$0
                                      $0
                                      $0
                                     $6.9
Clark County, NV
                                      $0
                                      $0
                                     $0.3
                                     $5.5
Crook County, OR
                                      $0
                                     $19.3
                                      $0
                                     $5.4
Harney County, OR
                                      $0
                                     $1.5
                                      $0
                                     $13.4
Jackson County, OR
                                      $0
                                      $0
                                     $0.3
                                     $8.4
Klamath County, OR
                                      $0
                                     $0.5
                                      $0
                                     $6.2
Lake County, OR
                                      $0
                                      $0
                                      $0
                                      $0
Lane County, OR
                                      $0
                                      $0
                                      $0
                                     $0.04
Box Elder County, UT
                                      $0
                                     $14.6
                                      $0
                                      $0
Cache County, UT
                                      $0
                                     $22.0
                                      $0
                                      $0
Davis County, UT
                                      $0
                                     $7.1
                                      $0
                                      $0
Salt Lake County, UT
                                      $0
                                     $16.0
                                      $0
                                      $0
Utah County, UT
                                      $0
                                     $12.3
                                      $0
                                      $0
Weber County, UT
                                      $0
                                     $3.2
                                      $0
                                      $0
King County, WA
                                      $0
                                      $0
                                      $0
                                     $0.8
Kittitas County, WA
                                      $0
                                      $0
                                      $0
                                      $0
Okanogan County, WA
                                      $0
                                     $13.3
                                      $0
                                      $0
Snohomish County, WA
                                      $0
                                     $0.7
                                      $0
                                      $0
Spokane County, WA
                                      $0
                                      $0
                                      $0
                                     $0.4
Yakima County, WA
                                      $0
                                      $0
                                      $0
                                      $0
Total
                                     $19.0
                                    $150.0
                                     $34.2
                                    $121.8

Table 4A-6	Summary of Estimated Annual Control Costs for California (26 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
Air District
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Alameda County, CA
Bay Area AQMD
                                     $0.03
                                     $0.03
                                     $2.0
                                     $14.5
Contra Costa County, CA
Bay Area AQMD
                                      $0
                                      $0
                                     $0.04
                                     $0.4
Marin County, CA
Bay Area AQMD
                                      $0
                                      $0
                                      $0
                                     $0.05
Napa County, CA
Bay Area AQMD
                                     $0.2
                                     $0.2
                                     $1.7
                                     $1.7
Santa Clara County, CA
Bay Area AQMD
                                      $0
                                      $0
                                     $1.0
                                     $16.8
Solano County, CA
Bay Area AQMD
                                      $0
                                      $0
                                      $0
                                     $6.7
Butte County, CA
Butte County AQMD
                                      $0
                                      $0
                                      $0
                                     $0.3
Sutter County, CA
Feather River AQMD
                                      $0
                                      $0
                                      $0
                                     $3.6
Imperial County, CA
Imperial County APCD
                                      $0
                                      $0
                                      $0
                                      $0
Plumas County, CA
Northern Sierra AQMD
                                      $0
                                      $0
                                      $0
                                      $0
Sacramento County, CA
Sacramento Metro AQMD
                                      $0
                                     $0.2
                                     $0.4
                                     $2.3
San Diego County, CA
San Diego County APCD
                                      $0
                                      $0
                                     $0.7
                                     $30.3
Fresno County, CA
San Joaquin Valley APCD
                                     $30.1
                                     $30.1
                                     $30.1
                                     $30.1
Kern County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
Kings County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
Madera County, CA
San Joaquin Valley APCD
                                     $9.1
                                     $9.1
                                     $9.1
                                     $9.1
Merced County, CA
San Joaquin Valley APCD
                                     $9.0
                                     $9.0
                                     $9.0
                                     $9.0
San Joaquin County, CA
San Joaquin Valley APCD
                                     $0.03
                                     $0.03
                                     $11.9
                                     $11.9
Stanislaus County, CA
San Joaquin Valley APCD
                                     $2.9
                                     $2.9
                                     $2.9
                                     $2.9
Tulare County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
San Luis Obispo County, CA
San Luis Obispo County APCD
                                      $0
                                      $0
                                     $1.2
                                     $1.2
Siskiyou County, CA
Siskiyou County APCD
                                      $0
                                     $16.9
                                      $0
                                      $0
Los Angeles County, CA
South Coast AQMD
                                     $12.9
                                     $12.9
                                     $12.9
                                     $12.9
Riverside County, CA
South Coast AQMD
                                      $0
                                      $0
                                      $0
                                      $0
San Bernardino County, CA
South Coast AQMD
                                      $0
                                      $0
                                      $0
                                      $0
Ventura County, CA
Ventura County APCD
                                      $0
                                     $9.1
                                     $1.8
                                     $9.1
Total

                                     $64.1
                                     $90.4
                                     $84.7
                                    $162.9

BENEFITS ANALYSIS APPROACH AND RESULTS
 Overview
This chapter presents the estimated human health-related and welfare benefits of meeting the proposed National Ambient Air Quality Standards (NAAQS) for particulate matter (PM). In this Regulatory Impact Analysis (RIA), we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). We quantify the number and economic value of the estimated avoided premature deaths and illnesses attributable to applying hypothetical national control strategies for the more stringent annual PM2.5 NAAQS standards with a sensitivity analysis for a more stringent 24-hour standard that reduces fine particulate matter (PM2.5) concentrations in 2032. Reducing directly emitted PM2.5 and PM2.5 precursor emissions would also improve environmental quality (U.S. EPA, 2019c, U.S. EPA, 2022a) and reduce the ecological effects of nitrogen and sulfur deposition. Because the EPA is proposing that the current secondary PM NAAQS standards be retained, we did not evaluate alternative secondary standard levels in this RIA, or any visibility-, climate change-, or materials-damage-related benefits of the proposed rule (Cox, 2019, U.S. EPA, 2019c).
The analysis in this chapter aims to characterize the benefits of the air quality changes resulting from the implementation of revised PM standard levels by answering two key questions:
 What is the estimated number and geographic distribution of avoided PM2.5-attributable premature deaths and illnesses expected to result from applying hypothetical national control strategies for a more stringent PM2.5 NAAQS? This chapter presents these results. As discussed in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - .
 What is the estimated number and geographic distribution of avoided PM2.5-attributable premature deaths and illnesses expected to result if we assume that areas identify all of the controls needed for compliance with the proposed and alternative PM2.5 NAAQS? Appendix 5A presents these results.
 What is the estimated economic value of these avoided impacts?
To answer these questions we perform a human health benefits analysis (NRC, 2002). Starting first with the Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2019b) and the Supplement to the ISA for Particulate Matter (U.S. EPA, 2022a), we identify the human health effects associated with ambient particles, which include premature death and a variety of morbidity effects associated with acute (hours-long) and chronic (months- or years- long) exposures. Table 5-2 summarizes human health categories monetized and reflected in the total value of the benefits reported and those categories not monetized due to limited data or resources. The list of benefits categories is neither exhaustive nor completely quantified. We excluded effects not identified as having a causal or likely to be causal relationship with the affected pollutants in the most recent PM ISA (U.S. EPA, 2019b,U.S. EPA, 2022a). In a Technical Support Document (TSD) accompanying this RIA we specify in detail our approach for identifying, selecting, and parametrizing concentration-response relationships and economic unit values to support this benefits analysis. Below in Section 5.1 we summarize this information for readers, describing how we updated our methods for quantifying the number and value of PM-related benefits to reflect the information reported in the PM ISA and supplement to the PM ISA.
This chapter contains a subset of the estimated health benefits of the proposed and alternative PM2.5 standard levels in 2032 that EPA was able to quantify, given available resources and methods. This benefits analysis relies on an array of data inputs -- including air quality modeling, health impact functions and valuation estimates -- which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. We employ several techniques to characterize this uncertainty, which are described in detail in section 5.5.
As described in Chapter 1, the analytical objectives of the NAAQS RIA are unique as compared to other RIAs, such as the recent Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c). The NAAQS RIAs illustrate the potential costs and benefits of attaining one or more revised air quality standard(s) nationwide; these estimated costs and benefits are estimated after we first assume the current standards have been attained. In this RIA, we illustrate the potential costs and benefits for the proposed and more stringent alternative standard levels nationwide. The NAAQS RIAs hypothesize the control strategies that States may choose to enact when implementing a revised NAAQS, but they cannot do so with perfect foresight; individual states will formulate air quality management plans whose mix of emissions controls may differ substantially from those we simulate here. Hence, NAAQS RIAs are illustrative. The benefits and costs estimated in a NAAQS RIA are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emissions reductions. By contrast, EPA is generally confident in the emissions projected to be reduced from rules affecting specific and well-characterized sources -- such as mobile and Electric Generating Units (U.S. EPA, 2019a). Hence, the emissions reduced by final rules affecting such sources are accounted for when simulating attainment with alternative NAAQS.
In the following sections of this chapter, we estimate health benefits occurring as an increment to a 2032 baseline in which the nation fully attains the current primary PM2.5 standards (i.e., an annual standard of 12 ug/m[3] and a 24-hour standard of 35 ug/m[3], hereafter referred to as "12/35"). This baseline accounts for: (1) promulgated regulations (Chapter 1, Section 1.3.); and (2) any additional illustrative emissions reductions needed to simulate attainment with 12/35 (Chapter 3, Section 3.1). We project PM2.5 levels in 2032 in certain areas would exceed 10/35, 10/30, 9/35 and 8/35, even after illustrative controls applied to simulate attainment with 12/35 and estimate emissions reductions needed to attain the alternative standard levels (Chapter 3, Table 3-2). Table 5-1 summarizes the total national monetized benefits resulting from applying the control strategies in 2032. Because the analyses in the RIA are national-level assessments and the ambient air quality issues are complex and local in nature, we do not currently have sufficiently detailed local information for the areas being analyzed, including local inventory information on emissions sources, higher resolution air quality modeling, and local information on emissions controls to estimate the control measures or strategies that might result in meeting the range of revised annual and 24-hour alternative standard levels in the proposal.
Whereas the main analysis in this chapter presents the benefits of the applied control strategies for the standards levels (Table 5-5 through Table 5-9), in Appendix 5A, we present the potential health and monetized benefits of full compliance with the alternative standard levels; the tables in Appendix 5A present potential health benefits regardless of whether the technology or control measures to achieve them is currently available or whether an agency submits information on cross-border transport or wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events, potentially affecting the amount of any additional control needed. The estimates reflect the value of the avoided PM2.5-attributable deaths and the value of morbidity impacts, including, for example, hospital admissions and emergency department visits for cardiovascular and respiratory health issues.
Table 5-1	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                    $17 + B
                                    $20 + B
                                    $43 + B
                                    $95 + B
  7% discount rate
                                    $16 + B
                                    $18 + B
                                    $39 + B
                                    $86 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                   $8.5 + B
                                   $9.6 + B
                                    $21 + B
                                    $46 + B
  7% discount rate
                                   $7.6 + B
                                   $8.6 + B
                                    $19 + B
                                    $41 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

 Because the method used in this analysis to simulate the control strategies does not also simulate changes in ambient concentrations of other pollutants, we were not able to quantify the additional benefits associated with reduced exposure to other pollutants. We also did not estimate the additional benefits from improvements in welfare effects, such as climate effects, ecosystem effects, and visibility (Cox, 2019, U.S. EPA, 2019c). With regard to potential climate benefits, we note that because the available evidence suggests direct PM control measures will be most effective in reducing ambient PM2.5 concentrations, and because we lack information on the CO2-related emissions changes that may result from such measures, we do not quantitatively estimate CO2-related climate benefits in this RIA.
Updated Methodology Presented in the RIA
In 2021, EPA published a TSD titled "Estimating PM2.5- and Ozone-Attributable Health Benefits" that accompanied the RIA for the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2021). As noted above, that TSD described the EPA's approach for quantifying the number and value of air pollution-related premature deaths and illnesses. Since publishing the Revised Cross-State Air Pollution Rule Update TSD, the EPA released a Supplement to the PM ISA (U.S. EPA, 2022a). EPA evaluated the new evidence reported in the Supplement to the PM ISA and revised the TSD accordingly; this process is described in detail within the TSD. The updated TSD will be published as a new document alongside this RIA. Key changes from the most recent version of the TSD are summarized below:
 Incorporated alternative long-term exposure mortality studies. We selected a hazard ratio from an analysis of the National Health Interview Survey (NHIS) (Pope III et al., 2019). Compared to the American Cancer Society study it replaces (Turner et al., 2016), the NHIS cohort reflects more recent years of PM2.5 concentrations and produces a larger number of estimated PM-attributable deaths. We also selected a hazard ratio from an extended analysis of the Medicare cohort (Wu et al., 2020). Compared to the study it replaces (Di et al., 2017), the Wu et al., 2020 analysis includes additional, and more current, years of PM2.5 concentrations and more person-time; this newer study produces a similar number of estimated PM-attributable deaths. We elaborate on our rationale for these choices in section <IP_ADDRESS> of the TSD.
 Altered our approach for estimating counts of Acute Myocardial Infarctions. We selected a risk estimate from an analysis of the Medicare cohort (Wei et al., 2019), in which the authors performed a case-crossover analysis of over 95 million Medicare inpatient hospital claims from 2000-2012. The risk estimate from this study replaces a pooled estimate of single- and multi-city studies that accounted for a smaller population, more limited geographic coverage and less recent PM2.5 concentrations; that latter approach yielded a range of estimated non-fatal heart attacks whose upper bound was significantly larger than the estimate reported in this RIA.
Human Health Benefits Analysis Methods
We estimate the quantity and economic value of air pollution-related effects using a "damage-function." This approach quantifies counts of air pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts, while assuming that each outcome is independent of one another. We construct this damage function by adapting primary research -- specifically, air pollution epidemiology studies and economic value studies -- from similar contexts. This approach is sometimes referred to as "benefits transfer." Below we describe the procedure we follow for: (1) selecting air pollution health endpoints to quantify; (2) calculating counts of air pollution effects using a health impact function; (3) calculating the economic value of the health impacts.
Selecting Air Pollution Health Endpoints to Quantify
As a first step in quantifying PM2.5-related human health impacts, the Agency consults the most recent PM ISA and the Supplement to the ISA for Particulate Matter (U.S. EPA, 2019b, U.S. EPA, 2022a). This document synthesizes the toxicological, clinical and epidemiological evidence to determine whether PM is causally related to an array of adverse human health outcomes associated with either acute (i.e., hours or days-long) or chronic (i.e., years-long) exposure; for each outcome, the ISA reports this relationship to be causal, likely to be causal, suggestive of a causal relationship, inadequate to infer a causal relationship or not likely to be a causal relationship. Historically, the Agency estimates the incidence of air pollution effects for those health endpoints that the ISA classified as either causal or likely-to-be-causal.
Consistent with economic theory, the willingness-to-pay (WTP) for reductions in exposure to environmental hazard will depend on the expected impact of those reductions on human health and other outcomes. All else equal, WTP is expected to be higher when there is stronger evidence of a causal relationship between exposure to the contaminant and changes in a health outcome (McGartland et al., 2017). For example, in the case where there is no evidence of a potential relationship the WTP would be expected to be zero and the effect should be excluded from the analysis. Alternatively, when there is some evidence of a relationship between exposure and the health outcome, but that evidence is insufficient to definitively conclude that there is a causal relationship, individuals may have a positive WTP for a reduction in exposure to that hazard (U.S. EPA-SAB, 2020; Kivi and Shogren, 2010). Lastly, the WTP for reductions in exposure to pollutants with strong evidence of a relationship between exposure and effect are likely positive and larger than for endpoints where evidence is weak, all else equal. Unfortunately, the economic literature currently lacks a settled approach for accounting for how WTP may vary with uncertainty about causal relationships.
Given this challenge, the Agency draws its assessment of the strength of evidence on the relationship between exposure to PM2.5 and potential health endpoints from the ISAs that are developed for the NAAQS process as discussed above. The focus on categories identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest is to estimate the pollutant-attributable human health benefits in which we are most confident. All else equal, this approach may underestimate the benefits of PM2.5 exposure reductions as individuals may be willing to pay to avoid specific risks where the evidence is insufficient to conclude they are "likely to be caus[ed]" by exposure to these pollutants.[6] At the same time, WTP may be lower for those health outcomes for which causality has not been definitively established. This approach treats relationships with ISA causality determinations of "likely to be causal" as if they were known to be causal, and therefore benefits could be overestimated. Table 5-2 reports the effects we quantified and those we did not quantify in this RIA. The list of benefit categories not quantified is not exhaustive. The table below omits welfare effects such as acidification and nutrient enrichment.
Table 5-2	Human Health Effects of Pollutants Potentially Affected by Attainment of the Primary PM2.5 NAAQS
                                   Pollutant
                                 Effect (age)
                               Effect Quantified
                               Effect Monetized
                               More Information
                                     PM2.5

Adult premature mortality based on cohort study estimates (>17 or >64)
                                      
                                      
PM ISA

Infant mortality (<1)
                                      
                                      
PM ISA

Non-fatal heart attacks (>18)
                                      
                                      
PM ISA

Hospital admissions - cardiovascular (all)
                                      
                                      
PM ISA

Hospital admissions - respiratory (<19 and >64)
                                      
                                      
PM ISA

Hospital admissions - Alzheimer's disease (>64)[2]
                                      
                                      
PM ISA

Hospital admissions - Parkinson's disease (>64) 2
                                      
                                      
PM ISA

Emergency department visits  -  cardiovascular (all)
                                      
                                      
PM ISA

Emergency department visits  -  respiratory (all)
                                      
                                      
PM ISA

Emergency hospital admissions (>65)
                                      
                                      
PM ISA

Non-fatal lung cancer (>29)[2]
                                      
                                      
PM ISA

Out-of-hospital cardiac arrest (all)[2]
                                      
                                       --
PM ISA

Stroke incidence (50-79)[2]
                                      
                                      
PM ISA

New onset asthma (<12)[2]
                                      
                                      
PM ISA

Exacerbated asthma  -  albuterol inhaler use (asthmatics, 6-13)
                                      
                                      
PM ISA

Lost work days (18-64)
                                      
                                      
PM ISA

Minor restricted-activity days (18-64)
                                      
                                       --
PM ISA

Other cardiovascular effects (e.g., doctor's visits, prescription medication)
                                       --
 --
PM ISA[1]

Other respiratory effects (e.g., pulmonary function, other ages)
 --
 --
PM ISA[1]

Other cancer effects (e.g., mutagenicity, genotoxicity)
 --
 --
PM ISA[1]

Other nervous system effects (e.g., dementia)
 --
 --
PM ISA[1]

Metabolic effects (e.g., diabetes, metabolic syndrome)
 --
 --
PM ISA[1]

Reproductive and developmental effects (e.g., low birth weight, pre-term births)
 --
 --
PM ISA[1]
1 We assess these benefits qualitatively due to epidemiological or economic data limitations.
2 Quantified endpoints have been added since the 2021 version of the Estimating PM2.5- and Ozone-Attributable Health Benefits TSD. Full details of the updates can be found in the TSD published alongside this RIA.

Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use the environmental Benefits Mapping and Analysis Program -- Community Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses attributable to photochemical modeled changes in annual mean PM2.5 for the year 2032 using a health impact function (Sacks et al., 2018). A health impact function combines information regarding: the concentration-response relationship between air quality changes and the risk of a given adverse outcome; the population exposed to the air quality change; the baseline rate of death or disease in that population; and, the air pollution concentration to which the population is exposed.
The following provides an example of a PM2.5 mortality risk health impact function. We estimate counts of PM2.5-related total deaths (yij) during each year i (i=2032) among adults aged 18 and older (a) in each county in the contiguous U.S. j (j=1,...,J where J is the total number of counties) as
yij= Σa yija
yija = moija x(e[β∙∆C]ij-1) x Pija,    Eq[1]
where moija is the baseline total mortality rate for adults aged a=18-99 in county j in year i stratified in 10-year age groups, β is the risk coefficient for total mortality for adults associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in county j in year i, and Pija is the number of county adult residents aged a=18-99 in county j in year i stratified into 5-year age groups.
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. In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analysis is limited to those health effects that are directly and specifically linked to PM2.5.
We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Künzli et al., 2000 and other, more recent health impact analyses, our estimates are based on the best available methods of benefits transfer.
Calculating the 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 WTP for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use these data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). The same type of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we instead use the cost of treating or mitigating the effect to economically value the health impact. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates generally (although not 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.
Benefits Analysis Data Inputs
In Figure 5-1, we summarize the key data inputs to the health impact and economic valuation estimates, which were calculated using BenMAP-CE model version 1.5.1 (Sacks et al., 2018). In the sections 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 Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c).
                Modeled Baseline and Post-Control Ambient PM2.5

                            PM2.5 Health Functions

                         Economic Valuation Functions

                          2032 Population Projections

                     PM2.5 Incremental Air Quality Change

                         PM2.5-Related Health Impacts

Woods & Poole Population Projections

                   Background Incidence and Prevalence Rates

                       Monetized PM2.5-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 Data

Modeled Baseline and Post-Control Ambient PM2.5

                            PM2.5 Health Functions

                         Economic Valuation Functions

                          2032 Population Projections

                     PM2.5 Incremental Air Quality Change

                         PM2.5-Related Health Impacts

Woods & Poole Population Projections

                   Background Incidence and Prevalence Rates

                       Monetized PM2.5-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 Data

Figure 5-1	Data Inputs and Outputs for the BenMAP-CE Model
Demographic Data
Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting models developed by Woods & Poole, Inc. (Woods & Poole, 2015). The Woods & Poole database contains county-level projections of population by age, sex, and race out to 2060, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S. to consider 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 (Hollmann et al., 2000). According to Woods & Poole, linking county-level growth projections together and constraining the projected population to a national-level total growth avoids potential errors introduced by forecasting each county independently (for example, the projected sum of county-level populations cannot exceed the national total). 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 179 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 production 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, population 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.
 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 by sex and race for each year through 2060 based on historical rates of mortality, 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 5 ug/m[3] decrease in daily PM2.5 levels is associated with a decrease in hospital admissions of 3%. A baseline incidence rate, necessary to convert this relative change into a number of cases, is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population.
Table 12 from the TSD (reproduced below as Table 5-3) summarizes the sources of baseline incidence rates and reports average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence 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. National-level incidence rates were used for most morbidity endpoints, whereas county-level data are available for premature mortality. 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 only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level.
Table 5-3	Baseline Incidence Rates for Use in Impact Functions
                                   Endpoint
                                   Parameter
                                     Rates

                                     Value
                                    Source
Mortality[1]
Daily or annual projected incidence to 2060 in 5-year increments (0--99)
Age-, cause-, race-, and county-stratified rates
CDC WONDER (2012 - 2014)
U.S. Census Bureau, 2012
Hospitalizations[2]
Daily incidence rates for all ages
Age-, region/state/county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Emergency Department Visits[2]
Daily emergency department visit incidence rates for all ages
Age-, region-, state-, county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Nonfatal Acute Myocardial Infarction
Daily nonfatal AMI incidence rate per person aged 18-99
Age-, region-, state-, and county- stratified rates
AHRQ, 2016
Asthma Symptoms
Daily incidence among asthmatic children

Wheeze (ages 5-12)
Cough (ages 5-12)
Shortness of breath (ages 5-12)
Albuterol use (ages 6-13)
Age- and race- stratified rates

2.2 puffs per day
Ostro et al., 2001

Rabinovitch et al., 2006
Asthma Onset
Annual incidence
0 - 4
5 - 11
12 - 17
0.0234
0.0111
0.0044
Winer et al., 2012
Alzheimer's Disease
Daily incidence rates for all ages
Age-, region-, state-, and county- stratified rates

2011-2014 HCUP data files
Parkinson's Disease
Annual incidence
18 - 44
45 - 64
65 - 84
85 - 99
0.0000011
0.0000366
0.0002001
0.0002483
HCUPnet
Allergic Rhinitis
Respondents aged 3-17 experiencing allergic rhinitis/hay fever symptoms within the year prior to the survey
0.192
Parker et al., 2009
Cardiac Arrest
Daily nonfatal incidence rates
0 - 17
18 - 39
40 - 64
65 - 99
0.00000002
0.00000009
0.00000056
0.00000133
Ensor et al., 2013, Rosenthal et al., 2008, Silverman et al., 2010
Lung Cancer
Annual nonfatal incidence
25 - 34
35 - 44
45 - 54
55 - 64
65 - 74
75 - 84
95 - 99
0.000001746
0.000014919
0.000067463
0.000208053
0.000052370
0.000576950
0.000557130
SEER, 2015 and Gharibvand et al., 2017
Stroke
Annual nonfatal incidence in ages 65-99
0.00446
Kloog et al., 2012
Work Loss Days
Daily incidence rate per person (18 - 64)
Aged 18 - 24
Aged 25 - 44
Aged 45 - 64
0.00540
0.00678
0.00492
Adams et al., 1999, Table 41; U.S. Census Bureau, 2000
School Loss Days
Rate per person per year, assuming 180 school days per year
9.9
Adams et al., 1999, Table 47
Minor Restricted-Activity Days
Daily MRAD incidence rate per person (18-64)
0.02137
Ostro and Rothschild, 1989, p. 243
CDC-Centers for Disease Control; NHS-National Health Interview Survey. Detailed references associated with this table are located in the TSD.
[1]Mortality rates are only available in 5-year increments. The Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2016).
[2]Baseline incidence rates now include corrections from the states of Indiana and Montana.

We projected mortality rates such that future mortality rates are consistent with our projections of population growth (U.S. EPA, 2018). To perform this calculation, we began first with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau projected national-level annual mortality rates stratified by age range, we projected these mortality rates to 2060 in 5-year increments (U.S. Census Bureau, 2009, U.S. EPA, 2018). Further information regarding this procedure may be found in the TSD for this RIA and the appendices to the BenMAP user manual (U.S. EPA, 2022b).
The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent (AHRQ, 2016) than the rates they replace and more accurately represent the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution-related illnesses. 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-related hospital admissions are scheduled, this assumption would underestimate these benefits.
Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits analysis is described fully in the TSD accompanying this RIA. Because of the substantial economic value associated with estimated counts of PM2.5-attributable deaths, we describe our rationale for selecting among long-term exposure epidemiologic studies below; a detailed description of all remaining endpoints may be found in the TSD.
PM2.5 Premature Mortality Effect Coefficients for Adults
A substantial body of published scientific literature documents the association between PM2.5 concentrations and the risk of premature death (U.S. EPA, 2019b U.S. EPA, 2022a). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA, completed as part of this review of the PM standards and reviewed by the Clean Air Scientific Advisory Committee (CASAC) (Sheppard, 2022), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the full body of scientific evidence (U.S. EPA, 2019b U.S. EPA, 2022a). The size of the mortality effect estimates from epidemiologic 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 endpoint quantified in this analysis. EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related premature death, following a systematic approach detailed in the TSD accompanying this RIA that is generally consistent with previous RIAs (e.g., U.S. EPA, 2011a, U.S. EPA, 2011b, U.S. EPA, 2011c, U.S. EPA, 2012a, U.S. EPA, 2012b, U.S. EPA, 2015a, U.S. EPA, 2019a).
As premature mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits assessment, quantifying effects using risk estimates reported from multiple long-term exposure studies using different cohorts helps account for uncertainty in the estimated number of PM-related premature deaths. Below we summarize the three identified studies and hazard ratios and then describe our rationale for quantifying premature PM-attributable deaths using two of these studies.
Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and all-cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using Medicare claims data from 2000-2016 representing over 573 million person-years of follow up and over 27 million deaths. This cohort included over 20% of the U.S. population and was, at the time of publishing, the largest air pollution study cohort to date. The authors modeled PM2.5 exposure at a 1-km[2] grid resolution using a hybrid ensemble-based prediction model that combined three machine learning models and relied on satellite data, land-use information, weather variables, chemical transport model simulation outputs, and monitor data. Wu et al., 2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression model, and three causal inference approaches (GPS estimation, GPS matching, and GPS weighting). All five statistical approaches provided consistent results; we report the results of the Cox proportional hazards model here. The authors adjusted for numerous individual-level and community-level confounders, and sensitivity analyses suggest that the results are robust to unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.066) and 95% confidence interval (1.058-1.074) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Wu et al., 2020, Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in place of the risk estimate from Di et al., 2017. These two epidemiologic studies share many attributes, including the Medicare cohort and statistical model used to characterize population exposure to PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and reflects more recent PM2.5 concentrations.
Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure and all-cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539 adults from the full cohort for whom body mass index (BMI) and smoking status data were available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5 concentrations derived from regulatory monitoring data and constructed in a universal kriging framework using geographic variables including land use, population, and satellite estimates. Pope III et al., 2019 assigned annual-average PM2.5 exposure from 1999-2015 to each individual by census tract and used complex (accounting for NHIS's sample design) and simple Cox proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio calculated using the complex model for the sub-cohort, which controls for individual-level covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and 95% confidence interval (1.08-1.15) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Pope III et al., 2019, Table 2, Subcohort). This study exhibits two key strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort is representative of the U.S. population, especially with respect to the distribution of individuals by race, ethnicity, income, and education.
     EPA has historically used estimated Hazard Ratios from extended analyses of the ACS cohort (Pope et al., 1995, Pope III et al., 2002, Krewski et al, 2009) to estimate PM-related risk of premature death. More recent ACS analyses (Pope et al., 2015, Turner et al., 2016):
 extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),
 evaluated 669,046 participants over 12,662,562 person-years of follow up and 237,201 observed deaths, and
 applied a more advanced exposure estimation approach than had previously been used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy framework with national-level land use regression models.
The total mortality hazard ratio best estimating risk from these ACS cohort studies was based on a random-effects Cox proportional hazard model incorporating multiple individual and ecological covariates (relative risk =1.06, 95% confidence intervals 1.04 - 1.08 per 10ug/m3 increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range (>29 vs >64), only the Wu et all., 2020 and Pope III et al., 2019 are included in the main benefits assessments, with Wu et al., 2020 representing results from both the Medicare and ACS cohorts.
Unquantified Human Health Benefits
Although we have quantified many of the health benefits associated with reducing exposure to PM2.5, as shown in Table 5-2, we are unable to quantify the health benefits of implementing the illustrative control strategies described in Chapter 3 associated with reducing ozone exposure, SO2 exposure, or NO2 exposure. This is because we focused on reducing direct PM emissions and do not have air quality modeling data for these pollutants. Although we used air quality surfaces that reflect applying the control strategies for the impact of each alternative combination of standard levels on ambient levels of PM2.5, this method does not simulate how the illustrative emissions reductions would affect ambient levels of ozone, SO2, or NO2. Below we provide a qualitative description of these health benefits. In general, previous analyses have shown that the monetized value of these additional health benefits is much smaller than PM2.5-related benefits (U.S. EPA, 2010, U.S. EPA, 2015a). The extent to which ozone, SO2, and/or NOx would be reduced would depend on the specific control strategies used to reduce PM2.5 in a given area.
Exposure to ambient ozone is associated with human health effects, including respiratory and metabolic morbidity (U.S. EPA, 2020a). Epidemiological researchers have associated ozone exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2020a). When adequate data and resources are available, EPA generally quantifies several health effects associated with exposure to ozone (e.g., U.S. EPA, 2014b, U.S. EPA, 2015a). These health effects include respiratory morbidity such as asthma attacks, hospital admissions, emergency department visits, and school loss days. The scientific literature suggests that exposure to ozone is also associated with chronic respiratory damage and premature aging of the lungs, but 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 (SO2 ISA) concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO2 (U.S. EPA, 2017). The immediate effect of SO2 on the respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting inflammation associated with this disease. A clear concentration-response relationship has been demonstrated in laboratory studies following exposures to SO2, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified three short-term morbidity endpoints that the SO2 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 SO2 ISA (U.S. EPA, 2017). The SO2 ISA also concluded that the relationship between short-term SO2 exposure and premature mortality was "suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a relationship between SO2 exposure and mortality, the number of studies was limited. Because we focused on reducing primary PM emissions, we did not quantify these benefits.
Epidemiological researchers have associated NO2 exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen -- Health Criteria (NO2 ISA) (U.S. EPA, 2016). The NO2 ISA provides a comprehensive review of the current evidence of health and environmental effects of NO2. The NO2 ISA concluded that "evidence for asthma attacks supports a causal relationship between short-term NO2 exposure and respiratory effects," and "evidence for development of asthma supports a likely to be causal relationship between long-term NO2 exposure and respiratory effects." These are stronger conclusions than those determined in the 2008 NO2 ISA (U.S. EPA, 2008).These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. These are stronger conclusions than those determined in the 2008 NO2 ISA (U.S. EPA, 2008). These epidemiologic and experimental studies encompass a number of endpoints including emergency department 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 - 20% increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms. The NO2 ISA concluded that the relationship between short-term NO2 exposure and premature mortality was "suggestive but not sufficient to infer a causal relationship" because it is difficult to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies consistently reported a relationship between NO2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. Because we focused on reducing primary PM emissions, we did not quantify these benefits.
Illustrative controls to meet the alternative standard levels are expected to reduce PM2.5 emissions from fossil fuel and wood combustion, as well as industrial processes, and consequentially is expected to lead to reduced Hazardous Air Pollutant (HAP) emissions. HAP emissions from EGUs and other industrial sources may contribute to increased cancer risks and other serious health effects, including damage to the immune system, as well as neurological, reproductive (e.g., reduced fertility), developmental, respiratory and other health problems. These public health implications of exposure to HAPs can be particularly pronounced for segments of the population that are especially vulnerable to some of these effects (e.g., children are especially vulnerable to neurological effects because their brains are still developing). Some HAPs can also detrimentally affect ecosystems used for recreational and commercial purposes.
Unquantified Welfare Benefits
      The Clean Air Act definition of welfare effects includes, but is not limited to, effects on soils, water, wildlife, vegetation, visibility, weather, and climate, as well as effects on man-made materials, economic values, and personal comfort and well-being. Detailed information regarding the ecological effects of nitrogen and sulfur deposition is available in the Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter-- Ecological Criteria (ISA) (U.S. EPA, 2020b).
      Particulate matter (PM) is composed of some or all of the following components: nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), metals, minerals (dust), and organic and elemental carbon. Nitrate, sulfate, and ammonium contribute to nitrogen (N) and sulfur (S) deposition, which causes substantial ecological effects. The ecological effects of deposition are grouped into three main categories: acidification, N enrichment/N driven eutrophication, and S enrichment. Ecological effects are further subdivided into terrestrial, wetland, freshwater, and estuarine/near-coastal ecosystems. These ecosystems and effects are linked by the connectivity of terrestrial and aquatic habitats through biogeochemical pathways of N and S.
      In the ISA, information on ecological effects from controlled exposure, field addition, ambient deposition, and toxicological studies, among others, are integrated to form conclusions about the causal relationships between NOy, SOx, and PM and ecological effects. A consistent and transparent framework (U.S. EPA, 2015b, Table II) is applied to classify the ecological effect evidence according to a five-level hierarchy:
      1. Causal relationship
      2. Likely to be a causal relationship
      3. Suggestive of, but not sufficient to infer, a causal relationship
      4. Inadequate to infer a causal relationship
      5. Not likely to be a causal relationship
      Table 5-4 summarizes the causal determinations for relationships between N and S deposition and ecological effects. Though not quantified in this RIA, it is reasonable to infer that reducing fine particle levels by controlling emissions of NOx and SOx will yield the ecological benefits detailed below.
Table 5-4 	Causal Determinations Identified in Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter -- Ecological Criteria 2020b
                                Effect Category
                             Causal Determination
N and acidifying deposition to terrestrial ecosystems

N and S deposition and alteration of soil biogeochemistry in terrestrial ecosystems
Section IS.5.1 and Appendix 4.1

                              Causal relationship
N deposition and the alteration of the physiology and growth of terrestrial organisms and the productivity of terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.1
                              Causal relationship
N deposition and the alteration of species richness, community composition, and biodiversity in terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.2
                              Causal relationship
Acidifying N and S deposition and the alteration of the physiology and growth of terrestrial organisms and the productivity of terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.1
                              Causal relationship
Acidifying N and S deposition and the alteration of species richness, community composition, and biodiversity in terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.2
                              Causal relationship
N and acidifying deposition to freshwater ecosystems

N and S deposition and alteration of freshwater biogeochemistry
Section IS.6.1 and Appendix 7.1.7
                              Causal relationship
Acidifying N and S deposition and changes in biota, including physiological impairment and alteration of species richness, community composition, and biodiversity in freshwater ecosystems
Section IS.6.3 and Appendix 8.6
                              Causal relationship
N deposition and changes in biota, including altered growth and productivity, species richness, community composition, and biodiversity due to N enrichment in freshwater ecosystems
Section IS.6.2 and Appendix 9.6
                              Causal relationship
N deposition to estuarine ecosystems

N deposition and alteration of biogeochemistry in estuarine and near-coastal marine systems
Section IS.7.1 and Appendix 7.2.10
                              Causal relationship
N deposition and changes in biota, including altered growth, total primary production, total algal community biomass, species richness, community composition, and biodiversity due to N enrichment in estuarine environments
Section IS.7.2 and Appendix 10.7
                              Causal relationship
N deposition to wetland ecosystems

N deposition and the alteration of biogeochemical cycling in wetlands
Section IS.8.1 and Appendix 11.10
                              Causal relationship
N deposition and the alteration of growth and productivity, species physiology, species richness, community composition, and biodiversity in wetlands
Section IS.8.2 and Appendix 11.10
                              Causal relationship
S deposition to wetland and freshwater ecosystems

S deposition and the alteration of mercury methylation in surface water, sediment, and soils in wetland and freshwater ecosystems
Section IS.9.1 and Appendix 12.7
                              Causal relationship
S deposition and changes in biota due to sulfide phytotoxicity, including alteration of growth and productivity, species physiology, species richness, community composition, and biodiversity in wetland and freshwater ecosystems
Section IS.9.2 and Appendix 12.7
                              Causal relationship

Visibility Impairment Benefits
Reducing PM2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people's enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009). Previous analyses (U.S. EPA, 2011d) show that visibility benefits can be a significant welfare benefit category. Without air quality modeling, we are unable to estimate visibility-related benefits, and we are also unable to determine whether the emission reductions associated with the proposal would be likely to have a significant impact on visibility in urban areas or Class I areas.
Climate Effects of PM2.5
In the climate section of Chapter 5 of the 2020 PM2.5 Primary NAAQS Policy Assessment it states "Thus, as in the last review, the data remain insufficient to conduct quantitative analyses for PM effects on climate in the current review." (U.S. EPA, 2020d) 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 change. In contrast, a pollutant that exerts negative radiative forcing reduces the amount of energy in the Earth's system and leads to cooling.
Atmospheric particles influence climate in multiple ways: directly absorbing light, scattering light, changing the reflectivity ("albedo") of snow and ice through deposition, and interacting with clouds. Depending on the particle's composition, the timing of emissions, and where it is in the atmosphere determine if it contributes to cooling or warming. The short atmospheric lifetime of particles, lasting from days to weeks, and the mechanisms by which particles affect climate, distinguish it from long‐lived greenhouse gases like CO2. This means that actions taken to reduce PM2.5 will have near term effects on climate change. The Intergovernmental Panel on Climate Change Sixth Assessment Report concludes that for forcers with short lifetimes, "the response in surface temperature occurs strongly, as soon as a sustained change in emissions is implemented" (Naik et al., 2021). 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, Arctic Council, Climate and Clean Air Coalition, and Convention on Long-Range Transboundary Air Pollution of the United Nations Economic Commission for Europe). Recent reports have concluded that short-lived compounds play a prominent role in keeping global warming below 1.5° C (IPCC, 2018), and are especially important in the rapidly warming Arctic (AMAP, 2021). While reducing long-lived GHGs such as CO2 is necessary to protect against long-term climate change, reducing short-lived forcers and would slow the rate of climate change within the first half of this century (UNEP, 2011).
Climate Effects of Carbonaceous Particles
The illustrative control strategies are focused on emissions sources that are significant sources of carbonaceous particles, including black carbon and organic carbon. Black Carbon (BC), also called soot, is the most strongly light‐absorbing component of PM2.5, and is formed by incomplete combustion of fossil fuels, biofuels, and biomass. Another contributor to carbonaceous particles is organic carbon (OC), which in addition to carbon are also composed of oxygen and hydrogen. Organic carbon particles can be directly emitted from the same sources as black carbon or formed in the atmosphere from chemical reactions. They can be light-absorbing, but most have a larger light-scattering component.
Both BC and organic carbon in the atmosphere influence climate in multiple ways: directly absorbing or reflecting light, modifying the rate of vertical mixing, and interacting with clouds. Light-absorbing particles also have an additional climate effect when deposited on snow and ice. These particles darken the surface and decrease albedo, thereby increasing absorption and accelerating melting (Hock et al., 2019; Meredith et al., 2019). Regional climate impacts of BC are highly variable, and sensitive regions such as the Arctic 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 snowpacks 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 occur 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 different 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 snowpack 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 (McKenzie Skiles et al 2018). Light-absorbing particles and especially BC can have an additional warming effect when deposited on snow and ice, and this effect is highly seasonal and regional.
Relative to greenhouse gases, the net effect of carbonaceous particles is both more regionally variable and more uncertain (Naik et al., 2021). Particles have a relatively short lifetime in the atmosphere, leading to spatial concentration differences, while greenhouse gases are more well mixed and have less global variability. The amount of light absorption by particles depends on the season, with different effects in the summer and winter. Lastly, even light-absorbing particles can also contribute to cooling (e.g., by shading the surface).
Climate Effects: Summary and Conclusions
The net climate change effect of carbonaceous aerosols in the illustrative control strategies depends on the location, timing, and type of the emissions controls. As described above, the black carbon emissions are more likely to contribute to warming and organic aerosols more likely to contribute to cooling. Emissions sources with larger amounts of light-absorbing aerosols, like diesel vehicles, or with emissions near snow or the Arctic, like residential wood combustion, are more likely to contribute to warming (Bond et al., 2013).
However, assessing the net effect is beyond the scope of this RIA and requires climate atmospheric modeling that has not been undertaken. Furthermore, there are uncertainties relevant to the assessment of the net climate change effects of PM2.5, especially at a regional scale (U.S. EPA, 2019b). Strategies that could be implemented by State and Local governments that would likely provide climate change mitigation benefits include prioritizing (i) emissions control actions that also achieve emissions reductions for warming agents like carbon dioxide, methane, and ozone precursors (carbon monoxide and volatile organic compounds), and (ii) sources of light-absorbing carbonaceous aerosols, especially diesel engines and residential wood combustion.
Economic Valuation Estimates
To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014a).
Characterizing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. The TSD accompanying this RIA details our approach to characterizing uncertainty in both quantitative and qualitative terms. That TSD describes the sources of uncertainty associated with key input parameters including emissions 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 regarding the future state of the country (i.e., regulations, technology, and human behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.
To characterize uncertainty and variability into this assessment, we incorporate three quantitative analyses described below and in greater detail within the TSD (Section 7.1):
1.	A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation studies;
2.	The quantification of PM-related mortality using alternative PM2.5 mortality effect estimates drawn from two long-term cohort studies; and
3. 	Presentation of 95[th] percentile confidence interval around each risk estimate.
      Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in Section 7.1 of the TSD:
 For adult all-cause mortality:
 The distributions of air quality concentrations experienced by the original cohort population (TSD Section <IP_ADDRESS>);
 Methods of estimating and assigning exposures in epidemiologic studies (TSD Section <IP_ADDRESS>);
 Confounding by ozone (TSD Section <IP_ADDRESS>); and
 The statistical technique used to generate hazard ratios in the epidemiologic study (TSD Section <IP_ADDRESS>).
      Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3), cardiovascular hospital admissions (TSD Section 7.1.4,), and respiratory hospital admissions (TSD Section 7.1.5);
Effect modification of PM2.5-attributable health effects in at-risk populations (TSD Section 7.1.6).
Quantitative consideration of baseline incidence rates and economic valuation estimates are provided in Section 7.3 and 7.4 of the TSD, respectively. Qualitative discussions of various sources of uncertainty can be found in Section 7.5 of the TSD.
Monte Carlo Assessment
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 variability across the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly 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 epidemiology 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 transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Sources of Uncertainty Treated Qualitatively
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 attributes are summarized below and described more fully in the TSD.
Key assumptions underlying the estimates for premature mortality, which account for over 98% of the total monetized benefits in this analysis, include the following:
 We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 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 reviewed by CASAC, concluded that "across exposure durations and health effects categories ... the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" (U.S. EPA, 2019b).
 We assume that the health impact function for fine particles is log-linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with the fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. The PM ISA concluded that "the majority of evidence continues to indicate a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality" U.S. EPA, 2019b .
 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 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 (Cameron, 2004), which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer.
Benefits Results
Benefits of the Applied Control Strategies for the Alternative Combinations of Primary PM2.5 Standard Levels
Applying the impact and valuation functions described previously in this chapter to the estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital admissions and emergency department visits) and the associated monetary values for those changes. Not all known PM health effects could be quantified or monetized.
We present two sets of tables  -  one set in this chapter and one set in Appendix 5A. First, Table 5-5 through Table 5-9 present benefits associated with the illustrative control strategies identified in Chapter 3. More specifically, for the proposed alternative standard level of 9/35 g/m[3], for the northeast we were able to identify approximately 97 percent of the reductions needed. For the southeast we were able to identify approximately 76 percent of the reductions needed. For the west, we were able to identify approximately 31 percent of the reductions needed, and for California the percentage is approximately 17 percent. As such, these tables present the benefits associated with the illustrative control strategies and reflect the remaining air quality challenges (discussed in Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6). Second, Table 5A-1 through 5A-5 in Appendix 5A present the potential benefits associated with fully meeting the proposed and alternative standards.
 Table 5-5 through Table 5-9 present the benefits results of applying the control strategies for the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ).
Table 5-5 presents the estimated avoided incidences of PM-related illnesses and premature mortality resulting from the control strategies applied to each of the alternative standard levels in 2032. Table 5-6 and Table 5-7 present the monetized valuation benefits (discounted at a 3% and 7% discount rate, respectively) of the avoided health outcomes presented in Table 5-5.
Table 5-8 and Table 5-9 present a summary of the monetized benefits associated with each of the alternative standard levels, both nationally and by region. The regional monetized benefits in Table 5-8 are presented in four regions: California (CA), the Northeastern (NE) states, the Southeastern (SE) states, and the Western (W) states. For Table 5-8 and Table 5-9, the monetized value of unquantified effects is represented by adding an unknown "B" to the aggregate total. This B represents both uncertainty and a bias in this analysis, as it reflects health and welfare benefits that we are unable to quantify.
For a more detailed description of the geographic distribution of the emissions reductions needed for each of the alternative standard levels, see the discussion in Chapter 3, Section 3.2.5. The estimated PM2.5 emissions reductions from control applications do not result in all counties in the northeast, southeast, west, and California meeting the proposed and more stringent alternative standard levels. For the proposed alternative standard level of 10/35 g/m[3] -  - , the northeast and southeast have sufficient estimated emissions reductions to reach attainment. For the west, the estimated emissions reductions are approximately 27 percent of the total needed to reach attainment, and for California the estimated emissions reductions are approximately 18 percent of the total needed to reach attainment.

Table 5-5 	Estimated Avoided PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                     1,700
                               (1,200 to 2,100)
                                     1,900
                               (1,400 to 2,400)
                                     4,200
                               (3,000 to 5,300)
                                     9,200
                               (6,600 to 12,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                      810
                                 (710 to 900)
                                      920
                                (810 to 1,000)
                                     2,000
                               (1,800 to 2,200)
                                     4,400
                               (3,900 to 4,900)
Woodruff et al., 2008 (infant mortality)
                                      1.6
                                (-0.99 to 4.0)
                                      1.8
                                 (-1.1 to 4.6)
                                      4.7
                                 (-3.0 to 12)
                                      11
                                 (-6.9 to 28)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      140
                                 (100 to 170)
                                      150
                                 (110 to 190)
                                      310
                                 (230 to 400)
                                      660
                                 (480 to 840)
Hospital admissions -- respiratory
                                      93
                                  (31 to 150)
                                      100
                                  (35 to 170)
                                      210
                                  (74 to 350)
                                      460
                                 (160 to 740)
ED visits--cardiovascular
                                      260
                                 (-100 to 610)
                                      290
                                 (-110 to 670)
                                      630
                                (-240 to 1,500)
                                     1,400
                                (-530 to 3,200)
ED visits -- respiratory
                                      490
                                 (95 to 1,000)
                                      530
                                (100 to 1,100)
                                     1,200
                                (240 to 2,600)
                                     2,700
                                (540 to 5,700)
Acute Myocardial Infarction
                                      29
                                  (5.9 to 17)
                                      32
                                  (19 to 45)
                                      67
                                  (39 to 94)
                                      140
                                  (83 to 200)
Cardiac arrest
                                      15
                                 (-5.9 to 33)
                                      16
                                 (-6.6 to 37)
                                      34
                                  (-14 to 76)
                                      72
                                 (-29 to 160)
Hospital admissions-- Alzheimer's Disease
                                      360
                                 (270 to 440)
                                      390
                                 (300 to 480)
                                      850
                                (640 to 1,000)
                                     1,900
                               (1,500 to 2,400)
Hospital admissions-- Parkinson's Disease
                                      48
                                  (25 to 70)
                                      54
                                  (28 to 79)
                                      120
                                  (63 to 180)
                                      270
                                 (140 to 390)
Stroke
                                      55
                                  (14 to 94)
                                      61
                                  (16 to 110)
                                      130
                                  (33 to 220)
                                      270
                                  (71 to 470)
Lung cancer
                                      65
                                  (20 to 110)
                                      73
                                  (22 to 120)
                                      150
                                  (46 to 250)
                                      320
                                  (99 to 530)
Hay Fever/Rhinitis
                                    15,000
                               (3,500 to 25,000)
                                    16,000
                               (4,000 to 28,000)
                                    35,000
                               (8,500 to 60,000)
                                    75,000
                              (18,000 to 130,000)
Asthma Onset
                                     2,200
                               (2,100 to 2,300)
                                     2,500
                               (2,400 to 2,600)
                                     5,400
                               (5,100 to 5,600)
                                    11,000
                              (11,000 to 12,000)
Asthma symptoms  -  Albuterol use
                                    310,000
                             (-150,000 to 750,000)
                                    350,000
                             (-170,000 to 850,000)
                                    740,000
                            (-360,000 to 1,800,000)
                                   1,600,000
                            (-780,000 to 3,900,000)
Lost work days
                                    110,000
                              (97,000 to 130,000)
                                    130,000
                             (110,000 to 150,000)
                                    270,000
                             (230,000 to 310,000)
                                    580,000
                             (490,000 to 660,000)
Minor restricted-activity days[d,f]
                                    680,000
                             (550,000 to 800,000)
                                    750,000
                             (610,000 to 890,000)
                                   1,600,000
                           (1,300,000 to 1,900,000)
                                   3,400,000
                           (2,700,000 to 4,000,000)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5. These values should not be added to one another.

Table 5-6 	Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                    17,000
                               (1,600 to 47,000)
                                    20,000
                               (1,800 to 53,000)
                                    43,000
                              (3,900 to 120,000)
                                    94,000
                              (8,600 to 260,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                     8,300
                                (770 to 22,000)
                                     9,400
                                (870 to 25,000)
                                    20,000
                               (1,900 to 54,000)
                                    45,000
                              (4,200 to 120,000)
Woodruff et al., 2008 (infant mortality)
                                      18
                                 (-9.9 to 70)
                                      20
                                  (-11 to 80)
                                      53
                                 (-30 to 210)
                                      120
                                 (-69 to 490)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      2.3
                                 (1.7 to 2.9)
                                      2.5
                                 (1.8 to 3.2)
                                      5.2
                                 (3.7 to 6.5)
                                      11
                                  (7.9 to 14)
Hospital admissions -- respiratory
                                      1.6
                                 (0.35 to 2.7)
                                      1.7
                                 (0.39 to 3.0)
                                      3.6
                                 (0.81 to 6.2)
                                      7.6
                                  (1.7 to 13)
ED visits--cardiovascular
                                     0.32
                                (-0.12 to 0.75)
                                     0.35
                                (-0.14 to 0.83)
                                     0.78
                                 (-0.3 to 1.8)
                                      1.7
                                 (-0.65 to 4)
ED visits -- respiratory
                                     0.45
                                (0.089 to 0.94)
                                      0.5
                                 (0.098 to 1)
                                      1.2
                                 (0.23 to 2.4)
                                      2.6
                                 (0.5 to 5.3)
Acute Myocardial Infarction
                                      1.5
                                 (0.88 to 2.1)
                                      1.7
                                 (0.97 to 2.4)
                                      3.5
                                 (2.0 to 4.9)
                                      7.4
                                  (4.3 to 10)
Cardiac arrest
                                     0.55
                                (-0.23 to 1.3)
                                     0.62
                                (-0.25 to 1.4)
                                      1.3
                                (-0.52 to 2.9)
                                      2.7
                                 (-1.1 to 6.2)
Hospital admissions-- Alzheimer's Disease
                                      4.6
                                 (3.5 to 5.7)
                                       5
                                 (3.8 to 6.2)
                                      11
                                  (8.3 to 13)
                                      25
                                  (19 to 31)
Hospital admissions-- Parkinson's Disease
                                     0.66
                                (0.34 to 0.96)
                                     0.74
                                 (0.38 to 1.1)
                                      1.7
                                 (0.86 to 2.4)
                                      3.7
                                 (1.9 to 5.3)
Stroke
                                       2
                                 (0.51 to 3.4)
                                      2.2
                                 (0.58 to 3.8)
                                      4.6
                                 (1.2 to 7.8)
                                      9.9
                                  (2.6 to 17)
Lung cancer
                                       1
                                 (0.31 to 1.7)
                                      1.1
                                 (0.35 to 1.9)
                                      2.3
                                 (0.71 to 3.8)
                                      4.9
                                 (1.5 to 8.1)
Hay Fever/Rhinitis
                                      9.3
                                  (2.3 to 16)
                                      11
                                  (2.5 to 18)
                                      22
                                  (5.4 to 38)
                                      48
                                  (12 to 82)
Asthma Onset
                                      100
                                  (98 to 110)
                                      120
                                 (110 to 130)
                                      250
                                 (240 to 270)
                                      540
                                 (510 to 570)
Asthma symptoms  -  Albuterol use
                                     0.11
                               (-0.055 to 0.28)
                                     0.13
                               (-0.062 to 0.31)
                                     0.27
                                (-0.13 to 0.66)
                                     0.59
                                (-0.29 to 1.4)
Lost work days
                                      21
                                  (17 to 24)
                                      23
                                  (19 to 26)
                                      48
                                  (41 to 56)
                                      100
                                  (88 to 120)
Minor restricted-activity days
                                 53
(28 to 80)
                                      59
                                  (31 to 89)
                                      120
                                  (64 to 190)
                                      260
                                 (140 to 400)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5. These values should not be added to one another.

Table 5-7 	Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                    16,000
                               (1,400 to 42,000)
                                    18,000
                               (1,600 to 47,000)
                                    38,000
                              (3,500 to 100,000)
                                    85,000
                              (7,700 to 230,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                     7,500
                                (690 to 20,000)
                                     8,500
                                (780 to 22,000)
                                    18,000
                               (1,700 to 49,000)
                                    41,000
                              (3,800 to 110,000)
Woodruff et al., 2008 (infant mortality)
                                      18
                                 (-9.9 to 70)
                                      20
                                  (-11 to 80)
                                      53
                                 (-30 to 210)
                                      120
                                 (-69 to 490)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      2.3
                                 (1.7 to 2.9)
                                      2.5
                                 (1.8 to 3.2)
                                      5.2
                                 (3.7 to 6.5)
                                      11
                                  (7.9 to 14)
Hospital admissions -- respiratory
                                      1.6
                                 (0.35 to 2.7)
                                      1.7
                                 (0.39 to 3.0)
                                      3.6
                                 (0.81 to 6.2)
                                      7.6
                                  (1.7 to 13)
ED visits--cardiovascular
                                     0.32
                                (-0.12 to 0.75)
                                     0.35
                                (-0.14 to 0.83)
                                     0.78
                                 (-0.3 to 1.8)
                                      1.7
                                 (-0.65 to 4)
ED visits -- respiratory
                                     0.45
                                (0.089 to 0.94)
                                      0.5
                                 (0.098 to 1)
                                      1.2
                                 (0.23 to 2.4)
                                      2.6
                                 (0.5 to 5.3)
Acute Myocardial Infarction
                                      1.5
                                 (0.86 to 2.1)
                                      1.6
                                 (0.97 to 2.4)
                                      3.4
                                 (2.0 to 4.8)
                                      7.3
                                  (4.2 to 10)
Cardiac arrest
                                     0.55
                                (-0.22 to 1.2)
                                     0.61
                                (-0.25 to 1.4)
                                      1.3
                                (-0.51 to 2.8)
                                      2.7
                                 (-1.1 to 6.1)
Hospital admissions-- Alzheimer's Disease
                                      4.6
                                 (3.5 to 5.7)
                                       5
                                 (3.8 to 6.2)
                                      11
                                  (8.3 to 13)
                                      25
                                  (19 to 31)
Hospital admissions-- Parkinson's Disease
                                     0.66
                                (0.34 to 0.96)
                                     0.74
                                 (0.38 to 1.1)
                                      1.7
                                 (0.86 to 2.4)
                                      3.7
                                 (1.9 to 5.3)
Stroke
                                       2
                                 (0.51 to 3.4)
                                      2.2
                                 (0.58 to 3.8)
                                      4.6
                                 (1.2 to 7.8)
                                      9.9
                                  (2.6 to 17)
Lung cancer
                                     0.72
                                 (0.22 to 1.2)
                                      0.8
                                 (0.25 to 1.3)
                                      1.6
                                 (0.5 to 2.7)
                                      3.4
                                 (1.1 to 5.7)
Hay Fever/Rhinitis
                                      9.3
                                  (2.3 to 16)
                                      11
                                  (2.5 to 18)
                                      22
                                  (5.4 to 38)
                                      48
                                  (12 to 82)
Asthma Onset
                                      65
                                  (60 to 69)
                                      73
                                  (68 to 78)
                                      160
                                 (150 to 170)
                                      340
                                 (310 to 360)
Asthma symptoms  -  Albuterol use
                                     0.11
                               (-0.055 to 0.28)
                                     0.13
                               (-0.062 to 0.31)
                                     0.27
                                (-0.13 to 0.66)
                                     0.59
                                (-0.29 to 1.4)
Lost work days
                                      21
                                  (17 to 24)
                                      23
                                  (19 to 26)
                                      48
                                  (41 to 56)
                                      100
                                  (88 to 120)
Minor restricted-activity days
                                 53
(28 to 80)
                                      59
                                  (31 to 89)
                                      120
                                  (64 to 190)
                                      260
                                 (140 to 400)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5. These values should not be added to one another.

Table 5-8	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                    $17 + B
                                    $20 + B
                                    $43 + B
                                    $95 + B
  7% discount rate
                                    $16 + B
                                    $18 + B
                                    $39 + B
                                    $86 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                   $8.5 + B
                                   $9.6 + B
                                    $21 + B
                                    $46 + B
  7% discount rate
                                   $7.6 + B
                                   $8.6 + B
                                    $19 + B
                                    $41 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

Table 5-9 is a summary of the monetized benefits associated with applying the control strategies for each of the alternative standard levels by four regions: California, the Northeast, the Southeast, and the West. The monetized benefits differ regionally and by each alternative standard level. For the proposed alternative standard level of 10/35 g/ - m - [3] - , because 15 of the 24 counties that need emissions reductions are counties in California, the majority of the benefits are incurred in California (Table 5-9). For California, we were able to identify approximately 18 percent of the reductions needed. In addition, as the alternative standard levels become more stringent, more counties in the northeast and southeast need emissions reductions. As additional controls are applied in those areas, those areas account for a relatively higher proportion of the benefits. For example, for alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/ - m - [3] - , more controls are available to apply in the northeast and their adjacent counties and the southeast and their adjacent counties. The benefits for those areas are higher than the costs for the west and California.
Table 5-9	Estimated Monetized Benefits by Region of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                                    Region
                                 10 ug/m[3]
                       annual &
35 ug/m[3] 24-hour
                                 10 ug/m[3]
                       annual &
30 ug/m[3] 24-hour
                                  9 ug/m[3]
                       annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                  California
                                    $13 + B
                                    $14 + B
                                    $17 + B
                                    $23 + B

                                   Northeast
                                   $2.3 + B
                                   $2.6 + B
                                    $15 + B
                                    $40 + B

                                   Southeast
                                   $1.8 + B
                                   $1.8 + B
                                   $8.8 + B
                                    $22 + B

                                     West
                                  $0.018 + B
                                   $1.1 + B
                                   $2.2 + B
                                    $11 + B
  7% discount rate
                                  California
                                    $12 + B
                                    $13 + B
                                    $16 + B
                                    $21 + B

                                   Northeast
                                    $2 + B
                                   $2.3 + B
                                    $13 + B
                                    $36 + B

                                   Southeast
                                   $1.6 + B
                                   $1.6 + B
                                   $7.9 + B
                                    $20 + B

                                     West
                                  $0.016 + B
                                    $1 + B
                                    $2 + B
                                   $9.5 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                  California
                                   $6.5 + B
                                   $6.9 + B
                                   $8.4 + B
                                    $11 + B

                                   Northeast
                                   $1.1 + B
                                   $1.3 + B
                                   $7.3 + B
                                    $19 + B

                                   Southeast
                                   $0.84 + B
                                   $0.84 + B
                                   $4.1 + B
                                    $10 + B

                                     West
                                  $0.0092 + B
                                   $0.56 + B
                                   $1.1 + B
                                   $5.1 + B
  7% discount rate
                                  California
                                   $5.8 + B
                                   $6.2 + B
                                   $7.5 + B
                                    $10 + B

                                   Northeast
                                    $1 + B
                                   $1.2 + B
                                   $6.6 + B
                                    $17 + B

                                   Southeast
                                   $0.75 + B
                                   $0.75 + B
                                   $3.6 + B
                                   $9.2 + B

                                     West
                                  $0.0082 + B
                                   $0.5 + B
                                   $0.97 + B
                                   $4.6 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

Discussion
      The estimated benefits to human health and the environment of the alternative PM2.5 daily and annual standard levels are substantial. We estimate that by 2032 the emissions reduced by the applied control strategies for the proposed annual primary standards would decrease the number of PM2.5-related premature deaths and illnesses. The emissions reduction strategies will also yield significant welfare benefits (see Section 5.3.5), though this RIA does not quantify those endpoints.
Inherent to any complex analysis quantifying the benefits of improved air quality, such as this one, are multiple sources of uncertainty. Some of these we characterized through our use of Monte Carlo techniques to sample the statistical error reported in the epidemiologic and economic studies supplying concentration-response parameters and economic unit values. Other key sources of uncertainty that affect the size and distribution of the estimated benefits -- including projected atmospheric conditions and source-level emissions, projected baseline rates of illness and disease, incomes and expected advances in healthcare -- remain unquantified. When evaluated within the context of these uncertainties, the estimated health impacts and monetized benefits in this RIA provide important information regarding the public health benefits associated with a revised PM NAAQS.
There are two important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c). First, 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. Second, those costs and benefits are calculated incremental to implementation of existing regulations as well as additional controls applied to reach the current standards and create the analytical baseline for the analysis. In short, NAAQS RIAs hypothesize, but do not predict, the strategies that States may follow to reduce emissions when implementing previous and revised NAAQS options. Setting a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs illustrate potential benefits and costs; these estimated values cannot be added, or directly compared, to the costs and benefits of regulations that require specific emissions control strategies to be implemented.
This latter type of regulatory action -- often referred to as an implementation rule -- reduces emissions for specific, well-characterized sources (see: Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c)). In general, the EPA is more confident in the magnitude and location of the emissions reductions for these implementation rules. As such, emissions reductions achieved under promulgated implementation rules such as the RCU have been reflected in the baseline of this NAAQS analysis. For this reason, the benefits estimated 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 accounts for the variability in PM2.5 concentrations over space and time. While the standard is designed to limit concentrations at the highest monitor in an area, EPA acknowledges that emissions controls implemented to meet the standard at the highest monitor will simultaneously result in lower PM2.5 concentrations in neighboring areas. In fact, the Policy Assessment for the Review of the National Ambient Air Quality Standards for Particulate Matter (U.S. EPA, 2022c) shows how different standard levels would affect the distribution of PM2.5 concentrations, as well as people's risk, across urban 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 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 the confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2022c). While benefits occurring below the standard 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 PM2.5 concentrations, there is no evidence of a threshold in PM2.5-related health effects in the epidemiology literature. Given 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.
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