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 APPENDIX 5A: BENEFITS OF THE PROPOSED AND ALTERNATIVE STANDARD LEVELS
 Overview
      In this Appendix, we estimate the potential health benefits resulting from identifying controls and emissions reductions to comply with the proposed and alternative standard levels, incremental 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]). In contrast the main analysis in Chapter 5, we present the national health impacts and monetized benefits resulting only from the applied control strategies identified in Chapter 3 for each of the alternative PM2.5 standard levels in 2032. After applying the control strategies for the main analysis, we estimated that PM2.5 emissions reductions would still be needed in certain areas to meet the 10/35, 10/30, 9/35 and 8/35 alternative standard levels. Additional information on estimating the emission reductions needed to meet each of the alternative standards is available in section 2A.3.4.2 of Appendix 2A. Also, additional information on the emissions reductions still needed is available in Chapter 3, Section 3.2.5. Lastly, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California that may still need emissions reductions. These challenges limit our ability to characterize how standard levels might be met given highly local influences that require more specific information beyond what is available for this type of national analysis. In this Appendix, we assume the remaining emissions reductions are identified to meet the proposed and more stringent alternative standard levels, and we present the resulting health and monetized benefits below. To the extent that the additional PM2.5 emissions reductions are not achieved, the health benefits reported below may be overestimated.
      For this appendix, the annual-mean PM2.5 concentration fields where existing and alternative NAAQS standard levels are just met were developed to estimate the emission changes resulting from fully meeting each of the proposed and more stringent alternative standard levels. Using the methods described in Chapter 5 of this RIA and the "Technical Support Document (TSD) for the PM2.5 NAAQS Proposal: Estimating PM2.5- and Ozone-Attributable Health Benefits" that will be published with this RIA, we estimate health benefits from achieving the proposed and more stringent alternative standard levels occurring as an increment to a 12/35 baseline. These benefits reflect the value of the avoided PM2.5-attributable deaths and the value of avoided morbidity impacts, including, for example, hospital admissions and emergency department visits for cardiovascular and respiratory health issues.
 5A.1	Benefits of the Proposed and More Stringent Alternative Standard Levels of Primary PM2.5 Standards
Applying the impact and valuation functions described in Chapter 5 and the TSD to the projected 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. Tables 5A-1 through 5A-5 present the benefits results for the proposed and more stringent alternative annual primary PM2.5 standard levels. Table 5A-1 presents the estimated avoided incidences of PM-related illnesses and premature mortality for achieving each alternative standard level in 2032. Tables 5A-2 and 5A-3 present the monetized valuation benefits of the avoided morbidity and premature mortality (at a 3% and 7% discount rate respectively) of the health outcomes in Table 5A-1 for each alternative standard level in 2032.
Tables 5A-4 and 5A-5 present a summary of the monetized benefits nationally and by region of achieving the alternative standard levels. The regional monetized benefits in Table 5A-5 are presented in four regions: California, the Northeast, the Southeast, and the West. For Tables 5A-4 and 5A-5, the monetized value of unquantified effects is represented by adding an unknown "B" to the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of monetized PM-related health benefits plus B, the sum of the non-monetized health and welfare benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefits categories that we are unable to quantify in this analysis.
Table 5A-1 	Estimated Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                     3,200
                               (2,300 to 4,100)
                                     3,800
                               (2,700 to 4,800)
                                     7,300
                               (5,200 to 9,300)
                                    15,000
                              (11,000 to 20,000)
Wu et al. (adult mortality ages 65-99 years)
                                     1,500
                               (1,300 to 1,700)
                                     1,800
                               (1,600 to 2,000)
                                     3,500
                               (3,100 to 3,900)
                                     7,400
                               (6,500 to 8,200)
Woodruff et al. (infant mortality)
                                      3.4
                                 (-2.1 to 8.6)
                                      3.9
                                 (-2.5 to 10)
                                      8.3
                                 (-5.2 to 21)
                                      18
                                  (-11 to 45)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      260
                                 (190 to 330)
                                      300
                                 (220 to 380)
                                      570
                                 (410 to 720)
                                     1,200
                                (840 to 1,500)
Hospital admissions -- respiratory
                                      180
                                  (64 to 300)
                                      210
                                  (72 to 330)
                                      400
                                 (140 to 650)
                                      810
                                (280 to 1,300)
ED visits--cardiovascular
                                      500
                                (-190 to 1,200)
                                      570
                                (-220 to 1,300)
                                     1,100
                                (-430 to 2,600)
                                     2,300
                                (-900 to 5,500)
ED visits -- respiratory
                                      990
                                (200 to 2,100)
                                     1,100
                                (220 to 2,300)
                                     2,300
                                (450 to 4,700)
                                     4,700
                                (920 to 9,800)
Acute Myocardial Infarction
                                      57
                                  (33 to 80)
                                      65
                                  (38 to 91)
                                      120
                                  (72 to 170)
                                      250
                                 (150 to 350)
Cardiac arrest
                                      28
                                  (-11 to 63)
                                      32
                                  (-13 to 73)
                                      61
                                 (-25 to 140)
                                      130
                                 (-51 to 280)
Hospital admissions-- Alzheimer's Disease
                                      610
                                 (470 to 740)
                                      690
                                 (520 to 840)
                                     1,400
                               (1,000 to 1,700)
                                     3,000
                               (2,300 to 3,600)
Hospital admissions-- Parkinson's Disease
                                      87
                                  (45 to 120)
                                      100
                                  (53 to 150)
                                      200
                                 (100 to 290)
                                      430
                                 (220 to 610)
Stroke
                                      100
                                  (27 to 180)
                                      120
                                  (31 to 210)
                                      230
                                  (59 to 390)
                                      470
                                 (120 to 810)
Lung cancer
                                      120
                                  (38 to 200)
                                      140
                                  (44 to 230)
                                      270
                                  (83 to 440)
                                      550
                                 (170 to 890)
Hay Fever/Rhinitis
                                    30,000
                               (7,400 to 52,000)
                                    35,000
                               (8,500 to 60,000)
                                    66,000
                              (16,000 to 110,000)
                                    130,000
                              (33,000 to 230,000)
Asthma Onset
                                     4,600
                               (4,400 to 4,800)
                                     5,300
                               (5,100 to 5,500)
                                    10,000
                               (9,700 to 10,000)
                                    20,000
                              (19,000 to 21,000)
Asthma symptoms  -  Albuterol use
                                    650,000
                            (-320,000 to 1,600,000)
                                    750,000
                            (-360,000 to 1,800,000)
                                   1,400,000
                            (-690,000 to 3,400,000)
                                   2,900,000
                           (-1,400,000 to 7,000,000)
Lost work days
                                    230,000
                             (190,000 to 260,000)
                                    260,000
                             (220,000 to 300,000)
                                    500,000
                             (420,000 to 570,000)
                                   1,000,000
                            (850,000 to 1,200,000)
Minor restricted-activity days
                                   1,300,000
                           (1,100,000 to 1,600,000)
                                   1,500,000
                           (1,200,000 to 1,800,000)
                                   2,900,000
                           (2,400,000 to 3,400,000)
                                   5,900,000
                           (4,800,000 to 7,000,000)
Note: Values rounded to two significant figures.

Table 5A-2 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                    33,000
                               (3,000 to 89,000)
                                    39,000
                              (3,500 to 100,000)
                                    75,000
                              (6,800 to 200,000)
                                    160,000
                              (14,000 to 430,000)
Wu et al. (adult mortality ages 65-99 years)
                                    16,000
                               (1,400 to 41,000)
                                    18,000
                               (1,700 to 49,000)
                                    36,000
                               (3,300 to 94,000)
                                    76,000
                              (7,000 to 200,000)
Woodruff et al. (infant mortality)
                                      38
                                 (-21 to 150)
                                      44
                                 (-25 to 180)
                                      94
                                 (-52 to 370)
                                      200
                                 (-110 to 800)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
	4.3
(3.1 to 5.4)
4.9
(3.5 to 6.2)
9.3
(6.8 to 12)
19
(14 to 24)
Hospital admissions -- respiratory
3.0
(0.70 to 5.3)
3.4
(0.79 to 5.9)
6.6
(1.5 to 11)
13
(3.1 to 23)
ED visits--cardiovascular
0.62
(-0.24 to 1.4)
0.7
(-0.27 to 1.6)
1.4
(-0.54 to 3.2)
2.9
(-1.1 to 6.7)
ED visits -- respiratory
0.92
(0.18 to 1.9)
1
(0.2 to 2.2)
2.1
(0.42 to 4.4)
4.4
(0.86 to 9.1)
Acute Myocardial Infarction
3.0
(1.7 to 4.1)
3.4
(2.0 to 4.7)
6.4
(3.7 to 9.0)
13
(7.6 to 18)
Cardiac arrest
1.1
(-0.43 to 2.4)
1.2
(-0.5 to 2.8)
2.3
(-0.95 to 5.2)
4.8
(-2 to 11)
Hospital admissions-- Alzheimer's Disease
7.8
(6 to 9.5)
8.8
(6.7 to 11)
18
(13 to 21)
38
(29 to 46)
Hospital admissions-- Parkinson's Disease
1.2
(0.62 to 1.7)
1.4
(0.72 to 2)
2.7
(0.86 to 2.4)
5.8
(3.1 to 8.3)
Stroke
3.7
(0.97 to 6.4)
4.4
(1.1 to 7.5)
8.3
(2.1 to 14)
17
(4.4 to 29)
Lung cancer
1.9
(0.59 to 3.1)
2.2
(0.68 to 3.6)
4.1
(1.3 to 6.7)
8.4
(2.6 to 14)
Hay Fever/Rhinitis
19
(4.7 to 33)
22
(5.4 to 38)
42
(10 to 73)
85
(21 to 150)
Asthma Onset
220
(200 to 230)
250
(230 to 260)
470
(440 to 500)
950
(890 to 1,000)
Asthma symptoms  -  Albuterol use
0.24
(-0.12 to 0.58)
0.27
(-0.13 to 0.67)
0.52
(-0.25 to 1.3)
1.1
(-0.51 to 2.6)
Lost work days
41
                                  (35 to 47)
47
                                  (40 to 54)
90
                                  (76 to 100)
180
                                 (150 to 210)
Minor restricted-activity days
100
(55 to 160)
120
(63 to 180)
230
(120 to 350)
460
(240 to 700)
Note: Values rounded to two significant figures.

Table 5A-3 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                    30,000
                               (2,700 to 80,000)
                                    35,000
                               (3,100 to 94,000)
                                    67,000
                              (6,100 to 180,000)
                                    140,000
                              (13,000 to 380,000)
Wu et al. (adult mortality ages 65-99 years)
                                    14,000
                               (1,300 to 37,000)
                                    17,000
                               (1,500 to 44,000)
                                    32,000
                               (3,000 to 85,000)
                                    68,000
                              (6,300 to 180,000)
Woodruff et al. (infant mortality)
                                      38
                                 (-21 to 150)
                                      44
                                 (-25 to 180)
                                      94
                                 (-52 to 370)
                                      200
                                 (-110 to 800)
Avoided Morbidity
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      4.3
                                 (3.1 to 5.4)
                                      4.9
                                 (3.5 to 6.2)
                                      9.3
                                  (6.8 to 12)
                                      19
                                  (14 to 24)
Hospital admissions -- respiratory
                                      3.0
                                 (0.70 to 5.3)
                                      3.4
                                 (0.79 to 5.9)
                                      6.6
                                  (1.5 to 11)
                                      13
                                  (3.1 to 23)
ED visits--cardiovascular
                                     0.62
                                (-0.24 to 1.4)
                                      0.7
                                (-0.27 to 1.6)
                                      1.4
                                (-0.54 to 3.2)
                                      2.9
                                 (-1.1 to 6.7)
ED visits -- respiratory
                                     0.92
                                 (0.18 to 1.9)
                                       1
                                 (0.2 to 2.2)
                                      2.1
                                 (0.42 to 4.4)
                                      4.4
                                 (0.86 to 9.1)
Acute Myocardial Infarction
                                      2.9
                                 (1.7 to 4.0)
                                      3.3
                                 (1.9 to 4.6)
                                      6.3
                                 (3.6 to 8.8)
                                      13
                                  (7.4 to 18)
Cardiac arrest
                                       1
                                (-0.43 to 2.4)
                                      1.2
                                 (-0.5 to 2.7)
                                      2.3
                                (-0.94 to 5.2)
                                      4.7
                                 (-1.9 to 11)
Hospital admissions-- Alzheimer's Disease
                                      7.8
                                  (6 to 9.5)
                                      8.8
                                  (6.7 to 11)
                                      18
                                  (13 to 21)
                                      38
                                  (29 to 46)
Hospital admissions-- Parkinson's Disease
                                      1.2
                                 (0.62 to 1.7)
                                      1.4
                                  (0.72 to 2)
                                      2.7
                                 (1.4 to 3.9)
                                      5.8
                                 (3.1 to 8.3)
Stroke
                                      3.7
                                 (0.97 to 6.4)
                                      4.4
                                 (1.1 to 7.5)
                                      8.3
                                  (2.1 to 14)
                                      17
                                  (4.4 to 29)
Lung cancer
                                      1.3
                                 (0.41 to 2.2)
                                      1.5
                                 (0.48 to 2.5)
                                      2.9
                                 (0.9 to 4.7)
                                      5.9
                                 (1.8 to 9.6)
Hay Fever/Rhinitis
                                      19
                                  (4.7 to 33)
                                      22
                                  (5.4 to 38)
                                      42
                                  (10 to 73)
                                      85
                                  (21 to 150)
Asthma Onset
                                      130
                                 (130 to 140)
                                      160
                                 (140 to 160)
                                      290
                                 (270 to 310)
                                      590
                                 (550 to 630)
Asthma symptoms  -  Albuterol use
                                     0.24
                                (-0.12 to 0.58)
                                     0.27
                                (-0.13 to 0.67)
                                     0.52
                                (-0.25 to 1.3)
                                      1.1
                                (-0.51 to 2.6)
Lost work days
                                      41
                                  (35 to 47)
                                      47
                                  (40 to 54)
                                      90
                                  (76 to 100)
                                      180
                                 (150 to 210)
Minor restricted-activity days
                                      100
                                  (55 to 160)
                                      120
                                  (63 to 180)
                                      230
                                 (120 to 350)
                                      460
                                 (240 to 700)
Note: Values rounded to two significant figures.

Table 5A-4	Total Estimated Monetized Benefits of Meeting the Proposed and More Stringent Alternative Primary 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 (2019)
  3% discount rate
                                    $33 + B
                                    $39 + B
                                    $76 + B
                                   $160 + B
  7% discount rate
                                    $30 + B
                                    $35 + B
                                    $68 + B
                                    $140+ 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
                                    $16 + B
                                    $19 + B
                                    $36 + B
                                    $77 + B
  7% discount rate
                                    $14 + B
                                    $17 + B
                                    $33 + B
                                    $69 + 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.

Table 5A-5	Total Estimated Monetized Benefits by Region of Meeting the Proposed and More Stringent Alternative Primary 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 (2019)
  3% discount rate
                                  California
                                    $29 + B
                                    $32 + B
                                    $49 + B
                                    $76 + B

                                   Northeast
                                   $2.3 + B
                                   $2.6 + B
                                    $15 + B
                                    $46 + B

                                   Southeast
                                   $1.8 + B
                                   $1.8 + B
                                   $9.6 + B
                                    $26 + B

                                     West
                                  $0.086 + B
                                   $2.8 + B
                                   $2.4 + B
                                    $12 + B
  7% discount rate
                                  California
                                    $26 + B
                                    $28 + B
                                    $44 + B
                                    $68 + B

                                   Northeast
                                    $2 + B
                                   $2.3 + B
                                    $13 + B
                                    $41 + B

                                   Southeast
                                   $1.6 + B
                                   $1.6 + B
                                   $8.6 + B
                                    $23 + B

                                     West
                                  $0.077 + B
                                   $2.6 + B
                                   $2.2 + B
                                    $11 + 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
                                    $14 + B
                                    $15 + B
                                    $24 + B
                                    $37 + B

                                   Northeast
                                   $1.1 + B
                                   $1.3 + B
                                   $7.2 + B
                                    $23 + B

                                   Southeast
                                   $0.84 + B
                                   $0.84 + B
                                   $4.4 + B
                                    $12 + B

                                     West
                                  $0.044 + B
                                   $1.4 + B
                                   $1.2 + B
                                   $5.9 + B
  7% discount rate
                                  California
                                    $13 + B
                                    $14 + B
                                    $21 + B
                                    $33 + B

                                   Northeast
                                    $1 + B
                                   $1.2 + B
                                   $6.4 + B
                                    $20 + B

                                   Southeast
                                   $0.75 + B
                                   $0.75 + B
                                    $4 + B
                                    $11 + B

                                     West
                                   $0.04 + B
                                   $1.3 + B
                                   $1.1 + B
                                   $5.3 + 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.

 5A.2		References
Pope III, CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M, Gilliat, KS, Vernon, SE and Robinson, AL (2019). Mortality risk and fine particulate air pollution in a large, representative cohort of US adults. Environmental health perspectives 127(7): 077007.
Wu, X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F (2020). Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Science advances 6(29): eaba5692.
Woodruff, TJ, Darrow, LA and Parker, JD (2008). Air pollution and postneonatal infant mortality in the United States, 1999 - 2002. Environmental Health Perspectives 116(1): 110-115.

  -  -  - ENVIRONMENTAL JUSTICE
 Introduction
Executive Order 12898 directs the EPA to "achiev[e] environmental justice (EJ) by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects" (59 FR 7629, February 16, 1994), termed disproportionate impacts in this chapter. Additionally, Executive Order 13985 was signed to advance racial equity and support underserved communities through Federal government actions (86 FR 7009, January 20, 2021). The EPA defines EJ as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. The EPA further defines the term fair treatment to mean that "no group of people should bear a disproportionate burden of environmental harms and risks, including those resulting from the negative environmental consequences of industrial, governmental, and commercial operations or programs and policies". Meaningful involvement means that: (1) potentially affected populations have an appropriate opportunity to participate in decisions about a proposed activity that will affect their environment and/or health; (2) the public's contribution can influence the regulatory Agency's decision; (3) the concerns of all participants involved will be considered in the decision-making process; and (4) the rule-writers and decision-makers seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that are extensive enough that they may merit Agency action. In general, the determination of whether a disproportionate impact exists is ultimately a policy judgment which, while informed by analysis, is the responsibility of the decision-maker. The terms "difference" or "differential" indicate an analytically discernible distinction in impacts or risks across population groups. It is the role of the analyst to assess and present differences in anticipated impacts across population groups of concern for both the baseline and proposed regulatory options, using the best available information (both quantitative and qualitative) to inform the decision-maker and the public.
A regulatory action may involve potential EJ concerns if it could: (1) create new disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; (2) exacerbate existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; or (3) present opportunities to address existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223; January 20, 2021) calls for procedures to "take into account the distributional consequences of regulations, including as part of a quantitative or qualitative analysis of the costs and benefits of regulations, to ensure that regulatory initiatives appropriately benefit, and do not inappropriately burden disadvantaged, vulnerable, or marginalized communities." Under Executive Order 13563, federal agencies may consider equity, human dignity, fairness, and distributional considerations, where appropriate and permitted by law. For purposes of analyzing regulatory impacts, the EPA relies upon its June 2016 "Technical Guidance for Assessing Environmental Justice in Regulatory Analysis," which provides recommendations that encourage analysts to conduct the highest quality analysis feasible, recognizing that data limitations, time, resource constraints, and analytical challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is to review the available evidence from the published literature and from community input on what factors may make population groups of concern more vulnerable to adverse effects (e.g., underlying risk factors that may contribute to higher exposures and/or impacts). It is also important to evaluate the data and methods available for conducting an EJ analysis. EJ analyses can be grouped into two types, both of which are informative, but not always feasible for a given rulemaking:
          Baseline: Describes the current (pre-control) distribution of exposures and risk, identifying potential disparities.
          Policy: Describes the distribution of exposures and risk after the regulatory option(s) have been applied (post-control), identifying how potential disparities change in response to the rulemaking.
EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or methodology for conducting EJ analyses, though a key consideration is consistency with the assumptions underlying other parts of the regulatory analysis when evaluating the baseline and regulatory options.
Analyzing EJ Impacts in This Proposal
In addition to the benefits assessment (Chapter 5), the EPA considers potential EJ concerns of this proposed rulemaking. A potential EJ concern is defined as "the actual or potential lack of fair treatment or meaningful involvement of minority populations, low-income populations, tribes, and indigenous peoples in the development, implementation and enforcement of environmental laws, regulations and policies" (U.S. EPA, 2015). For analytical purposes, this concept refers more specifically to "disproportionate impacts on minority populations, low-income populations, and/or indigenous peoples that may exist prior to or that may be created by the proposed regulatory action" (U.S. EPA, 2015). Although EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis, the EPA's EJ Technical Guidance (U.S. EPA, 2015) states that "[t]he analysis of potential EJ concerns for regulatory actions should address three questions:
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern in the baseline?
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern for the regulatory option(s) under consideration?
 For the regulatory option(s) under consideration, are potential EJ concerns created [, exacerbated,] or mitigated compared to the baseline?"
To address these questions, the EPA developed an analytical approach that considers the purpose and specifics of this proposed rulemaking, as well as the nature of known and potential exposures and health impacts. The purpose of this Regulatory Impact Analysis (RIA) is to provide estimates of the potential costs and benefits of the illustrative national control strategies in 2032 for the alternative standard levels analyzed. The alternative standard levels evaluated in the RIA are more stringent than the current standards. This means that in reducing emissions to reach lower standard levels, some areas above or near the current standards are expected to experience greater air quality improvements, and thus health improvements, than other areas already at or below lower alternative standard levels. As differences in both exposure and susceptibility (i.e., intrinsic individual risk factors) contribute to environmental impacts, the analytical approach used here first determines whether exposure (Section 6.2) and health effect (Section 6.3) disparities exist under the baseline scenario. The approach then evaluates if and how disparities are impacted when illustrative emissions control strategies are analyzed. Both the exposure and health effects analyses were developed using available scientific evidence from the current PM NAAQS reconsideration, for the future year 2032, and are associated with various uncertainties. Consistent with the methods the EPA uses to fully characterize the benefits of a regulatory action, these EJ analyses evaluate the full set of exposure and health outcome distributions resulting from this proposed action at the national scale. Recognizing, however, that only some areas of the U.S. are projected to exceed the proposed alternative standard levels, the EPA conducted a case study analysis to further examine the impacts of this proposed action on populations living in areas with the highest exposures and health risks in the baseline. By focusing on locations that are projected to exceed one of the analytical alternatives examined, this case study analysis considers the magnitude of exposure and health effect disparities across the smaller geographical scale where the impacts of alternative standard levels are expected (Section 6.4).
The EJ exposure assessment portion of the analysis focuses on associating ambient PM2.5 concentrations with various demographic variables. Because this type of analysis requires less a priori information, we were able to include a broad array of demographic characteristics. Estimating actual health outcomes modified by demographic population requires additional scientific information, which constrained the scope of the second portion of the assessment. We focused the EJ health effects analysis on populations and health outcomes with the strongest scientific support (U.S. EPA, 2019, U.S. EPA, 2020, U.S. EPA, 2022a). However, the EJ health effects analysis does not include information about differences in other factors that could affect the likelihood of adverse impacts (e.g., access to health care, BMI, etc.) across groups, due to limitations on the underlying data. Both the EJ exposure and health effects analyses are subject to uncertainties related to input parameters and assumptions. For example, both analyses focus on annual PM2.5 concentrations and do not evaluate whether concentrations experienced by different groups persist across the distribution of daily PM2.5 exposures. Additionally, the EJ health effects analysis is subject to additional uncertainties related to concentration-response relationships and baseline incidence data.
Since NAAQS RIAs are national-level assessments and air quality issues are complex and local in nature, the RIA presents costs and benefits of PM2.5 emission reductions associated with illustrative control strategies. Correspondingly, the main EJ analyses in this chapter also evaluates implications of air quality surfaces associated with the illustrative emission control strategies for both current (i.e., baseline) and alternative standard levels. However, the illustrative control strategies do not result in all counties identifying emissions reductions needed to meet either the current or more stringent alternative standard levels (Chapters 3). As such, the appendix to this chapter provides EJ implications of air quality scenarios associated with meeting the standards (labelled in some Section 6.6 figures as "Standards") and allows for direct comparison with results associated with the illustrative emissions control strategies (labelled in some Section 6.6 figures as "Controls").
Complex analyses using estimated parameters and inputs from numerous models are likely to include multiple sources of uncertainty. As this analysis is based on the same PM2.5 spatial fields as the benefits assessment (Appendix 2A), it is subject to similar types of uncertainty (Chapter 5, Section 5.4). A particularly germane limitation is the illustrative nature of the emission reductions in NAAQS RIAs; as a result, the EJ analyses in this chapter illustrate the estimated EJ impacts of the illustrative control strategies and may not reflect state-level implementation decisions. Relatedly, while proximity analyses can sometimes provide limited EJ information regarding the demographics of populations living near emissions sources, in this case state-level implementation decisions are unknown. Therefore, proximity analyses of populations living near individual sources that could potentially install controls would be highly uncertain and were not conducted in this EJ assessment. However, the EJ exposure and health analyses included in this chapter provide more relevant and high-confidence information than a proximity analysis, since these analyses relate actual PM2.5 concentrations (not just emissions) to various demographic populations.
As with all EJ analyses, data limitations make it quite possible that there exist additional disparities unidentified in this analysis. This is especially relevant for potential EJ characteristics and more granular spatial resolutions that were not evaluated. For example, results are provided here at national- and county-levels, potentially masking tract- or block-level EJ impacts. Additional uncertainties are briefly discussed in the summary of this analysis (Section 6.5).

EJ Analysis of Exposures Under Current Standard and Alternative Standard Levels
This EJ PM2.5 exposure analysis aims to evaluate the potential for EJ concerns related to PM2.5 exposures among potentially vulnerable populations from three perspectives, which correspond to the three EJ questions listed in Section 6.1. Specifically, the following questions are addressed:
 Are there disproportionate PM2.5 exposures under baseline/current PM NAAQS standard levels (question 1)?
 Are there disproportionate PM2.5 health effects under illustrative alternative PM NAAQS standard levels (question 2)?
 Are PM2.5 exposure disparities created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline (question 3)?
Population variables considered in this EJ exposure assessment include race/ethnicity, poverty status, educational attainment, age, and sex (Table 6-1). The results presented below reflect the control strategies described in Chapter 3.

Table 6-1 	Populations Included in the PM2.5 Exposure Analysis
                                  Population
                                    Groups
                                   Ethnicity
                            Hispanic; Non-Hispanic
                                     Race
                     Asian; American Indian; Black; White
                            Educational Attainment
               High school degree or more; No high school degree
                                Poverty Status
                Above the poverty line; Below the poverty line
                                      Age
             Children (0-17); Adults (18-64); Older Adults (65-99)
                                      Sex
                                 Female; Male

Total Exposure
We begin by considering the first two questions from EPA's EJ Technical Guidance (i.e., are there potential EJ concerns 1) in the baseline, and 2) for the regulatory option(s) under consideration) with respect to PM2.5 exposures. Estimated exposures as measured by the projected national and regional ambient PM2.5 concentrations experienced by various demographic populations for the current standards or alternative standard levels analyzed are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively. Information regarding identified emissions controls, as well as areas where air quality has been adjusted, is available in Chapters 2 and 3.
National
As NAAQS are national rules, we begin by evaluating annual average PM2.5 concentrations in absolute terms projected to be experienced by various demographic groups that may be of EJ concern, averaged across the contiguous US (national). Figure 6-1 shows the national average annual PM2.5 concentrations associated with the control strategy baseline scenario for the current annual standard of 12 ug/m[3] and current 24-hour standard of 35 ug/m[3] (12/35) as a heat map, with higher estimated annual PM2.5 concentrations shown in darker shades of blue. Populations with potential EJ concerns can be compared to the reference/overall population and/or other populations (i.e., White, Non-Hispanic, above the poverty line, more educated, and adults 18-64). On average, Asians, Blacks, Hispanics, and those over 25 without a high school education live in areas with higher annual PM2.5 concentrations than the reference population, with Hispanic and Asian populations experiencing the highest relative concentrations. The most substantial discrepancy in national average annual PM2.5 exposures is noted between Hispanic populations and non-Hispanic populations. It is noteworthy that the national average annual exposures for all demographic groups are well below the current annual NAAQS.

Figure 6-1	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls

Figure 6-1 also shows the national average total PM2.5 concentrations associated with control strategies applied for the potential alternative annual and 24-hour standard levels: 10/35, 10/30, 9/35, and 8/35. Although average concentrations under 10/35 and 10/30 are similar, most demographic groups are projected to experience greater annual PM2.5 concentration reductions after implementing the illustrative control strategies for lower alternative annual standard levels. However, after implementing the illustrative control strategies associated with all alternative standard levels evaluated, Asians, Blacks, Hispanics, those over 25 without a high school education, and those under the poverty level live in areas with higher projected annual PM2.5 concentrations than the reference population, again with Hispanic and Asian populations experiencing the highest average concentrations. This suggests that while emissions reductions associated with more stringent standard levels will result in air quality improvements across the board, disparities seen in the baseline likely remain, at least when considering the average national exposure levels by demographic group. These annual average exposures are also well below the current standards and all alternative standard levels evaluated.
While average PM2.5 concentrations can provide some insight when comparing population impacts, information on the full distribution of concentrations affords a more comprehensive understanding. This is because both demographic groups and ambient concentrations are unevenly distributed, meaning that average exposures may mask important disparities that occur on a more localized basis. To evaluate how the distribution of annual exposures varies within and across demographic groups at the county level, we plot the full array of exposures (including very high and very low exposures) projected to be experienced by different subpopulations. Distributional figures present the running sum of each population, converted to a percentage, on the y-axes (i.e., cumulative percent). Conversion of each total population to a percent of the total permits direct comparison of annual PM2.5 exposures across demographic populations with different absolute numbers. The x-axes show annual PM2.5 concentrations (ug/m[3]) from low to high. For Figure 6-2, PM2.5 concentrations are county-level averages from all counties in the contiguous U.S. In other words, plots compare the running sum of each population against increasing annual PM2.5 concentrations.
Information on the distribution of county-level PM2.5 concentrations associated with the illustrative control strategies associated with the current and alternative PM standard levels across and within populations can be found in Figure 6-2. The reference population in the top row shows that emissions reductions associated with the current or alternative standard levels yields a fairly smooth S-curve, with the majority of the population experiencing annual PM2.5 concentrations between 4 and 10 ug/m[3] under air quality scenarios associated with the control strategies for current standards (12/35). Lower PM2.5 concentrations remain similar across lower alternative standard levels, while higher concentrations are reduced.
To evaluate differential exposures, populations of potential EJ concern are shown with a colored line and can be compared to the respective reference population shown with a black line. Colored lines to the right of a black line suggest that the potential EJ population is experiencing disproportionately higher PM2.5 concentrations. The greatest disproportionate exposures are observed when considering ethnicity. The Hispanic population (dark orange) is predicted to experience higher PM2.5 concentrations than the non-Hispanic population (black) across a large portion of the exposure distribution. This difference is approximately 1 ug/m[3] at all concentrations above 6 ug/m[3].
Similarly, when considering race across the various standard levels evaluated, portions of the Asian (bright orange) and Black (blue) populations live in areas with higher PM2.5 concentrations than the White (black) population, and portions of the American Indian (light orange) population live in areas with lower PM2.5 concentrations. Interestingly, Black and White population exposures are very similar at concentrations above about 8 ug/m[3] under air quality scenarios associated with controls for 12/35 and about 7.5 ug/m[3] air quality scenarios associated with controls for 8/35. This could suggest that exposure disparities in the Black population occur in rural areas with lower PM2.5 concentrations. The Asian population experiences higher PM2.5 concentrations across a larger portion of the distribution, but higher exposures become more similar to the White distribution at lower alternative PM standard levels. Those living below the poverty level, those over 25 without a high school diploma, and the two sexes experience virtually identical distributions of exposure of all standard levels.

Figure 6-2	National Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls

Regional
As both emissions changes and overrepresentation of people/communities of color (POC/COC) vary with respect to location, we also parse the aggregated and distributional absolute PM2.5 concentration by geographic region (southeast [SE], northeast [NE], west [W], and California [CA]) (Figure 6-3 and Figure 6-4).[,] Across all current and alternative standard levels, average annual reference PM2.5 concentrations are highest in CA, followed by the SE and NE, and are lowest in the W (Figure 6-3). Comparing populations of potential EJ concern with their respective references within each region, disparities are observed in all four regions, though not all for the same demographic populations.
Regarding racial and ethnic disparities, annual PM2.5 concentrations for Black populations are substantially higher in the NE across the full distribution, but only slightly higher in the W and in CA. Also, concentrations for Black populations are slightly higher than concentrations for White populations only in the lowest ~50 percent of the populations in the SE. PM2.5 concentrations among Hispanics are higher than concentrations for Non-Hispanic populations in all four regions, although disparities are largest at higher PM2.5 concentrations in CA and smallest at lower PM2.5 concentrations in the NE. Total PM2.5 concentrations for Asian populations in the NE and SE are higher than the reference PM2.5 concentrations, but similar in the W and CA.
People living below the poverty level and people over 25 without a high school diploma experience similar annual PM2.5 concentrations to those above the poverty line and with a high school diploma in the NE, SE, and W, but experience higher PM2.5 concentrations in CA under controls associated with the current standards (12/35). Older adults (65-99) experience slightly lower PM2.5 concentrations associated with the illustrative control strategies for the more stringent alternative standard levels in all regions. Children experience higher annual PM2.5 concentrations in some areas in the W.

Figure 6-3	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls

Figure 6-4	Regional Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls
Exposure Changes
In addition to evaluating total/absolute exposures under control strategies associated with current/baseline and potential alternative standard levels (Section 6.2), we evaluate the extent to which exposures change for each demographic population, to compare improvements in air quality among populations. This begins to address the third question from EPA's EJ Technical Guidance: how disparities observed between demographic groups in the baseline scenario (12/35) are impacted (e.g., exacerbated/mitigated) under alternative standard levels. The national and regional changes in PM2.5 concentrations experienced by different demographic populations for the current and alternative standard levels are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively.
National
First, we consider how average exposures change across different demographic groups at the national level. Figure 6-5 shows the average PM2.5 concentration reduction and Figure 6-6 shows the distributions of county-level PM2.5 concentration exposure reductions for each population when moving from the current standard to alternative standard levels. The magnitude of these numbers is quite small because they are national averages and include individuals residing in 12km x 12km gridded areas not predicted to experience PM2.5 concentration reductions. For example, Figure 6-6 shows that only ~15% of the non-Hispanic population will experience PM2.5 concentration reductions when moving from the baseline of control strategies associated with the current standards to control strategies associated with the alternative standard levels of 10/35, whereas ~30% of the Hispanic population will experience PM2.5 concentration reductions under air quality scenarios associated with the same control strategies. Figure 6-6 also shows that greater reductions are expected in the ~30% of the Hispanic population projected to experience PM2.5 concentration reductions than the ~15% of the non-Hispanic population projected to experience PM2.5 concentration reductions. Together, these differences lead to an estimated four-fold greater reduction in average PM2.5 concentrations when moving from the baseline of air quality associated with control strategies for the current standards of 12/35 to control strategies associated with the proposed alternative standard level of 10/35 (12/35-10/35) in Figure 6-5. Colored lines again represent potential populations of EJ concern and black lines the respective reference population; however, in these figures, colored lines to the right of the black line now indicate greater relative air quality improvements.
In general, populations with higher total PM2.5 exposures (Section 6.2.1) are also expected to see the greatest reductions in average PM2.5 concentrations under the alternative standard levels. On average nationwide, Asians, Hispanics, and those over 25 without a high school diploma are predicted to experience substantially greater PM2.5 concentration reductions under air quality scenarios associated with control strategies for all alternative standard levels as compared to the reference population. Black populations may experience slightly smaller PM2.5 concentration reductions for alternative standard levels of 12/35-10/35 and 12/35-10/30 as compared to either the reference/overall population or other populations (Asian, Hispanic, and those over 25 without a high school diploma), but that disparity is smaller for control strategies associated with 12/35-9/35 or 12/35-8/35, and in fact average PM2.5 concentration improvements are on par or slightly greater than in the reference population for these more stringent alternative standard levels.

Figure 6-5	Heat Map of National Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Figure 6-6	National Distributions of Annual PM2.5 Concentration Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Next, we consider how average exposures change across different demographic groups at the regional level. Information on average and distributional exposure changes by region when moving from control strategies associated with the current standard to control strategies associated with alternative standard levels are available in Figure 6-7 and Figure 6-8, respectively. Similar to the average annual PM2.5 concentrations going from highest in CA, followed by the SE and NE, and being lowest in the W (Section <IP_ADDRESS>), average PM2.5 concentration reductions also follow the same order. Comparing how these reductions affect populations of potential EJ concern with each region, we note that there are differences across regions in terms of which demographic populations benefit the most (or least), particularly for 12/35-9/35 or 12/35-8/35.
Going through each region, the largest regional PM2.5 concentration reductions occur in CA, where Blacks, Hispanics, those below the poverty line, and those less educated are expected to experience greater PM2.5 concentration reductions when moving from the baseline to alternative standard levels. In the SE, there are greater PM2.5 concentration reductions for Asians, Hispanics, and those less educated under all alternative standard levels. Asian and Black populations in CA experience greater PM2.5 concentration reductions when moving from 12/35-8/35. In the NE for 12/35-9/35 and 12/35-8/35 there are greater PM2.5 concentration reductions for Blacks, and slightly greater PM2.5 concentration reductions for Asians. This is similar to the W, where Blacks, Hispanics, and those less educated are predicted to see greater PM2.5 concentration reductions for 12/35-9/35 and 12/35-8/35.

Figure 6-7	Heat Map of Regional Reductions in PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Figure 6-8	Regional Distributions of Total PM2.5 for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Proportional Changes in Exposure
To put the changes in exposure discussed in section 6.2.2 in perspective, especially in light of the disparities in the exposure baseline across population groups as discussed in section 6.2.1, it helps to consider whether the absolute changes represent equivalent (proportional) reductions in exposure. In some cases, moving to more stringent control strategies could both reduce total average exposures and reduce disparities in exposure across groups. However, it can be difficult to determine the relative proportionality of changes in PM2.5 concentrations for demographic populations using just the absolute exposure changes when moving from the current standard to a potential alternative standard level, like those shown in section 6.2.2.
In this section, the proportionality of PM2.5 concentration changes when moving from the current (baseline) to alternative standard levels under air quality scenarios associated with the illustrative emission control strategies is directly calculated. To compare air quality improvements on a percentage basis, first exposures under the current standard are divided by exposures under the alternative standard levels at the national and regional levels. Those results are then subtracted from 1 to get the remainder, and then multiplied by 100 to get the percent change. For example, if the average annual PM2.5 concentration in population A is 7 under control strategies associated with the current standard and 6 under an alternative standard level, the proportional change would be (1-(6/7)) x 100 = (1-0.857) x 100 = 0.143 x 100 = 14.3%. If the average annual PM2.5 concentration in population B is 6 under the current standard and 5 under an alternative standard level, the proportional change would be (1-(5/6)) x 100 = (1-0.833) x 100 = 0.167 x 100 = 16.7%. Therefore, even though the absolute reduction is equivalent, population B would experience a proportionally larger reduction under controls strategies associated with the alternate standard level because the starting concentration was lower. As average PM2.5 concentrations have been representative of the distributions, for simplicity we only present the average proportional reduction for each population and scenario, at the national and regional levels (<IP_ADDRESS> and <IP_ADDRESS>).
National
Nationally, alternative PM standard levels associated with control strategies reduce the average PM2.5 exposure concentrations experienced by the reference population by an increasing percentage as the alternative standards are lowered, with a 0.7% improvement for 12/35-10/35 and a 3.8% improvement for 12/35-8/35 (Figure 6-9). Non-Hispanics experience slightly smaller proportional reductions, 0.5% for 12/35-10/35 and 3.4% for 12/35-8/35. Hispanics and Asian populations are predicted to experience the proportionally largest reductions in PM2.5 concentrations under all alternative standard levels evaluated, followed by those less educated. Black populations experience smaller proportional PM2.5 concentration improvements than Whites when moving from 12/35-10/35 or 12/35-10/30, but greater proportional PM2.5 concentration improvements than Whites when moving from 12/35-9/35 or 12/35-8/35. This is likely due to the fact that gaps between the PM2.5 concentrations experienced by Black populations vs. those experienced by White populations in the baseline is greater at lower ambient PM2.5 concentrations (Figure 6-2, Figure 6-4, Figure 6-6, and Figure 6-8), with Black populations experiencing higher PM2.5 levels relative to Whites throughout the distribution but particularly at lower ambient concentrations. This leads to proportionally greater improvements for Black populations (i.e., a narrowing of disparities as compared to White populations) at lower alternative PM2.5 standards. Native Americans are estimated to experience the opposite, with slightly greater proportional PM2.5 concentration improvements than Whites when moving from 12/35-10/35 or 12/35-10/30, and smaller proportional PM2.5 concentration improvements than Whites when moving from 12/35-9/35 or 12/35-8/35. Older adults are estimated to experience proportionally smaller reductions in PM2.5 concentrations under all alternative standard levels evaluated; however older adults experience lower PM2.5 concentrations under air quality scenarios associated with control strategies for the baseline and all alternative NAAQS (Figure 6-1 through Figure 6-8).

Figure 6-9	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Regionally the greatest proportional reductions are estimated for CA when moving from the current to all alternative standards under air quality associated with the illustrative emission control strategies (Figure 6-10). Like the national analysis, percent reductions get larger as alternative standard levels decrease. In addition to trends observed at the national level (Section <IP_ADDRESS>), there are notable proportional reductions of PM2.5 concentrations for Hispanic populations in CA, the SE, and the W, as well as for Asian populations in the SE and CA for all alternative standard levels and in the W for 12/35-8/35.

Figure 6-10	Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current (12/35) to Alternative PM NAAQS Level (10/35, 10/30, 9/35, and 8/35 After Application of Controls

EJ Analysis of Health Effects under Current Standards and Alternative Standard Levels
In addition to comparing PM2.5 concentrations for potential demographic populations of concern in the EJ exposure analysis (Section 6.2.1), we conducted an EJ analysis of health effects. This analysis aims to evaluate the potential for EJ concerns related to PM2.5 health outcomes among populations potentially at increased risk of or to PM2.5 exposures from three perspectives, which correspond to the three EJ questions listed in Section 6.1. Specifically, the following questions are addressed:
 Are there disproportionate PM2.5 health effects (e.g., mortality) under baseline/current PM NAAQS standard levels (question 1)?
 Are there disproportionate PM2.5 health effects under illustrative alternative PM NAAQS standard levels (question 2)?
 Are disparities in PM2.5 health effects created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline (question 3)?
There is considerable scientific evidence that specific populations and lifestages are at increased risk of PM2.5-related health effects (Section 1.5.5 and Chapter 12 of U.S. EPA, 2019). Factors that may contribute to increased risk of PM2.5-related health effects include lifestage (e.g., children), pre-existing diseases (e.g., cardiovascular disease and respiratory disease), race/ethnicity, and socioeconomic status. Of these factors, the ISA found "adequate evidence" indicating that children and some races are at increased risk of PM2.5-related health effects, in part due to disparities in exposure. However, we lack associated epidemiologic information that would enable us to conduct a health effects analysis for children.
Therefore, due to the limited availability of both new scientific evidence in this NAAQS review and input information (U.S. EPA, 2019, U.S. EPA, 2022a), the one health endpoint for which we evaluate EJ implications is premature mortality. The PM ISA and PM ISA Supplement provided evidence that there are consistent racial and ethnic disparities in PM2.5 exposure across the U.S., particularly for Black/African Americans, as compared to non-Hispanic White populations. Additionally, some studies provided evidence of increased PM2.5-related mortality and other health effects from long-term exposure to PM2.5 among Black populations. Taken together, the 2019 PM ISA concluded that the evidence was adequate to conclude that race and ethnicity modify PM2.5-related risk, and that non-White individuals, particularly Black individuals, are at increased risk for PM2.5-related health effects, in part due to disparities in exposure (U.S. EPA, 2019, U.S. EPA, 2022a).
As such, this EJ health analysis assesses long-term PM2.5-attributable mortality rates stratified by racial and ethnic demographic populations.[,] Mortality is presented as a rate per 100,000 (100k) individuals to permit direct comparisons between population demographics with different total population counts. Additional information on the concentration-response functions and baseline incidence rates used as input information in this health EJ analysis can be found in Section <IP_ADDRESS> and Appendix C of the draft PM Policy Assessment (U.S. EPA, 2021).
Total Mortality Rates
National and regional relative disparities between the demographic-specific mortality rates under air quality scenarios associated with control strategies for the current and potential alternative lower standard levels are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively.
National
Figure 6-11 and Figure 6-12 show the national averages and distributions of estimated mortality rates per 100k individuals for each demographic population over the age of 64. These estimates are calculated using various inputs, including air quality changes, concentration-response functions, and baseline incidence. The greater magnitude concentration-response relationship between exposure and mortality for the Black population of older adults found by Di et al., 2017 results in estimated higher mortality rates in Blacks. Higher estimated average PM2.5 concentrations among Hispanics, as discussed in the previous sections, leads to larger mortality rates in Hispanics than in non-Hispanics even though the baseline incidence rate in Hispanics is slightly lower than the overall rate (U.S. EPA, 2021, Appendix C).

Figure 6-11	Heat Map of National Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Figure 6-12	National Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Regional
Regionally, the highest mortality rates for reference populations are in CA under air quality scenarios associated with control strategies for both current and alternative PM standard levels, followed by the NE, SE, and then the W (Figure 6-13 and Figure 6-14). Total mortality rates in the reference populations decrease slightly under alternative standard levels in all regions, and the most in CA. Within each of the four regions, average and distributional mortality rates are highest among Blacks and lowest among Asians, although there are differences in the ordinality of other races and ethnicities across regions. Interestingly, the distribution of Hispanic mortality rates in the SE suggests there may be a subset of locations in which Hispanics have higher baseline incidence rates, as the PM2.5 concentration differentials between Hispanic and non-Hispanic populations remained fairly constant across PM2.5 concentration distributions (Figure 6-4).

Figure 6-13	Heat Map of Regional Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Figure 6-14	Regional Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Mortality Rate Changes
National and regional relative changes in disparities between the demographic-specific mortality rates when moving from air quality associated with control strategies for the current to alternative standard levels are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively.
National
Nationally, the rate of PM2.5-attributable mortality is estimated to decrease for all races and ethnicities when moving from current alternative standard levels, and more so under lower alternate standard levels (Figure 6-15 and Figure 6-16). In addition, reductions in mortality rates are larger for all other races as compared to Whites, and for Hispanics as compared to non-Hispanics.

Figure 6-15	Heat Map of National Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Figure 6-16	National Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Of the four regions, the largest mortality rate reductions for the greatest percent of each population are estimated in CA when moving from the current to alternative standard levels (Figure 6-17 and Figure 6-18). Reductions are smaller in the other three regions, although reductions become more substantial in the other three regions for 12/35-9/35 or 12/35-8/35. When comparing across race and ethnicities, Blacks are predicted to see the largest mortality rate reductions and Whites are predicted to see the smallest rate reductions.

Figure 6-17	Heat Map of Regional Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current and Alternative PM NAAQS Levels After Application of Controls

Figure 6-18	Regional Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Proportional Changes in Mortality Rates
The proportional change in mortality rate for different demographic groups when moving from current to alternative PM2.5 standard levels associated with the illustrate control strategies is calculated in the same way we estimated proportional changes in PM2.5 concentrations in Section 6.2.3. Briefly, the mortality rate under the alternative standard level is divided by the mortality rate under the current standard, then subtracted from 1, and multiplied by 100 to get a percent. As the average mortality rates have been representative of the distributions, for simplicity we again only present the average proportional change for each population and scenario, at the national and regional levels (<IP_ADDRESS> and <IP_ADDRESS>).
National
Hispanics and Asians are estimated to experience proportionally larger reductions in mortality rates when moving from the current to alternative standard levels associated with control strategies, with the percent relative improvement increasing as standards are lowered (Figure 6-19).

Figure 6-19	Heat Map of National Average Percent Mortality Rate Reductions (per 100k People) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Hispanics and Asians are estimated to experience proportionally larger reductions in mortality rates when moving from current standard to alternative standard levels associated with control strategies in the SE and CA. Blacks experience proportionally larger reductions in mortality rates for 12/35-9/35 and 12/35-8/35.

Figure 6-20	Heat Map of Regional Average Percent Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

EJ Case Study of Exposure and Health Effects in Impacted Areas
The analyses presented above in sections 6.2 and 6.3 encompass the entire contiguous U.S., including areas that already meet potential alternative standards. Such areas would not be required to reduce emissions to meet the proposed more stringent standards, and therefore PM2.5 concentrations in these areas would not be expected to change as a result of EPA adopting more stringent PM2.5 standard level(s). Including such areas in the analysis reduces the resulting average exposure and mortality rate change estimates and potentially masks proportionally greater changes (i.e., reductions) in exposure and health impacts in areas that are projected to exceed the proposed alternative standards in the baseline. Areas that exceed the proposed alternative standards can be expected to experience the greatest PM2.5 concentration changes following the application of control strategies. Therefore, in addition to analyses of the whole contiguous U.S. (Sections 6.2 and 6.3), here we perform an EJ case study focusing on areas that are predicted to experience PM2.5 concentration changes when moving from the current standard of 12/35 to the alternative standard 9/35 under the emission control scenario described in Chapter 3.
This case study is intended to illustrate how changes in higher concentration areas compare to changes at the national scale; for purposes of this illustration, we focus on the single lower alternative standard of 9/35. The specific areas in which PM2.5 concentrations change when moving to a lower standard differ with each alternative lower standard, with the number of areas increasing as the standard lowers. As such, fewer areas would be included if we analyzed 10/35 or 10/30, and additional areas would be included if we analyzed 8/35. Also, the case study analysis is limited to the assessment of average PM2.5 exposures and risks and does not include all of the distributional information presented in the national analysis above. It is important to note that some of the limitations and caveats that affect the national scale analysis become even more relevant to this case study analysis. For example, 12 km grid scale air quality information may not be sufficiently resolved to detect hyperlocal differences in population exposures; this limitation becomes more important as we try to dial in on changes in exposure and risk in the considerably smaller areas included in the case study. Finally, the illustrative nature of the emission control strategy leading to emissions reductions in this NAAQS RIA may lead to increased uncertainties when looking only at areas in which PM2.5 concentrations are predicted to change, as PM2.5 concentrations in this analysis may not reflect state-level implementation decisions.
The subset of areas in which PM2.5 concentrations are predicted to change when moving from 12/35 to 9/35 are colored blue in Figure 6-21. The subset of areas constitutes approximately 5% of the area across the contiguous U.S. and just over a quarter of the population. Information regarding the other ~95% of areas, which are projected to already meet a standard of 9/35 and therefore are not projected to experience a change in PM2.5 concentrations under this more stringent standard, is also provided in certain figures for context.

Figure 6-21	Map of Areas in which PM2.5 Concentrations Change when Moving from 12/35 to 9/35 After Application of Controls

Exposures
Average annual PM2.5 concentrations and concentration changes for the various demographic populations analyzed are presented for the subset of areas in Figure 6-22. Columns labelled `12/35 (Subset)' and `9/35 (Subset)' provide average PM2.5 concentrations experienced by populations residing in the subset of ~5% of areas (~25% of people) where PM2.5 concentration changes when moving from 12/35 to 9/35. The far-right column labeled `No PM Changes' provides the average PM2.5 concentrations experienced by populations residing in the other ~95% of areas (~75% of people) that do not experience a change in PM2.5 concentration under the more stringent standard of 9/35. The column labelled `12/35-9/35 (Subset)' also shows the average PM2.5 concentration reduction afforded to each population residing in the subset of areas where concentrations change, when moving from 12/35 to 9/35.
Comparing these averages to national-level estimates (Figure 6-1), we note that as expected, we observe higher average baseline exposures in areas where air quality changes, but the overall pattern of exposure across groups is fairly similar to the national pattern. Like Figure 6-1, Figure 6-22 shows that the most substantial disparity in average annual PM2.5 exposures occurs between Hispanic populations and non-Hispanic populations. Further, in comparing the subset of areas where air quality changes to areas where it does not change, we note that average exposures in the subset of areas where air quality does change are at least 1 ug/m[3] higher than averages in the areas where air quality does not change under both the baseline and 9/35 scenarios. In addition, disparities are pronounced among certain demographics (e.g., the average baseline exposure among Hispanics living in areas where air quality does change is almost 2 ug/m[3] higher than exposures among Hispanics in areas that already meet 9/35). Similarly, the average air quality improvements experienced by populations living in areas where air quality does change are 2-4 times larger than when such changes are averaged over the entire contiguous U.S. (Figure 6-5).

Figure 6-22	Heat Map of National Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35

Average exposures of the subset of areas where air quality changes in each of the four regions analyzed show similar results, with larger average annual PM2.5 concentrations and concentration reductions for this subset of areas in all regions (Figure 6-23). In the subset of areas where air quality does change moving from 12/35 to 9/35, absolute concentration reductions are more similar across the regions than when all areas are included as in Sections 6.2 and 6.3, with the largest reductions predicted in the SE, followed by CA, the NE, and the W. We note that this is tied to the control strategy, which identified different available measures in each region.

Figure 6-23	Heat Map of Regional Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35

While absolute exposure and exposure reduction estimates are necessary foundational information, the proportionality of the reductions more clearly answers question 3 of the EJ Technical Guidance (U.S. EPA, 2015). Proportional exposure reductions (i.e., the percent change in PM2.5 concentrations when moving from 12/35 to 9/35 divided by the total exposure under 12/35) for the national and regional subset of areas in which PM2.5 concentrations changed when moving from 12/35 to 9/35 are shown in Figure 6-24. As expected, proportional reductions are also greater than the national proportions (Figure 6-9 and Figure 6-10). Nationally, all populations with exposures higher than the overall reference (i.e., Hispanic, Asian, Black, and those less educated) are predicted to have larger proportional exposure decreases than the reference population. CA reflects the national trend, although there are variations in the NE, SE, and W. For example, ethnic exposure disparities in the NE, Black exposure disparities in the SE, and educational attainment disparities in the W are not proportionally mitigated in the subset of areas with air quality improvements when moving from the current standard to 9/35. However, it is also important to note that Hispanics are underrepresented in the NE, and population counts are lowest in the W.

Figure 6-24	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations for Demographic Groups in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35 to 9/35

Mortality Rates
Although the mitigation of exposure disparities is predicted for all demographic groups at the national level and most demographic groups at the regional scale in areas in which PM2.5 concentrations are expected to change in moving from 12/35 to 9/35, it is also important to translate exposure disparities into health disparities when feasible, acknowledging that additional uncertainties are associated with estimating population-stratified health effects. To exemplify the potential importance of stratifying health impacts within various demographic of potential EJ concern, when employing the Di et al., 2017 population-stratified mortality hazard ratios (Table 6-2), the same PM2.5 exposure reduction will reduce the hazard of mortality ~3-fold more in Black populations than in White populations. Therefore, we also separate mortality rate impacts in the subset of areas where PM2.5 concentrations are expected to change when moving from 12/35 to 9/35 from areas that are not associated with PM2.5 concentration changes.
Average national annual mortality rates and mortality rate changes for the various demographic populations analyzed are presented in Figure 6-25. Similar to average PM2.5 concentrations (Figure 6-22), average mortality rates in the subset of areas where air quality changes are higher, and averages in the areas where air quality does not change are lower than in the analysis of all areas (Figure 6-11 and Figure 6-13). The mortality rate reductions are also 2-5 times larger (Figure 6-15 and Figure 6-17).

Figure 6-25	Heat Map of National Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality when Moving from 12/35 to 9/35

In the subset of areas in which PM2.5 air quality changes in moving from 12/35 to 9/35, absolute mortality rate reductions are larger and also more similar across the regions than when all areas are included as in Sections 6.2 and 6.3 (Figure 6-26).

Figure 6-26	Heat Map of Regional Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35 9/35, and 12/35-9/35, in the Subset of Areas that Do and Do Not Change When Moving from 12/35-9/35

Proportionally, mortality rate reductions associated with the change between the 12/35 and 9/35 scenarios are greatest for Black and Hispanic populations, helping to alleviate some of the disparities in the baseline (Figure 6-27). While mortality rate disparities for Blacks are predicted to be reduced in each region, impacts on disparities for Hispanics vary by region, with the greatest percent reduction in CA and the W. In comparing these reductions to the overall reductions in mortality rates nationally (Figure 6-19 and Figure 6-20), we note that the percent reductions are larger in the areas in which air quality changes when moving from 12/35 to 9/35, and that the pattern of results also varies somewhat by region (e.g., the greatest proportional rate reductions are seen among Asians in the SE, as compared to Blacks and Asians in CA in the analysis of all areas).

Figure 6-27	Heat Map of National and Regional Percent Reductions in Average Annual Total Mortality Rates (per 100K) by Demographic in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35-9/35
Summary
For this proposal, we quantitatively evaluate the potential for disparities in PM2.5 concentrations and mortality effects across different demographic populations for the current (12/35; baseline) and potential alternative PM2.5 NAAQS levels (10/35, 10/30, 9/35, and 8/35) under air quality scenarios associated with illustrative emission control strategies. Specifically, we provide information on totals, changes, and proportional changes in 1) annual average PM2.5 concentrations and 2) premature mortality as rates per 100,000 individuals across and within various demographic populations. Each type of analysis has strengths and weaknesses, but when taken together, can respond to the three questions from EPA's EJ Technical Guidance. Total concentration/mortality rate analyses provide information on absolute PM2.5 concentrations and mortality rates; however, it can be difficult to compare improvements in air quality/mortality rates among populations from total information. In contrast, comparing changes in concentration/mortality rates provides information on how improvements compare across and within populations, but does not provide information on which populations experience higher total concentration/mortality rates. Proportional changes are provided as a percent of the total concentration/mortality rate information, so although they are similar to absolute changes, they are more closely related to total concentration/mortality rate information.
EJ analyses were performed both at national and regional scales, as geography is relevant both to PM NAAQS attainment and population demographics. We also conducted a case study to examine the subset of areas in which air quality is projected to change after the application of controls outlined in Chapter 3 to illustrate how air quality improvements in the areas with the highest starting concentrations might be distributed demographically. For all of these analyses, we note that the results should be considered illustrative only, Further, as with all EJ analyses, data limitations make it possible that disparities may exist that our analysis did not identify. This is especially relevant for potential EJ characteristics, environmental impacts, and more granular spatial resolutions that were not evaluated. We note again that this analysis is based on air quality modeling conducted on a 12 by 12 km grid scale, which may mask more local disparities in exposure and risk. Additionally, EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis.
Whereas all populations experience reductions in PM2.5 concentrations and health effects at lower PM standard levels, to make conclusions regarding EJ impacts of this proposed rule we refer back to the three questions that EPA's EJ Technical Guidance (U.S. EPA, 2015) states should be addressed, which for purposes of the PM NAAQS RIA EJ analysis are:
 Are there disproportionate PM2.5 exposures/health effects under baseline/current PM NAAQS standard levels?
 Are there disproportionate PM2.5 exposures/health effects under illustrative alternative PM NAAQS standard levels?
 Are PM2.5 exposure/health effect disparities created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline?
Considering results of both the EJ exposure analysis (Section 6.2) and the EJ health effects analysis (Section 6.3), responses to the above three questions can be summarized as:
 Disparities in the baseline: Under air quality scenarios associated with control strategies for the baseline (12/35) PM NAAQS scenario, some populations are predicted to experience disproportionately higher annual PM2.5 concentrations nationally than the reference (overall) population, both in terms of aggregated average concentrations and across the distribution of air quality (Figure 6-1 and Figure 6-2). Specifically, Hispanics, Asians, Blacks, and those less educated (no high school) have higher national annual concentrations, on average and across the distributions, than both the overall reference population or other populations (e.g., non-Hispanic, White, and more educated). In particular, the Hispanic population is estimated to experience the highest concentrations, both on average and across PM2.5 concentration distributions, of all demographic groups analyzed. These disproportionalities are also observed at the regional level, though to different extents, as Asian concentrations in the W and CA are similar to the reference group, and those less educated are exposed to higher PM2.5 concentrations only in CA (Figure 6-3 and Figure 6-4). Similar, but magnified, trends are observed when evaluating only the areas in which air quality improvements are predicted.
      In terms of health effects, some demographic populations are also predicted to experience disproportionately higher rates of premature mortality than reference populations (Figure 6-11 through Figure 6-14). Black populations are estimated to have the highest national and regional mortality rates, both on average and across population distributions. This may be partly due to higher PM2.5 concentrations for this population, which could contribute to the higher magnitude concentration-response relationship between exposure concentrations and premature mortality (Di et al., 2017), as well as other underlying health factors which may increase susceptibility to adverse outcomes among Black populations. Hispanic mortality rates are disproportionately higher in the SE, W, and CA. Higher mortality rates are predicted for Asians and American Indians in CA and for American Indians in the SE. Similar, but larger, trends are also observed when evaluating only the areas in which air quality improvements are predicted.
 Disparities under alternative policy options: While more stringent control strategies reduce PM2.5 concentrations and mortality rates across all demographic groups, disparities seen in the baseline are also reflected in the policy options under consideration. Specifically, disproportionately higher PM2.5 concentrations and health effects remain for some populations estimated under air quality scenarios associated with the illustrative control strategies (10/35, 10/30, 9/35, and 8/35) (Figure 6-1 through Figure 6-4 and Figure 6-11 through Figure 6-14). Nationally and regionally, these patterns and the populations affected are similar to those seen in the baseline. and larger when considering only the subset of areas in which air quality improvements are expected.
 Relative impact of alternative policy options on disparities in the baseline: For most populations assessed, PM2.5 concentration disparities are mitigated in the illustrative air quality scenarios associated with control strategies for more stringent PM2.5 control strategies (10/35, 10/30, 9/35, and 8/35) as compared to the baseline (12/35), to differing degrees (Figure 6-1 through Figure 6-10). This conclusion is strengthened when restricting analyses to areas in which PM2.5 concentrations are predicted to decrease (Figure 6-29 through Figure 6-34). More specifically, increasing portions of certain populations of potential EJ concern are expected to experience greater PM2.5 concentration reductions as the control strategies become more stringent (Figure 6-6). At the national scale, Hispanics, Asians, and those less educated are estimated to see greater proportional reductions in PM2.5 concentrations than reference populations under all lower standard levels evaluated, with proportional reductions increasing as the standard levels decrease. However, concentrations in the Black population are estimated to proportionally decrease on par with reference concentrations. Average concentration reductions were also similar across Black and White populations when the spatial scale of the analysis was limited to those areas affected by the illustrative control strategies. Considering the four geographic regions, proportionally greater reductions in PM2.5 concentrations experienced by Asian, Hispanic, and less educated populations are most notable in the SE and CA, whereas PM2.5 concentration reductions among Black populations tend to be proportionally larger than among the reference population in CA, W, and the NE, especially under lower standard levels. Due to the higher prevalence of Black populations in the SE, the lack of proportional concentration reductions in that region may mask increased concentration reductions in other regions at the national level.
      In general, more stringent control strategies are also associated with reductions in mortality rate disparities. Specifically, the analysis shows that as the PM2.5 control strategies become increasingly stringent, differences in mortality rates across demographic groups decline, particularly for the lowest alternatives evaluated (12/35-9/35 and 12/35-8/35). Similar to the estimated changes in PM2.5 concentrations following reductions in PM2.5 concentrations under alternative standards, disparities in PM2.5 mortality rates across demographic groups are mitigated nationally for Hispanics in all the alternative PM standard levels (10/35, 10/30, 9/35, and 8/35) as compared to the baseline (Figure 6-19). Nationally, Black populations are predicted to experience proportionally similar mortality rate reductions to White populations under control strategies associated with 12/35-10/35 or 12/35-10/30, but greater reductions in mortality rates than White populations under control strategies associated with 12/35-9/35 or 12/35-8/35. While Asians are estimated to experience the greatest proportional mortality rate reductions of the races/ethnicities analyzed, they are predicted to initially experience disproportionally lower mortality rates under the baseline scenario. When the spatial scale of the analysis was limited to those areas affected by the illustrative control strategies for 9/35, Asian, Black and Hispanics experienced the greatest reduction in mortality rates nationally and in most regions.
Environmental Justice Appendix
Input Information
EJ Exposure Analysis Input Data
In Appendix 2A, the exposure assessment involves demographic population data projected out to the future year 2032. We use population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods & Poole, 2015). The Woods and 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 and Poole, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently (Woods & Poole, 2015).
EJ Health Effects Analysis Input Data
The health assessment requires input data in addition to the information used in the exposure assessment (Section <IP_ADDRESS>). As such, there are additional uncertainties, albeit similar to the benefits assessment results (Chapter 5). We evaluated the available studies and concentration-response functions to determine if sufficient information exists for use in a quantitative analysis and to determine which study or studies best characterizes at-risk nonwhite populations across the U.S. Of the available studies, Di et al., 2017 was a nationwide study, evaluated the largest study size over one of the most recent time spans, used a sophisticated exposure estimation technique, and provided sufficient information to apply risk models quantifying increased risks to the following nonwhite groups: Black, Asian, Native American, and Hispanic populations. Although Di et al., 2017 effect estimates were derived from a cohort aged 65 and older and the study did not provide a non-Hispanic concentration-response function, it was identified as best characterizing populations potentially at increased risk of long-term exposure and all-cause mortality. Health impact functions, including beta parameters and standard errors (SE), were developed for each at-risk population demographic described by Di et al., 2017 and are provided in Table 6-2.
Table 6-2	Hazard Ratios, Beta Coefficients, and Standard Errors (SE) from Di et al., 2017
                            Demographic Population
          Risk of Death Associated with a 10 ug/m3 Increase in PM2.5
                             Beta Coefficient (SE)
                                     White
                             1.063 (1.060, 1.065)
                                0.0061 (0.0001)
                                      All
                             1.073 (1.071, 1.075)
                                0.0070 (0.0001)
                                   Hispanic
                             1.116 (1.100, 1.133)
                                0.0110 (0.0008)
                                     Black
                             1.208 (1.199, 1.217)
                                0.0189 (0.0004)
                                     Asian
                             1.096 (1.075, 1.117)
                                0.0092 (0.0010)
                                Native American
                             1.100 (1.060, 1.140)
                                0.0095 (0.0019)

Concentration-response functions stratified by race and ethnicity were only available for ages greater than 64. While BenMAP-CE includes population information for 5-year age spans up to 84 and Di et al., 2017 provides stratified concentration-response functions for 10-year age spans (65-74, 75-84, and 85-99), the stratified concentration-response functions for 10-year age spans were not also stratified by race or ethnicity. Therefore, this analysis only evaluated a single age range group of 65-99 years.
BenMAP-CE includes baseline incidence rates at the most geographically- and age-specific levels available for each health endpoint assessed. For many locations within the U.S., these data are resolved at the county- or state-level, providing a better characterization of the geographic distribution of mortality rates than the national-level rates. Race- and ethnicity-stratified baseline incidence rates from 2007-2016 Census data were recently improved for the all-cause mortality health endpoint, by adding the geographic level option of rural/urban state between county-level and state-level. Both overall and race/ethnicity-stratified baseline rates are used in this analysis of EJ health impacts analysis.
To estimate race-stratified and age-stratified incidence rates at the county level, we downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC WONDER mortality database. Race-stratified incidence rates were calculated for the following age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. To address the frequent county-level data suppression for race-specific death counts, we stratified the county-level data into two broad race categories, White and Non-White populations. In a later step, we stratified the non-White incidence rates by race (Black, Asian, Native American) using the relative magnitudes of incidence values by race at the regional level, described in more detail below.
We followed methods outlined in Section D.1.1 of the BenMAP User Manual with one notable difference in methodology; we included an intermediate spatial scale between county and state for imputation purposes. We designated urban and rural counties within each state using CDC WONDER and, where possible, imputed missing data using the state-urban and state-rural classifications before relying on broader statewide data. We followed methods for dealing with suppressed and unreliable data at each spatial scale as described in Section D.1.1.
A pooled non-White incidence rate masks important differences in mortality risks by race. To estimate county-level mortality rates by individual race (Black, Asian, Native American), we applied regional race-specific incidence relationships to the county-level pooled non-White incidence rates. We calculated a weighted average of race-specific incidence rates using regional incidence rates for each region/age/race group normalized to one reference population (the Asian race group) and county population proportions based on race-specific county populations from CDC WONDER where available. In cases of population suppression across two or more races per county, we replaced all three race-specific population proportions derived from CDC WONDER with population proportions derived from 2010 Census data in BenMAP-CE (e.g., 50 percent Black, 30 percent Asian, 20 percent Native American).
To estimate ethnicity-stratified and age-stratified incidence rates at the county level, we downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC WONDER mortality database. Ethnicity-stratified incidence rates were calculated for the following age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. We stratified county-level data by Hispanic origin (Hispanic and non-Hispanic). We followed the methods outlined in Section D.1.1 to deal with suppressed and unreliable data. We also included an intermediate spatial scale between county and state designating urban and rural counties for imputation purposes, described in detail in Section D.1.3 of the BenMAP User Manual.
EJ Analysis of Total Exposures Associated with Meeting the Standards
In addition to air quality surfaces associated with the illustrative emission control strategies evaluated in the main EJ chapter, PM2.5 air quality surfaces associated with meeting the current and alternative standard levels were also developed. Air quality associated with meeting the standards was based on assumptions that emission controls could be identified to meet the required emission amounts (Appendix 2A). Results for both air quality scenarios are included in this appendix, to allow for direct comparisons. In general, for populations experiencing higher baseline PM2.5 concentrations and mortality rates, air quality scenarios associated with meeting the standards reduce disparities more so than air quality scenarios associated with the control strategies, especially for Hispanics populations in CA.
National and regional PM2.5 concentrations by demographic populations for air quality scenarios associated with both the control strategies and meeting the standards are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively.
National
At the national level, air quality scenarios associated with meeting the standards led to similar and/or slightly lower PM2.5 concentrations under the current and lower alternative standard levels than air quality scenarios associated with control strategies (Figure 6-28 and Figure 6-29). This may narrow disproportionate PM2.5 concentrations for certain populations, such as Hispanics, under air quality associated with more stringent alternative standard levels.

Figure 6-28	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies (Controls) or with Meeting the Standards (Standards) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35)

Figure 6-29	National Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels
Regional
Regionally, air quality scenarios associated with meeting the standards also led to similar or slightly lower PM2.5 concentrations as air quality scenarios associated with the current standards for more stringent standard levels, except for in CA, where air quality associated with the standards resulted in substantially lower PM2.5 concentrations (Figure 6-30 and Figure 6-31).
Comparing `Controls' and `Standards' in CA associated with the lower alternative standard levels allows for some insight into areas without known emission control strategies. For example, for the alternative standard level of 9/35 in CA, more than 90% of non-Hispanics and Hispanics are projected to experience annual PM2.5 concentrations <9 ug/m[3] when meeting an alternative standard level of 9/35 and a similar percentage of non-Hispanics are expected to experience annual PM2.5 concentrations <9 ug/m[3] associated with emission control strategies for 9/35. In contrast, only about 60% of Hispanics are expected to experience annual PM2.5 concentrations <9 ug/m[3] with controls associated with 9/35. Therefore, disparities between Hispanics and non-Hispanics predicted with controls at 9/35 are mitigated if CA were to meet the alternative standard level of 9/35.

Figure 6-30	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels

Figure 6-31	Regional Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels
EJ Analysis of Exposure Changes Associated with Meeting the Standards
National and regional changes in PM2.5 concentrations for demographic populations when moving from current to more stringent alternative standard levels for air quality scenarios associated with meeting the standards, and the ability to compare them with air quality scenarios associated with the illustrative emission control strategies, are provided in Sections <IP_ADDRESS> and <IP_ADDRESS>, respectively.
National
Nationally, PM2.5 concentration reductions for air quality scenarios associated with the illustrative emission control strategies are estimated to be similar or slightly greater than PM2.5 concentration reductions for air quality scenarios associated with meeting the standards when moving from current to more stringent standard levels (Figure 6-32 and Figure 6-33).

Figure 6-32	Heat Map of National Average Annual Reductions in PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels