1

- 1

From: Lydia Wegman/RTP/USEPA/US To: "Frey, Nathan J." <Nathan_J._Frey@omb.eop.gov>, "Lee, Amanda I."

<Amanda_I._Lee@omb.eop.gov> Cc: Joseph

- J Dougherty/DC/USEPA/US@EPA, Darryl Weatherhead/RTP/USEPA/US@EPA, Lillian

Bradley/RTP/USEPA/US@EPA, Bryan Hubbell/RTP/USEPA/US@EPA, Amy Lamson/RTP/USEPA /US@EPA Date: 12/14/2012 10:48 AM Subject: Response to interagency comments on the PM RIA

Nathan and Amanda, Attached below are the revisions to the RIA. As we discussed, we had made many edits to the Executive Summary prior to receiving your comments this morning. Those edits are:
1. Sections ES.2.1

- 3 was completely rewritten and we re

- drafted the analytical flowchart from Chapter 1 and added extensive language explaining in great detail what was performed at each step of the current analysis for the final rule. 2. Section ES.5 was completely rewritten to explain and fully describe the updates and important analytical differences between proposal and final. In addition, in response to your comments of today, we have added to this version of t he ES language related to emission reductions and included the table the comments identified. We have also updated the benefits language. The new language added to section ES.5 addresses the analytical differences between proposal and final. We also add ed a statement to the beginning paragraph of ES 2.1 pointing to section ES. 5. We plan to create a final version of the document based on this version so please let us know if you have any questions. Also, the revised benefits Chapters 5 and 6 are attached below as well.1

- 2 EXECUTIVE SUMMARY

ES.1 Overview Based on its review of the air quality criteria and the national ambient air quality standards (NAAQS) for particulate matter (PM), the U.S. Environmental Protection Agency ( EPA ) is making revisions to the primary standards for PM to provide requisite protection of public health and welfare. The EPA is revis ing the primary annual (health

- based) standard , retaining the primary 24

- hour standard, and retaining the secondary (welfare

- based) NAAQS for fine particles (generally referring to particles less than or equal to 2.5 micrometers [ μm ] in diameter

— PM 2.5 )

. T he EPA is retain ing the current primary and secondary 24

- hour PM 10 standards.
As has traditionally been done in NAAQS rulemaking s , the EPA has conducted a Regulatory Impact Analysis (RIA) to provide the public with illustrative estimates of the potential costs and health and welfare benefits of attaining the revised annual standard along with two alternat ive standards. In NAAQS rulemaking s , the RIA is prepared for informational purposes only, and the decisions related to the setting of the PM NAAQS standards are not in any way based on consideration of the information or analyses in the RIA. The RIA fulfil ls the requirements of Executive Order s 12866 and 13563 and guidelines of the Office of Management and Budget ’ s ( OMB ) Circular A

- 4

. 1
The control strategies presented in this RIA are illustrative and represent one set of control strategies State s might cho ose to implement in order to meet the final standards. As a result, b enefit and cost estimates provided in th is RIA cannot be added to benefits and costs f rom other regulations because each regulation is based on a different set of analytical assumptions and policy decisions

. T he costs and benefits identified in this RIA will not be realized until specific controls are mandated by future State Implementation Plans ( SIPs ) or other Federal regulations

. ES.2 Existing and Revised PM Air Quality Standards
T wo primary PM 2.5 standards provide public health protection from effects associated with fine particle exposures : the annual standard and the 24

- hour standard . The current annual standard is set at a level of 15.0 μg/m 3 , based on the 3

- year average of annual arithmetic mean PM 2.5 concentrations. The current 24

- hour standard is set at a level of 35 μg/m 3 , based on the

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

.1

- 3

3

- year average of the 98 th percentile of 24

- hour PM 2.5 concentration. In the RIA , the current primary PM 2.5 standard s , including both annual and 24

- hour standard s , are denoted as 15/ 35 μg/m 3

. Attainment of the 24

- hour standard is analyzed only in developing the scenario of attainment with the existing standards of 15/35 μg/m 3

. All other scenarios evaluate additional emissions reductions needed to a ttain alternative annual standards only

.
In th is PM NAAQS review, the EPA has revis ed and lowered the level of the primary annual PM 2.5 standard to 12 μ g/m 3 in conjunction with retaining the level of the 24

- hour standard at 35 μg/m 3 and th is standard is denoted as 12 μg/m 3

. In addition to the revised annual standard of 12 μg/m 3 , the RIA also analyzes the benefits and costs of incremental control strategies for two alternative annual standards of 13 μg/m 3 and 11 μg/m 3

. Currently, t he existing secondary (welfare

- based) PM 2.5 standards are identical in all respects to the primary standards. In this PM NAAQS review , the EPA i s retaining the current suite of secondary standards for 24

- hour and annual PM 2.5 . Thus, while the new primary annual standard will be revised to 12 μg/m 3 , the secondary annual standard will remain at 15 μ g/m 3

. Non

- visibility welfare effects are addressed by this suite of secondary standards, and PM

related visibility impairment is addressed by the secondary 24

- hour PM 2.5 standard , which EPA is leaving unchanged at 35 μ g/m 3

. The secondary standards will thus remain at 15/35 μg/m 3

.
W ith regard to the primary and secondary standards for particles less than or equal to 10 μm in diameter (PM 10 ), the EPA is retain ing the current primary and secondary 24

- hour PM 10 standards , which are both set at a level of 150 μg/m 3 , not to be exceeded more than once per year on average over 3 years ( U.S. EPA, 1997 )

. 2 Because the benefit

- cost analysis of the alt ernative PM 10 standards was conducted when the standard was promulgated in 19 9 7 , this RIA does not repeat that analysis here.

ES.2.1 Overview of the Analytical Steps in this RIA The goal of this RIA is to provide the best estimates of the costs and benefits of an illustrative attainment strategy to the meet the revised annual standard. The flowchart below (Figure ES

- 1) outlines the analytical steps taken to illustrate attainment with the revised annual standard of 12 μg/m 3 , and the following discussi on, by primary flowchart section, describes each of the steps taken. For important updates and analytical differences between proposal and final, see section ES

. 5 of this Executive Summary.

2 U.S. Environmental Protection Agency . 1997 . Regulatory Impact Analyses for the Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule. Available at: http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html

.1

- 4

Figure ES

- 1. PM RIA Analytical Flowchart

Establishing the Analy tical Baseline ( Flowchart Section 1 ) This section of the flowchart reflects the analytical steps taken to account for Federal and State rules and programs currently underway, as well as to reflect attainment of the current annual and daily standards of 15/35 μg/m 3 for the purpose of estimating the incremental costs and benefits of attaining the revised annual standard

. Detailed discussion s of the elements of this section of the flowchart are in Chapters 1 and 3 of the final RIA.

 Adjusted 2020 Base Case with “On the Books” Federal and State Rules and Programs

— The adjusted 2020 base case includes reductions expected to occur between 2007 and 2020 from existing (i.e., “on

- the

- books”) Federal and State control programs. This projection reflects air quality modeling of 2020 that accounts for major Federal and State pr ograms along with adjustments to these national

- level modeling results to account for PM 2.5 reductions expected, largely in the western US, from the implementation of episodic burn ban programs in certain counties and1

- 5 to remove the effects of atypical even ts such as wildfires and fireworks displays. (See Figure ES

- 2 below for illustration.) Below is a list of some of the major national rules reflected in the base case. Refer to Chapter 3, Section 3.2.1.4 for a more detailed discussion of the rules reflected in the 2020 base case emissions inventory.

– Light

- Duty Vehicle Tier 2 Rule (U.S. EPA, 1999 )

– Heavy Duty Diesel Rule (U.S. EPA, 2000 )

– Clean Air Nonroad Diesel Rule (U.S. EPA, 2004 )

– Regional Haze Regulations and Guidelines for Best Available Retrofit Technology Determinations (U.S. EPA, 2005b )

– NO x Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005 a )

– Emissions Standards for Loc omotives and Marine Compression

- Ignition Engines (U.S. EPA, 2008 a )

– Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 2008 b )

– C3 Oceangoing Vessels (U.S. EPA, 2010 a )

– Boiler MACT (U.S. EPA, 2011d)

– Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009 )

– Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010 b )

– Mercury and Air Toxics Standards (U.S. EPA, 2011 b )

– Cross

- State Air Pollution Rule (U.S. EPA, 2011 a ) 3

3 See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM 2.5 RIA and the reasons we believe CSAPR remains an appropriate proxy for this analysis

.1

- 6

Figure ES

- 2

. A nnual NAAQS Exce edances * in “Adjusted 2020 Base Case” Scenario

* 24

- hr PM 2.5 NAAQS Exceedances as Follows :

• San Joaquin Valley (6 counties); South Coast (2 counties); Imperial, CA; Allegheny, PA; Salt Lake, UT; Lake, OR; and Sacramento, CA

 Analytical Baseline with Attainment of 15/35

— The analytical baseline includes reductions from additional controls that the EPA estimates are needed to attain the current standard s (15/35 μg/m 3 ) for the purpose of estimating the incremental costs and benefits of attaining the revised annual standard of 12 μg/m 3

. Determining the level of emissions reductions needed to meet both the current annual and daily standards is done through adjusting a county’s projected design value (DV) concentration using the geographic area and poll utant specific air quality ratios (as described in Chapter 3 of the final RIA)

. In addition, for each area, it is necessary to determine which of the standards will be “controlling ,” i.e., which standard will require the most reductions to reach attainment of current standard

. This is important for establishing the analytical baseline because when the daily standard is controlling (as is the case in much of California), the emissions reductions required to meet the daily standard will result in reductions i n the annual design value to below the annual standard of 15 μg/m 3

. As a result, the annual PM 2.5 increment needed to attain alternative standards for each county will not be relative to an annual design value of 15 μg/m 3 , rather, the increment will vary by county based on the annual design value in that county that resulted from applying emissions1

- 7 controls to meet the 35 μg/m 3 daily standard. As shown in the map below, for counties labeled as exceeding the 12 μg/m 3 annual standard, the baseline design val ues can be any value between 12 and 15 μg/m 3

. Similarly, exceedances of the 11 μg/m 3 and 13 μg/m 3 standards can be anywhere between 11 or 13 and 15 μg/m 3

. The analytical baseline reflects attainment of 15/35 μg/m 3 by 2020

. We also modified the analytical baseline for counties in the South Coast Air Quality Management District and the San Joaquin Valley Air Pollution Control District to reflect reductions in mobile NO x emissions that th e se areas are expected to achieve between 2020 and 2025 due to fleet tur nover . These reductions in NO x emissions are not attributable to attainment of the current or revised PM standards, but reflect the impacts of other ”on

- the

- books” mobile programs so that these reductions are not included as either an incremental cost nor benefit to these area’s attaining the revised annual standard

. Figure ES

- 3. Annual NAAQS Exceedances in “Analytical Baseline” Scenario

• 13 Level: 2 Counties (Tulare, CA and Riverside, CA)

• 12 Level: 7 Counties (All in CA)

• 11 Level: 23 Counties

Control Strategies ( Flowchart Section 2 ) This section of the flowchart reflects analytical steps taken to analyze controls and emissions reductions needed beyond the current standard (15/35 μg/m 3 ) and other existing major rules to achieve the revised standard (12 μg/m 3 )

. We apply control options that might be available to State s for application by 2020. Detailed discussion of the elements of this section of the flowchart is in Chapter 4 of the final RIA.1

- 8  Select Controls by Emissions Sector and Pollutant

— Non

- EGU po int and nonpoint control measures were applied for the revised and alternative standards ’ control strategies. These controls were identified using the U.S. EPA’s Control Strategy Tool. Additional control measures were not applied to EGUs because of the ext ensive nature of controls resulting from the inclusion of MATS.

 Adjust 2020 Control Scenario to Meet Alternate Standards

— After identifying the known controls in the control scenario that were needed to meet the analytical baseline, additional known contro ls needed to meet the revised and alternative standards were identified. The EPA used air quality modeling results to determine whether the control scenario was sufficient to meet the revised and alternative standards for each geographic area. Where the co ntrol scenario modeling resulted in design value reductions below the level needed for the revised or alternative standards for specific geographic areas, county

- specific ratios of air quality response to emission reductions were used to determine the subs et of controls that were needed to attain the standard. Where it was determined that the control scenario was not sufficient in attaining the standard, these same response factors were used to calculate the amount of additional emission reductions beyond k nown controls needed to meet the standard. For the revised and alternative control strategy analysis, known controls for two sectors were used: non

- EGU point and area sources. Onroad mobile source controls were not used in the revised and alternative stand ards analysis because they were applied previously in the analytical baseline analysis where they were deemed to be most cost effective. Emission reductions were calculated for the known control strategy analysis and the cost analysis for emission reductio ns needed beyond known controls (“extrapolated” costs) for each alternative standard being analyzed. The EPA estimates the national

- scale emission reductions for revised annual standard of 12 μg/m 3 and two alternative annual standards (13 μg/m 3 and 11 μg/m 3 ) as shown in Table ES

- 1.

Due to Because the rules listed above and other emissions reductions should have substantially reduced ambient PM 2.5 concentrations by 2020 in the East, no additional controls are anticipated to be needed outside of Califo rnia (assuming no the absence of new sources). Specifically, our analysis estimates that in 2020 only 7 counties, all in California, will be out of attainment with the revised annual standard of 12ug/m 3 . Emissions reductions are needed in more locations for the alternative standard of 11ug/m 3

.1

- 9

Table ES

- 1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised

and Alternative Annual Primary PM 2.5 Standards, by Pollutant and Region in 2020 (tons) a

13 μg/m 3 12 μg/m 3 11 μg/m 3
Directly emitted PM 2.5 East 0 0 8,200
West 0 0 160
CA 730 4,000 10,600
SO 2 East 0 0 21,000
West 0 0 43
CA 0 0 0
NOx
East 0 0 9
West 0 0 0
CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table reflect both known and unknown controls. Estimates are rounded to two significant figure. Emission Reduction Estimates by Standard in 2020 (annual tons/year) a

Alternative Annual Standard s ( μg/m 3 ) PM 2.5 SO 2 NO x 13 730

- - 12 4,000

- - 11 19,000 21,000 9

a Estimates are rounded to two significant figures. Analysis of Costs ( Flowchart Section 3) This section of the flowchart reflects analytical steps taken to estimate the costs of both known and unknown controls

. Detailed discussion of the elements of this section of the flowchart is in Chapter 7 of the final RIA.

 Estimate Known Controls Engineering Costs, Estimate Extrapolated Costs

— We provide engineering cost estimates for the control strategies identified in Chapter 41

- 10 th at include control technologies on non

- EGU point sources and area sources. Engineering costs generally refer to the capital equipment expense, the site preparation costs for the application, and annual operating and maintenance costs. For this analysis, we included known controls for all of the geographic areas likely to exceed the revised and/or alternative standards. We also provide estimates for the engineering costs of the additional emissions reductions that are needed beyond the application of known c ontrols to reach full attainment of the alternative standards analyzed; the cost estimates derived from this approach are referred to as “extrapolated” costs. By definition, no cost data currently exists for the additional emissions reductions needed beyon d known controls

. We employ two methodologies for estimating the costs of unidentified future controls, and both approaches assume that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020.
Full Attainment (Flowchart Section 4) This section of the flowchart reflects analytical steps taken to estimate the costs of attainment, the benefits of attainment and the net benefits for the revised standard of 12 μg/m 3 . Detailed discussions of the elements of this section of the flowchart are in Chapter 5, Chapter 7, and Chapter 8 of the final RIA.

 Calculate Full Attainment Costs

— In Chapter 7 we present a summary of the total national costs of attaining the revised annual standard of 12 μg/m 3 and the altern ative annual standards of 13 μg/m 3 and 11 μg/m 3 in 2020. This summary includes the known and extrapolated costs. The total cost estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of 12 μg/m 3 ; $11 million and $100 mil lion for the alternative annual standard of 13 μg/m 3 ; and $320 million and $1,700 million for the alternative annual standard of 11 μg/m 3

.

 Calculate Full Attainment Benefits

— C hapter 5 presents the estimated human health benefits for the revised NAAQS

. W e q uantify the health

- related benefits of the fine particulate matter (PM 2.5 )

- related air quality improvements resulting from the illustrative emission s control scenarios that reduce emissions of directl y emitted particles and precursor pollutants including SO 2 and NO x to reach alternative PM 2.5 NAAQS levels in 2020. These benefits are relative to an analytical baseline reflecting nationwide attainment of the current primary PM 2.5 standards (i.e., annual s tandard of 15 μg/m 3 and 24

- hour standard of 35 μg/m 3 ) that includes promulgated national regulations and illustrative emission s controls to simulate attainment with 15/35 as well as a NO x emission adjustment to reflect expected reductions in mobile NO x emi ssions between 2020 and 2025.
The estimated benefits for the revised and alternative standards are in addition to the substantial benefits estimated for several recent implementation rules. Rules such as the Mercury and Air Toxics Standard (MATS) and othe r emission reductions will have substantially reduced ambient PM 2.5 concentrations by 2020 in the East,1

- 11 such that no additional controls would be needed in the East for the revised annual standard of 12 μg/m 3 . Thus, all of the estimated benefits occur in C alifornia because this is the only State that needs additional air quality improvement beyond the analytical baseline after accounting for air quality improvements from recent rules.

 Net Benefits

— Chapter 8 compares estimates of the benefits with costs and summarizes the net benefits of revised annual standard of 12 μg/m 3 and the alternative annual standards of 13 μg/m 3 and 11 μg/m 3 relative to the analytical baseline that includes recently promulgated national regulations and additional emissions reductions needed to attain the existing 15/35 μg/m 3 standards, as well as adjustments to NO x emissions in the San Joaquin and South Coast areas.

ES.2. 4 Health and Welfare Co

- Benefits
The EPA estimated impacts on human health (e.g., mortality and morbidity effects) under full attainment of the three alternative annual PM 2.5 standards. We considered an array of health impacts attributable to changes in PM 2.5 exposure and estimated these benefits using the BenMAP mod el (Abt Associates, 2012 ), which has been used in many recent RIAs ( e.g., U.S. EPA, 2006, 2011a, 2011b ), and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011c ). The monetized benefits estimated in the core analysis include avoided premature deaths (derived from effect coefficients in tow cohort studies [ Krewski et al. (2009) and Lepeule et al. (2012) ] for adults and one for infants [ Woodruff et al. (1997) ] for infants ) as well as avoided morbidity effects for 10 non

- fatal endpoints ranging in severity from lower respiratory symptoms to heart attacks. As not ed above, because California is the only state that needs additional air quality improvement beyond the analytical baseline after accounting for expected air quality improvements expected from recent rules, all of the benefits associated with the revised s tandard of 12ug/m3 occur in California.
Since the proposed rule, t T he EPA has incorporated an array of policy and technical updates to the benefits analysis approach applied in this RIA, including incorporation of the most recent follow

- up to the Harvard S ix Cities cohort study ( Lepeule et al., 2012 ), more recent demographic data projections, additional hospitalization and emergency department visit studies, inflation adjustment to 2010 dollars, and an expanded uncertainty assessment. Each of these updates is fully described in the health benefits chapter (Chapter 5) and summarized below in section ES.5

. Compared with the proposal benefits, the estimated benefits for the revised standard are about double due to a combination of updates to the analytic baseline The monetized benefits estimated in the core analysis include avoided premature deaths (derived from effect coefficients in Krewski et al. (2009) and Lepeule et al. (2012) for adults and1

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Woodruff et al. (1997) for infants) as well as avoided morbidity effects for 10 non
- fatal endpoints ranging in severity from lower respiratory symptoms to heart attacks.
Even though the primary standards are designed to protect against adverse effects to human health, the emission reductions will have welfare co

- benefits in addition to the direct human health benefits. The term welfare co

- benefits covers both environment al and societal benefits of reducing pollution, such as reductions in visibility impairment, materials damage, and ecosystem damage. Despite our attempts to quantify and monetize as many of the benefits as possible, the welfare co
- benefits associated with meeting the alternative standards are not quantified or monetized in this analysis. Unquantified health benefits are discussed in Chapter 5, and unquantified welfare co
- benefits are discussed in Chapter 6.
It is important to note that estimates of the heal th benefits from reduced PM 2.5 exposure reported here contain uncertaint ies , which are described in detail in Chapter 5 and Appendix 5b. Below are two key assumptions in the benefits analysis:

1. We assume that all fine particles, regardless of their chemi cal composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estim ates by particle type. The Integrated Science Assessment for Particulate Matter (PM ISA) , which was twice reviewed by CASAC, concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to al low differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009 ). These uncertainties are likely to be magnified in the current analysis to the extent that the emissions controls are less diverse when focusing on one small region of the country rather than a broader geography with more diverse emissions sources and the application of a more diverse set of controls.

2. We assume that health impact functions based on nationa l studies are representative for exposures and populations in California. I n addition to the national risk coefficients we use as our core estimates , the EPA considered the cohort studies conducted in California specifically. Although we have not calculat ed the benefits results using the cohort studies conducted in California , we provided the se risk coefficients to show how much the monetized benefits could have changed. Most of the California cohort studies report central effect estimates similar to the ( nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort studies conducted in California indicate statistically significant higher risks than the1

- 13 ris k estimates we applied from Lepeule et al. (2012), and four studies showed insignificant results.

3. We assume that the health impact function for fine particles is log

- linear without a threshold in this analysis. Thus, the estimates include health benefit s from reducing fine particles in areas with varied concentrations of PM 2.5, including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.
In general, we are more confide nt in the magnitude of the risks we estimate from simulated PM 2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies th at are used to estimate the benefits. Likewise, we are less confident in the ris k we estimate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. As noted in the preamble to the rule, the range from the 25 th to 10 th percentiles of the air quality data in the epidemiology studies is a reasonable range below which we start to have appreciably less confidence in the magnitude of the associations observed in the epidemiological studies. C oncentration benchmark analyses (e.g., 25 th percentile, 10 th percentile, one standard deviation below the mean , 4 and lowest measured level [ LML ] ) provide some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits

. Most of the estimated avoided premature deaths for this rulemaking occur at or above the lowest measured PM 2.5 concentration in the two studies that are used to estimate mortality benefits. T here are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less , and the sc ientific evidence provides no clear dividing line

. However , the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. Rather, t he core benefits estimates reported in this RIA (i.e., those based on Krewski et al. [ 2 009 ] and Lepeule et al. [ 2012 ] ) are the best measures because they reflect the full range of modeled air quality con centrations associated with the emission reduction strategies and because t he current body of scientific literature indicates that a no

threshold model provides the best estimate of PM

- related long

- term mortality. It is important to emphasize that “less co nfidence” does not mean “no confidence.”

The estimated benefits reflect illustrative control measures and emission reductions to lower PM 2.5 concentrations at monitors projected to exceed the revised and alternative annual standards. The result is that ai r quality is expected to improve in counties that exceed these standards as well as surrounding areas that do not exceed the alternative standards. In order to

4 A range of one standard deviation around the mean represents approximately 68% of normally distributed data and below the mean falls between the 25 th and 10 th percentiles.1

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make a direct comparison between the benefits and costs of the emission reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission reduction strategies applied regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in the counties that exceed the standards because it would omit benefits attributable to emission reduction in exceeding counties. In addition, we estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantiall y smaller than counties , and PM 2.5 concentrations can vary spatially within a county. Some grid cells in a county can be below the level of the alternative standard even though the highest monitor exceeds the alternative standard. Thus, emission reductions can lead to benefits in grid cells that are below the alternative standards within an exceeding county.

ES.2. 5 Cost Analysis Approach
The EPA estimated total costs under partial and full attainment of the alternative PM 2.5 standards. The engineering costs generally include the costs of purchasing, installing, and operating the referenced control technologies. The technologies and control strategies selected for analysis are illustrative of one way in which nonattainment are as could meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with alternative standards, and the EPA anticipates that State and local governments will consider program s that are best suited for local conditions.
The partial attainment cost analysis reflects the costs associated with app lying known controls

. Costs for full attainment include estimates for the engineering costs of the additional tons of emissions reductio ns that are needed beyond identified controls, referred to as extrapolated costs. By definition, no cost data currently exist for the additional emissions reductions needed beyond known controls. W e e mploy two methodologies for estimating the costs of un id entified future controls: a fixed

- cost methodology and a hybrid methodology ; b oth approaches assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strate gies and new control options make possible the emissions reductions needed for attainment by 2020. T he two approaches , however, implicitly reflect different assumptions about technological progress and innovation in emissions reductions strategies. The fix ed

- cost methodology uses a $15,000/ton estimate for each ton of PM 2.5 reduced, and t he hybrid methodology generates a total annual cost curve for PM 2.5 for unknown future controls that might be applied in order to move toward 2020 attainment. The hybrid methodology has the advantage of incorporating information about how significant the needed reductions from unspecified control technologies1

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are relative to the known control measures and matching that information with expected increasing per

- ton cost for applying unknown controls. Employing the fixed

- cost methodology, approximately 90% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspecified control technologies

. Employing the hybrid methodology, approximately 97% of total costs for attaining the revised annual standard of 12 μg/m 3 are from unspecified control technologies

. The EPA recognizes that the extrapolated portion of the engineering cost estimates reflects substantial uncertainty about which sectors and which technol ogies might become available for cost

- effective application in the future.

T he engineering cost estimates are limited in their scope. Our analysis focuses on the emissions reductions needed for attainment of the revised and alternative standards . Also, t he amendments to the ambient air monitoring regulations will revise the network design requirements for PM 2.5 monitoring sites, resulting in moving 21 monitors to established near

road monitoring stations by January 1, 2015. The incremental cost associated w ith moving these 21 monitors is a one

- time cost of $28,570. Lastly, the EPA understands that some State s will incur costs designing SIPs and implementing new control strategies to meet the revised standard. However, the EPA does not know what specific actions State s will take to design their SIPs to meet the revised standards ; therefore , we do not include estimated costs that government agencies may incur for managing the requirement , implementing these (or other) control strategies, or for off ering incentives that may be necessary to encourage the implementation of specific technologies, especially for technologies that are not necessarily market driven.

ES.2. 6 Comparison of Benefits and Costs The EPA’s illustrative analysis has estimated the health and welfare benefits and costs associated with the revised annual PM NAAQS. The results for 2020 suggest there will be significant health and welfare benefits and these benefits will outweigh the costs associated with the illustrative control strate gies in 2020. In the analysis , we estimate the net benefits of the revised annual PM 2.5 standard of 12 μg/m 3 and al ternative annual standards of 13 μg/m 3 and 11 μg/m 3 , incremental to the 2020 analytical baseline. For the revised annual standard of 12 μg/m 3
, net benefits are estimated to be $ 3

. 7 billion to $ 9 billion at a 3% discount rate and $ 3. 3 billi on to $ 8.1 billion at a 7% discount rate in 2020 (20 10 dollars )

. For an alternative annual standard of 13 μg/m 3
, net benefits are estimated to be $ 1. 2 b illion to $2

. 9 b illion at the 3% discount rate and $ 1.1 b illion to $2

. 6 b illion at the 7% discount rate. N et benefits of an alternative annual PM 2.5 standard of 11 μg/m 3 are estimated to be $ 1 1 billion to $ 2 9 bill i on at a 3% discount rate and $ 1 0 billion to $ 2 6 billion at a 7% discount rate in 2020

. See Table ES

- 2.1

- 16

For the revised annual s tandard of 12 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 12 to 171 times at a 3 % discount rate. For the alternative annual standard of 13 μg/m 3 , the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 13 to 272 times at a 3 % discount rate. For the alternative annual standard s of 11 μg/m 3
, the EPA estimates that the benefits of full attainment exceed the costs of full attainment by 8 to 90 times at a 3 % discount rate.
Table ES

- 2. Total Monetized Benefits, Total Co sts, and Net Benefits in 20 20 (m illions of

20 10 $)

— Full Attainment a

Alternative Annual Standard ( μg/m 3 ) Total Costs b Monetized Benefits d Net Benefits

3% Discount Rate c 7% Discount

Rate 3% Discount

Rate 7% Discount

Rate 3% Discount

Rate b 7% Discount

Rate
13 $11 to $100 $11 to $100 $1,300 to $2,900 $1,200 to

$2,600 $1, 2 00 to

$2, 9 00 $1,100 to

$2, 6 00
12 $53 to $350 $53 to 350 $4,000 to $9,100 $3,600 to

$8,200 $ 3,7 00 to

$ 9,0 00 $3,3 00 to

$ 8,1 00
11 $320 to

$1,700 $320 to

$1,700 $13,000 to

$29,000 $12,000 to

$26,000 $1 1 ,000 to

$2 9 ,000 $10 ,000 to

$2 6 ,000

a These estimates reflect incremental emissions reductions from an analytical baseline that gives an “ adjustment ” to the San Joaquin and South Coast areas in California for NO x emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b The two cost est imates do not represent lower

- and upper
- bound estimates but represent estimates generated by two different methodologies. The lower estimate is generated using the fixed

- cost methodology, which assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cost range for current, known controls. The higher estimate is generated using the hybrid methodology, which assumes that while additional controls may bec ome available by 2020, they become available at an increasing cost and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining the needed emissions reductions.

c Due to data limitations, we were unable to discou nt compliance costs for all sectors at 3%. See Chapter 7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by subtracting the costs at 7% from the monetized benefits at 3%.
d The re duction in premature deaths each year accounts for over 90% of total monetized benefits. Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure. Not all possible benefits or disbenefits are quantified and mone tized in this analysis. B is the sum of all unquantified benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. The range of ben efits reflects the range of the central estimates from two mortality cohor t studies (i.e., Krewski et al., 2009 and Lepeule et al., 20 12 ).1

- 17

Table ES

- 3. Benefit

- to
- Cost Ratios for Alternative Standa rds at 3% and 7% Based on

Projected Benefits and Costs in 2020

13 μ g/m 3 12 μ g/m 3 11 μ g/m 3

Benefit

- Cost Ratio 3% a 13 to 272 12 to 171 8 to 90
Benefit

- Cost Ratio 7% 11 to 246 11 to 154 7 to 81

a Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by subtracting the costs at 7% from the monetized benefits at 3%.
Figure ES

- 4 reflects the range of costs based on the calculation of costs using the fixed

cost approach and the hybrid approach. Additionally, we see the difference in the calculation of benefits based on using various studies.1

- 18

Figure ES

- 4
. Monetized Benefit to Cost Comparison for 12 μg/m 3 in 2020 (7% Discount Rate)1

- 19

T able ES

- 4
. Estimated Number of Avoided PM 2.5 Health Impacts for Standard Alternatives

—

Full Attainment in 2020 a

Health Effect Alternative Annual Standards

13 μg/m 3 12 μg/m 3 11 μg/m 3 Adult Mortality Krewski et al. (2009) (adult) 140 460 1,500 Lepeule et al. (2012) (adult) 330 1,000 3,300 Woodruff et al. (1997) (infant) 0 1 4
Non

- Fatal Heart Attacks (age >18) Peters et al. (2001) 160 480 1,600
Pooled estimate of 4 studies 17 52 170 Hospital admissions

— respiratory (all ages) 31 110 380 Hospital admissions

— cardiovascular (age > 18) 43 140 480 Emergency department visits for asthma ( all ages) 67 230 810 Acute bronchitis (age 8

– 12) 280 870 2,700 Lower respiratory symptoms (age 7

– 14) 3,500 11,000 34,000 Upper respiratory symptoms (asthmatics age 9

– 11) 5,100 16,000 49,000
Asthma exacerbation (age 6

– 18) 13,000 40,000 120,000 Lost work days (age 18

– 65) 22,000 71,000 230,000
Minor restricted

- activity days (age 18

– 65) 130,000 420,000 1,300,000

a Incidence estimates are rounded to whole numbers with no more than two significant figures.
ES. 3 Discussion and Conclusions An extensive body of scientific evidence documented in PM ISA indicates that PM 2.5 can penetrate deep into the lungs and cause serious health effects, including premature death and other non

- fatal illnesses (U.S. EPA, 2009 ). As described in the preamble to the rule , the revisions to the standards are based on an integrative assessment of an extensive body of new scientific evidence (U.S. EPA, 2009 ). Health studies published since the PM ISA (e.g., Pope et al. [2009] ) confirm that recent levels of PM 2.5 have had a significant impact on public health. Based on the air quality analysis in this RIA, the EPA projects that nearly all counties with PM 2.5 monitors in the U nited States would meet an annual standard of 12 μg/m 3 without additional F ederal , S tate , or local PM control programs. This demonstrates the substantial progress that the U nited States has made in reducing air pollution emissions over the last sever al decades.1

- 20

Regulations such as the EPA’s recent Mercury a nd Air Toxics Standards (MATS) and other F ederal programs such as diesel standards will provide substantial improvements in regional concentrations of PM 2.5 . Our analysis shows a few areas would sti ll need additional emissions reductions to address local sources of air pollution, including ports and uncontrolled industrial emissions. For this reason, we have designed the RIA analysis to focus on local controls in these few areas. We estimate that the se additional local controls would yield benefits well in excess of costs.
The setting of a NAAQS does not compel specific pollution reductions and as such does not directly result in costs or benefits. For this reason, NAAQS RIAs are merely illustrative. The NAAQS RIAs illustrate the potential costs and benefits of additional steps States could take to attain a revised air quality standard nationwide beyond rules already on the books. We base our illustrative estimates on an array of emission control strat egies for different sources. T he costs and benefits identified in this RIA will not be realized until specific controls are mandated by SIPs or other F ederal regulations. In short, NAAQS RIAs hypothesize, but do not prescribe, the control strategies that S tates may choose to enact when implementing a revised NAAQS.
It is important to emphasize that the EPA does not “double count” the costs or the benefits of our rules. E mission reductions achieved under rules that require specific actions from sources

— such as MATS

— are in the baseline of this NAAQS analysis, as are emission reductions needed to meet the current NAAQS. For this reason, the cost and benefits estimates provided in this RIA and all other NAAQS RIAs should not be added to the estimates for impleme ntation rules.
In calculating the costs, the EPA assume d the application of a significant number of

un identified future controls that would make possible the additional emission s reductions needed for attainment in 2020. EPA used two methodologies

— the fixe d

- cost and hybrid methodologies

— for estimating the costs of un identified future controls , and b oth approaches

assume either that existing technologies can be applied in particular combinations or to specific sources that we currently can’t predict or that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. Estimates generated by the two approaches do not represent lower

- and upper
- bound estimates but simply represent estimates generated by two different methodologies. The fixed

- cost methodology implicitly assumes that technological change and innovation will result in the availability of additional controls by 2020 that are similar in cost to the higher end of the cost range for current controls

. The hybrid methodology implicitly assumes that while additional controls become available by 2020, they become available at an increasing cost and the increasing cost1

- 21

varies by geographic area and by degree of difficulty associated with obtaining the nee ded emissions reductions.

F or the revised annual standard of 12 μg/m 3 , the total cost estimates comprise between 90 and 97 % extrapolated cost estimates , and the estimated total cost using the hybrid methodology is roughly 6.5 times more than the estimated total cost using the fixed

- cost methodology. Because the hybrid methodology reflects increasing marginal costs in areas needing a higher ratio of emission s reductions from unknown to known controls, it could be more representative of total costs. In an ef fort to consider the potential fitness of the

e xtrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Quality Management Plan (AQMP) , and we located data on recent emission reduction credit (ERC) transac tion s in both the SCAQMD and San Joaquin Valley Air Pollution Control District (SJV APCD). While this information provides context for the extrapolated cost estimates, the current relationship between available controls and costs to reduce emissions may or may not be applicable in 2020 because of changes in innovation and advances in technology.
The SCAQMD’s 2012 AQMP includes information on control measures to meet the current 24

- hour standard of 35 μg/m 3 , including further PM 2.5 controls for under

- fired charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used in the fixed

- cost methodology, as well as the initial value used for the hybrid methodology and is supportive of our selection of that value. In addition, the C alifornia Air Resources Board’s 2009 and 2010 Emission Reduction Offset Transaction Costs, Summary Report included PM 10 ERC prices in both the SCAQMD and the SJV APCD. To some degree, ERC transaction prices reflect a choice between installing a more string ent control and purchasing ERCs. Between 2009 and 2010 PM 10 ERC prices in SJV APCD ranged from $40,000 per ton per year (tpy) to $70,000/tpy, and PM 10 ERC prices in the SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These prices reflect bot h marginal costs that are higher than the fixed

- cost estimates and marginal costs that are not inconsistent with the higher cost estimates generated using the hybrid methodology. For further discussion of the total cost estimates, refer to Section 7.2.4 in Chapter 7 of this RIA.
Furthermore, the monetized benefits estimates presented in this RIA are not intended to capture the full burden of PM to public health but rather represent the incremental benefits expected upon attaining the revised annual primary standard of 12 μg/m 3

. In comparison , modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible for between 130,000 and 320,000 PM 2.5 - related deaths, or between 6.1% and 15% of total deaths1

- 22

from a ll causes in the continental United States. The monetized benefits associated with attaining the proposed range of standards appear modest when viewed within the context of the potential overall public health burden of PM 2.5 and ozone air pollution estimat ed by Fann et al. (2012) , but this is primarily because regulations already on the books will make great strides toward reducing future levels of PM. One important distinction between the total public health burden estimated for 2005 air pollution levels and the estimated benefits in this RIA is that ambient levels of PM 2.5 will have improved substantially by 2020 , due to major emissions reductions resulting from implementation of Federal regulations

. For example, we estimate that SO 2 emissions (an important PM 2.5 precursor) in the U nited States would fall from 14 million tons in 2005 to less than 5 million tons by 2020 (a reduction of 66%). For this reason, States will only need to achieve small air quality improvements to reach the proposed PM standards. As shown in the recent RIA for MATS (U.S. EPA, 2011b ), implementing other Federal and State air quality actions wi ll address a substantial fraction of the total public health burden of PM 2.5 and ozone air pollution.
The NAAQS are not set at levels that eliminate the risk of air pollution. Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature. The risk analysis prepared in support of this PM NAAQS reported risks below these levels, while acknowledging that t he confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2010 c ). While benefits occurring below the standard are assumed to be more uncertain than those occurring above the standard, the EPA consi ders these to be legitimate components of the total benefits estimate. Alt hough there are greater uncertainties at lower PM 2.5 concentrations, there is no evidence of a population

- level threshold in PM 2.5 - related health effects in the epidemiology literatu re. Lastly, the EPA recognizes that there are uncertainties in both the cost and benefit estimates provided in this RIA. T he EPA was unable to monetize fully all of the benefits associated with reaching these standards in this RIA, including other health e ffects of PM, visibility effects, ecosystem effects, and climate effects. If the EPA were able to monetize all of the benefits, the benefits would exceed the estimated costs by an even greater margin.1

- 23

ES. 4 Caveats and Limitations EPA acknowledges several important limitations of th is analysis. These include the following :
ES. 4
. 1 Benefits Caveats

 PM 2.5 mortality benefits represent a substantial proportion of tota l monetized benefits (over 98 %)

. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality, we include a set of 12 estimates of the concentration

- response function based on results of the PM 2.5 mortality expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertai nty in air quality estimates, baseline incidence rates, populations exposed , chemical composition, transferability of the effect estimate to diverse locations , and additional uncertainty around the mean estimates expressed by the experts . As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.

 Most of the estimated avoided premature deaths occur at or above the lowest measured PM 2.5 concentration in the two studies used to estimate mortality benefits. In general, we have greater confidence in risk estimates based on PM 2.5 concentrations where the bulk of the health and air quality data reside and somewhat less confidence where data density is lower.

 We a naly zed full attainment in 2020 , and projecting key variables introduces uncertainty. Inherent in any analysis of future r egulatory programs are uncertainties in projecting atmospheric conditions and source

- level emissions, as well as population, health baselines, incomes, technology, and other factors.

 There are uncertainties related to the health impact functions used in th e analysis. These include within

- study variability; pooling across stud ies ; the application of C

- R functions nationwide and for all particle species; extrapolation of impact functions across population s ; and various uncertainties in the C

- R function, inclu ding causality and shape of the function at low concentrations. Therefore, benefits may be under

or over
- estimates.

 T his analysis omits certain unquantified effects due to lack of data, time , and resources. These unquantified endpoints include other healt h and ecosystem effects. The EPA will continue to evaluate new methods and models and select those most appropriate for estimating the benefits of reductions in air pollution.

 Full benefits of the revised standards in San Joaquin and South Coast will not b e realized until 2025 when those areas are expected to demonstrate attainment with the revised standards. If we were to estimate the monetized benefits for 2025, those1

- 24 benefits would be higher due to population growth, aging of the population, and income g rowth over time.

ES. 5
. 2 Control Strategy and Cost Analysis Caveats and Limitations

 The control technologies applied as part of the illustrative control strategies represent technologies that are available currently and may not reflect emerging devices that may be available in future years to aid in attainment of the revised standard. In addition , the emission reductions calculated from the known controls (control efficiencies) assume that the control devices are properly installed and maintained. There is a lso variability in scale of application that is difficult to reflect for small area sources of emissions.

 The illustrative control strategy analysis estimates only one potential pathway to attainment. The control strategies are not recommendations for how the revised PM 2.5 standard should be implemented, and States will make all final decisions regarding implementation strategies for the revised NAAQS.

 The application of known controls is based on source information obtained from the emissions inventory. To the extent the inventory is lacking data on baseline controls from S IPs, we may analyze control options that are currently in place.

 The future

- year emissions used as a basis for developing the control strategies in this RIA have implicit assumptions r egarding e missions growth and control , which differ by sector. For some emission sectors , these future

- year emissions may not reflect new sources locating in thes e areas
.

 For two areas in California (South Coast Air Quality Management District and San Joaq uin Valley Air Pollution Control District ) the degree of projected nonattainment with the revised annual standard of 12 μg/m 3 is such that th ese areas are not expected to be able to demonstrate attainment with the new standard by 2020. These areas may qual ify for up to a 5

- year extension of their attainment date and are likely to have until 2025 to demonstrate attainment with the revised annual standard

.

 The control technologies applied do not reflect potential effects of technological change that may be a vailable in future years and the effects of “learning by doing” are not accounted for in the emissions reduction estimates. In our analysis , we do not have the necessary data for cumulative output, fuel sales, or emission reductions for all sectors include d in order to properly generate control costs that reflect learning

- curve impacts. We believe the effect of including these impacts would be to lower our estimates of costs for our control strategies in 2020.

 In addition to the application of known control s, the EPA assume s the application of
un identified future controls that make possible the additional emission reductions needed for attainment in 2020. By definition, no cost data currently exist for1

- 25 unidentified future technologies or innovative strategies and the cost estimat es for
un identified future controls reflect some uncertainty

.

 Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original data sources. If disaggregated control cost data are unavailable (i.e., where capital, equipment life value, and operation and maintenance [ O&M ] costs are not separated out), the EPA typically assumes that the estimated control costs are annualized using a 7% discount rate. When disaggregated contr ol cost data are available (i.e., where capital, equipment life value, and O&M costs are explicit) , we can recalculate costs using a 3% discount rate. In general, we have some disaggregated data available for non

- EGU point source controls , and we do not ha ve any disaggregated control cost data for area source controls. In this analysis, for the revised annual standard of 12 μg/m 3 and the alternative standard of 13 μg/m 3 we did not have any disaggregated known control cost data ; therefore, we were not able t o recalculate known control costs using a 3% discount rate.

 The EPA understands that some States will incur costs designing SIPs and implementing new control strategies to meet the revised annual standard. However, the EPA does not know what specific acti ons States will take to design their SIPs to meet the revised standards ; therefore we do not present estimated costs that government agencies may incur for managing the requirement , implementing these (or other) control strategies, or for offering incentiv es that may be necessary to encourage the implementation of specific technologies, especially for technologies that are not necessarily market driven.

ES. 5 Important Updates and Analytical Differences Between the PM NAAQS Proposal RIA and the Final RIA There have been several major improvements in the analytical components the EPA used to estimate benefits and costs between the proposal RIA (June 2012) and this RIA

ac companying the final PM NAAQS. Important updates to our emissions, air quality modeling and ambient data, air quality ratios, population projections, as well as currency year valuation resulted in an improved analytical base for our analysis for the final rule. Based on the complexity and magnitude of the updates and improvements made between the PM RIA proposal and final RIA, it would not be appropriate to perform a simple direct comparison of results. Therefore, each analysis stands alone and must be evaluated independently as such.
Below is a list summarizing some of the analytical changes between the p roposal RIA and the final RIA. Between the proposal RIA and the final RIA, we developed the control strategies based on an improved modeling platform and updated the approach in desi gning the control strategies. The improved modeling platform updated the current and projected air1

- 26

quality levels for each area across the nation and the updated approach allowed for more effective emissions reductions for each area in both attaining the current annual and daily standards for our analytical baselin e and in attainin g the revised annual standard. For example, in the proposal RIA we controlled precursor emissions of NO x and SO 2 over a broader region and these emissions reductions were not as effective in reducing design value for each area as the direc t PM 2.5 emissions reductions targeted in the final RIA

. These analytical improvements resulted in different estimates of costs and benefits between the proposal RIA and the final RIA in which we have more confidence in reflecting the approaches that State s will pursue to attain the current and revised standards.

 New modeling platform

— In the modeling platform for the final rule we included key updates to the current ambient data that generally show improved air quality when compared to modeling for the prop osed rule, although daily design values (DVs) for some areas in California increased due largely to wildfires in 2008. To address the increases in these areas we adjusted the ambient data for these atypical events.

 Future air quality with “on

- the

- books” co ntrols

— We also project PM 2.5 air quality levels between 2007 and the 2020 base case which were generally lower in the final RIA compared to the proposal RIA largely because the starting values for the ambient data were lower (i.e., final RIA air quality p rojections showed more improvements than proposal RIA)

.

 Analytical baseline with attainment of 15/35

— For the final RIA, EPA’s approach to attaining the existing standards of 15/35 μg/m 3 was improved with “ air quality adjustment ratios ” that were based on more focused sensitivity model runs for (i) specific areas like California counties; (ii) influential sources like residential wood combustion; and (iii) specific PM emission species like directly emitted PM 2.5 . In the proposal RIA, we conducted the analysis using more general air quality ratios that reflected multiple sources such as point and area and precursor PM emissions like NO x and SO 2 . As a result, in the final RIA, the daily standard of 35 μg/m 3 was attained more effectively and had less impact on annual DVs because of episodic, direct PM 2.5 reductions, while in the proposal RIA, the daily standard was attained less effectively because we pursued year

- around NO x and SO 2 reductions that necessitated more emissions reductions and h ad more impact on the annual DVs.

 Incremental air quality changes

— In the final RIA, the use of improved air quality adjustment ratios resulted in more incremental air quality improvement needed to attain the annual standard of 12 μg/m 3 in California. Thus , these larger incremental air quality improvements needed to attain the revised standard resulted in higher estimated health benefits in the final rule compared to the proposal RIA. As described above, this is because the daily standard of 35 μg/m 3 was at tained more1

- 27 effectively and had less impact on annual DVs acr oss the counties in California. In addition, the focus on direct PM 2.5 emissions reductions allowed for more effective controls to attain the revised standard with fewer PM 2.5 emissions reduction s thereby resulting in similar fixed costs estimates. 5

 Benefits analysis

— EPA incorporated several updates that affected the core benefits estimates in this RIA. Specifically, the EPA incorporated the most recent follow

- up to the Harvard Six Cities cohort study (Lepeule et al., 2012), which decreases the high end of the mo netized benefits range by 13%. The EPA also updated the demographic data projections to reflect the 2010 Census, which increased the monetized benefits by 4% percent for the revised standa rd. Additional epidemiology studies for hospitalizations and emergency department visits and updated survival rates for
non
- fatal heart attacks did not affect the rounded benefits estimates.

 Cost estimates

— In the final RIA, the EPA presents a range of cost s using both the fixed and hybrid methodologies to estimate the costs associated with unknown controls.

 Inflation

— The EPA updated the currency year in this RIA to use 2010 dollars, which increased both the costs and the monetized benefits by approximately 8% since the proposal.

ES. 6 References Abt Associates, Inc. 2012. BenMAP User ’ s Manual Appendices

. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September.
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. Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air pollution and the triggering of myocardial infarction.” Circulation 103 (23): 2810

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5 The hybrid methodology cost estimates increased between the proposal RIA and the final RIA largely because a large amount of emissions reductions were needed from one county with a low amount of known controls.1

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. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

— Benefits.pdf

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. 2008a

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. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

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. Available at: http://www.regulations.gov/#!documentDetail;D=EPA

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U.S. Environmental Protection Agency (U.S. EPA). 2009a. Economic Impacts of Revised MACT Standards for Hospital/Medical/Infectious Waste Incinerators. Available at: http:// www.epa.gov/ttn/atw/129/hmiwi/hmiwi_eia_090803.pdf

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. EPA

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CHAPTER 1 HUMAN HEALTH BENEFIT S ANALYSIS APPROACH AND RESULTS Synopsis This chapter present s the estimated human health benefits for the revised National Ambient Air Quality Standards (NAAQS) for particulate matter (PM) . In this chapter , we quantify the health

- related benefits of the fine particulate matter ( PM 2.5 )

- related air quality improvements resulting from the illustrative emission control scenarios that reduce emissions of directly emitted particles and precursor pollutants including SO 2 and NO x to reach alternative PM 2.5 NAAQS levels in 2020

. 6

T hese benefits are relative to a n analytical baseline reflecting nationwide attainment of the current primary PM 2.5 standards (i.e., annual standard of 15 μg/m 3 and 24

- hour standard of 35 μg/m 3 ) that includes promulgated national regulations and illustrativ e emission controls to simulate attainment with 15/35 as well as a NO x emission adjustment to reflect expected reductions in mobile NO x emissions between 2020 and 2025

. W e project PM 2.5 levels in certain

areas that would exceed the revised annual standard of 12 μg/m 3 as well as alternative annual standards of 13 and 11 μg/m 3 after simulate d attainment with 15/35 in the analytical baseline

. Table 5

- 1 summarizes the total monetized benefits of the revised and alternative PM 2.5 standards in 2020. These estimates reflect the sum of the economic value of estimated PM 2.5 mortality impacts identified and the value of all morbidity impacts.

The estimated benefits for the revised and alternative standards are in addition to the substa ntial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 2011d, 2011e). Rules such as the Mercury and Air Toxics Standard (MATS) and other emission reductions will have substantially reduced ambient PM 2.5 concentrations by 2020 in the East, such that no additional controls would be needed in the East for the revised annual standard of 12 μg/m 3 . Thus, all of the estimated benefits occur in California because this is the only state

6 The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that gives an adjustment to the San Joaquin and South Coast areas in California for NOx emissi ons reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.1

- 31

that needs additional air quality improvement beyond the analytical baseline after accounting for air quality improvements from recent rules. Because of the national focus of many of the inputs to the benefits model, benefits estimated for any particular location have greater uncertainty than benefit s occurring nationally. Compared with the proposal benefits, the estimated benefits for the revised standard are about double due to a combination of updates to the analytic baseline (See Chapter 3). We believe that all of these updates improve our estimate of benefits for the revised annual primary standard.

Table 5

- 1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM 2.5 Standards in 2020 Incremental to the An alytical Baseline ( billions of 2010$ ) a

Benefits Estimate 13 μg/m 3 12 μg/m 3 11 μg/m 3
Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Krewski et al. (2009) 3% discount rate $1.3 + B $ 4.0 +B $13 + B 7% discount rate $1.2 + B $3. 6 +B $12 + B Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Lepeule et al. (2012) 3% discount rate $2.9 + B $ 9.1 +B $ 29 + B 7% discount rate $2.6 + B $ 8.2 +B $ 26 + B

a Rounded to two significant figures. Avoided premature deaths account for over 98 % of monetized benefits here , which are discount ed over the SAB

- recommended 20

- year segmented lag. It was not all possible to quantify all benefits due to dat a limitations in this analysis. “ B ” is the sum of all unquantified health benefits and welfare co

- benefits

. As we describe in detail below, we estimate PM

- related health impacts using concentration

- response relationships drawn from the epidemiological literature. In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies that are used in th e benefits estimates

. Likewise, we are less confiden t in the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. As noted in the preamble to the rule, the range from the 25 th to 10 th percentiles of the air quality data in the epidemiology studies is a reasonable range below which we start to have appreciably less confi dence in the magnitude of the associations observed in the epidemiological studies. M ost of the estimated avoided premature deaths occur at or above the lowest measured PM 2.5 concentration in the two studies that are used to estimate mortality benefits.1

- 32

In addition to PM 2.5 benefits, implementation of emissions controls to reach some of the alternative PM 2.5 standards would reduce other ambient pollutants , such as SO 2 , NO 2 , and

ozone
. However, because the method used in this analysis to simulate attainment does not account for changes in ambient concentrations of other pollutants , we were not able to quantify the co
- benefits of reduced exposure to other pollutants

. In addition, due to data and

m ethodology limitations, we were unable to estimate additional health benefits associated with exposure to PM 2.5 or the additional co
- benefits from improvements in welfare effects (i.e.,

non
- health effects) associated with emission reductions to attain the primary standard , such as visibility

. We describe the unquantified health benefits in this chapter and the unquantified welfare co
- benefits in Chapter 6

. Overview This chapter contains a subset of the estimated health benefits of the revised and alternative PM 2.5 standards in 2020 that the EPA was able to quantify, given the available resources and methods . The analysis in this chapter aims to characterize the benefits of the air quality changes resulting from the implementation of new PM standard s by answering two key questions:

1. What are the health effects of changes in ambient particulate matter (PM 2.5 ) resulting from reductions in directly emitted PM 2.5 and precursors due to the attainment of a new PM 2.5 standard ?

2. What is the economic value of these effects?
In this analysis , we consider an array of health impacts attributable to changes in PM 2.5 . The Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b ) identif ies the human health effects associated with ambi ent particles , which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Table 5

- 2 summarizes human health categories contained within the core benefits estimate as well as those categories that were unquantified due to limited data or resources. It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete. To ident ify human health benefits that we could quantify with confidence we selected endpoints that were classified as causal or likely causal in the PM ISA , and we excluded effects not identified as having at least a causal, likely causal, or suggestive relations hip with the affected pollutants in the most recent comprehensive scientific assessment, such as an ISA. This decision does not imply that additional relationships between these and other human health and environmental c o
- benefits and the affected pollutants do1

- 33

not exist. Due to this decision criterion, we excluded some effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb exposure).
The benefits analysis in this chapter relies o n an array of data inputs

— including air quality modeling, health impact functions and valuation estimates among others

— which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. We employ severa l techniques to characterize this uncertainty, which are described in detail in section 5.4.1

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Table 5

- 2

. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain

the Primary PM 2.5 Standards

Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information Improved Human Health
Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort

study estimates and expert elicitation estimates (age >25 or age >30)   Section 5.6

Infant mortality (age <1)   Section 5.6
Non

- fatal heart attacks (age > 18)   Section 5.6 Hospital admissions

— respiratory (all ages)   Section 5.6 Hospital admissions

— cardiovascular (age >20)   Section 5.6 Emergency department visits for asthma (all ages)   Section 5.6

Acute bronchitis (age 8

– 12)   Section 5.6 Lower respiratory symptoms (age 7

– 14)   Section 5.6 Upper respiratory symptoms (asthmatics age 9

– 11)   Section 5.6

Asthma exacerbation (asthmatics age 6

– 18)   Section 5.6 Lost work days (age 18

– 65)   Section 5.6
Minor restricted

- activity days (age 18

– 65)   Section 5.6 Chronic Bronchitis (age >26)

— a

— a Section 5.6 Emergency department visits for cardiovascular effects (all ages) — a

— a Section 5.6

Strokes and cerebrovascular disease (age 50

– 79) — a

— a Section 5.6

Other cardiovascular effects (e.g., other ages)

— — PM ISA b Other respiratory effects (e.g., pulmonary function, non

- asthma ER visits, non

- bronchitis chronic diseases, other ages and populations) — — PM ISA b

Reproductive and developmental effects (e.g., low birth weight, pre

- term births, etc.) — — PM ISA b,c

Cancer, mutagenicity, and genotoxicity effects

— — PM ISA b,c (continued)1

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Table 5

- 2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain

the Primary PM 2.5 Standards (continued)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information
Reduced incidence of mortality from exposure to ozone Premature mortality based on short

- term

study estimates (all ages) — — Ozone ISA d

Premature mortality based on long

- term study estimates (age 30

– 99) — — Ozone ISA d

Hospital admissions

— respiratory causes (age > 65) — — Ozone ISA d

Hospital admissions

— respiratory causes (age <2) — — Ozone ISA d

Emergency department visits for asthma (all ages) — — Ozone ISA d

Minor restricted

- activity days (age 18

– 65)

— — Ozone ISA d School absence days (age 5

– 17)

— — Ozone ISA d Decreased outdoor worker productivity (age 18

– 65) — — Ozone ISA d

Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA b

Cardiovascular and nervous system effects

— — Ozone ISA c Reproductive and developmental effects

— — Ozone ISA c
Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages)

— — NO 2 ISA d

Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA d

Respiratory emergency department visits (all ages) — — NO 2 ISA d

Asthma exacerbation (asthmatics age 4

– 18)

— — NO 2 ISA d Acute respiratory symptoms (age 7

– 14)

— — NO 2 ISA d Premature mortality

— — NO 2 ISA b,c

Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA b,c

(continued)1

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Table 5

- 2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain

the Primary PM 2.5 S tandards (continued)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information
Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65)

— — SO 2 ISA d

Asthma emergency department visits (all ages) — — SO 2 ISA d

Asthma exacerbation (asthmatics age 4

– 12)

— — SO 2 ISA d Acute respiratory symptoms (age 7

– 14)

— — SO 2 ISA d Premature mortality

— — SO 2 ISA b,c Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA b,c

Reduced incidence of morbidity from exposure to methylmercury (through role of sulfate in methylation) Neurologic effects

— IQ loss

— — IRIS; NRC, 2000 d

Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 b

Cardiovascular effects

— — IRIS; NRC, 2000 b,c Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 b,c

a We quantify these benefits in a sensitivity analysis, but not in the core analysis.
b We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

c We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.
d We assess these benefits qualitative ly due to time and resource limitations for this analysis. As described in Chapter 1 of this RIA, there are important differences worth noting in the design an d analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide based on an a rray of emission reduction strategies for different sources including known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predic t, the emission reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are merely illustrative and the estimated costs and benefi ts are not intended to be added to the1

- 37

costs and benefits of other regulations that result in specific costs of control and emission reductions. By contrast, the emission reductions from implementation rules are generally for specific, well

- characterized s ources, such as the recent MATS rule addressing emissions from coal and oil

- fired electricity generating units (U.S. EPA, 2011d). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules. As su ch, emission reductions achieved under promulgated implementation rules such as MATS have been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules will be reflected in the baseline for the next PM NAAQS review. For this reason , the benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for implementation rules. Updated Methodology Presented in this RIA The benefits analysis presented in this chapter incorporates an array of policy and technical changes that the Agency has adopted since the previous review of the PM 2.5 standards in 2006, and since publication of the proposal RIA for this rulemaking . Below we note the aspects of this analysis that differ from the proposal RI A (U.S. EPA, 2012 a ) :
1. Incorporation of the newest Harvard Six Cities mortality study . In 2012 , Lepeule et al. published an extended analysis of the Six Cities cohort. Compared to the study it replaces ( Laden et al. , 200 6 ), this new analysis follows the cohort for a longer time and includes more years of PM 2.5 monitoring data . The all

- cause PM 2.5 mortality risk coefficient drawn from Lepeule et al. (2012 ) is roughly similar to the Laden et al. (200 6 ) risk coefficient applied in the EPA’s recent analyses of long

- term PM 2.5 mortality and has narrower confidence intervals

. 2. Updated demographic data

. We updated the population demographic data in BenMAP to reflect the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier demographic projection data from Woods and Poole (2007).

3. Incorporation of new morbidity studies . Since the publication of the PM ISA (U.S. EPA, 2009 ) the epidemiological literature has produced several new studies examining the association between short

- term PM 2.5 exposure and respiratory and cardiovascular hospitalizations, respiratory and cardiovascular emergency department visits , and stroke . Upon ca reful evaluation of this new literature identified in the Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure (“Provisional Assessment”) (U.S. EPA, 2012b) we added several new studies to our health impact ass essment.1

- 38

4. Updated the survival rates for non

- fatal acute myocardial infarctions

. Based on recent data from Agency for Healthcare Research and Quality ’ s Healthcare Utilization Project National Inpatient Sample database ( AHRQ , 2009) , we identified death rates for adults hospitalized with acute myocardial infarction stratified by age. These rates replace the survival rates from Rosamond et al. (1999).

5

. Expanded uncertainty assessment

. We clarified the comprehensive assessment of the various uncertain pa rameters and assumptions within the benefits analysis and expanded the evaluation of air quality benchmarks (previously the LML analysis)

. Although the list above identifies the major changes implemented since the proposal RIA , the EPA has also updated several additional components of the benefits analysis since the 2006 PM NAAQS RIA (U.S. EPA, 2006 a ). In the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009 a ), the Agency no longer assumed a concentration threshold in the concentration

response functio n for PM 2.5 - related health effects and began using the benefits derived from the two major cohort studies of PM 2.5 and mortality as the core benefits estimates , while still including a range of sensitivity estimates based on the EPA ’ s PM 2.5 mortality expert elicitation

. In the NO 2 NAAQS proposal RIA (U.S. EPA, 2009a ), we revised the estimate used for the value

of

- a
- statistical life to be consistent with Agency guidance

. In the proposed CSAPR (previously the “ Transport Rule ” ) (U.S. EPA, 2010g) , we incor porated the “ lowest measured level ” assessment to help characterize uncertainty in estimates of benefits of reductions in PM 2.5 at lower baseline concentrations of PM 2.5 . In the final CSAPR (U.S. EPA, 2011c ), we updated the baseline incidence rates for hos pital admissions and emergency department visits and asthma prevalence rates. We direct the reader to each of these RIAs for more information on these changes. In the proposal RIA for this NAAQS review (U.S. EPA, 2012 a ) , we updated the American Cancer Society cohort study to Krewski et al. (2009), updated health endpoints in the core and sensitivity analyses, incorporated new morbidity studies, updated the median wage data in the cost

- of

- illness studies, and expan ded the uncertainty assessment.

Human He alth Benefits Analysis Methods
We follow a “ damage

- function ” approach in calculating total benefits of the modeled changes in environmental quality. 7 This approach estimates changes in individual health endpoints (specific effects that can be associated wi th changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoints

. Total benefits are calculated simply as the sum of the values for all non

- overlapping health

7 The damage function app roach is a more comprehensive method of estimating total benefits than the hedonic price approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude of health risks associated with their home purchase. See discus sion of hedonic studies in Chapter 6.1

- 39

endpoints. The “ damage

- function ” approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Fann et al

. , 201 2 a ; Tagaris et al., 2009)

.
To assess economic value in a damage

- function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be directly valued, as is the case for changes in visibili ty. In other cases, such as for changes in ozone and PM, a impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to PM 2.5 .
We note at the outset that the EPA rarely has the time or resources to perform extensive new research to measure directly either th e health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2000) and other , more recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio

- demographic and econo mic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates. Health Impact Assessment
The health impact assessment (HIA) quantifies the changes in the incidence of adverse health impacts re sulting from changes in human exposure to PM 2.5 and ozone air quality. HIAs are a well

- established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population

- level changes in exposure to pol lutants (Levy et al. , 2009). PC

- based tools such as the environmental Ben efits M apping and A nalysis P rogram (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population pro jections

— provided that key input data are available, including air quality estimates and risk coefficients (Abt Associates, 2010) . Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant leve ls (Hubbell et al. , 2005; Tagaris et al. , 2009; Fann et al. , 201 2 a ) . The EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting air1

- 40

quality ( e.g., U.S. EPA, 201 1d ). For this assessment, the HIA is limited to those health effects that are directly linked to ambient PM 2.5 concentrations. There may be other indirect health impacts associated with implementing emissions controls, such as occupational health

e xposures. The HIA approach used in this analysis involves three basic steps: (1) utilizing projections of PM 2.5 air quality 8 and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in populati on
- level exposure; (3) calculating health impacts by applying concentration

- response relationships drawn from the epidemiological literature (Hubbell et al. , 2009) to this change in population exposure.
A typical health impact function might look as follow s:
(5

. 1)
where y 0 is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the populati on affected by the change in air quality; x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Figure 5

- 1 provides a simplified overview of this approach.

8 Projections of ambient PM 2.5 concentrations for this analysis were generated using the Community Multiscale Air Quality model (CMAQ). See Chapter 3 of this RIA for more information on the air quality modeling.1

- 41

Figure 5

- 1. Illustration of BenMAP Approach

Economic Valuation of Health Impacts After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante willingness to p ay (WTP) for changes in risk. E pidemiological studies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 μg/m 3 for PM 2.5 )

. These relativ e risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 to change s in the incidence of a health effect. In order to value these changes in incidence, WTP for changes in risk need to be converted into WTP per statistical incid ence. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in1

- 42

10,000 to 1 in 10,000 (a reduction of 1 in 1 0,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital adm issions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the hea lth effects causing the admission. These cost

- of

- illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.

We use the BenMAP model version 4.0.5 2 (Abt Associates, 2010 ) to estimate the health impacts and monetized health benefits for the proposed standard

. Figure 5

- 2 shows the dat a inputs and outputs for the BenMAP model.

Modeled Baseline and Post

- Control Ambient PM 2.5

PM 2.5 Health Functions

Economic Valuation Functions 2020 Population Projections

PM 2.5 Incremental Air Quality Change

PM 2.5 - Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 - related Benefits Blue identifies a user

- selected input within the BenMAP model Green identifies a data input generated outside of the BenMAP model Census Population Data1

- 43

Figure 5

- 2. Data Inputs and Outputs for the BenMAP Model

Uncertainty Characterization In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty

. T his analysis is no exception. As outlined both in this and preceding chapters, this analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions reg arding the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analysis are compounded, even small uncert ainties can have large effects on the total quantified benefits.
After reviewing the EPA ’ s approach, the National Research Council (NRC) (2002, 2008) , which is part of the National Academies of Science, concluded that the EPA ’ s general methodology for calc ulating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA has continued work to improve the characterization of uncertainty in both health incidence and benefits estimates. In response t o these recommendations, we have expanded our previous analyses to incorporate additional quantitative and qualitative characterizations of uncertainty. Although we have not yet been able to make as much progress towards a full, probabilistic uncertainty a ssessment as envisioned by the NAS as we had hoped, we have added a number of additional quantitative and qualitative analyses to highlight the impact that uncertain assumptions may have on the benefits estimates. In addition, for some inputs into the bene fits analysis, such as the air quality data, it is difficult to address uncertainty probabilistically due to the complexity of the underlying air quality models and emission inputs. Therefore, we decline to make up alternative assumptions simply for the pu rpose of probabilistic uncertainty characterization when there is no scientific literature to support alternate assumptions.
To characterize uncertainty and variability, we follow an approach that combines elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2011a), and uses a tiered approach developed by the World Health Organization (WHO) for characterizing uncertainty1

- 44

(WHO, 2008). We present this tiered assessment as well as an assessment of the potential impact and magnitude of each aspect of uncertainty In Appendix 5c

. D ata limitations prevent us from treating each source of uncertainty quantitatively and from reaching a full

- probabilistic simulation of our results , but we were able to consider the influence of uncertainty in the risk coefficients and economic valuation functions by incorporating six quantitative analyses described in more detail below :
1. A Monte Carlo assessment that accounts for random sampling error a nd between study variability in the epidemiological and economic valuation studies;

2. A concentration benchmark assessment that characterizes the distribution of avoided PM 2.5 - related deaths relative to specific concentrations in the long

- term epidemiolog ical studies used to estimate PM 2.5 - related mortality;

3. The quantification of PM

- related mortality using alternative PM 2.5 mortality effect estimates drawn from two long

- term cohort studies and an expert elicitation ;
4. S ensitivity analys e s of several aspects of PM

- related benefits ;
5. Distributional analyses of PM 2.5 - related benefits by locati on, race, income, and education; and

6. An analysis of the influence of various parameters on total monetized benefits

. Monte Carlo A ssessment Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variabi lity across the economic valuation functions. The Monte Carlo simulation in the BenMAP software random ly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies . The confidence intervals around the monetized benefits incorporate the ep idemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed and transferabilit y of the effect estimate to diverse locations. As a result, the reported confidence1

- 45

intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates. Concentration Benchmark Analysis for PM 2.5 We include a concentration benchmark assessment, which identifies the baseline (i.e. , pre
- rule) annual mean PM 2.5 levels at which populations are exposed and specific concentrations in the two long

- term cohort stud ies we use to quantify mortality impacts. This analysis characterize s avoided PM 2.5 - related deaths relative to the 10 th and 25 th percentiles of the air quality data used the Krewski et al. (2009) study as well as the lowest measured level (LML) of the Krewski et al. (2009) and Lepeule et al. (20) studi es.
Alternative Conce ntration

- Response Functions for PM 2.5 – Related Mortality We assign the greatest economic value to the reduction in PM 2.5 related mortality risk

. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. To better understand the concentration

- response relationship between PM 2.5 exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 9 In general, th e results of the expert elicitation support the conclusion that the benefits of PM 2.5 control are very likely to be substantial.

Alternative concentration

- response functions are useful for assessing uncertainty beyond random statistical error, including un certainty in the functional form of the model or alternative study design. Thus, we include the expert elicitation results as well as standard errors approaches to provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. 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 deriv ed from each of the twelve experts to better characterize the degree of variability in the expert responses.
In previous RIAs, the EPA presented benefits estimates using concentration response functions derived from the PM 2.5 Expert Elicitation (Roman et a l., 2008) 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 2.5 benefits may be, and

9 Expe rt elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of multiple experts, are obtained (Ayyub, 2002).1

- 46

the EPA ’ s independent Science Advisory Board (SAB) recommended refinements to the way EPA present ed the results of the elicitation (U.S. EPA

- SAB, 2008). Therefore, we began to present the cohort

- based studies (Krewski et al., 2009; Laden et al., 2006) 10 as our core estimates in the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009 a ). Using alternate relationships between PM 2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert

- based estimates of the mean PM 2.5 effect on mortality fall between the two epidemiology

- based estimates (Roman et al., 2008)

. In addition to these studies, we have included a discussion or other recent multi

- state cohort studies conducted in North America, but we have not estimated benefits using the effect coefficients from these studies. Please note that the benefits estimates results presented are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the effect coefficients provided in those studies or by experts

. In addition, the experts provided distributions around their mean PM 2.5 effect estimates, which provides more information regarding the overall range of uncertainty , and this over all range is larger than the range of the mean effect estimates from each of the experts.
Even these multiple characterizations with confidence intervals omit the contribution to overall uncertainty from uncertainty in air quality changes, baseline inciden ce rates, and populations exposed. Furthermore, the approach presented here does not yet include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the estimates. This information should be interpreted within the context of the larg er uncertainty surrounding the entire analysis. Sensitivity Analyses For some aspects of uncertainty, we have sufficient data to conduct sensitivity analyses. In this analysis, we performed five such analyses for the revised standard level . In particular,

we:
1. Assessed the sensitivity of the economic value of reductions in the risk of PM 2.5 -

related death according to differing assumptions regarding the lag between PM 2.5 exposure and premature death. The timing of such premature deaths affects the magnitud e of the discounted PM 2.5 - related mortality benefits

. I n this sensitivity assessment , we consider 6 alternative cessation lags.

10 We have since updated the the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent follow

- up publication of this cohort (Lepeule et al, 2012).1

- 47

2. Characterized the sensitivity of the economic value of the health endpoints valued using willingness

- to

- pay estimates to a higher and a lower assumption regarding income elasticity. As we discuss below, economic theory argues that individual willingness to pay increases as personal income grows. The relationship between growth in personal income and willingness

- to

- pay to red uce mortality and morbidity risk is characterized by the income growth factor.

3. Summarize d the avoided cases of certain health endpoints for which we either lacked an appropriate economic value (cardiovascular hospital admissions and stroke) or in which we no longer had sufficient confidence to retain in our primary benefits estimate (chronic bronchitis).

4. Assessed the sensitivity of the benefits results to the new population data from the 2010 C ensus
. 5. Assessed the sensitivity of the benefits results to an analysis year of 2025.
Distributional Assessment
In an Appendix to the proposal RIA , we characterize d the distribution of PM 2.5 - related benefits based on the geographic distribution of race and education in areas where the illustrative emission reduction strategies would reduce PM 2.5 concentrations

. In that assessment , we aim ed to answer two key questions:

1. What wa s the estimated distribution of PM 2.5 - related mortality risk based on the
race a nd education characteristics o f the population living within areas projected to exceed alternative combinations of the proposed primary PM 2.5 standards ?

2. How would air quality improvements within these counties change the distribution of risk among populations of different races and educational attainment ? 11

Th at assessment wa s generally consistent with the distributional assessments performed in support of MATS ( U.S. EPA, 2011 c ), with one key difference. The e nvironmental j ustice analyses accompanying the MATS RIA applied CMAQ

- mo deled PM 2.5 predictions that represent the change in air quality after the implementation of each rule. By contrast, the proposal RIA aim ed to illustrate the potential benefits and costs of attaining alternative primary PM 2.5 standards ; the states will ultimately implement attainment strategies, which may differ greatly from the least

- cost strategy the EPA modeled here. Alternative emission reduction strategies

— particularly those that maximize benefits to human health and provide a more

11 In this analysis we assess the change in risk among populations of different race and educational attainment. As we discuss further in the methodology, we consider this last variable because of the availability of education

modified PM 2.5 mortality risk estimates.1

- 48

equitable distribution of risk

— are also available to the states, though not modeled here (Fann et al. , 2012b). Due in part to time constraints, the EPA did not perform such an analysis for the final RIA, and instead refers readers to the Appendix noted ab ove. Influence Analysis

— Quantitative Assessment of Uncertainty In the past few years, the EPA has initiated several projects to improve the characterization of uncertainty for benefits analysis. In particular, the EPA recently completed the first phase of a quantitative uncertainty analysis of benefits, hereafter referred to as the

“ Influence Analysis ” (Mansfield et al., 2009). The Influence Analysis diagramed the uncertain components of each step within the benefits analysis process, identified plausible r anges for a sensitivity analysis, and assessed the sensitivity to total benefits to changes in each component. Although this analysis does not quite fulfill the goal of a full probabilistic assessment, it accomplished the necessary first steps and identified the challenges to accomplishing that goal. Below are some of the preliminary observations from the first phase of th e project.

 The components that contribute the most to uncertainty of the monetized benefits and mortality incidence (in order of importance) are the value

- of

- a
- statistical

- life ( VSL ) , the concentration

- response ( C

- R ) function for mortality, and change in PM 2.5 concentration.

 The components that contribute the least to uncertainty of the monetized benefits and mortality incidence are population, morbidity valuation, and income elasticity.

 The choice of a C

- R function f or mortality affects the mortality incidence and monetized benefits more than other sources of uncertainty within each C

- R function.

 Alternative cessation lag structures for mortality have a moderate effect on the monetized benefits.

 Because the health imp act function is essentially linear, the key components show the same sensitivity across all mortality C

- R functions even if the midpoints differ significantly from one expert to another. Qualitative Assessment of Uncertainty and Other Analysis Limitations Although we strive to incorporate as many quantitative assessments of uncertainty as possible , there are several aspects we are only able to address qualitatively. These aspects are important factors to consider when evaluating the relative benefits of the emission reduction strategies for the revised and alternative annual standards:1

- 49

T he total monetized benefits presented in this chapter are based on our interpretation of the best available scientific literature and methods and supported by the EPA ’ s indep endent SAB (Health Effects Subcommittee) ( SAB

- HES ) (U.S. EPA

- SAB, 2010 a ) and the National Academies of Science ( NAS ) (NRC, 2002). The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality, which account for over 9 8 % of the total monetized benefits in this analysis , i nclude the following:

1. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA, which was twice reviewed by Clean Air Scientific Advisory Committee (SAB

- CASAC ) , concluded that “many constituents of PM 2.5 can be link ed with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b). These uncertainties are likely to be magnified in the c urrent analysis to the extent that the emissions controls are less diverse when focusing on one small region of the country rather than a broader geography with more diverse emissions sources and the application of a more diverse set of controls.

2. We a ssume that health impact functions based on national studies are representative for exposures and populations in California. I n addition to the national risk coefficients we use as our core estimates , the EPA considered the cohort studies conducted in California specifically. Although we have not calculated the benefits results using the cohort studies conducted in California , we provided the se risk coefficients to show how much the monetized benefits could have changed. Most of the California cohort st udies report central effect estimates similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al. (2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort studies conducted in California indica te statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012), and four studies showed insignificant results.

3. We assume that the health impact function for fine particles is log

- linear without a threshold in thi s analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest m odeled concentrations.

4

. We assume that there is a “ cessation ” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that1

- 50 some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB

- HES (U.S. EPA

- SAB, 2004c), which affects the valuation of mortality benefits at different discount rates.

5

. To characterize the uncertainty in th e relationship between PM 2.5 and premature mortality (which account for over 98 % of total monetized benefits in this analysis ), 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 estim ates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.
As previously described, we strive to monetize as man y of the benefits anticipated from the revised and alternative standards as possible given data and resource limitations , but the monetized benefits estimated in this RIA inevitably only reflect a portion of the benefits. Specifically, only certain benefit s attributable to the health impacts associated with exposure to ambient fine particles have been monetized in this analysis. Data and m ethodological limitations prevented the EPA from quantifying or monetizing the benefits from several important health be nefit categories from emission reduction strategies to reach the revised annual standard in this RIA , including potential co
- benefits from reducing ozone exposure , NO 2 exposure, SO 2 exposure, and methylmercury exposure (see section 5.6.5 for more information)

. If we could fully monetize all of the benefit categories, the total monetized benefits would exceed the costs by an even greater margin than we currently estimate

.
To more fully address all these uncertainties inc luding those we cannot quantify, we apply a four

- tiered approach using the WHO uncertainty framework (WHO, 2008) , which provides a means for systematically linking the characterization of uncertainty to the sophistication of the underlying risk assessment

. The EPA has applied similar approaches in analyses (U.S. EPA, 2010b, 2011a). Using this framework, we summarize the key uncertainties in the health benefits analysis, including our assessment of the direction of potential bias, magnitude of impact on the monetized benefits, degree of confidence in our analytical approach, and our ability to assess the source of uncertainty. More information on this approach and the uncertainty characterization are available in Appendix 5 B

.1

- 51

Benefits Analysis Data Inputs
In Figure 5

- 2, we summarized the key data inputs to the health impact and economic valuation estimate. Below we summarize the data sources for each of these inputs, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation. We indicate where we have updated key data inputs since the benefits analysis conducted for the MATS RIA (U.S. EPA, 2011d ) and the proposal RIA (U.S. EPA, 2012 a )

. Demographic D ata Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use population projections based on economic forecasting models developed by Woods and P oole, Inc

. (Woods and Poole, 2012 ). The Woods and Poole (WP) database contain s county

- level projections of population by age, sex, and race out to 2040, relativ e to a baseline using the 2010 C ensus data; the proposal RIA incorporated WP projections relative to a baseline using 2000 C ensus data. An analysis exploring the sensitivity of population and health impact estimates to this update can be found in Appendix 5 .A

. Projections in each county are determined simultaneously with every other county in the United States to take into account patterns of economic growth and migration. The sum of growth in county

- level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to WP, linking county

- level growth projections together and cons training to a national

- level total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four

- stage process:

 First, national

- level variables such as income, employment, and populations are forecasted.

 Second, employment projections are made for 17 9 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004 ) , using an “ export

- base ” approach, which relies on linking industrial

- sector production of non

- locally consumed productio n items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export

- based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.

 Third, populati on is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort

- component method based on fertility and mortality in each area.1

- 52  Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year of age by sex and race for each year through 20 4 0 based on historical rates of mortal ity, fertility, and migration.
Baseline Incidence and Prevalence Estimates Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 10 μg/m 3 decrease in daily PM 2.5 levels might be associated with a decrease in hospital admissions of 3 % . The baseline incidence of the health effect is necessary to convert this relative change into a number of cases. A baseline incidence rate is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to bas eline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, t hat number must be multiplied by the millions of people in the total population.

Table 5

- 3 summarizes the sources of baseline incidence rates and provides average incidence rates for the endpoints included in the analysis. For both baseline incidence and p revalence data, we used age

- specific rates where available. We applied concentration

response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. In most cases, we used a singl e national incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the onl y available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level. County, state and regional incidence rates are available for hospital ad missions, and county

- level data are available for premature mortality.
We projected mortality rates such that future mortality rates are consistent with our projections of population growth (Abt Associates, 2012 ). To perform this calculation, we began fir st with an average of 2004

– 2006 cause
- specific mortality rates. Using Census Bureau projected national

- level annual mortality rates stratified by age range, we projected these1

- 53

mortality rates to 2050 in 5

- year increments (Abt Associates, 2012; U.S. Bureau of the Census 2002).
The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we have revised the baseline incidence rates for acute myocar dial infarction. These revised rates are more recent (AHRQ, 2007), which provides a better representation of the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution

- related illnesses. Also, the new baseline incidence rates are more spatially refined. For many locations within the U.S., these data are resolved at the county

- or state

- level, providing a better characterization of the geographic distribution of hospital and eme rgency department visits than the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution

- related visits are likely to be unscheduled. If air pollution

- re lated hospital admissions are scheduled, this assumption would underestimate these benefits.
For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma in the population are needed to de fine the applicable population. Table 5

- 4 lists the prevalence rates used to determine the applicable population for asthma symptoms. Note that these reflect current asthma prevalence and assume no change in prevalence rates in future years. We updated the se rates in the CSAPR RIA (U.S. EPA, 2011c).1

- 54

Table 5

- 3

. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact

Functions, General Population

Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate

projected to 202 0 a Age

- , cause
- , and county

specific rate CDC WONDER (2004

– 2006) U.S. Census bureau, 2000 Hospitalizations Daily hospitalization rate Age

- , region

- , state

- , county

- and cause

- specific rate 2007 HCUP data files b

ER Visits Daily ER visit rate for asthma and

cardiovascular events Age

- , region

- , state

- , county

- and cause

- specific rate 2007 HCUP data files b

Cerebrovascular events Incidence of new cerebrovascular

events among populations 50

– 79 0.001575I Table 3 of Miller et al. (2007) Chronic Bronchitis c Annual prevalence rate per

person Aged 18

– 44 Aged 45

– 64 Aged 65 and older 0.0315 0.0549 0.0563 American Lung Association

(2010a, Table 4).

Annual incidence rate per person 0.00378 Abbey et al. (1993, Table 3) Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial

infarction incidence rate per person, 18+ Age

- , region

- , state

- , and county

- specific rate 2007 HCUP data files ; b adjusted by 0.93 for probability of surviving after 28 days (Rosamond et al., 1999) Asthma Exacerbations Incidence among asthmatic

African

- American children daily wheeze daily cough daily shortness of breath 0.173 0.145 0.074 Ostro et al. (2001)

Acute Bronchitis Annual bronchitis incidence rate,

children 0.043 American Lung Association (2002c, Table 11) Lower Respiratory Symptoms Daily lower respiratory symptom

incidence among children d 0.0012 Schwartz et al. (1994, Table 2) Upper Respiratory Symptoms Daily upper respiratory symptom

incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)

(continued)1

- 55

Table 5

- 3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact

Functions, General Population (continued)

Endpoint Parameter Rates Value Source Work Loss Days Daily WLD incidence rate per

person (18

– 65) Aged 18

– 24 Aged 25

– 44 Aged 45

– 64 0.00540 0.00678 0.00492 1996 HIS (Adams,

Hendershot, and Marano, 1999, Table 41); U.S. Census Bureau (2000)

School Loss Days Rate per person per year,

assuming 180 school days per year 9.9 National Center for Education Statistics (1996) and 1996 HIS (Adams et al., 1999, Table 47); Minor Restricted

Activity Days Daily MRAD incidence rate per

person 0.02137 Ostro and Roths child (1989, p. 243)

a Mortality rates are only available at 5

- year increments.
b Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional

level hospital and emergency department discharges for a variety of ICD codes (AHRQ, 2007).

c Assessed in sensitivity analysis only. The rate numbers may be slightly different from those in Table 4 because we received more current estimates from ALA.
d Lower respiratory symptoms are defined as two or more of the following: co ugh, chest pain, phlegm, and wheeze.
Table 5

- 4
. Asthma Prevalence Rates

Population Group Asthma Prevalence Rates
Value Source All Ages 0.0780 American Lung Association (2010b, Table 7) < 18 0.0941 5

– 17 0.1070 18

– 44 0.0719 45

– 64 0.0745 65+ 0.0716 African American, 5

– 17 0.1776 American Lung Association (2010b, Table 9) African American, <18 0.1553 American Lung Association a

a Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008). Effect Coefficients The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with the EPA ’ s Integrated1

- 56

Science Assessments (which replace previous Criteria Documents), with i nput and advice from the SAB

- HES, a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1 990 to 2020 (U.S. EPA, 2011a)

. In addition , we have included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009 b ) and the Provisional Assessment (U.S. EPA, 2012 b )

. 12 I n 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 f ocus on endpoints reflecting public health impacts (like hospital admissions) rather than physiological responses (such as changes in clinical measures like Forced Expiratory Volume [ FEV1 ] ).
There are several types of data that can support the determinatio n of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these dat a sources provide important contributions to the weight of evidence surrounding a particular health impact. However, only epidemiology studies provide direct concentration

- response relationships that can be used to evaluate population

- level impacts of redu ctions in ambient pollution levels in a health impact assessment.

For the data

- derived estimates, we relied on the published scientific literature to ascertain the relationship between PM 2.5 and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5

- 5 . These criteria include consideration of whether the study was peer

- reviewed, the match between the pollutant studied and the pollut ant of interest, the study design and location, and characteristics of the study population, among other considerations. In general, the use of concentration

- response functions from more than a single study can provide a more representative distribution of the effect estimate . However, there are often differences between studies examining the same endpoint, making it difficult to pool the results in a consistent manner. For example, studies may examine different pollutants or different age

12 T he peer

- reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB

. The new studies from the PM ISA and Provisional Assessme nt for health endpoints not previously quantified in EPA’s RIAs are presented in a sensitivity analysis in Appendix 5B, but these new endpoints have not been incorporated into the core benefits analysis.1

- 57

Table 5

- 5
. Crite ria Used When Selecting C

- R Functions

Consideration Comments
Peer

- Reviewed Research Peer

- reviewed research is preferred to research that has not undergone the peer

- review process. Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual

- level confounding variables that cannot be controlled for in ecologic al studies. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect effects. Studies that are more recent are also preferred because of possib le changes in pollution mixes, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years will be included. Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national

- level health impacts. When available, multi

- city studies are preferred to single city studies bec ause they provide a more generalizable representation of the concentration

- response function. Study Size Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic children). Study Location U.S. studies are more desirable than non
- U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. National estimates are most appropriate when benefits are nationally distributed; the impact of reg ional differences may be important when benefits only accrue to a single area. Pollutants Included in Model When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important to use properly specified impact functions tha t include both pollutants. Using single

- pollutant models in cases where both pollutants are expected to affect a health outcome can lead to double

- counting when pollutants are correlated. Measure of PM For this analysis, impact functions based on PM 2.5 ar e preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors, and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing tha t there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction. Economically Valuable Health Effects Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms. These health effects are not quantified in this analysis. Non

- overlapping Endpoints Although the benefits associated with each individual health endpoint may be analyzed separately, care must be exe rcised in selecting health endpoints to include in the overall benefits analysis because of the possibility of double

- counting of benefits.
groups. For this reason, we consider very carefully the set of studies available examining each endpoint and select a consistent subset that provides a good balance of population coverage1

- 58

and match with the pollutant of interest. In many cases, either because of a lack of multiple studies, consistency problems, or clear superiority in the quality or comprehensive ness of one study over others, a single published study is selected as the basis of the effect estimate.
When several effect estimates for a pollutant and a given health endpoint have been selected, they are quantitatively combined or pooled to derive a m ore robust estimate of the relationship. The BenMAP Manual Technical Appendices provides details of the procedures used to combine multiple impact functions ( Abt Associates, 2012 ). In general, we used fixed or random effects models to pool estimates from d ifferent single city studies of the same endpoint. Fixed effects pooling simply weights each study’s estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects pooling accounts for both w ithin

- study variance and between

- study variability, due, for example, to differences in population susceptibility. We used the fixed effects model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesi s, in which case we would use the random effects model. 13 Pooled impact functions are used to estimate hospital admissions and asthma exacerbations. When combining evidence across multi

- city studies (e.g., cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates drawn from e ach multi

- city study are themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than weighting by the inverse of the variance reported in each study. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices ( Abt Associates, 2012 ).
Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This applies to both impact functions defined by a single e ffect estimate and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in fact, vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of PM), location

- specific effect estimates are generally not available.
The specific studies from which effect estimates for the core analysis are drawn are included in Table 5

- 6. We highlight in blue those studies that have been added since the benefits analysis conducted for the MATS RIA (U.S. EPA, 2011d), and we highlight those studies

13 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling process. Prior versions of the model calculated population variance, while the version used here calculated sample variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates between the proposal and final RIA.1

- 59

in red that have been added since the proposal RIA (U.S. EPA, 2012a). In all cases where effect estimates are drawn directly from epidemiological studies, standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Table 5

- 7 summarizes those health endpoints and studies we have included as in sensitivity analyses.
Table 5

- 6

. Health Endpoints and Epidemiological Studies Used to Quantif y Health Impacts

in the Core Analysis a

Endpoint Study Study Population Risk Estimate (95 th Percentile Confidence Interval) a Premature Mortality Premature mortality

— cohort study, all

- cause Krewski et al. (2009) >29 years >24 years RR = 1.06 (1.04

– 1.06) per

10 μg/m 3 Lepeule et al. (2012) RR = 1. 14 (1.07

– 1. 22 ) per 10 μg/m 3 Premature mortality, total exposures PM 2.5 Expert Elicitation (Roman et al.,

2008) >24 years Varies by expert

Premature mortality

— all

- cause Woodruff et al. (1997) Infant (<1 year) OR = 1.04 (1.02

– 1.07) per 10 μg/m 3 Chronic Illness Nonfatal heart attacks Peters et al. (2001) Adults (>18 years) OR = 1.62 (1.13

– 2.34) per

20 μg/m 3 Pooled estimate: Pope et al. (2006) β = 0.00481 (0.00199) Sullivan et al. (2005) β = 0.00198 (0.00224) Nonfatal heart attacks (cont’d) Zanobetti et al. (2009) β = 0.00225 (0.000591) Zanobetti and Schwartz (2006) β = 0.0053 (0.00221) Hospital Admissions Respiratory Zanobetti et al. (2009)

— ICD 460

- 519 (All respiratory) >64 years β=0.00207 (0.00446)
Kloog et al. (2012)

— ICD 460

- 519 (All Respiratory β=0.0007 (0.000961)
Moolgavkar (2000)

— ICD 490

– 496 (Chronic lung disease) 18

– 64 years 1.02 (1.01

– 1.03) per 36 μg/m 3 Babin et al. (2007)

— ICD 493 (asthma) <19 β=0.002 (0.004337) Sheppard (2003)

— ICD 493 (asthma) < 18 RR = 1.04 (1.01

– 1.06) per 11.8 μg/m 3 (continued) Table 5

- 6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts

in the Core Analysis a (continued)

Endpoint Study Study Population Risk Estimate

(95th Percentile Confidence Interval) a Cardiovascular Pooled estimate:

Zanobetti et al. (2009)

— ICD 390

- 459 (all cardiovascular) >64 years β=0.00189 (0.000283)
Peng et al. (2009)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

- and β=0.00068 (0.000214)1

- 60 peripheral vascular disease) Peng et al. (2008)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

- and peripheral vascular disease) β=0.00071 (0.00013) Bell et al. (2008)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

- and peripheral vascular disease) β=0.0008 (0.000107) Moolgavkar (2000)

— ICD 390

– 429 (all cardiovascular) 20

– 64 years RR=1.04 (t statistic: 4.1) per 10 μg/m 3 Asthma

- related ER visits Pooled estimate:

Mar et al. (2010) All ages RR = 1.04 (1.01

– 1.07) per 7 μg/m 3 Slaughter et al. (2005) RR = 1.03 (0.98

– 1.09) per 10 μg/m 3 Glad et al. (2012) β=0.00392 (0.002843) Other Health Endpoints Acute bronchitis Dockery et al. (1996) 8

– 12 years OR = 1.50 (0.91

– 2.47) per 14.9 μg/m 3 Asthma exacerbations Pooled estimate: Ostro et al. (2001) (cough, wheeze and shortness of breath) b 6

– 18 years b OR = 1.03 (0.98

– 1.07) OR = 1.06 (1.01

– 1.11) OR = 1.08 (1.00

– 1.17) per 30 μg/m 3 Mar et al. (2004) (cough, shortness of breath) RR = 1.21 (1

– 1.47) per RR = 1.13 (0.86

– 1.48) per 10 μg/m 3 Work loss days Ostro (1987) 18

– 65 years β=0.0046 (0.00036) Acute respiratory symptoms Ostro and Rothschild (1989) (Minor restricted

activity days) 18

– 65 years β=0.00220 (0.000658) Upper respiratory symptoms Pope et al. (1991) Asthmatics, 9

– 11 years 1.003 (1

– 1.006) per

10 μg/m 3 Lower respiratory symptoms Schwartz and Neas (2000) 7

– 14 years OR = 1.11 (1.58

– 1.58) per 15 μg/m 3

a Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies highlighted in red represent update s incorporated since the proposal RIA (U.S. EPA, 2012a).
b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (20 04) study. Based on advice from the SAB

- HES, we extended the applied population to 6

- 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA

- SAB (2004) and NRC (2002).1

- 61

Table 5

- 7
. Health Endpoints and E pidemiological Studies Used to Quantify Health Impacts

in the Sensitivity Analysis a

Endpoint Study Study Population Chronic Illness Chronic bronchitis Abbey et al. (1995) >26 years Stroke Miller et al. (2007) 50

– 79 years Hospital Admissions Cardiovascular ED Visits Metzger et al. (2004) 0

– 99 Tolbert et al. (2007) 0

– 99 Mathes et al. (2011) 40

– 99

a Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d). Studies highlighted in red represent updated incorporated since the proposal RIA (U.S. EPA, 2012a).
5.6.3.1 PM 2.5 Premature Mortality Effect Coefficients Core Mortality Effect Coefficients for Adults

. A substantial body of published scientific literature documents the association between elevated PM 2.5 concentrations and increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the this revi ew of the PM standards, which was twice reviewed by the SAB

- CASAC (U.S. EPA

- SAB, 2009b, 2009c), concluded that there is a causal relationship between mortality and both long

- term and short

- term exposure to PM 2.5 based on the entire body of scientific evide nce (U.S. EPA, 2009b). The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpo int quantified in this analysis.
Researchers have found statistically significant associations between PM 2.5 and premature mortality using different types of study designs. Time

- series methods have been used to relate short

- term (often day

- to

- day) changes in PM 2.5 concentrations and changes in daily mortality rates up to several days after a period of elevated PM 2.5 concentrations. Cohort methods have been used to examine the potential relationship between community

- level PM 2.5 exposures over multiple years (i.e., long

- term exposures) and community

- level annual mortality rates that have been adjusted for individual level risk factors. When choosing between using short

- term studies or cohort studies for estimating mortality benefi ts, cohort analyses are thought to capture more of the public health impact of exposure to air pollution over time because they account for the effects of long

- term exposures as well as some fraction of short

term exposures (Kunzli et al., 2001; NRC, 2002) . The NRC stated that “ it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure ”1

- 62

(NRC, 2002, p. 108). The NRC further notes that “ the overall effect estimates may be a combination of effect s from long

- term exposure plus some fraction from short

- term exposure. The amount of overlap is unknown ” (NRC, 2002, p. 108

- 9). To avoid double counting, we focus on applying the risk coefficients from the long

- term cohort studies in estimating the mortali ty impacts of reductions in PM 2.5 . Over the last two decades, several studies using “ prospective cohort ” designs have been published that are consistent with the earlier body of literature. Two prospective cohort studies, often referred to as the Harvard “ Six Cities Study ” (Dockery et al., 1993; Laden et al., 2006; Lepeule et al., 2012) and the “ American Cancer Society ” or “ ACS study ” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of ambi ent PM 2.5 concentrations and mortality. These studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. The credibility of these two studies is further enhanced by t he fact that the initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect Institute (HEI) and by a Special Pane l of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow

- up period for the ACS cohort pro vides additional validation of the findings of these original studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SAB

HES also supported using these two cohorts for analyses of the benef its of PM reduction s, and concluded, “ the selection of these cohort studies as the underlying basis for PM mortality benefit estimates to be a good choice. These are widely cited, well studied and extensively reviewed data sets ” (U.S. EPA

- SAB, 2010a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present benefits estimates using relative risk estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using both ACS an d Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2006a, 2010c, 2011c, 2011d). The PM ISA concludes that the ACS and Six Cities cohorts provide the strongest evidence of the association between long

- term PM 2.5 exposure and premature mortal ity with support from a number of additional cohort studies (described below).
The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional refinements to the analysis of PM

- related mortality by (a) extending the follow

- up period by 2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of1

- 63

urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, or community

- level, co

- variates; (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well

suited for the assessment of mortality risk from long

- term PM 2.5 exposures for the EPA ’s benefits analyses.
In 2009, the SAB

- HES agai n reviewed the choice of mortality risk coefficients for benefits analysis, concluding that “ [t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the SAB

HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate ” (U.S. EPA

- SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the support for inc luding this study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 2010b) utilized risk coefficients drawn from the Krewski et al . (2009) study, the most recent reanalysis of the ACS cohort data. The risk assessment cited a number of advantages that informed the selection of the Krewski et al. (2009) study as the source of the core effect estimates, including the extended period of observation, the rigorous examination of model forms and effect estimates, the coverage for ecological variables, and the large dataset with over 1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The SAB

- CASAC also provided extensive peer review of t he risk assessment and supported the use of effect estimates from this study (U.S. EPA

- SAB, 2009a, 2010b, c).

Consistent with the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b) which was reviewed by the SAB

- CASAC (U.S. EPA

- SA B, 2009), we use the all

- cause mortality risk estimate based on the random

- effects Cox proportional hazard model that incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04

– 1.08 per 10μg/m 3 increase in PM 2.5 ). The re lative risk estimate (1.06 per 10μg/m 3 increase in PM 2.5 ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Ci ties study, which was published after the last SAB

- HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model specifications, lower exposures, and averaging time using eleven additional years of cohort follow

- up tha t incorporated recent lower exposures. The authors found significant associations between PM 2.5 exposure and increased risk of all

- cause, cardiovascular and lung cancer mortality. The authors also concluded that the concentration

- response relationship was linear1

- 64

down to PM 2.5 concentrations of 8 μg/m 3 , and that mortality rate ratios for PM 2.5 fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all

- cause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07

– 1.22 per 10 μg/m 3 increase in PM 2.5 ). The relative risk estimate is slightly smaller th an the risk estimate drawn from Laden et al

. (2006), with relatively smaller confidence intervals.
Implicit in the calculation of PM 2.5 - related premature mortality impacts are several key assumptions, which are described in further detail later in this cha pter. First, we assume that there is a “ cessation ” lag in time between the reduction in PM exposure and the full reduction in mortality risk that affects the timing (and thus discounted monetary valuation) of the resulting premature deaths (see section 5.6 .6.1). Second, following conclusions of the PM ISA, we assume that all fine particles are equally potent in causing premature mortality (see section 5.7.2). Third, following conclusions of the PM ISA, we assume that the health impact function for fine part icles is linear within the range of ambient concentrations affected by these standards (see section 5.7.4).
Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities cohorts, several recent cohort studies conducted in North America provide evidence for the relationship between long

- term exposure to PM 2.5 and the risk of premature death. Many of these additional cohort studies are described in the PM ISA (U.S. EPA, 2009) and the Provisional Assessment (U.S. EPA, 2012 b ) (and th us not summarized here). 14 , 15 Table 5

- 8 provides the effect estimates from each of these cohort studies for all

- cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality as well as the lowest measured air quality level (LML) and mean concentration in the study.
We also draw upon the results of the 2006 expert elicitation sponsored by the EPA (Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 expert

- defined concentration

- response functions. The PM 2.5 expert elicitation and the derivation of effect estimates from the expert elicitation results are described in detail in the

14 It is important to note that the newer studies in the Provisional Assessment are published in peer

- reviewed journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated Science Assessment nor gone through review by the SAB. In addition, only the ACS and H6C cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings. 15 In this chapter, we only describe multi

- state cohort studies. There are additional cohort studies that we have not included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies focusing on methods development. In Appendix 5 A, we provide additional information regarding cohort studies in California, which is the only state for which we identified single state cohorts.1

- 65

2006 PM 2.5 NAAQS RIA (U.S. EPA, 2006a), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and so we sum marize here the key attributes of this study relative to the interpretation of the estimates of PM

- related mortality reported here. We describe also how the epidemiological literature has evolved since the expert elicitation was conducted in 2005 and 2006.
Table 5

- 8. Summary of Effect estimates from Associated With Change in Long

- Term

Exposure to PM 2.5 in Recent Cohort Studies in North America

Study Cohort (age) LML (μg/m 3 ) Mean

(μg/m 3 ) Hazard Ratios per 10 μg/m 3 Change in PM 2.5 (95 th percentile confidence intervals) All Causes Cardiovascular Cardiopulmonary IHD Pope et al. (2002) ACS

(age >30) 7.5 18.2 1.06 (1.02

– 1.11) 1.12

(1.08

– 1.15) 1.09

(1.03

– 1.16) N/A

Laden et al. (2006) Six Cities

(age > 25) 10 16.4 1.16 (1.07

– 1.26) 1.28

(1.13

– 1.44) N/A N/A

Lipfert et al. (2006) a Veterans

(age 39

– 63) <14.1 14.3 1.15 (1.05

– 1.25) N/A N/A N/A

Miller et al. (2007) b WHI

(age 50

– 79) 3.4 13.5 N/A 1.76 (1.25

– 2.47) N/A 2.21 (1.17

– 4.16) Eftim et al. (2008) Medicare (age

> 65) 6 13.6 1.21 (1.15

– 1.27) N/A N/A N/A

Zeger et al. (2008) c Medicare (age

> 65) <9.8 13.2 1.068 (1.049

– 1.087) N/A N/A N/A

Krewski et al. (2009) d ACS

(age >30) 5.8 14 1.06 (1.04

– 1.08) N/A 1.13 (1.10

– 1.16) 1.24

(1.19

– 1.29) Puett et al. (2009) b NHS

(age 30

– 55) 5.8 13.9 1.26 (1.02

– 1.54) N/A N/A 2.02 (1.07

– 3.78) Crouse et al. (2011) d,e Canadian

census 1.9 8.7 1.06 (1.01

– 1.10) N/A N/A N/A

Puett et al. (2011) f Health

Professionals (age 40

– 75) <14.4 17.8 0.86 (0.70

– 1.00) 1.02

(0.84

– 1.23) N/A N/A

Lepeule et al. (2012) d Six Cities

(age > 25) 8 15.9 1.14 (1.07

– 1.22) 1.26

(1.14

– 1.40) N/A N/A

a Low socio

- economic status ( SES ) men only. Used traffic proximity as a surrogate of exposure.
b Women only.

c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the Western U.S.
d Random effects Cox model with individual and ecologic covariates.

e Canadian population.
f Men with high socioeconomic status only. The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in mortality Formatted: Spanish (International Sort)1

- 66

among the adult U.S. population resulting from reductions in ambient annual avera ge PM 2.5 levels. These distributions were obtained through a formal interview protocol using methods designed to elicit subjective expert judgments. These experts were selected through a peer

nomination process and included experts in epidemiology, toxicol ogy, and medicine. The elicitation interview consisted of a protocol of carefully structured questions, both qualitative and quantitative, about the nature of the PM 2.5 - mortality relationship designed to build twelve individual distributions for the coeffi cient (or slope) of the C

- R function relating changes in annual average PM 2.5 exposures to annual, adult all

- cause mortality. The elicitation also provided useful information regarding uncertainty characterization in the PM 2.5 - mortality relationship. Speci fically, during their interviews, the experts highlighted several uncertainties inherent within the epidemiology literature, such as causality, concentration thresholds, effect modification, the role of short

- and long

- term exposures, potential confounding , and exposure misclassification. In Appendix 5c, we evaluate each of these uncertainties in the context of this health impact assessment. For several of these uncertainties, such as causality, we are able to use the expert

- derived functions to quantify th e impacts of applying different assumptions. The elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006).
Prior to providing a quantitative estimate of the risk of premature death associated with long

- term PM 2.5 exposure, the experts answered a series of “ conditioning questions. ” One such question asked the experts to identify which epidemiological studies they found most informative. The “ ideal study attributes ” 16 according to the experts included:

 Geographic representation of the entire U.S. (e.g., monitoring sites across the country)

 Collection of information on individual risk factors and residential information both at the beginning and throughout the follow

- up period

 Large sample size that is representative of the general U.S. population

 Collection of genetic information from cohort members to identify and assess potential effect modifiers

 Monitoring of individual exposures (e.g., with a personal monitor)

 Collection of data on levels of several co

- pollutants (not only those that are monitored for compliance purposes

 Accurate characterization of outcome (i.e., cause of death)

16 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5

- 5 of this chapter.1

- 67  Follow

- up for a long period of time, up to a lifetime

 Prospective study design
Although no single epidemiological study completely satisfies each of these cr iteria, the experts determined that the ACS and Six Cities cohort studies best satisfy a majority of these ideal attributes. To varying degrees the studies examining these two cohorts: are geographically representative; have collected information on indivi dual risk factors; include a large sample size; have collected data on co

- pollutants in the case of the ACS study; have accurately characterized the health outcome; include a long (and growing) follow

- up period; and, are prospective in nature. The experts also noted a series of limitations in these two cohort studies. In the case of the Six Cities study (Laden et al., 2006), the experts identified the “ small sample size, limited number of cities, and concerns about representativeness of the six cities for t he U.S. as a whole ” as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that the “ method of recruitment for the study, which resulted in a group with higher income, more education, and a greater proportion of whites tha n is representative of the general U.S. population ” represented a shortcoming. Several experts also argued that because the ACS study relied upon “ ...whatever monitors were available to the study...a single monitor represent[ed] exposure for an entire metropol itan area...whereas [the Six Cities study] often had exposures assigned at the county level. ” Despite these limitations, the experts considered the Pope et al. (2002) extended analysis of the ACS cohort and the Laden et al. (2006) extended analysis of the Si x Cities cohort to be particularly influential in their opinions (see Exhibit 3

- 3 of the elicitation summary report [ IEc, 2006 ] ).
Since the completion of the EPA ’ s expert elicitation in 2006, additional epidemiology literature has become available, includ ing 9 new multi

- state cohort studies shown in Table 5

- 8. This newer literature addresses some of the weaknesses identified in the prior literature. For example, in an attempt to improve its characterization of population exposure the most recent extended a nalysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that employ more spatially resolved estimates of population exposure.
In light of the availability of this newer literature, we have updated the presentation of results in the R IA. Specifically, we focus the core analysis on results derived from the two most recent studies of the ACS and Six Cities cohorts (Krewski et al. , 2009; Lepeule et al. , 2012). Because the other multi

- state cohorts generally have limited geography and age/gender representativeness, these limitations preclude us from using these studies in our core benefits results, and we instead present the risk coefficients from these o ther multi

- state cohorts in1

- 68

Table 5

- 8. In addition, we now present the full distribution s of the expert

- derived results in a probabilistic graphic (rather than cascading the expert

- derived results throughout the results tables as done in prior RIAs). We do not combine the expert results in order to preserve the breadth and diversity of opinion on the expert panel (Roman et. al., 2006). This presentation of the expert

- derived results is generally consistent with SAB advice (U.S. EPA

- SAB, 2008), which

recomme nded that the EPA empha si ze that “scientific differences existed only with respect to the magnitude of the effect of PM 2.5 on mortality, not whether such an effect existed ” and that the expert elicitation “supports the conclusion that the benefits of PM 2. 5 control are very likely to be substantial”. Although it is possible that the newer literature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.
Mortality Effect Coefficie nts for Infants

. In addition to the adult mortality studies described above, several studies show an association between PM exposure and premature mortality in children under 5 years of age. 17 The PM ISA states that less evidence is available regarding the potential impact of PM 2.5 exposure on infant mortality than on adult mortality and the results of studies in several countries include a range of findings with some finding significant associ ations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the post

- neonatal period and for respiratory

- related mortality, although this trend is not consistent across all studies. In addition, compared to avoided premature de aths estimated for adult mortality, avoided premature deaths for infants are significantly smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller ri sk coefficients associated with exposure to PM 2.5 . In 2004, the SAB

- HES noted the release of the WHO Global Burden of Disease Study focusing on ambient air, which cites several recently published time

- series studies relating daily PM exposure to mortality in children (U.S. EPA

- SAB, 2004a). The SAB

- HES also cites the study by Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory inflammation and infections in children. A study by Chay and Greenstone (2003) found that

r eductions in TSP caused by the recession of 1981

– 1982 were statistically associated with reductions in infant mortality at the county level. With regard to the cohort study conducted by Woodruff et al. (1997), the SAB

- HES notes several strengths of the study, including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a

17 For the purposes of this analysis, we only calculate benefits for infants age 0

– 1, not all children under 5 years old.1

- 69

variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these findings, the SAB

- HES recommended that the EPA incorpora te infant mortality into the primary benefits estimate and that infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA

- SAB, 2004a).
In 2010, the SAB

- HES again noted the increasing body of literature relating infant mortality and PM exposure and supported the inclusion of infant mortality in the monetized benefits (U.S. EPA

- SAB, 2010a). The SAB

- HES generally supported the approach of estimating infant mortality based on Woodruff et al. (1997) and note d that a more recent study by Woodruff et al. (2006) continued to find associations between PM 2.5 and infant mortality in California

. The SAB

- HES also noted, “when PM 10 results are scaled to estimate PM 2.5 impacts, the results yield similar risk estimates. ” Consistent with the Costs and Benefits of the Clean Air Act (U.S. EPA, 2011a) , we continue to rely on the earlier 1997 study in part due to the national

– scale of the earlier study

. 5.6.3.2 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)
Nonfatal heart attacks have been linked with short

- term exposures to PM 2.5 in the United States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 and nonfatal he art attacks. The Peters et al. (2001) study exhibits a number of strengths. In particular, it includes a robust characterization of populations experiencing acute myocardial infarctions (AMIs). The researchers interviewed patients within 4 days of their AM I events and, for inclusion in the study, patients were required to meet a series of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms and the ability to indicate the time, place and other characteristics of their AM I pain in an interview.
Since the publication of Peters et al. (2001), a number of other single and multi

- city studies have appeared in the literature. These studies include Sullivan et al. (2005), which considered the risk of PM 2.5 - related hospitalization for AMIs in King County, WA; Pope et al. (2006), based in Wasatch Range, UT; Zanobetti and Schwartz (2006), based in Boston , MA ; and, Zanobetti et al. (2009), a multi

- city study of 26 U.S. communities. Each of these single and multi

- city studies, with the exception of Pope et al. (2006), measure AMIs using hospital discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with1

- 70

angiographically characterized patients

— arguably a more precise indicator of AMI. Because the Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces a more comprehensive estimate of acute ischemic heart disease events than the other studies. However, unlike the Peters study (Peters et al., 2006), Pope and colleagu es did not measure the time of symptom onset, and PM 2.5 data were not measured on an hourly basis.
As a means of recognizing the strengths of the Peters study while also incorporating the newer evidence found in the four single and multi

- city studies, we p resent a range of AMI estimates. The upper end of the range is calculated using the Peters study while the lower end of the range is the result of an equal

- weights pooling of these four newer studies. It is important to note that when calculating the incid ence of nonfatal AMI , the fraction of fatal heart attacks is subtracted to ensure that there is no double

- counting with premature mortality estimates. Specifically, we apply an adjustment factor in the concentration

- response function to reflect the probabi lity of surviving a heart attack. B ased on recent data from the Agency for Healthcare Research and Quality ’ s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocar dial infarction stratified by age (e.g., 1.85 2 % for age s 18

– 44, 2.8 188 % for age s 45

– 64, and 7.43 39 % for age s 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and thus replace the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).

5.6. 3

. 3 Hospital Admissions and Emergency Department Visits Because of the availability of detailed hospital admission and discharge records, there is an extensive body of literature examining the relationship between hospital admissions and air pollution. For this reason , we pool together the incidence estimates using several different studies for many of the hospital admission endpoints. In addition, some studies have examined the re lationship between air pollution and emergency department visits. Since most emergency department visits do not result in an admission to the hospital ( i.e., most people going to the emergency department are treated and return home), we treat hospital admissions and emergency department visits separately, taking account of the fraction of emergency department visits that are admitted to the hospital. Specifically, within the baseline incidence rates, we p arse out the scheduled hospital visits from unscheduled ones as well as the hospital visits that originated in the emergency department

. The two main groups of hospital admissions estimated in this analysis are respiratory admissions and cardiovascular adm issions. There is not much evidence linking PM 2.5 with other1

- 71

types of hospital admissions. Both asthma

- and cardiovascular

- related visits have been linked to PM 2.5 in the United States , though as we note below, we are able to assign an economic value to as thma

- related events only. To estimate the effects of PM 2.5 air pollution reductions on asthma

- related ER visits, we use the effect estimate from a study of children 18 and under by Mar et al. (2010) , Slaughter et al. ( 2005 ) , and Glad et al. (2012)

. The first two studies examine d populations 0 to 9 9 in Washington State , while Glad et al. examined populations 0

- 99 in Pittsburgh, PA . Mar and colleagues perform their study in Tacoma, while Slaughter and colleagues base their study in Spokane. We apply ra ndom/fixed effects pooling to combine evidence across these two studies.
To estimate avoided incidences of cardiovascular hospital admissions associated with PM 2.5 , we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 to 64, while the remainder estimate risk among adults over 64. 18 Total cardiovascular hospital admissions are thus the sum of the pooled estimate for adults over 65 and the single study estimate for adults 20 to 64. Cardiovascular hospital admissions include admissions for myocardial infarctions. To avoid double

- counting benefits from reductions in myocardial infarctions when applying the impact function for cardio vascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions. We applied equal weights pooling to the multi

- city studies assessing risk among adults over 64 because these st udies already incorporated pooling across the city

- level estimates. One potential limitation of our approach is that while the Zanobetti et al

. (2009) study assesses all cardiovascular risk, Bell et al. (2008) , and Peng et al., (2008, 2009) studies estimate a subset of cardiovascular hospitalizations as well as certain cerebro

- and peripheral

vascular diseases. To address the potential for the pooling of these four studies to produce a biased estimate, we match the pooled risk estimate with a baselin e incidence rate that excludes cerebro

- and peripheral

- vascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, though this would prevent us from drawing upon the strengths of the three multi

- city studies.

18 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence criteria. However, given that no other estimates are available for this age group, we chose to use the existing study. Given the very small (<5 % ) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions between the original and rean alyzed results, we do not expect this choice to introduce much bias. For a discussion of the GAM convergence criteria, and how it affected the size of effect coefficients reported by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/st

timeseries.htm

.1

- 72

To estim ate avoided incidences of respiratory hospital admissions associated with PM 2.5 , we used a number of studies examining total respiratory hospital admissions as well as asthma and chronic lung disease. We estimated impacts among three age groups: adults ove r 65, adults 18 to 6 4 and children 0 to 1 7. For adults over 65, the multi

- city stud ies by Zanobetti et al. (2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions (defined as ICD codes 460

– 519). We pool these tw o studies using equal weights. Moolgavkar et al. ( 2003 ) examines PM 2.5 and chronic lung disease hospital admissions (less asthma) in Los Angeles, CA among adults 18 to 6 4. For children 0 to 18, we pool two studies using random/fixed effects. The first is B abin et al. (2007) which assessed PM 2.5 and asthma hospital admissions in Washington, DC among children 1 to 1 8; we adjusted the age range for this study to apply to children 0 to 1 8. The second is Sheppard et al. (2003) which assessed PM 2.5 and asthma hos pitalizations in Seattle, Washington , among children 0 to 1 8.

5.6

. 3

. 4 Acute Health Events and School/Work Loss Days In addition to mortality, chronic illness, and hospital admissions, a number of acute health effects not requiring hospitalization are associated with exposure to PM 2.5 . The sources for the effect estimates used to quantify these effects are described below.
Approximately 4 % of U.S. children between the ages of 5 and 17 experience episodes of acute bronchitis annually ( ALA , 2002). Acute b ronchitis is characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 19 with the exception of cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate developed from Dockery et al. (1996). Health effects from air pollution can also result in missed days of work (either from personal symptoms or from caring for a sick family member). Days of work lost due to PM 2.5 were estimated using an effect estimate developed from Ostro (1987). Children may also be absent from school because of respiratory or other diseases caused by exposure to air pollution , but we have not quantified these effects for this rule

. We estimate three types of acute respiratory symptoms: lower respiratory symptoms, upper respirator y symptoms, and minor restricted activity days (MRAD). Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory sym ptoms in asthmatic children aged 9 to 11 are estimated using an effect estimate developed

19 See http://www.nlm.nih.gov/medlineplus/ency/article/ 001087.htm , accessed April 2012

.1

- 73

from Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air pollution), children with asthma can be more susceptible to a variety o f upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory symptoms has thus focused on effects in asthmatics.
Minor restricted activity days (MRAD) result when individuals reduce most usual daily activities and replace them with less strenuous activities or rest, yet not to the point of missing work or school. For example, a mechanic who would usually be doing physical work most of the day will inste ad spend the day at a desk doing paper and phone work because of difficulty breathing or chest pain. The effect of PM 2.5 on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989).
More recently published literature examining t he relationship between short

- term PM 2.5 exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009) , but proved to be unsuitable for use in this benefits analysis. In particular, the best available study ( Patel et al., 2010) speci fied a population aged 13

– 20, which overlaps with the population in which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of double

- counting impacts, we do not estimate changes in acute respiratory symptoms and asthma exac erbation among populations of the same age.

For this RIA , we have followed the SAB

- HES recommendations regarding asthma exacerbations in developing the core estimate (U.S. EPA

- SAB, 2004 a )

. Although certain studies of acute respiratory events characterize these impacts among only asthmatic populations, others consider the full population, including both asthmatics and non

- asthmatics. For this reason, incidence estimates derived from studies focused o nly on asthmatics cannot be added to estimates from studies that consider the full population

— to do so would double

- count impacts. To prevent such double

- counting, we estimat ed the exacerbation of asthma among children and excluded adults from the calculat ion. Asthma exacerbations occurring in adults are assumed to be captured in the general population endpoints such as work loss days and MRADs. Finally, note also the important distinction between the exacerbation of asthma among asthmatic populations, and the onset of asthma among populations not previously suffering from asthma; in this RIA , we quantify the exacerbation of asthma among asthmatic populations and not the onset of new cases of asthma.
To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. Ostro et al. (2001) followed a1

- 74

group of 138 African

- American children in Los Angeles for 13 weeks, recording daily occurrences of respiratory symptoms a ssociated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between PM 2.5 , measured as a 12

- hour average, and the daily prevalence of shortness of breath and wheeze endpoints. Although the association was not statistically significant for cough, the results were still positive and close to significance; consequently, we decided to include this endpoint, along with shortness of breath and wheeze, in generating incidence estimate s (see below).

Mar et al. (2004) studied the effects of various size fractions of particulate matter on respiratory symptoms of adults and children with asthma, monitored over many months. The study was conducted in Spok ane, Washington, a semiarid city with diverse sources of particulate matter. Data on respiratory symptoms and medication use were recorded daily by the study ’ s subjects, while air pollution data was collected by the local air agency and Washington State Un iversity. Subjects in the study consisted of 16 adults

— the majority of whom participated for over a year

— and nine children, all of whom were studied for over eight months. Among the children, the authors found a strong association between cough symptoms

an d several metrics of particulate matter, including PM 2.5 . However, the authors found no association between respiratory symptoms and PM of any metric in adults. Mar et al. therefore concluded that the discrepancy in results between children and adults was due either to the way in which air quality was monitored, or a greater sensitivity of children than adults to increased levels of PM air pollution.
We employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a single asthma exacerbation incidence estimate. First, we used random/fixed effects pooling to combine the Ostro and Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma exacerbation.

5.6

. 3 .5 Effect Coefficients Selected for the Sensitivity Analyses
Chronic Bronchitis. Chronic bronchitis is characterized by mucus in the lungs and a persistent wet co ugh for at least 3 months a year for several years in a row. C hronic bronchitis affects an estimated 5 % of the U.S. population ( ALA , 1999). A limited number of studies have estimated the impact of air pollution on new incidences of chronic bronchitis. Schwartz (1993) and Abbey et al. (1995) provide evidence that long

- term PM 2.5 exposure gives rise to the1

- 75

development of chronic bronchitis in adults in the United States; these remain the two most recent studies observing a relationship between long

- term e xposure to PM 2.5 and the onset of chronic bronchitis in the U.S . The absence of newer studies finding a relationship between long

term PM 2.5 exposure and chronic bronchitis argues for moving this endpoint from the core benefits analysis to a sensitivity an alysis

. In their review of the scientific literature on chronic obstructive pulmonary disease (COPD), which includes chronic bronchitis and emphysema, the American Thoracic Society concluded that air pollution is “ associated with COPD, but sufficient crite ria for causation were not met ” (Eisner et al., 2010).

Stroke

. The PM ISA (U.S. EPA, 2009) includes several new studies that have examined the relationship between PM 2.5 exposure and cerebrovascular events ( U.S. EPA, 2009). Time

series studies have generally been inconsistent with several studies showing positive associations (Dominici et al. , 2006 ; Metzger et al. , 2004 ; Lippman et al. , 2000 ; Lisabeth et al. , 2008). Several other studies have demonstrated null or negative associations (Anderson et al

. , 2001 ; Barnett et al. , 2006 ; Peel et al. , 2007). In general, these studies examined cerebrovascular disease as a group, though a few studies partition ischemic and hemorrhagic strokes separately (Lisabeth et al. , 2008). A key limitation of these time

- ser ies studies is that they use hospital discharge rates as the diagnosis and relatively short lags (0

– 2 days)

— this is problematic, as discharge rates are an imperfect diagnosis and strokes may occur several days before admission to the hospital.
Longer

- term prospective cohort studies of PM 2.5 and stroke include Miller et al. (2007)

. Miller et al. (2007) estimated the change in risk among post

- menopausal women enrolled in the Women ’ s Health Initiative ( U.S. EPA, 2009 b ). After adjusting for age, race, smoking status, educational level, household income, body

- mass index, diabetes, hypertension, and hypercholesterolemia, hazard ratios were estimated for the first cardiovascular event. Because this study considers first

- ti me cardiovascular events, a key challenge to incorporating this study into the core health impact assessment is match ing the baseline incidence rate correctly , and we have approximated this information using the data in the study

. In addition, Wellenius e t al. ( 2012 ) examined the association of PM 2.5 with neurologist confirmed ischemic stroke in Boston adults in a time

- stratified case

- crossover study. A key feature of this study is that it included the time of stroke sympt om onset for most patients. Similar to the challenge with Miller et al. (2007), we do not have baseline incidence rates, and we do not have sufficient data from the study to approximate it. Three factors argue for treating this endpoint in the sensitivity analysis: (1) the epidemiological literature examining PM

- related cerebrovascular events is still evolving; (2) there are special uncertainties associated1

- 76

with quantifying this endpoint; (3) we have not yet identified an appropriate method for estimating t he economic value of this endpoint.

Cardiovascular Emergency Department Visits. A large number of recent U.S.

- based studies provide support for an association between short

- term increases in PM 2.5 and increased risk of ED visits for ischemic heart diseases ( U.S. EPA, 2009 b ). Both Metzger et al. (2004) and Tolbert et al. (2007) published interim results from the Study of Particles and Health in Atlanta (SOPHIA), finding a relationship between PM 2.5 exposure and cardiovascular emergency department visits. These cardiovascular emergency department visits are distinct from cardiovascular hospital admissions and non

- fatal heart attacks. To ensure no double

- counting, we excluded ICD

- 9

- 411 (ischemic heart disease) from the baseline incidence rates for cardiovascular emergency department visits. Mathes et al. (2011) find relationships between PM 2.5 levels and cardiovascular emergency department visits in New York City. The principal challenge to incorporati ng these studies is the absence of readily

- available economic valuation estimates for cardiovascular emergency department visits. Until we develop an approach for estimating the economic value of this endpoint, we will quantify these ED visits in a sensiti vity analysis only

. Unquantified Human Health Benefits

T he illustrative emission reduction strategies to reach the revised and alternative annual standards described in Chapter 4 would reduce emissions of directly emitted particles, as well

as SO 2 , and NO x for an alternative standard for 11 μg/m 3

. The extent to which down wind exposure to secondary pollutants ozone, and mercury would actually be reduced would depend on the specific control strategy that States would use to reduce PM 2.5 in a given area as we ll as local geographic and meteorological conditions

. Although we have quantified many of the health benefits associated with reducing exposure to PM 2.5 , as shown in Table 5

- 2, we are unable to quantify the health benefits associated with reducing the potential for ozone exposure, SO 2 exposure, NO 2 exposure or contamination o f local water bodies with mercury due to the absence of air quality modeling data for these pollutants in this analysis . Although the method we applied simulate d the impact of attaining the revised and alternative annual standards on ambient levels of PM 2.5 , this method does not simulate how the illustrative emission reductions would affect ambient levels of ozone, SO 2 , or NO 2 . Furthermore, the air quality modeling conducted for this analysis did not assess mercury, so we are unable to estimate mercury deposition associated with the illustrative controls or subsequent bioaccumulation and exposure

. Below we provide a qualitative d escription of these health1

- 77

benefits. In general, previous analyses have shown that the monetized value of these additional health benefits is much smaller than PM 2.5 - related benefits (U.S. EPA, 2010a, 2010 c, 2010 d).
Reducing NO x emissions also reduce s ozone concentrations in most areas. Reducing ambient ozone concentrations is associated with significant human health benefits, including mortality and respiratory morbidity (U.S. EPA, 2008a, 2010d ). Epidemiological researchers have associated ozone exposu re with adverse health effects in numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2006b ; U.S. EPA, 2012c ). When adequate data and resources are available, the EPA generally quantifies several health effects associated with exposure to ozone (e.g., U.S. EPA, 2008a, 2010d , 2011a , 2011c ). These health effects include respiratory morbidity such as asthma attacks, hospital and emergency department visits, school loss days, as well as premature mortality. T he scientific literature suggests that exposure to ozone is also associated with chronic respiratory damage and premature aging of the lungs , but the EPA has not quantified these effects in benefits analyses previously

. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Sulfur Dioxide

— Health Criteria (SO 2 ISA) concluded that there is a causal relationship between respiratory health effects and short

- term exposure to SO 2 (U.S. EPA, 2008 c ). The imme diate effect of SO 2 on the respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration

response relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identi fied four short

- term morbidity endpoints that the SO 2 ISA identified as a “ causal relationship ” : asthma exacerbation, respiratory

- related emergency department visits, and respiratory

- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short

- term SO 2 exposure and premature mortality was

“ suggestive of a causal relationship ” because it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these benefits due to data constraints.
Epidemiological researchers have associated NO 2 exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated1

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Science Assessment for Oxides of Nitrogen

— Health Criteria (NO 2 ISA) (U.S. EPA, 2008b ). The NO 2 ISA provides a comprehensive review of the current evidence of health and environmental effects of NO 2 . The NO 2 ISA concluded that the evidence “ is sufficient t o infer a likely causal relationship between short

- term NO 2 exposure and adverse effects on the respiratory system

. ” These epidemiologic and experimental studies encompass a number of endpoints including emergency d epartment visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted in the United States and Canada generally indicate a 2

– 2 0% increase in risks for ED visits and hospital admissio ns and higher risks for respiratory symptoms. The NO 2 ISA concluded that the relationship between short

- term NO 2 exposure and premature mortality was “ suggestive but not sufficient to infer a causal relationship ” because it is difficult to attribute the mo rtality risk effects to NO 2 alone. Although the NO 2 ISA stated that studies consistently reported a relationship between NO 2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. We did not quantify these benef its due to data constraints.
Economic Valuation Estimates Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large population. Therefore, the appropriate economic measure is WTP for changes in risk of a health effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those changes in risk to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical incidences by dividing individual WTP for a risk reduction by the related observed chang e in risk.
WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we instead used the cost of treating or mitigating the effect to estimate the economic value . COI estimates generally ( al though not necessarily in all cases) understate the true value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).

W e provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 5

- 9 . All values are in constant year 2006 dollars, adjusted for growth in real income for WTP estimates out to 2020 using projections provide d by Standard and1

- 79

Poor ’ s , which is discussed in further detail in Section 5.6.8. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real income increases. Several of the valuation studies used in this analysi s were conducted in the late 1980s and early 1990s , and we are in the process of reviewing the literature to update these unit values . The discussion below provides additional details on valuing specific PM 2.5 - related related endpoints.

5.6.6.1 Mortality V

aluation Following the advice of the SAB’s Environmental Economics Advisory Committee (SAB

EEAC) , the EPA currently uses the value of statistical life (VSL) approach in calculating the core estimate of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA

- SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people. For a period of time (2004

– 2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a 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 $5.5 million (2000$) 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 SAB or other peer
- review group.*** E.O. 12866 Review

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### Table 5

- 9

. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a

Health Endpoint Central Estimate of Value Per Statistical Inc idence Derivation of Distributions of Estimates 1990 Income Level 2020 Income Level Premature Mortality (Value of a Statistical Life) $ 8 , 0 00 ,000 $ 9 , 6 00 ,000 The EPA currently recommends a central VSL of $ 4

. 8m ( 1990 $ , 1990 income ) 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 the EPA ’ s Guidelin es for Preparing Economic Analyses (U.S. EPA, 2010 e ). Nonfatal Myocardial Infarction (heart attack) 3% discount rat e Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55

– 64 Age 65 and over

7% discount rate Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55

– 6 4 Age 65 and over $ 98 ,000 $ 110 ,000 $ 120 ,000 $ 200 ,000 $ 98 ,000

$ 97 ,000 $ 110 ,000 $ 110 ,000 $ 190 ,000 $ 97 ,000 $98,000 $110,000 $120,000 $200,000 $98,000

$97,000 $110,000 $110,000 $190,000 $97,000 No distributional information available. Age

- specific cost

- of

- illness

values reflect lost earnings and direct medical costs over a 5

- year period following a nonfatal MI. Lost earnings estimates are based on 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 2000$ : age of onset: at 3% at 7% 25

– 44 $ 9,000 $ 8,000 45

– 54 $ 13,000 $12,000 55

– 65 $ 77,000 $69,000 Direct medical expenses (2000$) : An average of: 1. Wittels et al. (1990) ($ 100 , 000

— no discounting) 2. Russell et al. (1998), 5

- year period ($22, 000 at 3% discount rate; $21, 000 at 7% discount rate) (continued)


### Table 5

- 9

. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a 

### (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Hospital Admissions Chronic Lung Disease (18

– 64) $21,000 $21,000 No distributional information available. The COI estimates (lost*** E.O. 12866 Review

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1 - 81 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 chronic lung illnesses) reported i n Agency for Healthcare Research and Quality (2007) (www.ahrq.gov). Asthma Admissions (0

– 64) $ 16 ,000 $ 16 ,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 asthma category illnesses) reported in Agency for Healthcare Research and Quality (200 7 ) (www.ahrq.gov). All Cardiovascular Age 18

– 64 Age 65

– 99 $ 42 ,000 $ 41 , 000 $ 42 ,000 $ 41 ,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 (200 7 ) (www.ahrq.gov). All respiratory (ages 65+) $ 36 ,000 $ 36 ,0 00 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 respiratory category illnesses) reported in Agency for Healthcare Research and Quality, 200 7 (www.ahrq.gov). Emerg ency Department Visits for Asthma $ 430 $ 430 No distributional information available. Simple average of two unit COI values (2000$) : (1) $ 310 , from Smith et al. (1997) and (2) $260, from Stanford et al. (1999). (continued)*** E.O. 12866 Review

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### Table 5

- 9

. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a 

### (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) $ 31 $ 33 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 seve n types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43 (2000$)

. Lower Respiratory Symptoms (LRS) $ 20 $ 21 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 additivit y 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 un iform distribution between $6.9 and $ 25 (2000$)

. Asthma Exacerbations $ 54 $ 58 Asthma exacerbations are valued at $45 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 i s moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $16 and $7 1 (2000$)

. (continued)*** E.O. 12866 Review

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### Table 5

- 9

. Unit Values for Economic Valuation of Health Endpoints ( 2010$ ) a 

### (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2020 Income Level Respiratory Ailments Not Requiring Hospitalization (continued) Acute Bronchitis $ 450 $ 480 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 $10 is the sum of the mid

- ra nge values recommended by IEc (1994) 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

- activ ity day, or $110 (2000$)

. Work Loss Days (WLDs) Variable (U.S. median = $ 1 5 0 ) Variable (U.S. median = $ 1 5 0 ) No distribution available. Point estimate is based on county

- specific median annual wages divided by 52 and then by 5

— to get median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. (Geolytics, 2002) Minor Restricted Activity Days (MRADs) $ 64 $ 68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be triangular with a minimum of $22 and a maxim um of $83, with a most likely value of $52 (2000$) . 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 $16.00) 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.

a All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Ap pendi x J of the BenMAP user manual ( Abt Associates, 2012 )

.ES

- 1

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 the EPA and the SAB on combining estimates f rom the various data sources. In addition, the Agency consulted several times with the 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 techni ques 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) 1 wh ile 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 s tudies is $4.8 million (1990$) or $6.3 million (2000$). 2 The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB

- EEAC ’ s specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper on “ Valuing Mortality Risk Reductions in Environmental Policy, ” which recently underwent review by the SAB

- EEAC . A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA

- SAB, 2011). Draft guidance responding to SAB recommendations will be de veloped shortly.
The economics literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of con tinuing discussion within the economics and public policy analysis community. The EPA strives to use the best economic science in its analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy and Viscusi, 2008) , we use a single VSL for all reductions in mortality risk.

1 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), 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. 2 In this analysis, we adjust the VSL to account for a different cur rency year ( 2010$ ) and to account for income growth to 2020 . After applying these adjustments to the $6.3 million value, the VSL is $8. 9 M.*** E.O. 12866 Review

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ES

- 2 Although there are several differences between the labor market studies the EPA uses to derive a VSL estima te and the PM 2.5 air pollution context addressed here, those differences in the affected populations and the nature of the risks imply both upward and downward adjustments. Table 5

- 1 0 lists some of these differences and the expected effect on the VSL estim ate for air pollution

- related mortality. In the absence of a comprehensive and balanced set of adjustment factors, the EPA believes it is reasonable to continue to use the $4.8 million (1990$) value adjusted for inflation and income growth over time while acknowledging the significant limitations and uncertainties in the available literature.
Table 5

- 1 0

. Influence of Applied VSL Attributes on the Size of the Economic Benefits of

Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

Attribute Expected Direction of Bias Age Uncertain, perhaps overestimate Life Expectancy/Health Status Uncertain, perhaps overestimate Attitudes Toward Risk Underestimate Income Uncertain Voluntary vs. Involuntary Uncertain, perhaps underestimate Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

The SAB

- EEAC has reviewed many potential VSL adjustments and the state of the economics literature. The SAB

- EEAC advised the EPA to “continue to use a wage

- risk

- based VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be made is the timing of the risk” (U.S. EPA

- SAB, 2000). In developing our core estimate of the be nefits of premature mortality reductions, we have followed this advice.
For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of changes in health effects. We assumed for this analysis that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure and discounted over the period between exposure and premature mortality. Although the structure of the lag is uncertain, the 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 PM 2.5 (U.S. EPA

- SAB, 2004c). Additio nal cessation lag structures are described and assessed in Appendix 5.A of this RIA. To take this into account in the valuation of reductions in premature mortality, we discount the value of premature mortality occurring in*** E.O. 12866 Review

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- 3 future years using rates of 3% a nd 7%. 3 Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. Further discussion of this topic appears in the EPA ’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).
Uncertainties Specific to Premature Mortality Valuation

. The economic benefits associated with reductions in the risk of premature mortality are the largest category of monetized benefits in this RIA . In addition, in prior analyses, the EPA has identified valuation of mortality

- related benefits as the largest co ntributor to the range of uncertainty in monetized benefits (Mansfield et al., 2009). 4 Because of the uncertainty in estimates of the value of reducing premature mortality risk, it is important to adequately characterize and understand the various types of economic approaches available for valuing reductions in mortality risk. Such an assessment also requires an understanding of how alternative valuation approaches reflect that some individuals may be more susceptible to air pollution

- induced mortality or

r eflect differences in the nature of the risk presented by air pollution relative to the risks studied in the relevant economics literature.
The health science literature on air pollution indicates that several human characteristics affect the degree to whi ch mortality risk affects an individual. For example, some age groups appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health status prior to exposure also affects susceptibility. An ideal benefits estimate of mo rtality risk reduction would reflect these human characteristics, in addition to an individual ’ s WTP to improve one ’ s own chances of survival plus WTP to improve other individuals ’ survival rates. The ideal measure would also take into account the specific nature of the risk reduction commodity that is provided to individuals, as well as the context in which risk is reduced. To measure this value, it is important to assess how reductions in air pollution reduce the risk of dying from the time that reduction s take effect onward and how individuals value these changes. Each individual ’ s survival curve, or the probability of surviving beyond a

3 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the fe deral government. To comply with Circular A

- 4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “ social rate of time preference ” discounting concept. A 7% rate is consistent with an “ opport unity cost of capital ” concept to reflect the time value of resources directed to meet regulatory requirements. 4 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect estimates and economic valuati on estimates. Additional sources of model error such as those examined in the PM 2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative contribution of sources of uncertainty.*** E.O. 12866 Review

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- 4 given age, should shift as a result of an environmental quality improvement. For example, changing the current probabil ity of survival for an individual also shifts future probabilities of that individual ’ s survival. This probability shift will differ across individuals because survival curves depend on such characteristics as age, health state, and the current age to whic h the individual is likely to survive.
Although a survival curve approach provides a theoretically preferred method for valuing the benefits of reduced risk of premature mortality associated with reducing air pollution, the approach requires a great deal o f data to implement. The economic valuation literature does not yet include good estimates of the value of this risk reduction commodity. As a result, in this study we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across

- study variation: There is considerable uncertainty as to whether the available literature on VSL provides adequate estimates of the VSL for risk reductions from air pollution reductio n. Although there is considerable variation in the analytical designs and data used in the existing literature, the majority of the studies involve the value of risks to a middle

- aged working population. Most of the studies examine differences in wages of risky occupations, using a hedonic wage approach. Certain characteristics of both the population affected and the mortality risk facing that population are believed to affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP based on the current VSL literature for valuing the mortality

- related benefits of reductions in air pollution concentrations therefore depends not only on the quality of the studies (i.e., how well they measure what they are trying to measure), but also on the extent to which the risks being valued are similar and the extent to which the subjects in the studies are similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage

- risk studies to the context of the PM NAAQS analysis rests on the assumption that, within a reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For example, suppose a study provides a result that the average WT P for a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a reduc tion of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). Under the assumption of linearity, the estimate of the VSL does not depend on the particular amount of risk reduction being valued. This assumption has been shown to be reasonable provided the change in the risk being valued is within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998).*** E.O. 12866 Review

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- 5  Voluntariness of risks evaluated: Although job

- related mortality risks may differ in several ways from air pollution

- related mortality risks, the most important difference may be that job

- related risks are incurred voluntarily, or generally assumed to be, whereas air pollution

- related risks are incurred involuntarily. S ome evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., Lichtenstein and Slovic, 2006) . If this is the case, WTP estimates based on wage

- risk studies may understate WTP to reduce involun tarily incurred air pollution

- related mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of the risk may be that some workplace mortality risks tend to involve sudden, catastrophic events, whereas air pollut ion

- related risks tend to involve longer periods of disease and suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted death involving prolonged suffering and loss of dignity and personal control is greater than the WTP t o avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 2005; Alberini and Scasny, 2011) . To the extent that the mortality risks addressed in this assessment are associated with longer periods of illness or greater pain and suffering t han are the risks addressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.

 Self

- selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) suggests that VSL estimates based on hedonic wage studies may overstate the average value of a risk reduction. This is based on the fact that the risk

- wage trade

off revealed in hedonic studies reflects the preferences of the marginal worker (i.e., that worker who demands t he highest compensation for his risk reduction for a given job). This worker must have either a higher workplace risk than the average worker in a given occupation, a lower risk tolerance than the average worker in that occupation, or both. Conversely, the marginal worker should have a higher risk tolerance than workers employed in less

- risky sectors. However, the risk estimate used in hedonic studies is generally based on average risk, so the VSL may be biased, in an ambiguous direction, because the wage d ifferential and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) finds that because individuals reevaluate their baseline risk of death as they age, the marginal value of risk reductions does not decline with age as predicted by some lifetime consumption models. This research supports findings in recent stated preference studies that suggest only small reductions in the value of mortality risk reductions with increasing age (e.g., Alberini et al., 2004)

. 5.6.6.2 Nonfatal Myocardial Infarctions Valuation We were not able to identify a suitable WTP value for reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two components: the direct medical costs*** E.O. 12866 Review

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- 6 and the opportu nity cost (lost earnings) associated with the illness event. Because the costs associated with a myocardial infarction extend beyond the initial event itself, we consider costs incurred over several years. Using age

- specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for someone between the ages of 55 and 65. The rounded corresponding age

- specific estimates of lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates for these age groups.
We found three possible sources in the literature of estimates of the direct medical costs of m yocardial infarction, which provide significantly different values (see Table 5

- 1 1 ):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hos pital and survived hospitalization. (There does not appear to be any discounting used.) This estimated cost is based on a medical cost model, which incorporated therapeutic options, projected outcomes, and prices (using “ knowledgeable cardiologists ” as consultants). The model used medical data and medical decision algorithms to estimate the probabilities of certain events and/or medical procedures being used. The authors note that the average length of hospitalization for acute myocardial infarction has decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their study. It is unclear how much further the length of stay for myocardial infarction may have decreased from 1983 to the present. The average length of stay for ICD code 410 (myocardial infarction) in the year

- 2000 Agency for Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days ( AHRQ , 2000) . However, this may include patients who died in the hospital (not included among our nonfatal myocardial infarction cases), and whose length of stay was therefore substantially shorter than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10

- year costs of $45,000 in rounded 1997$ (using a 3% disc ount rate) for myocardial infarction patients, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were included.*** E.O. 12866 Review

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- 7 Table 5

- 11. Alternative Direct Medical Cost of Illness Esti mates for Nonfatal Heart Attacks a

Study Direct Medical Costs (2010$) Over an x

- Year Period, for x = Wittels et al. (1990) $1 6 0,000 b 5 Russell et al. (1998) $3 3 ,000 c 5 Average (5

- year) costs $ 98 ,000 5 Eisenstein et al. (2001) $ 7 4,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual ( Abt Associates, 2012 ).
b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c Using a 3% discount rate. Discounted values as reported in the study. As noted above, the estimates from these three studies are substantially different, and we have not adequately resolved the sources of differences in the estimates. Because the wage
- related opportunity cos t estimates from Cropper and Krupnick (1990) cover a 5

- year period, we used estimates for medical costs that similarly cover a 5

- year period (i.e., estimates from Wittels et al. (1990) and Russell et al. (1998). We used a simple average of the two 5

- year
e stimates, or rounded to $ 85 ,000, and added it to the 5

- year opportunity cost estimate. The resulting estimates are given in Table 5

- 1 2

. Table 5

- 1 2

. Estimated Costs Over a 5

- Year Period of a Nonfatal Myocardial Infarction (in

2010$ ) a

Age Group Opportunity Cost Medical Cost b Total Cost
0

– 24 $0 $ 98 ,000 $ 98 ,000 25

– 44 $1 2 ,000 c $ 98 ,000 $ 110 ,000 45

– 54 $ 17 ,000 c $ 98 ,000 $1 20 ,000 55

– 65 $ 100 ,000 c $ 98 ,000 $ 200 ,000 > 65 $0 $ 98 ,000 $ 98 ,000

a All estimates rounded to two significant digits , so estimates may not sum across columns . Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual ( Abt Associates, 2012 ).
b An average of the 5

- year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c From Cropper and Krupnick (1990), using a 3% discount rate for illustration

. Hospital Admissions and Emergency Department Valuation In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific illnesses, we derive COI estimates for use in the benefits analys is. The International Classification of Diseases (WHO, 1977) code

- specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital*** E.O. 12866 Review

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- 8 (based on the average length of a hospital stay for the illness). We based all estimates of hospital charges and length of stays on statistics provided by the Agency for Healthcare Research and Quality ’ s Healthcare Utilization Project National Inpatient Sample (NIS) database ( AHRQ , 2007). We estim ated the opportunity cost of a day spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized individual is in the workforce. To estimate the lost daily wage, we divided the median weekly wage reported by the 2007 Ame rican Community Survey (ACS) by five and deflated the result to year 2010$ using the CPI

- U

“ all items ” ( Abt Associates, 2012 ). The resulting national average lost daily wage is $1 48 . The total cost

- of

- illness estimate for an ICD code

- specific hospital stay lasting n days, then, was the mean hospital charge plus $1 48 multiplied by n. In general, the mean length of stay has decreased since the 2000 database used in previous version of BenMAP while the mean hospital charge has increased. We provide the rounded unit values in 2 01 0$ for the COI functions used in this analysis in Table 5

- 1 3

. Table 5

- 1 3

. Unit Values for Hospital Admissions

End Point ICD Codes Age Range Mean Hospital Charge (20 1 0$) Mean Length

of Stay (days) Total Cost of Illness

(unit value in 20 1 0$) min. max. HA, Chronic Lung Disease 490

– 496 18 64 $19,000 3.9 $21,000 HA, Asthma 493 0 64 $14,000 3.0 $16,000 HA, All Cardiovascular 390

– 429 18 64 $ 40 ,000 4.1 $ 42 ,000 HA, All Cardiovascular 390

– 429 65 99 $ 37 ,000 4.9 $ 41 ,000 HA, All Respiratory 460

– 519 65 99 $ 3 1,000 6.1 $ 36 ,000

* All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the BenMAP user manual ( Abt Associates, 2012 ). To value asthma emergency department visits, we used a simple average of two estimates from the health economics literature. The first estimate comes from Smith et al. (1997), who reported approximately 1.2 million asthma

- related emergency department visit s in 1987, at a total cost of $186 million (1987$). The average cost per visit that year was $155; in 2010$ , that cost was $4 64 (using the CPI

- U for medical care to adjust to 2010$ ). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma

related emergency department visit at $335, based on 1996

– 1997 data. A simple average of the two estimates yields a unit value of $3 8 8.*** E.O. 12866 Review

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- 9 Minor Restricted Activity Days Valuation No studies are reported to have estimated WTP to avoi d a minor restricted activity day. However, Neumann et al. (1994) derived an estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throa t congestion and sinusitis. This estimate of WTP to avoid a minor respiratory restricted activity day is $38 (1990$), or about $6 8 ( 2010$ ). Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely tha t most MRADs associated with ozone and PM 2.5 exposure are, in fact, minor respiratory restricted activity days. For the purpose of valuing this health endpoint, we used the estimate of mean WTP to avoid a minor respiratory restricted activity day.
Growth i n WTP Reflecting National Income Growth Over Time Our analysis accounts for expected growth in real income over time. This is a distinct concept from inflation and currency year. Economic theory argues that WTP for most goods (such as environmental protect ion) will increase if real incomes increase. There is substantial empirical evidence that the income elasticity 5 of WTP for health risk reductions is positive, although there is uncertainty about its exact value. Thus, as real income increases, the WTP for environmental improvements also increases. Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that income elas ticity is substantially less than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a slower rate than real income.
The effects of real income changes on WTP estimates can influence benefits estimates in two different ways: through real (national average) income growth between the year a WTP study was conducted and the year for which benefits are estimated, and through differences in income between study populations and the affected populations at a particular time. Th e SAB

EEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust WTP to account for cross

- sectional income differences “ because of the sensitivity of making such distinctions, and because of insufficient evidence available at present ” (U.S. EPA

- SAB, 2000). An advisory by another committee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis (SAB

- Council ) , has provided conflicting advice. While agreeing with “ the general principle that the willingnes s to pay to reduce mortality risks is likely to increase with growth in real income ” and that “ [t]he same increase should be assumed for the

5 Income elasticity is a co mmon economic measure equal to the percentage change in WTP for a 1% change in income.*** E.O. 12866 Review

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- 10 WTP for serious nonfatal health effects, ” they note that “ given the limitations and uncertainties in the available empirical evidence, the Council does not support the use of the proposed adjustments for aggregate income growth as part of the primary analysis ” (U.S. EPA

- SAB, 2004b). Until these conflicting advisories have been reconciled, the EPA will continue to adjus t valuation estimates to reflect income growth using the methods described below, while providing sensitivity analyses for alternative income growth adjustment factors.
Based on a review of the available income elasticity literature, we adjusted the valuat ion of human health benefits upward to account for projected growth in real U.S. income. Faced with a dearth of estimates of income elasticities derived from time

- series studies, we applied estimates derived from cross

- sectional studies in our analysis. Details of the procedure can be found in Kleckner and Neumann (1999). We note that the literature has evolved since the publication of this memo and that an array of newer studies identifying potentially suitable income elasticity estimates are available ( IE c , 2012). The EPA anticipates seeking an SAB review of these studies, and its approach to adjusting WTP estimates to account for changes in personal income, in 2013. As such, these newer studies have not yet been incorporated into the benefits analysis. An abbreviated description of the procedure we used to account for WTP for real income growth between 1990 and 2020 is presented below.
Reported income elasticities suggest that the severity of a health effect is a primary determinant of the strength of the relationship between changes in real income and WTP. As such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note that because of the variety of empirical so urces used in deriving the income elasticities, there may appear to be inconsistencies in the magnitudes of the income elasticities relative to the severity of the effects ( a priori one might expect that more severe outcomes would show less income elastici ty of WTP). We have not imposed any additional restrictions on the empirical estimates of income elasticity. One explanation for the seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often expressed as a short term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions are based on longer term measures of payment (such as wages or annual income). Economic theory suggests that relationships become more elastic as the length of time g rows, reflecting the ability to adjust spending over a longer time period (U.S. EPA, 2010e, p.A

- 9)

. Based on this theory, it would be expected that WTP for reducing long term risks would be more elastic than WTP for reducing short term risks. The relative magnitude of the income elasticity of WTP for visibility compared with those for health effects suggests that visibility is not as much of a necessity as health, thus,*** E.O. 12866 Review

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- 11 WTP is more elastic with respect to income. The elasticity values used to adjust estima tes of benefits in 2020 are presented in Table 5

- 1 4

. 6

Table 5

- 1 4
. Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Central Elasticity Estimate
Minor Health Effect 0.14 Severe and Chronic Health Effects 0.45
Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income growth

. In addition to elasticity estimates, projections of real gross domestic product (GDP) and populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, and Kallan, 2000). These population estimates are based on application of a cohort

- component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the years between 2000 and 2020 , we applied growth rates based on the U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010. 7 We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor ’ s (2000) for the years 2010 to 2020

. 8
Using the method outlined in Kleckner and Neumann (1999) and the population and income data described above, we calculated WTP adjustment factors for each of the elasticity estimates listed in Table 5

- 1 5

. Benefits for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor. For premature mortality, we ap plied

6 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real income (see Chapter 6)

. 7 U.S. Bureau of Economic Analysis, Table 2A

— Real Gross Domestic Product (1997) and U.S. Bureau of Economic Analysis, The Economic and Budget Outlook: An Update , Table 4

— Economic Projections for Calendar Years 1997 Through 2007 (1997) . Note that projections for 2007 to 2010 are based on aver age GDP growth rates between 1999 and 2007. 8 In previous analyses, we used the Standard and Poor ’ s projections of GDP directly. This led to an apparent discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the re lative growth rates for GDP derived from the Standard and Poor ’ s projections to the 2010 projected GDP based on the Bureau of Economic Analysis projections.*** E.O. 12866 Review

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- 12 the income adjustment factor specific to the analysis year, but we do not adjust for income growth over the 20

- year cessation lag. Our approach could underestimate the benefits for the later years of the lag.
There is some uncertainty regarding the total costs of illness in the future. Specifically, the nature of medical treatment is changing, including a shift towards more outpatient treatment. Although we adjust the COI estimates for inflation, we do not have data to project COI estimates for the c ost of treatment in the future or income growth over time, which leads to an inherent though unavoidable inconsistency between COI

- and WTP

- based estimates This approach may underpredict benefits in future years because it is likely that increases in real U.S. income would also result in increased COI (due, for example, to increases in wages paid to medical workers) and increased cost of work loss days and lost worker productivity (reflecting that if worker incomes are higher, the losses resulting from redu ced worker production would also be higher). In addition, cost

- of

- illness estimates do not include sequelae costs or pain and suffering, the value of which would likely increase in the future. To the extent that costs would be expected to increase over tim e, this increase may be partially offset by advancement in medical technology that improves the effectiveness of treatment at lower costs. For these reasons, we believe that the cost

- of

- illness estimates in this RIA may underestimate ( on net ) the total eco nomic value of avoided health impacts.
Table 5

- 1 5
. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2020 Minor Health Effect 1.07 Severe and Chronic Health Effects 1.22 Premature Mortality 1.20

a Based on elasticity values reported in Table 5

- 3, U.S. Census population projections, and projections of real GDP per capita.
Benefits Results Benefits of the Revised and Alternative Annual Primary PM 2.5 Standard

s Applying the impact and valuation functions described previously in this chapter to the estimated changes in PM 2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of acute bronchitis and hospital admissions) and the associated monetary values for those changes. Not all known PM health effects could be quantified or monetized. The monetized value of these unquantified effects is represented by adding an unknown “ B ” to the aggregate total. The estimate of total monetize d health benefits is thus*** E.O. 12866 Review

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- 13 equal to the subset of monetized PM

- related health benefits plus B, the sum of the non

monetized health benefits and welfare co
- benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefit s categories that we are unable to monetize in this analysis.

Table 5

- 16 shows the population

- weighted air quality change for the alternative standards averaged across the continental U.S. Tables 5

- 17 through 5

- 2 2 present the benefits results for the annual PM 2.5 standards. These benefits are relative to a 2020 analytical baseline reflecting attainment nationwide of the current primary PM 2.5 standards (i.e., 15/35) that includes promulgated national regulations and illustrative emission controls to sim ulate attainment with 15/35 as well as a n adjustment to NO x emission s to reflect expected reductions in mobile NO x emissions between 2020 and 2025. 9 Figure 5

- 3 graphicall y display s the total monetized benefits of the revised annual primary standard of 12 μg/m 3
, using alternative concentration

- response functions at discount rates of 3% and 7%. Figure 5

- 4 graphically displays the cumulative distribution of total monetized benefits using the 2 epidemiology

derived and the 12 expert

- derived relationships betwe en PM 2.5 and mortality for the revised standard , which provides the full range of uncertainty within and across the expert

- derived relationships

. Table 5

- 16

. Population

- Weighted Air Quality Change for the Revised and Alternative Annual

Primary PM 2.5 Standards Relative to Analytical Baseline

Standard Population

- Weighted Air Quality Change
13 μg/m 3 0. 014 μg/m 3
12 μg/m 3 0. 04 3 μg/m 3
11 μg/m 3 0. 207 μg/m 3

9 The estimates in this chapter reflect incremental emissions reductions from an analytical baselin e that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of t he revised standards in those two areas will not be realized until 2025.*** E.O. 12866 Review

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- 14 Table 5

- 1 7
. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised

and Alternative Annual Primary PM 2.5 Standard s , by Pollutant and Region in 2020 (tons) a

13 μg/m 3 12 μg/m 3 11 μg/m 3
Directly emitted PM 2.5 East 0 0 8, 200
West 0 0 160
CA 730 4,000 10, 600
SO 2 East 0 0 21,000
West 0 0 43
CA 0 0 0
NOx
East 0 0 9
West 0 0 0
CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table reflect both known and unknown controls.*** E.O. 12866 Review

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- 15 Table 5

- 1 8

. Estimated number of Avoided PM 2.5 Health Impacts for the Revised and

Alternative Annual Primary PM 2.5 Standards (Incremental to the Analytical Baseline ) a

Health Effect Revised and Alterative Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3 Avoided Mortality Krewski et al. (2009) (adult mortality age 30+) 140 460 1,500 (100

-- 190) (320

-- 590) (1,000

-- 1,900) Lepeule et al. ( 2012 ) (adult mortality age 25+) 330 1,000 3,300 (180

-- 480) (560

-- 1,500) (1,800

-- 4,800) Woodruff et al. (1997) (infant mortality) 0 1 4 (0

-- 1) (1

-- 2) (2

-- 6) Avoided Morbidity Non

- fatal heart attacks Peters et al. (2001) (age >18) 160 480 1,600 (49

-- 260) (150

-- 800) (480

-- 2,600) Pooled estimate of 4 studies (age >18) 17 52 170 (8

-- 41) (24

-- 130) (78

-- 410) Hospital admissions

— respiratory (all ages) b 31 110 380 (

- 9

-- 58) (

- 30

-- 200) (

- 100

-- 720) Hospital admissions

— cardiovascular (age > 18) 43 140 480 (20

-- 76) (66

-- 240) (230

-- 0,840) Emergency department visits for asthma ( all ages ) b 67 230 810 (

- 22

-- 140) (

- 74

-- 470) (

- 260

-- 1,600) Acute bronchitis (ages 8

– 12) b 280 870 2,700 (

- 36

-- 580) (

- 110

-- 1,800) (

- 350

-- 5,500) Lower respiratory symptoms (ages 7

– 14) 3,500 11,000 34,000 (1500

-- 5500) (4,900

-- 17,000) (15,000

-- 53,000) Upper respiratory symptoms (asthmatics ages 9

– 11) 5,100 16,000 49,000 (1300

-- 8900) (4,100

-- 28,000) (12,000

-- 86,000) Asthma exacerbation (asthmatics ages 6

– 18) 13,000 40,000 120,000 (270

-- 81000) (850

-- 250,000) (2,600

-- 770,000) Lost work days (ages 18

– 65) 22,000 71,000 230,000 (19000

-- 25000) (61,000

-- 81,000) (190,000

-- 260,000) Minor restricted

- activity days (ages 18

– 65) 130,000 420,000 1,300,000 (110,000

-- 150,000) (350,000

-- 490,000) (1,100,000

-- 1,600,000)

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025. Additional health endpoints , such as cardiovascular emerge ncy department visits, are only quantified in a sensitivity analysis in Table 5.A

- 6 because we do not yet have a valuation estimate for this endpoint.
b The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflec t the statistical power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts.*** E.O. 12866 Review

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- 16 Table 5

- 1 9

. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual P rimary

PM 2.5 Standards (Incremental to Analytical Baseline ) (Millions of 2006$, 3% discount rate ) a

Health Effect Revised and Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3 Avoided Mortality b Krewski et al. (2009) (adult mortality age 30+) $1,300 $4,000 $13,000 ($120

-- $3,500) ($370

-- $11,000) ($1,200

-- $35,000) Lepeule et al. ( 2012 ) (adult mortality age 25+) $2,900 $9,000 $29,000 ($250

-- $8,100) ($800

-- $26,000) ($2,600

-- $82,000) Woodruff et al. (1997) (infant mortality) $3.4 $11 $35 ($0.29

-- $10) ($0.91

-- $32) ($3.0

-- $100) Avoided Morbidity Non

- fatal heart attacks Peters et al. (2001) (age >18) $18 $55 $180 ($3.0

-- $46) ($9.1

-- $140) ($31

-- $460) Pooled estimate of 4 studies (age >18) $2.0 $6.0 $20.0 ($0.43

-- $6.8) ($1.3

-- $21) ($4.4

-- $68) Hospital admissions

— respiratory (all ages) c $0.86 $3.0 $11 (

- $0.22

-- $1.6) (

- $0.8

-- $5.5) (

- $2.7

-- $20) Hospital admissions

— cardiovascular (age > 18) $1.70 $5.3 $18 ($0.85

-- $2.8) ($2.7

-- $9.2) ($10

-- $32) Emergency department visits for asthma ( all ages ) $0.03 $0.10 $0.34 (

- $0.0052

-- $0.061) (

- $0.018

-- $0.21) (

- $0.063

-- $0.73) Acute bronchitis (ages 8

– 12) c $0.13 $0.42 $1.30 (

- $0.0060

-- $0.37) (

- $0.019

-- $1.2) (

- $0.059

-- $3.5) Lower respiratory symptoms (ages 7

– 14) $0.08 $0.24 $0.71 ($0.025

-- $0.10) ($0.078

-- $0.47) ($0.24

-- $1.4) Upper respiratory symptoms (asthmatics ages 9

– 11) $0.17 $0.54 $1.6 ($0.038

-- $0.42) ($0.12

-- $1.30) ($0.36

-- $4.0) Asthma exacerbation (asthmatics ages 6

– 18) $0.7 $2.30 $7.0 ($0.027

-- $5.2) ($0.085

-- $16) ($0.26

-- $49) Lost work days (ages 18

– 65) $3.3 $11 $35 ($2.90

-- $3.70) ($9.4

-- $12) ($30

-- $39) Minor restricted

- activity days (ages 18

– 65) $8.8 $29 $91 ($4.70

-- $13) ($15

-- $43) ($48

-- $140)

a All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in Table 5

- 2 or Section 5.6.5 or welfare co

- benefits noted in Chapter 6. These estimates reflect incremental emissions reductions from an analy tical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over th e SAB

- recommended 20

- year segmented lag structure.

c The negative estimates at the 5 th percentile confidence estimates for this morbidity endpoint reflects the statistical power of the study used to calculate these health impacts. These results do not sugg est that reducing air pollution results in additional health impacts.*** E.O. 12866 Review

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- 17 Table 5

- 20

. Monetized PM 2.5 Health Benefits for the Revised and Alternative Annual Primary

PM 2.5 Standards (Incremental to Analytical Baseline ) (Millions of 2006$, 7% discount rate ) a

Health Effect Revised and Alterative Annual Standards (95 th percentile confidence interval) 13 μg/m 3 12 μg/m 3 11 μg/m 3 Avoided Mortality b Krewski et al. (2009) (adult mortality age 30+) $1,100 $3,600 $11,000 ($110

-- $3,100) ($330

-- $9,800) ($1,100

-- $31,000) Lepeule et al. ( 2012 ) (adult mortality age 25+) $2,600 $8,100 $26,000 ($230

-- $7,300) ($720

-- $23,000) ($2,300

-- $74,000) Woodruff et al. (1997) (infant mortality) $3.4 $11 $35 ($0.29

-- $100) ($0.91

-- $32) ($3.0

-- $100) Avoided Morbidity Non

- fatal heart attacks Peters et al. (2001) (age >18) $18 $54 $180 ($2.8

-- $46) ($8.4

-- $140) ($28

-- $450) Pooled estimate of 4 studies (age >18) $1.9 $5.9 $20 ($0.40

-- $6.7) ($1.2

-- $20) ($4.1

-- $67) Hospital admissions

— respiratory (all ages) c $0.86 $3.0 $11 (

- $0.22

-- $1.6) (

- $0.8

-- $5.5) (

- $2.7

-- $20) Hospital admissions

— cardiovascular (age > 18) $1.70 $5.3 $18 ($0.85

-- $2.8) ($2.7

-- $9.2) ($10

-- $32) Emergency department visits for asthma ( all ages ) $0.03 $0.10 $0.34 (

- $0.0052

-- $0.061) (

- $0.018

-- $0.21) (

- $0.063

-- $0.73) Acute bronchitis (ages 8

– 12) c $0.13 $0.42 $1.3 (

- $0.0060

-- $0.37) (

- $0.019

-- $1.2) (

- $0.059

-- $3.5) Lower respiratory symptoms (ages 7

– 14) $0.08 $0.24 $0.71 ($0.025

-- $0.10) ($0.078

-- $0.47) ($0.24

-- $1.4) Upper respiratory symptoms (asthmatics ages 9

– 11) $0.17 $0.54 $1.6 ($0.038

-- $0.42) ($0.12

-- $1.30) ($0.36

-- $4.0) Asthma exacerbation (asthmatics ages 6

– 18) $0.7 $2.30 $7.0 ($0.027

-- $5.2) ($0.085

-- $16) ($0.26

-- $49) Lost work days (ages 18

– 65) $3.3 $11 $35 ($2.90

-- $3.70) ($9.4

-- $12) ($30

-- $39) Minor restricted

- activity days (ages 18

– 65) $8.8 $29 $91 ($4.70

-- $13) ($15

-- $43) ($48

-- $140)

a All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in Table 5

- 2 or Section 5.6.5 or welfare co

- benefits noted in Chapter 6. These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting ov er the SAB

- recommended 20

- year segmented lag structure.

c The negative estimates at the 5 th percentile confidence estimates for this morbidity endpoint reflects the statistical power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts.*** E.O. 12866 Review

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- 18 Table 5

- 2 1
. Total Estimated Monetized Benefits of the for Revised and Alternative Annual

Primary PM 2.5 Standards (Incremental to the Analytical Baseline ) ( b illions of 2006$) a ,b

Benefits Estimate 13 μg/m 3 12 μg/m 3 11 μg/m
Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Krewski et al. (2009)
3% discount rate $ 1.3 + B $ 4.0 +B $ 13 + B
7% discount rate $ 1.2 + B $ 3. 6 +B $ 12 + B
Economic value of avoided PM 2.5

- related morbidities and premature deaths using PM 2.5 mortality estimate from Lepeule et al. (20 12 )
3% discount rate $ 2.9 + B $ 9.1 +B $ 29 + B
7% discount rate $ 2.6 + B $ 8. 2 +B $ 26 + B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over the SAB

recommended 20

- year segmented lag structure. N ot all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co

- benefits . Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncerta in than those benefits that we were able to quantify.

b These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
Table 5

- 2 2

. Regional Breakdown of Moneti zed Benefits Results

Region Revised and Alterative Annual Standards 13 μg/m 3 12 μg/m 3 11 μg/m 3

East a 0% 0% 23 %
California b 100 % 100 % 77 %
Rest of West 0% 0% <1%

a Includes Texas and those states to the north and east. Several recent rules such as MATS and CSAPR will have substantially reduced PM 2.5 levels by 2020 in the East, thus few additional control s would be needed to reach 12 μg/m 3

. b For 12 and 13 μg/m 3
, all of the benefits occur in California because this highly populated area is where the most air quality improvement beyond the analytical baseline is needed to reach these levels. These estimates reflect incremental emissions reductions from an analytical baseline t hat gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.*** E.O. 12866 Review

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- 19 Figure 5

- 3. Estimated PM 2.5 - Related Premature Mortalities Avoided According to Epidemiology or Expert

- Derived PM 2.5 Mortality Risk Estimate for 12 μg/m 3 in 2020 a

a These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when those areas a re expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.*** E.O. 12866 Review

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- 20 Figure 5

- 4. Total Monetized Benefits Using 2 Epidemiology

- Derived and 12

- Expert Derived Relations hips Between PM 2.5 and Premature Mortality for 12 μg/m 3 in 2020 a

a These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions red uctions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025. Uncertainty in Benefits Results Mortality benefits account for 98% of total monetized benefits, in part because we are unable to quantify most of the non

- health benefits. The next largest benefit is for reductions in chronic illness (nonfatal heart attacks), although this value is more t han an order of magnitude lower than for premature mortality. Hospital admissions for respiratory and cardiovascular causes, MRADs and work loss days account for the majority of the remaining benefits. The remaining categories each account for a small perc entage of total benefit; however, they represent a large number of avoided incidences affecting many individuals. A comparison of the incidence table to the monetary benefits table reveals that there is not always a close correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For example, we estimate almost 1 , 00 0 times more work loss days would be avoided than premature mortalities, yet work loss days account*** E.O. 12866 Review

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- 21 for only a very small f raction of total monetized benefits. This reflects the fact that many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure of WTP. As such, the true value of these effects may be higher than that reported in the tables above.

PM 2.5 mortality benefits represent a substantial proportion of total monetized benefits (over 98% in this analysis), and these estimates h ave the following key assumptions and uncertainties.

1. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differential effects estimates by particle type. The PM ISA, which was twice reviewed by SAB

- CASAC, concluded that “many constituents of PM 2.5 can be linked with mul tiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is lo g
- linear without a threshold in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of prematu re mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB

- HES (U.S. EPA

- SAB, 2004c), which affects the valuation of mortality benefits at different discount rates.

Given that reductions in premature mortality dominate the size of the overall monetized benefits, more focus on uncertainty in mortality

- related benefits gives us greater confidence in our uncertainty characterization surrounding total benefits. Estimated Life Years Gained and Reduction in the Percentage of Deaths Attributable to PM 2.5 In their 2008 review of the EPA ’ s approach to estimating ozone

- related mortality benefits, NRC indicated, “ EPA should consider placing greater emphasis on reporting decreases in age

- sp ecific death rates in the relevant population and develop models for consistent*** E.O. 12866 Review

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- 22 calculation of changes in life expectancy and changes in number of deaths at all ages ” (NRC, 2008). In addition, NRC noted in an earlier report that “ [f]rom a public

- health perspective, life

years lost might be more relevant than annual number of mortality cases ” (NRC, 2002). This advice is consistent with that of the SAB

- HES, which agreed that “ ...the interpretation of mortality risk results is enhanced if estimates of lost li fe

- years can be made ” ( U.S. EPA

- SAB , 2004 a). To address these recommendations, we use simplifying assumptions to estimate the number of life years that might be gained. We also estimate the reduction in the percentage of deaths attributed to PM 2.5 resultin g from the illustrative emission reduction strategies to reach the revised annual primary standard . The EPA included similar estimates of life years gained in a previous assessment of PM 2.5 benefits (U.S. EPA, 2006a, 2011a), the latter of which was peer

re viewed by the SAB

- HES (U.S. EPA

- SAB, 2010a).
Changes in life years and changes in life expectancy at birth are frequently conflated, thus it is important to distinguish these two very different metrics at the outset. Life expectancy varies by age. CDC defi nes life expectancy as the “ average number of years of life remaining for persons who have attained a given age ” (CDC, 2011). In other words, changes in life expectancy refer to an average change for the entire population, and refer to the future. Over the past 50 years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 2001). For example, life expectancy at birth was estimated in 2007 to be 77.9 years for an average person born in the U.S., but for people surviving to age 60, es timated life expectancy is 82.5 years (i.e., 4.6 years more than life expectancy at birth) (CDC, 2011). Life years, on the other hand, measure the amount of time that an individual loses if they die before the age of their life expectancy. Life years refer to individuals, and refer to the past, e.g., when the individual has already died. If a 60

- year old individual dies, we estimate that this individual would lose about 22.5 years of life (i.e., the average population life expectancy for an individual of th is age minus this person ’ s age at death).

5.7.3.1 Estimated Life Years Gained For estimating the potential life years gained by reducing exposure to PM 2.5 in the U.S. adult population, we use the same general approach as Hubbell (2006) and Fann et al. (201 2a). We have not estimated the change in average life expectancy at birth in this RIA. Hubbell (2006) estimated that reducing exposure to PM 2.5 from air pollution regulations may result in an average gain of 15 years of life for those adults prematurely dy ing from PM 2.5 exposure. In contrast, Pope et al. (2009) estimated changes in average life expectancy at birth over a twenty year period, suggesting that reducing exposure to air pollution may increase average life expectancy at birth by approximately 7 mo nths, which was 15% of the overall increase in life*** E.O. 12866 Review

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- 23 expectancy at birth from 1980 through 2000. These results are not necessarily inconsistent because they are reporting different metrics. Because life expectancy is an average of the entire population (inc luding both those whose deaths would likely be attributed to PM exposure as well as those whose deaths would not), average life expectancy changes associated with PM exposure would be expected to always be significantly smaller than the average number of l ife years lost by an individual who is projected to die prematurely from PM exposure.
To estimate the potential distribution of life years gained for population subgroups defined by the age range at which their reduction in PM 2.5 exposure is modeled to occ ur we use standard life tables available from the CDC (2003) and the following formula:

(5

. 2)

where LE i is the average remaining life expectancy for age interval i, M i is the estimated change in number of deaths in age interval i, and n is the number of age intervals.
To get M i (the estimate the number of avoided premature deaths attributed to changes in PM 2.5 exposure in 2020), we use a health impact function that inc orporates risk coefficients estimated for the adult population in the U.S. and age

- specific mortality rates. That is, we use risk coefficients that do not vary by age, but use baseline mortality rates that do. Because mortality rates for younger population s are much lower than mortality rates for older populations, most but not all, of the avoided deaths tend to be in older populations. Table 5

- 2 3 summarizes the modeled number of life years gained by reducing PM 2.5 exposure to 12 μg/m 3 in 2020. We then calculated the average number of life years gained per avoided premature mort ality. Figure 5

- 5 shows the potential life years gained as a result of meeting a primary standard of 12 μg/m 3 in 2020, partitioned by the age when exposure reduction occurred, not necessarily age at death.
By comparing the projected age distribution of the avoided premature deaths with the age distribution of life years gained, we observed that about half of the deaths occur in populations age 75

– 99, but half of the life years wou ld occur in populations younger than 65. This is because the younger populations have the potential to lose more life years per death than older populations based on changes in PM 2.5 exposure in 2020. W e estimate that the average individual who would other wise have died prematurely from PM exposure would gain 16 additional years of life. However, this approach does not account for whether or not people who are older are more likely to be susceptible to the health effects of PM or whether that*** E.O. 12866 Review

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- 24 susceptibility was in and of itself cause d by PM exposure (for a more complete discussion of this issue, see Kunzli et al., 2001).
Table 5

- 2 3

. Sum of Life Years Gained by Age Range Attributed to the Revised Annual Primary

PM 2.5 Standard of 12 μg/m 3 in 2020 a,b

Age Range b Krewski et al. (2009) Risk Coefficient c Lepeule et al. (20 12 ) Risk Coefficient
25

– 29

— 610 30

– 34 210 470 35

– 44 550 1,200 45

– 54 1,000 2,300 55

– 64 1,600 3,500 65

– 74 1,700 3,900 75

– 84 1,300 2,900 85

– 99 560 1,300 Total life years gained 7,000 16,000 Average life years gained per individual 15.0 16.0

a Estimates rounded to two significant figures.
b Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.

c The youngest age in the population cohort of this study is 30.*** E.O. 12866 Review

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- 25 Figure 5

- 5
. Estimated Life Years Gained as a Result of the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020, Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death a

a As shown in these charts, slightly more than half of the avoided premature deaths occur in populations age 75

– 99, but slightly more than half of the avoided life years occur in populations age <65 due to the fact that the younger populations would lose more life years per death than older populations. Results would be similar for other standard levels on a percent age basis. Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.
b The youngest age in t he population cohort of this study used to estimate PM 2.5 mortality incidence is 30.

5.7.3.2 Percent of PM

- related Mortality Reduced To estimate the percentage of all

- cause mortality attributed to reduced PM 2.5 exposure in 2020 as a result of the illustrative emission reduction strategies, we use M i from the equation above, dividing the number of excess deaths estimated for each alternative standard by the total number of deaths in each county. Table 5

- 2 4 shows the reduction in all

- cause mortality attributed to reducing PM 2.5 exposure to the revised annual standard of 12 μg/m 3 in 2020. Figure 5

- 6 shows the percentage of avoided premature deaths attributed to meeting the revised primary annual standard of 12 μg/m 3 in 2020, partitioned by the age when exposure reduction occurred, not necessarily age at death. Age 30

- 34
Age 35

- 44

Age 45

- 54

Age 55

- 64 Age 65

- 74 Age 75

- 84 Age

85

- 99 Age 25

- 29 Age 30

- 34

Age 35

- 44

Age 45

- 54

Age 55

- 64 Age 65

- 74 Age 75

- 84 Age 85

- 99

Calculated using Lepeule et al. (2012) risk coefficient Calculated using Krewski et al. (2009) risk coefficient b*** E.O. 12866 Review

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- 26 Table 5

- 2 4
. Estimated Reduction in the Percentage of All

- Cause Mortality Attributed to the

Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 a,b

Age Ran ge b Krewski et al. (2009) Risk Coefficient c Lepeule et al. ( 2012 ) Risk Coefficient
25

– 29

— 0.80% 30

– 34 0.35% 0.78% 35

– 44 0.32% 0.73% 45

– 54 0.32% 0.73% 55

– 64 0.33% 0.73% 65

– 74 0.33% 0.73% 75

– 84 0.32% 0.73% 85

– 99 0.29% 0.65%

a Rounded to two significant figures. Results would be similar for other standard levels on a percentage basis. These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in C alifornia for NOx emissions reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
b Because we assume that there is a “ cessation ” lag between PM exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.

c The youngest age in the populat ion cohort of this study is 30. The relative distributions of the potential number of life years gained (Figure 5

- 5) and the estimated avoided mortalities (Figure 5

- 6) would be similar across alternative annual standards. Because reduction in PM exposure i s not associated with an immediate improvement in chronic health conditions, we assume that there is a “cessation” lag between PM exposures and the reduction in the risk of premature death. There is uncertainty regarding the specific ages at which people “ avoid” death relative to the change in exposure. While the structure of the lag is uncertain, some studies suggest that most of the premature deaths are avoided within the first 3 years after the change in exposure, while others are unable to identify

conc lusively a critical window of exposure (U.S. EPA, 2004c; Schwartz, 2008; Krewski et al. 2009). These studies did not examine whether the cessation lag was modified by either age at the time when exposure is reduced or the extent of cumulative lifetime expo sure.*** E.O. 12866 Review

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- 27 Figure 5

- 6

. Estimate of Avoided Premature Deaths Attributed to the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 , Partitioned by the Age When Exposure Reduction Occurred, Not Necessarily Age at Death a

a As shown in these charts, slightly more than half of the avoided premature deaths occur in populations age 75

- 99, but slightly more than half of the avoided life ye ars occur in populations age <65 due to the fact that the younger populations would lose more life years per death than older populations. Results would be similar for other standard levels on a percentage basis. Because we assume that there is a “ cessatio n ” lag between PM exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that people die relative to the change in exposure.

b The youngest age in the population cohort of this study is 30.
Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks In this analysis, we estimate the number of avoided PM 2.5 - related deaths occurring due to PM 2.5 reductions down to various PM 2.5 concentration benchmarks, including the Lowest Measured Level (LML) of each long

- term PM 2.5 mortality study. This analysis is one of several sensitivities that the EPA has historically performed that characterize the uncertainty associated with the PM

- mortality relationship and the economic value of reducing the risk of premature death (Roman et al., 2008; U.S. EPA, 2006a, 2011a; Mansfield, 2009).

Our review of the curr ent body of scientific literature indicates that a log

- linear no

threshold model provides the best estimate of PM

- related long

- term mortality. The PM ISA (U.S. EPA, 2009b), which was twice reviewed by the EPA ’ s Clean Air Scientific Advisory Committee (U.S. EPA

- SAB, 2009a, 2009b), concluded that the evidence supports the use of a

no
- threshold log

- linear model while also recognizing potential uncertainty about the exact

Age 30

- 34 Age 35

- 44 Age 45

- 54

Age 55

- 64

Age 65

- 74 Age 75

- 84 Age 85

- 99 Age 25

- 29 Age

30

- 34 Age 35

- 44 Age 45

- 54
Age 55

- 64

Age 65

- 74 Age 75

- 84 Age 85

- 99

Calculated using Krewski et al. (2009) risk coefficient b Calculated using Lepeule et al. (2012) risk coefficient

.*** E.O. 12866 Review

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- 28 shape of the concentration

- response function. 10 Consistent with this finding, we estimate b enefits associated with the full range of PM 2.5 exposure in conjunction with sensitivity analyses to recognize the potential uncertainty at lower concentrations. Specifically, we incorporated a LML assessment, a method the EPA has employed in several recen t RIAs (U.S. EPA, 2010g, 2011c, 2011d). In addition, we have incorporated an assessment using specific concentration benchmarks identified in the EPA ’ s Policy Assessment for Particulate Matter (U.S. EPA, 2011b).
These two approaches summarize the distribut ion of avoided PM 2.5 - related mortality impacts relative to baseline (i.e., pre

- rule) annual mean PM 2.5 levels. The LML approach compares the percentage of avoided premature deaths estimated to occur above and below the minimum observed air quality level of each long

- term cohort study we use to quantify PM. In the air quality benchmark approach, we summari ze the impacts occurring at different points in the distribution of the air quality data used in these same epidemiology studies.

O ur confidence in the est imated number of premature deaths avoided (but not in the existence of a causal relationship between PM and premature mortality) diminishes as we estimate these impacts in locations where PM 2.5 levels are below the LML. This interpretation is consistent wi th the Policy Assessment (U.S. EPA, 2011b) and advice from SAB

- CASAC during their peer review (U.S. EPA

- SAB, 2010d). As noted in the preamble to the final rule, the Policy Assessment (U.S. EPA, 2011b) concludes that the range from the 25 th to the 10 th percentile is a reasonable range of the air quality distribution below which we start to have appreciably less confidence in the magnitude of the associations observed in the epidemiological studies. In general, we are more confident in the magnitude of t he risks we estimate from simulated PM 2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies at are used to estimate the benefits. Likewise, we are less confident in the risk we estimate from simulat ed PM 2.5 concentrations that fall below the bulk of the observed data in these studies. However, there are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less , and the scienti fic evidence provides no clear dividing line

. For these reasons, we consider the LML as well as one standard deviation below the

mean 11 air quality levels when characterizing the distribution of mortality impacts. It is

10 F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mortality relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration

- Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010f). 11 A range of one standard deviation around the mean represents approximately 68 percent of normally distributed data, and, below the mean falls between the 25 th and 10 th percentiles.*** E.O. 12866 Review

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- 29 important to emphasize that “ less confidence ” does not mean “ no confidence .” In addition, while we may have less confidence in the magnitude of the risk, we still have high confidence that PM 2.5 is causally associated with risk at those lower air quality concentrations. To clarify this con cept, Figure 5

- 7 graphically displays the spectrum of confidence using illustrative concentration benchmarks from the major epidemiology studies cited in this chapter.

Figure 5

- 7
. Relationship between the Size of the PM Mortality Estimates and the PM 2.5 Concentration Observed in the Epidemiology Study Although these types of concentration benchmark analyses (e.g., LML , one standard deviation below the mean , etc. ) provide some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements Rather, the core benefits estimates reported in this RIA (i.e., those based on Krewski et al. (2009) and Lepeule et al. (2012)) are the best measures because they reflect the full range of modeled air quality concentrations associated with the emission reduction strategies. In reviewing the Policy Assessment , SAB

- CASAC confirmed t hat “ [a]lthough there is increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below which there is no risk for adverse health effects) ” (U.S. EPA

- SAB, 2010d). In addition, in reviewing the Costs and Benefits of the Cle an Air Act (U.S. EPA, 2011a), the SAB

- HES noted that

“ [t]his [no

- threshold] decision is supported by the data, which are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts using data from more recent years, during w hich time PM concentrations have fallen, continue to report strong associations with mortality ” (U.S. EPA

- SAB, 2010a). Therefore, the best estimate of benefits includes*** E.O. 12866 Review

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- 30 estimates below and above these concentration benchmarks but uncertainty is higher in t he magnitude of health benefits estimated at lower concentrations, with the lowest confidence below the LML. Estimated health impacts reflecting air quality improvements below and above these concentration benchmarks are appropriately included in the total benefits estimate. In other words, our higher confidence in the estimated benefits above these concentration benchmarks should not imply an absence of confidence in the benefits estimated below these concentration benchmarks.

We estimate that most of the avoided PM

- related impacts we estimate in this analysis occur among populations exposed at or above the LML of the Lepeule et al. (2012) study, while a majority of the impacts occur at or above the LML of the Krewski et al. (2009) study. We show the estima ted reduction in incidence of premature mortality above and below the LML or air quality benchmarks of these studies in Tables 5

- 2 5 , and we graphically display the distribution of PM 2.5 - related mortality impacts for the selected standard in Figures 5

- 8 and 5

- 9

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- 31 Table 5

- 2 5
. Estimated Reduction in Incidence of Adult Premature Mortality Occurring Above

and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies a

Revised and Alternative Standard Level Epidemiology Study Total Reduced

Mortality Incidence Allocation of Reduced Mortality Incidence

Below 1 S td

. D ev . B elow AQ M ean At or Above

1 S td

. D ev . B elow AQ M ean Below

LML At or

Above LML 13 μg/m 3 Krewski et al.

(2009) 140 79 66 6 140 (54%) (46%) (4%) (96%) Lepeule et al. (2012) 330 N/A N/A 130 200 (38%) (62%) 12 μg/m 3 Krewski et al.

(2009) 460 200 260 14 440 (43%) (57%) (3%) (97%) Lepeule et al. (2012) 1,000 N/A N/A 310 720 (30%) (70%) 11 μg/m 3 Krewski et al.

(2009) 1,500 690 770 27 1,400 (47%) (53%) (2%) (98%) Lepeule et al. (2012) 3,300 N/A N/A 1,000 2,300 (31%) (69%)

a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates may not sum across columns. One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th percentile. For Krewski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3 . For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one standard devi ation below the mean. I t is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.*** E.O. 12866 Review

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- 32 Of total PM 2.5 - Related deaths avoided for 12 μg/m 3 : 97 % occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. 70 % occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study. Figure 5

- 8

. Number of Premature PM 2.5 - related Deaths Avoided for the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mort ality Study*** E.O. 12866 Review

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- 33 Of total PM 2.5 - Related deaths avoided for 12 μg/m 3 : 97 % occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. 70 % occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study. Figure 5

- 9

. Number of Premature PM 2.5 - related Deaths Avoided for the Revised Annual Primary PM 2.5 Standard of 12 μg/m 3 in 2020 According to the Baseline Level of PM 2.5 and the Lowest Measured Air Quality Levels of Each Mortality Study When interpreting these LML and air quality benchmarks results, it is important to understand that the avoided PM 2.5 deaths are estimated to occur from PM 2.5 reductions in the baseline air quality simulation, which assumes that 15/35 is alr eady met. When simulating attainment with revised and alternative standards, we adjust the design value at each monitor exceeding the standard alternative to equal that standard and use an air quality interpolation technique to simulate the change in PM le vels surrounding that monitor. This technique tends to simulate the greatest air quality changes nearest the monitor. We estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantial ly smaller than counties and PM 2.5 concentrations vary spatially within a county. Therefore, there may be a small number of grid cells with concentrations slightly greater than 15 μg/m 3 in the gridded baseline even though all monitors could meet an annual standard of 15 μg/m 3 . In addition, some grid cells in a county can be below the level of a standard even though the highest monitor value is above that standard. Thus, emission*** E.O. 12866 Review

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- 34 reductions can lead to benefits in grid cells that are below a standard even wi thin a county with a monitor that exceeds that standard. Furthermore, our approach to simulating attainment can lead to benefits in counties that are below alternative standard. Emission reduction strategies designed to reduce PM 2.5 concentrations at a giv en monitor will frequently improve air quality in neighboring counties. In order to make a direct comparison between the benefits and costs of these emission reduction strategies, it is appropriate to include all the benefits occurring as a result of the e mission reduction strategies applied regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in counties that exceed the alternative standards because it would omit benefits attributable to emis sion reductions in exceeding counties. Figure 5

- 10 provides an illustration of this concept.

As this illustration shows, because 12km grid cells are much smaller than counties and because PM 2.5 concentrations vary within a county, there can be modeled grid cells with PM 2.5 concentrations below an standard level even when the c ounty exceeds that level. Because we model benefits using grid cells , this is a key reason why the LML graphs show benefi ts at levels below the alternative standards. In addition, emission reductions in an exceeding county can have benefits in a neighboring county that does not exceed. Figure 5

- 10. Illustration of Relative Size of County with Exceeding Monitor and Modeled G rid Cells*** E.O. 12866 Review

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- 35 While the LML of each study is important to consider when characterizing and interpreting the overall level of PM 2.5 - related benefits, as discussed earlier in this chapter, the EPA believes that both of the cohort

- based mortality estimates are suitable for use in air pollution health impact analyses. When estimating PM

- related premature deaths avoided using risk coefficients drawn from the Lepeule et al. (2012) analysis of the Harvard Six Cities and the Krewski et al. (2009) analysis of the ACS cohorts there are innumerable other attributes that may affect the size of the reported effect estimates

— including differences in population demographics, the size of the cohort, activity patterns and particle composition among others. The LML assessment p resented here provides a limited representation of one key difference between the two studies.
Additional Sensitivity Analys e s The details of these sensitivity analyses are provided in A ppendix 5 .A , and summarized here. The use of an alternate lag structure would change the PM 2.5 - related mortality benefits discounted at 3% discounted by between 10% and

– 27%; when discounted at 7%, these benefits change by between 22% and −52%. When applying higher and lower income growth adjustments, the monetary va lue of PM 2.5 - related premature mortality changes between 33% and −14%; the value of acute endpoints changes between 8% and −4%. Using the updated population projection data , the rounded estimates of total monetized benefits increases by 4. 4 % for 12 μg/m 3 . These sensitivity analysis results would be similar on a percentage basis for the alternative annual standards.

Discussion
Th e analysis in this Chapter demonstrates the potential for significant health benefits of the illustrative emission controls appli ed to simulate attainment with the revised annual primary PM 2.5 standard. We estimate that by 2020 the emissions reductions to reach the revised annual primary standard would have reduced the number of PM 2.5 - related premature mortalities and produce substantial non

- mortality benefits. This rule promises to yield significant welfare impacts as well (see Chapter 6) , though the quantification of those endpoints is absent in this RIA. Even considering the quantified and unquantified uncertainties identifi ed in this chapter, we believe that the implementing the revised annual PM 2.5 standard of 12 μg/m 3 would have substantial public health benefits that outweigh the costs (see Chapter 7)

. Inherent in any complex RIA such as this one are multiple sources of u ncertainty. Some of these we characterized through our quantification of statistical error in the concentration*** E.O. 12866 Review

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- 36 response relationships and our use of the expert elicitation

- derived PM 2.5 mortality functions. Others, including the projection of atmospheric conditions and source

- level emissions, the projection of baseline morbidity rates, incomes and technological development are unquantified. When evaluated within the context of these uncertainties, the health impact and monetized benefits estimates in this RIA can provide useful information regarding the public health benefits associated with the revised PM NAAQS.
There are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality standard nationwide based on an array of emission reduction strategies for different sources , incremental to implementation of e xisting regulations and controls needed to attain the current standards . In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. By contrast, the emi ssion reductions from implementation rules are generally for specific, well

- characterized sources, such as the recent MATS rule (U.S. EPA, 2011d). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementa tion rules. As such, emission reductions achieved under promulgated implementation rules such as MATS have been reflected in the baseline of this NAAQS analysis. S ubsequent implementation rules will be reflected in the baseline for the next PM NAAQS review

. For this reason, the benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for implementation rules.
In setting the NAAQS, the EPA considers that PM 2.5 concentrations vary over space and time. Whil e the standard is designed to limit concentrations at the highest monitor in an area, it is understood that emission controls put in place to meet the standard of the highest monitor will simultaneously result in lower PM 2.5 concentrations throughout the entire area. In fact, the Quantitative R isk and E

xposure A ssessment for Particulate Matter (U.S. EPA, 2010b) shows how different standard levels would affect the entire distribution of PM 2.5 concentrations, and thus people ’ s exposures and risk, across urb an areas. For this reason, it is inappropriate to use the NAAQS level as a bright line for health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely

. Instead, the Administrator sets the NAAQS at a level requisite t o protect public health with an*** E.O. 12866 Review

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- 37 adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature

. The risk analysis prepared in support of this PM NAAQS reported risks below these levels, while acknowledging that the confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2010 b ). While benefits occurring below the sta ndard may be somewhat more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate

. Though there are greater uncertainties at lower PM 2.5 concentrations, there is no evidence of a threshold in PM 2.5 - related health effects in the epidemiology literature. G iven that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming thresholds or other non

- linear concentration

response functions for the purposes of benefits analysis.

The estimated benefits for the revised and alternative annual standards are in addition to the substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 2011d, 2011e). Rules such as MATS and other emission reductions will have substantially

r educed ambient PM 2.5 concentrations by 2020 in the East, such that no additional controls would be needed to reach 12 μg/m 3 in the East beyond the analytical baseline

. These rules that have already been promulgated have tremendous combined benefits that ex plain why the number of avoided premature deaths associated with this NAAQS revision are smaller than were estimated in the previous PM NAAQS RIA (U.S. EPA, 2006a) for the year 2020 and even smaller than the mortality risks estimated for the current year i n the PM REA (U.S. EPA, 2010b)

. In addition, because our analytical baseline excludes benefits associated with attaining the current annual an d daily standards as well as the mobile NOx emissions anticipated by 2025, including th e benefits associated with those air quality improvements would result in higher benefits than we have estimated here.

F or the revised annual standard of 12 μg/m 3 , all of the estimated benefits occur in California because this is the only state that needs additional air quality impr ovement beyond the analytical baseline after accounting for the air quality improvements from recent rules. Because all of the monetized human health benefits are projected to occur in California, we have consider ed the cohort studies conducted in Californ ia specifically in addition to the national risk coefficients we use as our core estimates . Although we have not calculated the benefits results using these cohort studies, we provided the risk coefficients from these California cohorts to show how much the monetized benefits c ould have changed if we used these cohort s tudies . Most of the Calif ornia cohort studies report central effect estimates similar to the (nation

- wide) all

- cause mortality risk estimate we applied from Krewski et al.*** E.O. 12866 Review

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- 38 (2009) and Lepeule et al. (20 12 ) albeit with wider confidence intervals. Three cohort studies conducted in Ca lifornia indicate statistically significant higher risks than the risk estimates we applied from Lepeule et al. (2012) , and four studies showed insignificant results.
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- 56 CHAPTER 2 WELFARE CO

- BENEFITS OF THE PRIMARY STAND ARD

6

. 1 Synopsis
T he emission reductions to meet the revised primary standard will have “welfare” co

benefits in addition to human health benefits, including changes in visibility, materials damage, ecological effects from PM deposition, ecological effects from nitrogen and sulfur emissions, vegetation effects from ozone exposure, ecological effects from mercury deposition, and climate effects. 12 Despite our goal to quantify and monetize as many of the benefits as possible for the revised primary standard, the welfare co

- benefits of the revised primary standard remain unquantified and nonmonetized in this RIA due to data, methodology, and resource limitations. Specifically, we do not have air quality model runs for the regulatory baseline and the alternative standard levels that would allow us to calculate the visibili ty co

- benefits of attaining the revised primary standard even though we have a complete methodology for estimating these co

- benefits. However, using the approach described in this chapter, we provide the results of an illustrative analysis in Appendix 6.B using the 2020 base case and 2020 control case simulation s that were used to develop the air quality ratios.

6

. 2 Introduction to Welfare Benefits
Illustrative e mission reduction strategies to attain the revised and alternative annual primary standard have numerous documented effects on environmental quality that affect human welfare. The Clean Air Act define s welfare effects to include any non

- health effects, including direct damages to property, either through impacts on material structures or by soi ling of surfaces, direct economic damages in the form of lost productivity of crops and trees, indirect damages through alteration of ecosystem functions, and indirect economic damages through the loss in value of recreational experiences or the existence value of important

resources. The EPA ’ s Integrated Science Assessments for Particulate Matter (hereafter, “ PM ISA ” ) (U.S. EPA, 2009b ) , NO X /SO X — Ecological Criteria (U.S. EPA, 2008), and Ozone (U.S. EPA, 201 2a ) identify numerous physical and ecological effects known to be causally linked to these pollutants. This chapter describes these individual effects and how we would quantify and monetize them if there is enough data to do so. These welfare effects include changes in visibility, materials damage, ecological effects from PM deposition, ecological effects from

12 While we understand that “welfare” can include health and non

- health effects in the economic sense, we use the term “welfare” in this RIA in the same context as the definitions in the Clean Air Act for NAAQS. In practice, welfare benefits represent non

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- 57 nitrogen and sulfur emissions, vegetation effects from ozone exposure, ecological effects from mercury deposition, and cli mate effects.

These welfare co
- benefits are associated with reductions in emissions of specific pollutants resulting from illustrative e mission reduction strategies to attain the revised and alternative annual primary standard , not the form or intent of an y specific standard. Even though the primary standards are designed to protect against adverse effects to human health, the emission reductions have welfare co

- benefits in addition to the direct human health benefits.

The impacts of the illustrative emissi on reduction strategies can be grouped into four categories: directly emitted PM (e.g., metals, organic compounds, dust), reductions of PM 2.5 precursors (e.g., NO x , SO x , VOCs), other ancillary reductions from the illustrative emission reduction strategies (e.g., mercury and CO 2 ) , and secondary co

- pollutant formation from PM precursors (e.g., ozone from NO x and VOCs). Regardless of the category, these emission changes are anticipated to affect ambient concentrations and deposition, and consequently affect pu blic welfare. It is therefore appropriate and reasonable to include all the benefits and

co
- benefits associated with these emission reductions to provide a comprehensive understanding of the likely public impacts of attaining the revised or alternative annual standards. Table 6

- 1 shows the welfare effects associated with the various pollutants (either directly or as a precursor to secondary formation of PM or ozone) that would be reduced by the illustrative emission reduction strategies to attain the rev ised and alternative annual standard.

Based on the EPA’s previous analyses, we believe the welfare co
- benefits associated with these non

- health benefit categories could be significant (U.S. EPA, 2011b). Despite our goal to quantify and monetize as many of the benefits and co

- benefits as possible, w elfare co
benefits of the revised primary standard remain unquantified and nonmonetized in this RIA due to data, methodology, and resource limitations. Therefore, the total benefits would be larger than we have es timated

. The monetized value of these unquantified effects is represented by adding an unknown “ B, ” which includes both unmonetized health benefits and welfare co
benefits , to the aggregate total for the cost

- benefit comparison. These unquantified benefits and co

- benefits may be substantial, although the magnitude is highly uncertain. W e include a qualitative description of the anticipated welfare effects in this chapter to characterize the type and potential extent of those co
- benefits , as identified i n Table 6

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- 58 Table 6

- 1. Welfare Effects by Pollutants Potentially Affected by Attainment of the PM

NAAQS

Pollutant Atmospheric Effects Atmospheric and Deposition Effects Depo sition Effects

Visibility Impairment Vegetation

Injury (SO 2 ) Vegetation

Injury (Ozone) Materials

Damage Climate Ecosystem

Effects

— (Organics & Metals) Acidification

(freshwater) Nitrogen

Enrichment Mercury

Methylation

Direct PM 2.5    

NO x      

SO 2      
VOCs    

PM 10    Hg  
CO 2 

 = Welfare category affected by this pollutant. The remainder of this chapter is organized as follows: Section 6.3 provides a qualitative discussion of the visibility co

- benefits and describes our approach to estimat e those visibility

co
- benefits if we had the data to do so . Sections 6.4 through 6.6 provide qualitative co
- benefits for the unquantified benefits categories of materials damage, climate, and ecosystem effects

. References are provid ed in Section 6.7. Additional information regarding technical details of the visibility co
- benefits approach i s provided in Appendix 6A

. The illustrative visibility co

- benefits results for the specific modeled scenario (not the revised standard scenario) a re provided in Appendix 6.B

. Table 6

- 2

. Quantified and Unquantified Welfare Co

- Benefits

Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information*** E.O. 12866 Review

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- 59 Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Improved Environment Reduced visibility impairment Visibility in Class 1 areas in SE, SW, and CA regions a a Section 6.3 , Appendix 6.B Visibility in Class 1 areas in other regions — a Section 6.3 , Appendix 6.B Visibility in 8 cities

— a Section 6.3 , Appendix 6.B Visibility in other residential
areas — a Section 6.3 , Appendix 6.B Reduced climate effects Global climate impacts from CO 2 — — Section 6.5, SCC TSD b Climate impacts from ozone and PM — — Section 6.5, Ozone ISA, PM ISA c Other climate impacts (e.g., other GHGs, other impacts) — — Section 6.5, IPCC c Reduced effects on materials Household soiling

— — Section 6.4, PM ISA c Materials damage (e.g., corrosion, increased wear) — — Section 6.4, PM ISA c Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — Section 6.6.1, PM ISA c
Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Section 6.6.4, Ozone ISA c Reduced vegetation growth and reproduction — — Section 6.6.4, Ozone ISA b Yield and quality of commercial forest products and crops — — Section 6.6.4, Ozone ISA b,d
Damage to urban ornamental plants — — Section 6.6.4, Ozone ISA c Carbon sequestration in terrestrial ecosystems — — Ozone ISA c

Recreational demand associated with forest aesthetics — — Ozone ISA c*** E.O. 12866 Review

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- 60 (continued) Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Other non

- use effects Ozone ISA c Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf

gas exchange, community composition) — — Ozone ISA 2

Table 6

- 2. Quantified and Unquantified Welfare Co

- Benefits (Cont.)

Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Improved Environment (Cont.) Reduced effects from acid deposition Recreational fishing

— — Section 6.6.2, NO x SO x ISA b Tree mortality and decline

— — Section 6.6.2, NO x SO x ISA c Commercial fishing and forestry effects — — Section 6.6.2, NO x SO x ISA c Recreational demand in terrestrial and aquatic ecosystems — — Section 6.6.2, NO x SO x ISA c

Other non

- use effects Section 6.6.2, NO x SO x ISA c Ecosystem functions (e.g., biogeochemical cycles) — — Section 6.6.2, NO x SO x ISA c Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — Section 6.6.2, NO x SO x ISA c
Coastal eutrophication

— — Section 6.6.2, NO x SO x ISA c Recreational demand in terrestrial and estuarine ecosystems — — Section 6.6.2, NO x SO x ISA c

Other non

- use effects Section 6.6.2, NO x SO x ISA c Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — Section 6.6.2, NO x SO x ISA c*** E.O. 12866 Review

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- 61 Benefits Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — Section 6.6.2, NO x SO x ISA c Injury to vegetation from NO x exposure — — Section 6.6.2, NO x SO x ISA c Reduced ecosystem effects from exposure to methylmercury (through the role of sulfate in methylation) Effects on fish, birds, and mammals (e.g., reproductive effects) — — Section 6.2 and 6.6.3, Mercury Study RTC c,d Commercial, subsistence and recreational fishing — — Section 6.2 and 6.6.3, Mercury Study RTC c

a We quantify these co
- benefits in a n illustrative analysis using the methods discussed in this chapter for the specific modeled scenario. These results are provided in Appendix 6.B , but these results of that illustrative scenario are not an estimate of the co

- benefits for the revised primary standard

. b We assess these co
- benefits qualitative ly due to time and resource limitations for this RIA

. c We assess these co
- benefits qualitatively because we do not have sufficient confidence in available data or methods. d We assess these co
- benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.
6

. 3 Visibility Co

- Benefits Approach

6

. 3 .1 Visibility and Light Extinction Background
The illustrative emission reduction strategi es designed to attain the revised and alternative annual standards would reduce emissions of directly emitted PM 2.5 as well as precursor emissions such as NO x and SO 2 for an alternative annual standard at 11 μg/m 3

. These emission reductions would improve the level of visibility because these suspended particles and gases impair visibility by scattering and absorbing light (U.S. EPA, 2009b). Visibility is also referred to as visual air quality (VAQ) , 13 and it directly affects people ’ s enjoyment of a variety of daily activities (U.S. EPA, 2009b). Good visibility increases quality of life where individuals live and work, and where they travel for recreational activities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009b). This section discusses the economic co
- benefits associated with improved visibility as a result of emission reductions associated with the revised and alternative annual standards.

13 We use the term VAQ to refer to the visibility effects caused solely by air quality conditions, excluding fog.*** E.O. 12866 Review

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- 62 Air pollution affects light extinction, a measure of how m uch the components of the atmosphere scatter and absorb light. More light extinction means that the clarity of visual images and visual range is reduced, all else held constant

. 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, elementa l 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 (e.g., 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 6

- 1 for an illustration of the important factors affecting visibility.
According to the PM ISA, there is strong and consistent evidenc e that PM is the overwhelming source of visibility impairment in both urban and remote areas (U.S. EPA, 2009b). After reviewing all of the evidence, the PM ISA concluded that the evidence was sufficient to conclude that a causal relationship exists between PM and visibility impairment.*** E.O. 12866 Review

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- 63 Figure 6

- 1. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999)
Visibility is commonly measured as either light extinction (β ext ), which is defined as the loss of light per unit of distance in terms of inverse megameters (Mm - 1 ), or using the deciview (dv) metric, which is a logarithmic function of extinction (Pitchford and Malm, 1994). Deciviews, a unitless measure of visibility, are standardized for a reference distance in such a way that one deciview corresponds to a change of about 10% in available light. 14 Pitchford and Malm (1994) characterize a change of one deciview as “ a small but perceptible scenic change under many circumstances. ” 15 Extinc tion and deciviews are both 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. Using the relationships derived by Pitchford and Malm (1994),

14 Note that deciviews are inversely related to visual range, such that a decrease in deciviews implies an increase in visual rang e (i.e., improved visibility). Conversely, an increase in deciviews implies a decrease in visual range (i.e., decreased visibility). Deciview, in effect, is a measure of the lack of visibility. 15 An instantaneous change of less than 1 deciview (i.e., less than 10% in the light extinction budget) represents a measurable improvement in visibility but may not be perceptible to the eye. The visibility co
- benefits approach described in this chapter reflects annual average changes in visibility, which are likely made up of periods with changes less than one deciview and periods with changes exceeding one deciview. Annual averages appear to more closely correspond to the economic literature relied upon for valuation of visibility changes in this analysis.*** E.O. 12866 Review

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- 64 (6.1)
where VR denotes visual range (in kilometers) and β ext denotes light extinction (in Mm - 1 ). 16
Annual average visibility conditions (reflecting light extinction due to both anthropogenic and non
- anthropogenic sources) vary regionally across the U.S. and by season (U.S. EPA, 2009b). Particulate sulfate is the dominant source of regional haze in the eastern U.S

. (>50% of the particulate light extinction) and an important contributor to haze else where in the country (>20% of particulate light extinction) (U.S. EPA, 2009b). Particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Smoke plumes from large wildfires dominate many of the worst haze periods in the western U.S., while Asian dust only caused a few of the worst haze episodes, primarily in the more northerly regions of the west (U.S. EPA, 2009b). Higher visibility impairment levels in the Ea st are due to generally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels (U.S. EPA, 2009b). Humidity increases visibility impairment because some particles such as ammonium sulfate and ammonium nit rate absorb water and form droplets that become larger when relative humidity increases, thus resulting in increased light scattering (U.S. EPA, 2009b).
Reductions in air pollution from implementation of various programs associated with the Clean Air Act A mendments of 1990 (CAAA) provisions have resulted in substantial improvements in visibility, and will continue to do so in the future. Because trends in haze are closely associated with trends in particulate sulfate and nitrate due to the simple relationsh ip between their concentration and light extinction, visibility trends have improved as emissions of SO 2 and NO x have decreased over time due to air pollution regulations such as the Acid Rain Program (U.S. EPA, 2009b). For example, Figure 6

- 2 shows that v isual range increased nearly 50% in the eastern U.S. since 1992. 17 The EPA’s r ecent regulations such as the Cross

- State Air Pollution Rule (U.S. EPA, 2011 c ) and the Mercury and Air Toxics Standard (U.S. EPA, 2011 d ) are anticipated to reduce SO 2 emissions do wn to 2 million tons nationally, which would lead to substantial further improve ment in visibility levels in the Eastern U.S

. Calculated from light extinction efficiencies from Trijonis et al. (1987, 1988), annual average visual range under natural conditi ons in the East is estimated to be 150 km ± 45 km (i.e., 65 to 120 miles) and 230 km ± 35 km (i.e., 120 to 165 miles) in the West (Irving, 1991). Figure 6

- 2 reflects the average

16 It has been noted that, for a given deciview value, there can be many different visual ranges, depending on the other factors that affect visual range

— su ch as light angle and altitude. See Appendix 6a for more detail. 17 In Figure 6

- 2, the “ best days ” are defined as the best 20% of days, the “ mid

- range days ” are defined as the middle 20%, and the “ worst days ” are defined as the worst 20% of days (IMPROVE, 2 010).*** E.O. 12866 Review

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- 65 trends in visual ranges at select monitors in the eastern and western areas of the U.S. since 1992 using data from the IMPROVE monitoring network (U.S. EPA (2008) updated; IMPROVE (2010)). As an illustration of the improvements in visibility att ributable to the CAAA, Figure 6

- 3 depicts the modeled improvements in visibility associat ed with all the CAAA provisions in 2020 compared to a counterfactual scenario without the CAAA (U.S. EPA, 2011b). While visibility trends have improved in most National Parks, the recent data show that these areas continue to suffer from visibility impairm ent beyond natural background levels (U.S. EPA, 2009b).

Figure 6

- 2. Visibility in Selected National Parks and Wilderness Areas in the U.S., 1992

– 20 08 a,b (Source: U.S. EPA (2008) updated, IMPROVE (2010))*** E.O. 12866 Review

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- 66 Figure 6

- 3. Estimated Improvement in Annual Average Visibility Levels Associated with the CAAA Provisions in 2020 Source: U.S. EPA, 2011b 18

18 It is important to note that visibility levels shown in these maps were modeled differently than the modeling conducted for this analysis using an earlier method that we would currently use , including coarser grid resolution (i.e., 36 km instead of 12 km). In addition, please note that these maps present annual average visibility levels , which are different than the short

- term averages being considered for the secondary standard

.*** E.O. 12866 Review

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- 67 6. 3

. 3 Quantifying Light Extinction for Assessing Visibility Co

- benefits For this RIA, we do not have air quality model runs for the regulatory baseline and the

a lternative standard levels that would allow us to calculate the visibility co

- benefits of attaining the revised primary standard. However, we provide an illustrative analysis in Appendix 6.B using the 2020 base case and 2020 control case simulations that w ere used to develop the air quality ratios. 19 In our approach , we generate light extinction estimates using the CMAQ model in conjunction with the IMPROVE (Interagency Monitoring of Protected Visual Environments) algorithm that estimates light extinction as a function of PM concentrations and relative humidity levels (U.S. EPA, 2009b). 20 The procedure for calculating light extinction associated with the revised and alternative annual s tandards is described in detail in Chapter 3 of th is RIA. In addition, Appendix 6.A describes how the spatial resolution of the light extinction estimates would be adjusted in our approach

. It is important to note that the light extinction estimates used in our approach represent annual averages, which is different from the a veraging times currently being considered for the secondary PM NAAQS. While the annual averages are influenced by days with extremely impaired visibility, the light extinction data is not sufficient to provide higher temporal resolution than quarterly aver ages. While we suspect that the most impaired days would have disproportionately improved visibility from the emission reductions associated with attaining the revised or alternative primary standards, we are not able to quantify those impacts. These data gaps result in an underestimate of visibility co
- benefits associated with extreme days. We recognize that recent advice from the Science Advisory Board’s Advisory Council on Clean Air Compliance Analysis ( SAB

- Council ) recommends estimating visibility co
- be nefits considering daytime visibility on days with severe impairment (U.S. EPA

- SAB, 2010 a ), but the available data and valuation studies do not allow such fine temporal resolution.

19 These simulations are described in Chapter 3. 20 According to the PM ISA, the IMPROVE algorithm performs reasonably well despite its simplicity (U.S. EPA, 2009b).*** E.O. 12866 Review

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- 68 While our approach is a substantial improvement in the methods to estimat e light extinction nationally, we are still developing a method to estimate coarse particle concentrations for the entire continental U.S. for estimating light extinction. As an interim solution, our approach includes sensitivity analyses to show the potent ial impact of omitting coarse particles from the light extinction estimates for recreational and residential visibility . For the s e sensitivity analys e s, we selected the levels of coarse particles to represent the full range of possible annual concentration s from a recent report on the IMPROVE monitoring network (Debell et al., 2006). Specifically, for these sensitivity analyses, we assume four levels of coarse particles: no coarse particles, 5 μg/m 3 nationwide, 15 μg/m 3 in the Southwest with 5 μg/m 3 in the rest of the country, and 15 μg/m 3 in the Southwest with 8 μg/m 3 in the rest of the country. 21 In Table 6

- 3, we provide a qualitative assessment of how key assumptions in the estimation of light extinction would affect the visibility co
- benefits.
Table 6

- 3

. Key Assumptions in the Light Extinction Estimates Affecting the Visibility Co

Benefits Approach a

Key Assumption Direction of Bias Magnitude of

Effect The light extinction estimates are annual averages to correspond with the valuation studies. People may value large changes to the haziest days differently than small changes to many days. We assume that annual average light extinction is the most appropriate temporal scale for estimating visibility benefits. Potential Underestimate Medium

Coarse particles are a component of light extinction, but we were unable to include coarse particles in the light extinction estimates. We provide sensitivity analyses with up to 15 μg/m 3 in the Southwest and 8 μg/m 3 in the rest of the country. Potential Overestimate Very Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

.

21 We define “ Southwest ” for this sensitivity analysis to be the states of California, Nevada, Utah, Arizona, New Mexico, Colorado, and Texas.*** E.O. 12866 Review

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- 69 6

. 3

. 3 Visibility Valuation Overview In the Clean Air Act Amendments of 1977, the U.S. Government recognized visibility ’ s value to society by establishing a national goal to protect national parks and wilderness areas from visibility impairment caused by manmade pollution. 22 Air pollution impairs visibility in both residential and recreational settings, and an individual ’ s willingness to pay (WTP ) to improve visibility differs in these two settings. Benefits of residential visibility relate to the impact of visibility changes on an individual ’ s daily life (e.g., at home, at work, and while engaged in routine recreational activities). Benefits of r ecreational visibility relate to the impact of visibility changes manifested at parks and wilderness areas that are expected to be experienced by recreational visitors.
Both recreational and residential visibility benefits 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 birdwatching. Nonuse values are based on a belief that the environment ought to exist free of human

- induced haze. This includes the value of better visibility for use by others now and in the future (bequest value). Nonuse values may be more important for recreational areas, particularly national parks and monuments.
The relationship between a household ’ s WTP and changes in visibility can be derived from a number of contingent valuation (CV) studies published in the peer

- reviewed economics literature. The studies used in the approach to estimate the residential and recreational visibility co
- benefits ass ociated with the revised and alternative annual standards are described in the following sections. In addition to CV studies, hedonic valuation studies ( Beron et al., 2001, 2004) also demonstrate that visibility has value, but we are unable to apply these valuation estimates in the context of estimating the visibility co
- benefits associated with national regulations that reduce air pollution (Leggett and Neumann, 2004)

. 6. 3

. 3.1 Visibility Valuation Approach
In our approach , we assume that individuals value visibility for aesthetic reasons rather than viewing visibility as a proxy for other impacts associated with air pollution, such as health or ecological improvements. Some studies in the literature indicate that individuals may have difficulty distinguishi ng visibility from other aspects of air pollution (e.g., McClelland et al., 1993; Chestnut and Rowe, 1990c; Carson, Mitchell, and Rudd, 1990). Because visual air quality is inherently multi

- attribute, it is a challenge for all visibility valuation studies to isolate the

22 See Section 169(a) of the Clean Air Act.*** E.O. 12866 Review

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- 70 value of visibility from the collection of intertwined benefits. Each study used in our approach attempts to isolate visibility from other effect categories, but the different studies take different approaches (U.S. EPA, 2009b). 23 Ho wever, t he degree to which the studies were successful in convincing respondents to focus solely on visibility is unclear
Similarly, it is important to try to distinguish residential visibility from recreational visibility co
- benefits, specifically whether these can these can be treated as distinct and additive benefit categories based on the available literature. In our approach , we assume that residential and recreational visibility co
- benefits are distinct and separable. It is conceivable that respondents to the recreational visibility survey may have partially included values for their own residential visibility when evaluating changes at national parks and wilderness areas located in their region of the country. In our app roach, we take care to minimize the number of overlapping areas and their contributions. Specifically, we believe that the potential for double

counting recreational and residential visibility is minimal for several reasons. First, in our approach, we only include a subset of areas in the primary estimates of recreational and residential visibility co
- benefits, which overlap in only a few places. 24 Second, a number of the overlapping counties are wilderness areas, which would contribute little to the overall monetized visibility co

- benefits due to low visitation rates, rather than highly visited national parks. For example, Los Angeles County is home to the San Gabriel Wilderness Area, which has 10 thousand annual visitors (NPS, 2008). If we were to exclude t he residential visibility co
benefits that accrue to 10 million residents in Los Angeles County and only include the very small recreational visibility co
- benefits for the wilderness area, we would be substantially biasing the overall estimates downward. F or these reasons, we believe that the potential for double

- counting is minimal.
In the next sections, we describe the methodology and limitations of the recreational and residential visibility approach . Consistent with the health benefits analysis, the mon etized visibility co
- benefits would be adjusted for inflation and income growth. These co
- benefits would be specific to the analysis year , and as population and income increase over time, these

co
- benefits would be expected to increase each year for the sa me incremental change in light extinction.

23 See Leggett and Neumann (2004) for a more detailed discussion of this issue. 24 As described in detail in Sections 6.3.3 and 6.3.4, our approach includes only a subset of visibility co
- benefits in the m ain benefits estimates, while providing the rest of the visibility co
- benefits in sensitivity analyses.*** E.O. 12866 Review

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- 71 6

. 3

. 4 Recreational Visibility

6

. 3

. 4 .1 Methodology
Our approach for estimating recreational visibility co
- benefits is well

- establis hed and has been used in numerous analyses by the EPA (U.S. EPA, 1999; 2005 ; 20 06 ; 2010; 2011b). In our approach , recreational visibility co
- benefits apply to Class 1 areas, such as National Parks and Wilderness Areas. 25 Although other recreational settings such as National Forests, state parks, or even hiking trails or roadside areas hav e important scenic vistas, a lack of suitable economic valuation literature to identify these other areas and/or a lack of visitation data prevents us from generating estimates for those recreational vista areas.
Under the 1999 Regional Haze Rule (64 FR 35 714), states are required to set goals develop long

- term strategies to improve visibility in Class 1 areas, with the goal of achieving natural background visibility levels by 2064. In conjunction with the U.S. National Park Service (NPS), the U.S. Forest S ervice (USFS), other Federal land managers, and State organizations in the U.S., the EPA has supported visibility monitoring in national parks and wilderness areas since 1988. The monitoring network known as IMPROVE includes 156 sites that represent the Cl ass 1 areas across the country (U.S. EPA, 2009b). 26 The IMPROVE monitoring network measures fine particles, coarse particles, and key PM 2.5 constituents that affect visibility, such as sulfate, nitrate, organic and elemental carbon, soil dust, and several other elements. Figure 6

- 4 identifies where each of these parks are located in the U.S.

25 Hereafter referred to as Class 1 areas, which are defined as areas of the country such as national parks, national wilderness areas, and national monu ments that have been set aside under Section 169(a) of the Clean Air Act to receive the most stringent degree of air quality protection. Mandatory Class 1 federal lands fall under the jurisdiction of three federal agencies, the National Park Service, the F ish and Wildlife Service, and the Forest Service. EPA has designated 156 areas as mandatory Class 1 federal areas for visibility protection, including national parks that exceed 6,000 acres and wilderness areas that exceed 5,000 acres (40 CFR § 81.400 ). 26 The formula used to estimate light extinction from concentrations of PM constituents and relative humidity is referred to as the IMPROVE algorithm.*** E.O. 12866 Review

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- 72 Figure 6

- 4. Mandatory Class 1 Areas in the U.S.

F or recreational visibility, the EPA relies upon a contingent valuation (CV) survey conducted in 1988 ( Chestnut and Rowe , 199 0a; 1990b ) to estimate the recreational visibility co

benefits

. Although there are several other studies in the literature on recreational visibility valuation, they are even older and use less robust methods. In the EPA ’ s judgment , despite the inherent limitations in the survey , the Chestnut and Rowe study contains many of the elements of a valid CV study and is sufficiently reliable to serve as the basis for monetary estimates of the

co
- benefits of visibility changes in recreational areas

. E ven though this specific report has not been published in a peer

- reviewed journal , these data were subjected to peer review as part of the National Acid Precipitation Assessment Program (NAPAP) and have been applied to estimate visibility benefits in several peer

- re viewed journal articles (Chestnut and Dennis, 1997; Burtraw et al., 1998; Delucchi et al, 2002; Chestnut and Mills, 2005) , as well as several peer reviewed in reports ( NAPAP, 1991; SAMI, 2002 ; U.S. EPA, 1999b; 2011b )

. 27 This study serves as

27 In 1999 as part of The Benefits and Costs of the Clean Air Act, 1990

– 2010 (U.S. EPA, 1999b), the EPA as ked the SAB

- Council whether they concurred with the EPA’s proposed use of the WTP values from Chestnut and Rowe (1990a,b) to support the main (“primary”) benefit estimate. The SAB

- Council responded, “[t]he Council believes that EPA should rely on the peer

- reviewed literature as the basis for its regulatory analyses. Although many members of the Council believe that the Chestnut and Rowe study is the best available , we believe that the*** E.O. 12866 Review

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- 73 an essential input to our approach for estimating the co
- benefits from improving recreational visibility.

In our approach , we assume that the household WTP is higher if the Class 1 recreational area is located close to the person ’ s home (i.e., in the same region of the country). People appear to be willing to pay more for visibility improvements at parks and wilderness areas that are in the same region as their household than at those that are not in the same region as their househol d (Chestnut and Rowe, 1990a, 1990b). 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. However, studies have also found many people who had never visite d and never planned to visit the parks still had positive values for visibility improvements in those locations (Chestnut and Rowe, 1990b).
The Chestnut and Rowe survey measured the demand for visibility in Class 1 areas managed by the NPS in three broad r egions of the country: California, the Colorado Plateau (Southwest), and the Southeast. 28 Respondents in five states were asked about their WTP to protect national parks or NPS

- managed wilderness areas within a particular region. The survey used photographs reflecting different visibility levels in the specified recreational areas. The authors used the survey data to estimate household WTP values for improved visibility in each region.
The separate regions were developed to capture differences in household W TP values based on proximity to recreational areas. Chestnut (1997) also concluded that, for a given region, a substantial proportion of the WTP is attributable to one specific park within the region. This so called “ indicator park ” is the most well

- known and frequently visited park within a particular region. The indicator parks for the three studied park regions are Yosemite National

general principle should be adhered to.” ” (U. S. EPA

- SAB, 1999). In response to this comment, the EPA clarified to the SAB

- Council that the visibility valuation functions and the sources of estimates for the coefficients for the functions were originally developed as part of NAPAP and were subjected t o peer review as part of that program (Chestnut and Rowe, 1990c). The Integrated Assessment Report , including the data contained therein, received several rounds of public and peer review (NAPAP, 1991). After this clarification, the SAB

- Council identified no concerns in their final review letter with the EPA using the Chestnut and Rowe data to quantify recreational visibility benefits in the main benefits estimate (U.S. EPA

- SAB, 1999 b ). In July 2010 as part of The Benefits and Costs of t he Clean Air Act 1990 to 2020 (U.S. EPA, 2011b), the SAB

- Council stated that the studies were dated, but the EPA “used what the Council understands to be the only relevant studies” (U.S. EPA

- SAB, 2010a). In addition, the SAB

- Council identified no con cerns with quantifying recreational visibility benefits using these data in the main benefits estimate (U.S. EPA

- SAB, 2010a). 28 The Colorado Plateau (Southwest) region is defined as the states of Colorado, New Mexico, Arizona, and Utah. The Southeast regi on is defined as the states of West Virginia, Virginia, North Carolina, South Carolina, Georgia, Florida, Alabama, Mississippi, Louisiana, Tennessee, and Kentucky. The California region includes the state of California and one wilderness area in Nevada.*** E.O. 12866 Review

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- 74 Park for the California region, the Grand Canyon National Park for the Southwest region, and Shenandoah National Park for t he Southeast region. In accordance with the methodology in Chestnut (1997), our approach calculates the benefits from households for a particular region for a given change in visibility at a particular Class 1 area. In theory, summing benefits from househo lds in all regions would yield the total monetary benefits associated with a given visibility improvement at a particular park, which could then be summed with other parks and regions to estimate national benefits. Because recreational visibility benefits may reflect the value an individual places on visibility improvements regardless of whether the person plans to visit the park, all households in the U.S. are assumed to derive some benefit from improvements to Class 1 areas.

To value recreational visibili ty improvements associated with its rulemakings, the EPA developed a valuation WTP equation function based on the baseline of visibility, the magnitude of the visibility improvement, and household income. This function requires light extinction estimates measured as visual range. The behavioral parameters of this equation were taken from an analysis of the survey described in Chestnut and Rowe (1990a, 1990b). These parameters were used to calibrate WTP for the visibility changes resulting from this rule. 29 As an example, household WTP for a visibility improvement at a park in its region takes the following form:

(6.2)

where: i indexes region, k indexes park, m = household income, ρ = shape parameter (0. 1), γ = parameter corresponding to the visibility at in

- region parks,
Q 0 = starting visibility, and
Q 1 = visibility after change.

29 The parameters for each region are available in Appendix 6a of this RIA.*** E.O. 12866 Review

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- 75 As discussed in more detail in Appendix 6 .A of this RIA, our approach to valuing recreational visibility changes is an application of the Constant Elasticity of Substitution (CES) utility function approach and is based on the preference calibration method developed by Smith, Van Houtven, and Pattanayak (2002). 30 Ava ilable evidence indicates that households are willing to pay more for a given visibility improvement as their income increases (Chestnut, 1997). Using the income elasticity calculated by Chestnut (1997), the recreational visibility benefits assume a 1% inc rease in income is associated with a 0.9% increase in WTP for a given change in visibility. WTP responses reported in Chestnut and Rowe (1990a, 1990b) were also region

- specific, rather than park

- specific. As visibility improvements are not constant across all parks in a region, we must infer park

- specific visibility parameters in order to calculate WTP for projected visibility changes. As the quantity and quality of parks differs between regions, we apportion the regional WTP parameters based on relative vi sitation rates at the different parks, because this statistic likely captures both park quality (more people visit parks with more desirable attributes, so collective WTP is likely higher) and quantity (more people visit parks in a region if the parks are more numerous, so collective WTP is likely higher). 31 We also adjust the

co
- benefits for inflation and growth in real income.
Recreational visibility co
- benefits can be calculated as the sum of the household WTPs for changes in light extinction. We assume t hat each household is valuing the first or only visibility change that occurs in a particular area. The co
- benefits at particular areas can be calculated by assuming that the subset of visibility changes of interest is the first or the only set of changes being valued by households. Estimating benefit components in this way will yield slightly upwardly biased estimates of co
- benefits , because disposable in come is not reduced by the WTPs for any prior visibility improvements. The upward bias should be extremely small, however, because all of the WTPs for visibility changes are very small relative to income.
In our approach, t he primary estimate for recreatio nal visibility only includes co
- benefits for 86 Class 1 areas in the original study regions (i.e., California, the Southwest, and the

30 The Constant Elasticity of Substitution utility function has been chosen for use in this analysis due to its flexibility when illustrating the degree of substitutability present in various economic relationships (in this case, the tradeoff between income and improvements in visibility). 31 We use 2008 park visitation data from the National Park Service Statistical Abstracts (NPS, 2008), as this is the most current data available. Whe re the data for a particular park was not representative of normal visitation rates at that park (for example due to fire damage that occurred during that year), we substitute data from the prior year. We use 1997 visitation data for those wilderness areas not included in the National Park Service Statistical Abstracts, as more current data is not readily available. As visitation rates for Wilderness Areas are small compared to visitation rates in National Parks, the inaccuracies generated by using 1997 dat a are likely to also be small.*** E.O. 12866 Review

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- 76 Southeast). 32 These co
- benefits reflect the value to households living in the same region as the Class 1 area as well as va lues for all households in the United States living outside the state containing the Class 1 area. The Chestnut and Rowe study did not measure values for visibility improvement in Class 1 areas in the Northwest, Northern Rockies, and Rest of U.S. regions. 33 In order to obtain estimates of WTP for visibility changes for the 70 additional Class 1 areas in these non
- studied regions, we have to transfer the WTP values from the studied regions. 34 This co
- benefits transfer approach introduces additional uncertainty into the estimates. However, we have taken steps to adjust the WTP values to account for the possibility that a visibility improvement in parks within one region may not necessarily represent the same vi sibility improvement at parks within a different region in terms of environmental improvement. This may be due to differences in the scenic vistas at different parks, uniqueness of the parks, or other factors, such as public familiarity with the park resou rce. To account for this potential difference, we adjusted the transferred WTP being transferred by the ratio of visitor days in the two regions. 35 A complete description of the co
- benefits transfer method used to infer values for visibility changes in Clas s 1 areas outside the study regions is provided in Appendix 6a of this RIA.

Table 6

- 4 indicates which studied park regions we used to estimate the value in the non

studied park regions in our approach

. Figure 6

- 5 shows how the visitation rates vary across Class 1 areas and regions and indicates whether each Class 1 area is located within one of the studied regions.
Table 6

- 4. WTP for Visibility Improvements in Class 1 Areas in Non

- Studied Park Regions
Park Region Source of WTP Estimate 1. Northwest Benefits transfer from California 2. Northern Rockies Benefits transfer from Colorado Plateau 3. Rest of U.S. Benefits transfer from Southeast

32 The 86 Class 1 areas in the three studied park regions represented 68% of the total visitor days to Class 1 areas in 2008 (NPS, 2008). 33 The Northwest region is defined as the states of Washington and Oregon. The Northern Rockies region include s the states of Idaho, Montana, Wyoming, North Dakota, and South Dakota. The Rest of the U.S. region includes all other states not included in the other 5 regions. 34 The 70 additional Class 1 areas represented 32% of the total visito r days to Class 1 areas in 2008 (NPS, 2008). 35 For example, if total park visitation in a transfer region was less than visitation in a study region, transferred WTP would be adjusted downward by the ratio of the two.*** E.O. 12866 Review

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- 77 Figure 6

- 5. Visitation Rates and Park Regions for Class 1 Areas a

a The colors in this map correspond to the park regions used in the valuation study and the extrapolation to parks in other regions. Red = California, light red = Northwest (extrapolated from California), blue = Colorado Plateau, light blue = Northern Rockies ( extrapolated from Colorado Plateau), green = Southeast, light green = Rest of U.S. (extrapolated from Southeast). In a more recent study, Smith et al. (2005) conducted a contingent valuation survey that updated and expanded a portion of the 1988 survey by Chestnut and Rowe (1990). Specifically, the Smith et al. (2005) survey relied on a panel maintained by Knowledge Networks with 2,020 participants completing the survey. Similar to the Chestnut and Rowe survey, the Smith et al. survey assessed WTP for changes in summertime visibility using the base photograph of Shenandoah National Park. Unlike the Chestnut and Rowe survey, t he Smith et al. survey only assessed the Shenandoah National Park, did not estimate in

- region estimates of WTP, and evaluated several options for incorporating budgetary constraints into the survey. The authors concluded that WTP for recreational visibilit y is skewed and sensitive to information about budgetary constraints. We are still evaluating the potential error identified by Smith et al. (2005) regarding the visibility levels in the photographs for Shenandoah National Park in the Chestnut and Rowe sur vey (1990a,b).
Even though this survey represents several advantages over the older survey (e.g., more recent, national, demographically representative, larger sample, etc

. ), we are unable ys*** E.O. 12866 Review

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- 78 incorporate the results generated by this survey into our existing method for calculating recreational visibility co
- benefits because the survey did not account for the differences between WTP for in

- region parks and out

- of

- region parks. This omission precludes us from combining this new survey for only 1 region of the co untry with the WTP for the other regions of the country from Chestnut and Rowe (1990). Furthermore, Smith et al. (2005) provide a variety of WTP estimates reflecting different versions of the survey and different methods of summarizing the typical response , which makes it difficult to select estimates to incorporate into the recreational visibility benefits calculation.

6

. 3

. 4 .2 Recreational Visibility Limitations, Caveats, and Uncertainties Our approach relies upon several data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), relative humidity measurements, park information, economic data and assumptions for monetizing co
- benefits

. Each of these inputs may contain uncertainty that would affect the recreational visibility co
benefits estimates. Though we are unable to quantify the cumulative effect of all of these uncertainties in our approach , we do provide information on uncertainty based on the available data, including model eva luation 36 and sensitivity analyses to characterize major omissions ( i.e., benefits from parks in non

- studied park regions and inclusion of coarse particles). Although we strive to incorporate as many quantitative assessments of uncertainty as possible, we a re severely limited by the available data, and there are several aspects that we are only able to address qualitatively. A summary of the key assumptions including direction and magnitude of bias is provided in Table 6

- 5

. One major source of uncertainty for the estimation of recreational visibility co
- benefits is the benefits transfer process. Choices regarding the functional form and key parameters of the estimating equation for WTP for the affected population could have s ignificant effects on the magnitude of the estimates. Assumptions about how individuals respond to changes in visibility that are either very small or outside the range covered in the Chestnut and Rowe study could also affect the estimates.
Table 6

- 5
. Summary of Key Assumptions in Estimating R ecreational Visibility Co

- benefits a

Key Assumption Direction of Bias Potential

Magnitude of Effect

36 See Chapter 4 for more information on model evaluation.*** E.O. 12866 Review

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- 79 Chestnut and Rowe study covers parks in three regions: California, Southwest, and Southeast. Benefits to other reg ions in the U.S. are not included in the primary benefits estimate. Underestimate Medium

Benefits to other recreational settings, such as National Forests and state parks, are not included in our approach

. Underestimate Medium

- Low

Chestnut and Rowe study conducted on populations in five states. These results are applied to the entire U.S. population. Unclear Unclear

Individuals have a greater WTP for visibility changes in parks within their region. Unclear Unclear
WTP values reflect only visibili ty improvements and not overall air quality improvements. Potential Overestimate Unclear
We assume that there are 2.68 people per household. Because this estimate has been decreasing over time, this may underestimate the number of households. Potential Underestimate Medium

- Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

. Since the valuation of recreational visibility co
- benefits relies upon one study (Chestnut and Rowe, 1990a; 1990b), all of the uncertainties within that study also pertain to any analysis that uses it

. In general, the survey design and implementation reflect the period in which the Chestnut and Rowe study was con ducted. Since that time, many improvements to the design of stated preference surveys have been developed (e.g., Arrow, 1993) , but we are currently unaware of newer studies that we could incorporate into our visibility co
- benefits methodology

. Although Che stnut and Rowe still offers the best available WTP estimates, the study has a number of limitations, including:

 The vintage of the survey (late 1980s) invites questions whether the values would still be valid for current populations, or more importantly fo r our approach , future populations in 2020.

 The survey focused on visibility improvements in and around specific national parks and wilderness areas. Given that national parks and wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate obtained from this survey can be transferred to other national parks and wilderness areas, even other parks within the studied park regions, without introducing additional uncertainty.

 The survey focused only on populations in five states, so the application of the estimated values to populations outside those states requires that preferences of populations in the five surveyed states be similar to those of non

- surveyed states.*** E.O. 12866 Review

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- 80  There is an inherent difficulty in separating values expressed for visibility improvements from an overall value for improved air quality. The survey attempted to control for this by informing respondents that “ other households are being asked about visibility, human health, and vegetation protections in urban areas and at national parks in other regions. ” However, most of the respondents did not feel that they were able to segregate recreational visibility at national parks entirely from residential visibility and health effects.

 It is not clear exactly what visibility i mprovements the respondents to the survey were valuing. The WTP question asked about changes in average visibility, but the survey respondents were shown photographs of only daytime, summer conditions, when visibility is generally at its worst. It is possi ble that the respondents believed those visibility conditions held year

- round, in which case they would have been valuing much larger overall improvements in visibility than what otherwise would be the case. In our approach , the EPA assumed that respondent s provided values for changes in annual average visibility. Because most policies would result in a shift in the distribution of visibility (usually affecting the worst days more than the best days), the annual average may not be the most relevant metric f or policy analysis.

 The survey did not include reminders of possible substitutes (e.g., visibility at other parks) or budget constraints. These reminders are considered to be best practice for stated preference surveys.

6

. 3

. 5 Residential Visibility

6

. 3

. 5 .1 Methodology Residential visibility co
- benefits are those that occur from visibility changes in urban, suburban, and rural areas where people live. These co
- benefits are important because s ome people living in certain urban areas may place a high value on unique scenic resources in or near these areas that are outside of Class 1 areas. For example, the State of Colorado established a local visibility standard for the Denver metropolitan area in 1990 (Ely et al., 1991). In our approach , residential visibilit y improvements are defined as those that occur specifically in Metropo litan Statistical Areas (MSAs).

In the Urban

-

focused Visibility Assessment (U.S. EPA, 2010b) and the Policy Assessment for the Review of the PM NAAQS (U.S. EPA, 2011a), several preference studies provide the foundation for the secondary PM NAAQS. 37 The three completed survey studies (all in the west) included Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near Vancouver, Briti sh Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC

37 For more detail about these preference studies, including information about study designs and sampling protocols, please see Section 2 of the Particulate Matter Urban

- Focused Visibility Assessment (U.S. EPA, 2010b).*** E.O. 12866 Review

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- 81 Research & Consulting, 2003). A pilot focus group study was conducted in Washington, DC on behalf of the EPA to inform the 2006 PM NAAQS review (Abt Associates Inc., 2001). Although t hese studies indicate that some individuals considered the visual air quality associated with ambient levels of air pollution in urban areas to be unacceptable, the se studies do not provide sufficient information on which to develop monetized co
- benefits e stimates. Specifically, the public perception studies do not provide preferences expressed in dollar values, even though they do suggest that the co
- benefits associated with improving residential visibility are positive

. Studies in the peer

- reviewed litera ture support a non

- zero value for residential visibility ( e.g., Brookshire et al., 1982; Loehman et al., 1994). Furthermore, Chestnut and Rowe (1990c) conclude that residential visibility co
- benefits are likely to be at least as high as recreational visibi lity co
- benefits because of the quantity of time most people spend in and near their homes and the substantial number of people affected. In previous assessments, the EPA used a study on residential visibility valuation conducted in 1990 (McClelland et al., 1993). Consistent with advice from SAB

- Council , the EPA designated the McClelland et al. study as significantly less reliable for regulatory benefit

- cost analysis, alth ough it does provide useful estimates on the order of magnitude of residential visibility co
- benefits (U.S. EPA

- SAB, 1999). 38 In our approach for estimating residential visibility co
- benefits , we replaced the previous methodology with a new benefits transfe r approach and incorporated additional valuation studies. This new approach was developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011) 39 and reviewed by the SAB

- Council (U. S. EPA

- SAB, 2004, 2010 a, 20 10b ).
To value residential visibility improvements, the new approach draws upon information reported in the Brookshire et al. (1979), Loehman et al. (1985) and Tolley et al. (198 6 ) studies. 40

38 EPA ’ s Advisory Co uncil on Clean Air Compliance Analysis noted that the McClelland et al. (1993) study may not incorporate two potentially important adjustments. First, their study does not account for the “ warm glow ” effect, in which respondents may provide higher willingness to pay estimates simply because they favor “ good
causes ” such as environmental improvement. Second, while the study accounts for non

- response bias, it may not employ the best available methods. A s a result of these concerns, the Council recommended that reside ntial visibility be omitted from the overall primary benefits estimate. (U. S. EPA

- SAB, 1999) 39 This report is also known as the Second Prospective 812 analysis. 40 Loehman et al. (1994) and Brookshire et al. (1982) published results in peer

- reviewed journals based on the same underlying data we obtained from Loehman et al. ( 1985) and Brookshire et al. (1979 ). While t he specific details need to compute visibility benefits using Tolley et al. ( 198 6 ) were not published in a peer

- reviewed journal , the overall work including study and survey design was subject to peer review during study development (see Leggett and Neumann , 2004 and Patterson et al., 2005 ). In addition, Tolley subsequently published a book (Tolley and Fabian, 1988) based on this research, which notes in the preface that the methods were critiqued throughout by various external economists . The EPA does not claim that this external critique necessarily constituted a formal peer review process, but we provide this information for transparency regarding the review*** E.O. 12866 Review

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- 82 Each of the studies provides estimates of household WTP to improve visibility conditions. While uncertainty exists regarding the precision of these older, stated

- preference residential valuation studies, we believe their results support the argument that individuals have a non

zero value for residential visibility improvements. These studies provide primary visibility values for Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C. 41
In accordance with Chestnut and Rowe (1990c), we utilize the WTP estimates and the associated chan ge in visual range from each study to estimate the β parameter for the eight study areas. The β parameter represents the WTP for a specific improvement in visibility in a specific location. Where studies provide multiple estimates for visual range improvem ents, we estimate β by regressing the natural log of the ratio of visual range following and prior to improvement against WTP. To express these value estimates in comparable terms across study locations, we express household WTP for a change in visual rang e in a specific MSA using the following function:
(6.3)

where:
VR 0 = mean annual visual range in miles before the improvement,

VR 1 = mean annual visual range in miles after the improvement, and β = parameter.

Total residential visi bility co
- benefits within a particular MSA are driven by visibility improvements, population density, and the WTP value applied. Only those people living within in an MSA are assumed to receive co
- benefits from improved residential visibility. In other

wor ds, unlike recreational visibility, we do not assume a non

- use value by people who live outside the MSA for residential visibility. Table 6

- 6 provides a summary of these valuation estimates for each study location, as well as an illustrative implied WTP value for a 10% improvement in visual range. As shown, the implied annual per

- household WTP estimates for a hypothetical 10% improvement ranges from $21 to $220, depending on the study area. It is not

of this work. The use of these studies as the only available information to estimate residential visibility co

- benefits in the main estimate was supported by the SAB

- Council (U.S. EPA

- SAB, 1999a, 2010a). 41 Recognizing potential fundamental issues associated with data collected in Cincinnati and Miami (e.g., see Chestnut et al. (1986) and Chestnut and Rowe (1990c), we do not include values for these cities in our analysis. The 8 MSAs where the valuation studi es were conducted represent 15% of the total US population in 2020 (U.S. Census).*** E.O. 12866 Review

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- 83 surprising that such a range of values exists, as these study areas all feature different landscapes and vistas, populations and prevailing visibility conditions.
Table 6

- 6

. Summary of Residential V isibility Valuation Estimates

City Study β Estimate Implied WTP for

10% Improvement in Visual Range (1990$, 1990 income) Implied WTP for

10% Improvement in Visual Range (2006$, 2020 income) Atlanta Tolley et al. ( 198 6 ) 321 $31 $72 Boston Tolley et al. ( 198 6 ) 398 $38 $89 Chicago Tolley et al. ( 198 6 ) 310 $30 $69 Denver Tolley et al. ( 198 6 ) 696 $66 $155 Los Angeles Brookshire et al.

(1979) 94 $9 $21

Mobile Tolley et al. ( 198 6 ) 313 $30 $70 San Francisco Loehman et al.

(1985) 989 $94 $220
Washington, DC Tolley et al. ( 198 6 ) 614 $59 $137

Similar to recreational visibility co
- benefits , we then incorporate preference calibration using the method developed by Smith, Van Houtven, and Pattanayak (2002), which is discussed in more detail in Appendix 6a of this RIA. This preference calibration is a change since The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) intended to address the SAB

- Council’s concern (U.S. EPA

- SAB, 2010a) regarding the inconsistency regarding household income in the estimation of recreational and residential visibility. To express these “ preference

- calibrated ” value estimates across study locations, we express household WTP for a change in visual range in a specific MSA using the following function:

(6

- 4)

where: m = household income, ρ = shape parameter (0.1),*** E.O. 12866 Review

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- 84 θ = WTP parameter corresponding to the visibility at MSA,
VR 0 = starting visibility, and
VR 1 = visibility after change.
While the primary estimate for residential visibility includes co
- benefits in only the eight MSAs included in the valuation studies, people living in other urban areas also have non

- zero values for residential visibility. For th is reason, our approach includes a sensitivity analysis for the extrapolated residential visibility in the 351 additional MSAs. 42 Because there is considerable uncertainty about the validity of this benefit transfer approach, these extrapolated co
- benefits are included in a sensitivity analysis only. This is an important distinction between the approach used in The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ), where all cities were included in the total benefi ts approach. We believe that it is appropriate to deviate from the previous approach in order to be consistent with the approach used to estimate recreational visibility co
- benefits and to recognize the uncertainty associated with extrapolating beyond the studied cities. Figure 6

- 6 indicates the study cities as well as the assignment of the other MSAs to the study cities.
The degree to which the three studies were successful in convincing respondents to focus solely on visibility is unclear, as none of the three studies includes follow

- up questions necessary to investigate the issue. Furthermore, no other residential visibi lity CV studies provide evidence regarding the degree to which health effects are embedded in visibility values. Although the McClelland et al. (1991) study has a follow

- up question designed to allocate WTP across several categories, the CV question in the McClelland et al. study was focused on air pollution generally rather than visibility. As a result, we do not adjust the results from these studies to account for potentially embedded health effects.
There are many factors that could influence WTP for res idential visibility, and these factors vary across urban areas. In our approach , we utilize the benefit transfer approach developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) report , but we recogni ze that there are alternative methods that we could have used. We assigned a valuation study area to each MSA based on two factors: geographic proximity to one of the eight study cities and elevation. Any MSA with a county elevation above 1 , 500 meters was assigned the Denver valuation instead of the nearest study area. 43 Because

42 The 351 additional MSAs plus the 8 study area MSAs represent 84% of the total US population in 2020 (U.S. Census). 43 Elevation data represent the county

- level maximum, whic h were calculated using the ArcGIS Spatial Analyst tool

“ Zonal Statistics ” using the geographic database HYDRO1K for North America (U.S. Geological Survey, 1997). This*** E.O. 12866 Review

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- 85 residents of Denver have a dramatic view of the Rocky Mountains that is rarely obstructed by trees, it is plausible that they might have a greater interest in protecting visibility t han a city without nearby mountains. The geographic proximity factor is constrained in two areas. The San Francisco valuation study is only assigned to the six counties in the San Francisco Bay area MSAs because the study is unique among the three regardin g the temporal description of visibility conditions, landscape/vistas, and prevailing weather conditions. In addition, the Los Angeles valuation was assigned to the Riverside MSA despite exceeding the elevation threshold. 44

dataset and associated documentation are available on the Internet at DEMhttp://eros.usg s.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/namerica

. 44 Riverside MSA is assigned to the Los Angeles study area because a significant portion of Riverside County itself is located in the South Coast Air Quality Management District, which ca n be considered by to be part of the same regulated airshed as Los Angeles. The geographic assignment is preserved despite exceeding the elevation threshold because Riverside is adjacent to one of the study cities and this region has a particular set of lo cation

specific characteristics that set it apart from Denver.*** E.O. 12866 Review

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- 86 Figure 6

- 6

. Residential Visibility Study City Assignment

6. 3

. 5 .2 Residential Visibility Limitations, Caveats, and Uncertainties Similar to recreational visibility co
- benefits , there are many data inputs into the residential visibility co
- benefits that contribute to overall uncert ainty. Our approach includes sensitivity analyses to characterize major omissions (i.e., co
- benefits in other MSAs and coarse particles). A summary of the key assumptions including direction and magnitude of bias in our approach is provided in Table 6

- 7.

T he valuation studies relied upon for the residential visibility co
- benefits , although representing the best available estimates, have a number of limitations. These include the following:

 The survey design and implementation reflects the period in which th e surveys were conducted. Since that time, many improvements to the stated preference methods have been developed.

 The vintage of the surveys (1970s and 1980s) invites questions whether the values are still valid for current populations, or more importantl y for our approach , future populations in 2020.*** E.O. 12866 Review

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- 87  The survey focused only on populations in eight cities, so the transfer of the WTP estimates values to populations outside those cities requires that their preferences be similar to those in non

- surveyed citi es, as well as the visibility attributes be similar across study and transfer MSAs.

 There is an inherent difficulty in separating values expressed for visibility improvements from an overall value for improved air quality. The studies attempted to control for this, but most of the respondents did not feel that they were able to segregate residential visibility entirely from recreational visibility and health effects.
Table 6

- 7
. Summary of Key Assumptions in the Residential Visibility Co

- benefits a

Key Assumption Direction of Bias Magnitude of

Effect Residential and recreational visibility benefits are distinct and separable. Potential Overestimate Medium

- Low
Estimates residential visibility benefits are limited to populations within the boundaries of MSAs. Areas outside of an MSA are not included in our approach

. Underestimate Low

WTP values reflect only visibility improvements and not overall air quality improvements. Potential Overestimate Medium

- Low
WTP values from studies in Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C. can be accurately transferred to MSAs across the U.S. based on proximity and elevation Unclear Unclear

We assume that there are 2.68 peopl e per household. Because this estimate has been decreasing over time, this may underestimate the number of households. Potential Underestimate Medium

- Low

a A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA

. 6. 3

. 5.3 Using Hedonic Economic Literature to E stimate Visibility Co

- benefits
The hedonic model assumes that consumers d o not value the consumption of a good directly, but rather value the characteristics contained within a good. In the contex t of property values, the consumer values both the physical attributes of the property (i.e., number of rooms, square footage, etc.) as well as geographic and environmental attributes (e.g., proximity to parks, visibility, etc.). Following the technique de veloped by Rosen (1974), property characteristics are regressed on the observed price of the properties within a given housing market to estimate the contribution of each characteristic to the overall price.

Numerous studies have applied hedonic methods t o estimate the willingness to pay (WTP) for air quality changes (see Smith and Huang (1995) and Boyle and Kiel (2001) for*** E.O. 12866 Review

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- 88 literature reviews), but fewer researchers have focused specifically on visibility. Studies that have estimated the WTP to improve vis ibility have focused on specific housing markets like Los Angeles (Murdoch and Thayer, 1998; Beron et al., 2001) or San Francisco (Graves et al., 1998), and it is unclear if these results would be more broadly applicable to the rest of the county. While th e literature demonstrates a link between pollutant concentrations and home

- buying behavior, it is difficult to partition the WTP for changes in pollution between health and aesthetic concerns. Murdoch and Thayer (1998) use visibility as a surr ogate for env ironmental quality and Beron et al. (2001) acknowledge that their parameter estimates likely reflect a combination of visual aesthetics and an absence of health effects. Delucchi et al. (2002) deals with this issue by partitioning WTP estimates from a hedo nic model into health and visibility components using results from previous contingent valuation studies, and find that the estimate of visibility co
- benefits is similar to estimates based on contingent valuation alone

. In 2004, the Advisory Council on Clean Air Compliance Analysis ( SAB

- Council ) recommended that the EPA evaluate the available studies addressing residential visibility and consider the possibility of using hedonic property models to estimate residential visibility co
be nefits (U.S. EPA

- SAB, 2004). In response to this recommendation, the EPA evaluated the existing economic literature, and determined that there were substantial limitations that precluded the Agency from using these studies to make inferences regarding indi viduals ’ WTP for improved visibility (Leggett and Neumann, 2004). Specifically, the literature did not provide support for the assumption that market participants are aware of the spatial variation in visibility, and consider this variation when purchasing a home, and can successfully separate visibility effects from health effects (Leggett and Neumann, 2004). This conclusion is also supported by Delucchi et al

. (2004), which found that hedonic price analysis does not capture all of the health effects of ai r pollution because homebuyers may not be fully informed about these effects .
Research since 2004 has attempted to address limitations of the hedonic method through the use of U.S. Census microdata (Bayer et al., 2009), spatial statistic al methods (Anseli n and Le Gallo, 2006; Anselin and L ozana
- Gracia , 2009; Beron et al., 2004; Kim et al., 2010 ) and more complete air quality data and information about nonattainment status (Chay and Greenstone, 2005). However, none of these studies specifically address visi bility, and they are therefore of limited use at this time. While the current state of the literature does not provide a basis for using hedonics

- based approaches , continued innovations in methodology and the further development of national, micro

- level ho using and demographic datasets may open possibilities for national

- scale hedonics

- based benefit analysis in the future.*** E.O. 12866 Review

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- 89 Regardless of whether we use hedonic models or stated preference surveys to estimate

co
- benefits arising from improved visibility, it i s important to emphasize that estimates of WTP for residential or recreational visibility improvements are not substitutes for health benefits. As previously mentioned, people often have difficulty separating their health concerns from their aesthetic conc erns when evaluating preferences for visibility, which could overestimate visibility co
- benefits if not properly controlled. However, because we use a damage

- function approach to estimate health benefits (see Chapter 5 of this RIA), the health benefits est imates are unaffected by any potential confounding with visibility preferences.

6

. 3

. 6 Discussion of Visibility Co

- benefits As described in the previous sections of this chapter, the estimation of visibility co
benefits is complex and suffers from unavoida ble limitations. While we are confident that the underlying scientific literature supports a non

- zero estimate for visibility co
- benefits attributable to emission reductions, we are less confident in the magnitude of those co
- benefits outside of previously studied locations. While acknowledging these limitations, it is important to note that this general approach was included in The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) , whic h was reviewed by the SAB

- Council (U.S. EPA

- SAB, 2010 a, 2010b )

. Although the SAB

- Council highlighted concerns with the visibility approach used in the study, it did not recommend that visibility benefits be excluded. W e have addressed the SAB

- Council’s concern regarding inconsistency between estimation of residential and recreational visibility in our approach

. However, we do not have the data to address the SAB

- Council’s concern regarding inclu sion of night

- time benefits of visibility improvements in our annual average , which may lead to an underestimation of visibility benefits

. To minimize uncertainties related to extrapolation and geographical double counting, our approach only include s a subset of monetized vi sibility co
- benefits in the core monetized visibility co

- benefits estimate to correspond with our higher level of confidence in recreational co
- benefits within the study regions and residential co
- benefits within the study cities. Although we are confident that visibility co
- benefits extend beyond these studied areas, we are less confident about the magnitude of those co
- benefits

. However, it is unclear the degree to which the visibility valuation surveys were successful in contro l l ing for potential double counting embedded health benefits.

Consistent with the approach described in the proposal RIA , we have described a revised approach for estimating visibility co
- benefits , including light extinction estimation methods, visitation data for Class 1 areas (used in extrapolating co
- benefits ) , valuation studies for residential visibility co
- benefits , and the benefit transfer technique for residential co
-*** E.O. 12866 Review

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- 90 benefits

. Including residential visibility co
- benefits in the core visibility co

- benefits estimates reflec ts an evolution in our understanding of the nature and importance of the effect on public welfare from visibility impairment to a more multifaceted approach that includes non

- Class 1 areas, such as urban areas. This evolution has occurred in conjunction wi th the expansion of available PM data and information from associated studies of public perception, valuation and personal comfort and well

- being. While visibility preference studies ( Abt Associates Inc., 2001, Ely et al., 1991, Pryor, 1996, BBC Research & Consulting, 2003 ) also provide support for a non

zero benefits estimate, these surveys did not include questions that would enable monetization of those preferences.
Despite these improvements, we are limited by the available peer

- reviewed studies on visi bility co
- benefits , which have not undergone a similar expansion as the health literature. Each of these valuation studies has limitations, which are identified in the section s 6.3.4.2 and 6.3.5.2. When the SAB

- Council reviewed the visibility benefits anal ysis for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011 ) , they also lamented on the need for additional research to improve methods and estimates (U.S. EPA

SAB, 2010 a, 2010b ). Because of time and resource constraints, performing original research for regulatory analyses of specific policy actions is infeasible. Most importantly, we are interested in recently published national

- scale visibility valuation studies that incorporate current CV best practices, as the existing studies are limited to specific subset of geographic areas. Other important research questions that remain unresolved include identifying factors that affect valuation preferences in order to facilitate benefits transfer from the original stu dies to transfer sites across localities, disentangling health and ecosystem valuation from visibility valuation, usefulness of preference calibration, and potential role of hedonic valuation approaches. Many of these same research needs were identified by Cropper (2000), but they have yet to be addressed by the research community.
In Appendix 6.B, w e provide the results of an illustrative analysis of the visibility co
benefits associated with the 2020 base case 2020 control case simulation described in Cha pter 3 that were used to develop the air quality ratios; however, we do not have air quality model runs for the regulatory baseline and the alternative standard levels that would allow us to calculate the visibility co

- benefits of attaining the revised pri mary standard.

6

. 4 Materials Damage Co

- benefits Building materials including metals, stones, cements, and paints undergo natural weathering processes from exposure to environmental elements (e.g., wind, moisture,*** E.O. 12866 Review

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- 91 temperature fluctuations, sunlight, etc. ). Pollution can worsen and accelerate these effects. Deposition of PM is associated with both physical damage (materials damage effects) and impaired aesthetic qualities (soiling effects). Wet and dry deposition of PM can physically affect materials, adding to the effects of natural weathering processes, by potentially promoting or accelerating the corrosion of metals, by degrading paints and by deteriorating building materials such as stone, concrete and marble (U.S. EPA, 2009b). The effects of PM are

exacer bated by the presence of acidic gases and can be additive or synergistic due to the complex mixture of pollutants in the air and surface characteristics of the material. Acidic deposition has been shown to have an effect on materials including zinc/galvani zed steel and other metal, carbonate stone (as monuments and building facings), and surface coatings (paints) (Irving, 1991). The effects on historic buildings and outdoor works of art are of particular concern because of the uniqueness and irreplaceabilit y of many of these objects.
The PM ISA concludes that evidence is sufficient to support a causal relationship between PM and effects on materials (U.S. EPA, 2009b). Considerable research has been conducted on the effects of air pollutants on metal surfaces due to the economic importance of these materials, especially steel, zinc, aluminum, and copper. Moisture is the single greatest factor promoting metal corrosion; however, deposited PM can have additive, antagonistic or synergistic effects. In general, SO 2 is more corrosive than NO x although mixtures of NO x , SO 2 and other particulate matter corrode some metals at a faster rate than either pollutant alone (U.S. EPA, 2008). Metal structures are usually coated by alkaline corrosion product layers and thus are subject to increased corrosion by acidic deposition. In addition, research has demonstrated that iron, copper, and aluminum

- based products are subject to increased corrosion due to pollution (Irving, 1991). Information from both the PM ISA (U.S. EPA, 2009 b) and NO x /SO x ISA (U.S. EPA, 2008) suggest that the extent of damage to metals due to ambient PM is variable and dependent upon the type of metal, prevailing environmental conditions, rate of natural weathering and presence or absence of other pollutants
In addition, the deposition of PM can cause soiling, which is the accumulation of dirt, dust, and ash on exposed surfaces such as metal, glass, stone and paint. Particles consisting primarily of carbonaceous compounds can cause soiling of commonly used bui lding materials and culturally important items such as statues and works of art. Soiling occurs when PM accumulates on an object and alters the optical characteristics (appearance). The reflectivity of a surface may be changed or presence of particulates m ay alter light transmission. These effects can reduce the aesthetic value of a structure or result in reversible or irreversible damage to statues, artwork and architecturally or culturally significant buildings. Due to soiling*** E.O. 12866 Review

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- 92 of building surfaces by PM, the frequency and duration of cleaning or repainting may be increased. In addition to natural factors, exposure to PM may give painted surfaces a dirty appearance. Pigments in works of art can be degraded or discolored by atmospheric pollutants, especially sulfates (U.S. EPA, 2008). Previous assessments estimated household soiling co
benefits based on the Manuel et al. (1982) study of consumer expenditures on cleaning and household maintenance. However, the data used to estimate household soiling damages in the Manuel et al. study is from a 1972 consumer expenditure survey and as such may not accurately represent consumer preferences in the future. In light of this significant limitation, we believe that this study cannot provide reliable estimates of the li kely magnitude of the co
benefits of reduced PM household soiling.

In order to estimate the monetized co
- benefits associated with reducing materials damage and household soiling, quantitative relationships are needed between particle size, concentration, c hemical concentrations and frequency of maintenance and repair. Such an analysis would require three steps:

1. Develop a national inventory of sensitive materials;

2. Derive concentration

- response functions that relate material damage to change in pollution concentration or deposition; and,

3. Estimate the value of lost materials and/or repair of damage.
Due to data limitations and uncertainties inherent in each of these steps, we have chosen not to include a monetized estimate of materials damage a nd household soiling in this analysis. The PM ISA concluded that there is considerable uncertainty with regard to interaction of co

- pollutants in regards to materials damage and soiling processes (U.S. EPA, 2009b). Previous benefits analyses by the EPA hav e provided quantitative estimates of materials damage (U.S. EPA, 2011b) and household soiling damage (U.S. EPA, 1999). Consistent with SAB advice (U.S. EPA, 1998), we determined that the existing data are not sufficient to calculate a reliable estimate of future year household soiling damages (U.S. EPA, 1998). These previous analyses have shown that materials damage co
- benefits are significantly smaller than the health benefits associated with reduced exposure to PM 2.5 and ozone, or even visibility co
benef its

. However, studies of materials damage to historic buildings and outdoor artwork in Sweden (Grosclaude and Soguel, 1994) indicate that these co
- benefits could be an order of magnitude larger than household soiling co
- benefits

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- 93 In the absence of quantifi ed co
- benefits , we provide a qualitative description of the avoided damage associated with reducing PM and PM precursor pollutants. Table 6

- 8 shows the effect of various PM 2.5 precursor pollutants and other co

- pollutants on various materials.
Table 6

- 8

. Ma terials Damaged by Pollutants Affected by this Rule (U.S. EPA, 2011b)
Pollutant Unquantified Effects / Damage to: Sulfur oxides Infrastructural materials

— galvanized and painted carbon steel

Commercial buildings

— carbonate stone, metal, and painted wood surfaces Residential buildings

— carbonate stone, metal, and painted wood surfaces Monuments

— carbonate stone and metal Structural aesthetics Automotive finishes

—

painted metal Hydrogen ion and nitrogen oxides Infrastructural materials

— galvanized and painted carbon steel

Zinc

- based metal products, such as galvanized steel Commercial and residential buildings

— carbonate stone, metal, and wood surfaces Monuments

— carbonate stone and metal Structural aesthetics Automotive finishes

—

painted metal Carbon dioxide Zinc

- based metal products, such as galvanized steel Formaldehyde Zinc

- based metal products, such as galvanized steel Particulate matter Household cleanliness (i.e., household soiling) Ozone Rubber products (e.g., tires)

6. 5 Climate Co

- benefits Actions taken by s tate and l ocal governments to implement the revised annual primary standard are likely to have implications for climate change because emission reductions ultimately implemented to meet the standard may have impacts on emissions of long

- l ived greenhouse gas (GHG) such as carbon dioxide (CO 2 ), short

- lived climate forcers such as black carbon (BC), and cooling aerosols like organic carbon (OC). Our ability to quantify the climate effects of these revised standard is limited due to lack of av ailable information on the co

controlled GHG emission reductions, the energy and associated climate gas implications of control technologies assumed in the illustrative regulatory alternative s, and remaining uncertainties regarding the impact of long

- lived and short

- lived climate forcer impacts on climate change. For this RIA, we discuss qualitatively the implications of potential emission*** E.O. 12866 Review

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- 94 reductions in warming and cooling aerosols and changes in long

- lived GHG emissions such as CO 2 for the regulatory alter native s. Implementation strategies undertaken by s tate and l ocal governments to comply with the standard s may differ from the illustrative emission reduction strategies in this RIA

. It is important to note that the net climate forcing depends on the specif ic combinations of emission reductions chosen to meet the revised standard because of the differences in warming and cooling potential of the difference pollutants

. 6

. 5 .1 Climate Effects of Short Lived Climate Forcers Pollutants that affect the energy balance of the earth are referred to as climate forcers. A pollutant that increases the amount of energy in the Earth ’ s climate system is said to exert

“ positive radiative forcing, ” which leads to warming and climate chang e. In contrast, a pollutant that exerts negative radiative forcing reduces the amount of energy in the Earth ’ s system and leads to cooling.
Long

- lived gases such as CO 2 differ from short

- lived pollutants such as BC in the length of time they remain in the atmosphere affecting the earth ’ s energy balance. Long

- lived gases remain in the atmosphere for hundreds to thousands of years. Short

- lived climate forcers (SLCFs), in contrast, remain in the atmosphere for short periods of time ranging from days to weeks. The potential to affect near

- term climate change and the rate of climate change with policies to address these emissions is gaining attention nationally and internationally ( e.g., Black Carbon Report to Congress ( U.S. EPA, 2012b ), Arctic Council Task Force , Global Methane Initiative, and Convention on Long

- Range Trans

- boundary Air Pollution of the United Nations Economic Commission for Europe). A recent United Nations Environmental Programme (UNEP) study provides the most comprehensive analysis to date of t he co
- benefits of measures to reduce SLCFs including methane, ozone, and black carbon assessing the health, climate, and agricultural co
- benefits of a suite of mitigation technologies. The report concludes that the climate is changing now, and these change s have the potential to “ trigger abrupt transitions such as the release of carbon from thawing permafrost and biodiversity loss. ” While reducing long

- lived GHGs such as CO 2 is necessary to protect against long

- term climate change, reducing SLCF gases inclu ding BC and ozone is beneficial and will slow the rate of climate change within the first half of this century (UNEP , 2011).

6

. 5 .1.1 Climate Effects of Black Carbon Black carbon is the most strongly light

‐ absorbing component of PM 2.5 , and is formed by incomplete combustion of fossil fuels, biofuels, and biomass. The short atmospheric lifetime of BC lasting from days to weeks and the mechanisms by which BC affects climate distinguish it*** E.O. 12866 Review

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- 95 from long

‐ lived GHGs like CO 2 . This means that ac tions taken to reduce the BC constituents in direct PM 2.5 will have almost immediate effects on climate change. Emissions sources and ambient concentrations of BC vary geographically and temporally resulting in climate effects that are more regionally and seasonally dependent than the effects of long‐lived, well‐mixed GHGs. Likewise, mitigation actions for BC will produce different climate impacts depending on the region, season, and emission source category affected.
BC influences climate in multiple ways: directly absorbing light , reducing the reflectivity ( “ albedo ” ) of snow and ice through deposition, and interacting with clouds. BC affects climate directly by absorbing both incoming and outgoing radiation of all wavelengths. In contrast, GHGs mainly trap outgoing infrared radiation from the earth ’ s surface. Per unit of mass in the atmosphere, BC can absorb a million times more energy than CO 2 (Bond and Sun 2005). This strong absorptive capacity is the property most relevant to its potential to affect the Earth ’ s climate. W hen BC is deposited on snow and ice, it darkens the surface and decreases albedo , thereby increasing absorption and accelerating melting. Finally, BC also affects climate indirectly by altering the properties of clouds, affecting cloud reflectivity, precipitation, and surface dimming. These indirect impacts of BC are associated with all ambient particles and may lead to cooling, but are not associated with l ong
- lived well mixed GHGs.
Regional climate impacts of BC are highly variable, and sensitive regions such as the Arctic and the Himalayas are particularly vulnerable to the warming and melting effects of BC. Snow and ice cover in the Western U.S. has also been affected by BC. Specifically, deposition of BC on mountain glaciers and snow packs produces a positive snow and ice albedo effect, contributing to the melting of snowpack earlier in the spring and reducing the amount of snowmelt that normally would o ccur later in the spring and summer (Hadley et al. 2010). This has implications for freshwater resources in regions of the U.S. dependent on snow‐fed or glacier‐fed water systems. In the Sierra Nevada mountain range, Hadley et al. (2010) found BC at differ ent depths in the snowpack, deposited over the winter months by snowfall. In the spring, the continuous uncovering of the BC contributed to the early melt. A model capturing the effects of soot on snow in the western U.S. shows significant decreases in sno wpack between December and May (Qian et al., 2009). Snow water equivalent (the amount of water that would be produced by melting all the snow) is reduced 2‐50 millimeters (mm) in mountainous areas, particularly over the Central Rockies, Sierra Nevadas, and western Canada. A study found that biomass burning emissions in Alaska and the Rocky Mountain region during the summer can enhance snowmelt. Dust deposition on snow, at high concentrations, can have similar effects to BC (Koch et al., 2007). Similarly, a study done by Painter et al. (2007) in the*** E.O. 12866 Review

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- 96 San Juan Mountains in Colorado indicated a decrease in snow cover duration of 18‐35 days as a result of dust transported from non‐local desert sources.

The illustrative emission reduction strategies evaluated for this rule include reductions in BC emissions that will tend to have a beneficial cooling effect on the atmosphere. BC and elemental carbon ( EC ) (or particulate elemental carbon (PEC)) are used interchangeably in this report because the EPA t raditionally estimates EC emissions rather than BC and for the purpose of this analysis these measures are essentially equivalent.

6

. 5 .1.2 Climate Effects of Nitrates, Sulfate, and Organic Carbon (excluding BC) The composition of the total emissions mixtu re is also relevant as to whether emissions are warming or cooling to the atmosphere . P ollutants such as SO 2 , NO X , and most OC particles tend to produce a cooling influence on climate

. Exceptions include OC deposition on snow and ice , which leads to increased melting.
In addition, it is important to account for the indirect effects of all PM constituents on climate: all aerosols (including BC) affect climate indirectly by changing the reflectivity and lifetime of clouds. The net indirect effect of all aerosols is very uncertain but is thought to be a net cooling influence.

6

. 5 .1.3 Climate Effects of Ozone Ozone changes due to this revised annual standard are not estimated for this analysis but may occur due to the NO x reductions estimated. Ozone is a w ell

- known SLCF (U.S. EPA, 2006). Stratospheric ozone (the upper ozone layer) is beneficial because it protects life on Earth from the sun ’ s harmful ultraviolet (UV) radiation. In contrast, tropospheric ozone (ozone in the lower atmosphere) is a harmful air pollutant that adversely affects human health and the environment and contributes significantly to regional and global climate change. Due to its short atmospheric lifetime, tropospheric ozone concentrations exhibit large spatial and temporal variability (U.S. EPA, 2009). The discernible influence of ground level ozone on climate leads to increases in global surface temperature and changes in hydrological cycles. While reducing long

- lived GHGs such as CO 2 is necessary to protect against long

- term climate c hange, reducing SLCF gases including ozone is beneficial and will slow the rate of climate change within the first half of this century (UNEP , 2011).

6

. 5 .1.4 SLCFs Summary and Conclusions Assessing the net climate impact of SLCFs for the illustrative emiss ion reduction strategies is outside the scope of this regulatory analysis and requires climate atmospheric*** E.O. 12866 Review

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- 97 modeling not undertaken due to time and resource constraints. Information about the amount of BC relative to non

- BC constituents emitted from a sourc e is important. In general, these non

BC constituents are emitted in greater volume than BC, counteracting the warming influence of BC. Qualitatively, it seems likely that BC emission reductions associated with direct ly emitted PM 2.5 reductions will be beneficial for the climate in terms of reduced radiative forcing and deposition on snow and ice. Reductions in OC, sulfates and nitrates are likely to produce warming in the atmosphere. The indirect impacts of aerosols on clouds and precipitation remain the subject of great uncertainty making it more difficult to estimate the quantitative impact of aerosol reductions on climate.

6

. 5 .2 Climate Effects of Long

- Lived Greenhouse Gases
The EPA Administrator found in 2009 that elevated concentrations of the six major GHGs, including CO 2 , endanger the public health and public welfare of current and future generations (FR 77 66496). While addressing short

- lived climate forcers can result in near

- term (and sometimes regionally specific) co
- benefits as well a s reductions in the rate of warming, reductions of long

- term warming would require mitigation of long

- lived GHGs. We are unable to quantify the impact of the illustrative emission reduction strategies for this rulemaking on long

- lived climate gases due lac k of available data

. However, State and Local governments may want to consider human health, welfare, and climate implications of regulatory strategies undertaken to implement the promulgated PM standards.

6

. 6 Ecosystem Co

- benefits and Services
The effects of air pollution on the health and stability of ecosystems are potentially very important. At present, it is difficult to measure the impact of reducing air pollution in a national scale analysis across different types of ecosystems and different pollutan t effects. Previous science assessments by the EPA (U.S. EPA, 2006a; 2008c; 2009b) have determined that air pollution can be directly linked to aquatic and terrestrial acidification, nutrient enrichment, vegetation injury, and metal bioaccumulation in animals. Ecosystem services are a useful conceptual framework for analyzing the impact of ecosystem changes on public welfare.
Ecosystem service s can be generally defined as the benefits that individuals and organizations obtain from ecosystems. The EPA has defined ecological goods and services as the “ outputs of ecological functions or processes that directly or indirectly contribute to social

we lfare or have the potential to do so in the future. Some outputs may be bought and sold, but most are not marketed ” (U.S. EPA, 2006c). Figure 6

- 7 provides the Millennium Ecosystem Assessment ’ s schematic demonstrating the connections between the categories of ecosystem*** E.O. 12866 Review

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- 98 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).

Figure 6

- 7
. Linkages between Categories of Ecosystem Se rvices and Components of Human Well

- Being from Millennium Ecosystem Assessment (MEA, 2005)

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem s ervices 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 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*** E.O. 12866 Review

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- 99 reductions in them (U.S. EPA, 2006c). There are three primary approaches for estimating the monetary va lue 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 co ncepts 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 analys is 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 depicte d in Figure 6

- 8

.

Figure 6

- 8

. Schematic of the Benefits Assessment Pro cess (U.S. EPA, 2006c)

6

. 6 .1 Ecosystem Co

- benefits for Metallic and Organic Constituents of PM Several significant ecological effects are associated with deposition of chemical constituents of ambient PM such as metals and organics (U.S. EPA, 2009b). The trace metal constituents of PM include cadmium, copper, chromium, mercury, nickel, zinc, and lead. The organics include persistent organic pollutants (POPs), polyaromatic hydroca rbons (PAHs) and polybromiated diphenyl ethers (PBDEs). Exposure to PM for direct effects occur via deposition ( e.g., wet, dry or occult) to vegetation surfaces, while indirect effects occur via deposition to*** E.O. 12866 Review

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- 100 ecosystem soils or surface waters where the dep osited constituents of PM then interacts with biological organisms. While both fine and coarse

- mode particles may affect plants and other organisms, more often the chemical constituents drive the ecosystem response to PM (Grantz et al., 2003). Ecological e ffects of PM include direct effects to metabolic processes of plant foliage; contribution to total metal loading resulting in alteration of soil biogeochemistry and microbiology, plant and animal growth and reproduction; and contribution to total organics loading resulting in bioaccumulation and biomagnification across trophic levels.
The PM ISA concludes that a causal relationship is likely to exist between deposition of PM and a variety of effects on individual organisms and ecosystems (U.S. EPA 2009b). M ost direct ecosystem effects associated with particulate pollution occur in severely polluted areas near industrial point sources (quarries, cement kilns, metal smelting) (U.S. EPA, 2009b). However the PM ISA also finds, in many cases, it is difficult to c haracterize the nature and magnitude of effects and to quantify relationships between ambient concentrations of PM and ecosystem response due to significant data gaps and uncertainties as well as considerable variability that exists in the components of PM and their various ecological effects (U.S. EPA, 2009b).
Particulate matter can adversely impact plants and ecosystem services provided by plants by deposition to vegetative surfaces (U.S. EPA, 2009b). Particulates deposited on the surfaces of leaves and n eedles can block light, altering the radiation received by the plant. PM deposition near sources of heavy deposition can obstruct stomata limiting gas exchange, damage leaf cuticles and increase plant temperatures (U.S. EPA, 2009b). Plants growing on roads ides exhibit impact damage from near

- road PM deposition, having higher levels of organics and heavy metals, and accumulate salt from road de

- icing during winter months (U.S. EPA, 2009b). In addition, atmospheric PM can convert direct solar radiation to dif fuse radiation, which is more uniformly distributed in a tree canopy, allowing radiation to reach lower leaves (U.S. EPA, 2009b). Decreases in crop yields (a provisioning service) due to reductions in solar radiation have been attributed to regional scale air pollution in other counties with especially severe regional haze (Chameides et al., 1999).
In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil or foliage. Copper, zinc, and nickel have been shown to be directly tox ic to vegetation under field conditions (U.S. EPA, 2009b). The ability of vegetation to take up heavy metals is dependent upon the amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants from soils and vegetative surfaces ca n disrupt photosynthesis, alter pigments and mineral content, reduce plant vigor, decrease frost hardiness and impair root development.*** E.O. 12866 Review

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- 101 Particulate matter can also contain organic air toxic pollutants, including PAHs, which are a class of polycyclic organi c matter (POM). PAHs can accumulate in sediments and bioaccumulate in freshwater, flora and fauna. The uptake of organics depends on the plant species, site of deposition, physical and chemical properties of the organic compound and prevailing environmenta l conditions (U.S. EPA, 2009b). Different species can have different uptake rates of PAHs. For example, zucchini ( Cucurbita pepo ) accumulated significantly more PAHs than related plant species (Parrish et al., 2006). PAHs can accumulate to high enough

conc entrations in some coastal environments to pose an environmental health threat that includes cancer in fish populations, toxicity to organisms living in the sediment and risks to those (e.g., migratory birds) that consume these organisms (Simcik et al., 19 96; Simcik et al., 1999). Atmospheric deposition of particles is thought to be the major source of PAHs to the sediments of Lake Michigan, Chesapeake Bay, Tampa Bay and other coastal areas of the U.S. (Arz avus, Dickhut, and Canuel, 2001 ).

Contamination of plant leaves by heavy metals can lead to elevated concentrations in the soil. Trace metals absorbed into the plant, frequently bind to the leaf tissue, and then are lost when the leaf drops. As the fallen leaves decompose, the heavy metals are transferred into the soil (Cotrufo et al

. , 1995; Niklinska et al., 1998). Many of the major indirect plant responses to PM deposition are chiefly soil

- mediated and depend on the chemical composition of individual components of deposited PM. Upon entering the soil envi ronment, PM pollutants can alter ecological processes of energy flow and nutrient cycling, inhibit nutrient uptake to plants, change microbial community structure and, affect biodiversity. Accumulation of heavy metals in soils depends on factors such as lo cal soil characteristics, geologic origin of parent soils, and metal bioavailability. Heavy metals, such as zinc, copper, and cadmium, and some pesticides can interfere with microorganisms that are responsible for decomposition of soil litter, an important regulating ecosystem service that serves as a source of soil nutrients (U.S. EPA, 2009b). Surface litter decomposition is reduced in soils having high metal concentrations. Soil communities have associated bacteria, fungi, and invertebrates that are essen tial to soil nutrient cycling processes. Changes to the relative species abundance and community composition are associated with deposited PM to soil biota (U.S. EPA, 2009b).
Atmospheric deposition can be the primary source of some organics and metals to watersheds. Deposition of PM to surfaces in urban settings increases the metal and organic component of storm water runoff (U.S. EPA, 2009b). This atmospherically

- associated pollutant burden can then be toxic to aquatic biota. The contribution of atmospher ically deposited PAHs to aquatic food webs was demonstrated in high elevation mountain lakes with no other*** E.O. 12866 Review

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- 102 anthropogenic contaminant sources (U.S. EPA, 2009b). Metals associated with PM deposition limit phytoplankton growth, affecting aquatic trophic struc ture. Long

- range atmospheric transport of 47 pesticides and degradation products to the snowpack in seven national parks in the Western U.S. was recently quantified indicating PM

- associated contaminant inputs to receiving waters during spring snowmelt (Hag eman et al., 2006).
The recently completed Western Airborne Contaminants Assessment Project (WACAP) is the most comprehensive database on contaminant transport and PM depositional effects on sensitive ecosystems in the Western U.S. (Landers et al., 2008). In this project, the transport, fate, and ecological impacts of anthropogenic contaminants from atmospheric sources were assessed from 2002 to 2007 in seven ecosystem components (air, snow, water, sediment, lichen, conifer needles and fish) in eight core n ational parks. The study concluded that bioaccumulation of semi

- volatile organic compounds occurred throughout park ecosystems, an elevational gradient in PM deposition exists with greater accumulation in higher altitude areas, and contaminants accumulate in proximity to individual agriculture and industry sources, which is counter to the original working hypothesis that most of the contaminants would originate from Eastern Europe and Asia.
Although there is considerable data on impacts of PM on ecological receptors, few studies link ambient PM levels to observed effect. This is due, in part, to the nature, deposition, transport and fate of PM in ecosystems. Some of the difficulties in quantifying the ecosystem

co
- benefits associated with reduced PM depositi on include the following:

 PM is not a single pollutant, but a heterogeneous mixture of particles differing in size, origin and chemical composition. Since vegetation and other ecosystem components are affected more by particulate chemistry than size fracti on, exposure to a given mass concentration of airborne PM may lead to widely differing plant or ecosystem responses, depending on the particular mix of deposited particles.

 Composition of ambient PM varies in time and space and the particulate mixture may have synergistic, antagonistic or additive effects on ecological receptors depending upon the chemical species present.

 Presence of co

- pollutants makes it difficult to attribute observed effects to ecological receptors to PM alone or one component of depos ited PM.

 Ecosystem effects linked to PM are difficult to determine because the changes may not be observed until pollutant deposition has occurred for many decades. Furthermore, many PM components bioaccumulate over time in organisms or plants, making corr elations to ambient levels of PM difficult.*** E.O. 12866 Review

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- 103  Multiple ecological stressors can confound attempts to link specific ecosystem responses to PM deposition. These stressors can be anthropogenic ( e.g., habitat destruction, eutrophication, other pollutants) or nat ural ( e.g., drought, fire, disease). Deposited PM interacts with other stressors to affect ecosystem patterns and processes.

 Each ecosystem has a unique topography, underlying bedrock, soils, climate, meteorology, hydrologic regime, natural and land use hi story, and species composition. Sensitivity of ecosystem response can be highly variable in space and time. Because of this variety and lack of data for most ecosystems, extrapolating these effects from one ecosystem to another is highly uncertain.

6

. 6 .2 E cosystem Co

- benefits from Reductions in Nitrogen and Sulfur Emissions Emissions of the PM precursors, such as n itrogen and sulfur oxides occur over large regions of North America. Once these pollutants are lofted to the middle and upper troposphere, they t ypically 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 meteorologica l conditions encountered along the transport path (U.S. EPA, 2008c). Secondary particles are formed from NO X and SO 2 gaseous 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 rep resented in Figures 6

- 9 and 6

- 1 0

. Although there is some evidence that nitrogen deposition may have positive effects on agricultural and forest output through passive fertilization, it is likely that the overall value is very small relative to other health and welfare effects. In addition to deposition effects, SO 2 can affect vegetation at ambient levels near pollution sources.*** E.O. 12866 Review

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- 104 NO X Atmospheric Fate and Transport

Deposition Process

Acidification

Aquatic Ecosystems Terrestrial

Ecosystems Nutrient Enrichment

Aquatic Ecosystems Terrestrial

Ecosystems*** E.O. 12866 Review

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- 105 Figure 6

- 9

. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

Figure 6

- 1 0

. Nitrogen and Sulfur Cycling , and Interactions in the Environment Source: U.S. EPA, 2008c SO 2 Atmospheric Fate and Transport

Deposition Process

Acidification

Aquatic Ecosystems Terrestrial Ecosystems Methylmercury Production*** E.O. 12866 Review

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- 106 The atmospheric 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 tha n 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 condensat ion 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 NO X and SO 2 emissions will decrease both acid depositi on and PM concentrations, but not necessarily in a linear fashion (U.S. EPA, 2008c). Sulfuric acid is also deposited on surfaces by dry deposition and can contribute to environmental effects (U.S. EPA, 2008c).

6

. 6 .2.1 Ecological Effects of Acidification Deposition of nitrogen and 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, 2008c). Major terrestrial effects include a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) (U.S. EPA, 2008 c). 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, 2008c). Decreases in the acid neutralizing capacity and increases in inorgan ic aluminum concentration contribute to declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems (U.S. EPA, 2008c).
Geology (particularly surficial geology) is the principal factor governing the sensitivity of terrestri al and aquatic ecosystems to acidification from nitrogen and sulfur deposition (U.S. EPA, 2008c). Geologic formations having low base cation supply generally underlie the watersheds of acid

- sensitive lakes and streams. Other factors contribute to the sensi tivity of soils and surface waters to acidifying deposition, including topography, soil chemistry, land use, and hydrologic flow path (U.S. EPA, 2008c).
Aquatic Acidification. Aquatic effects of acidification have been well studied in the U.S. and elsewher e 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. The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between acidifying deposition and effects on biogeochemistry*** E.O. 12866 Review

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- 107 related to aquatic ecosystems and biota in aquatic ecosystems (U.S. EPA, 2008c). Effects have been most clearly documented for fish, aquatic insects, oth er 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 com monly during low

- flow conditions, except where chronic acidity conditions are severe. Biological effects of episodes include reduced fish condition factor 45 , changes in species composition and declines in aquatic species richness across multiple taxa, ecosy stems 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 bi odiversity. 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 6

- 9 provides a summary of the biological effects experienced at various ANC levels.
Table 6

- 9

. 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 epi sodes 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

μe q/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 s pecies that are sensitive to acidifying deposition are affected.

45 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 ac idification on an individual fish (U.S. EPA, 2008f).*** E.O. 12866 Review

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- 108 Category Label ANC Levels Expected Ecological Effects 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, 2008c). 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 6

- 11 illustrates those areas of the U.S. where aquatic ecosystems are at risk from acidification.
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

su pplies, 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 sour ce 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 rat es 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.*** E.O. 12866 Review

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- 109 Figure 6

- 11. Areas Potentially Sensitive to Aquatic Acidification Source: U.S. EPA, 2008c Inland surface waters support several cultural services, including aesthetic and educational services and recreational fishing. Recreational fishing in lakes and stre ams is among the most popular outdoor recreational activities in the northeastern United States. 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. 46 For recreation days, consumer surplus value is most commonly measured using recreation demand, travel c ost models.
Another estimate of the overarching ecological co
- 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 fo r 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

46 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.*** E.O. 12866 Review

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- 110 scope version with large improvements due to th e 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 p er 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 NO x and SO x emissions were estimated to range from $291 mil lion to $829 million (U.S. EPA, 2009c). 47
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 b y 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 servic es.

Terrestrial Acidification. 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 catio ns (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, 2008c). These direct effects can, in turn, influence the response of these plants to climatic stresses such as droughts and cold temperatures. They can also influence the sensitivi ty 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, 2008c). The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between acidify ing deposition and effects on biogeochemistry related to terrestrial ecosystems and biota in terrestrial ecosystems (U.S. EPA, 2008c).
Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial acidification, which is the greatest concern regarding nitrogen and sulfur deposition U.S. EPA (2008c). Figure 6

- 1 2 depicts the areas across the U.S. that are potentially sensitive to terrestrial acidification.

47 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.*** E.O. 12866 Review

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- 111 Figure 6

- 1 2

. Areas Potentially Sensitive to Terrestrial Acidification
Source: U. S. EPA, 2008c 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 from acidifying deposition. This change in nutrient availability may reduce t he 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 6

- 1 3 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S.
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).*** E.O. 12866 Review

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- 112 Figure 6

- 1 3

. Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S. Source: U.S. EPA, 2008c 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). 48 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 tim ber 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),

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- 113 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. Virg inia 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 statistic s 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 p opulation visited a wilderness or primitive area during the previous year and engaged in day hiking (Cordell et al., 2008). 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 wil dlife 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 200 7 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 (U.S. EPA, 2009c). 49
As previously mentioned, it is d ifficult to estimate the p ortion of these recreational services that are specifically attributabl e 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 y ear involving fall color viewing (Spencer and Holecek, 2007). In a separate study conducted in

49 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.*** E.O. 12866 Review

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- 114 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 v alues 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 from damages as sociated 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 n umber 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 (U.S. EPA, 2 009c). 50
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 servic es is very difficult to quantify in a meaningful way, as is the reduction in the value of these services associated with total nitrogen and sulfur deposition. As terrestrial acidification contributes to root damages, reduced biomass growth, and tree mortal ity, all of these services are likely to be affected; however, the magnitude of these impacts is currently very uncertain.

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. 6 .2.2 Ecological Effects from Nitrogen Enrichment Aquatic Enrichment

. The ISA for NO x /SO x — Ecological Criteria concluded that the evidence is sufficient to infer a causal relationship between nitrogen deposition and the alteration of species richness, species composition, and biodiversity in wetland, freshwater aquatic and coastal marine ecosystems (U.S. EPA, 2008c).

One of the main adverse ecological effects resulting from nitrogen deposition, particularly in the Mid

- Atlantic region of the United States, is the effect associated with nutrient enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the Natio nal Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered from moderately high or high levels of eutrophication due to excessive inputs of both

50 These estimates reflect the marginal value of the service for the hypothetical program described in the survey, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.*** E.O. 12866 Review

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- 115 N and phosphorus, and a majority of these estuaries are located in the coasta l area from North Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid

- Atlantic region, the contribution of atmospheric distribution to total N loads is estimated to range between 10% and 58% (Valigura et al., 2001).
Eutrophication in estuaries is associated with a range of adverse ecological effects. The conceptual framework developed by NOAA emphasizes four main types of eutrophication effects

— low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic vegetation (SAV), a nd low water clarity. Low DO disrupts aquatic habitats, causing stress to fish and shellfish, which, in the short

- term, can lead to episodic fish kills and, in the long

- term, can damage overall growth in fish and shellfish populations. Low DO also degrades the aesthetic qualities of surface water. In addition to often being toxic to fish and shellfish, and leading to fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also be harmful to human health. SAV provides critical habitat for many aquatic species in estuaries and, in some instances, can also protect shorelines by reducing wave strength; therefore, declines in SAV due to nutrient enrichment are an important source of concern. Low water clarity is in part the result of accum ulations of both algae and sediments in estuarine waters. In addition to contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of the estuarine environment.
Estuaries in the eastern United States are an important so urce of food production, in particular fish and shellfish production. The estuaries are capable of supporting large stocks of resident commercial species, and they serve as the breeding grounds and interim habitat for several migratory species. To provide an indication of the magnitude of provisioning services associated with coastal fisheries, from 2005 to 2007, the average value of total catch was $1.5 billion per year. It is not known, however, what percentage of this value is directly attributable to or dependent upon the estuaries in these states.
In addition to affecting provisioning services through commercial fish harvests, eutrophication in estuaries may also affect the demand for seafood. For example, a well

publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following the outbreak (Lipton, 1999).

Estuaries in the United States also provide an important and substantial variety of cultural ecosystem services, including water

- based recreational and aesthetic services. The*** E.O. 12866 Review

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- 116 water quality in the estuary directly affects the quality of these experiences. For example, there were 26 million days of saltwater fishing coastal states from North Carolina to Massachusetts in 2006 (FWA and Census, 2007). Assuming an average consumer surplus value for a fishing day at $36 (in 2007 dollars) in the Northeast and $87 in the South east (Kaval and Loomis, 2003), the aggregate value was approximately $1.3 billion (in 2007 dollars) (U.S. EPA, 2009c). 51 In addition, almost 6 million adults participated in motorboating in coastal states from North Carolina to Massachusetts, for a total of nearly 63 million days annually during 1999

– 2000 (Leeworthy and Wiley, 2001). Using a national daily value estimate of $32 (in 2007 dollars) for motorboating (Kaval and Loomis (2003), the aggregate value of these coastal motorboating outings was $2 billion per year (U.S. EPA, 2009c). 52 Almost 7 million participated in birdwatching for 175 million days per year, and more t han 3 million participated in visits to non

- beach coastal waterside

areas. Estuaries and marshes have the potential to support a wide range of regulating services, including climate, biological, and water regulation; pollution detoxification; erosion preve ntion; and protection against natural hazards from declines in SAV (MEA, 2005). SAV can help reduce wave energy levels and thus protect shorelines against excessive erosion, which increases the risks of episodic flooding and associated damages to near

- shor e properties or public infrastructure or even contribute to shoreline retreat.
We are unable to provide an estimate of the aquatic enrichment co
- benefits associated with the revised or alternative annual standard s due to data, time, and resource limitation s.

Terrestrial Enrichment

. Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess of natural background levels, through either atmospheric deposition or direct application. Evidence presented in the ISA for NO x /SO x (U.S. EPA, 2008c) supports a causal relationship between atmospheric N deposition and biogeochemical cycling and fluxes of N and carbon in terrestrial systems. Furthermore, evidence summarized in the report supports a causal link between atmospheric N deposition and changes in the types and number of species and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as a result, it may take as much as 50 years or more to see changes in ecosystem conditions and indicators. This lo ng time scale also affects the timing of the ecosystem service changes. The ISA for NO x /SO x — Ecological Criteria concluded that the

51 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. 52 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.*** E.O. 12866 Review

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- 117 evidence is sufficient to infer a causal relationship between nitrogen deposition and the alteration of species richness, spe cies composition, and biodiversity in terrestrial ecosystems (U.S. EPA, 2008c).
One of the main provisioning services potentially affected by N deposition is grazing opportunities offered by grasslands for livestock production in the Central U.S. Although N deposition on these grasslands can offer supplementary nutritive value and promote overall grass production, there are concerns that fertilization may favor invasive grasses and shift the species composition away from native grasses. This process may ult imately reduce the productivity of grasslands for livestock production. Losses due to invasive grasses can be significant; for example, based on a bioeconomic model of cattle grazing in the upper Great Plains, Leitch, Leistritz, and Bangsund (1996) and Lei stritz, Bangsund, and Hodur (2004) estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, and Wyoming. 53 However, the contribution of N deposition to these losses is still uncertain.

Terrestrial nutrient enrichment also affects cultural and regulating services. For example, in California, Coastal Sage Scrub (CSS) habitat concerns focus on a decline in CSS and an increase in nonnative grasses and other species, impacts on the viability of threatened and endangered species associated with CSS, and an increase in fire frequency. Changes in Mixed Conifer Forest (MCF) include changes in habitat suitability and increased tree mortality, increased fire intensity, and a change in the forest ’ s nutrient cycling that may affect surfa ce water quality through nitrate leaching (U.S. EPA, 2008c). CSS and MCF are an integral part of the California landscape, and together the ranges of these habitats include the densely populated and valuable coastline and the mountain areas. Numerous threa tened and endangered species at both the state and federal levels reside in CSS and MCF. The value that California residents and the U.S. population as a whole place on CSS and MCF habitats is reflected in the various federal, state, and local government m easures that have been put in place to protect these habitats, including the Endangered Species Act, conservation planning programs, and private and local land trusts. CSS and MCF habitat are showcased in many popular recreation areas in California, includ ing several national parks and monuments. In addition, millions of individuals are involved in fishing, hunting, and wildlife viewing in California every year (DOI, 2007). The quality of these trips depends in part on the health of the ecosystems and their ability to support the diversity of plants and animals found in important habitats found in CSS or MCF ecosystems and the parks associated with those ecosystems.

53 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.*** E.O. 12866 Review

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- 118 Based on analyses in the NO x SO x REA average values of the total benefits in 2006 from fishin g, hunting, and wildlife viewing away from home in California were approximately $950 million, $170 million, and $3.6 billion, respectively (U.S. EPA, 2009c). 54 In addition, data from California State Parks (2003) indicate that in 2002, 69% of adult residen ts participated in trail hiking for an average of 24 days per year. The aggregate annual benefit for California residents from trail hiking in 2007 was $11 billion (U.S. EPA, 2009c). 55 It is not currently possible to quantify the loss in value of services d ue to nitrogen deposition as those losses are already reflected in the estimates of the contemporaneous total value of these recreational activities. Restoration of services through decreases in nitrogen deposition would likely increase the total value of recreational services.
Fire regulation is also an important regulating service that could be affected by nutrient enrichment of the CSS and MCF ecosystems by encouraging growth of more flammable grasses, increasing fuel loads, and altering the fire cycle. Over the 5

- year period from 2004 to 2008, Southern California experienced, on average, over 4,000 fires per year burning, on average, over 400,000 acres per year (National Association of State Foresters [NASF], 2009). It is not possible at this time to qua ntify the contribution of nitrogen deposition, among many other factors, to increased fire risk.
We are unable to provide an estimate of the terrestrial nutrient enrichment co
- benefits associated with the revised or alternative annual st andards due to data, time, and resource limitations. Methods are not yet available to allow estimation of changes in ecosystem services due to nitrogen deposition.

6

. 7 .2.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide Uptake of gaseous sulfur dioxide in a pla nt canopy is a complex process involving adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO 2 penetrates into leaves through to the stomata, although there is evidence for limited pathways via the cuticle (U.S. EPA, 2008c). Poll utants 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

su sceptibility 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 and

54 These estimates refle ct 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. 55 These estimates reflect the total value of the service, not the marginal change in the value of the serv ice as a result of the emission reductions achieved by this rule.*** E.O. 12866 Review

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- 119 nitrogen than vascular plants (U.S. EPA, 2008c). Acute foliar injury u sually 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 SO 2 exposures over the gro wing season. Besides foliar injury, chronic exposure to low SO 2 concentrations can result in reduced photosynthesis, growth, and yield of plants (U.S. EPA, 2008c). These effects are cumulative over the season and are often not associated with visible folia r injury. As with foliar injury, these effects vary among species and growing environment. SO 2 is also considered the primary factor causing the death of lichens in many urban and industrial areas (Hutchinson et al., 1996). The ISA for NO x /SO x — Ecological C riteria concluded that the evidence is sufficient to infer a causal relationship between SO 2 injury to vegetation (U.S. EPA, 2008c).

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. 6 .2.4 Mercury

- Related Co

- benefits Associated with the Role of Sulfate in Mercury Methylation Mercury is a persistent, bioaccumulative toxic metal that is emitted from in three forms: gaseous elemental Hg (Hg 0 ), oxidized Hg compounds (Hg +2 ), and particle

- bound Hg (Hg P ). Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils, after Hg has precipitated from the air and deposited into waterbodies or land. Once formed, MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger predatory fish may have MeHg concentrations many times, typically on the order of one m illion times, that of the concentrations in the freshwater body in which they live.

The NO x SO x ISA

— Ecological Criteria concluded that evidence is sufficient to infer a causal relationship between sulfur deposition and increased mercury methylation in wetl ands and aquatic environments (U.S. EPA, 2008c). Specifically, there appears to be a relationship between SO 4 2

- deposition and mercury methylation; however, the rate of mercury methylation varies according to several spatial and biogeochemical factors whos e influence has not been fully quantified (see Figure 6

- 1 4 ). Therefore, the correlation between SO 4 2

- deposition and MeHg could not be quantified for the purpose of interpolating the association across waterbodies or regions. Nevertheless, because changes in MeHg in ecosystems represent changes in significant human and ecological health risks, the association between sulfur and mercury cannot be neglected (U.S. EPA, 2008c).*** E.O. 12866 Review

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- 120 Figure 6

- 1 4
. Spatial and Biogeochemical Factors Influencing MeHg Production

As research evolves and the computational capacity of models expands to meet the complexity of mercury methylation processes in ecosystems, the role of interacting factors may be better parsed out to identify ecosystems or regions that are more likely to g enerate higher concentrations of MeHg. Figure 6

- 1 5 illustrates the type of current and forward

- looking research being developed by the U.S. Geological Survey (USGS) to synthesize the contributing factors of mercury and to develop a map of sensitive watersh eds. The mercury score referenced in Figure 6

- 1 5 is based on SO 4 2

- concentrations, acid neutralizing capacity (ANC), levels of dissolved organic carbon and pH, mercury species concentrations, and soil types to gauge the methylation sensitivity (Myers et al ., 2007).*** E.O. 12866 Review

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- 121 Figure 6

- 1 5
. Preliminary USGS Map of Mercury Methylation

– Sensitive Watersheds Source: Myers et al., 2007 Interdependent biogeochemical factors preclude the existence of simple sulfate

- related mercury methylation models. It is clear that decreasing sulfate deposition is likely to result in decreased MeHg concentrations. Future research may allow for the charac terization of a usable sulfate

- MeHg response curve; however, no regional or classification calculation scale can be created at this time because of the number of confounding factors.

Decreases in SO 4 2

- deposition have already shown promising reductions in MeHg. Observed decreases in MeHg fish tissue concentrations have been linked to decreased acidification and declining SO 4 2

- and mercury deposition in Little Rock Lake, WI (Hrabik and Watras, 2002), and to decreased SO 4 2

- deposition in Isle Royale in Lake S uperior, MI (Drevnick et al., 2007). Although the possibility exists that reductions in SO 4 2

- emissions could generate a pulse in MeHg production because of decreased sulfide inhibition in sulfate

- saturated waters, this effect would likely involve a limite d number of U.S. waters (Harmon et al., 2007). Also, because of the diffusion and outward flow of both mercury

- sulfide complexes and SO 4 2

- , increased mercury methylation downstream may still occur in sulfate

- enriched ecosystems with increased organic matte r and/or downstream transport capabilities.*** E.O. 12866 Review

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- 122 Remediation of sediments heavily contaminated with mercury has yielded significant reductions of MeHg in biotic tissues. Establishing quantitative relations in biotic responses to MeHg levels as a result of chang es in atmospheric mercury deposition, however, presents difficulties because direct associations can be confounded by all of the factors discussed in this section. Current research does suggest that the levels of MeHg and total mercury in ecosystems are po sitively correlated, so that reductions in mercury deposited into ecosystems would also eventually lead to reductions in MeHg in biotic tissues. Ultimately, an integrated approach that involves the reduction of both sulfur and mercury emissions may be most efficient because of the variability in ecosystem responses. Reducing SO X emissions could have a beneficial effect on levels of MeHg in many waters of the United States.
MeHg is the only form of mercury that biomagnifies in the food web. Concentrations of MeHg in fish are generally on the order of a million times the MeHg concentration in water. In addition to mercury deposition, key factors affecting MeHg production and accumulation in fish include the amount and forms of sulfur and carbon species present in a given waterbody. Thus, two adjoining water bodies receiving the same deposition can have significantly different fish mercury concentrations.
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. In general, higher mercury concentrations are expected in top predators, which are often large fish relative to other species in a waterbody.
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.

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. 6 .3 Ecosystem Co

- benefits from Reductions in Mercury Emissions Deposition of mercury to waterbodies can also have an impact on ecosystems and wildli fe. Mercury contamination is present in all environmental media with aquatic systems experiencing the greatest exposures due to bioaccumulation. Bioaccumulation refers to the net uptake of a contaminant from all possible pathways and includes the accumulat ion that may occur by direct exposure to contaminated media as well as uptake from food.
Atmospheric mercury enters freshwater ecosystems by direct deposition and through runoff from terrestrial watersheds. Once mercury deposits, it may be converted to org anic*** E.O. 12866 Review

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- 123 methylmercury mediated primarily by sulfate

- reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly increasing mercury toxicity and potential to bioaccumulate in aquatic foodwebs. A number of key biogeochemical control s influence the production of methylmercury in aquatic ecosystems. These include sulfur, pH, organic matter, iron, mercury “ aging , ” and bacteria type and activity (Munthe et al., 2007).
Wet and dry deposition of oxidized mercury is a dominant pathway for b ringing mercury to terrestrial surfaces. In forest ecosystems, elemental mercury may also be absorbed by plants stomatally, incorporated by foliar tissues and released in litterfall (Ericksen et al., 2003). Mercury in throughfall, direct deposition in prec ipitation, and uptake of dissolved mercury by roots (Rea et al., 2002) are also important in mercury accumulation in terrestrial ecosystems.
Soils have significant capacity to store large quantities of atmospherically deposited mercury where it can leach i nto groundwater and surface waters. The risk of mercury exposure extends to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians that receive mercury deposition or from aquatic systems near the forest areas they inhabit (Berge ron et al., 2010a, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010).
Numerous studies have generated field data on the levels of mercury in a variety of wild species. Many of the data from these environmental studies are anecdotal in nature rather than representative or statistically designed studies. The body of work examining the effects of these exposures is growing but still incomplete given the complexities of the natural world. A large portion of the adverse effect research cond ucted to date has been carried out in the laboratory setting rather than in the wild; thus, conclusions about overarching ecosystem health and population effects are difficult to make at this time. In the sections that follow numerous effects have been ide ntified at d iffering exposure levels.

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. 6 .3.1 Mercury Effects on Fish A review of the literature on effects of mercury on fish (Crump and Trudeau, 2009) reports results for numerous species including trout, bass (large and smallmouth), northern pike, carp, walleye, salmon and others from laboratory and field studies. The effects studied are reproductive and include deficits in sperm and egg formation, histopathological changes in testes and ovaries, and disruption of reproductive hormone synthesis. These st udies were conducted in areas from New York to Washington and while many were conducted by adding MeHg to water or diet many were conducted at current environmental levels. While we cannot determine at this time whether these reproductive deficits are affe cting fish populations across the United States it should be noted that it is possible that over time reproductive deficits could*** E.O. 12866 Review

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- 124 have an effect on populations. Lower fish populations would conceivably impact the ecosystem services like recreational fishin g derived from having healthy aquatic ecosystems quite apart from the effects of consumption advisories due to the human health effects of mercury.

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. 6 .3.2 Mercury Effects on Birds In addition to effects on fish, mercury also affects avian species. In prev ious reports (U.S. EPA , 1997; 2005), much of the focus has been on large piscivorous species in particular the common loon. The loon is most visible to the public during the summer breeding season on northern lakes and they have become an important symbol of wilderness in these areas (McIntyre and Barr, 1997). A multitude of loon watch, preservation, and protection groups have formed over the past few decades and have been instrumental in promoting conservation, education, monitoring, and research of breedi ng loons (McIntyre and Evers, 2000; Evers, 2006). Significant adverse effects on breeding loons from mercury have been found to occur including behavioral (reduced nest

- sitting), physiological (flight feather asymmetry) and reproductive (chicks fledged/ter ritorial pair) effects (Evers, 2008). Additionally Evers, et al (2008) report that they believe that the weight of evidence indicates that population

- level effects occur in parts of Maine and New Hampshire, and potentially in broad areas of the loon ’ s rang e. Recently attention has turned to other piscivorous species such as the white ibis, and great snowy egret. While considered to be fish

- eating generally these wading birds have a very wide diet including crayfish, crabs, snails, insects and frogs. These s pecies are experiencing a range of adverse effects due to exposure to mercury. The white ibis has been observed to have decreased foraging efficiency (Adams and Frederick, 2008). Additionally ibises have been shown to exhibit decreased reproductive success and altered pair behavior (Frederick and Jayasena, 2010). These effects include significantly more unproductive nests, male/male pairing, reduced courtship behavior (head bobbing and pair bowing) and lower nestling production by exposed males. In this stu dy, a worst

- case scenario suggested by the results could involve up to a 50% reduction in fledglings due to MeHg in diet. These estimates may be conservative if male/male pairing in the wild it could result in a shortage of partners for females and the effect of homosexual breeding would be magnified. In egrets, mercury has been implicated in the decline of the species in sou th Florida (Sepulveda, et al., 1999) and Hoffman (2010) has shown that egrets show liver and possibly kidney effects. While ibises and egrets are most abundant in coastal areas and these studies were conducted in south Florida and Nevada the ranges of ibis es and egrets extend to a large portion of the United States. Ibis territory can range inland to Oklahoma, Arkansas and Tennessee. Egret range covers virtually the entire United States except the mountain west.*** E.O. 12866 Review

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- 125 Insectivorous birds have also been shown to s uffer adverse effects due to mercury exposure. These songbirds such as Bicknell ’ s thrush, tree swallows and the great tit have shown reduced reproduction, survival, and changes in singing behavior. Exposed tree swallows produced fewer fledglings (Brasso, 2 008), lower survival (Hallinger, 2010) and had compromised immune competence (Hawley, 2009). The great tit has exhibited reduced singing behavior and smaller song repertoire in an area of high contamination in the vicinity of a metallurgic smelter in Fland ers (Gorissen, 2005).

6

. 6 .3.3 Mercury Effects on Mammals In mammals, adverse effects have been observed in mink and river otter, both fish eating species. For otter from Maine and Vermont maximum concentrations on Hg in fur nearly equal or exceed a concent ration associated with mortality and concentration in liver for mink in Massachusetts/Connecticut and the levels in fur from mink in Maine exceed concentrations associated with acute mortality (Yates, 2005). Adverse sublethal effects may be associated with lower Hg concentrations and consequently be more widespread than potential acute effects. These effects may include increased activity, poorer maze performance, abnormal startle reflex, and impaired escape and avoidance behavior (Scheuhammer et al., 2007)

. 6

. 6 .3.4 Mercury Ecological Conclusions The studies cited here provide a glimpse of the scope of mercury effects on wildlife particularly reproductive and survival effects. These effects range across species from fish to mammals and spatially across a wid e area of the United States. The literature is far from complete however. Much more research is required to establish a link between the ecological effects on wildlife and the effect on ecosystem services (services that the environment provides to people) for example recreational fishing, bird watching and wildlife viewing. The EPA is not, however, currently able to quantify or monetize the co
- benefits of reducing mercury exposures affecting provision of ecosystem services.

6

. 6 .4 Vegetation Co

- benefits from Reductions in Ambient Ozone
Illustrative emission reduction s trategies that include NO x emission reductions would affect ambient ozone concentrations. Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006a; Fox and Mickler, 1996). Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2006a). In terms of forest productivity and ec osystem diversity, ozone may be the pollutant with the greatest potential for regional

- scale forest impacts (U.S. EPA, 2006a). Studies have demonstrated repeatedly that*** E.O. 12866 Review

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- 126 ozone concentrations commonly observed in polluted areas can have substantial impacts o n plant function (De Steiguer et al., 1990; Pye, 1988).
When ozone is present in the air, it can enter the leaves of plants, where it can cause significant cellular damage. Like carbon dioxide (CO 2 ) and other gaseous substances, ozone enters plant tissues primarily through the stomata in leaves in a process called “ uptake ” (Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its reaction products, reaches the interior of plant cells, it can inhibit or damage essenti al cellular components and functions, including enzyme activities, lipids, and cellular membranes, disrupting the plant ’ s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 2006a; Tingey and Taylor, 1982). With fewer resources availab le, the plant reallocates existing resources away from root growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have shown that plants stre ssed in these ways may exhibit a general loss of vigor, which can lead to secondary impacts that modify plants ’ responses to other environmental factors. Specifically, plants may become more sensitive to other air pollutants, or more susceptible to disease , pest infestation, harsh weather (e.g., drought, frost) and other environmental stresses, which can all produce a loss in plant vigor in ozone

- sensitive species that over time may lead to premature plant death. Furthermore, there is evidence that ozone

ca n interfere with the formation of mycorrhiza, essential symbiotic fungi associated with the roots of most terrestrial plants, by reducing the amount of carbon available for transfer from the host to the symbiont (U.S. EPA, 2006a).
This ozone damage may or may not be accompanied by visible injury on leaves, and likewise, visible foliar injury may or may not be a symptom of the other types of plant damage described above. Foliar injury is usually the first visible sign of injury to plants from ozone exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic value of ornamental vegetation and trees in urban landscapes and negatively affects scenic vistas in protected natural areas.
Ozone can produce both acute and chronic injury in sensitive species depending on the concentration level and the du ration of the exposure. Ozone effects also tend to accumulate over the growing season of the plant, so that even lower concentrations experienced for a longer duration have the potential to create chronic stress on sensitive vegetation. Not all plants, how ever, are equally sensitive to ozone. Much of the variation in sensitivity between*** E.O. 12866 Review

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- 127 individual plants or whole species is related to the plant ’ s ability to regulate the extent of gas exchange via leaf stomata (e.g., avoidance of ozone uptake through closure of stomata) and the relative ability of species to detoxify ozone

- generated reactive oxygen free radicals (U.S. EPA, 2006a; Winner, 1994). After injuries have occurred, plants may be capable of repairing the damage to a limited extent (U.S. EPA, 2006a). B ecause of the differing sensitivities among plants to ozone, ozone pollution can also exert a selective pressure that leads to changes in plant community composition. Given the range of plant sensitivities and the fact that numerous other environmental fac tors modify plant uptake and response to ozone, it is not possible to identify threshold values above which ozone is consistently toxic for all plants.
Because plants are at the base of the food web in many ecosystems, changes to the plant community can af fect associated organisms and ecosystems (including the suitability of habitats that support threatened or endangered species and below ground organisms living in the root zone). Ozone impacts at the community and ecosystem level vary widely depending upon numerous factors, including concentration and temporal variation of tropospheric ozone, species composition, soil properties and climatic factors (U.S. EPA, 2006a). In most instances, responses to chronic or recurrent exposure in forested ecosystems are s ubtle and not observable for many years. These injuries can cause stand

- level forest decline in sensitive ecosystems (U.S. EPA, 2006a, McBride et al., 1985; Miller et al., 1982). It is not yet possible to predict ecosystem responses to ozone with certainty ; however, considerable knowledge of potential ecosystem responses is available through long

- term observations in highly damaged forests in the U.S. ( U.S. EPA, 2006a).

6

. 6 .4.1 Ozone Effects on Forests Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity and influencing the diversity, health, and vigor of individual species (U.S. EPA, 2006a). Ozone has been shown in numerous studies to have a strong effect on the health of many plants, inclu ding a variety of co mmercial and ecologically impor tant forest tree species throughout the United States (U.S. EPA, 2007b).
In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program (formerly known as Forest Health Monitoring), FIA looks for visible foliar injury of ozone

- sensitive forest plant species at each ground monitoring site across the country (excluding woodlots and urban trees) that me ets certain minimum criteria. Because ozone injury is cumulative over the course of the growing season, examinations are conducted in July and August, when ozone concentrations and associated injury are typically highest.*** E.O. 12866 Review

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- 128 Monitoring of ozone injury to plan ts by the U.S. Forest Service has expanded over the last 15 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002. Since 2002, the monitoring program has further expanded to 1,130 monitoring sites in 45 stat es. Figure 6

- 16 shows the results of this monitoring program for the year 2002 broken down by U.S. EPA Regions. 56 Figure 6

- 17 identifies the counties that were included in Figure 6

- 1 6 , and provides the county

- level data regarding the presence or absence of ozone
related injury. As shown in Figure 6

- 1 6 , large geographic areas of EPA Regions 6, 8, and 10 were not included in the assessment. Ozone damage to forest plants is classified using a subjective five

- category biosite index based on expert opinion, but d esigned to be equivalent from site to site. Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar injury to highly sensi tive or moderately sensitive plants, respectively), and high or severe foliar injury, which wo uld be expected to result in tree

- level or ecosystem

- level responses, respectively (U.S. EPA, 2006a; Coulston, 2004). The highest percentages of observed high and severe foliar injury, which are most likely to be associated with tree or ecosystem

- level

res ponses, are primarily found in the Mid

- Atlantic and Southeast regions. While the assessment showed considerable regional variation in ozone injury, this assessment targeted different

ozone
- sensitive species in different parts of the country with varying oz one sensitivity, which contributes to the apparent regional differences. It is important to note that ozone can have other, more significant impacts on forest plants ( e.g., reduced biomass growth in trees) prior to showing signs of visible foliar injury (U .S. EPA, 2006a).
Assessing the impact of ground

- level ozone on forests in the U.S. involves understanding the risks to sensitive tree species from ambient ozone concentrations and accounting for the prevalence of those species within the forest. As a way to quantify the risks to particular plants from ground

- level ozone, scientists have developed ozone

- exposure/tree

- response functions by exposing tree seedlings to different ozone levels and measuring reductions in growth as

“ biomass loss. ” Typical ly, seedlings are used because they are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are identical, and the decreases predicted using the

se edlings should be related to the decrease in overall plant fitness for mature trees, but the

56 The data are based on averages of all observations collected in 2002, which is the last year for which data are publicly available. For more information, please consult EPA ’ s 2008 Report on the Environment (U.S. EPA, 2008b).*** E.O. 12866 Review

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- 129 c Degree of Injury: These categories reflect a subjective index based on expert opinion. Ozone can have other, more significant impacts on forest plants (e.g., r educed biomass growth in trees) prior to showing signs of visible foliar injury.*** E.O. 12866 Review

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- 130 Figure 6

- 16. Visible Foliar Injury to Forest Plants from Ozone in U.S. by EPA Regions a,b,c

Figure 6

- 17. Presence and Absence of Visible Foliar Injury, as Measured by U.S. Fo rest Service, 2002 Source: U.S. EPA, 2007b magnitude of the effect may be higher or lower depending on the tree species (Chappelka and Samuelson, 1998). In areas where certain ozone

- sensitive species dominate the forest community, the biomass loss from ozo ne can be sig nificant. Experts have identified 2% annual biomass loss as a level of concern, which would cause long term ecological harm as the short

term negative effects on seedlings compound to affect long

- term forest health (Heck and Cowling, 1997).

O zone damage to the plants including the trees and understory in a forest can affect the ability of the forest to sustain suitable habitat for associated species particularly threatened and endangered species that have existence value

— a nonuse ecosystem ser vice

— for the public. Similarly, damage to trees and the loss of biomass can affect the forest ’ s provisioning services in the form of timber for various commercial uses. In addition, ozone can cause discoloration of leaves and more rapid senescence (early s hedding of leaves), which could negatively affect fall

color tourism because the fall foliage would be less available or less attractive. Beyond the aesthetic damage to fall color vistas, forests provide the public with many other recreational*** E.O. 12866 Review

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- 131 and educatio nal services that may be affected by reduced forest health including hiking, wildlife viewing (including bird watching), camping, picnicking, and hunting. Another potential effect of biomass loss in forests is the subsequent loss of climate regulation serv ice in the form of reduced ability to sequester carbon and alteration of hydrologic cycles.
Some of the common tree species in the United States that are sensitive to ozone are black cherry ( Prunus serotina ), tulip

- poplar ( Liriodendron tulipifera) , and eas tern white pine ( Pinus strobus)

. Ozone

- exposure/tree

- response functions have been developed for each of these tree species, as well as for aspen ( Populus tremuliodes ), and ponderosa pine ( Pinus ponderosa ) (U.S. EPA, 2007b).

6

. 6 .4.2 Ozone Effects on Crops Laboratory and field experiments have shown reductions in yields for agronomic crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and wheat). Damage to crops from ozone exposures includes yield losses (i.e., in terms of weight, number, or size of the plant part that is harvested), as well as changes in crop quality (i.e., physical appearance, chemical composition, or the ability to withstand storage) (U.S. EPA, 2007b). The most extensive field experiments, conducted u nder the National Crop Loss Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN results show that

“ several economically important crop species are sensitive to ozone levels typical of those found in the United States ” (U.S. EPA , 2006a). In addition, economic studies have shown reduced economic co
- benefits as a result of predicted reductions in crop yields, directly affecting the amount and quality of the provisioning service provided by these crops, associated with observed ozon e levels (Kopp et al., 1985; Adams et al., 1986; Adams et al., 1989). In addition, visible foliar injury by itself can reduce the market value of certain leafy crops (such as spinach, lettuce). According to the Ozone Staff Paper, there has been no evidence that crops are becoming more tolerant of ozone (U.S. EPA, 2007b). Using the Agriculture Simulation Model (AGSIM) (Taylor, 1994) to calculate the agricultural benefits of reductions in ozone exposure, the EPA estimated that attaining a W126 standard of 13 ppm
- hr would produce monetized benefits of approximately $400 million to $620 million in 2006 (inflated to 2006 dollars) (U.S. EPA, 2007b). 57

57 These estimates illustrate the value of vegetation effects from a substantial reduction of ozone concentrations, not the marginal change in ozone concentrations anticipated a result of the emission reductions achieved by this rule.*** E.O. 12866 Review

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- 132 6

. 6 .4.3 Ozone Effects on Ornamental Plants Urban ornamental plants are an additional vegetation category likely to experience some degree of negative effects associated with exposure to ambient ozone levels. Several ornamental species have been listed as sensitive to ozone (Abt Associates, 1995). Because ozone causes visible foliar injury, the aesthetic value of orname ntal plants (such as petunia, geranium, and poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007b). Sensitive ornamental species would require more frequent replacement and/or increased maintenance (fertilizer or pesticide application) to maint ain the desired appearance because of exposure to ambient ozone (U.S. EPA, 2007b). In addition, many businesses rely on healthy

- looking vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, farmers of leafy crops, etc.). The ornamental landscaping industry is a multi

- billion dollar industry that affects both private property owners/tenants and governmental units responsible for public areas (Abt Associates, 1995). Preliminary data from the 2007 Economic Census indic ate that the landscaping services industry, which is primarily engaged in providing landscape care and maintenance services and installing trees, shrubs, plants, lawns, or gardens, was valued at $53 billion (U.S. Census Bureau, 2010). Therefore, urban orna mentals represent a potentially large unquantified benefit category. This aesthetic damage may affect the enjoyment of urban parks by the public and homeowners ’ enjoyment of their landscaping and gardening activities. In addition, homeowners may experience a reduction in home value or a home may linger on the market longer due to decreased aesthetic appeal. In the absence of adequate exposure

- response functions and economic damage functions for the potential range of effects relevant to ornamental plants, w e cannot conduct a quantitative analysis to estimate these effects.
We are unable to provide an estimate of the ozone crop co
- benefits associated with the revised or alternative annual standard s due to data, time, and resource limitations.

6

. 7 References
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