

Auto plants

Pulp and Paper

Large Municipal Waste Combustors

Small Municipal Waste Combustors

Plants closed in preparation for 2005 inventory

3

Small Municipal Waste Combustors (SMWC) 4

Solid Waste Rules (Section 129d/111d)

Hospital/Medical/Infectious Waste Incinerator Regulations EPA, 2005

MACT rules, plant-level, PM & SO2: Lime Manufacturing 5

Stationary Area Assumptions

Residential Wood Combustion Growth and Changeouts to year 2020 6

EGU Point Controls

Clean Air Interstate Rule 7; EPA, 2005

Onroad Mobile and Nonroad Mobile Controls (list includes all key mobile control strategies but is not 

exhaustive)

Tier 2 Rule EPA, 1999

2007 Onroad Heavy-Duty Rule EPA, 2000

Final Mobile Source Air Toxics Rule (MSAT2) EPA, 2007

Renewable Fuel Standard EPA, 2010

Clean Air Nonroad Diesel Final Rule – Tier 4 8, EPA, 2004

Control of Emissions from Nonroad Large-Spark Ignition Engines and Recreational Engines 

(Marine and Land Based): “Pentathalon Rule”

Clean Bus USA Program

Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition 

Engines Less than 30 Liters per Cylinder

8,9,10

Aircraft, Locomotives, and Commercial Marine Assumptions

Aircraft:

Itinerant (ITN) operations at airports to year 2020 11

Locomotives:

Energy Information Administration (EIA) fuel consumption projections for freight rail

Clean Air Nonroad Diesel Final Rule – Tier 4

Locomotive Emissions Final Rulemaking, December 17, 1997

Control of Emissions of Air Pollution from Locomotives and Marine

EPA, 2009; 12; 9
3-5

Control Strategies

Approach or 

Reference:

Commercial Marine:

EIA fuel consumption projections for diesel-fueled vessels

OTAQ ECA C3 Base 2020 inventory for residual-fueled vessels

Clean Air Nonroad Diesel Final Rule – Tier 4

Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999

Tier 1 Marine Diesel Engines, February 28, 2003

12; EPA, 2009

1. For ALCOA consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; 

for MOTIVA: used information sent by State of Delaware

2. Used data provided by Brenda Shine, EPA, OAQPS

3. Closures obtained from EPA sector leads; most verified using the world wide web.

4. Used data provided by Walt Stevenson, EPA, OAQPS

5. Percent reductions recommended are determined from the existing plant estimated 

baselines and estimated reductions as shown in the Federal Register Notice for the 

rule. SO2 % reduction will therefore be 6147/30,783 = 20% and PM10 and PM2.5 

reductions will both be 3786/13588 = 28%

6. Expected benefits of woodstoves change-out program: 

http://www.epa.gov/woodstoves/index.html

7. http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf

8. http://www.epa.gov/nonroad-diesel/2004fr.htm

9. http://www.epa.gov/cleanschoolbus/

10. http://www.epa.gov/otaq/marinesi.htm

11. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, 

December 2007: http://www.apo.data.faa.gov/main/taf.asp

12. http://www.epa.gov/nonroad-diesel/2004fr.htm

In brief, these CMAQ emissions were at 12 km horizontal resolution for two modeling 

domains which, collectively, cover the lower 48 States and adjacent portions of Canada and 

Mexico. The boundaries of these two domains are shown in Figure 3.2. The spatial 

distribution of the emissions for 2005 and 2020 can be seen in Figures 3.3 and 3.4 respectively. 

In both figures, the lines radiating from the coast are the commercial marine vessel emissions. 

Figure 3.5 shows the reduction in emissions between 2005 (16.3 million tons) and 2020 (9.6 

million tons) by source sector (EGU, non-EGU point, commercial marine vessel, and other 

sources) with the decrease from 2005 to 2020 due mostly to decreases in EGU emissions. 

3.2.1 2020 Design Value Calculation Methodology

Ambient monitored data were assigned to CMAQ grid cells using ArcGIS. Since there 

were areas of the country where the eastern and western domains overlapped, monitors in 

these overlapping areas were assigned to the eastern or western grid cells by using a 

“combined grid.” This combined grid was a mesh of the eastern and western domains, with 

overlapping areas assigned eastern grid cells or western grid cells based on the location relative 

to the dividing line shown in Figure 3.2. Figure 3.2 shows the assignment of monitors to the 
3-6

two domains. An example of monitors in both domains was the El Paso County monitors. 

These monitors were assigned to the western domain. The gridded 2006 and 2020 emissions

were also assigned to the combined grid based on the same grid assignments as the monitors. 

Figure 3.2. Monitor domain assignments. Western domain is outlined in blue and eastern 

domain outlined in red. Black vertical line denotes dividing line between eastern and 

western domains for monitor assignments. Monitors in blue were assigned to the western 

domain and monitors in red were assigned to the eastern domain.
3-7

Figure 3.3. 2005 annual 12 km gridded SO2 emissions (tons).
3-8

Figure 3.4. 2020 annual 12 km gridded SO2 emissions (tons).
3-9

Figure 3.5. 2005 and 2020 SO2 emissions (tons) by source sector.

Once the monitors and emissions were assigned to the combined grid, for each monitor, 

a 9x9 matrix of grid cells was selected, centered on the monitor’s grid cell. An example is 

shown in Figure 3.6. The 9x9 matrix represented an approximate domain of emissions 

extending out 50 km from the monitor, the upper range of near-field dispersion. Since the 

design values were based on hourly concentrations, extending the radius of influential 

emissions on the monitor grid cell to 50 km was considered appropriate.
3-10

Figure 3.6. 9 x 9 matrix of 12km grid cells centered on CMAQ cell containing an SO2 monitor 

(star).

Once the matrices of grid cells were created for each monitor, the 2005 and 2020 

gridded emissions were summed for each year across the 81 grid cells to result in total 2005

and 2020 emissions for each monitor. The summed 2020 emissions were then divided by the 

2005 emissions to get an emissions change ratio:

2005

2020

E

E

Eratio = (3.1)

Where E2020 are the summed 81 grid cell emissions for 2020, E2005 are the summed 81 

grid cell emissions for 2005 and Eratio is the ratio of 2020 emissions to 2005 emissions.

The 2005-2007 99th percentile design value concentrations were then multiplied by the 

emissions ratio to calculate the 2020 design values. 

ratio DV = DV × E 2020"99 2005−2007:99 (3.2)
3-11

Where Eratio is as defined above, DV2005-2007:99 is the 2005-2007 3-year averaged design 

value for the 99th percentile, and DV2020:99 is the projected 2020 design value for the 99th

percentile.

After calculating the 2020 design values, a ppb/ton estimate was calculated by:

( )

( ) 2020 2005

20205:99 2005 2007:99

99 / E E

DV DV ppb ton − − = − (3.3)

Where E2020 and E2005 are the summed emissions as defined for Equation 3.1, DV2005-

2007:99 and DV2020:99 are as defined above and ppb/ton99 is the ppb/ton estimate for the 99th

percentile.

Residual nonattainment estimates for the three alternative standards of 50, 75, and 100 

ppb were calculated by subtracting the alternative standard from the 2020 design value. The 

absolute values of the alternative standards (50, 75, or 100 ppb) were not subtracted but rather 

the highest value that would meet the standards (50.4, 75.4, and 100.4 ppb) if design values 

were rounded to the nearest whole ppb. Once residual nonattainment was calculated for each 

alternative standard, for monitors exceeding the standards, tons needed for control were 

calculated by dividing residual nonattainment by the ppb/ton estimate:

99

99:

99: ppb /ton

NA

Tons AS

AS = (3.4)

Where ppb/ton99 is as defined above, NA99:AS is the residual nonattainment for 

alternative standard AS (50, 75, or 100 ppb) for the 99th percentile, and Tons99:AS are the tons 

needed to reach attainment for alternative standard AS for the 99th percentile.

3.2.2 Methodology Limitations

While the approach described in Section 3.2.1 is reasonable for a national analysis, there 

are limitations to the approach that may be better addressed by other methods such as nearfield dispersion modeling on a case by case basis or fine scale CMAQ modeling. Given the 

number of monitors in the analysis, dispersion modeling for all monitors would not be feasible. 

Also, given that the CMAQ concentrations associated with the emissions used in this analysis 

are at 12 km horizontal resolution and that SO2 is affected by nearby stationary sources, the 

CMAQ results may not be reasonable for this analysis, due to allocation of individual emission 

points within the grid cell. Limitations of this analysis include:

• Distance from source to monitor is not factored in the emissions sums used in Equation 

3.1. All emission sources, regardless of distance and tonnage, are weighted equally. 
3-12

Using Figure 3.6 as an example, a source may be located in the most northwestern grid 

cell and a source may be located in the same grid cell that contains the monitor. No 

distance weighting is applied to either source, based on its proximity to the monitor. 

They are both added to the emissions sum as is. Some monitors’ emission sums may 

include large emission sources that are farther away from the monitor than smaller 

emission sources but the large emissions sources dominate the emissions used to 

calculate the ratio in Equation 3.1. These large sources, may have large changes in 

emissions from 2005 to 2020 and these changes could drastically affect the emissions 

ratio. Given the nature of the projection approach described in Section 3.2.1, these 

large emission changes may overestimate or underestimate the concentration change at 

the monitor given the distance from the source to the monitor and the factors 

mentioned in the points below, meteorology and terrain. 

• Meteorology and terrain influences are not factored into the analysis. A source may not 

have a significant impact on a monitor because the prevailing wind direction is not from 

the source to the monitor, or the terrain between the source and monitor is configured 

such that the source does not have a significant impact on the monitor. This would also 

depend on building downwash effects and stack parameters such as stack height, exit 

temperature, stack diameter, and exit velocity.

3.3 Results

3.3.1. Nonattainment results

Table 3.4 lists the number of monitors and counties exceeding the three alternative standards 

for the 99th percentile 2020 design values. The number of counties exceeding each of the 

alternative standards decreased from 2005-2007 to 2020. Figure 3.7 shows the maximum 2020 

design value for monitored counties for the 99th percentile design values. Counties in blue, 

green, and scarlet exceed the 50 ppb alternative standard. Table 3.5 lists the top 10 counties in 

2020 for the 99th percentile design value along with residual nonattainment and tons needed 

for control to meet attainment. A complete list of 2020 design values for all monitors can be 

found in Appendix 3.
3-13

Table 3.4. Number of monitors and counties exceeding 50, 75, and 100 ppb alternative 

standards for the 99th percentile design values for 2020.

Alternative standard 

(ppb)

Number of monitors Number of counties

50 71 56

75 27 24

100 11 9

Figure 3.7. 2020 design values (ppb) for 99th percentile daily 1-hour maximum SO2

concentrations. Values shown are county maxima.
3-14

Table 3.5. Top 10 2020 counties 99th percentile design values (ppb).

State County 2020 DV

Alternative standards (ppb)

50 75 100

Residual 

nonattainment

Tons for 

control

Residual 

nonattainment

Tons for 

control

Residual 

nonattainment

Tons for 

control

MO Jefferson 285.5 235.1 139,033 210.1 124,249 185.1 109,464

AZ Gila 284.8 234.4 21,930 209.4 19,591 184.4 17,252

PA Warren 217.2 166.8 10,379 141.8 8,824 116.8 7,268

WI Oneida 175.3 124.9 6,866 99.9 5,491 74.9 4,117

TN Montgomery 144.3 93.9 19,764 68.9 14,502 43.9 9,240

IN Wayne 134.3 83.9 24,088 58.9 16,911 33.9 9,733

IA Muscatine 126.2 75.8 27,365 50.8 18,340 25.8 9,314

OK Muskogee 104.9 54.5 45,542 29.5 24,651 4.5 3,760

OH Summit 103.9 53.5 26,690 28.5 14,218 3.5 1,746

PA Northampton 100.4 50.0 20,652 25.0 10.326 - -
3-15

3.3.2 Example monitors

This section describes the emissions changes for two monitors’ 99th percentile design 

values shown in Figures 3.8 and 3.9. One monitor’s design value, Tazewell County, IL decreased 

from 2005-2007 to 2020 (Figure 3.8) and the other monitor’s (Montgomery County, TN) design 

value increased from 2005-2007 to 2020 (Figure 3.9). Emissions summaries in the 81 cell 

matrices for both monitors are shown in Figure 3.10. 

Figure 3.8. Location of monitor in Tazewell County, IL.
3-16

Figure 3.9. Location of monitor in Montgomery County, TN.
3-17

Figure 3.10. Tazewell County, IL and Montgomery County, TN monitors emissions (tons) for 

2005 and 2020.

3.3.2.1 Tazewell County

Emissions affecting the Tazewell County monitor decreased from approximately 94,000 

tons in 2005 to approximately 38,000 tons in 2020 (Figure 3.10 a and b). The decrease was 

mostly due to decreases in EGU emissions. The decrease caused the EGU sector drop from 

about 75% of the emissions to around 40% of the emissions. Figure 3.11 shows the spatial 

distribution of 2005 total emissions (all sources) within 50 km of the monitor and Figure 3.12

shows the spatial distribution of 2020 total emissions within 50 km of the monitor. The 

decrease in emissions can be seen as the emissions become more uniform outside of the 

“hotspot” grid cells.
3-18

Figure 3.11. 2005 12 km grid cell SO2 total emissions (tons) for Tazewell County monitor. The 

red star represents the monitor location.
3-19

Figure 3.12. 2020 12 km grid cell SO2 total emissions (tons) for Tazewell County monitor. The 

red star represents the monitor location.
3-20

3.3.2.2 Montgomery County

The design value for Montgomery County increased from 2005-07 to 2020 due to an 

increase in EGU emissions (Figure 3.10 c and d). Figures analogous to Figure 3.11 and Figure 

3.12 are shown in Figure 3.13 and Figure 3.14. While emissions decrease outside the “hotspot” 

grid cells, the emissions within those hotspots increase from 2005 to 2020, as these are the 

locations of EGU facilities and the emissions increase from 2005 to 2020.
3-21

Figure 3.13. 2005 12 km grid cell SO2 total emissions (tons) for Montgomery County monitor. 

The red star represents the monitor location.
3-22

Figure 3.14. 2020 12 km grid cell SO2 total emissions (tons) for Montgomery County monitor. 

The red star represents the monitor location.
3-23

3.4 Summary

In summary, 2020 baseline NO2 design value concentrations were projected from 2005-

2007 observed design values using CMAQ emissions output from 2005 and 2020. Results of the 

projections showed that, in 2020, nonattainment occurred for all three alternative standards 

(50, 75, and 100 ppb). However, the number of counties exceeding the standards dropped from 

the 2005-2007 period.

3.5 References

U.S. Environmental Protection Agency (EPA). 1999. Air Pollution from New Motor Vehicles: Tier 

2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control, U.S. Environmental 

Protection Agency, Office of Transportation and Air Quality, Assessment and Standards 

Division, Ann Arbor, MI 48105, EPA420-R-99-023, December 1999. Available at 

http://www.epa.gov/tier2/frm/ria/r99023.pdf.

U.S. Environmental Protection Agency (EPA). 2000. Regulatory Impact Analysis: Heavy-Duty

Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, U.S.

Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and

Standards Division, Ann Arbor, MI 48105, EPA420-R-00-026, December 2000. Available at

http://www.epa.gov/otaq/highway-diesel/regs/exec-sum.pdf

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of

Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of

Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105,

EPA420-R-04-007, May 2004. Available at http://www.epa.gov/nonroaddiesel/

2004fr/420r04007.pdf.

U.S. Environmental Protection Agency (EPA). 2005. Clean Air Interstate Rule Emissions Inventory 

Technical Support Document, U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, March 2005. Available at

http://www.epa.gov/cair/pdfs/finaltech01.pdf.

U.S Environmental Protection Agency (EPA), 2007. Control of Hazardous Air Pollutants from 

Mobile Sources; Final Rule. 40 CFR Parts 59, 80,85, 86, et al. Available at 

http://www.epa.gov/fedrgstr/EPA-AIR/2007/February/Day-26/a2667a.pdf
3-24

U.S. Environmental Protection Agency (EPA). 2008. Air Quality Modeling Platform for the Ozone 

National Ambient Air Quality Standard Final Rule Regulatory Impact Analysis. North 

Carolina. EPA-454/R-08-003.

U.S. Environmental Protection Agency (EPA), 2009. Regulatory Impact Analysis: Control of 

Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines 

Less than 30 Liters Per Cylinder. U.S. Environmental Protection Agency Office of 

Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, 

EPA420-R-08-001a, May 2009. Available at: 

http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf

U.S. Environmental Protection Agency (EPA), 2010. RFS2 Emissions Inventory for Air Quality 

Modeling Technical Support Document, February, 2010. Available at: 

http://www.epa.gov/otaq/renewablefuels/420r10005.pdf. 
3-1

Appendix 3a: 2005-2007 and 2020 Design Values

Table 3a-1 lists the 2005-2007 design values used in projecting 2020 design values for all 

monitors meeting the completeness criteria described in Section 3.1 of Chapter 3. Design 

values in black are below the 50 ppb alternative standard. Design values in blue exceed the 50 

ppb alternative standard but are below 75 ppb. Design values in green exceed the 75 ppb 

alternative standard but are below 100 ppb. Values in red exceed 100 ppb. Exceedances of the 

alternative standards are based on the criteria discussed in Section 3.1 of Chapter 3.

Table 3a-1. SO2 2005-2007 and 2020 projected 99th percentile design values (ppb).

State County Monitor 2005-07 2020

AL Jefferson 1003 63.3 19.3

AZ Gila 9 131.6 131.2

AZ Gila 1001 286.0 284.8

AZ Maricopa 3002 14.0 4.1

AZ Maricopa 3003 9.3 2.8

AZ Pima 1011 14.0 16.5

AR Pulaski 7 10.0 12.5

CA Contra Costa 2 18.6 12.5

CA Contra Costa 6 18.0 11.6

CA Contra Costa 1002 12.3 8.1

CA Contra Costa 1004 14.6 9.4

CA Contra Costa 2001 22.6 14.8

CA Contra Costa 3001 25.6 17.2

CA Imperial 5 20.9 20.4

CA Los Angeles 1002 6.6 4.0

CA Los Angeles 1103 10.6 6.3

CA Los Angeles 4002 27.6 15.6

CA Los Angeles 5005 19.6 11.6

CA Orange 1003 9.3 5.4

CA Sacramento 2 5.0 4.5

CA Sacramento 6 5.6 5.1

CA San Bernardino 306 10.0 8.2

CA San Bernardino 1234 11.3 19.6

CA San Bernardino 2002 8.0 7.2

CA San Diego 1 9.6 8.6

CA San Francisco 5 15.3 9.9

CA Santa Barbara 8 4.0 0.6

CA Santa Barbara 1013 4.6 2.0

CA Santa Barbara 1020 44.3 6.7

CA Santa Barbara 1025 8.0 1.3

CA Santa Barbara 2004 5.6 1.6

CA Santa Barbara 2011 3.3 0.5
3

-2

State County Monitor 2005

-07 2020

CA Santa Barbara 4003 2.6 1.3

CA Solano 4 10.0 6.5

CO Denver 2 32.6 66.8

CT Fairfield 12 35.6 46.4

CT Fairfield 1123 25.3 24.2

CT Fairfield 9003 27.6 29.4

CT New Haven 27 60.6 60.9

CT New Haven 2123 27.8 22.8

DE New Castle 1008 125.0 48.7

DE New Castle 2004 49.6 23.0

FL Broward 10 64.635.4

FL Duval 80 21.3 17.6

FL Duval 81 69.0 57.0

FL Duval 97 42.0 34.5

FL Escambia 4 76.3 26.7

FL Hamilton 15 31.6 24.5

FL Hillsborough 81 47.3 20.6

FL Hillsborough 95 42.6 19.1

FL Hillsborough 109 119.0 53.5

FL Hillsborough 1035 71.3 32.1

FL Orange 2002 11.3 4.7

FL Pinellas 23 96.3 36.4

FL Pinellas 3002 42.0 15.8

FL Pinellas 5002 77.6 27.8

FL Pinellas 5003 83.3 43.2

FL Putnam 1008 51.6 11.7

GA Chatham 21 62.3 57.5

GA Chatham 1002 94.6 87.4

GA Floyd 3 110.0 10.2

GA Fulton 48 73.0 10.2

GA Fulton 55 60.0 22.7

ID Bannock 4 69.6 61.7

IL Cook 50 37.0 27.7

IL Cook 63 40.6 29.2

IL Cook 76 45.6 33.3

IL Cook 1601 104.0 63.7

IL Cook 4002 68.3 48.9

IL Macon 13 47.0 48.6

IL Macoupin 2 27.0 13.8

IL Madison 1010 83.6 52.6

IL Madison 3007 59.0 37.1

IL Madison 3009 142.0 89.4

IL Peoria 24 73.6 31.1

IL Randolph 1 29.6 20.9

IL St. Clair 10 91.3 59.4

IL Sangamon 6 110.6 99.3

IL Tazewell 4 222.3 89.3

IL Wabash 1 152.3 40.5

IL Wabash 1001 125.3 33.3
3

-3

State County Monitor 2005

-07 2020

IL Will 13 64.6 32.0

IN Daviess 2 112.6 36.5

IN Dearborn 4 109.6 36.4

IN Floyd 4 140.3 52.7

IN Floyd 7 159.6 59.9

IN Floyd 1004 176.3 66.2

IN Fountain 1 183.0 56.0

IN Gibson 1 108.6 28.8

IN Hendricks 2 41.0 19.5

IN Jasper 2 57.0 56.9

IN Lake 22 92.0 81.8

IN Lake 2008 42.6 32.8

IN La Porte 5 27.3 27.0

IN Marion 42 92.3 36.2

IN Marion 57 117.3 45.5

IN Marion 73 62.0 24.4

IN Morgan 1001 129.6 52.5

IN Pike 5 19.3 6.2

IN Porter 11 63.6 59.6

IN Spencer 10 60.0 15.9

IN Vanderburgh 12 67.3 18.9

IN Vanderburgh 1002 35.0 9.1

IN Vigo 18 93.6 28.4

IN Vigo 1014 125.0 31.8

IN Warrick 2 148.3 38.3

IN Wayne 6 106.7 134.3

IN Wayne 7 84.1 105.9

IA Cerro Gordo 18 13.2 12.3

IA Clinton 19 48.3 41.3

IA Linn 29 46.0 48.8

IA Linn 31 88.6 94.0

IA Muscatine 16 122.1 91.7

IA Muscatine 17 65.5 50.0

IA Muscatine 20 165.1 126.2

IA Scott 15 27.6 21.0

IA Van Buren 6 6.9 6.8

KS Montgomery 6 16.6 15.0

KS Sumner 2 8.6 4.7

KS Trego 1 4.3 2.1

KS Wyandotte 21 50.0 33.2

KY Boyd 17 60.3 19.1

KY Daviess 5 71.0 20.0

KY Greenup 7 46.0 13.3

KY Jefferson 1041 150.6 73.4

KY Livingston 4 53.3 53.5

KY McCracken 1024 26.3 26.2

LA Bossier 8 20.6 16.7

LA Calcasieu 8 42.3 36.1

LA East Baton Rouge 9 65.3 54.6
3

-4

State County Monitor 2005

-07 2020

LA Ouachita 4 22.3 20.4

ME Hancock 103 6.3 5.4

MD Baltimore 3001 99.3 43.3

MA Bristol 1004 64.3 21.5

MA Hampden 16 39.0 29.7

MA Hampshire 4002 17.0 13.0

MA Suffolk 2 26.6 17.1

MA Suffolk 20 23.0 14.7

MA Suffolk 21 32.3 20.6

MA Suffolk 40 40.3 25.9

MA Suffolk 42 27.3 17.5

MA Worcester 23 20.6 17.7

MN Anoka 1002 21.3 10.4

MN Dakota 20 18.0 7.2

MN Dakota 423 14.0 5.6

MN Dakota 441 7.0 2.8

MN Dakota 442 8.0 3.2

MO Greene 26 67.6 48.0

MO Greene 32 25.0 17.7

MO Greene 37 90.6 65.0

MO Greene 40 81.3 58.3

MO Greene 41 25.6 18.3

MO Jackson 34 156.3 97.4

MO Jefferson 4 350.6 285.5

MO St. Louis 3001 49.6 34.6

MO St. Louis city7 56.6 40.3

MO St. Louis city 86 67.6 47.2

MT Yellowstone 16 40.0 46.3

MT Yellowstone 1065 68.0 73.3

MT Yellowstone 2005 54.6 58.8

NE Douglas 53 89.3 87.6

NE Douglas 55 18.6 18.2

NV Clark 539 8.0 6.3

NH Hillsborough 20 58.3 20.6

NH Merrimack 1006 157.0 51.8

NH Rockingham 14 59.6 28.3

NJ Atlantic 5 19.0 11.7

NJ Bergen 5001 29.3 21.6

NJ Burlington 1001 27.6 12.8

NJ Camden 3 38.0 16.7

NJ Camden 1001 26.6 13.3

NJ Cumberland 7 23.0 8.6

NJ Gloucester 2 32.6 13.9

NJ Hudson 6 42.0 33.7

NJ Hudson 1002 47.6 38.2

NJ Middlesex 2003 29.3 12.1

NJ Morris 3001 36.0 14.4

NJ Union 4 51.0 23.2

NM Eddy 1004 4.6 4.6
3

-5

State County Monitor 2005

-07 2020

NM Grant 1003 4.0 2.1

NM San Juan 9 12.6 5.3

NM San Juan 1005 77.0 33.0

NY Albany 12 22.0 21.0 NY Chautauqua 6 61.4 41.5

NY Chautauqua 11 32.1 28.7

NY Chemung 3 24.6 24.8

NY Erie 5 30.6 16.4

NY Erie 4002 118.6 75.9

NY Essex 3 9.9 9.2

NY Franklin 4 9.1 8.3

NY Hamilton 5 10.3 9.2

NY Herkimer 5 9.8 8.8

NY Madison 6 20.0 27.2

NY Monroe 1007 52.0 58.6

NY New York 56 62.6 44.3

NY Niagara 2008 21.7 13.8

NY Onondaga 1015 17.0 39.8

NY Putnam 5 21.9 20.0

NY Queens 124 44.0 33.4

NY Schenectady 3 23.0 21.9

NY Suffolk 9 56.0 75.6

NY Ulster 1005 15.5 15.2

NC Beaufort 6 47.3 45.9

NC New Hanover 6 87.6 58.4

ND Billings 2 6.3 3.1

ND Burke 4 29.4 29.2

ND Cass 1004 5.5 4.1

ND Dunn 3 11.6 8.8

ND McKenzie 2 11.0 5.6

ND McKenzie 104 17.6 12.3

ND McKenzie 111 25.6 16.9

ND Mercer 4 35.0 18.8

ND Mercer 102 35.3 19.0 ND Mercer 118 34.3 18.5

ND Mercer 123 39.0 21.0

ND Mercer 124 37.3 21.7

ND Oliver 2 56.3 30.4

ND Williams 103 44.3 37.3

OH Adams 1 88.3 21.8

OH Allen 2 22.3 19.6

OH Ashtabula 1001 36.6 30.3

OH Butler 4 72.0 29.0

OH Butler 1004 57.3 23.6

OH Clark 3 40.0 62.8

OH Columbiana 22 121.3 42.7

OH Cuyahoga 45 65.0 35.2

OH Cuyahoga 60 84.3 45.7

OH Cuyahoga 65 87.0 47.2
3

-6

State County Monitor 2005

-07 2020

OH Franklin 34 41.6 14.9

OH Hamilton 10 123.6 49.9

OH Jefferson 17 175.6 52.6

OH Lake 3 53.3 27.1

OH Lake 3002 180.3 94.7

OH Lawrence 6 53.3 15.4

OH Lucas 8 68.3 32.4

OH Lucas 24 53.3 25.3

OH Mahoning 13 63.0 48.4

OH Meigs 1001 98.6 25.3

OH Scioto 13 36.6 20.6

OH Scioto 20 51.8 17.4

OH Summit 17 108.0 103.9

OH Summit 22 62.0 59.6

OH Tuscarawas 6 71.0 15.8

OK Kay 602 40.3 67.8

OK Kay 9010 14.6 24.3

OK Muskogee 167 65.6 104.9

OK Oklahoma 1037 6.6 4.8

OK Tulsa 175 65.3 51.3

OK Tulsa 235 61.3 48.2

OK Tulsa 501 48.6 38.2

PA Allegheny 10 71.3 18.4

PA Allegheny 21 73.0 31.5

PA Allegheny 64 142.0 60.0

PA Allegheny 67 67.0 22.5

PA Beaver 2 140.0 48.1

PA Beaver 14 69.0 34.2

PA Blair 801 58.6 57.2

PA Bucks 12 37.3 17.3

PA Cambria 11 86.3 34.4

PA Centre 100 31.0 25.8

PA Dauphin 401 64.6 15.7

PA Erie 3 54.0 30.4

PA Indiana 4 111.3 47.0

PA Lackawanna 2006 40.6 20.5

PA Lancaster 7 66.0 19.5

PA Lawrence 15 95.0 44.0

PA Lehigh 4 52.6 30.1

PA Lycoming 100 50.3 7.0

PA Mercer 100 45.3 30.6

PA Montgomery 13 32.3 16.4

PA Northampton 25 46.6 26.3

PA Northampton 8000 187.0 100.4

PA Perry 301 33.6 6.4

PA Philadelphia 55 40.0 17.4

PA Schuylkill 3 55.3 10.1

PA Warren 3 63.0 63.9

PA Warren 4 214.0 217.2
3

-7

State County Monitor 2005

-07 2020

PA Washington 5 79.6 32.8

PA Washington 200 79.6 20.0

PA Washington 5001 90.0 29.6

PA Westmoreland 8 76.6 30.3

PA York 8 104.0 30.7

SC Barnwell 1 17.0 19.1

SC Charleston 3 37.3 24.4

SC Charleston 46 23.6 9.6

SC Georgetown 6 55.0 14.2

SC Greenville 8 27.0 15.8

SC Greenville 9 25.0 14.6

SC Lexington 8 96.3 68.9

SC Oconee 1 20.0 17.7

SC Richland 7 28.6 20.1

SC Richland 1003 36.3 25.5

SD Custer 132 4.3 3.2

SD Jackson 1 3.6 1.5

SD Minnehaha 7 18.0 15.2

TN Blount 2 196.3 60.0

TN Blount 6 84.9 25.6

TNBradley 102 85.3 80.2

TN Davidson 11 23.6 26.1

TN Montgomery 6 53.0 66.1

TN Montgomery 106 115.6 144.3

TN Shelby 46 65.3 49.0

TN Shelby 1034 81.3 56.5

TN Sullivan 7 170.6 88.2

TN Sullivan 9 141.8 73.3

TX Dallas 69 11.6 10.3

TX El Paso 37 9.3 9.1

TX El Paso 53 12.6 12.4

TX Galveston 5 59.0 42.9

TX Gregg 1 78.3 38.9

TX Harris 46 34.0 27.4

TX Harris 51 31.0 24.9

TX Harris 62 55.3 43.7

TX Harris 70 68.6 54.3

TX Harris 1035 74.6 58.9

TX Harris 1050 17.3 12.7

TX Jefferson 9123.0 98.9

TX Jefferson 11 94.6 74.9

TX Kaufman 5 15.3 13.4

TX Nueces 25 24.0 12.4

TX Nueces 26 8.0 4.1

TX Nueces 32 36.0 18.7

UT Davis 4 22.6 24.1

UT Salt Lake 1001 32.0 34.5

VT Rutland 2 48.2 45.5

VA Charles City 2 88.6 24.9
3-8

State County Monitor 2005-07 2020

VA Fairfax 5 25.6 6.8

VA Fairfax 1005 37.0 8.2

VA Fairfax 5001 37.3 14.6

VA Rockingham 3 14.6 13.0

VA Alexandria city 9 55.3 12.2

VA Hampton city 4 64.0 46.3

VA Richmond city 24 62.0 15.2

WV Brooke 5 150.3 45.0

WV Brooke 7 164.6 49.3

WV Brooke 11 155.3 46.5

WV Cabell 6 41.6 7.4

WV Hancock 5 164.0 56.3

WV Hancock 7 132.0 42.4

WV Hancock 8 115.3 40.6

WV Hancock 9 136.6 43.9

WV Hancock 15 121.3 42.7

WV Hancock 1004 135.6 43.6

WV Kanawha 10 88.0 22.4

WV Marshall 1002 155.0 41.8

WV Monongalia 3 171.3 41.5

WV Wood 1002 130.6 37.8

WI Brown 5 74.3 64.7

WI Oneida 996 179.0 175.3
4-1

Chapter 4: Emissions Controls Analysis – Design and Analytical Results

Synopsis

This chapter documents the illustrative emission control strategy we applied to simulate 

attainment with the alternative standards being analyzed for the final SO2 NAAQS. Section 4.1 

describes the approach we followed to select emissions controls to simulate attainment in each 

geographic area of analysis. Section 4.2 summarizes the emission reductions we simulated in 

each area based on current knowledge of identified emission controls, while Section 4.3 

presents the air quality impacts of these emissions reductions. Section 4.4 discusses the 

application of additional controls, beyond the level of control already assumed to be in place 

for the analysis year1

Generally, we expect that the nation will be able to make significant progress towards 

attainment of a tighter SO2 NAAQS without the addition of new controls beyond those already

being planned for the attainment of existing PM2.5 standards by the year 2020. As States 

, that we estimate will be necessary to reach attainment in certain monitor 

areas. Section 4.5 discusses key limitations in the approach we used to estimate the optimal 

control strategies for each alternative standard.

The final rule will set a new short-term SO2 primary standard based on the average of 

the 99th percentile of 1-hour daily maximum concentrations from three consecutive years of 

data. This new standard will be set at 75 parts per billion (ppb). OMB Circular A-4 requires the 

RIA to contain, in addition to analysis of the impacts of the final NAAQS, analysis of a level more 

stringent and a level less stringent than the final NAAQS. For a more stringent standard level, 

we chose an alternative primary standard of 50 parts per billion (ppb). We also include 

analyses for a less stringent standard, 100 ppb. 

For the range of alternative standards, we analyzed the impact that additional emissions 

controls applied to numerous sectors would have on predicted ambient SO2 concentrations, 

incremental to the baseline set of controls. Thus the analysis for a revised standard focuses 

specifically on incremental improvements beyond the current standards, and uses control 

options that might be available to states for application by 2020. The hypothetical control 

strategy presented in this RIA is one illustrative option for achieving emissions reductions to 

move towards a national attainment of a tighter standard. It is not a recommendation for how 

a tighter SO2 standard should be implemented, and states will make all final decisions regarding 

implementation strategies once a final NAAQS has been set. 

 1 Note that the baseline or starting point for this analysis includes rules that are already “on the books” and will 

take affect prior to the analysis year, as well as control strategies applied in the recent PM and Ozone NAAQS RIAs.
4-2

develop their plans for attaining these existing standards, they are likely to consider adding 

controls to reduce sulfur dioxide, as SO2 is a precursor to both PM2.5. In addition, proposed 

standards such as the Portland cement NESHAP, the ICI boilers NESHAPs, and the eventual rule 

to replace the existing CAIR may also yield in total considerable additional reductions of SO2 

emissions if they are implemented as proposed. These controls will also directly help areas 

meet a tighter SO2 standard. 

As part of our economic analysis of the tighter SO2 standard, our 2020 analysis baseline 

assumes that States will put in place the necessary control strategies to attain the current PM2.5

standards. The cost of these control strategies was included in the RIAs for those rulemakings. 

We do not include the cost of those controls in this analysis, in order to prevent counting the 

cost of installing and operating the controls twice. Of course, the health and environmental 

benefits resulting from installation of those controls were attributed to attaining those 

standards, and are not counted again for the analysis of this SO2 standard.

In addition, we include the SO2 control requirements for Category 3 (C3) marine vessels 

that will be affected by a new mobile source rule promulgated by EPA in December 2009.2

 2 Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder. Signed 

on December 18, 2009. For more information on this final rule and its RIA, please refer to 

 

These requirements call for changes in the diesel fuel program to allow for use of lower sulfur 

fuel (1,000 ppm sulfur content) in U.S.-flagged C3 marine vessels beginning in 2011. Reductions 

of SO2 associated with this final rule are included in our 2020 analysis baseline. Thus, we 

estimate no costs or benefits associated with these reductions. 

It is important to note also that this analysis does not attempt to estimate attainment or 

nonattainment for any areas of the country other than those counties currently served by one 

of the 349 monitors in the current network. Chapter 3 explains that the current network is 

focused on longer terms indicators that that included in this final rule. 

Finally, we note that because it was not possible, in this analysis, to bring all areas into 

attainment with the alternative standards in all areas using only identified (or known) controls, 

EPA conducted a second step in the analysis, and estimated the cost of further tons of emission 

reductions needed to attain the alternative primary NAAQS. It is uncertain what controls States 

would put in place to attain a tighter standard, since additional abatement strategies are not 

currently recognized as being commercially available. We should also note that because of data 

and resource limitations, we are not able to adequately represent in this analysis the impacts of 

some local emission control programs such as discussed in Chapter 3.

http://www.epa.gov/otaq/oceanvessels.htm. 
4-3

4.1 Developing the Identified Control Strategy Analysis

The 2020 baseline air quality estimates revealed that 27 monitors in 24 counties had 

projected design values exceeding 75 ppb. We then developed a hypothetical control strategy 

that could be adopted to bring the current highest emitting monitor in each of those counties 

into attainment with a primary standard of 75 ppb, as well as additional target levels of 50 ppb 

and 100 ppb, by 2020. (For more information on the development of the air quality estimates 

for this analysis see Chapter 3.) Controls for three emissions sectors were included in the 

control analysis: Non-Electricity Generating Unit Point Sources (nonEGU), Non-Point Area 

Sources (Area), and Electricity Generating Unit Point Sources (EGU). Each of these sectors is 

defined below for clarity.

• NonEGU point sources as defined in the National Emissions Inventory (NEI) are 

stationary sources that emit 100 tons per year or more of at least one criteria 

pollutant. NonEGU point sources are found across a wide variety of industries, such 

as chemical manufacturing, cement manufacturing, petroleum refineries, and iron 

and steel mills. 

• Area Sources3

• Electricity Generating Unit Point Sources are stationary sources of 25 megawatts 

(MW) capacity or greater producing and selling electricity to the grid, such as fossilfuel-fired boilers and combustion turbines.

are stationary sources that are too numerous or whose emissions are 

too small to be individually included in a stationary source emissions inventory. 

Area sources are the activities where aggregated source emissions information is 

maintained for the entire source category instead of each point source, and are 

reported at the county level.

It should be noted that no additional SO2 controls beyond our baseline are applied to 

onroad and nonroad mobile sources because mobile source measures to reduce sulfur content 

from diesel engine rules will be well-applied in onroad and nonroad mobile source fleets by 

2020, and thus there is little capability to achieve further reductions for this analysis beyond 

those described in this report.

We began the control strategy analysis by applying controls to EGUs first before 

applying controls to other sources. We applied controls in this sequence for the following 

reasons: 1) there are many more SO2 emissions from EGUs than from non-EGU sources in the 

areas included in this analysis, and 2) SO2 reductions from EGUs are less costly than from other 

 3 Area Sources include the nonpoint emissions sector only.
4-4

source categories included in this analysis. Chapter 6 provides a table showing that the EGU 

control costs for SO2 as estimated for this analysis have a lower annual cost/ton compared to 

those from the non-EGU point and area source categories.

The air quality impact of the needed emissions reductions was calculated using impact 

ratios as discussed further in Chapter 3. The results of analyzing the control strategy indicate 

that there were four areas projected not to attain 75 ppb in 2020 using all identified control 

measures. To complete the analysis, EPA then extrapolated the additional emission reductions 

required to reach attainment. The methodology used to develop those estimates and those 

calculations are presented in Section 4.4. 

4.1.1 Controls Applied for EGU Sector

The baseline in this RIA for EGUs accounts for extensive reductions in SO2 emissions 

from EGUs as implemented in the Clean Air Interstate Rule (CAIR).

4

In this analysis, EGU controls were applied to uncontrolled coal-fired units of size 25 

MW and larger within the 50 km radius of violating monitors. Each unit was retrofitted with a 

Wet Flue Gas Desulfurization (FGD) scrubber with 95 percent SO2 reduction efficiency. This 

control measure is applicable to coal-fired EGUs with unit capacities above 25 MW.

 While the US District Court 

for District of Columbia has remanded the CAIR, it still is in full effect. The Agency is working at 

this time on a proposal to replace the CAIR, but that proposal is not yet complete. No 

additional controls for SO2 from EGUs are implemented in the baseline. 

The Integrated Planning Model (IPM) was used to develop the baseline emissions for the 

control strategy applied for the alternative standards. Historically, EPA has used the IPM model 

to assess the cost and effectiveness of additional EGU controls for a large number of 

rulemakings (e.g., CAIR, NOx SIP call, Ozone NAAQS, etc.). For this RIA, we applied controls on 

a unit by unit basis to obtain reductions from units that contribute to nonattainment at 

violating monitors in 2020. The end result of this approach mimics an approach which could be 

used by individual states as they try to apply targeted controls on EGUs which affect attainment 

in a specific area.

5

 4 For more information on the CAIR rule, please refer to 

 More 

http://www.epa.gov/airmarkt/progsregs/cair/. 5 Costs of FGD scrubber applications increase progressively as EGU capacity approaches 25 MW. At an capital cost 

of more than $1000/kW, it is typically more economical to retire a unit than to operate it with a scrubber. It is 

possible to duct emissions from more than one EGU to a single scrubber, but that approach is not included in this 

analysis. 
4-5

information on EGU SO2 measures, particularly for EGUs with 100 MW or larger capacity, can be 

found in the documentation for the IPM version used for this RIA.

6

4.1.2 Controls Applied for the NonEGU Point and Area Sectors

 

NonEGU point and Area control measures were identified using AirControlNET 4.2 as 

well as the Control Strategy Tool7

The SO2 emission control measures used in this analysis are similar to those used in the 

PM2.5 RIA prepared about three years ago. FGD scrubbers can achieve 95% control of SO2 for 

non-EGU point sources and for utility boilers. Spray dryer absorbers (SDA) are another 

commonly employed technology, and SDA can achieve up to 90% control of SO2. For specific 

source categories, other types of control technologies are available that are more specific to 

the sources controlled. The following table lists these technologies. For more information on 

these technologies, please refer to the AirControlNET 4.2 control measures documentation 

report.

(CoST). To reduce nonEGU point SO2 emissions, least cost 

control measures were identified for emission sources within 50 km of the violating monitor 

(see Chapter 3 for rationale). Area source emissions data are generated at the county level, 

and therefore controls for this emission sector were applied to the county containing the 

violating monitor. 

8

 6 Documentation on the version of IPM used for this RIA can be found at 

http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html. 7 See http://www.epa.gov/ttn/ecas/cost.htm for a description of CoST.

8 For a complete description of AirControlNET control technologies see AirControlNET 4.2 control measures 

documentation report, prepared by E.H. Pechan and Associates. May 2008. More information on AirControlNET 

(in this case, version 4.1) and the control technologies included in the tool are available at

http://www.epa.gov/ttn/ecas/AirControlNET.htm. 
4-6

Table 4-1: Example SO2 Control Measures for Non-EGU Point Sources Applied in Identified 

Control Measures Control Strategy Analysesa

Control Measure Sectors to which These Control 

Measures Can Be Applied

Control 

Efficiency 

(percent)

Average Annualized 

Cost/ton (2006$)

Wet and Dry FGD 

scrubbers and SDA

ICI boilers—all fuel types, kraft 

pulp mills, Mineral Products (e.g., 

Portland cement plants (all fuel 

types), primary metal plants, 

petroleum refineries

95—FGD 

scrubbers, 

90 - for SDA

$800-$8,000—FGD

$900 – 7,000—SDA

Increase percentage sulfur 

conversion to meet 

sulfuric acid NSPS (99.7% 

reduction)

Sulfur recovery plants 75 to 95 $4,000

Sulfur recovery and/or tail 

gas treatment 

Sulfuric Acid Plants 95-98 $1,000 – 4,000

Cesium promoted catalyst

Sulfuric Acid Plants with DoubleAbsorption process 50% $1,000

Sources: AirControlNET 4.2 control measures documentation report, May 2008, NESCAUM Report on 

Applicability of NOx, SO2, and PM Control Measures to Industrial Boilers, November 2008 available at 

http://www.nescaum.org/documents/ici-boilers-20081118-final.pdf , and Comprehensive Industry Document on 

Sulphuric Acid Plant, Govt. of India Central Pollution Control Board, May 2007. The estimates for these control 

measures reflect applications of control where there is no SO2 control measure currently operating except for 

the Cesium promoted catalyst.

In applying these SO2 controls, we employ a decision rule in which we do not apply 

controls to any non-EGU source with 50 tons/year of emissions or less. This decision rule is the 

same one we employed for such sources in the PM2.5 RIA completed four years ago.9

The analysis for non-EGUs mostly applied controls to the following source categories: 

industrial boilers, commercial and institutional boilers, sulfuric acid plants (both standalone and 

at other facilities such as copper and lead smelters), primary metal plants (iron and steel mills, 

 The 

reason for applying this decision rule is based on a finding that most point sources with 

emissions of this level or less had SO2 controls already on them. This decision rule aids in gap 

filling for a lack of information regarding existing controls on nonEGU sources. In addition, we 

also apply the decision rule that we do not apply SO2 nonEGU point source controls that yield 

emission reductions of 50 tons/year or less. We apply this decision rule in order to reduce the 

number the sources affected our non-EGU control strategies to those sources whose reductions 

are relatively more cost-effective. 

 9 PM2.5 RIA, Chapter 3, p. 3-10. This RIA was completed in October, 2006 and is available at 

http://www.epa.gov/ttn/ecas/ria.html. 
4-7

lead smelters), mineral products (primarily cement kilns) and petroleum refineries. These 

source categories are the most prevalent SO2 emitters in the areas included in this analysis. 

4.1.3 Data Quality for this Analysis

The estimates of emission reductions associated with our control strategies above are 

subject to important limitations and uncertainties. EPA’s analysis is based on its best judgment 

for various input assumptions that are uncertain. As a general matter, the Agency selects the 

best available information from available engineering studies of air pollution controls and has 

set up what it believes is the most reasonable framework for analyzing the cost, emission 

changes, and other impacts of regulatory control.
4-8

4.2 SO2 Emission Reductions Achieved with Identified Controls Analysis

We identified illustrative control strategies that might be employed to reduce emissions 

to bring air quality into compliance with the alternative standard being analyzed. As part of this 

exercise, we considered the cost-effectiveness of various control options and selected the 

lowest cost controls, based on available cost information. Applying identified control measures, 

we were able to illustrate attainment for most, but not all of the areas. 10 

Table 4.2 presents the emission reductions achieved through applying identical control 

measures, both by sector and in total. As this table reveals, a majority of the emission 

reductions were achieved through EGU emission controls. As indicated in this table, the 

estimate emission reductions from the identified controls applied in this analysis under the 75

ppb alternative standard in 2020 are 372,000 tons. About 260,000 tons of the reductions are 

from EGUs, and 112,000 are from non-EGU point sources. For the other alternative standards, 

the total emission reductions in 2020 are estimated to range from 186,000 tons for the 100 ppb 

standard to 803,000 tons for the 50 ppb standard. For all of these standards, this analysis 

shows that roughly 60 to 70 percent of these reductions are from EGUs. Most of the remaining 

reductions obtained come from non-EGU point sources. Reductions from area sources are 

generally a very small portion of those estimated except for the 50 ppb alternative standard, 

where 1.8 percent of reductions come from this sector. 

Table 4.2: Emission Reductions from Identified Controls in 2020 in Total and by Sector (Tons) a

for Each Alternative Standard

50 ppb 75 ppb 100 ppb

Total Emission Reductions 

from Identified Controls:b 800,000 370,000 190,000

EGUs 540,000 260,000 110,000

Non-EGUs 250,000 110,000 79,000

Area Sources 15,000 200 100

a

All estimates rounded to two significant figures. As such, totals may not sum down columns. b

These values represent emission reductions for the identified control strategy analysis. There were locations not 

able to attain the alternative standard being analyzed with identified controls only.

Table 4.3 presents the emission reductions by individual non-EGU point source category

in 2020. As this table shows, the majority of reductions are from industrial boilers for all 

alternative standards except for 100 ppb. The percentage of non-EGU point source reductions 

from industrial boilers ranges from 50 (50 ppb) to 33 (100 ppb). Reductions from primary metal 

 10 As will be discussed below, the application of identified controls was insufficient to bring all monitor areas into 

compliance with the alternative standards. 
4-9

units provide most of the reductions at 100 ppb (59 percent) and this source category has the 

next highest percent of reductions for the other alternative standards (21 percent at 50 ppb, 43

percent at 75 ppb).

Table 4.3: Emission Reductions from Identified Controls By Non-EGU Point Source Category in 

2020 in Total (Tons)a for Each Alternative Standard

50 ppb 75 ppb 100 ppb

Total Non-EGU Emission 

Reductions from Identified 

Controls:b

246,000 112,000 79,000

Industrial Boilers 124,000 49,000 26,000

Sulfuric Acid Plants 3,000 2,000 1,000

Commercial/Institutional 

Boilers

20,000 4,000 4,000

Primary Metal Products 52,000 48,000 47,000

Petroleum Refineries 23,000 6,000 1,000

Mineral Products 22,000 5,000 600

a

All estimates rounded to two significant figures. As such, totals may not sum down columns. b

These values represent emission reductions for the identified control strategy analysis. There were locations not 

able to attain the alternative standard being analyzed with identified controls only.

Table 4.4 presents the SO2 emissions reductions realized in each geographic area under 

the control strategies applied for the final standard of 75 ppb and also for the other two 

alternative standards. 

Table 4.4: Emission Reductions by County in 2020 for Each Alternative Standard Analyzed a

State County 50 ppb 75 ppb 100 ppb

Arizona Gila Co 9,000 9,000 9,000

Colorado Denver Co 10,000 - -

Connecticut New Haven Co 8,000 - -

Florida Duval Co 5,100 - -

Florida Hillsborough Co 1,300 - -

Georgia Chatham Co 19,000 5,400 -

Idaho Bannock Co 590 - -

Illinois Cook Co 39,000 - -

Illinois Madison Co 29,000 14,000 -

Illinois St Clair Co 82,000 - -

Illinois Sangamon Co 22,000 11,000 -

Illinois Tazewell Co 17,000 6,700 -

Indiana Floyd Co 15,000 - -

Indiana Fountain Co 9,000 - -

Indiana Jasper Co 21,000 - -

Indiana Lake Co 65,000 20,000 -

Indiana Morgan Co 3,300 - -

Indiana Porter Co 50,000 - -
4-10

Indiana Wayne Co 10,000 10,000 9,800

Iowa Linn Co 9,200 4,700 -

Iowa Muscatine Co 27,000 21,000 11,000

Kentucky Jefferson Co 16,000 - -

Kentucky Livingston Co 4,900 - -

Louisiana East Baton Rouge Par 12,000 - -

Missouri Greene Co 3,000 - -

Missouri Jackson Co 25,000 13,000 -

Missouri Jefferson Co 130,000 130,000 120,000

Montana Yellowstone Co 6,100 - -

Nebraska Douglas Co 24,000 24,000 -

New Hampshire Merrimack Co 2,700 - -

New York Erie Co 8,200 3,200 -

New York Monroe Co 12,000 - -

New York Suffolk Co 11,000 4,400 -

North Carolina New Hanover Co 6,200 - -

Ohio Clark Co 6,000 - -

Ohio Jefferson Co 12,000 - -

Ohio Lake Co 34,000 15,000 -

Ohio Summit Co 22,000 15,000 3,100

Oklahoma Kay Co 18,000 - -

Oklahoma Muskogee Co 52,000 35,000 17,000

Oklahoma Tulsa Co 15,000 - -

Pennsylvania Allegheny Co 8,800 - -

Pennsylvania Blair Co 4,300 - -

Pennsylvania Northampton Co 21,000 12,000 -

Pennsylvania Warren Co 6,100 6,100 6,100

South Carolina Lexington Co 7,800 - -

Tennessee Blount Co 4,000 - -

Tennessee Bradley Co 11,000 1,200 -

Tennessee Montgomery Co 1,000 1,000 1,000

Tennessee Shelby Co 4,900 - -

Tennessee Sullivan Co 24,000 8,400 -

Texas Harris Co 28,000 - -

Texas Jefferson Co 12,000 7,000 -

West Virginia Hancock Co 25,000 - -

Wisconsin Brown Co 11,000 - -

Wisconsin Oneida Co 7,000 7,000 7,000 a All estimates rounded to two significant figures.
4-11

4.3 Impacts Using Identified Controls

As discussed in Chapter 3, we estimated the overall change in ambient air quality 

achieved as a result of each of the control strategies identified above using an impact ratio of 

emission reductions to air quality improvement. Table 4.5 presents a detailed breakdown of 

the estimated ambient SO2 concentrations in 2020 at each of the counties that do not reach 

attainment under one or more of the alternative standards.

According to the data presented in Table 4.5, 20 of the 24 monitor areas are expected to 

reach attainment with a standard of 75 ppb following implementation of the identified control 

strategy. For four areas, identified controls are not sufficient to reach attainment with the 

standard of 75 ppb. For the areas projected to violate the NAAQS with the application of 

identified controls, we assume that emission reductions beyond identified controls will be 

applied, as discussed further below.

Table 4.5: 2020 SO2 Design Values after Application of Identified Controls for Alternative 

Standards

State County 50 ppb 75 ppb 100 ppb

Arizona Gila Co 188.9 188.9 188.9

Colorado Denver Co 50.3

Connecticut New Haven Co 46.9

Florida Duval Co 50.4

Florida Hillsborough Co 52.5

Georgia Chatham Co 34.4 72.1

Idaho Bannock Co 41.2

Illinois Cook Co 39.6

Illinois Madison Co 57.0 74.0

Illinois St Clair Co 20.1

Illinois Sangamon Co 35.9 67.5

Illinois Tazewell Co 47.9 73.5

Indiana Floyd Co 53.2

Indiana Fountain Co 46.3

Indiana Jasper Co 33.6

Indiana Lake Co 49.1 71.5

Indiana Morgan Co 47.8

Indiana Porter Co 37.4

Indiana Wayne Co 98.1 98.1 100.2

Iowa Linn Co 50.8 71.7

Iowa Muscatine Co 50.0 68.3 96.9

Kentucky Jefferson Co 54.6

Kentucky Livingston Co 50.2

Louisiana East Baton Rouge Par 48.6

Missouri Greene Co 44.5

Missouri Jackson Co 47.3 71.9
4-12

Missouri Jefferson Co 66.4 73.8 78.7

Montana Yellowstone Co 45.8

Nebraska Douglas Co 47.2 47.2

New Hampshire Merrimack Co 42.6

New York Erie Co 51.5 66.4

New York Monroe Co 46.5

New York Suffolk Co 66.4 72.0

North Carolina New Hanover Co 44.7

Ohio Clark Co 50.7

Ohio Jefferson Co 46.0

Ohio Lake Co 37.3 70.4

Ohio Summit Co 59.2 74.6 97.6

Oklahoma Kay Co 41.2

Oklahoma Muskogee Co 42.2 63.2 84.2

Oklahoma Tulsa Co 28.3

Pennsylvania Allegheny Co 57.0

Pennsylvania Blair Co 50.1

Pennsylvania Northampton Co 49.8 70.4

Pennsylvania Warren Co 118.8 118.8 118.8

South Carolina Lexington Co 39.2

Tennessee Blount Co 52.9

Tennessee Bradley Co 33.2 75.2

Tennessee Montgomery Co 139.5 139.5 139.5

Tennessee Shelby Co 46.0

Tennessee Sullivan Co 45.2 73.3

Texas Harris Co 42.4

Texas Jefferson Co 49.6 69.3

West Virginia Hancock Co 42.7

Wisconsin Brown Co 47.2

Wisconsin Oneida Co 47.1 47.1 47.1

Table 4.6 Number of Areas Projected to be in Nonattainment for Each Alternative Standard 

After Application of Identified Controls in 2020a

50 ppb 75 ppb 100 ppb

Number of Areas Needing Emission 

Reductions Beyond Identified Controls

16 4 3

a There are 56 areas included in this analysis. 

4.4 Emission Reductions Needed Beyond Identified Controls

As shown through the identified control strategy analysis, there were not enough 

identified controls for every area in the analysis to achieve attainment with neither the 75 ppb 

final standard nor the other alternative standards in 2020. Therefore additional emission 

reductions will be needed for these areas to attain these alternative standards. Table 4.7

shows the emission reductions needed beyond identified controls for counties to attain the 

alternative standards being analyzed. The total emission reductions for full attainment of each 
4-13

alternative standard are also included in this table. Table 4.8 presents the emission reductions 

needed for each area beyond identified controls for each alternative standard. Chapter 6 

presents the discussion of extrapolated costs associated with the emission reductions needed 

beyond identified controls.

Table 4.7: Total Emission Reductions and those from Extrapolated Controls in 2020 in Total 

and by Sector (Tons)a for Each Alternative Standard

50 ppb 75 ppb 100 ppb

Total Emission Reductions 

from Identified and 

Unidentified Controls

920,000 350,000 170,000

Total Emission Reductions 

from Unidentified Controls 110,000 33,000 18,000

Unidentified Reductions 

from EGUs 33,000 5,000 -

Unidentified Reductions 

from non-EGUs 54,000 22,000 15,000

Unidentified Reductions 

from Area Sources 19,000 6,400 3,000

a All estimates rounded to two significant figures.

Table 4.8: Emission Reductions Needed Beyond Identified Controls in 2020

State County 50 ppb 75 ppb 100 ppb

Arizona Gila Co 13,000 11,000 8,300

Colorado Denver Co - - -

Connecticut New Haven Co - - -

Florida Duval Co - - -

Florida Hillsborough Co 2,800 - -

Georgia Chatham Co - - -

Idaho Bannock Co - - -

Illinois Cook Co - - -

Illinois Madison Co 5,800 - -

Illinois St Clair Co - - -

Illinois Sangamon Co - - -

Illinois Tazewell Co - - -

Indiana Floyd Co 3,200 - -

Indiana Fountain Co - - -

Indiana Jasper Co - - -

Indiana Lake Co - - -

Indiana Morgan Co - - -

Indiana Porter Co - - -

Indiana Wayne Co 14,000 6,500 -

Iowa Linn Co 84 - -

Iowa Muscatine Co - - -

Kentucky Jefferson Co 3,500 - -

Kentucky Livingston Co - - -

Louisiana East Baton Rouge Par - - -
4-14

Missouri Greene Co - - -

Missouri Jackson Co - - -

Missouri Jefferson Co 9,500 - -

Montana Yellowstone Co - - -

Nebraska Douglas Co - - -

New Hampshire Merrimack Co - - -

New York Erie Co 360 - -

New York Monroe Co - - -

New York Suffolk Co 19,000 - -

North Carolina New Hanover Co - - -

Ohio Clark Co 130 - -

Ohio Jefferson Co - - -

Ohio Lake Co - - -

Ohio Summit Co 4,400 - -

Oklahoma Kay Co - - -

Oklahoma Muskogee Co - - -

Oklahoma Tulsa Co - - -

Pennsylvania Allegheny Co 20,000 - -

Pennsylvania Blair Co - - -

Pennsylvania Northampton Co - - -

Pennsylvania Warren Co 4,300 2,700 1,100

South Carolina Lexington Co - - -

Tennessee Blount Co 1,400 - -

Tennessee Bradley Co - - -

Tennessee Montgomery Co 19,000 13,000 8,200

Tennessee Shelby Co - - -

Tennessee Sullivan Co - - -

Texas Harris Co - - -

Texas Jefferson Co - - -

West Virginia Hancock Co - - -

Wisconsin Brown Co - - -

Wisconsin Oneida Co - - - a All estimates rounded to two significant figures.

4.5 Key Limitations 

The estimates of emission reductions associated with the control strategies described 

above are subject to important limitations and uncertainties. We summarize these limitations 

as follows:

• Actual State Implementation Plans May Differ from our Simulation: In order to reach 

attainment with the final NAAQS, each state will develop its own implementation 

plan implementing a combination of emissions controls that may differ from those 

simulated in this analysis. This analysis therefore represents an approximation of 

the emissions reductions that would be required to reach attainment and should not 

be treated as a precise estimate.
4-15

• Use of Existing CMAQ Model Runs: This analysis represents a screening level 

analysis. We did not conduct new regional scale modeling specifically targeting SO2. 

More explanation on the screening level analysis done for this RIA can be found in 

Chapter 3. 

• Analysis Year of 2020: Data limitations necessitated the choice of an analysis year of 

2020, as opposed to the presumptive implementation year of 2017. Emission

inventory projections are available for 5-year increments; i.e. we have inventories 

for 2015 and 2020, but not 2017. In addition, the CMAQ model runs upon which we 

relied were also based on an analysis year of 2020.

• Unidentified controls: We have limited information on available controls for some of 

the monitor areas included in this analysis. For a number of small non-EGU and 

area sources, there is little or no information available on SO2 controls. 
5‐1

Chapter 5:  Benefits Analysis Approach and Results

Synopsis

EPA estimated the monetized human health benefits of reducing cases of morbidity

among populations exposed to SO2 and cases of morbidity and premature mortality among

populations exposed to PM2.5 in 2020 for the selected standard and alternative standard levels

in 2006$.  Because SO2 is also a precursor to PM2.5, reducing SO2 emissions in the projected

non‐attainment areas will also reduce PM2.5 formation, human exposure and the incidence of

PM2.5‐related health effects. For the selected SO2 standard at 75 ppb (99th percentile, daily 1‐

hour maximum), the total monetized benefits would be $15 to $37 billion at a 3% discount rate

and $14 to $33 billion at a 7% discount rate.  For an SO2 standard at 50 ppb, the total

monetized benefits would be $34 to $83 billion at a 3% discount rate and $31 to $75 billion at a

7% discount rate.  For an SO2 standard at 100 ppb, the total monetized benefits would be $7.4

to $18 billion at a 3% discount rate and $6.7 to $16 billion at a 7% discount rate.   

   

These estimates reflect EPA’s most current interpretation of the scientific literature and

are consistent with the methodology used for the proposal RIA. These benefits are incremental

to an air quality baseline that reflects attainment with the 2008 ozone and 2006 PM2.5National

Ambient Air Quality Standards (NAAQS).  More than 99% of the total dollar benefits are

attributable to reductions in PM2.5 exposure resulting from SO2 emission controls.  Higher or

lower estimates of benefits are possible using other assumptions; examples of this are provided

in Figure 5.1 for the selected standard of 75 ppb.  Methodological limitations prevented EPA

from quantifying the impacts to, or monetizing the benefits from several important benefit

categories, including ecosystem effects from sulfur deposition, improvements in visibility, and

materials damage.  Other direct benefits from reduced SO2 exposure have not been quantified,

including reductions in premature mortality.
5‐2

Figure 5.1: Total Monetized Benefits (SO2 and PM2.5) of Attaining 75 ppb in 2020*

$0

$10

$20

$30

$40

$50

Pope et al  

Laden et al

Billions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

*This graph shows the estimated total monetized benefits in 2020 for the selected standard  of 75 ppb using the no‐

threshold model at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. study and the

Laden et al. study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality.  The results

shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the

concentration‐response function provided in those studies.  Graphs for alternative standards would show a similar

pattern.

5.1 Introduction

This chapter documents our analysis of health benefits expected to result from

achieving alternative levels of the SO2 NAAQS in 2020, relative to baseline ambient

concentrations that represent attainment with previously promulgated regulations, including

the 2008 ozone and 2006 PM2.5 NAAQS.  We first describe our approach for estimating and

monetizing the health benefits associated with reductions of SO2.  Next, we provide a summary

of our results, including an analysis of the sensitivity of several assumptions in our model.  We

then estimate the PM2.5 co‐benefits from controlling SO2 emissions.  Finally, we discuss the key

results of the benefits analysis and indicate limitations and areas of uncertainty in our

approach.

5.2 Primary Benefits Approach

This section presents our approach for estimating avoided adverse health effects due to

SO2 exposure in humans resulting from achieving alternative levels of the SO2NAAQS, relative
5‐3

to a baseline concentration of ambient SO2.  First, we summarize the scientific evidence

concerning potential health effects of SO2 exposure, and then we present the health endpoints

we selected for our primary benefits estimate.  Next, we describe our benefits model, including

the key input data and assumptions.  Finally, we describe our approach for assigning an

economic value to the SO2 health benefits.  The approach for estimating the benefits associated

with exposure to PM is described in section 5.7.   

We estimated the economic benefits from annual avoided health effects expected to

result from achieving alternative levels of the SO2NAAQS (the “control scenarios”) in the year

2020.  We estimated benefits in the control scenarios relative to the incidence of health effects

consistent with the ambient SO2 concentration expected  in 2020 (the “baseline”).  Note that

this “baseline” reflects emissions reductions and ambient air quality improvements that we

anticipate will result from implementation of other air quality rules, including compliance with

previously promulgated regulations, including the 2008 ozone and 2006 PM2.5 NAAQS.1

   

We compare benefits across three alternative SO2NAAQS levels: 50 ppb, 75 ppb, and

100 ppb (99th percentile).  Consistent with EPA’s approach for RIA benefits assessments, we

estimate the health effects associated with an incremental difference in ambient

concentrations between a baseline scenario and a pollution control strategy.  As indicated in

Chapter 4, several areas of the country may not be able to attain the alternative standard levels

using known pollution control methods.  For this reason, we provide an estimate of the benefits

associated with partially attaining the standard using known controls as well as the full

attainment results in Table 5.13 of this chapter.  Because some areas require emission

reductions from unknown sources to attain the various standards, the results are sensitive to

assuming full attainment.  All of the other results tables in this chapter assume full attainment

with the various standard levels.  The full attainment results include extrapolated tons from

unknown controls, which were spread across the sectors in proportion to the emissions in the

county.2

5.3 Overview of analytical framework for benefits analysis  

5.3.1 Benefits Model  

For the SO2 benefits analysis, we use the Environmental Benefits Mapping and Analysis

Program (BenMAP, version 3) (Abt Associates, 2008) to estimate the health benefits occurring

as a result of implementing alternative SO2NAAQS levels.  Although EPA has used BenMAP

                                                           

1 See Chapter 2 of this RIA for more information on the rules incorporated into the baseline. 2 See Chapter 4 of this RIA for more information on the extrapolated tons estimated to reach full attainment.
5‐4

extensively to estimate the health benefits of reducing exposure to PM2.5 and ozone in previous

RIAs, the proposal RIA was the first RIA in which EPA used BenMAP to estimate the health

benefits directly attributable to reducing exposure to SO2 to support a change in the NAAQS.  

Figure 5.2 below shows the major components of, and data inputs to, the BenMAP model.   

Figure 5.2: Diagram of Inputs to BenMAP model for SO2Analysis

5.3.2 Air Quality Estimates

As Figure 5.2 shows, the primary input to any benefits assessment is the estimated

changes in ambient air quality expected to result from a simulated control strategy or

attainment of a particular standard.  EPA typically relies upon air quality modeling to generate

these data, but time and technical limitations described in Chapter 3 prevented us from

generating new air quality modeling to simulate the changes in ambient SO2 resulting from

each control strategy.  Instead, we utilize the ambient SO2 concentrations modeled by CMAQ as

part of the upcoming PM NAAQS RIA as our baseline.3

   

                                                           

3 See Chapter 3 for more detail regarding the air quality data used in this analysis.

Census 

Population Data 

Modeled 

Baseline 2020 

Ambient SO2

Concentrations 

SO2 Health 

Functions 

Valuation 

Functions 

2020 

Population 

Projections 

SO2 Incremental 

Air Quality Change 

SO2-Related Health 

Impacts 

Woods & 

Poole 

Population 

Projections 

Monitor Rollback 

of Design Values to 

Full Attainment 

Background 

Incidence and 

Prevalence Rates 

Monetized Benefits
5‐5

The CMAQ air quality model provides projects both design values at SO2monitors and

air quality concentrations at 12 km by 12 km grid cells nationwide.  To estimate the benefits of

fully attaining the standards in all areas, EPA employed the “monitor rollback” approach to

approximate the air quality change resulting from just attaining alternative SO2NAAQS at each

design value monitor.  Figure 5.3 depicts the rollback process, which differs from the technique

described in Chapter 3.  The emission control strategy estimated the level of emission

reductions necessary to attain each alternate NAAQS standard, whereas the approach

described here aims to estimate the change in population exposure associated with attaining an

alternate NAAQS.  This approach relies on data from the existing SO2monitoring network and

the inverse distance squared variant of the Veronoi Neighborhood Averaging (VNA)

interpolation method to adjust the CMAQ‐modeled SO2 concentrations such that each area just

attains the standard alternatives.  We believe that the interpolation method using inverse

distance squared most appropriately reflects the exposure gradient for SO2 around each

monitor (EPA, 2008c).  A sensitivity analysis in Table 5.6 shows that the results are not

particularly sensitive to the interpolation method.

Figure 5.3: Diagram of Rollback Method

Step 2.  Rollback SO2 monitor design

values to just attain each standard

alternative

Alternative 1: 50 ppb

Alternative 2: 75 ppb

Alternative 3: 100 ppb

Use modeled air quality data

to establish ratios between

99th percentile SO2 design

value and SO2 air quality

metric at each monitor.*

Step 1.  Receive 12 km

CMAQ baseline air quality

modeling

Step 3.  Interpolate

incremental reduction

in design value change

to 12 km grid using

VNA in BenMAP and

calculate benefits

Convert interpolated

DV change to

equivalent change in

SO2 metric and adjust

Calculate

benefits for

each

standard

*Metrics used in the epidemiology studies include the 24‐hr mean, 3‐hr mean, 8‐hr max, and 1‐hr max.
5‐6

  Because the VNA rollback approach interpolates monitor values, it is most reliable in

areas with a denser monitoring network.  In areas with a sparser monitoring network, there is

less observed monitoring data to support the VNA interpolation and we have less confidence in

the predicted air quality values further away from the monitors.  For this reason, we

interpolated air quality values—and estimated health impacts—within the CMAQ grid cells that

are located within 50 km of the monitor, assuming that emission changes within this radius  

would affect the SO2 concentration at each monitor.  Limiting the interpolation to this radius

attempts to account for the limitations of the VNA approach, the air quality data limitations

identified in Chapter 3 and ensures that the benefits and costs analyses consider a consistent

geographic area.4

  Therefore, the primary benefits analysis assesses health impacts occurring to

populations living in the CMAQ grid cells located within the 50 km buffer for the specific

geographic areas assumed to not attain the alternate standard levels.  We test the sensitivity of

this assumption relative to other exposure buffers in Table 5.6.  

   

5.4 Estimating Avoided Health Effects from SO2 Exposure

Following an extensive evaluation of health evidence from epidemiologic and laboratory

studies, the U.S. EPA has concluded that there is a causal relationship between respiratory

health effects and short‐term exposure to SO2 (U.S. EPA, 2008c).  The immediate effect of SO2

on the respiratory system in humans is bronchoconstriction.  This response is mediated by

chemosensitive receptors in the tracheobronchial tree, which trigger reflexes at the central

nervous system level resulting in bronchoconstriction, mucus secretion, mucosal vasodilation,

cough, and apnea followed by rapid shallow breathing.  In some cases, local nervous system

reflexes also may be involved.  Asthmatics are more sensitive to the effects of SO2 likely

resulting from preexisting inflammation associated with this disease.  This inflammation may

lead to enhanced release of mediators, alterations in the autonomic nervous system and/or

sensitization of the chemosensitive receptors.  These biological processes are likely to underlie

the bronchoconstriction and decreased lung function observed in response to SO2 exposure.  A

clear concentration‐response relationship has been demonstrated in laboratory studies

following exposures to SO2 at concentrations between 20 and 100 ppb, both in terms of

increasing severity of effect and percentage of asthmatics adversely affected.  

5.4.1 Selection of Health Endpoints for SO2

Epidemiological researchers have associated SO2 exposure with adverse health effects in

numerous toxicological, clinical and epidemiological studies, as described in the Integrated

                                                           

4

Please see Chapter 3 for more information regarding the technical basis for the 50 km assumption.
5‐7

Science Assessment for Oxides of Sulfur ‐ Health Criteria (Final Report) (U.S. EPA, 2008c);

hereafter, “SO2 ISA”).  The SO2 ISA provides a comprehensive review of the current evidence of

health and environmental effects of SO2.   

Previous reviews of the SO2 primary NAAQS, most recently in 1996, did not include a

quantitative benefits assessment for SO2 exposure.  As the first health benefits assessment for

SO2 exposure, we build on the methodology and lessons learned from the SO2 risk and exposure

assessment (U.S. EPA, 2009c) and the benefits assessments for the recent PM2.5,O3, and NO2

NAAQS (U.S. EPA, 2006a; U.S. EPA, 2008a; U.S. EPA, 2010a; U.S. EPA, 2010b).

We quantified SO2‐related health endpoints for which the SO2 ISA provides the strongest

evidence of an effect.  In general, we follow a weight of evidence approach, based on the

biological plausibility of effects, availability of concentration‐response functions from well

conducted peer‐reviewed epidemiological studies, cohesiveness of results across studies, and a

focus on endpoints reflecting public health impacts (like hospital admissions) rather than

physiological responses (such as changes in clinical measures like Forced Expiratory Volume

(FEV1)).  The differing evidence and associated strength of the evidence for these different

effects is described in detail in the SO2 ISA.   

Although a number of adverse health effects have been found to be associated with SO2

exposure, this benefits analysis only includes a subset due to limitations in understanding and

quantifying the dose‐response relationship for some of these health endpoints.  In this analysis,

we only estimated the benefits for those endpoints with sufficient evidence to support a

quantified concentration‐response relationship using the information presented in the SO2 ISA,

which contains an extensive literature review for several health endpoints related to SO2

exposure.  Because the ISA only included studies published or accepted for publication through

April 2008, we also performed supplemental literature searches in the online search engine

PubMed® to identify relevant studies published between January 2008, and the present.5

  

Based on our review of this information, we quantified four short‐term respiratory morbidity

endpoints that the SO2 ISA identified as a “causal relationship”: acute respiratory symptoms,

asthma exacerbation, respiratory‐related emergency department visits, and respiratory‐related

hospitalizations.   

Table 5.1 presents the health effects related to SO2 exposure quantified in this benefits

analysis.  In addition, the table includes other endpoints potentially linked to SO2 exposure, but

which we are not yet ready to quantify with dose‐response functions.  For a list of the health

                                                           

5 The O’Connor et al. study (2008) is the only study included in this analysis that was published after the cut‐off

date for inclusion in the SO2 ISA.
5‐8

effects related to PM2.5 exposure that we quantify in this analysis, please see Table 5.6 in

section 5.7.   

The SO2 ISA concluded that the relationship between short‐term SO2 exposure and

premature mortality was “suggestive of a causal relationship” because it is difficult to attribute

the mortality risk effects to SO2 alone.  Therefore, we decided not to quantify premature

mortality from SO2 exposure in this analysis despite evidence suggesting a positive association

(U.S. EPA, 2008c).  Although the SO2 ISA stated that studies are generally consistent in reporting

a relationship between SO2 exposure and mortality, there was a lack of robustness of the

observed associations to adjustment for co‐pollutants.  As the literature continues to evolve,  

we may revisit this decision in future benefits assessment for SO2.

As noted in Table 5.1, we are not able to quantify several welfare benefit categories in

this analysis because we are limited by the available data or resources.  Although we cannot

quantify the ecosystem benefits of reducing sulfur deposition or visibility improvements in this

analysis, we provide a qualitative analysis in section 5.9.   

Table 5.1: Human Health and Welfare Effects of SO2

Pollutant /

Effect

Quantified and Monetized in Primary

Estimates a

Unquantified Effects b, c  

Changes in:

SO2 /Health Respiratory Hospital Admissions

Asthma ER visits

Asthma exacerbation

Acute Respiratory symptoms

Premature mortality

Pulmonary function

Other respiratory emergency department visits

Other respiratory hospital admissions

SO2 /Welfare Visibility improvements

Commercial fishing and forestry from acidic deposition

Recreation in terrestrial and aquatic ecosystems from

acid deposition

Increased mercury methylation a Primary quantified and monetized effects are those included when determining the primary estimate of total

monetized benefits of the alternative standards.   b

The categorization of unquantified toxic health and welfare effects is not exhaustive. c

Health endpoints in the unquantified benefits column include both a) those for which there is not consensus on

causality and those for which causality has been determined but empirical data are not available to allow

calculation of benefits.

5.4.2 Selection of Concentration‐Response Functions

After identifying the health endpoints to quantify in this analysis, we then selected

concentration‐response functions drawn from the epidemiological literature identified in the

SO2 ISA.  We considered several factors, in the order below, in selecting the appropriate

epidemiological studies and concentration‐response functions for this benefits assessment.   
5‐9

1. We considered ambient SO2 studies that were identified as key studies in the SO2

ISA (or a more recent study), excluding those affected by the general additive

model (GAM) S‐Plus issue.6   

2. We judged that studies conducted in the United States are preferable to those

conducted outside the United States, given the potential for effect estimates to

be affected by factors such as the ambient pollutant mix, the placement of

monitors, activity patterns of the population, and characteristics of the

healthcare system especially for hospital admissions and emergency department

visits.  We include Canadian studies in sensitivity analyses, when available.   

3. We only incorporated concentration‐response functions for which there was a

corresponding valuation function.  Currently, we only have a valuation function

for asthma‐related emergency department visits, but we do not have a valuation

function for all‐respiratory‐related emergency department visits.   

4. We preferred concentration‐response functions that correspond to the age

ranges most relevant to the specific health endpoint, with non‐overlapping ICD‐9

codes.  We preferred completeness when selecting functions that correspond to

particular age ranges and ICD codes.  Age ranges and ICD codes associated with

the selected functions are identified in Table 5.2.

5. We preferred multi‐city studies or combined multiple single city studies, when

available.   

6. When available, we judged that effect estimates with distributed or cumulative

lag structures were most appropriate for this analysis.   

7. When available, we selected SO2 concentration‐response functions based on

multi‐pollutant models.  Studies with multi‐pollutant models are identified in

Table 5.2.

These criteria reflect our preferences for study selection, and it was possible to satisfy

many of these, but not all.  There are trade‐offs inherent in selecting among a range of studies,

as not all studies met all criteria outlined above.  At minimum, we ensured that none of the

studies were GAM affected, we selected only U.S. based studies, and we quantified health

endpoints for which there was a corresponding valuation function.

We believe that U.S.‐based studies are most appropriate studies to use in this analysis

to estimate the number of hospital admissions associated with SO2 exposure because of the

                                                           

6 The S‐Plus statistical software is widely used for nonlinear regression analysis in time‐series research of health

effects.  However, in 2002, a problem was discovered with the software’s default conversion criteria in the

general additive model (GAM), which resulted in biased relative risk estimates in many studies.  This analysis

does not include any studies that encountered this problem.  For more information on this issue, please see U.S.

EPA (2002).
5‐10

characteristics of the ambient air, population, and healthcare system.  Using only U.S.‐based

studies, we are limited to one epidemiology study for hospital admissions (Schwartz, 1996).  

However, there are several Canada‐based epidemiology studies that also estimate respiratory

hospital admissions (Fung, 2006; Luginaah, 2005; Yang, 2003).  Table 5.12 provides the

sensitivity of the SO2 benefits using the effect estimates from the Canadian studies.  Compared

to the U.S. based study, the Canadian studies produce a substantially larger estimate of hospital

admissions associated with SO2 exposure.   

When selecting concentration‐response functions to use in this analysis, we reviewed

the scientific evidence regarding the presence of thresholds in the concentration‐response

functions for SO2 ‐related health effects to determine whether the function is approximately

linear across the relevant concentration range.  The SO2 ISA concluded that, “The overall limited

evidence from epidemiologic studies examining the concentration‐response function of SO2

health effects is inconclusive regarding the presence of an effect threshold at current ambient

levels.”  For this reason, we have not incorporated thresholds in the concentration‐response

functions for SO2 ‐related health effects in this analysis.

Table 5.2 shows the studies and health endpoints that we selected for this analysis.  

Table 5.3 shows the baseline health data used in combination with these health functions.  

Following these tables is a description of each of the epidemiology studies used in this analysis.
5‐11

Table 5.2: SO2 ‐Related Health Endpoints Quantified, Studies Used to Develop Health Impact

Functions and Sub‐Populations to which They Apply

Endpoint Study Study

Population

Hospital Admissions      

    

All respiratory Schwartz et al., 1996 – ICD‐9 460‐519 65 ‐ 99

Emergency Department Visits

Asthma

Pooled Estimate: All ages

Ito et al. (2007)—ICD‐9   493

Michaud (2004) – ICD‐9 493

NYDOH (2006)b

—ICD‐9   493

Peel et al. (2005)—ICD‐9   493

Wilson (2005) – ICD‐9 493

Other Health Endpoints

Asthma exacerbations

Pooled estimate: 4 ‐ 12

Mortimer et al. (2002) (one or more symptoms)a

O’Connor et al. (2008) (slow play, missed school daysc

,

nighttime asthma)a, b

Schildcrout et al. (2006) (one or more symptoms)a

    

Acute Respiratory

Symptoms Schwartz et al. (1994)b 7 ‐ 14

a

  The original study populations were 4 to 9 for the Mortimer et al. (2002) study and 5 to 12 for the O’Connor et al.

(2008) study and the Schildcrout et al. (2006) study.  We extended the applied population to facilitate the pooling

process, recognizing the common biological basis for the effect in children in the broader age group.  See: National

Research Council (NRC).  2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations.  

Washington, DC: The National Academies Press, pg 117. b Study specifies a multipollutant model. c The form of this one function was not clear from the study.  For this analysis, we assumed that it was log‐linear,

but we have subsequently determined that it is logistic.  This adds a small amount to uncertainty regarding the

asthmas incidence estimates, but this uncertainty is obscured by the rounding of the monetized estimates.  
5‐12

Table 5.3: National Average Baseline Incidence Rates used to Calculate SO2 ‐Related Health

Impacts a

Endpoint Source

Notes Rate per 100 people per year by Age Group

<18 18–24 25–34 35–44 45–54 55–64 65+

Respiratory

Hospital

Admissions

1999 NHDS

public use data

files b

incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.629

Asthma ER

visits

2000 NHAMCS

public use data

files c

; 1999

NHDS public use

data files b

incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232

Minor

Restricted

Activity Days

(MRADs)

Schwartz (1994,

table 2) incidence 0.416 — — — — — —

Asthma

Exacerbations

Mortimer et al.

(2002)

Incidence (and

prevalence) among

asthmatic children

Any morning symptom 0.116 (0.0567) d

O’Connor et al.

(2008)

Incidence (and

prevalence) among

asthmatic children

Missed school

One or more symptoms

Slow play

Nighttime asthma

0.057 (0.0567) d

0.207 (0.0567) d

0.157 (0.0567) d

0.121 (0.0567) d

Schildcrout et

al. (2006)

Incidence (and

prevalence) among

asthmatic children

One or more symptoms 0.52 (0.0567) d

a

The following abbreviations are used to describe the national surveys conducted by the National Center for Health

Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—

National Hospital Ambulatory Medical Care Survey. b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/ c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/   d We assume that this prevalence rate for ages 5 to 9 is also applicable down to age 4.   

Schwartz et al. (1996)

Schwartz et al. (1996) is a review paper with an example drawn from hospital

admissions of the elderly in Cleveland, Ohio from 1988‐1990.  The authors argued that the

central issue is control for seasonality.  They illustrated the use of categorical variables for

weather and sinusoidal terms for filtering season in the Cleveland example.  After controlling

for season, weather, and day of the week effects, hospital admissions of persons aged 65 and

older in Cleveland for respiratory illness was associated with ozone (RR = 1.09, 95% CI 1.02,

1.16) and PM10 (RR = 1.12, 95% CI 1.01, 1.24), and marginally associated with SO2 (RR = 1.03,

95% CI = 0.99, 1.06).  All of the relative risks are for a 100 micrograms/m3 increase in the

pollutant.  
5‐13

Fung et al. (2006) – Sensitivity Analysis

Fung et al. (2006) assessed the impact of ambient gaseous pollutants (SO2, NO2, CO, and

O3) and particulate matters (PM10, PM2.5, and PM10‐2.5) as well as the coefficient of haze (COH)

on recurrent respiratory hospital admissions (ICD‐9 codes 460‐519) among the elderly in

Vancouver, Canada, for the period of June 1, 1995, to March 31, 1999, using a new method

proposed by Dewanji and Moolgavkar (2000; 2002). The authors found significant associations

between respiratory hospital admissions and 3‐day, 5‐day, and 7‐day moving averages of the

ambient SO2 concentrations, with the strongest association observed at the 7‐day lag (RR =

1.044, 95% CI: 1.018‐1.070).  The authors also found PM10‐2.5 for 3‐day and 5‐day lag to be

significant, with the strongest association at 5‐day lag (RR = 1.020, 95% CI: 1.001‐1.039).  No

significant associations with admission were found with current day exposure.

Luginaah et al. (2005) – Sensitivity analysis  

Luginaah et al. (2005) assessed the association between air pollution and daily

respiratory hospitalization (ICD‐9 codes 460‐519) for different age and sex groups from 1995 to

2000. The pollutants included were NO2, SO2, CO, O3, PM10, coefficient of haze (COH), and total

reduced sulfur (TRS).  The authors estimated relative risks (RR) using both time‐series and case‐

crossover methods after controlling for appropriate confounders (temperature, humidity, and

change in barometric pressure).  The results of both analyses were consistent.  They found

associations between NO2, SO2, CO, COH, or PM10 and daily hospital admission of respiratory

diseases especially among females.  For females 0‐14 years of age, there was 1‐day delayed

effect of NO2 (RR = 1.19, case‐crossover method), a current‐day SO2 (RR = 1.11, time series),

and current‐day and 1‐ and 2‐day delayed effects for CO by case crossover (RR = 1.15, 1.19,

1.22, respectively).  Time‐series analysis showed that 1‐day delayed effect of PM10 on

respiratory admissions of adult males (15‐64 years of age), with an RR of 1.18.  COH had

significant effects on female respiratory hospitalization, especially for 2‐day delayed effects on

adult females, with RRs of 1.15 and 1.29 using time‐series and case‐crossover analysis,

respectively.  There were no significant associations between O3 and TRS with respiratory

admissions.

Yang et al. (2003) – Sensitivity analysis

Yang et al. (2003) examined the impact of ozone, nitrogen dioxide, sulfur dioxide,

carbon monoxide, and coefficient of haze on daily respiratory admissions (ICD‐9 codes 460‐519)

in both young children (<3 years of age) and the elderly (65‐99 years of age) in greater
5‐14

Vancouver, British Columbia during the 13‐yr period 1986‐1998.  Bidirectional case‐crossover

analysis was used to investigate associations and odds ratios were reported for single‐pollutant,

two‐pollutant and multiple‐pollutant models.  Sulfur dioxide was found marginally significant in

all models for elderly.   

Ito et al. (2007)

Ito et al. (2007) assessed associations between air pollution and asthma emergency

department visits in New York City for all ages.  Specifically they examined the temporal

relationships among air pollution and weather variables in the context of air pollution health

effects models.  The authors compiled daily data for PM2.5, O3, NO2, SO2, CO, temperature, dew

point, relative humidity, wind speed, and barometric pressure for New York City for the years

1999‐2002.The authors evaluated the relationship between the various pollutants' risk

estimates and their respective concurvities, and discuss the limitations that the results imply

about the interpretability of multi‐pollutant health effects models.

Michaud et al. (2004)

Michaud et al. (2004) examined the association of emergency department (ED) visits in

Hilo, Hawai'i, from January 1997 to May 2001 with volcanic fog, or "vog", measured as sulfur

dioxide (SO2) and submicrometer particulate matter (PM1).  Log‐linear regression models were

used with robust standard errors.  The authors studied four diagnostic groups: asthma/COPD;

cardiac; flu, cold, and pneumonia; and gastroenteritis.  Before adjustments, highly significant

associations with vog‐related air quality were seen for all diagnostic groups except

gastroenteritis.  After adjusting for month, year, and day of the week, only asthma/COPD had

consistently positive associations with air quality.  They found that the strongest associations

were for SO2 with a 3‐day lag (6.8% per 10 ppb; P=0.001) and PM1, with a 1‐day lag (13.8% per

10 μg/m3

; P=0.011).

NYDOH (2006)

New York State Department of Health (NYDOH) investigated whether day‐to‐day

variations in air pollution were associated with asthma emergency department (ED) visits in

Manhattan and Bronx, NYC and compared the magnitude of the air pollution effect between

the two communities.  NYDOH (2006) used Poisson regression to test for effects of 14 key air

contaminants on daily ED visits, with control for temporal cycles, temperature, and day‐of‐week

effects. The core analysis utilized the average exposure for the 0‐ to 4‐day lags.  Mean daily SO2

was found significantly associated with asthma ED visits in Bronx but not Manhattan.  Their
5‐15

findings of more significant air pollution effects in the Bronx are likely to relate in part to

greater statistical power for identifying effects in the Bronx where baseline ED visits were

greater, but they may also reflect greater sensitivity to air pollution effects in the Bronx.

Peel et al. (2005)

Peel et al. (2005) examined the associations between air pollution and respiratory

emergency department visits (i.e., asthma (ICD‐9 code 493, 786.09), COPD (491,492,496), URI

(460‐466, 477), pneumonia (480‐486), and an all respiratory‐disease group) in Atlanta, GA from

1 January 1993 to 31 August 2000. They used 3‐Day Moving Average (Lags of 0, 1, and 2 Days)

and unconstrained distributed lag (Lags of 0 to 13 Days) in the Poisson regression analyses.  In

single‐pollutant models, positive associations persisted beyond 3 days for several outcomes,

and over a week for asthma.  The effects of NO2, CO or PM10 on asthma ED visits were found

significant but SO2 or O3 were not significantly associated with asthma ED visits.

Wilson et al. (2005)

Daily emergency room (ER) visits for all respiratory (ICD‐9 codes 460‐519) and asthma

(ICD‐9 code 493) were compared with daily SO2, O3, and weather variables over the period

1998‐2000 in Portland, Maine and 1996‐2000 in Manchester, New Hampshire.  Seasonal

variability was removed from all variables using nonparametric smoothed function (LOESS).  

Wilson et al.(2005) used generalized additive models to estimate the effect of elevated levels of

pollutants on ER visits.  Relative risks of pollutants were reported over their inter‐quartile range

(IQR, the 75th ‐25th percentile pollutant values).  In Portland, an IQR increase in SO2 was

associated with a 5% (95% CI 2‐7%) increase in all respiratory ER visits and a 6% (95% CI 1‐12%)

increase in asthma visits.  An IQR increase in O3 was associated with a 5% (95% CI 1‐10%)

increase in Portland asthmatic ER visits.  No significant associations were found in Manchester,

New Hampshire, possibly due to statistical limitations of analyzing a smaller population.  The

absence of statistical evidence for a relationship should not be used as evidence of no

relationship.  This analysis reveals that, on a daily basis, elevated SO2 and O3 have a significant

impact on public health in Portland, Maine.

Villeneuve et al. (2007) – Sensitivity Analysis  

Villeneuve et al. (2007) examined the associations between air pollution and emergency

department (ED) visits for asthma among individuals two years of age and older in the census

metropolitan area of Edmonton, Canada between April 1, 1992 and March 31, 2002 using a

time stratified case‐crossover design.  Daily air pollution levels for the entire region were
5‐16

estimated from three fixed‐site monitoring stations.  Odds ratios and their corresponding 95%

confidence intervals were estimated using conditional logistic regression with adjustment for

temperature, relative humidity and seasonal epidemic of viral related respiratory disease.  

Villeneuve et al.(2007) found positive associations for asthma ED visits with outdoor air

pollution levels between April and September, but such associations were absent during the

remainder of the year.  Effects were strongest among young children (2‐4 years of age) and

elderly (>75 years of age).  Air pollution risk estimates were largely unchanged after adjustment

for aeroallergen levels.  This study is not included in the SO2 ISA only because it was published

after the cut‐off date, but it met all of the other criteria for inclusion in this analysis.

Mortimer et al. (2002)

Mortimer et al. (2002) examined the effect of daily ambient air pollution within a cohort

of 846 asthmatic children residing in eight urban areas of the USA between June 1 to August 31,

1993, using data from the National Cooperative Inner‐City Asthma Study.  Daily air pollution

concentrations were extracted from the Aerometric Information Retrieval System database

from the Environment Protection Agency in the USA.  Logistic models were used to evaluate the

effects of several air pollutants (O3, NO2, SO2 and PM10) on peak expiratory flow rate (PEFR) and

symptoms in 846 children (ages 4‐9 yrs) with a history of asthma.  In single pollutant models,

each pollutant was associated with an increased incidence of morning symptoms: (odds ratio

(OR) = 1.16 (95% CI 1.02‐1.30) per IQR increase in 4‐day average O3, OR = 1.32 (95% CI 1.03‐

1.70) per IQR increase in 2‐day average SO2, OR = 1.48 (95% CI 1.02‐2.16) per IQR increase in 6‐

day average NO2 and OR = 1.26 (95% CI 1.0‐1.59) per IQR increase in 2‐day average PM10.  This

longitudinal analysis supports previous time‐series findings that at levels below current USA air‐

quality standards, summer‐air pollution is significantly related to symptoms and decreased

pulmonary function among children with asthma.

O'Connor et al. (2008)  

O'Connor et al. (2008) investigated the association between fluctuations in outdoor air

pollution and asthma exacerbation (wheeze‐cough, nighttime asthma, slow play and school

absence) among 861 inner‐city children (5‐12 years of age) with asthma in seven US urban

communities.  Asthma symptom data were collected every 2 months during the 2‐year study

period.  Daily pollution measurements were obtained from the Aerometric Information

Retrieval System between August 1998 and July 2001.  The relationship of symptoms to

fluctuations in pollutant concentrations was examined by using logistic models.  In single‐

pollutant models, significant or nearly significant positive associations were observed between

higher NO2 concentrations and each of the health outcomes.  The O3, PM2.5, and SO2
5‐17

concentrations did not appear significantly associated with symptoms or school absence except

for a significant association between PM2.5 and school absence.  This study is not included in the

SO2 ISA only because it was published after the cut‐off date, but it met all of the other criteria

for inclusion in this analysis.

Schildcrout et al. (2006)

Schildcrout et al. (2006) investigated the relation between ambient concentrations of

the five criteria pollutants (PM10, O3, NO2, SO2, and CO) and asthma exacerbations (daily

symptoms and use of rescue inhalers) among 990 children in eight North American cities during

the 22‐month prerandomization phase (November 1993‐September 1995) of the Childhood

Asthma Management Program.  Short‐term effects of CO, NO2, PM10, SO2, and warm‐season O3

were examined in both one‐pollutant and two‐pollutant models, using lags of up to 2 days in

logistic and Poisson regressions.  Lags in CO and NO2 were positively associated with both

measures of asthma exacerbation, and the 3‐day moving sum of SO2 levels was marginally

related to asthma symptoms.  PM10 and O3 were unrelated to exacerbations.  The strongest

effects tended to be seen with 2‐day lags, where a 1‐parts‐per‐million change in CO and a 20‐

parts‐per‐billion change in NO2 were associated with symptom odds ratios of 1.08 (95%

confidence interval (CI): 1.02, 1.15) and 1.09 (95% CI: 1.03, 1.15), respectively.  

Schwartz et al. (1994)

Schwartz et al. (1994) studied the association between ambient air pollution exposures

and respiratory illness among 1,844 school children (7‐14 years of age) in six U.S. cities during

five warm season months between April and August. Daily measurements of ambient sulfur

dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), inhalable particles (PM10), respirable particles

(PM2.5), light scattering, and sulfate particles were made, along with integrated 24‐h measures

of aerosol strong acidity.  Significant associations in single pollutant models were found

between SO2, NO2, or PM2.5 and incidence of cough, and between sulfur dioxide and incidence

of lower respiratory symptoms.  Significant associations were also found between incidence of

coughing symptoms and incidence of lower respiratory symptoms and PM10, and a marginally

significant association between upper respiratory symptoms and PM10.
5‐18

Delfino et al. (2003) – Sensitivity Analysis

Delfino et al. (2003) conducted a panel study of 22 Hispanic children with asthma who

were 10‐16 years old and living in a Los Angeles community with high traffic density.  Subjects

filled out symptom diaries daily for up to 3 months (November 1999 through January 2000).  

Pollutants included ambient hourly values of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide

(SO2), and carbon monoxide (CO) and 24‐hr values of volatile organic compounds (VOCs),

particulate matter with aerodynamic diameter < 10 micro (PM10), and elemental carbon (EC)

and organic carbon (OC) PM10 fractions.  Asthma symptom severity was regressed on pollutants

using logistic models.  The authors found positive associations of symptoms with criteria air

pollutants (O3, NO2, SO2, and PM10).  Selected adjusted odds ratio for more severe asthma

symptoms from interquartile range increases in pollutants was, for 2.5 ppb 8‐hr max SO2, 1.36

[95% confidence interval (CI), 1.08‐1.71].  Their findings support the view that air toxins in the

pollutant mix from traffic and industrial sources may have adverse effects on asthma in

children.

5.4.3 Pooling Multiple Health Studies  

After selecting which health endpoints to analyze and which epidemiology studies

provide appropriate effect estimates, we then selected a method to combine the multiple

health studies to provide a single benefits estimate for each health endpoint.  The purpose of

pooling multiple studies together is to generate a more robust estimate by combining the

evidence across multiple studies and cities.  Because we used a single study for acute

respiratory symptoms and a single study for hospital admission for asthma, there was no

pooling necessary for those endpoints.   

See Table 5.2 for more information on how the asthma studies were adjusted.  Because

asthma represents the largest benefits category in this analysis, we tested the sensitivity of the

SO2 benefits to alternate pooling choices in Table 5.6.   

5.5 Valuation of Avoided Health Effects from SO2 Exposure

The selection of valuation functions very similar to the NO2 NAAQS RIA (U.S. EPA, 2010b)

and the PM2.5 NAAQS RIA (U.S. EPA, 2006a) with a couple exceptions.  First, in this analysis, we

estimated changes in all respiratory hospital admissions.  This is consistent with the PM2.5

NAAQS RIA, but inconsistent with the NO2 NAAQS RIA, which estimated changes for only a

subset of respiratory hospital admissions (i.e., chronic lung disease and asthma) because

concentration‐response functions were only available for the subset.  Second, in this analysis,
5‐19

we used the any‐of‐19 symptoms valuation function for acute respiratory symptoms.  This is

consistent with the NO2 NAAQS RIA, but inconsistent with the PM2.5NAAQS RIA, which used the

valuation function for “minor‐restricted activity day” (MRADs).  The valuation for any‐of‐19‐

symptoms is approximately 50% of the valuation for MRADs.  Consistent with economic theory,

these valuation functions include adjustments for inflation (2006$) and income growth over

time (2020 income levels).  Table 5.4 provides the unit values used to monetize the benefits of

reduced exposure to SO2.  

Table 5.4: Central Unit Values SO2Health Endpoints (2006$)*

Health Endpoint

Central Unit Value Per

Statistical Incidence  

(2020 income level)

Derivation of Distributions of Estimates

Hospital Admissions and ER Visits

Respiratory Hospital

Admissions $24,000

No distributional information available. The COI point 

estimates (lost earnings plus direct medical costs) are based 

on ICD-9 code level information (e.g., average hospital care 

costs, average length of hospital stay, and weighted share of 

total COPD category illnesses) reported in Agency for 

Healthcare Research and Quality, 2000 (www.ahrq.gov).

Asthma Emergency

Room Visits   $370  

No distributional information available.  Simple average of

two unit COI values:  

(1) $400 (2006$), from Smith et al. (1997) and

(2) $340 (2006$), from Stanford et al. (1999).

Respiratory Ailments Not Requiring Hospitalization

Asthma Exacerbation $53  

Asthma exacerbations are valued at $49 (2006$) per

incidence, based on the mean of average WTP estimates for

the four severity definitions of a “bad asthma day,” described

in Rowe and Chestnut (1986).  This study surveyed asthmatics

to estimate WTP for avoidance of a “bad asthma day,” as

defined by the subjects.  For purposes of valuation, an

asthma exacerbation is assumed to be equivalent to a day in

which asthma is moderate or worse as reported in the Rowe

and Chestnut (1986) study.  The value is assumed have a

uniform distribution between $19 and $83 (2006$).

Acute Respiratory

Symptoms $30  

The valuation estimate for "any of 19 acute respiratory

symptoms” is derived from Krupnick et al. (1990) assuming

that this health endpoint consists either of upper respiratory

symptoms (URS) or lower respiratory symptoms (LRS), or

both.  We assumed the following probabilities for a day of

"any of 19 acute respiratory symptoms": URS with 40 percent

probability, LRS with 40 percent probability, and both with 20

percent probability.  The point estimate of WTP to avoid a

day of “the presence of any of 19 acute respiratory

symptoms” is $28 (2006$).  The value is assumed have a

uniform distribution between $0 and $56 (2006$).

*All estimates rounded to two significant figures. All values have been inflated to reflect values in 2006 dollars and

income levels in 2020.   
5‐20

5.6 Health Benefits of Reducing Exposure to SO2 Results

EPA estimated the monetized human health benefits of reducing cases of morbidity

among populations exposed to SO2 in 2020 for the selected standard and the alternative

standard levels in 2006$.  For the selected SO2 standard at 75 ppb, the monetized benefits from

reduced SO2 exposure would be $2.2 million in 2020.  Figure 5.4 shows the breakdown of the

monetized SO2 benefits by health endpoint.  Table 5.5 shows the incidences of health effects

and monetized benefits of attaining the alternative standard levels by health endpoint.  

Because all health effects from SO2 exposure are expected to occur within the analysis year, the

monetized benefits for SO2 do not need to be discounted.  Please note that these benefits do

not include any of the benefits listed as “unquantified” in Table 5.1, nor do they include the PM

co‐benefits, which are presented in the section 5.7.   

Figure 5.4: Breakdown of Monetized SO2Health Benefits by Endpoint  
5‐21

Table 5.5: SO2 Health Benefits of Attaining Alternate Standard Levels in 2020 in 2006$  

(95th percentile confidence interval)

Incidence Valuation

50 ppb

Acute Respiratory Symptoms 38,000 (‐21,000 ‐‐ 97,000) $1,100,000 (‐$730,000 ‐‐ $4,200,000)

Hospital Admissions, Respiratory 170 (‐10 ‐‐ 360)    $4,100,000 ($120,000 ‐‐ $8,100,000)

Asthma Exacerbation 55,000 (7,800 ‐‐ 130,000) $2,900,000 ($440,000 ‐‐ $8,800,000)

Emergency Room Visits, Respiratory 930 (‐230 ‐‐ 2,600)    $340,000 (‐$53,000 ‐‐ $940,000)

     Total $8,500,000 (‐$210,000 ‐‐ $22,000,000)

75 ppb

Acute Respiratory Symptoms 9,400 (‐5,200 ‐‐ 24,000) $280,000 (‐$180,000 ‐‐ $1,100,000)

Hospital Admissions, Respiratory 46 (‐3 ‐‐ 95) $1,100,000 ($33,000 ‐‐ $2,100,000)

Asthma Exacerbation 14,000 (1,900 ‐‐ 33,000) $720,000 ($110,000 ‐‐ $2,200,000)

Emergency Room Visits, Respiratory 260 (‐65 ‐‐ 720) $95,000 (‐$15,000 ‐‐ $260,000)

     Total $2,200,000 (‐$52,000 ‐‐ $5,600,000)

100 ppb

Acute Respiratory Symptoms 2,600 (‐1,500 ‐‐ 6,700) $80,000 (‐$50,000 ‐‐ $290,000)

Hospital Admissions, Respiratory 13 (‐1 ‐‐ 27) $310,000 ($9,500 ‐‐ $620,000)

Asthma Exacerbation 3,800 (530 ‐‐ 9,200) $200,000 ($30,000 ‐‐ $610,000)

Emergency Room Visits, Respiratory 74 (‐19 ‐‐ 200) $27,000 (‐$4,400 ‐‐ $74,000)

     Total $620,000 (‐$15,000 ‐‐ $1,600,000)

*All estimates are rounded to two significant figures.  The negative 5th percentile incidence estimates for acute

respiratory symptoms are a result of the weak statistical power of the study and should not be inferred to indicate

that decreased SO2 exposure may cause an increase in this health endpoint.

In Table 5.6, we present the results of sensitivity analyses for the SO2 benefits.  We

indicate each input parameter, the value used as the default, and the values for the sensitivity

analyses, and then we provide the total monetary benefits for each input and the percent

change from the default value.   

Table 5.6: Sensitivity Analyses for SO2 Health Benefits to Fully Attain 50 ppb Standard

      Total SO2 Benefits

(millions of 2006$)  

% Change

from Default

Exposure Estimation Method

50km radius $2.2 N/A

75km radius $2.7 25%

100km radius $3.1 42%

150km radius $3.7 71%

Location of Hospital Admission

Studies  

w/US‐based studies only $2.2 N/A

w/Canada‐based studies only $12 438%

Asthma Pooling Method Pool all endpoints together $2.2 N/A

One or more symptoms only $2.2 ‐0.2%

Interpolation Method Inverse distance squared $2.2 N/A

Inverse distance $2.5 12%
5‐22

5.7 PM2.5 Co‐Benefits

Because SO2 is also a precursor to PM2.5, reducing SO2 emissions in the projected non‐

attainment areas will also reduce PM2.5 formation, human exposure and the incidence of PM2.5‐

related health effects.  In this analysis, we estimated the co‐benefits of reducing PM2.5 exposure

for the alternative standards.  Due to analytical limitations, it was not possible to provide a

comprehensive estimate of PM2.5‐related benefits.  Instead, we used the “benefit‐per‐ton”

method to estimate these benefits (Fann et al, 2009).  Please see Chapter 4 for more

information on the tons of emission reductions calculated for the control strategy.7

   

The PM2.5 benefit‐per‐ton methodology incorporates key assumptions described in

detail below.  These PM2.5 benefit‐per‐ton estimates provide the total monetized human health

benefits (the sum of premature mortality and premature morbidity) of reducing one ton of

PM2.5 from a specified source.  EPA has used the benefit per‐ton technique in previous RIAs,

including the recent Ozone NAAQS RIA (U.S. EPA, 2010a) and NO2 NAAQS RIA (U.S. EPA, 2010b).  

Table 5.7 shows the quantified and unquantified benefits captured in those benefit‐per‐ton

estimates.   

Table 5.7: Human Health and Welfare Effects of PM2.5

Pollutant /

Effect

Quantified and Monetized

in Primary Estimates

Unquantified Effects

Changes in:

PM2.5 Adult premature mortality

Bronchitis: chronic and acute

Hospital admissions: respiratory and

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial infarction)

Lower and upper respiratory illness

Minor restricted‐activity days

Work loss days

Asthma exacerbations (asthmatic population)

Infant mortality

Subchronic bronchitis cases

Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic

bronchitis

Non‐asthma respiratory emergency room visits

Visibility

Household soiling

Consistent with the Portland Cement NESHAP, the benefits estimates utilize the

concentration‐response functions as reported in the epidemiology literature, as well as the 12

functions obtained in EPA’s expert elicitation study as a sensitivity analysis.   

                                                           

7

  Pollution controls installed to comply with this standard would also reduce ambient PM2.5 concentrations.  This

illustrative analysis is incremental to the 2006 PM NAAQS, so these benefits are in addition to those estimates

for that rule.  Furthermore, the controls installed to comply with this standard might also help states attain a

more stringent PM NAAQS if one is promulgated in 2011.
5‐23

 One estimate is based on the concentration‐response (C‐R) function developed

from the extended analysis of American Cancer Society (ACS) cohort, as reported in

Pope et al. (2002), a study that EPA has previously used to generate its primary

benefits estimate.  When calculating the estimate, EPA applied the effect coefficient

as reported in the study without an adjustment for assumed concentration

threshold of 10 μg/m3 as was done in recent (2006‐2009) Office of Air and Radiation

RIAs.

 One estimate is based on the C‐R function developed from the extended analysis

of the Harvard Six Cities cohort, as reported by Laden et al. (2006).  This study,

published after the completion of the Staff Paper for the 2006 PM2.5NAAQS, has

been used as an alternative estimate in the PM2.5NAAQS RIA and PM2.5 co‐benefits

estimates in RIAs completed since the PM2.5NAAQS.  When calculating the estimate,

EPA applied the effect coefficient as reported in the study without an adjustment for

assumed concentration threshold of 10 μg/m3 as was done in recent (2006‐2009)

RIAs.  

 Twelve estimates are based on the C‐R functions from EPA’s expert elicitation

study (IEc, 2006; Roman et al., 2008) on the PM2.5 ‐mortality relationship and

interpreted for benefits analysis in EPA’s final RIA for the PM2.5NAAQS.  For that

study, twelve experts (labeled A through L) provided independent estimates of the

PM2.5 ‐mortality concentration‐response function.  EPA practice has been to develop

independent estimates of PM2.5 ‐mortality estimates corresponding to the

concentration‐response function provided by each of the twelve experts, to better

characterize the degree of variability in the expert responses.

The effect coefficients are drawn from epidemiology studies examining two large

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six

Cities cohort (Laden et al., 2006).8

  These are logical choices for anchor points in our

presentation because, while both studies are well designed and peer reviewed, there are

strengths and weaknesses inherent in each, which we believe argues for using both studies to

generate  benefits estimates.  Previously, EPA had calculated benefits based on these two

empirical studies, but derived the range of benefits, including the minimum and maximum

results, from an expert elicitation of the relationship between exposure to PM2.5 and premature

mortality (Roman et al., 2008).  Within this assessment, we include the benefits estimates

derived from the concentration‐response function provided by each of the twelve experts to

better characterize the uncertainty in the concentration‐response function for mortality and

the degree of variability in the expert responses.  Because the experts used these cohort

studies to inform their concentration‐response functions, benefits estimates using these

functions generally fall between results using these epidemiology studies (see Figure 5.1).  In

                                                           

8 These two studies specify multi‐pollutant models that control for SO2, among other co‐pollutants.
5‐24

general, the expert elicitation results support the conclusion that the benefits of PM2.5 control

are very likely to be substantial.

Readers interested in reviewing the general methodology for creating the benefit‐per‐

ton estimates used in this analysis should consult Fann et al. (2009) or the Technical Support

Document (TSD) accompanying the ozone NAAQS RIA (USEPA 2008a).  As described in the

documentation for the benefit per‐ton estimates cited above, national per‐ton estimates are

developed for selected pollutant/source category combinations.  The per‐ton values calculated

therefore apply only to tons reduced from those specific pollutant/source combinations (e.g.,

SO2 emitted from electric generating units; SO2 emitted from area sources).  Our estimate of

PM2.5 co‐control benefits is therefore based on the total PM2.5 emissions controlled by sector

and multiplied by this per‐ton value.   

The benefit‐per‐ton coefficients in this analysis were derived using modified versions of

the health impact functions used in the PM NAAQS Regulatory Impact Analysis.  Specifically,

this analysis uses the benefit‐per‐ton estimates first applied in the Portland Cement NESHAP

RIA (U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an

expanded geographic scope of the benefit‐per‐ton calculation, and the functions directly from

the epidemiology studies without an adjustment for an assumed threshold.9  Removing the

threshold assumption is a key difference between the method used in this analysis of PM‐co

benefits and the methods used in RIAs prior to Portland Cement, and we now calculate

incremental benefits down to the lowest modeled PM2.5 air quality levels.   

EPA strives to use the best available science to support our benefits analyses, and we

recognize that interpretation of the science regarding air pollution and health is dynamic and

evolving.  Based on our review of the body of scientific literature, EPA applied the no‐threshold

model in this analysis. EPA's final Integrated Science Assessment (2009d), which was recently

reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA‐SAB, 2009a; U.S. EPA‐SAB,

2009b), concluded that the scientific literature consistently finds that a no‐threshold log‐linear

model most adequately portrays the PM‐mortality concentration‐response relationship while

recognizing potential uncertainty about the exact shape of the concentration‐response

function.  In Table 5‐12, we include an estimate of the sensitivity of the results to an assumed

threshold at 10 μg/m3

.

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods

used to estimate air quality benefits evolve over time to reflect the Agency’s most current

                                                           

9 The benefit‐per‐ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as

discussed on the next page.   
5‐25

interpretation of the scientific and economic literature.  For a period of time (2004‐2008), the

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life

(VSL) estimate derived from a limited analysis of some of the available studies.  OAR arrived at a

VSL using a range of $1 million to $10 million (2000$) consistent with two meta‐analyses of the

wage‐risk literature.  The $1 million value represented the lower end of the interquartile range

from the Mrozek and Taylor (2002) meta‐analysis of 33 studies.  The $10 million value

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta‐

analysis of 43 studies.  The mean estimate of $5.5 million (2000$)10 was also consistent with the

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta‐analysis.  However, the

Agency neither changed its official guidance on the use of VSL in rule‐makings nor subjected the

interim estimate to a scientific peer‐review process through the Science Advisory Board (SAB)

or other peer‐review group.   

During this time, the Agency continued work to update its guidance on valuing mortality

risk reductions, including commissioning a report from meta‐analytic experts to evaluate

methodological questions raised by EPA and the SAB on combining estimates from the various

data sources.  In addition, the Agency consulted several times with the Science Advisory Board

Environmental Economics Advisory Committee (SAB‐EEAC) on the issue.  With input from the

meta‐analytic experts, the SAB‐EEAC advised the Agency to update its guidance using specific,

appropriate meta‐analytic techniques to combine estimates from unique data sources and

different studies, including those using different methodologies (i.e., wage‐risk and stated

preference) (U.S. EPA‐SAB, 2007).   

    

Until updated guidance is available, the Agency determined that a single, peer‐reviewed

estimate applied consistently best reflects the SAB‐EEAC advice it has received.  Therefore, the

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines

for Preparing Economic Analyses (U.S. EPA, 2000)11 while the Agency continues its efforts to

update its guidance on this issue.  This approach calculates a mean value across VSL estimates

derived from 26 labor market and contingent valuation studies published between 1974 and

1991.  The mean VSL across these studies is $6.3 million (2000$).12 The Agency is committed to

using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions

                                                           

10  After adjusting the VSL to account for a different currency year (2006$) and to account for income growth to

2020, the $5.5 million VSL is $7.7m.   11 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008d), EPA retained the VSL endorsed by the SAB

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in

the near future.  Therefore, this report does not represent final agency policy.   12  In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income

growth to 2020.  After applying these adjustments to the $6.3 million value, the VSL is $8.9m.   
5‐26

and has made significant progress in responding to the SAB‐EEAC’s specific recommendations.  

The Agency anticipates presenting results from this effort to the SAB‐EEAC in Spring 2010 and

that draft guidance will be available shortly thereafter.

Table 5.8 provides the unit values used to monetize the benefits of reduced exposure to

PM2.5.  Figure 5.5 illustrates the relative breakdown of the monetized PM2.5 health benefits.   

Table 5.8: Unit Values used for Economic Valuation of PM2.5Health Endpoints (2006$)*

Health Endpoint

Central Estimate

of Value Per

Statistical

Incidence (2020

income level)

Derivation of Distributions of Estimates

Premature

Mortality  

(Value of a

Statistical Life)

$8,900,000  

EPA currently recommends a central VSL of $6.3m (2000$) based on

a Weibull distribution fitted to 26 published VSL estimates (5

contingent valuation and 21 labor market studies).  The underlying

studies, the distribution parameters, and other useful information

are available in Appendix B of EPA's current Guidelines for Preparing

Economic Analyses (U.S. EPA, 2000).   

Chronic Bronchitis

(CB) $490,000  

The WTP to avoid a case of pollution‐related CB is calculated as WTPx

= WTP13 * e‐β*(13‐x) , where x is the severity of an average CB case,

WTP13 is the WTP for a severe case of CB, and $ is the parameter

relating WTP to severity, based on the regression results reported in

Krupnick and Cropper (1992).  The distribution of WTP for an average

severity‐level case of CB was generated by Monte Carlo methods,

drawing from each of three distributions: (1) WTP to avoid a severe

case of CB is assigned a 1/9 probability of being each of the first nine

deciles of the distribution of WTP responses in Viscusi et al. (1991); 2)

the severity of a pollution‐related case of CB (relative to the case

described in the Viscusi study) is assumed to have a triangular

distribution, with the most likely value at severity level 6.5 and

endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of

WTP with respect to severity is normally distributed with mean =

0.18 and standard deviation = 0.0669 (from Krupnick and Cropper

[1992]). This process and the rationale for choosing it is described in

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

(U.S. EPA, 1999b).   

Nonfatal Myocardial Infarction  

(heart attack)

  

No distributional information available.  Age‐specific cost‐of‐illness

values reflect lost earnings and direct medical costs over a 5‐year on

period following a nonfatal MI. Lost earnings estimates are based

Cropper and Krupnick (1990).  Direct medical costs are based on

simple average of estimates from Russell et al. (1998) and Wittels et

al. (1990).

Lost earnings: Cropper and Krupnick (1990).  Present discounted

value of 5 years of lost earnings in (2006$):

age of onset: at 3%, at 7%

25–44:  $11,000,  $10,000

45–54:  $17,000, $15,000

55–65:  $96,000, $86,000

3% discount rate   

Age 0–24 $80,000  

Age 25–44 $96,000  

Age 45–54 $100,000  

Age 55–65 $180,000  

Age 66 and over $80,000  
5‐27

Direct medical expenses: An average of:

7% discount rate    1. Wittels et al. (1990) ($130,000—no discounting)

Age 0–24 $80,000   2. Russell et al. (1998), 5‐year period ($29,000 at 3%, $27,000 at

7%)

Age 25–44 $88,000    

Age 45–54 $92,000    

Age 55–65 $160,000    

Age 66 and over $78,000     

Hospital Admissions and ER Visits

Chronic

Obstructive

Pulmonary Disease

(COPD)

$17,000  

No distributional information available.  The COI estimates (lost

earnings plus direct medical costs) are based on ICD‐9 code‐level

information (e.g., average hospital care costs, average length of

hospital stay, and weighted share of total COPD category illnesses)

reported in Agency for Healthcare Research and Quality (2000)

(www.ahrq.gov).   

Asthma

Admissions $8,900  

No distributional information available.  The COI estimates (lost

earnings plus direct medical costs) are based on ICD‐9 code‐level

information (e.g., average hospital care costs, average length of

hospital stay, and weighted share of total asthma category illnesses)

reported in Agency for Healthcare Research and Quality (2000)

(www.ahrq.gov).   

All Cardiovascular $25,000  

No distributional information available.  The COI estimates (lost

earnings plus direct medical costs) are based on ICD‐9 code‐level

information (e.g., average hospital care costs, average length of

hospital stay, and weighted share of total cardiovascular category

illnesses) reported in Agency for Healthcare Research and Quality

(2000) (www.ahrq.gov).   

All respiratory

(ages 65+) $25,000  

No distributions available.  The COI point estimates (lost earnings

plus direct medical costs) are based on ICD‐9 code level information

(e.g., average hospital care costs, average length of hospital stay, and

weighted share of total COPD category illnesses) reported in Agency

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

All respiratory

(ages 0–2) $10,000  

No distributions available.  The COI point estimates (lost earnings

plus direct medical costs) are based on ICD‐9 code level information

(e.g., average hospital care costs, average length of hospital stay, and

weighted share of total COPD category illnesses) reported in Agency

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

Emergency Room

Visits for Asthma $370  

No distributional information available.  Simple average of two unit

COI values:

(1) $400 (2006$), from Smith et al. (1997) and

(2) $340 (2006$), from Stanford et al. (1999).

Respiratory Ailments Not Requiring Hospitalization
5‐28

Upper Respiratory

Symptoms  

(URS)

$31  

Combinations of the three symptoms for which WTP estimates are

available that closely match those listed by Pope et al. result in seven

different “symptom clusters,” each describing a “type” of URS.  A

dollar value was derived for each type of URS, using mid‐range

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster

and assuming additivity of WTPs.  In the absence of information

surrounding the frequency with which each of the seven types of URS

occurs within the URS symptom complex, we assumed a uniform

distribution between $11 and $50 (2006$).

Lower Respiratory

Symptoms  

(LRS)

$19  

Combinations of the four symptoms for which WTP estimates are

available that closely match those listed by Schwartz et al. result in

11 different “symptom clusters,” each describing a “type” of LRS.  A

dollar value was derived for each type of LRS, using mid‐range

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster

and assuming additivity of WTPs.  The dollar value for LRS is the

average of the dollar values for the 11 different types of LRS.  In the

absence of information surrounding the frequency with which each

of the 11 types of LRS occurs within the LRS symptom complex, we

assumed a uniform distribution between $8 and $29 (2006$).

Asthma

Exacerbations $53  

Asthma exacerbations are valued at $49 (2006$) per incidence, based

on the mean of average WTP estimates for the four severity

definitions of a “bad asthma day,” described in Rowe and Chestnut

(1986).  This study surveyed asthmatics to estimate WTP for

avoidance of a “bad asthma day,” as defined by the subjects.  For

purposes of valuation, an asthma exacerbation is assumed to be

equivalent to a day in which asthma is moderate or worse as

reported in the Rowe and Chestnut (1986) study.  The value is

assumed have a uniform distribution between $19 and $83 (2006$).

Acute Bronchitis $440  

Assumes a 6‐day episode, with the distribution of the daily value

specified as uniform with the low and high values based on those

recommended for related respiratory symptoms in Neumann et al.

(1994).  The low daily estimate of $12 (2006$) is the sum of the mid‐

range values recommended by IEc for two symptoms believed to be

associated with acute bronchitis: coughing and chest tightness.  The

high daily estimate was taken to be twice the value of a minor

respiratory restricted‐activity day, or $130 (2006$).   

Work Loss Days

(WLDs) Variable

No distribution available.  Point estimate is based on county‐specific

median annual wages divided by 50 (assuming 2 weeks of vacation)

and then by 5—to get median daily wage.  U.S. Year 2000 Census,

compiled by Geolytics, Inc.

Minor Restricted

Activity Days

(MRADs)

$63  

Median WTP estimate to avoid one MRAD from Tolley et al. (1986).

Distribution is assumed to be triangular with a minimum of $26 and a

maximum of $97 (2006$).  Range is based on assumption that value

should exceed WTP for a single mild symptom (the highest estimate

for a single symptom—for eye irritation—is $19 (2006$)) and be less

than that for a WLD.  The triangular distribution acknowledges that

the actual value is likely to be closer to the point estimate than either

extreme.

*All estimates rounded to two significant figures.  All values have been inflated to reflect values in 2006 dollars.   
5‐29

Figure 5.5: Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from

Pope et al.*

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

AsthmaExacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%

*This pie chart is an illustrative breakdown of the monetized PM co‐benefits, using the results based on Pope et al.

(2002) as an example.  Using the Laden et al. (2006) function for premature mortality, the percentage of total

monetized benefits due to adult mortality would be 97%.  This chart shows the breakdown using a 3% discount

rate, and the results would be similar if a 7% discount rate was used.   

Because epidemiology studies have indicated that there is a lag between exposure to

PM2.5 and premature mortality, the discount rate has a substantial effect on the final monetized

benefits.13   We provide the PM co‐benefit results using discount rates of 3% and 7% in Table

5.11 and the total monetized benefits (i.e., SO2 and PM2.5) results using both discount rates in

Table 5.13.  We test the sensitivity of the PM results to discount rates of 3% and 7% in Table

5.12.   

                                                           

13 To comply with Circular A‐4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003).

These benefits are estimated for a specific analysis year (i.e., 2020), and most of the PM benefits occur within

that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality.  For AMIs, we

assume 5 years of follow‐up medical costs and lost wages.  For premature mortality, we assume that there is a

“cessation” lag between PM exposures and the total realization of changes in health effects. Although the

structure of the lag is uncertain, EPA follows the advice of the SAB‐HES to assume a segmented lag structure

characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to

20 after the reduction in PM2.5 (U.S. EPA‐SAB, 2004). Changes in the lag assumptions do not change the total

number of estimated deaths but rather the timing of those deaths.  Therefore, discounting only affects the AMI

costs after the analysis year and the valuation of premature mortalities that occur after the analysis year.  As

such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized

benefits using a 3% discount rate.
5‐30

The benefit‐per‐ton estimates are provided in Table 5.9 and the health incidences are

provided in Table 5.10.  Table 5.11 shows the monetized results using the two epidemiology‐

based estimates as well as the 12 expert‐based estimates.  Figure 5.6 provides a graphical

breakdown of the PM2.5 co‐benefits by sector.  Figure 5.7 provides a graphical representation of

all 14 of the PM2.5 co‐benefits, at both a 3 percent and 7 percent discount rate.   

Table 5.9:  PM2.5 Co‐benefits associated with reducing SO2 emissions (2006$)*

PM2.5 Precursor Benefit per Ton Estimate

(Pope)

Benefit per Ton Estimate  

(Laden)

SO2 EGU: $42,000 $100,000

SO2 non‐EGU: $30,000 $74,000

SO2 area: $19,000 $47,000

*Estimates have been rounded to two significant figures.  Confidence intervals are not available for benefit per‐ton

estimates.  Estimates shown use a 3% discount rate.  Estimates at a 7% discount rate would be approximately 9%

lower.  

Table 5.10:  Summary of Reductions in Health Incidences from PM2.5 Co‐Benefits to Attain

Alternate Standard Levels in 2020*

   50 ppb 75 ppb 100 ppb

Avoided Premature Mortality     

Pope 5,100 2,300 1,100

Laden 13,000 5,900 2,900

Woodruff (Infant Mortality) 20 9 5

Avoided Morbidity     

Chronic Bronchitis 3,500 1,600 780

Acute Myocardial Infarction 8,600 3,900 1,900

Hospital Admissions, Respiratory 1,300 570 280

Hospital Admissions, Cardiovascular 2,800 1,300 620

Emergency Room Visits, Respiratory 4,900 2,200 1,100

Acute Bronchitis 8,200 3,700 1,800

Work Loss Days 650,000 290,000 150,000

Asthma Exacerbation 90,000 41,000 20,000

Acute Respiratory Symptoms 3,900,000 1,700,000 870,000

Lower Respiratory Symptoms 98,000 44,000 22,000

Upper Respiratory Symptoms 74,000 33,000 17,000

*All estimates are for the analysis year (2020) and are rounded to two significant figures.  All fine particles are

assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different propensity to form

PM2.5.  These results reflect full attainment with the various standard levels, including extrapolated tons, which

were spread across the sectors in proportion to the emissions in the county.    
5‐31

Table 5.11: All PM2.5 Co‐Benefits Estimates to Attain Alternate Standard Levels in 2020 at

discount rates of 3% and 7% (in millions of 2006$)*

   50 ppb 75 ppb 100 ppb

   3% 7% 3% 7% 3% 7%

Benefit‐per‐ton Coefficients Derived from Epidemiology Literature

Pope et al. $34,000 $31,000 $15,000 $14,000 $7,400 $6,700

Laden et al. $83,000 $75,000 $37,000 $34,000 $18,000 $16,000

Benefit‐per‐ton Coefficients Derived from Expert Elicitation

Expert A $88,000 $79,000 $40,000 $36,000 $19,000 $17,000

Expert B $67,000 $61,000 $30,000 $27,000 $15,000 $13,000

Expert C $67,000 $60,000 $30,000 $27,000 $15,000 $13,000

Expert D $47,000 $43,000 $21,000 $19,000 $10,000 $9,400

Expert E $110,000 $98,000 $49,000 $44,000 $24,000 $21,000

Expert F $61,000 $55,000 $27,000 $25,000 $13,000 $12,000

Expert G $40,000 $36,000 $18,000 $16,000 $8,700 $7,900

Expert H $50,000 $46,000 $23,000 $21,000 $11,000 $9,900

Expert I $66,000 $60,000 $30,000 $27,000 $14,000 $13,000

Expert J $54,000 $49,000 $24,000 $22,000 $12,000 $11,000

Expert K $13,000 $12,000 $5,900 $5,400 $2,900 $2,600

Expert L $49,000 $44,000 $22,000 $20,000 $11,000 $9,600

*

All estimates are rounded to two significant figures.  Estimates do not include confidence intervals because they

were derived through the benefit‐per‐ton technique described above.  The benefits estimates from the Expert

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated

with the concentration‐response function.  These results reflect full attainment with the various standard levels,

including extrapolated tons, which were spread across the sectors in proportion to the emissions in the county.

In Table 5.12, we present the results of sensitivity analyses for the PM co‐benefits.  We

indicate each input parameter, the value used as the default, and the values for the sensitivity

analyses, and then we provide the total monetary benefits for each input and the percent

change from the default value.   
5‐32

Table 5.12: Sensitivity Analyses for PM2.5Health Co‐Benefits to Fully Attain 75 ppb

    Total PM2.5 Co‐Benefits

(billions of 2006$)

% Change from

Default

Threshold Assumption (with

Epidemiology Study)

No Threshold (Pope) $15 N/A

No Threshold (Laden) $37 N/A

Threshold (Pope)* $10 ‐33%

Threshold (Laden)* $22 ‐41%

Discount Rate  (with

Epidemiology Study)

3% (Pope) $15 N/A

3% (Laden) $37 N/A

7% (Pope) $14 ‐8%

7% (Laden) $34 ‐9%

Simulated Attainment  

(using Pope)

Full attainment $15 N/A

Partial Attainment $14 ‐7%

*The Threshold model is not directly comparable to the no‐threshold model.  The threshold model estimates do

not include two technical updates, and they are based on data for 2015, instead of 2020.  Directly comparable

estimates are not available.   

Figure 5.6: Monetized PM2.5 Co‐Benefits of Fully Attaining 75 ppb by PM2.5 Precursor

$‐

$10

$20

$30

$40

Pope et al Laden et al

Billions of 2006$

PM2.5 Mortality Function

SO2 area SO2 non‐EGU SO2 EGU

* All estimates are for the analysis year (2020).  All fine particles are assumed to have equivalent health effects, but

each PM2.5 precursor pollutant has a different propensity to form PM2.5.  Results using a 7% discount rate would

show a similar breakdown.  These results reflect full attainment with the various standard levels, including

extrapolated tons, which were spread across the sectors in proportion to the emissions in the county.
5‐33

Figure 5.7: Monetized PM2.5 Co‐Benefits of Fully Attaining 75 ppb*

$0

$10

$20

$30

$40

$50

Pope et al  

Laden et al

Billions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

* This graph shows the estimated co‐ benefits in 2020 for the selected standard of 75 ppb using the no‐threshold model

at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. study and the Laden et al. study, as

well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality.  The results shown are not the direct

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration‐response

function provided in those studies.  Graphs for alternative standards would show a similar pattern. These results reflect

full attainment with the various standard levels, including extrapolated tons, which were spread across the sectors

in proportion to the emissions in the county.

5.8 Summary of Total Monetized Benefits (SO2 and PM2.5)

EPA estimated the monetized human health benefits of reducing cases of morbidity and

premature mortality among populations exposed to SO2 and PM2.5 in 2020 for each of the

alternative standard levels in 2006$.  For the selected SO2 standard at 75 ppb, the total

monetized benefits would be $15 to $37 billion at a 3% discount rate and $14 to $34 billion at a

7% discount rate.   

All of the results in this chapter present benefits estimates that assume full attainment

with the alternative standard levels.  Partial attainment only incorporates the emission

reductions from identified controls without the extrapolated emission reductions.14  These

results are shown in Table 5.13 along with the full attainment at discount rates of 3% and 7%.  

Table 5.14 shows the total incidences of avoided health effects.  Figure 5.8 provides a graphical

                                                           

14 See Chapter 4 for more information regarding the control strategy, including the identified and extrapolated

emission reductions.   
5‐34

representation of all 14 total monetized benefits estimates, at both a 3 percent and 7 percent

discount rate, for the selected standard of 75 ppb, respectively.   

Table 5.13: Total Monetized Benefits to attain Alternate Standard Levels at Discount Rates of

3% and 7% for Full and Partial Attainment (millions of 2006$)a,c

    SO2

PM2.5

(Pope)

PM2.5

(Laden)

TOTAL

(with Pope)

TOTAL

(with Laden )

50 ppb

3% Full Attainment $8.5 $34,000 $83,000 $34,000 $83,000

7% Full Attainment $8.5 $31,000 $75,000 $31,000 $75,000

3% Partial Attainment ‐

b $30,000 $74,000 $30,000 $74,000

7% Partial Attainment ‐

b $28,000 $67,000 $28,000 $67,000

75 ppb

3% Full Attainment $2.2 $15,000 $37,000 $15,000 $37,000

7% Full Attainment $2.2 $14,000 $34,000 $14,000 $34,000

3% Partial Attainment ‐

b $14,000 $35,000 $14,000 $35,000

7% Partial Attainment ‐

b $13,000 $31,000 $13,000 $31,000

100 ppb

3% Full Attainment $0.62 $7,400 $18,000 $7,400 $18,000

7% Full Attainment $0.62 $6,700 $16,000 $6,700 $16,000

3% Partial Attainment ‐

b $6,900 $17,000 $6,900 $17,000

7% Partial Attainment ‐

b $6,200 $15,000 $6,200 $15,000

a Estimates have been rounded to two significant figures and therefore summation may not match table estimates. b The approach used to simulate air quality changes for SO2 did not provide the data needed to distinguish partial

attainment benefits from full attainment benefits from reduced SO2 exposure. Therefore, a portion of the SO2

benefits is attributable to the known controls and a portion of the SO2 benefits are attributable to the extrapolated

controls.   c These models assume that all fine particles, regardless of their chemical composition, are equally potent in

causing premature mortality because there is no clear scientific evidence that would support the development of

differential effects estimates by particle type.  Reductions in SO2 emissions from multiple sectors to meet the SO2

NAAQS would primarily reduce the sulfate fraction of PM2.5.  Because this rule targets a specific particle precursor

(i.e., SO2), this introduces some uncertainty into the results of the analysis.
5‐35

Table 5.14:  Summary of Reductions in Health Incidences from SO2 and PM2.5 to attain

Alternate Standard Levels*

   50 ppb 75 ppb 100 ppb

Avoided Premature Mortality     

Pope 5,100 2,300 1,100

Laden 13,000 5,900 2,900

Woodruff (Infant Mortality) 20 9 5

Avoided Morbidity     

Chronic Bronchitis 3,500 1,600 780

Acute Myocardial Infarction 8,600 3,900 1,900

Hospital Admissions, Respiratory 1,400 570 280

Hospital Admissions, Cardiovascular 2,800 1,300 620

Emergency Room Visits, Respiratory 5,800 2,500 1,200

Acute Bronchitis 8,200 3,700 1,800

Work Loss Days 650,000 290,000 150,000

Asthma Exacerbation 150,000 54,000 24,000

Acute Respiratory Symptoms 3,900,000 1,700,000 870,000

Lower Respiratory Symptoms 98,000 44,000 22,000

Upper Respiratory Symptoms 74,000 33,000 17,000

*All estimates are for the analysis year (2020) and are rounded to two significant figures.  All fine particles are

assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different propensity to form

PM2.5.  These results reflect full attainment with the various standard levels, including extrapolated tons, which

were spread across the sectors in proportion to the emissions in the county.    
5‐36

Figure 5.8: Total Monetized Benefits (SO2 and PM2.5) of Fully Attaining 75 ppb in 2020*

$0

$10

$20

$30

$40

$50

Pope et al  

Laden et al

Billions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

* This graphs shows the estimated total monetized benefits in 2020 for the selected standard of 75 ppb using the no‐

threshold model at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. study and the

Laden et al. study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality.  The results

shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the

concentration‐response function provided in those studies.  Graphs for alternative standards would show a similar

pattern.

5.9 Unquantified Welfare Benefits

The monetized benefits estimated in this RIA only reflect the portion of benefits

attributable to the health effect reductions associated with ambient fine particles and direct

exposure to SO2. Data, resource, and methodological limitations prevented EPA from

quantifying or monetizing the benefits from several important benefit categories, including

benefits from reducing ecosystem effects and visibility impairment.  In this section, we provide

a qualitative assessment of two welfare benefit categories: ecosystem benefits of reducing

sulfur deposition and visibility improvements.

5.9.1 Ecosystem Benefits of Reduced Sulfur Deposition

Ecosystem services can be generally defined as the benefits that individuals and

organizations obtain from ecosystems.  EPA has defined ecological goods and services as the

“outputs of ecological functions or processes that directly or indirectly contribute to social

welfare or have the potential to do so in the future.  Some outputs may be bought and sold, but
5‐37

most are not marketed” (U.S. EPA, 2006c).  Figure 5.9 provides the Millennium Ecosystem

Assessment’s schematic demonstrating the connections between the categories of ecosystem

services and human well‐being.  The interrelatedness of these categories means that any one

ecosystem may provide multiple services.  Changes in these services can affect human well‐

being by affecting security, health, social relationships, and access to basic material goods

(MEA, 2005).  

   

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are classified

into four main categories:

1. Provisioning: Products obtained from ecosystems, such as the production of food and

water

2. Regulating: Benefits obtained from the regulation of ecosystem processes, such as the

control of climate and disease

3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual

enrichment, cognitive development, reflection, recreation, and aesthetic experiences

4. Supporting: Services necessary for the production of all other ecosystem services, such

as nutrient cycles and crop pollination

Figure 5.9.  Linkages between categories of ecosystem services and components of human

well‐being from Millennium Ecosystem Assessment (MEA, 2005)

The monetization of ecosystem services generally involves estimating the value of

ecological goods and services based on what people are willing to pay (WTP) to increase

ecological services or by what people are willing to accept (WTA) in compensation for
5‐38

reductions in them (U.S. EPA, 2006c).  There are three primary approaches for estimating the

monetary value of ecosystem services: market‐based approaches, revealed preference

methods, and stated preference methods (U.S. EPA, 2006c).  Because economic valuation of

ecosystem services can be difficult, nonmonetary valuation using biophysical measurements

and concepts also can be used.  An example of a nonmonetary valuation method is the use of

relative‐value indicators (e.g., a flow chart indicating uses of a water body, such as boatable,

fishable, swimmable, etc.).  It is necessary to recognize that in the analysis of the environmental

responses associated with any particular policy or environmental management action, only a

subset of the ecosystem services likely to be affected are readily identified.  Of those ecosystem

services that are identified, only a subset of the changes can be quantified.  Within those

services whose changes can be quantified, only a few will likely be monetized, and many will

remain nonmonetized.  The stepwise concept leading up to the valuation of ecosystems

services is graphically depicted in Figure 5.10.  

Figure 5.10:  Schematic of the benefits assessment process (U.S. EPA, 2006c)

Science of Sulfur Deposition

Sulfur emissions occur over large regions of North America.  Once these pollutants are

lofted to the middle and upper troposphere, they typically have a much longer lifetime and,

with the generally stronger winds at these altitudes, can be transported long distances from

their source regions. The length scale of this transport is highly variable owing to differing

chemical and meteorological conditions encountered along the transport path (U.S. EPA,

2008f).  Sulfur is primarily emitted as SO2, and secondary particles are formed from SOX gaseous
5‐39

emissions and associated chemical reactions in the atmosphere.  Deposition can occur in either

a wet (i.e., rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., gases or particles).  Together

these emissions are deposited onto terrestrial and aquatic ecosystems across the U.S.,

contributing to the problems of acidification, nutrient enrichment, and methylmercury

production as represented in Figure 5‐11.   

Figure 5‐11: Schematic of Ecological Effects of Sulfur Deposition

The lifetimes of particles vary with particle size.  Accumulation‐mode particles such as

sulfates are kept in suspension by normal air motions and have a lower deposition velocity than

coarse‐mode particles; they can be transported thousands of kilometers and remain in the

atmosphere for a number of days.  They are removed from the atmosphere primarily by cloud

processes.  Particulates affect acid deposition by serving as cloud condensation nuclei and

contribute directly to the acidification of rain.  In addition, the gas‐phase species that lead to

the dry deposition of acidity are also precursors of particles.  Therefore, reductions in SO2

emissions will decrease both acid deposition and PM concentrations, but not necessarily in a

linear fashion (U.S. EPA, 2008f).  Sulfuric acid is also deposited on surfaces by dry deposition

and can contribute to environmental effects (U.S. EPA, 2008f).   

Ecological Effects of Acidification       

Deposition of sulfur can cause acidification, which alters biogeochemistry and affects

animal and plant life in terrestrial and aquatic ecosystems across the U.S.  Soil acidification is a

natural process, but is often accelerated by acidifying deposition, which can decrease

concentrations of exchangeable base cations in soils (U.S. EPA, 2008f). Major terrestrial effects

SO2 Atmospheric 

Fate and Transport 

Deposition 

Processes

Acidification

MeHg Production

Aquatic Terrestrial Aquatic Terrestrial
5‐40

include a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple

(Acer saccharum) (U.S. EPA, 2008f).  Biological effects of acidification in terrestrial ecosystems

are generally linked to aluminum toxicity and decreased ability of plant roots to take up base

cations (U.S. EPA, 2008f). Decreases in the acid neutralizing capacity and increases in inorganic

aluminum concentration contribute to declines in zooplankton, macro invertebrates, and fish

species richness in aquatic ecosystems (U.S. EPA, 2008f).   

  

Geology (particularly surficial geology) is the principal factor governing the sensitivity of

terrestrial and aquatic ecosystems to acidification from sulfur deposition (U.S. EPA, 2008f).  

Geologic formations having low base cation supply generally underlie the watersheds of acid‐

sensitive lakes and streams.  Other factors contribute to the sensitivity of soils and surface

waters to acidifying deposition, including topography, soil chemistry, land use, and hydrologic

flow path (U.S. EPA, 2008f).

Aquatic Ecosystems

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at

various trophic levels.  These studies indicate that aquatic biota have been affected by

acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems.  Effects

have been most clearly documented for fish, aquatic insects, other invertebrates, and algae.  

Biological effects are primarily attributable to a combination of low pH and high inorganic

aluminum concentrations.  Such conditions occur more frequently during rainfall and snowmelt

that cause high flows of water and less commonly during low‐flow conditions, except where

chronic acidity conditions are severe.  Biological effects of episodes include reduced fish

condition factor15, changes in species composition and declines in aquatic species richness

across multiple taxa, ecosystems and regions.  These conditions may also result in direct fish

mortality (Van Sickle et al., 1996).  Biological effects in aquatic ecosystems can be divided into

two major categories: effects on health, vigor, and reproductive success; and effects on

biodiversity. Surface water with ANC values greater than 50 μeq/L generally provides moderate

protection for most fish (i.e., brook trout, others) and other aquatic organisms (U.S. EPA,

2009c).  Table 5‐15 provides a summary of the biological effects experienced at various ANC

levels.   

                                                           

15 Condition factor is an index that describes the relationship between fish weight and length, and is one measure

of sublethal acidification stress that has been used to quantify effects of acidification on an individual fish

(U.S.EPA, 2008f).
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Table 5‐15:  Aquatic Status Categories

Category Label ANC Levels                 Expected Ecological Effects

Acute

Concern

<0 micro

equivalent per

Liter (μeq/L)

Near complete loss of fish populations is expected.  Planktonic communities

have extremely low diversity and are dominated by acidophilic forms.  The

number of individuals in plankton species that are present is greatly reduced.

Severe  

Concern

0–20 μeq/L

Highly sensitive to episodic acidification.  During episodes of high acidifying

deposition, brook trout populations may experience lethal effects.  Diversity and

distribution of zooplankton communities decline sharply.   

Elevated

Concern

20–50 μeq/L

Fish species richness is greatly reduced (i.e., more than half of expected species

can be missing).  On average, brook trout populations experience sublethal

effects, including loss of health, reproduction capacity, and fitness.  Diversity

and distribution of zooplankton communities decline.

Moderate

Concern

50–100 μeq/L

Fish species richness begins to decline (i.e., sensitive species are lost from

lakes).  Brook trout populations are sensitive and variable, with possible

sublethal effects.  Diversity and distribution of zooplankton communities also

begin to decline as species that are sensitive to acidifying deposition are

affected.

Low

Concern

>100 μeq/L

Fish species richness may be unaffected.  Reproducing brook trout populations

are expected where habitat is suitable.  Zooplankton communities are

unaffected and exhibit expected diversity and distribution.

A number of national and regional assessments have been conducted to estimate the

distribution and extent of surface water acidity in the U.S (U.S. EPA, 2008f).  As a result, several

regions of the U.S. have been identified as containing a large number of lakes and streams that

are seriously impacted by acidification.  Figure 5‐12 illustrates those areas of the U.S. where

aquatic ecosystems are at risk from acidification.   
5‐42

Figure 5‐12: Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008f)

Because acidification primarily affects the diversity and abundance of aquatic biota, it

also affects the ecosystem services that are derived from the fish and other aquatic life found in

these surface waters.

While acidification is unlikely to have serious negative effects on, for example, water

supplies, it can limit the productivity of surface waters as a source of food (i.e., fish).  In the

northeastern United States, the surface waters affected by acidification are not a major source

of commercially raised or caught fish; however, they are a source of food for some recreational

and subsistence fishermen and for other consumers.  For example, there is evidence that

certain population subgroups in the northeastern United States, such as the Hmong and

Chippewa ethnic groups, have particularly high rates of self‐caught fish consumption (Hutchison

and Kraft, 1994; Peterson et al., 1994).  However, it is not known if and how their consumption

patterns are affected by the reductions in available fish populations caused by surface water

acidification.

Inland surface waters support several cultural services, including aesthetic and

educational services and recreational fishing.  Recreational fishing in lakes and streams is

among the most popular outdoor recreational activities in the northeastern United States.  
5‐43

Based on studies conducted in the northeastern United States, Kaval and Loomis (2003)

estimated average consumer surplus values per day of $36 for recreational fishing (in 2007

dollars); therefore, the implied total annual value of freshwater fishing in the northeastern

United States was $5.1 billion in 2006.16  For recreation days, consumer surplus value is most

commonly measured using recreation demand, travel cost models.  

Another estimate of the overarching ecological benefits associated with reducing lake

acidification levels in Adirondacks National Park can be derived from the contingent valuation

(CV) survey (Banzhaf et al., 2006), which elicited values for specific improvements in

acidification‐related water quality and ecological conditions in Adirondack lakes.  The survey

described a base version with minor improvements said to result from the program, and a

scope version with large improvements due to the program and a gradually worsening status

quo.  After adapting and transferring the results of this study and converting the 10‐year annual

payments to permanent annual payments using discount rates of 3% and 5%, the WTP

estimates ranged from $48 to $107 per year per household (in 2004 dollars) for the base

version and $54 to $154 for the scope version.  Using these estimates, the aggregate annual

benefits of eliminating all anthropogenic sources of NOx and SOx emissions were estimated to

range from $291 million to $829 million (U.S. EPA, 2009c).17

In addition, inland surface waters provide a number of regulating services associated

with hydrological and climate regulation by providing environments that sustain aquatic food

webs.  These services are disrupted by the toxic effects of acidification on fish and other aquatic

life.  Although it is difficult to quantify these services and how they are affected by acidification,

some of these services may be captured through measures of provisioning and cultural services.  

Terrestrial Ecosystems

Acidifying deposition has altered major biogeochemical processes in the U.S. by

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching

from soil to drainage waters, depleting base cations (especially calcium and magnesium) from

soils, and increasing the mobility of aluminum.  Inorganic aluminum is toxic to some tree roots.  

Plants affected by high levels of aluminum from the soil often have reduced root growth, which

restricts the ability of the plant to take up water and nutrients, especially calcium (U. S. EPA,

2008f).  These direct effects can, in turn, influence the response of these plants to climatic

                                                           

16 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.    17 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

result of the emission reductions achieved by this rule.   
5‐44

stresses such as droughts and cold temperatures.  They can also influence the sensitivity of

plants to other stresses, including insect pests and disease (Joslin et al., 1992) leading to

increased mortality of canopy trees.  In the U.S., terrestrial effects of acidification are best

described for forested ecosystems (especially red spruce and sugar maple ecosystems) with

additional information on other plant communities, including shrubs and lichen (U.S. EPA,

2008f).   

Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial

acidification, which is the greatest concern regarding sulfur deposition U.S. EPA (2008f).  Figure

5‐13 depicts the areas across the U.S. that are potentially sensitive to terrestrial acidification.  

Figure 5‐13: Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008f)

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing

gradual losses of base cation nutrients from the soil due to accelerated leaching for acidifying

deposition.  This change in nutrient availability may reduce the quality of forest nutrition over

the long term.  Evidence suggests that red spruce and sugar maple in some areas in the eastern

U.S. have experienced declining health because of this deposition.  For red spruce, (Picea

rubens) dieback or decline has been observed across high elevation landscapes of the

northeastern U.S., and to a lesser extent, the southeastern U.S., and acidifying deposition has

been implicated as a causal factor (DeHayes et al., 1999).  Figure 5‐14 shows the distribution of

red spruce (brown) and sugar maple (green) in the eastern U.S.  
5‐45

Figure 5‐14: Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S.

(U.S. EPA, 2008f)

Terrestrial acidification affects several important ecological endpoints, including

declines in habitat for threatened and endangered species (cultural), declines in forest

aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil

erosion and reductions in water retention (cultural and regulating).

Forests in the northeastern United States provide several important and valuable

provisioning services in the form of tree products.  Sugar maples are a particularly important

commercial hardwood tree species, providing timber and maple syrup.  In the United States,

sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006), and annual

production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% of

worldwide production.  The total annual value of U.S. production in these years was

approximately $160 million (NASS, 2008).  Red spruce is also used in a variety of products

including lumber, pulpwood, poles, plywood, and musical instruments.  The total removal of

red spruce saw timber from timberland in the United States was over 300 million board feet in

2006 (USFS, 2006).  

Forests in the northeastern United States are also an important source of cultural

ecosystem services—nonuse (i.e., existence value for threatened and endangered species),
5‐46

recreational, and aesthetic services.  Red spruce forests are home to two federally listed species

and one delisted species:

1. Spruce‐fir moss spider (Microhexura montivaga)—endangered

2. Rock gnome lichen (Gymnoderma lineare)—endangered

3. Virginia northern flying squirrel (Glaucomys sabrinus fuscus)—delisted, but important

Forestlands support a wide variety of outdoor recreational activities, including fishing,

hiking, camping, off‐road driving, hunting, and wildlife viewing.  Regional statistics on

recreational activities that are specifically forest based are not available; however, more

general data on outdoor recreation provide some insights into the overall level of recreational

services provided by forests.  More than 30% of the U.S. adult population visited a wilderness

or primitive area during the previous year and engaged in day hiking (Cordell et al., 2005).  

From 1999 to 2004, 16% of adults in the northeastern United States participated in off‐road

vehicle recreation, for an average of 27 days per year (Cordell et al., 2005).  The average

consumer surplus value per day of off‐road driving in the United States was $25 (in 2007

dollars), and the implied total annual value of off‐road driving recreation in the northeastern

United States was more than $9 billion (Kaval and Loomis, 2003).  More than 5% of adults in the

northeastern United States participated in nearly 84 million hunting days (U.S. FWS and U.S.

Census Bureau, 2007).  Ten percent of adults in northeastern states participated in wildlife

viewing away from home on 122 million days in 2006.  For these recreational activities in the

northeastern United States, Kaval and Loomis (2003) estimated average consumer surplus

values per day of $52 for hunting and $34 for wildlife viewing (in 2007 dollars).  The implied

total annual value of hunting and wildlife viewing in the northeastern United States was,

therefore, $4.4 billion and $4.2 billion, respectively, in 2006.

As previously mentioned, it is difficult to estimate the portion of these recreational

services that are specifically attributable to forests and to the health of specific tree species.  

However, one recreational activity that is directly dependent on forest conditions is fall color

viewing.  Sugar maple trees, in particular, are known for their bright colors and are, therefore,

an essential aesthetic component of most fall color landscapes.  A survey of residents in the

Great Lakes area found that roughly 30% of residents reported at least one trip in the previous

year involving fall color viewing (Spencer and Holecek, 2007).  In a separate study conducted in

Vermont, Brown (2002) reported that more than 22% of households visiting Vermont in 2001

made the trip primarily for viewing fall colors.  

Two studies estimated values for protecting high‐elevation spruce forests in the

southern Appalachian Mountains.  Kramer et al. (2003) conducted a contingent valuation study

estimating households’ WTP for programs to protect remaining high‐elevation spruce forests
5‐47

from damages associated with air pollution and insect infestation.  Median household WTP was

estimated to be roughly $29 (in 2007 dollars) for a smaller program, and $44 for the more

extensive program.  Jenkins et al. (2002) conducted a very similar study in seven Southern

Appalachian states on a potential program to maintain forest conditions at status quo levels.  

The overall mean annual WTP for the forest protection programs was $208 (in 2007 dollars).  

Multiplying the average WTP estimate from these studies by the total number of households in

the seven‐state Appalachian region results in an aggregate annual range of $470 million to $3.4

billion for avoiding a significant decline in the health of high‐elevation spruce forests in the

Southern Appalachian region.

   

Forests in the northeastern United States also support and provide a wide variety of

valuable regulating services, including soil stabilization and erosion control, water regulation,

and climate regulation.  The total value of these ecosystem services is very difficult to quantify

in a meaningful way, as is the reduction in the value of these services associated with total

sulfur deposition.  As terrestrial acidification contributes to root damages, reduced biomass

growth, and tree mortality, all of these services are likely to be affected; however, the

magnitude of these impacts is currently very uncertain.

Ecological Effects of Associated with Sulfate in the Mercury Methylation Process          

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated

compound, methylmercury (U.S. EPA, 2008f).  The contaminant is concentrated in higher

trophic levels, including fish eaten by humans.  Experimental evidence has established that only

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S.

EPA, 2008f).  Many variables influence how much mercury accumulates in fish, but elevated

mercury levels in fish can only occur where substantial amounts of methylmercury are present

(U.S. EPA, 2008f).  Current evidence indicates that in watersheds where mercury is present,

increased sulfate deposition very likely results in methylmercury accumulation in fish (Drevnick

et al., 2007; Munthe et al., 2007).  The ISA for Oxides of Nitrogen and Sulfur: Ecological Criteria

ISA concluded that evidence is sufficient to infer a casual relationship between sulfur deposition

and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008f).  

Establishing the quantitative relationship between sulfate and mercury methylation in

natural settings is difficult because of the presence of multiple interacting factors in aquatic and

terrestrial environments, including wetlands, aquatic environments where sulfate, sulfur‐

reducing bacteria (SRB), and inorganic mercury are present (U.S. EPA, 2008f).  These are the

three primary requirements for bacterially‐mediated conversion to methylmercury.  Additional

factors affecting conversion include the presence of anoxic conditions, temperature, the
5‐48

presence and types of organic matter, the presence and types of mercury‐binding species, and

watershed effects (e.g., watershed type, land cover, water body limnology, and runoff loading).  

With regard to methylmercury, the highest concentrations in the environment generally occur

at or near the sedimentary surface, below the oxic–anoxic boundary.  Although mercury

methylation can occur within the water column, there is generally a far greater contribution of

mercury methylation from sediments because of anoxia and of greater concentrations of SRB,

substrate, and sulfate.  Figure 5‐15 depicts the mercury cycle.   

Figure 5‐15: The mercury cycle in an ecosystem (USGS, 2006)

Figure 5‐16 illustrates a map of mercury‐sensitive watersheds based on sulfate

concentrations, ANC, levels of dissolved organic carbon and pH, mercury species

concentrations, and soil types to gauge the methylation sensitivity (Myers et al., 2007).
5‐49

Figure 5.16: Preliminary USGS map of mercury methylation–sensitive watersheds  

(Myers et al., 2007)

Decreases in sulfate deposition/emissions have already shown reductions in

methylmercury (U.S. EPA, 2008f).  Observed decreases in methylmercury fish tissue

concentrations have been linked to decreased acidification and declining sulfate and mercury

deposition (Hrabik and Watras, 2002; Drevnick et al., 2007).  

In the U.S., consumption of fish and shellfish are the main sources of methylmercury

exposure to humans.  Methylmercury builds up more in some types of fish and shellfish than in

others.  The levels of methylmercury in high and shellfish vary widely depending on what they

eat, how long they live, and how high they are in the food chain.  Most fish, including ocean

species and local freshwater fish, contain some methylmercury.  For example, in recent studies

by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish samples contained some

methylmercury.

State‐level fish consumption advisories for mercury are based on state criteria, many of

which are based on EPA’s fish tissue criterion for methylmercury (U.S. EPA, 2001) or on U.S.

Food and Drug Administration’s action levels (U.S. FDA, 2001). In 2008, there were 3,361 fish

advisories issued at least in part for mercury contamination (80% of all fish advisories), covering
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16.8 million lake acres (40% of total lake acreage) and 1.3 million river miles (35% of total river

miles) over all 50 states, one U.S. territory, and 3 tribes (U.S. EPA, 2009f).  Recently, the U.S.

Geological Survey (USGS) examined mercury levels in top‐predator fish, bed sediment, and

water from 291 streams across the U.S. (Scudder et al., 2009).  USGS detected mercury

contamination in every fish sampled, and the concentration of mercury in fish exceeded EPA’s

criterion in 27% of the sites sampled.   

The ecosystem service most directly affected by sulfate‐mediated mercury methylation

is the provision of fish for consumption as a food source.  This service is of particular

importance to groups engaged in subsistence fishing, pregnant women and young children.  

While it is not possible to quantify the reduction in fish consumption due to the presence of

methylmercury in fish from sulfur deposition, it is likely, given the number of state advisories

and the EPA/FDA guidelines (U.S. EPA/FDA, 2004) on consumption for pregnant women and

young children, that this service is negatively affected.  

Research shows that most people’s fish consumption does not cause a mercury‐related

health concern.  However, certain people may be at higher risk because of their routinely high

consumption of fish (e.g., tribal and other subsistence fishers and their families who rely heavily

on fish for a substantial part of their diet).  It has been demonstrated that high levels of

methylmercury in the bloodstream of unborn babies and young children may harm the

developing nervous system, making the child less able to think and learn. Moreover, mercury

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people

of all ages.  The majority of fish consumed in the U.S. are ocean species.  The methylmercury

concentrations in ocean fish species are primarily influences by the global mercury pool.  

However, the methylmercury found in local fish can be due, at least partly, to mercury

emissions from local sources.   

Several studies suggest that the methylmercury content of fish may reduce these

cardio‐protective effects of fish consumption.  Some of these studies also suggest that

methylmercury may cause adverse effects to the cardiovascular system.  For example, the NRC

(2000) review of the literature concerning methylmercury health effects took note of two

epidemiological studies that found an association between dietary exposure to methylmercury

and adverse cardiovascular effects.18 Moreover, in a study of 1,833 males in Finland aged 42 to

60 years, Solonen et al. (1995) observed a relationship between methylmercury exposure via

                                                           

18National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press.

Washington, DC. pp.168‐173.
5‐51

fish consumption and acute myocardial infarction (AMI or heart attacks), coronary heart

disease, cardiovascular disease, and all‐cause mortality.19  The NRC also noted a study of 917

seven year old children in the Faroe Islands, whose initial exposure to methylmercury was in

utero although post natal exposures may have occurred as well.  At seven years of age, these

children exhibited an increase in blood pressure and a decrease in heart rate variability.20 Based

on these and other studies, NRC concluded in 2000 that, while “the data base is not as

extensive for cardiovascular effects as it is for other end points (i.e. neurologic effects) the

cardiovascular system appears to be a target for methylmercury toxicity.”21   

Since publication of the NRC report there have been some 30 published papers

presenting the findings of studies that have examined the possible cardiovascular effects of

methylmercury exposure.  These studies include epidemiological, toxicological, and

toxicokinetic investigations.  Over a dozen review papers have also been published.   If there is

a causal relationship between methylmercury exposure and adverse cardiovascular effects,

then reducing exposure to methylmercury would result in public health benefits from reduced

cardiovascular effects.

In early 2010, EPA sponsored a workshop in which a group of experts were asked to

assess the plausibility of a causal relationship between methylmercury exposure and

cardiovascular health effects and to advise EPA on methodologies for estimating population

level cardiovascular health impacts of reduced methylmercury exposure. The report from that

workshop is in preparation.

Because establishing the quantitative relationship between sulfate and mercury

methylation in natural settings is difficult, we were unable to model the changes in the

methylation process, bioaccumulation in fish tissue, and human consumption of mercury‐

contaminated fish that would be needed in order to estimate the human health benefits from

reducing sulfate emissions in this rule.  

                                                           

19Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91

(3):645‐655. 20Sorensen, N, K. Murata, E. Budtz‐Jorgensen, P. Weihe, and  Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370‐375. 21National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press.

Washington, DC.  p. 229.
5‐52

Ecological Effects Associated with Gaseous Sulfur Dioxide   

Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving

adsorption to surfaces (leaves, stems, and soil) and absorption into leaves.  SO2 penetrates into

leaves through to the stomata, although there is evidence for limited pathways via the cuticle.  

Pollutants must be transported from the bulk air to the leaf boundary layer in order to get to

the stomata.  When the stomata are closed, as occurs under dark or drought conditions,

resistance to gas uptake is very high and the plant has a very low degree of susceptibility to

injury.  In contrast, mosses and lichens do not have a protective cuticle barrier to gaseous

pollutants or stomates and are generally more sensitive to gaseous sulfur than vascular plants

(U.S. EPA, 2008f).  Acute foliar injury usually happens within hours of exposure, involves a rapid

absorption of a toxic dose, and involves collapse or necrosis of plant tissues.  Another type of

visible injury is termed chronic injury and is usually a result of variable SO2 exposures over the

growing season.  Besides foliar injury, chronic exposure to low SO2 concentrations can result in

reduced photosynthesis, growth, and yield of plants (U.S. EPA, 2008f).  These effects are

cumulative over the season and are often not associated with visible foliar injury.  As with foliar

injury, these effects vary among species and growing environment.  SO2 is also considered the

primary factor causing the death of lichens in many urban and industrial areas (Hutchinson et

al., 1996).  

5.9.2 Visibility Improvements

Reductions in SO2 emissions and secondary formation of PM2.5 due to the alternative

standards will improve the level of visibility throughout the United States.  These suspended

particles and gases degrade visibility by scattering and absorbing light.  Visibility directly affects

people’s enjoyment of a variety of daily activities.  Individuals value visibility both in the places

they live and work, in the places they travel to for recreational purposes, and at sites of unique

public value, such as the Great Smokey Mountains National Park.  Without the necessary air

quality data, we were unable to calculate the predicted change in visibility due to control

strategy to attain various alternate standard levels.  However, in this section, we describe the

process by which SO2 emissions impair visibility and how this impairment affects the public.   

Visual air quality (VAQ) is commonly measured as either light extinction, which is defined

as the loss of light per unit of distance in terms of inverse megameters (Mm‐1

) or the deciview

(dv) metric (Pitchford and Malm, 1993), which is a logarithmic function of extinction.  Extinction

and deciviews are physical measures of the amount of visibility impairment (e.g., the amount of

“haze”), with both extinction and deciview increasing as the amount of haze increases.  

Pitchford and Malm characterize a change of one deciview as “a small but perceptible scenic
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change under many circumstances.” Light extinction is the optical characteristic of the

atmosphere that occurs when light is either scattered or absorbed, which converts the light to

heat.  Particulate matter and gases can both scatter and absorb light.  Fine particles with

significant light‐extinction efficiencies include sulfates, nitrates, organic carbon, elemental

carbon, and soil (Sisler, 1996).  The extent to which any amount of light extinction affects a

person’s ability to view a scene depends on both scene and light characteristics.  For example,

the appearance of a nearby object (i.e. a building) is generally less sensitive to a change in light

extinction than the appearance of a similar object at a greater distance.  See Figure 5‐17 for an

illustration of the important factors affecting visibility.  

Figure 5‐17: Important factors involved in seeing a scenic vista (Malm, 1999)

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal

land managers, and State organizations in the U.S., the U.S. EPA has supported visibility

monitoring in national parks and wilderness areas since 1988.  The monitoring network known

as IMPROVE (Interagency Monitoring of Protected Visual Environments) now includes 150 sites

that represent almost all of the Class I areas across the country (see Figure 5‐18) (U.S. EPA,

2009d).  
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Figure 5‐18: Mandatory Class I Areas in the U.S.  

Annual average visibility conditions (reflecting light extinction due to both

anthropogenic and non‐anthropogenic sources) vary regionally across the U.S. (U.S. EPA,

2009d).  The rural East generally has higher levels of impairment than remote sites in the West,

with the exception of urban‐influenced sites such as San Gorgonio Wilderness (CA) and Point

Reyes National Seashore (CA), which have annual average levels comparable to certain sites in

the Northeast (U.S. EPA, 2004).  Higher visibility impairment levels in the East are due to

generally higher concentrations of fine particles, particularly sulfates, and higher average

relative humidity levels.  While visibility trends have improved in most Class I areas, the recent

data show that these areas continue to suffer from visibility impairment.  In eastern parks,

average visual range has decreased from 90 miles to 15‐25 miles, and in the West, visual range

has decreased from 140 miles to 35‐90 miles (U.S. EPA, 2004; U.S. EPA, 1999b).   

  

Visibility has direct significance to people’s enjoyment of daily activities and their overall

sense of wellbeing (U.S. EPA, 2009d).  Good visibility increases the quality of life where

individuals live and work, and where they engage in recreational activities.  When the necessary

AQ data is available, EPA generally considers benefits from these two categories of visibility

changes: residential visibility (i.e., the visibility in and around the locations where people live)

and recreational visibility (i.e., visibility at Class I national parks and wilderness areas.)  In both
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cases, economic benefits are believed to consist of use values and nonuse values.  Use values

include the aesthetic benefits of better visibility, improved road and air safety, and enhanced

recreation in activities like hunting and bird watching.  Nonuse values are based on people’s

beliefs that the environment ought to exist free of human‐induced haze.  Nonuse values may be

more important for recreational areas, particularly national parks and monuments.  In addition,

evidence suggests that an individual’s WTP for improvements in visibility at a Class I area is

influenced by whether it is in the region in which the individual lives, or whether it is

somewhere else (Chestnut and Rowe, 1990).  In general, people appear to be willing to pay

more for visibility improvements at parks and wilderness areas that are “in‐region” than at

those that are “out‐of‐region.”  This is plausible, because people are more likely to visit, be

familiar with, and care about parks and wilderness areas in their own part of the country.  EPA

generally uses a contingent valuation study as the basis for monetary estimates of the benefits

of visibility changes in recreational areas (Chestnut and Rowe, 1990).  To estimate the

monetized value of visibility changes, an analyst would multiply the willingness‐to‐pay

estimates by the amount of visibility impairment, but this information in unavailable for this

analysis.   

5.10 Limitations and Uncertainties

The National Research Council (NRC) (2002) concluded that EPA’s general methodology

for calculating the benefits of reducing air pollution is reasonable and informative in spite of

inherent uncertainties.  To address these inherent uncertainties, NRC highlighted the need to

conduct rigorous quantitative analysis of uncertainty and to present benefits estimates to

decisionmakers in ways that foster an appropriate appreciation of their inherent uncertainty.  

In response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a

comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling

elements on both health incidence and benefits estimates.  Components of that strategy

include emissions modeling, air quality modeling, health effects incidence estimation, and

valuation.  

In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo

analysis, sensitivity analysis, and alternate concentration‐response functions for PM mortality.  

We also provide a qualitative assessment for those aspects that we are unable to address

quantitatively in this analysis.  Each of these analyses is described in detail in the following

sections.   

  

This analysis includes many data sources as inputs, including emission inventories, air

quality data from models (with their associated parameters and inputs), population data, health
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effect estimates from epidemiology studies, and economic data for monetizing benefits.  Each

of these inputs may be uncertain and would affect the benefits estimate.  When the

uncertainties from each stage of the analysis are compounded, small uncertainties can have

large effects on the total quantified benefits.  In this analysis, we are unable to quantify the

cumulative effect of all of these uncertainties, but we provide the following analyses to

characterize many of the largest sources of uncertainty.   

5.10.1 Monte Carlo analysis

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random

sampling error associated with the concentration response functions and economic valuation

functions.  Monte Carlo simulation uses random sampling from distributions of parameters to

characterize the effects of uncertainty on output variables, such as incidence of morbidity.  

Specifically, we used Monte Carlo methods to generate confidence intervals around the

estimated health impact and dollar benefits.  The reported standard errors in the

epidemiological studies determined the distributions for individual effect estimates, as shown

in Table 5.6 for SO2 benefits.  Unfortunately, the associated confidence intervals are not

available for the PM2.5 co‐benefits due to limitations in the benefit‐per‐ton methodology.   

5.10.2 Sensitivity analyses

We performed a variety of sensitivity analyses on the benefits results to assess the

sensitivity of the primary results to various data inputs and assumptions.  We then changed

each default input one at a time and recalculated the total monetized benefits to assess the

percent change from the default.  In Tables 5.6 and 5.12, we provided the results of this

sensitivity analysis.  We indicate each input parameter, the value used as the default, and the

values for the sensitivity analyses, and then we provide the total monetary benefits for each

input and the percent change from the default value.  This sensitivity analysis indicates that the

results are most sensitive to assumptions regarding the attainment status and the threshold

assumption in the PM‐mortality relationship, and the results are less sensitive to alternate

assumptions regarding the interpolation method, discount rate, and various assumptions

regarding SO2 exposure.  To account for the large difference in magnitude between benefits

from reduced SO2 exposure and PM2.5 exposure, we provide separate sensitivity analyses.  We

show the sensitivity analysis for selected standard (75 ppb), but other standard levels would

show similar sensitivity to these perturbations, albeit with smaller magnitudes.  Descriptions of

the sensitivity analyses are provided in the relevant sections of this chapter.
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5.10.3 Alternate concentration‐response functions for PM mortality

PM2.5mortality co‐benefits are the largest benefit category that we monetized in this

analysis.  To better understand the concentration‐response relationship between PM2.5

exposure and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al.,

2008; IEc, 2006).  In general, the results of the expert elicitation support the conclusion that the

benefits of PM2.5 control are very likely to be substantial.  In previous RIAs, EPA presented

benefits estimates using concentration response functions derived from the PM2.5 Expert

Elicitation as a range from the lowest expert value (Expert K) to the highest expert value (Expert

E).  However, this approach did not indicate the agency’s judgment on what the best estimate

of PM benefits may be, and EPA’s Science Advisory Board described this presentation as

misleading.  Therefore, we began to present the cohort‐based studies (Pope et al, 2002; and

Laden et al., 2006) as our core estimates in the Portland Cement RIA (U.S. EPA, 2009a).  Using

alternate relationships between PM2.5 and premature mortality supplied by experts, higher and

lower benefits estimates are plausible, but most of the expert‐based estimates fall between the

two epidemiology‐based estimates (Roman et al., 2008).   

In this analysis, we present the results derived from the expert elicitation as indicative of

the uncertainty associated with a major component of the health impact functions, and we

provide the independent estimates derived from each of the twelve experts to better

characterize the degree of variability in the expert responses.  In this chapter, we provide the

results using the concentration‐response functions derived from the expert elicitation in both

tabular (Table 5.11) and graphical form (Figure 5.1).  Please note that these results are not the

direct results from the studies or expert elicitation; rather, the estimates are based in part on

the concentration‐response function provided in those studies.  Because in this RIA we estimate

PM co‐benefits using benefit‐per‐ton estimates, technical limitations prevent us from providing

the associated credible intervals with the expert functions.   

5.10.4 Qualitative assessment of uncertainty and other analysis limitations  

Although we strive to incorporate as many quantitative assessments of uncertainty,

there are several aspects for which we are only able to address qualitatively.  These aspects are

important factors to consider when evaluating the relative benefits of the attainment strategies

for each of the alternative standards:  
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1. The 12 km by 12 km resolution of the air quality modeling grid may be too coarse to

accurately estimate the potential near‐field health benefits of reducing SO2 emissions.  

These uncertainties likely result in an underestimate of the SO2‐related benefits.  

2. The interpolation techniques used to estimate the full attainment benefits from reduced

SO2 exposure of the alternative standards contributed some uncertainty to the analysis.  

The great majority of benefits estimated for the various standard levels were derived

through interpolation.  As noted previously in this chapter, these benefits are likely to

be more uncertain than if we had modeled the air quality scenario for both SO2 and

PM2.5.  In general, the VNA interpolation approach will underestimate benefits because

it does not account for the broader spatial distribution of air quality changes that may

occur due to the implementation of a regional emission control program.

3. There are many uncertainties associated with the health impact functions used in this

modeling effort.  These include: within study variability (the precision with which a given

study estimates the relationship between air quality changes and health effects); across

study variation (different published studies of the same pollutant/health effect

relationship typically do not report identical findings and in some instances the

differences are substantial); the application of C‐R functions nationwide (does not

account for any relationship between region and health effect, to the extent that such a

relationship exists); extrapolation of impact functions across population (we assumed

that certain health impact functions applied to age ranges broader than that considered

in the original epidemiological study); and various uncertainties in the C‐R function,

including causality and thresholds.  These uncertainties may under‐ or over‐estimate

benefits.  

4. Co‐pollutants present in the ambient air may have contributed to the health effects

attributed to SO2 in single pollutant models.  Risks attributed to SO2might be

overestimated where concentration‐response functions are based on single pollutant

models.  If co‐pollutants are highly correlated with SO2, their inclusion in an SO2 health

effects model can lead to misleading conclusions in identifying a specific causal

pollutant.  Because this collinearity exists, many of the studies reported statistically

insignificant effect estimates for both SO2 and the co‐pollutants; this is due in part to the

loss of statistical power as these models control for co‐pollutants.  Where available, we

have selected multipollutant effect estimates to control for the potential confounding

effects of co‐pollutants; these include NYDOH (2006), Schwartz et al. (1994) and

O’Connor et al. (2008).  The remaining studies include single pollutant models.   

5. This analysis is for the year 2020, and projecting key variables introduces uncertainty.  

Inherent in any analysis of future regulatory programs are uncertainties in projecting
5‐59

atmospheric conditions and source level emissions, as well as population, health

baselines, incomes, technology, and other factors.   

6. This analysis omits certain unquantified effects due to lack of data, time and resources.  

These unquantified endpoints include other health effects, ecosystem effects, and

visibility.  EPA will continue to evaluate new methods and models and select those most

appropriate for estimating the benefits of reductions in air pollution.  Enhanced

collaboration between air quality modelers, epidemiologists, toxicologists, ecologists,

and economists should result in a more tightly integrated analytical framework for

measuring benefits of air pollution policies.  

7. PM2.5 co‐benefits represent a substantial proportion of total monetized benefits (over

99% of total monetized benefits), and these estimates are subject to a number of

assumptions and uncertainties.   

a. PM2.5 co‐benefits were derived through benefit per‐ton estimates, which do not

reflect local variability in population density, meteorology, exposure, baseline

health incidence rates, or other local factors that might lead to an over‐estimate

or under‐estimate of the actual benefits of controlling directly emitted fine

particulates.   

b. We assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality.  This is an important assumption,

because PM2.5 produced via transported precursors emitted from EGUs may

differ significantly from direct PM2.5 released from diesel engines and other

industrial sources, but no clear scientific grounds exist for supporting differential

effects estimates by particle type.  

c. We assume that the health impact function for fine particles is linear down to

the lowest air quality levels modeled in this analysis.  Thus, the estimates include

health benefits from reducing fine particles in areas with varied concentrations

of PM2.5, including both regions that are in attainment with fine particle standard

and those that do not meet the standard down to the lowest modeled

concentrations.  

d. To characterize the uncertainty in the relationship between PM2.5 and premature

mortality, we include a set of twelve estimates based on results of the expert

elicitation study in addition to our core estimates.  Even these multiple

characterizations omit the uncertainty in air quality estimates, baseline incidence

rates, populations exposed and transferability of the effect estimate to diverse

locations.  As a result, the reported confidence intervals and range of estimates

give an incomplete picture about the overall uncertainty in the PM2.5 estimates.  
5‐60

This information should be interpreted within the context of the larger

uncertainty surrounding the entire analysis.  For more information on the

uncertainties associated with PM2.5 co‐benefits, please consult the PM2.5NAAQS

RIA (Table 5.5).

5.11 Discussion

The results of this benefits analysis suggest that fully attaining the selected SO2 standard

of 75 ppb would produce important health benefits from reduced SO2 exposure in the form of

fewer respiratory hospitalizations, respiratory emergency department visits and cases of acute

respiratory symptoms.  In addition, attaining the selected SO2 standard standards would also

produce substantial health co‐benefits from reducing PM2.5 exposure in the form of avoided

premature mortality and other morbidity effects.   

The proposal version of this analysis was the first time that EPA has estimated the

monetized human health benefits of reducing exposure to SO2 to support a change in the

NAAQS.  In contrast to recent PM2.5 and ozone‐related benefits assessments, there was far less

analytical precedent on which to base this assessment.  For this reason, we developed entirely

new components of the health impact analysis, including the identification of health endpoints

to be quantified and the selection of relevant effect estimates within the epidemiology

literature.  Because we did not receive any substantive comments on this approach during the

comment period, we duplicated this methodology using the updated air quality estimates for

the final RIA.  As the SO2 health literature continues to evolve, EPA will reassess the health

endpoints and risk estimates used in this analysis.

While the monetized benefits of reduced SO2 exposure appear small when compared to

the monetized benefits of reduced PM2.5 exposure, readers should not necessarily infer that the

total monetized benefits of attaining a new SO2 standard are minimal.  As shown in Table 5.13,

the monetized PM2.5 co‐benefits represent over 99% of the total monetized benefits.  This

result is consistent with other recent RIAs, where the PM2.5 co‐benefits represent a large

proportion of total monetized benefits.  This result is amplified in this RIA by the decision not to

quantify SO2‐related premature mortality and other morbidity endpoints due to the

uncertainties associated with estimating those endpoints.  Studies have shown that there is a

relationship between SO2 exposure and premature mortality, but that relationship is limited by

potential confounding.  Because premature mortality generally comprises over 90% of the total

monetized benefits, this decision may substantially underestimate the monetized health

benefits of reduced SO2 exposure.   
5‐61

We were unable to quantify the benefits from several welfare benefit categories.  We

lacked the necessary air quality data to quantify the benefits from improvements in visibility

from reducing light‐scattering particles.  Previous RIAs for ozone (U.S. EPA, 2008a) and PM2.5

(U.S. EPA, 2006a) indicate that visibility is an important benefit category, and previous efforts to

monetize those benefits have only included a subset of visibility benefits, excluding benefits in

urban areas and many national and state parks.  Even this subset accounted for up to 5% of

total monetized benefits in the Ozone NAAQS RIA (U.S. EPA, 2008a).   

We were also unable to quantify the ecosystem benefits of reduced sulfur deposition

because we lacked the necessary air quality data and resources to run the ecosystem benefits

models.  Previous assessments (U.S. EPA, 1999a; U.S. EPA, 2005; U.S. EPA, 2009e) indicate that

ecosystem benefits are also an important benefits category, but those efforts were only able to

monetize a tiny subset of ecosystem benefits in specific geographic locations, such as

recreational fishing effects from lake acidification in the Adirondacks.  We were also unable to

quantify the benefits of decreased mercury methylation from sulfate deposition. Quantifying

the relationship between sulfate and mercury methylation in natural settings is difficult, but

some studies have shown that decreasing sulfate deposition can also decrease methylmercury.  

In section 5.7 of this RIA, we discuss the revised presentation using benefits based on

Pope et al. and Laden et al. as the core estimates instead of using the range based on the low

and high end of the expert elicitation.  This change was incorporated in direct response to

recommendations from EPA’s Science Advisory Board (U.S.EPA‐SAB, 2008).  Although using

benefit‐per‐ton estimates limited our ability to incorporate all of their suggestions fully, we

have incorporated the following recommendations into this analysis:   

 Added “bottom line” statements where appropriate

 Clarified that the benefits results shown are not the actual judgments of the experts

 Acknowledged uncertainties exist at each stage of the analytic process, although

difficult to quantify when using benefit‐per‐ton estimates

 Did not use the expert elicitation range to characterize the uncertainty as it focuses on

the most extreme judgments with zero weight to all the others,  

 Described the rationale for using expert elicitation in the context of the regulatory

process (to characterize uncertainty)

 Identified results based on epidemiology studies and expert elicitation separately

 Showed central mass of expert opinion using graphs

 Presented the quantitative results using diverse tables and more graphics
5‐62

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6-1

Chapter 6: Cost Analysis Approach and Results

Synopsis

This chapter describes our illustrative analysis of the engineering costs and monitoring 

costs associated with attaining the final and alternative standards for the National Ambient Air 

Quality Standard (NAAQS) for SO2. We present our analysis of these costs in four separate 

sections. Section 6.1 presents the cost estimates. Sections 6.2 and 6.3 summarize the 

illustrative economic and energy impacts of these standards, respectively, while Section 6.4

outlines the main limitations of the analysis. As mentioned previously, the analysis is presented 

here for the final standard of 75 ppb, and two alternative standards: 50 ppb and 100 ppb in the 

year 2020. 

Section 6.1 breaks out discussion of cost estimates into five subsections. The first 

subsection summarizes the data and methods that we employed to estimate the costs 

associated with the control strategies outlined in Chapter 4. The second subsection presents 

county level estimates of the costs of identified controls associated with the regulatory 

alternatives examined in this RIA. Following this discussion, the third subsection describes the 

approach used to estimate the extrapolated costs of unspecified emission reductions that may 

be needed to comply with the final and alternative standards. The fourth subsection provides a 

brief discussion of the monitoring costs associated with the final NAAQS. The fifth subsection 

provides the estimated total costs of the regulatory alternatives examined. This section 

concludes with a discussion of technological innovation and how that affects regulatory cost 

estimates. 

This analysis does not estimate the projected attainment status of areas of the country 

other than those counties currently served by one of the approximately 349 monitors with 

complete data in the current network. It is important to note that the final rule will require a 

monitoring network wholly comprised of monitors sited at locations of expected maximum 

hourly concentrations. Only about one third of the existing SO2 network may be sourceoriented and/or in the locations of maximum concentration required by the final rule because 

the current network is focused on population areas and community-wide ambient levels of SO2. 

Actual monitored levels using the new monitoring network may be higher than levels measured 

using the existing network. We recognize that once a network of monitors located at 

maximum-concentration is put in place, more areas could find themselves exceeding the new 

SO2 NAAQS. However for this RIA analysis, we lack sufficient data to predict which counties 

might exceed the new NAAQS after implementation of the new monitoring network. Therefore 

we lack a credible analytic path to estimating costs and benefits for such a future scenario.
6-2

In addition, this chapter presents cost estimates associated with both identified control 

measures and unspecified emission reductions needed to reach attainment. Identified control 

measures include known measures for known sources that may be implemented to attain the 

alternative standard, whereas the achievement of unspecified emission reductions requires 

implementation of hypothetical additional measures in areas that would not attain the selected 

standard following the implementation of identified controls to known sources. 

Note that the universe of sources achieving unspecified emission reductions beyond 

identified controls is not completely understood; therefore we are not able to identify known 

control devices, work practices, or other control measures to achieve these reductions. We 

calculated extrapolated costs for unspecified emission reductions using a fixed cost per ton 

approach. The analysis presents hypothetical costs of attaining the SO2 NAAQS, subject to 

States’ abilities to find emission reductions whose costs are finite, although likely to be higher 

than those of the identified control measures we believe to exist. Section 6.1 below describes 

in more detail our approaches for estimating both the costs of identified controls and the 

extrapolated costs of unspecified emission reductions needed beyond identified controls.

As is discussed throughout this RIA, the technologies and control strategies selected for 

this analysis are illustrative of one approach that nonattainment areas may employ to comply 

with the revised SO2 standard. Potential control programs may be designed and implemented 

in a number of ways, and EPA anticipates that State and Local governments will consider those 

programs that are best suited for local conditions. As such, the costs described in this chapter 

generally cover the annualized costs of purchasing, installing, and operating the referenced 

technologies. We also present monitoring costs. Because we are uncertain of the specific 

actions that State Agencies will take to design State Implementation Plans to meet the revised 

standard, we do not estimate the costs that government agencies may incur to implement 

these control strategies.

6.1 Engineering Cost Estimates

6.1.1 Data and Methods: Identified Control Costs

Consistent with the emissions control strategy analysis presented in Chapter 4, our 

analysis of the costs associated with the final SO2 NAAQS focuses SO2 emission controls for 

EGU sources first, then nonEGU point sources, and then area sources. 
6-3

6.1.1.1 EGU Sources

We used equations for wet FGD scrubber controls used in the Integrated Planning 

Model (IPM) to estimate the control cost for SO2 reductions from EGUs. Equations are available 

for estimating capital and annual costs, and these equations are dependent on unit capacity 

and capacity factor (fraction of hours in a year that an EGU operates). Annual costs for control 

measures applied in IPM include those for fixed and variable operating and maintenance 

(O&M) items and annualized capital costs calculated using a capital recovery factor and are 

specifically applicable to EGUs. 

6.1.1.2 NonEGU Point and Area Sources

After designing the hypothetical control strategy using the methodology discussed in 

Chapter 4, EPA used the Control Strategy Tool (CoST) and AirControlNET to estimate 

engineering control costs for nonEGU and Area sources. CoST calculates engineering costs 

using three different methods: (1) by multiplying an average annualized cost per ton estimate 

against the total tons of a pollutant reduced to derive a total cost estimate; (2) by calculating 

cost using an equation that incorporates key plant information; or (3) by using both cost per ton 

and cost equations. Most control cost information within CoST has been developed based on 

the cost per ton approach. This is because estimating engineering costs using an equation 

requires more data, and parameters used in other non-cost per ton methods may not be readily 

available or broadly representative across sources within the emissions inventory. The costing 

equations used in CoST require either plant capacity or stack flow to determine annual, capital 

and/or operating and maintenance (O&M) costs. Capital costs are converted to annual costs 

using the capital recovery factor (CRF)1

Engineering costs will differ based upon quantity of emissions reduced, plant capacity, 

or stack flow which can vary by emissions inventory year. Engineering costs will also differ in a 

nominal sense by the year the costs are calculated for (i.e., 1999$ versus 2006$).

. Where possible, cost calculations are used to calculate 

total annual control cost (TACC) which is a function of the capital (CC) and O&M costs. The 

capital recovery factor incorporates the interest rate and equipment life (in years) of the 

control equipment. Operating costs are calculated as a function of annual O&M and other 

variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M. 

2

 1 For more information on this cost methodology and the role of AirControlNET in control strategy analysis, see 

Section 6 of the 2006 PM RIA, AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or the EPA Air 

Pollution Control Cost Manual, Section 1, Chapter 2, found at http://www.epa.gov/ttn/catc/products.html#cccinfo. 

For capital 

2 The engineering costs will not be any different in a real (inflation-adjusted) sense if calculated in 2006 versus 

1999 dollars if properly escalated. For this analysis, all costs are reported in real 2006 dollars. 
6-4

investment, we do not assume early capital investment in order to attain standards by 2020. 

For 2020, our estimate of annualized costs represents a “snapshot” of the annualized costs, 

which include annualized capital and O&M costs, for those controls included in our identified 

control strategy analysis. Our engineering cost analysis uses the equivalent uniform annual 

costs (EUAC) method, in which annualized costs are calculated based on the equipment life for 

the control measure along with the interest rate by use of the CRF as mentioned previously in 

this chapter. Annualized costs are estimated as equal for each year the control is expected to 

operate. Hence, our annualized costs for nonEGU point and area sources estimated for 2020 

are the same whether the control measure is installed in 2019 or in 2010. We make no 

presumption of additional capital investment in years beyond 2020. The EUAC method is 

discussed in detail in the EPA Air Pollution Control Cost Manual3. Applied controls and their 

respective engineering costs are provided in the SO2 NAAQS docket.

6.1.2 Identified Control Strategy Analysis Engineering Costs

In this section, we provide engineering cost estimates of the control strategies identified 

in Chapter 4 that include control measures applied to nonEGU sources, area sources, and EGUs. 

Engineering costs generally refer to the expense of capital equipment installation, the site 

preparation costs for the application, and annual operating and maintenance costs. 

The total annualized cost of control in each geographic area of our analysis for the 

hypothetical control scenario is provided in Table 6.1. These numbers reflect the engineering 

costs across all sectors. Estimates are annualized at a discount rate of 7%. 

Table 6.1 summarizes these costs in total and by sector nationwide. As indicated in the 

table, the estimated annualized costs of these controls under the 75 ppb final standard in 2020 

are $960 million per year (2006$). For the other 2 alternative standards examined, in 2020 the 

annualized costs range from $470 million to $2,600 million. Consistent with Chapter 4's 

summary of the air quality impacts associated with identified controls, the cost estimates in 

Table 6.1 reflect partial attainment with the alternative standard being examined in this RIA. 

Consistent with the identified control strategy analysis emission reductions presented in 

Chapter 4, a majority of the costs are from controls applied to EGU sources, but a relatively 

large share of costs is borne by nonEGU point sources. 

The costs of the EGU strategy reflect application of controls (described in Chapter 4) 

where needed to obtain as much reductions as possible to attain each alternative standard. 

 3 http://epa.gov/ttn/catc/products.html#cccinfo
6-5

Table 6.2 presents the identified control costs in 2020 by county for each alternative 

standard. These costs are shown for a 7 percent discount rate. 

Table 6.1: Annual Control Costs of Identified Controls in 2020 in Total and by Sector

(Millions of 2006$) a, b

50 ppb 75 ppb 100 ppb

Total Costs for Identified Controlsc, d $ 2,600 $ 960 $ 470

EGUs $ 1,700 $ 700 $ 300

nonEGUs $ 900 $ 260 $ 170 

Area Sources $ 40 $ 0.55 $ 0.24

a All estimates rounded to two significant figures. As such, totals will not sum down columns.

b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a 

2020 baseline.

c Total annualized costs were calculated using a 7% discount rate d

These values represent partial attainment costs for the identified control strategy analysis. There were locations 

not able to attain the alternative standard being analyzed with identified controls only.

Table 6.2: Identified Controls – Total Annual Cost by County in 2020 (Millions of 2006$)a,b,c,d

state county 50 ppb 75 ppb 100 ppb

Arizona Gila Co $8.8 $8.8 $8.8

Colorado Denver Co $39.0

Connecticut New Haven Co $8.2

Florida Duval Co $24.0

Florida Hillsborough Co $3.2

Georgia Chatham Co $42.0 $12.0

Idaho Bannock Co $0.6

Illinois Cook Co $16.0

Illinois Madison Co $65.0 $31.0

Illinois St Clair Co

Illinois Sangamon Co $60.0 $30.0

Illinois Tazewell Co $120.0 $27.0

Indiana Floyd Co $0.14

Indiana Fountain Co $19.0

Indiana Jasper Co

Indiana Lake Co $210.0 $49.0

Indiana Morgan Co $10.0

Indiana Porter Co

Indiana Wayne Co $47.0 $47.0 $35.0

Iowa Linn Co $26.0 $18.0

Iowa Muscatine Co $89.0 $65.0 $31.0

Kentucky Jefferson Co $85.0

Kentucky Livingston Co $11.0

Louisiana East Baton Rouge Par $29.0
6-6

state county 50 ppb 75 ppb 100 ppb

Missouri Greene Co $16.0

Missouri Jackson Co $59.0 $26.0

Missouri Jefferson Co $310.0 $280.0 $280.0

Montana Yellowstone Co $12.0

Nebraska Douglas Co $17.0 $17.0

New Hampshire Merrimack Co $19.0

New York Erie Co $38.0 $14.0

New York Monroe Co $7.5

New York Suffolk Co $50.0 $21.0

North Carolina New Hanover Co $19.0

Ohio Clark Co $19.0

Ohio Jefferson Co $18.0

Ohio Lake Co $110.0 $47.0

Ohio Summit Co $76.0 $19.0 $3.0

Oklahoma Kay Co $28.0

Oklahoma Muskogee Co $78.0 $51.0 $25.0

Oklahoma Tulsa Co $24.0

Pennsylvania Allegheny Co $160.0

Pennsylvania Blair Co $38.0

Pennsylvania Northampton Co $61.0 $28.0

Pennsylvania Warren Co $29.0 $29.0 $29.0

South Carolina Lexington Co $22.0

Tennessee Blount Co $36.0

Tennessee Bradley Co $39.0 $2.9

Tennessee Montgomery Co $38.0 $38.0 $38.0

Tennessee Shelby Co $16.0

Tennessee Sullivan Co $110.0 $47.0

Texas Harris Co $66.0

Texas Jefferson Co $61.0 $28.0

West Virginia Hancock Co $30.0

Wisconsin Brown Co $40.0

Wisconsin Oneida Co $22.0 $22.0 $22.0 a All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a 

2020 baseline.

c Total annualized costs were calculated using a 7% discount rate. d

These values represent partial attainment costs for the identified control strategy analysis. There were locations 

not able to attain the alternative standard being analyzed with identified controls only.
6-7

6.1.3 Extrapolated Costs 

Prior to presenting the methodology for estimating costs for unspecified 

emission reductions, it is important to provide information from EPA’s Science Advisory 

Board (SAB) Council Advisory on the issue of estimating costs of unidentified control 

measures.4

 4 U.S. Environmental Protection Agency, Advisory Council on Clean Air Compliance Analysis (COUNCIL), 

Council Advisory on OAR’s Direct Cost Report and Uncertainty Analysis Plan, Washington, DC. June 8, 

2007.

 812 Council Advisory, Direct Cost Report, Unidentified Measures

 (charge question 2.a):

 “The Project Team has been unable to identify measures that yield

 sufficient emission reductions to comply with the National Ambient

 Air Quality Standards (NAAQS) and relies on unidentified pollution

 control measures to make up the difference. Emission reductions

 attributed to unidentified measures appear to account for a large

 share of emission reductions required for a few large metropolitan

 areas but a relatively small share of emission reductions in other

 locations and nationwide.

 “The Council agrees with the Project Team that there is little

 credibility and hence limited value to assigning costs to these

 unidentified measures. It suggests taking great care in reporting

 cost estimates in cases where unidentified measures account for a

 significant share of emission reductions. At a minimum, the

 components of the total cost associated with identified and

 unidentified measures should be clearly distinguished. In some

 cases, it may be preferable to not quantify the costs of

 unidentified measures and to simply report the quantity and share

 of emissions reductions attributed to these measures.

 “When assigning costs to unidentified measures, the Council

 suggests that a simple, transparent method that is sensitive to

 the degree of uncertainty about these costs is best. Of the three

 approaches outlined, assuming a fixed cost/ton appears to be the
6-8

 simplest and most straightforward. Uncertainty might be

 represented using alternative fixed costs per ton of emissions

 avoided.”

EPA has considered this advice and the requirements of E.O. 12866 and OMB 

circular A-4, which provides guidance on the estimation of benefits and costs of 

regulations.

As indicated above the identified control costs do not result in attainment of the 

selected or alternative standards in four areas. In these areas, unspecified emission 

reductions needed beyond identified controls will likely be necessary to reach 

attainment. 

Taking into consideration the above SAB advice, we estimated the costs of 

unspecified future emission reductions using a fixed (annualized) cost per ton approach. 

In previous analyses we have estimated the extrapolated costs using other marginal cost 

based approaches in addition to the fixed cost per ton approach. We examine the data 

available for each analysis and determine on a case by case basis the appropriate 

extrapolation technique. Due to the limited number of control measures applied in this 

analysis across all sectors, we concluded that it would not be credible to establish a 

marginal cost-based approach or a representative value for the costs of further SO2

emission reductions. We also recognize that the emissions from EGUs are the largest 

for these areas. In addition, there is also limited information on SO2 controls applied to 

non-EGUs beyond the scope of this analysis, especially for small sources. For these

reasons, we have relied upon a simple fixed cost approach utilized for that analysis to 

represent the fixed cost of unspecified emission reductions for this analysis. The 

primary estimate presented is $15,000 (2006$), with sensitivities of $10,000/ton and 

$20,000/ton. Use of $15,000/ton as a fixed cost estimate is commensurate with the 

cost of nonEGU SO2 control measures as applied in the PM2.5 RIA three years ago. This 

fixed costs is also much higher than reported costs for SO2 controls such as wet FGD 

scrubbers for industrial boilers are reported to be up to at least $5,200/ton (2006$).5 

Also, this estimate is considerably greater than the current and futures prices for SO2

emissions allowances traded for compliance with the CAIR program.6

 5 Applicability and Feasibility of NOx, SO2, and PM Emissions Control Technologies for Industrial, 

Commercial, and Institutional (ICI) Boilers. NESCAUM, November 2008. Available on the Internet at 

 Finally, as 

http://www.nescaum.org/documents/ici-boilers-20081118-final.pdf/. 6 The Evolving SO2 Allowance Market: Title IV, CAIR, and Beyond. Palmer, Karen, Resources for the 

Future and Evans, David, US EPA/OPEI, July 13, 2009. Available on the Internet at 

http://www.rff.org/Publications/WPC/Pages/090713-Evolving-SO2-Allowance-Market.aspx. 
6-9

mentioned above, the use of a fixed cost per ton of $15,000/ton is consistent with what 

an advisory committee to the Section 812 second prospective analysis on the Clean Air 

Act Amendments suggested in June 2007 for estimating the costs of reductions from 

unidentified controls. 

The estimation of costs for emission reductions needed to reach attainment 

many years in the future is inherently difficult. We expect that additional control 

measures that we were not able to identify may be developed by 2020. As described 

later in this chapter, our experience with Clean Air Act implementation shows that 

technological advances and development of innovative strategies can make possible 

cost effective emissions reductions that are unforeseen today, and can reduce costs of 

some emerging technologies over time. But we cannot precisely predict the amount of 

technology advance in the future. The relationship of the cost of additional future 

controls to the cost of control options available today is not at all clear. Available, 

currently known control measures increase in costs per ton beyond the range of what 

has ever been implemented and because they are not currently required can not serve 

as an accurate representation of expected costs of implementation. Such measures 

would still not provide the needed additional control for full attainment in the analysis 

year 2020. History has shown that when faced with potentially costly controls 

requirements, firms could adapt by changing their production process or innovate to 

develop more cost effective ways of meeting control requirements. We recognize that a 

single fixed cost of control of $15,000 per ton of emissions reductions does not account 

for the significant emissions cuts that are necessary in some areas and so its use 

provides an estimate that is likely to differ from actual future costs. Yet, the limited 

emission controls dataset applied for the identified control strategy analysis significantly 

limits our ability to estimate full attainment costs using more sophisticated methods. 

In the economics literature there are a variety of theoretical ways to estimate 

the cost of more stringent emissions reductions than can be achieved by known 

technologies. One method would be to estimate the cost of reducing all remaining tons 

by simply extrapolating the cost curve using data on cost and effectiveness of all known 

controls. This method can imply the last ton of reductions costs an amount which is 

thousands of times higher than the fixed cost presumed above (i.e., $15,000 per ton). 

This result is highly unlikely given the uncertainty surrounding the assumptions implicit 

in this estimate (e.g. projecting 10 years into the future, not including factors for 

technological innovation and improvements, not including societal and economy wide 

changes from dealing with climate change). Such a result does not necessarily mean 

that such costs will be incurred, because of uncertainties about future control 
6-10

technology, economic activity and the possibility of deferment of full attainment dates. 

Another variant on this approach is to develop a method which simulates technological 

change by causing shifts in the cost curve over time to reflect that innovation can 

reduce costs of control.

In addition, it is theoretically possible to consider the cost of a geographic area 

changing to a different type of economic structure over time (e.g. moving from a one 

type of manufacturing to another or from manufacturing to a more service oriented 

economy) as another way to predict the cost of meeting a tighter standard. This would 

be a challenging, data intensive exercise that would be very area specific. Nationwide 

estimates would have to be built from an area by area basis. In some areas, mobile 

sources may be a significant source of emissions; some areas are experimenting with 

congestion pricing as a means of restructuring how people and goods travel to reduce 

emissions. 

In the absence of more robust methods for estimating these costs, EPA is 

following the SAB advice to keep the approach simple and transparent. If commentors 

have different assumptions about the cost of attainment, it is easy for them to calculate 

the cost of attaining a tighter standard using the fixed cost formula. EPA is going to 

continue to work on most robust methods of developing these estimates. EPA will 

continue to improve methods of estimating the costs of full attainment when healthbased standards require emissions cuts greater than can be achieved by all known 

engineering controls. Over the course of the next several months EPA, in partnership 

with OMB and interested federal agencies will be investigating different ways of 

estimating these extrapolated full attainment costs, including consideration of ways of 

incorporating technological change and other factors. In addition, EPA is looking into 

developing approaches to characterize different future states of the world. These 

scenarios (similar to the goal of the IPCC scenarios for the outcome of climate change, 

for example) would allow us to consider a range of possibilities. Many criteria pollutant 

emissions result from combustion processes used to make energy, transport goods and 

people and other industrial operations. Our alternative futures could represent 

different types of power generation that could become more prevalent under different 

circumstances. For example, in one scenario solar or wind power would prevail leading 

to reductions in the burning of coal for power generation. In contrast, in another 

scenario coal use remains consistent with current usage but is subject to more 

emissions reductions. Another could presume significant inroads for electric vehicles. 

EPA will be considering this approach as another method for projecting a range of 

possibilities for the cost of attaining a tighter standard. This research will include a 
6-11

review of how best to characterize the likely adoption by 2020 (or similar target years) 

of new technologies (e.g., solar, wind and others unrelated to fossil fuel combustion, as 

well as more fuel-efficient vehicles), that are expected to have the ancillary benefit of 

facilitating compliance with new standards for criteria air pollutants. It will also include 

consideration of control measures that depend on behavioral change (such as 

congestion pricing) rather than simply the adoption of engineering controls. 

The approach outlined above represents a significant amount of theoretical and 

applied analysis and the development of new methodologies for doing this analysis. 

Data supporting our cost approach is in the SO2 NAAQS RIA docket and we welcome 

ideas from the public on suggestions for analytical methods to estimate these future 

costs and plans to hopefully utilize portions of it in the proposed PM2.5 NAAQS RIA to 

be released with the rest of the material accompanying the standard.

Table 6.3 presents the extrapolated costs for each alternative standard analyzed. 

See Chapter 4 for a complete discussion of the air quality projections for these counties.

Table 6.3: Extrapolated Costs Estimated for the Alternative Standards 

(Millions of 2006$) a, b

50 ppb 75ppb 100 ppb

Total Extrapolated Costs

($10,000/ton): $ 1,200 $ 330 $ 180

Total Extrapolated Costs 

($15,000/ton): $ 1,800 $ 500 $ 260

Total Extrapolated Costs 

($20,000/ton): $ 2,400 $ 670 $ 350

a All estimates rounded to two significant figures. As such, totals will not sum down columns.

b Estimates of extrapolated costs are assumed using a 7% discount rate. Given the fixed cost per ton 

approach used here, 3% discount rate estimates could not be calculated.
6-12

6.1.4 Monitoring Costs

The final amendments would revise the technical requirements for SO2 monitoring sites;

require the siting and operation of additional SO2 ambient air monitors, and the reporting of 

the collected ambientmonitoring data to EPA’s Air Quality System (AQS). We have estimated 

the burden based on the monitoring requirements of this rule. Details of the burden estimate 

are contained in the information collection request (ICR) accompanying the final rule.7

6.1.5 Summary of Cost Estimates

The ICR 

estimates annualized costs of a new monitoring network at approximately $15 million per year

(2006 dollars).

Table 6.4 provides a summary of total costs to achieve the alternative standards in the 

year 2020, and this summary includes the sensitivity estimates. As mentioned previously, we 

use $15,000/ton as our primary estimate of the extrapolated costs on a per ton reduction basis, 

and $10,000/ton and $20,000/ton are used as sensitivities. Using that estimate, we find that 

the total annualized costs for the 75 ppb final standard in 2020 are $1.0 billion (2006$) using 

seven percent as the discount rate and applying the primary estimate of the extrapolated costs, 

and the costs for the other alternative standards range from $0.5 billion to $2.6 billion (2006$). 

The portion of these costs accounted for by identified controls ranges from 59 percent for the 

50 ppb standard to 64 percent for the 100 ppb standard. Hence, the portion of these costs 

accounted for by extrapolated controls ranges from 41 percent for the 50 ppb standard to 36

percent for the 100 ppb standard.

Finally, Table 6.5 present the annual cost/ton for the identified controls by sector as 

applied for the alternative standards in 2020. For each alternative standard, the annual 

cost/ton for reductions from the non-EGU sector is the most expensive. For the 75 ppb final 

standard, reductions from non-EGUs occur at $2,400/ton while the annual cost/ton for EGU 

sector is $2,700/ton. All of these estimates are for reductions in 2020 in 2006 dollars and using 

a seven percent discount rate. 

The significant difference between the costs of identified controls alone and the cost of 

achieving attainment (i.e. including both identified controls and emission reductions beyond 

identified controls) in this and other areas reflects the limited information available to EPA on 

 7 ICR 2358.01, May 2009.
6-13

the control measures that sources may implement. Although AirControlNET contains 

information on a large number of different point source controls, we would expect that State 

and local air quality managers would have access to additional information on the controls 

available to the most significant sources.

Table 6.4: Total Annual Costs for Alternative Standards (Millions of 2006$)a, b

50 ppb 75 ppb 100 ppb

Identified Control Costs $ 2,600 $ 960 $ 470

Monitoring Costs $2.1 $2.1 $2.1

Extrapolated Costs

Fixed Cost 

($10,000/ton) $ 1,200 $ 330 $ 180

d

Fixed Cost 

($15,000/ton) $ 1,800 $ 500 $ 260

Fixed Cost 

($20,000/ton) $ 2,400 $ 670 $ 350

Total Costs

Fixed Cost 

($10,000/ton) $ 3,800 $ 1,300 $ 650

d

Fixed Cost 

($15,000/ton) $ 4,400 $ 1,500 $ 730

Fixed Cost 

($20,000/ton) $ 5,000 $ 1,600 $ 820

a All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a 

2020. c Values reflect a 7% discount rate.

d Our primary estimate of extrapolated costs is, as mentioned earlier in this RIA, based on a fixed annual cost of 

$15,000/ton. This estimate of extrapolated costs is incorporated into our estimate of total costs for the alternative 

standards.
6-14

Table 6.5: Annual Cost per Ton of Identified Controls applied for the Alternative Standards by 

Emissions Sector (2006$) a, b

Emissions Sector 50 ppb 75 ppb 100 ppb

NonEGU $ 2,400 $ 2,700 $ 2,800 

Area $ 2,500 $ 2,200 $ 2,100 

EGU $ 2,700 $ 2,700 $ 2,800 a All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a 

2020 baseline.

6.1.6 Technology Innovation and Regulatory Cost Estimates

There are many examples in which technological innovation and “learning by doing” 

have made it possible to achieve greater emissions reductions than had been feasible earlier, or 

have reduced the costs of emission control in relation to original estimates. Studies8

Constantly increasing marginal costs are likely to induce the type of innovation that 

would result in lower costs than estimated early in this chapter. Breakthrough technologies in 

control equipment could by 2020 result in a rightward shift in the marginal cost curve for such 

equipment (Figure 6.1)

have 

suggested that costs of some EPA programs have been less than originally estimated due in part 

to inadequate inability to predict and account for future technological innovation in regulatory 

impact analyses.

9

 8 Harrington et al. (2000) and previous studies cited by Harrington.

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost Estimates.” Journal of 

Policy Analysis and Management 19(2):297-322. 9 Figure 6.1 shows a linear marginal abatement cost curve. It is possible that the shape of the marginal abatement 

cost curve is non-linear.

as well as perhaps a decrease in its slope, reducing marginal costs per 

unit of abatement, and thus deviate from the assumption of a static marginal cost curve. In 

addition, elevated abatement costs may result in significant increases in the cost of production 

and would likely induce production efficiencies, in particular those related to energy inputs, 

which would lower emissions from the production side. 
6-15

Figure 6.1: Technological Innovation Reflected by Marginal Cost Shift

6.1.6.1 Examples of Technological Advances in Pollution Control

There are numerous examples of low-emission technologies developed and/or 

commercialized over the past 15 or 20 years, such as:

• Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions

• Scrubbers which achieve 95% and even greater SO2 control on boilers

• Sophisticated new valve seals and leak detection equipment for refineries and 

chemical plans

• Low or zero VOC paints, consumer products and cleaning processes

• Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents

• Water and powder-based coatings to replace petroleum-based formulations

• Vehicles far cleaner than believed possible in the late 1980s due to 

improvements in evaporative controls, catalyst design and fuel control systems

for light-duty vehicles; and treatment devices and retrofit technologies for 

heavy-duty engines

MC0 MC1

Cumulative SO2 Reductions

Cost/

Ton

Induced Technology Shift

Slope = 

? 0

Slope = ? 1
6-16

• Idle-reduction technologies for engines, including truck stop electrification 

efforts

• Market penetration of gas-electric hybrid vehicles, and clean fuels

• The development of retrofit technology to reduce emissions from in-use vehicles 

and non-road equipment

These technologies were not commercially available two decades ago, and some were 

not even in existence. Yet today, all of these technologies are on the market, and many are 

widely employed. Several are key components of major pollution control programs and most of 

the examples are discussed further below. 

What is known as “learning by doing” or “learning curve impacts”, which is a concept 

distinct from technological innovation, has also made it possible to achieve greater emissions 

reductions than had been feasible earlier, or have reduced the costs of emission control in 

relation to original estimates. Learning curve impacts can be defined generally as the extent to 

which variable costs (of production and/or pollution control) decline as firms gain experience 

with a specific technology. Such impacts have been identified to occur in a number of studies 

conducted for various production processes. Impacts such as these would manifest themselves 

as a lowering of expected costs for operation of technologies in the future below what they 

may have been. 

The magnitude of learning curve impacts on pollution control costs has been estimated 

for a variety of sectors as part of the cost analyses done for the Draft Direct Cost Report for the 

second EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990.10

T.P. Wright, in 1936, was the first to characterize the relationship between increased 

productivity and cumulative production. He analyzed man-hours required to assemble 

successive airplane bodies. He suggested the relationship is a log linear function, since he 

observed a constant linear reduction in man-hours every time the total number of airplanes 

assembled was doubled. The relationship he devised between number assembled and assembly 

In 

that report, learning curve adjustments were included for those sectors and technologies for 

which learning curve data was available. A typical learning curve adjustment example is to 

reduce either capital or O&M costs by a certain percentage given a doubling of output from 

that sector or for that technology. In other words, capital or O&M costs will be reduced by 

some percentage for every doubling of output for the given sector or technology. 

 10 E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean Air Act Second Section 

812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office of Air and Radiation, February 2007. 

Available at http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf. 
6-17

time is called Wright’s Equation (Gumerman and Marnay, 2004)11

The percentage adjustments to costs can range from 5 to 20 percent, depending on the 

sector and technology. Learning curve adjustments were prepared in a memo by IEc supplied to 

US EPA and applied for the mobile source sector (both onroad and nonroad) and for application 

of various EGU control technologies within the Draft Direct Cost Report.

. This equation, shown below, 

has been shown to be widely applicable in manufacturing:

Wright’s Equation: CN = Co * Nb

, 

Where:

N = cumulative production

CN = cost to produce Nth unit of capacity

Co = cost to produce the first unit

B = learning parameter = ln (1-LR)/ln(2), where

LR = learning by doing rate, or cost reduction per doubling of capacity or 

output. 

12

 11 Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National 

Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California 

at Berkeley, Berkeley, CA. January 2004, LBNL-52559. 12 Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the 

Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August 

13, 2007. 

Advice received from 

the SAB Advisory Council on Clean Air Compliance Analysis in June 2007 indicated an interest in 

expanding the treatment of learning curves to those portions of the cost analysis for which no 

learning curve impact data are currently available. Examples of these sectors are non-EGU point 

sources and area sources. The memo by IEc outlined various approaches by which learning 

curve impacts can be addressed for those sectors. The recommended learning curve impact 

adjustment for virtually every sector considered in the Draft Direct Cost Report is a 10% 

reduction in O&M costs for two doubling of cumulative output, with proxies such as cumulative 

fuel sales or cumulative emission reductions being used when output data was unavailable. 

For this RIA, we do not have the necessary data for cumulative output, fuel sales, or 

emission reductions for all sectors included in our analysis in order to properly generate control 

costs that reflect learning curve impacts. Clearly, the effect of including these impacts would be 

to lower our estimates of costs for our control strategies in 2020, but we are not able to include 

such an analysis in this RIA. 
6-18

6.1.6.2 Influence on Regulatory Cost Estimates

Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher 

than later estimates, in part because of inability to predict technological advances. Over longer 

time horizons the opportunity for technical advances is greater.

• Multi-rule study: Harrington et al. of Resources for the Future13

Based on the case study results and existing literature, the authors identified 

technological innovation as one of five explanations of why predicted and actual regulatory cost 

estimates differ: “Most regulatory cost estimates ignore the possibility of technological 

innovation ... Technical change is, after all, notoriously difficult to forecast ... In numerous case 

studies actual compliance costs are lower than predicted because of unanticipated use of new 

technology.” 

It should be noted that many (though not all) of the EPA rules examined by Harrington 

had compliance dates of several years, which allowed a limited period for technical innovation. 

conducted an 

analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued by 

EPA and the Occupational Safety and Health Administration (OSHA), and found a tendency for 

predicted costs to overstate actual implementation costs. Costs were considered accurate if 

they fell within the analysis error bounds or if they fall within 25 percent (greater or less than) 

the predicted amount. They found that predicted total costs were overestimated for 14 of the 

28 rules, while total costs were underestimated for only three rules. Differences can result 

because of quantity differences (e.g., overestimate of pollution reductions) or differences in 

per-unit costs (e.g., cost per unit of pollution reduction). Per-unit costs of regulations were 

overestimated in 14 cases, while they were underestimated in six cases. In the case of EPA 

rules, the agency overestimated per-unit costs for five regulations, underestimated them for 

four regulations (three of these were relatively small pesticide rules), and accurately estimated 

them for four. Based on examination of eight economic incentive rules, “for those rules that 

employed economic incentive mechanisms, overestimation of per-unit costs seems to be the 

norm,” the study said. It is worth noting here, that the controls applied for this NAAQS do not 

use an economic incentive mechanism. In addition, Harrington also states that overestimation 

of total costs can be due to error in the quantity of emission reductions achieved, which would 

also cause the benefits to be overestimated. 

 13 Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost Estimates.” Journal 

of Policy Analysis and Management 19(2):297-322.
6-19

• Acid Rain SO2 Trading Program: Recent cost estimates of the Acid Rain SO2 

trading program by Resources for the Future (RFF) and MIT have been as much as 83 percent 

lower than originally projected by EPA.14 As noted in the RIA for the Clean Air Interstate Rule, 

the ex ante numbers in 1989 were an overestimate in part because of the limitation of 

economic modeling to predict technological improvement of pollution controls and other 

compliance options such as fuel switching. The fuel switching from high-sulfur to low-sulfur coal

was spurred by a reduction in rail transportation costs due to deregulation of rail rates during 

the 1990’s Harrington et al. report that scrubbing turned out to be more efficient (95% removal 

vs. 80-85% removal) and more reliable (95% vs. 85% reliability) than expected, and that 

unanticipated opportunities arose to blend low and high sulfur coal in older boilers up to a 

40/60 mixture, compared with the 5/95 mixture originally estimated.

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billiona

Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in 1995$. 

• EPA Fuel Control Rules: A 2002 study by EPA’s Office of Transportation and Air 

Quality15 examined EPA vehicle and fuels rules and found a general pattern that “all ex ante 

estimates tended to exceed actual price impacts, with the EPA estimates exceeding actual 

prices by the smallest amount.” The paper notes that cost is not the same as price, but suggests 

that a comparison nonetheless can be instructive.16

 14 Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur Dioxide Control by 

Electric Utilities: What Are the Gains from Trade?” Journal of Political Economy 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap-and-Trade Program. 

Massachusetts Institute of Technology Center for Energy and Environmental Policy Research. 15 Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to 

Actual Price Changes,” Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Technical 

Paper published by the Society of Automotive Engineers. SAE 2002-01-1980. 16 The paper notes: “Cost is not the same as price. This simple statement reflects the fact that a lot happens 

between a producer’s determination of manufacturing cost and its decisions about what the market will bear in 

terms of price change.” 

An example focusing on fuel rules is 

provided in Table 6.6:
6-20

Table 6.6: Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes for EPA 

Fuel Control Rulesa

Inflation-adjusted Cost Estimates (c/gal) Actual Price 

EPA DOE API Other Changes (c/gal)

Gasoline

Phase 2 RVP Control (7.8 RVP—

Summer) (1995$)

1.1 1.8 0.5

Reformulated Gasoline Phase 1 

(1997$)

3.1-5.1 3.4-4.1 8.2-14.0 7.4 (CRA) 2.2

Reformulated Gasoline Phase 2 

(Summer) (2000$)

4.6-6.8 7.6-10.2 10.8-19.4 12 7.2 (5.1, when 

corrected to 5yr 

MTBE price)

30 ppm sulfur gasoline (Tier 2) 1.7-1.9 2.9-3.4 2.6 5.7 (NPRA), 

3.1 (AIAM)

N/A

Diesel

500 ppm sulfur highway diesel fuel 

(1997$)

1.9-2.4 3.3 (NPRA) 2.2

15 ppm sulfur highway diesel fuel 4.5 4.2-6.0 6.2 4.2-6.1 

(NPRA)

N/A

a Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to 

Actual Price Changes,” Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Technical 

Paper published by the Society of Automotive Engineers. SAE 2002-01-1980.

• Chlorofluorocarbon (CFC) Phase-Out: EPA used a combination of regulatory, 

market based (i.e., a cap-and-trade system among manufacturers), and voluntary approaches 

to phase out the most harmful ozone depleting substances. This was done more efficiently than 

either EPA or industry originally anticipated. The phaseout for Class I substances was 

implemented 4-6 years faster, included 13 more chemicals, and cost 30 percent less than was 

predicted at the time the 1990 Clean Air Act Amendments were enacted.17

The Harrington study states, “When the original cost analysis was performed for the CFC 

phase-out it was not anticipated that the hydrofluorocarbon HFC-134a could be substituted for 

CFC-12 in refrigeration. However, as Hammit18

 17 Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Before the Subcommittee on Energy and air Quality of the committee on 

Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.

18 Hammit, J.K. (2000). “Are the costs of proposed environmental regulations overestimated? Evidence from the 

CFC phaseout.” Environmental and Resource Economics, 16(#3): 281-302.

notes, ‘since 1991 most new U.S. automobile air 

conditioners have contained HFC-134a (a compound for which no commercial production 

technology was available in 1986) instead of CFC-12” (p.13). He cites a similar story for HCFRC141b and 142b, which are currently substituting for CFC-11 in important foam-blowing 

applications.” 
6-21

Additional examples of decreasing costs of emissions controls include: SCR catalyst costs 

decreasing from $11k-$14k/m3 in 1998 to $3.5k-$5k/m3 in 2004, and improved low NOx 

burners reduced emissions by 50% from 1993-2003 while the associated capital cost dropped 

from $25-$38/kW to $15/kW19. Also, FGD scrubber capital costs have been estimated to have 

decreased by more than 50 percent from 1976 to 2005, and the operating and maintenance 

(O&M) costs decreased by more than 50% from 1982 to 2005. Many process improvements 

contributed to lowering the capital costs, especially improved understanding and control of 

process chemistry, improved materials of construction, simplified absorber designs, and other 

factors that improved reliability.20

We cannot estimate the precise interplay between EPA regulation and technology 

improvement, but it is clear that a priori cost estimation often results in overestimation of costs 

because changes in technology (whatever the cause) make less costly control possible. 

6.2 Economic Impacts

The assessment of economic impacts in Table 6.7 was conducted based on those source 

categories which are assumed in this analysis to become controlled. The impacts presented 

here are a comparison of the control costs to the revenues for industries affected by control 

strategies applied for the 75 ppb final standard. Control costs are allocated to specific source 

categories by North American Industry Classification System (NAICS) code. 

 19 ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring Innovation and Growth While Cleaning 

the Air. Washington, DC. Available at http://www.icfi.com/Markets/Environment/doc_files/caaa-success.pdf. 

20 Yeh, Sonia and Rubin, Edward. February 2007. “Incorporating Technological Learning in the Coal Utility 

Environmental Cost (CUECost) Model: Estimating the Future Cost Trends of SO2, NOx, and Mercury Control 

Technologies.” Prepared for ARCADIS Geraghty and Miller, Research Triangle Park, NC 27711. Available at 

http://steps.ucdavis.edu/People/slyeh/syeh-resources/Drft%20Fnl%20Rpt%20Lrng%20for%20CUECost_v3.pdf. 
6-22

Table 6.7: Identified Cost/Revenue Ratios by Affected Industry for Illustrative Control 

Strategy for the Final SO2 Standard (75 ppb) in 2020 (Millions of 2006$)a, b, c

NAICS 

Code

Industry Description 3% Discount 

Rated

7% Discount 

Rate

Industry 

Revenue in 

2007e

Cost/Revenue 

Ratio

2211 Electric Power Generation, 

Transmission and Distribution

699 699 440,000 0.16%

311 Food Manufacturing 55 19.9 589,000 <0.01%

312 Beverage and Tobacco Product 

Manufacturing

1.3 7.0 128,000 <0.01%

322 Paper Manufacturing $143 $31.2 $170,000 < 0.01%

324 Petroleum and Coal Products 

Manufacturing

$245 $39.5 $590,000 < 0.01%

325 Chemical Manufacturing $12.8 $12.8 $720,000 < 0.01%

326 Plastics and Rubber Products 

Manufacturing

6.2 6.2 211,000 <0.01%

327 Nonmetallic Mineral Product 

Manufacturing

266 43.5 128,000 <0.01%

331 Primary Metal Manufacturing $ $43.6 $250,000 < 0.01%

332 Fabricated metal product 

manufacturing

0.4 0.4 344,000 < 0.01%

333 Machinery manufacturing 3.0 3.0 19,700 < 0.01%

336 Transportation equipment 

manufacturing

2.9 0.8 737,000 < 0.01%

611 Educational services 137 51.9 47,000 0.13%

a All estimates rounded to two significant figures. As such, totals will not sum down columns.

b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a 

2020 baseline. c NAICS codes were unavailable for area source controls. These controls account for less than 2% of the total 

identified control strategy costs. d Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and 

where equipment life values were available. For the identified control strategy, data for calculating annualized 

costs at a 3% discount was available for point sources. Therefore, the total annualized identified control cost value 

presented in this referenced cell is an aggregation of engineering costs at 3% and 7% discount rate.

e Source: U.S. Census Bureau 2007 Economic Census. Industry-level data on revenues can be found at 

http://factfinder.census.gov/servlet/IBQTable?_bm=y&-fds_name=EC0700A1&-_skip=0&-ds_name=EC0700A1&-

_lang=en. f

No data on budget or revenues for this NAICS code is included in the 2007 Economic Census. 

6.3 Energy Impacts

This section summarizes the energy consumption impacts associated with control strategies 

applied for the final SO2 NAAQS of 75 ppb. The SO2 NAAQS revisions do not constitute a 

“significant energy action” as defined in Executive Order 13211; this information merely 

represents impacts of the illustrative control strategy applied in the RIA. The rule does not 

prescribe specific control strategies by which these ambient standards will be met. Such 
6-23

strategies will be developed by States on a case-by-case basis, and EPA cannot predict whether 

the control options selected by States will include regulations on energy suppliers, distributors, 

or users. Thus, EPA concludes that this rule is not likely to have any adverse energy effects as 

defined in Executive Order 13211.

For this RIA, implementation of the control measures needed for attainment with the 

alternative standards will likely lead to increased energy consumption among SO2 emitting 

facilities. In addition, because the energy consumption and impacts on various energy markets 

associated with emission reductions beyond identified controls is uncertain, we only consider 

the energy impacts associated with identified controls. 

With respect to energy supply and prices, the analysis in Table 6.7 suggests that at the 

electric power industry level, the annualized costs associated with the illustrative control 

strategy for the final standard (75 ppb) represent only about 0.16 percent of its revenues in 

2020. In addition, for the other industries affected under the 75 ppb standard, no other 

industry has annualized costs of more than 0.13 percent of its revenues. As a result we can 

conclude that impacts to supply and electricity price are small

6.4 Limitations and Uncertainties Associated with Engineering Cost Estimates

• EPA bases its estimates of emissions control costs on the best available information 

from engineering studies of air pollution controls and has developed a reliable 

modeling framework for analyzing the cost, emissions changes, and other impacts of 

regulatory controls. The annualized cost estimates of the private compliance costs 

are meant to show the increase in production (engineering) costs to the various 

affected sectors in our control strategy analyses. To estimate these annualized costs, 

EPA uses conventional and widely-accepted approaches that are commonplace for 

estimating engineering costs in annual terms. However, our engineering cost analysis 

is subject to uncertainties and limitations.

• One of these limitations is that we do not have sufficient information for all of our 

known control measures to calculate cost estimates that vary with an interest rate. 

We are able to calculate annualized costs at an interest rate other than 7% (e.g., 3% 

interest rate) where there is sufficient information—available capital cost data, and 

equipment life—to annualize the costs for individual control measures. For the vast 

majority of nonEGU point source control measures, we do have sufficient capital cost 

and equipment life data for individual control measures to prepare annualized capital 

costs using the standard capital recovery factor. Hence, we are able to provide 
6-24

annualized cost estimates at different interest rates for the point source control 

measures.

• For area source control measures, the engineering cost information is available only 

in annualized cost/ton terms. We have extremely limited capital cost and equipment 

life data for area source control measures. We know that these annualized cost/ton 

estimates reflect an interest rate of 7% because these estimates are typically 

products of technical memos and reports prepared as part of rules issued by EPA over 

the last 10 years or so, and the costs estimated in these reports have followed the 

policy provided in OMB Circular A-4 that recommends the use of 7% as the interest 

rate for annualizing regulatory costs. Capital cost information for these area source 

controls, however, is often limited since these measures are often not the traditional 

add-on controls where the capital cost is well known and convenient to estimate. The 

limited availability of useful capital cost data for such control measures has led to our 

use of annualized cost/ton estimates to represent the engineering costs of these 

controls in our cost tools and hence in this RIA. 

• There are some unquantified costs that are not adequately captured in this 

illustrative analysis. These costs include the costs of federal and State administration 

of control programs, which we believe are less than the alternative of States 

developing approvable SIPs, securing EPA approval of those SIPs, and Federal/State 

enforcement. The analysis also did not consider transactional costs and/or effects on 

labor supply in the illustrative analysis. 
7-1

Chapter 7: Estimates of Costs and Benefits

Synopsis

As discussed above, this RIA analyzes alternative primary standards of 50 parts per 

billion (ppb), 75 ppb, and 100 ppb. Our assessment of the lower bound SO2 target NAAQS 

includes several key elements, including specification of baseline SO2 emissions and 

concentrations; development of illustrative control strategies to attain the standard in 2020; 

and analyses of the control costs and health benefits of reaching the various alternative 

standards. We also note that because it was not possible, in this analysis, to bring all areas into 

attainment with the selected standard of 75 ppb in all areas using only identified controls, EPA 

conducted a second step in the analysis, and estimated the cost of unspecified emission 

reductions needed to attain the alternative primary NAAQS. 

This analysis does not estimate the projected attainment status of areas of the country 

other than those counties currently served by one of the approximately 488 monitors in the

current network. It is important to note that the rule would require a monitoring network 

wholly comprised of monitors sited at locations of expected maximum hourly concentrations. 

Only about one third of the existing SO2 network may be source-oriented and/or in the 

locations of maximum concentration required by the proposed rule because the current 

network is focused on population areas and community-wide ambient levels of SO2. Actual 

monitored levels using the new monitoring network may be higher than levels measured using 

the existing network. We recognize that once a network of monitors located at maximumconcentration is put in place, more areas could find themselves exceeding the new SO2 NAAQS. 

However for this RIA analysis, we lack sufficient data to predict which counties might exceed 

the new NAAQS after implementation of the new monitoring network. Therefore we lack a 

credible analytic path to estimating costs and benefits for such a future scenario.

7.1 Benefits and Costs

We estimated the benefits and costs for four alternative SO2 NAAQS levels: 50 ppb, 75 

ppb, and 100 ppb (99th percentile). These costs and benefits are associated with an 

incremental difference in ambient concentrations between a baseline scenario and a pollution 

control strategy. As indicated above and in Chapter 4, several areas of the country may not be 

able to attain some alternative standard using known pollution control methods. Because 

some areas require substantial emission reductions from unknown sources to attain the various 

standards, the results are very sensitive to assuming full attainment. For this reason, we 

provide the full attainment and the partial attainment results for both benefits and costs.
7-2

Costs

Our analysis of the costs associated with the range of alternative NAAQS focuses on SO2

emission controls for electric generating units (EGU) and nonEGU stationary and area sources. 

EGU, nonEGU and area source controls largely include measures from the Control Strategy Tool 

(CoST), and the AirControlNET control technology database. For these sources, we estimated 

costs based on the cost equations included in AirControlNET. 

As indicated in the above discussion on illustrative control strategies, implementation of 

the SO2 control measures identified from AirControlNET and other sources does not result in 

attainment with the selected NAAQS in several areas. In these areas, additional unspecified 

emission reductions might be necessary to reach some alternative standard levels. In order to 

bring these monitor areas into attainment, we calculated controls costs using a fixed cost per 

ton approach similar to that used in the ozone RIA analysis. We recognize that a single fixed 

cost of control of $15,000 per ton of emissions reductions does not account for the significant 

emissions cuts that are necessary in some areas, and so its use provides an estimate that is 

likely to differ from actual future costs. 

Benefits

EPA estimated the monetized human health benefits of reducing cases of morbidity 

among populations exposed to SO2 and cases of morbidity and premature mortality among 

populations exposed to PM2.5 in 2020 for the selected standard and alternative standard levels 

in 2006$. Because SO2 is also a precursor to PM2.5, reducing SO2 emissions in the projected 

non-attainment areas will also reduce PM2.5 formation, human exposure and the incidence of 

PM2.5-related health effects. For the selected SO2 standard at 75 ppb (99th percentile, daily 1-

hour maximum), the total monetized benefits would be $15 to $37 billion at a 3% discount rate 

and $14 to $33 billion at a 7% discount rate. For an SO2 standard at 50 ppb, the total 

monetized benefits would be $34 to $83 billion at a 3% discount rate and $31 to $75 billion at a 

7% discount rate. For an SO2 standard at 100 ppb, the total monetized benefits would be $7.4 

to $18 billion at a 3% discount rate and $6.7 to $16 billion at a 7% discount rate. 

These estimates reflect EPA’s most current interpretation of the scientific literature and 

are consistent with the methodology used for the proposal RIA. These benefits are incremental 

to an air quality baseline that reflects attainment with the 2008 ozone and 2006 PM2.5 National 

Ambient Air Quality Standards (NAAQS). More than 99% of the total dollar benefits are 

attributable to reductions in PM2.5 exposure resulting from SO2 emission reductions. Higher or 

lower estimates of benefits are possible using other assumptions; examples of this are provided 
7-3

in Figure 5.1 for the selected standard of 75 ppb. Methodological limitations prevented EPA 

from quantifying the impacts to, or monetizing the benefits from several important benefit 

categories, including ecosystem effects from sulfur deposition, improvements in visibility, and 

materials damage. Other direct benefits from reduced SO2 exposure have not been quantified, 

including reductions in premature mortality.

When estimating the SO2- and PM2.5-related human health benefits and compliance 

costs in Table 7.1 below, EPA applied methods and assumptions consistent with the state-ofthe-science for human health impact assessment, economics and air quality analysis. EPA 

applied its best professional judgment in performing this analysis and believes that these 

estimates provide a reasonable indication of the expected benefits and costs to the nation of 

the selected SO2 standard and alternatives considered by the Agency. The Regulatory Impacts 

Analysis (RIA) available in the docket describes in detail the empirical basis for EPA's 

assumptions and characterizes the various sources of uncertainties affecting the estimates 

below.

EPA's 2009 Integrated Science Assessment for Particulate Matter concluded, based on 

the scientific literature, that a no-threshold log-linear model most adequately portrays the PMmortality concentration-response relationship. Nonetheless, consistent with historical practice 

and our commitment to characterizing the uncertainty in our benefits estimates, EPA has 

included a sensitivity analysis with an assumed threshold in the PM-mortality health impact 

function in the RIA. EPA has included a sensitivity analysis in the RIA to help inform our 

understanding of the health benefits which can be achieved at lower air quality concentration 

levels. While the primary estimate and the sensitivity analysis are not directly comparable, due 

to differences in population data and use of different analysis years, as well as the difference in 

the assumption of a threshold in the sensitivity analysis, comparison of the two results provide 

a rough sense of the proportion of the health benefits that occur at lower PM2.5 air quality 

levels. Using a threshold of 10 μg/m3 is an arbitrary choice (EPA could have assumed 6, 8, or 12 

μg/m3 for the sensitivity analysis). Assuming a threshold of 10 μg/m3, the sensitivity analysis 

shows that roughly one-third of the benefits occur at air quality levels below that threshold. 

Because the primary estimates reflect EPA’s current methods and data, EPA notes that caution 

should be exercised when comparing the results of the primary and sensitivity analyses. EPA 

appreciates the value of sensitivity analyses in highlighting the uncertainty in the benefits 

estimates and will continue to work to refine these analyses, particularly in those instances in 

which air quality modeling data are available. 

Table 7.1 presents total national primary estimates of costs and benefits for a 3% 

discount rate and a 7% discount rate. The net benefits were calculated by subtracting the total 
7-4

cost estimate from the two estimates of total benefits. As indicated above, implementation of 

the SO2 control measures identified from AirControlNET and other sources does not result in 

attainment with the all target NAAQS levels in several areas. In these areas, additional 

unspecified emission reductions might be necessary to reach some alternative standard levels. 

The first part of the table, labeled Partial attainment (known controls), shows only those 

benefits and costs from control measures we were able to identify. The second part of the 

table, labeled Unidentified Controls, shows only additional benefits and costs resulting from 

unidentified controls. The third part of the table, labeled Full attainment, shows total benefits 

and costs resulting from both identified and unidentified controls. It is important to emphasize 

that we were able to identify control measures for a significant portion of attainment for many 

of those counties that would not fully attain the target NAAQS level with identified controls. 

Note also that in addition to separating full and partial attainment, the table also separates the 

portion of benefits associated with reduced SO2 exposure (i.e., SO2 benefits) from the additional 

benefits associated with reducing SO2 emissions, which are precursors to PM2.5 formation –

(i.e., the PM2.5 co-benefits). For instance, for the selected standard of 75 ppb, $2.2 million in 

benefits are associated with reduced SO2 exposure while $15 billion to $37 billion are 

associated with reduced PM2.5 exposure.
7-5

Table 7.1: Monetized Benefits and Costs to Attain Alternate Standard Levels in 2020 (millions 

of 2006$) a

# Counties

Fully

Controlled

Discount 

Rate

Monetized 

SO2

Benefits

Monetized PM2.5

Co-Benefits c,d Costs Net Benefits

Partial 

Attainment

(identified controls)

50 ppb 40 3%

7% -

b $30,000 to $74,000 $2,600 $27,000 to $71,000

$28,000 to $67,000 $25,000 to $64,000

75 ppb 20 3%

7% -

b $14,000 to $35,000 $960 $13,000 to $34,000

$13,000 to $31,000 $12,000 to $30,000

100 ppb 6 3% -

b $6,900 to $17,000 $470 $6,400 to $17,000

7% $6,200 to $15,000 $5,700 to $15,000

Unidentified 

Controls

50 ppb 16 3% -

b $4,000 to $9,000 $1,800 $2,200 to $7,200

7% $3,000 to $8,000 $1,200 to $6,200

75 ppb 4 3% -

b $1,000 to $3,000 $500 $500 to $1,500

7% $1,000 to $3,000 $500 to $2,500

100 ppb 3 3% -

b $500 to $1,000 $260 $240 to $740

7% $500 to $1,000 $240 to $740

Full Attainment

50 ppb 56

3% $8.50 $34,000 to $83,000 $4,400 $30,000 to $79,000

7% $31,000 to $75,000 $27,000 to $71,000

75 ppb 24

3% $2.20 $15,000 to $37,000 $1,500 $14,000 to $36,000

7% $14,000 to $34,000 $13,000 to $33,000

100 ppb 9

3% $0.60 $7,400 to $18,000 $730 $6,700 to $17,000

7% $6,700 to $16,000 $6,000 to $15,000 a Estimates have been rounded to two significant figures and therefore summation may not match table estimates. b The approach used to simulate air quality changes for SO2 did not provide the data needed to distinguish partial 

attainment benefits from full attainment benefits from reduced SO2 exposure. Therefore, a portion of the SO2

benefits is attributable to the known controls and a portion of the SO2 benefits are attributable to the unidentified

controls. Because all SO2-related benefits are short-term effects, the results are identical for all discount rates. c Benefits are shown as a range from Pope et al (2002) to Laden et al. (2006). Monetized benefits do not include 

unquantified benefits, such as other health effects, reduced sulfur deposition, or improvements in visibility.

d These models assume that all fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality because there is no clear scientific evidence that would support the development of 

differential effects estimates by particle type. Reductions in SO2 emissions from multiple sectors to meet the SO2

NAAQS would primarily reduce the sulfate fraction of PM2.5. Because this rule targets a specific particle precursor 

(i.e., SO2), this introduces some uncertainty into the results of the analysis.

7.2 Discussion of Uncertainties and Limitations

Air Quality, Emissions, and Control Strategies

The estimates of emission reductions associated with the control strategies described 

above are subject to important limitations and uncertainties. We summarize these limitations 

as follows:

• Actual State Implementation Plans May Differ from our Simulation: In order to reach 

attainment with the proposed NAAQS, each state will develop its own 
7-6

implementation plan implementing a combination of emissions controls that may 

differ from those simulated in this analysis. This analysis therefore represents an 

approximation of the emissions reductions that would be required to reach 

attainment and should not be treated as a precise estimate.

• Use of Existing CMAQ Model Runs: This analysis represents a screening level 

analysis. We did not conduct new regional scale modeling specifically targets to SO2; 

instead we relied upon impact ratios developed from model runs used in the 

analysis underlying the PM2.5 NAAQS. 

• Unidentified controls: We have limited information on available controls for some of 

the monitor areas included in this analysis. For a number of small non-EGU and 

area sources, there is little or no information available on SO2 controls. 

Costs

• We do not have sufficient information for all of our known control measures to calculate 

cost estimates that vary with an interest rate. We are able to calculate annualized costs 

at an interest rate other than 7% (e.g., 3% interest rate) where there is sufficient 

information—available capital cost data, and equipment life—to annualize the costs for 

individual control measures. For the vast majority of nonEGU point source control 

measures, we do have sufficient capital cost and equipment life data for individual 

control measures to prepare annualized capital costs using the standard capital recovery 

factor. Hence, we are able to provide annualized cost estimates at different interest 

rates for the point source control measures.

• There are some unquantified costs that are not adequately captured in this illustrative 

analysis. These costs include the costs of federal and State administration of control 

programs, which we believe are less than the alternative of States developing 

approvable SIPs, securing EPA approval of those SIPs, and Federal/State enforcement. 

Additionally, control measure costs referred to as “no cost” may require limited 

government agency resources for administration and oversight of the program not 

included in this analysis; those costs are generally outweighed by the saving to the 

industrial, commercial, or private sector. The Agency also did not consider transactional 

costs and/or effects on labor supply in the illustrative analysis. 
7-7

Benefits

Although we strive to incorporate as many quantitative assessments of uncertainty, 

there are several aspects for which we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the attainment strategies 

for each of the alternative standards: 

1. The 12 km CMAQ grid, which is the air quality modeling resolution, may be too coarse 

to accurately estimate the potential near-field health benefits of reducing SO2 emissions. 

These uncertainties may under- or over-estimate benefits. 

2. The interpolation techniques used to estimate the full attainment benefits of the 

alternative standards contributed some uncertainty to the analysis. The great majority 

of benefits estimated for the various standard alternatives were derived through 

interpolation. As noted previously in this chapter, these benefits are likely to be more 

uncertain than if we had modeled the air quality scenario for both SO2 and PM2.5. In 

general, the VNA interpolation approach will under-estimate benefits because it does 

not account for the broader spatial distribution of air quality changes that may occur 

due to the implementation of a regional emission control program.

3. There are many uncertainties associated with the health impact functions used in this 

modeling effort. These include: within study variability (the precision with which a given 

study estimates the relationship between air quality changes and health effects); across 

study variation (different published studies of the same pollutant/health effect 

relationship typically do not report identical findings and in some instances the 

differences are substantial); the application of C-R functions nationwide (does not 

account for any relationship between region and health effect, to the extent that such a 

relationship exists); extrapolation of impact functions across population (we assumed 

that certain health impact functions applied to age ranges broader than that considered 

in the original epidemiological study); and various uncertainties in the C-R function, 

including causality and thresholds. These uncertainties may under- or over-estimate 

benefits. 

4. Co-pollutants present in the ambient air may have contributed to the health effects 

attributed to SO2 in single pollutant models. Risks attributed to SO2 might be 

overestimated where concentration-response functions are based on single pollutant 

models. If co-pollutants are highly correlated with SO2, their inclusion in an SO2 health 

effects model can lead to misleading conclusions in identifying a specific causal 

pollutant. Because this collinearity exists, many of the studies reported statistically 

insignificant effect estimates for both SO2 and the co-pollutants; this is due in part to the 

loss of statistical power as these models control for co-pollutants. Where available, we 
7-8

have selected multipollutant effect estimates to control for the potential confounding 

effects of co-pollutants; these include NYDOH (2006), Schwartz et al. (1994) and 

O’Connor et al. (2008). The remaining studies include single pollutant models. 

5. This analysis is for the year 2020, and projecting key variables introduces uncertainty. 

Inherent in any analysis of future regulatory programs are uncertainties in projecting 

atmospheric conditions and source level emissions, as well as population, health 

baselines, incomes, technology, and other factors. 

6. This analysis omits certain unquantified effects due to lack of data, time and resources. 

These unquantified endpoints include other health effects, ecosystem effects, and 

visibility. EPA will continue to evaluate new methods and models and select those most 

appropriate for estimating the benefits of reductions in air pollution. Enhanced 

collaboration between air quality modelers, epidemiologists, toxicologists, ecologists, 

and economists should result in a more tightly integrated analytical framework for 

measuring benefits of air pollution policies. 

7. PM2.5 co-benefits represent a substantial proportion of total monetized benefits (over 

99% of total monetized benefits), and these estimates are subject to a number of 

assumptions and uncertainties. 

a. PM2.5 co-benefits were derived through benefit per-ton estimates, which do not 

reflect local variability in population density, meteorology, exposure, baseline 

health incidence rates, or other local factors that might lead to an over-estimate 

or under-estimate of the actual benefits of controlling directly emitted fine 

particulates. 

b. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may 

differ significantly from direct PM2.5 released from diesel engines and other 

industrial sources, but no clear scientific grounds exist for supporting differential 

effects estimates by particle type. 

c. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates 

include health benefits from reducing fine particles in areas with varied 

concentrations of PM2.5, including both regions that are in attainment with fine 

particle standard and those that do not meet the standard down to the lowest 

modeled concentrations. 

d. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized benefits), 

we include a set of twelve estimates based on results of the expert elicitation 

study in addition to our core estimates. Even these multiple characterizations 
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omit the uncertainty in air quality estimates, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse 

locations. As a result, the reported confidence intervals and range of estimates 

give an incomplete picture about the overall uncertainty in the PM2.5 estimates. 

This information should be interpreted within the context of the larger 

uncertainty surrounding the entire analysis. For more information on the 

uncertainties associated with PM2.5 co-benefits, please consult the PM2.5 NAAQS 

RIA (Table 5.5).

While the monetized benefits of reduced SO2 exposure appear small when compared to the 

monetized benefits of reduced PM2.5 exposure, readers should not necessarily infer that the 

total monetized benefits of attaining a new SO2 standard are minimal. For this rule, the

monetized PM2.5 co-benefits represent over 99% of the total monetized benefits. This result is 

consistent with other recent RIAs, where the PM2.5 co-benefits represent a large proportion of 

total monetized benefits. This result is amplified in this RIA by the decision not to quantify SO2-

related premature mortality and other morbidity endpoints due to the uncertainties associated 

with estimating those endpoints. Studies have shown that there is a relationship between SO2 

exposure and premature mortality, but that relationship is limited by potential confounding. 

Because premature mortality generally comprises over 90% of the total monetized benefits, 

this decision may substantially underestimate the monetized health benefits of reduced SO2 

exposure.

In addition, we were unable to quantify the benefits from several welfare benefit 

categories. We lacked the necessary air quality data to quantify the benefits from 

improvements in visibility from reducing light-scattering particles. Previous RIAs for ozone (U.S. 

EPA, 2008a) and PM2.5 (U.S. EPA, 2006a) indicate that visibility is an important benefit category, 

and previous efforts to monetize those benefits have only included a subset of visibility 

benefits, excluding benefits in urban areas and many national and state parks. Even this subset 

accounted for up to 5% of total monetized benefits in the Ozone NAAQS RIA (U.S. EPA, 2008a). 

We were also unable to quantify the ecosystem benefits of reduced sulfur deposition 

because we lacked the necessary air quality data, and the methodology to estimate ecosystem 

benefits is still being developed. Previous assessments (U.S. EPA, 1999; U.S. EPA, 2005; U.S. 

EPA, 2009e) indicate that ecosystem benefits are also an important benefits category, but those 

efforts were only able to monetize a tiny subset of ecosystem benefits in specific geographic 

locations, such as recreational fishing effects from lake acidification in the Adirondacks. We 

were also unable to quantify the benefits of decreased mercury methylation from sulfate 

deposition. Quantifying the relationship between sulfate and mercury methylation in natural 
7-10

settings is difficult, but some studies have shown that decreasing sulfate deposition can also 

decrease methylmercury.
8-1

Chapter 8: Statutory and Executive Order Reviews

1.0 Executive Order 12866: Regulatory Planning and Review

Under section 3(f)(1) of Executive Order 12866 (58 FR 51735, October 4, 1993), this 

action is an “economically significant regulatory action” because it is likely to have an 

annual effect on the economy of $100 million or more. Accordingly, EPA submitted this 

action to the Office of Management and Budget (OMB) for review under EO 12866 and 

any changes made in response to OMB recommendations have been documented in the 

docket for this action. In addition, EPA prepared a Regulatory Impact Analysis (RIA) of 

the potential costs and benefits associated with this action. However, the CAA and 

judicial decisions make clear that the economic and technical feasibility of attaining the 

national ambient standards cannot be considered in setting or revising NAAQS, although 

such factors may be considered in the development of State implementation plans to 

implement the standards. Accordingly, although an RIA has been prepared, the results 

of the RIA have not been considered by EPA in developing this final rule.

2.0 Paperwork Reduction Act

The information collection requirements in this final rule have been submitted 

for approval to the Office of Management and Budget (OMB) under the Paperwork 

Reduction Act, 44 U.S.C. 3501 et seq. The Information Collection Request (ICR) 

document prepared by EPA for these proposed revisions to part 58 has been assigned 

EPA ICR number 2370.01.

The information collected under 40 CFR part 53 (e.g., test results, monitoring 

records, instruction manual, and other associated information) is needed to determine 

whether a candidate method intended for use in determining attainment of the NAAQS 

in 40 CFR part 50 will meet the design, performance, and/or comparability requirements 

for designation as a Federal reference method (FRM) or Federal equivalent method 

(FEM). We do not expect the number of FRM or FEM determinations to increase over 

the number that is currently used to estimate burden associated with SO2 FRM/FEM 

determinations provided in the current ICR for 40 CFR part 53 (EPA ICR numbers 

2370.01). As such, no change in the burden estimate for 40 CFR part 53 has been made 

as part of this rulemaking. 

The information collected and reported under 40 CFR part 58 is needed to 

determine compliance with the NAAQS, to characterize air quality and associated health 
8-2

impacts, to develop emissions control strategies, and to measure progress for the air 

pollution program. The amendments would revise the technical requirements for SO2 

monitoring sites, require the siting and operation of additional SO2 ambient air 

monitors, and the reporting of the collected ambient SO2 monitoring data to EPA’s Air 

Quality System (AQS). This Information Collection is estimated to involve 102 

respondents for a total approximate cost of $15,203,762 (total capital, and labor and 

non-labor operation and maintenance) and a total burden of 207,662 hours. The labor 

costs associated with these hours is $11,130,409. Included in the $15,203,762 total are 

other costs of non-labor operations and maintenance of $1,104,377 and equipment and 

contract costs of $2,968,975. In addition to the costs at the State and local air quality 

management agencies, there is a burden to EPA of total of 14,749 hours and 

$1,060,621. Burden is defined at 5 CFR 1320.3(b). State, local, and tribal entities are 

eligible for State assistance grants provided by the Federal government under the CAA 

which can be used for monitors and related activities.

An agency may not conduct or sponsor, and a person is not required to respond 

to, a collection of information unless it displays a currently valid OMB control number. 

The OMB control numbers for EPA's regulations in 40 CFR are listed in 40 CFR part 9. 

3.0 Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the 

agency certifies that the rule will not have a significant economic impact on a substantial 

number of small entities. Small entities include small businesses, small organizations, 

and small governmental jurisdictions.

For purposes of assessing the impacts of this rule on small entities, small entity is 

defined as: (1) a small business that is a small industrial entity as defined by the Small 

Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental 

jurisdiction that is a government of a city, county, town, school district or special district 

with a population of less than 50,000; and (3) a small organization that is any not-forprofit enterprise which is independently owned and operated and is not dominant in its 

field.

After considering the economic impacts of this proposed rule on small entities, I 

certify that this action will not have a significant economic impact on a substantial 
8-3

number of small entities. This final rule will not impose any requirements on small 

entities. Rather, this rule establishes national standards for allowable concentrations of 

SO2 in ambient air as required by section 109 of the CAA. American Trucking Ass’ns v. 

EPA, 175 F. 3d 1027, 1044-45 (D.C. Cir. 1999) (NAAQS do not have significant impacts 

upon small entities because NAAQS themselves impose no regulations upon small 

entities). Similarly, the amendments to 40 CFR Part 58 address the requirements for 

States to collect information and report compliance with the NAAQS and will not impose 

any requirements on small entities. 

4.0 Unfunded Mandates Reform Act

This action is not subject to the requirements of sections 202 and 205 of the 

UMRA. EPA has determined that this proposed rule does not contain a Federal mandate 

that may result in expenditures of $100 million or more for State, local, and tribal 

governments, in the aggregate, or the private sector in any one year. The revisions to 

the SO2 NAAQS impose no enforceable duty on any State, local or Tribal governments or 

the private sector. The expected costs associated with the monitoring requirements are 

described in EPA’s ICR document, but those costs are not expected to exceed $100 

million in the aggregate for any year. Furthermore, as indicated previously, in setting a 

NAAQS, EPA cannot consider the economic or technological feasibility of attaining 

ambient air quality standards. Because the CAA prohibits EPA from considering the 

types of estimates and assessments described in section 202 when setting the NAAQS, 

the UMRA does not require EPA to prepare a written statement under section 202 for 

the revisions to the SO2 NAAQS. 

With regard to implementation guidance, the CAA imposes the obligation for 

States to submit SIPs to implement the SO2 NAAQS. In this final rule, EPA is merely 

providing an interpretation of those requirements. However, even if this rule did 

establish an independent obligation for States to submit SIPs, it is questionable whether 

an obligation to submit a SIP revision would constitute a Federal mandate in any case. 

The obligation for a State to submit a SIP that arises out of section 110 and section 191 

of the CAA is not legally enforceable by a court of law, and at most is a condition for 

continued receipt of highway funds. Therefore, it is possible to view an action requiring 

such a submittal as not creating any enforceable duty within the meaning of U.S.C. 658 

for purposes of the UMRA. Even if it did, the duty could be viewed as falling within the 

exception for a condition of Federal assistance under U.S.C. 658.
8-4

EPA has determined that this final rule contains no regulatory requirements that 

might significantly or uniquely affect small governments because it imposes no 

enforceable duty on any small governments. Therefore, the rule is not subject to the 

requirements of section 203 of the UMRA.

5.0 Executive Order 13132: Federalism

This final rule does not have federalism implications. It will not have substantial 

direct effects on the States, on the relationship between the national government and 

the States, or on the distribution of power and responsibilities among the various levels 

of government, as specified in Executive Order 13132. The rule does not alter the 

relationship between the Federal government and the States regarding the 

establishment and implementation of air quality improvement programs as codified in 

the CAA. Under section 109 of the CAA, EPA is mandated to establish NAAQS; however, 

CAA section 116 preserves the rights of States to establish more stringent requirements 

if deemed necessary by a State. Furthermore, this rule does not impact CAA section 107 

which establishes that the States have primary responsibility for implementation of the 

NAAQS. Finally, as noted in section E (above) on UMRA, this rule does not impose 

significant costs on State, local, or tribal governments or the private sector. Thus, 

Executive Order 13132 does not apply to this rule.

6.0 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments

Executive Order 13175, entitled “Consultation and Coordination with Indian 

Tribal Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an 

accountable process to ensure “meaningful and timely input by tribal officials in the 

development of regulatory policies that have tribal implications.” This final rule does 

not have tribal implications, as specified in Executive Order 13175. It does not have a 

substantial direct effect on one or more Indian tribes, on the relationship between the 

Federal government and Indian tribes, or on the distribution of power and 

responsibilities between the Federal government and tribes. The rule does not alter the 

relationship between the Federal government and tribes as established in the CAA and 

the TAR. Under section 109 of the CAA, EPA is mandated to establish NAAQS; however, 

this rule does not infringe existing tribal authorities to regulate air quality under their 

own programs or under programs submitted to EPA for approval. Furthermore, this rule 

does not affect the flexibility afforded to tribes in seeking to implement CAA programs 

consistent with the TAR, nor does it impose any new obligation on tribes to adopt or 
8-5

implement any NAAQS. Finally, as noted in section E (above) on UMRA, this rule does 

not impose significant costs on tribal governments. Thus, Executive Order 13175 does 

not apply to this rule. 

7.0 Executive Order 13045: Protection of Children from Environmental Health & 

Safety Risks

This action is subject to Executive Order (62 FR 19885, April 23, 1997) because it 

is an economically significant regulatory action as defined by Executive Order 12866, 

and we believe that the environmental health risk addressed by this action has a 

disproportionate effect on children. This final rule will establish uniform national 

ambient air quality standards for SO2; these standards are designed to protect public 

health with an adequate margin of safety, as required by CAA section 109. The 

protection offered by these standards may be especially important for asthmatics, 

including asthmatic children, because respiratory effects in asthmatics are among the 

most sensitive health endpoints for SO2 exposure. Because asthmatic children are 

considered a sensitive population, we have evaluated the potential health effects of 

exposure to SO2 pollution among asthmatic children. These effects and the size of the 

population affected are discussed in chapters 3 and 4 of the ISA; chapters 3, 4, 7, 8, 9 of 

the REA, and sections II.A through II.E of the preamble. 

8.0 Executive Order 13211: Actions that Significantly Affect Energy Supply, 

Distribution or Use

This rule is not a “significant energy action” as defined in Executive Order 13211, 

“Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or 

Use” (66 FR 28355; May 22, 2001) because it is not likely to have a significant adverse 

effect on the supply, distribution, or use of energy. The purpose of this rule is to 

establish revised NAAQS for SO2. The rule does not prescribe specific control strategies 

by which these ambient standards will be met. Such strategies will be developed by 

States on a case-by-case basis, and EPA cannot predict whether the control options 

selected by States will include regulations on energy suppliers, distributors, or users. 

Thus, EPA concludes that this rule is not likely to have any adverse energy effects.

9.0 National Technology Transfer and Advancement Act

Section 12(d) of the National Technology Transfer and Advancement Act of 1995 

(NTTAA), Public Law 104-113, section 12(d) (15 U.S.C. 27) directs EPA to use voluntary 
8-6

consensus standards in its regulatory activities unless to do so would be inconsistent 

with applicable law or otherwise impractical. Voluntary consensus standards are 

technical standards (e.g., materials specifications, test methods, sampling procedures, 

and business practices) that are developed or adopted by voluntary consensus 

standards bodies. The NTTAA directs EPA to provide Congress, through OMB, 

explanations when the Agency decides not to use available and applicable voluntary 

consensus standards.

This rulemaking involves technical standards with regard to ambient monitoring 

of SO2. The use of this voluntary consensus standard would be impractical because the 

analysis method does not provide for the method detection limits necessary to 

adequately characterize ambient SO2 concentrations for the purpose of determining 

compliance with the revisions to the SO2 NAAQS. 

10.0 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations

Executive Order 12898 (59 FR 7629; Feb. 16, 1994) establishes federal executive 

policy on environmental justice. Its main provision directs federal agencies, to the 

greatest extent practicable and permitted by law, to make environmental justice part of 

their mission by identifying and addressing, as appropriate, disproportionately high and 

adverse human health or environmental effects of their programs, policies, and 

activities on minority populations and low-income populations in the United States. 

EPA has determined that this final rule will not have disproportionately high and 

adverse human health or environmental effects on minority or low-income populations 

because it increases the level of environmental protection for all affected populations 

without having any disproportionately high and adverse human health effects on any 

population, including any minority or low-income population. The rule will establish 

uniform national standards for SO2 in ambient air.