Comments
Abstract
Rationale: Adverse health impacts from outdoor air pollution occur across the United States, but the magnitude of these impacts varies widely by geographic region. Ambient pollutant concentrations, emission sources, baseline health conditions, and population sizes and distributions are all important factors that need to be taken into account to quantify local health burdens.
Objectives: To determine health impacts from ambient air pollution concentrations in the United States that exceed the levels recommended by the American Thoracic Society.
Methods: Using a methodology that has been well established in previous “Health of the Air” reports, this study provides policy-relevant estimates for every monitored county and city in the United States for the adverse health impacts of outdoor pollution concentrations using U.S. Environmental Protection Agency design values for years 2018–2020. Additionally, for the first time, the report includes adverse birth outcomes as well as estimates of health impacts specifically attributable to wildland fires using an exposure dataset generated through Community Multiscale Air Quality simulations.
Results: The adverse health burdens attributable to air pollution occur across the entire age spectrum, including adverse birth outcomes (10,660 preterm and/or low-weight births; 95% confidence interval [CI], 3,180–18,330), in addition to mortality impacts (21,300 avoidable deaths; 95% CI, 16,180–26,200), lung cancer incidence (3,000 new cases; 95% CI, 1,550–4,390), multiple types of cardiovascular and respiratory morbidity (748,660 events; 95% CI, 326,050–1,057,080), and adversely impacted days (52.4 million days; 95% CI, 7.9–92.4 million days). Two different estimates of mortality impacts from wildland fires were created based on assumptions regarding the underlying toxicity of particles from wildland fires (low estimate of 4,080 deaths, 95% CI, 240–7,890; middle estimate of 28,000 deaths, 95% CI, 27,300–28,700).
Conclusions: This year’s report identified sizable health benefits that would be expected to occur across the United States with compliance with more health-protective air quality standards such as those recommended by the American Thoracic Society. This study also indicates that a large number of excess deaths are attributable to emissions from wildland fires; air quality management strategies outside what is required by the Clean Air Act will be needed to best address this important source of air pollution and its associated health risks.
This article serves as the fifth “Health of the Air” report, which has regularly provided updated estimates of the health impacts of outdoor air pollution at the local and national levels in the United States since it was first published in AnnalsATS in 2016 (1). The report is a joint effort between the American Thoracic Society (ATS) and environmental health researchers at New York University’s Marron Institute of Urban Management. This year’s report coincides with the U.S. Environmental Protection Agency (EPA) actively reviewing the National Ambient Air Quality Standards for fine particulate matter (i.e., <2.5 µm in diameter; PM2.5) and ozone (O3), both of which are evaluated in this report. Although health estimates are always included as part of the EPA’s official review process, this report emphasizes not just the magnitude of effects at the national level but brings the focus much closer to home by providing estimates of adverse health impacts for increased levels of these pollutants for all cities and counties with a regulatory air quality monitor.
The general structure of the report has stayed largely the same, specifically in regard to quantifying the adverse health impacts across a range of health endpoints for ambient pollution concentrations greater than recommended by the ATS (8 μg/m3 for long-term PM2.5, 25 μg/m3 for short-term PM2.5, and 60 parts per billion [ppb] for O3). At the same time, the report has also evolved by incorporating additional elements in each successive publication. Some of these previous improvements included adding estimates of incident lung cancer (1), looking at decadal trends (2), and providing updated health estimates based on a revision of the underlying recommendations from ATS (3). These efforts have not only provided evidence supporting broad calls for improved air quality standards at the federal level, but, perhaps more importantly, provided policy-relevant health information at the local level that can inform ongoing discussions on how to best prioritize and address unhealthy levels of outdoor air pollution at the local level.
This year’s Health of the Air report includes two important improvements: the inclusion of health estimates specifically attributable to wildland fire emissions across the United States and the inclusion of preterm birth and low-birthweight outcomes as new health endpoints. Both of these advancements reflect a growing interest in one of the most important sources of outdoor air pollution in the United States and a recognition that increased levels of outdoor air pollution exert adverse health impacts throughout all stages of life, including before birth.
There is an increasing awareness of the important contribution of wildland fires to ambient pollution in the United States (4–6). Wildland fires (including wildfires, prescribed fires, and agricultural burns) constitute approximately 40% of primary PM2.5 emissions and 25% of total PM2.5 in the United States and dominate the interannual PM2.5 variability (7, 8). Because of a combination of climate change (9–11) and a century of fire suppression (12), wildfire seasons are lengthening and increasing in intensity, producing emissions that may soon counteract decades of regional PM2.5 reductions (13, 14). Primary pollutants emitted from wildland fires also contribute to the formation of downwind ground-level ozone (O3), although conditions near fires may constrain O3 formation (15, 16). Because the Clean Air Act’s exceptional events rule excludes wildfire smoke from National Ambient Air Quality Standards attainment decisions, complete reliance on these standards cannot currently address issues of increasing adverse health impacts from wildland fires.
Preterm birth and low birth weight are global health challenges that increase an infant’s risk for numerous acute health risks, as well as increasing the lifelong risk of several chronic diseases (see Figure 1) (17–19). For example, diminished lung health is one of the most common long-term consequences of preterm birth (20, 21). Although treatments to enhance lung growth postnatally have increased the survival of infants born preterm, numerous studies suggest diminished lung function into adulthood (20, 22). Previous studies have defined the positive relationship between ambient air pollution exposure (PM2.5 and O3) and the risks of preterm birth and low birth weight (23–25). Regarding periods of susceptibility, it is thought that chronic exposure to air pollution throughout pregnancy results in reduced oxygen and nutrients, causing intrauterine growth restriction and low birth weight (26, 27), and more acute exposures can additionally increase the risk of the initiation of labor and premature rupture of membranes, leading to an increased risk of preterm birth (Figure 1) (28). These adverse birth outcomes are not presently accounted for in regulatory impact assessments for PM2.5 or ozone, although previous evaluations suggest substantial economic damages from these effects (29–31).
Figure 1. Clinical significance of air pollution–driven adverse birth outcomes. (A) How the timing of air pollution exposure during pregnancy effects the risk of the adverse birth outcomes included in this analysis. Specifically, exposure from weeks 1 to 37 increases the susceptibility to preterm birth, defined as birth between weeks 25 and 36; exposure throughout pregnancy restricts fetal growth, which can result in low birth weight of preterm and full-term births. (B) Acute and chronic health risks following preterm and low-weight births (17–19, 28).
[More] [Minimize]This study uses a methodological structure described in previous Health of the Air reports (1–3, 32) with updates reflecting the latest epidemiological research. These methods largely follow those used by the EPA in its regulatory review processes, with additional details described herein.
Daily PM2.5 and O3 concentrations were retrieved from the EPA Air Quality System for all monitored counties in the United States, defined as those with valid design values for 2018–2020. Design values are a statistic used by the EPA to define county-level air quality and determine whether locations are in compliance with federal regulatory levels (33). Design values are based on the 3-year average of the annual mean concentrations for long-term PM2.5, the 24-hour 98th percentile concentration for short-term PM2.5, and the 3-year average of the fourth-highest daily 8-hour maximum concentration for O3. From these values, baseline and control datasets for each county were created using a 24-hour metric for PM2.5 and an 8-hour maximum metric for O3. The pollution increment considered in this health analysis corresponds to the difference between design values (12 μg/m3 for long-term PM2.5, 35 μg/m3 for short-term PM2.5, and 70 ppb for O3) and ATS-recommended standards (8 μg/m3 for long-term PM2.5, 25 μg/m3 for short-term PM2.5, and 60 ppb for O3).
County-level baseline incidence information for respiratory and all-cause mortality was obtained from the National Center for Health Statistics Mortality Data on the Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research database (34), with other incidence numbers derived from the EPA’s Environmental Benefits Mapping and Analysis Program Community Edition (version 1.5) (35). Each of these endpoints used baseline incidence data from 2019 to avoid the confounding effects of coronavirus disease (COVID-19) on respiratory hospitalizations and deaths in 2020. County-level baseline incidence data (2018–2020) for preterm births and low-weight births were acquired from the Centers for Disease Control and Prevention’s National Center for Health Statistics natality public-use files (36). Three-year incidence values were obtained and averaged to annual counts for this endpoint to reduce the frequency of suppressed counts in counties with low totals.
The EPA’s Environmental Benefits Mapping and Analysis Program Community Edition was used to conduct county-level health impact assessments of the delta between current air pollution levels and those recommended by the ATS. Health impact functions (also called concentration–response functions) represent the relationship between pollutant concentration changes and specific health outcomes based on risk values derived from the epidemiological literature (37). This study uses health impact functions specifically designated as EPA “standard health functions” (updated in 2020) to calculate county-level health impacts of pollution levels exceeding ATS recommendations (Table E1 in the online supplement lists all the studies used). Health functions for O3-associated preterm birth were derived from the study of Rappazzo and coworkers (23), which compiled results across 19 cohort and case-control studies. Health functions for O3-related low birth weight were derived from the study of Li and coworkers (24), a meta-analysis of 14 cohort studies. For PM2.5-associated preterm birth, we used summary effect values from a recent meta-regression (25) that included 40 separate studies for each outcome. For PM2.5-associated low-birthweight health functions, the studies of Ghosh and coworkers (25) and Li and coworkers (24), which also conducted a meta-analysis across 29 studies, were coupled using fixed effects pooling.
This year’s report includes an additional analysis of the health impacts of air pollution from wildland fire pollution in all counties in the contiguous United States using high-resolution, satellite-derived wildland fire smoke exposure data (38). Using a blended Moderate-Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite fire detection product, two Community Multiscale Air Quality simulations with and without fire emissions were performed to produce high-resolution PM2.5 predictions for the contiguous United States. The Community Multiscale Air Quality estimates align well with urban and rural area ground measurements, making it an ideal model for nationwide analysis (39). In this study, daily with- and without-fire concentrations of PM2.5 (24-h mean) and O3 (8-h max) were aggregated to the county level using area-weighted analysis for 2020. Health functions for mortality risks for wildland fires were estimated using standard (40, 41) and wildland fire–specific health functions (42). The pollution increment considered in the wildland fire health impact assessment corresponds to the difference between daily pollutant predictions from the with-fire and without-fire models.
Of the 3,144 total counties in the contiguous United States, there were 515 with valid PM2.5 monitoring data, with 210 exceeding the ATS recommendation for long-term PM2.5 (8 μg/m3) and 101 exceeding the recommendation for short-term PM2.5 (25 μg/m3); 75 counties exceeded both. Of the 693 counties with valid O3 data, 487 exceeded the ATS recommendation (60 ppb).
Preventable national annual health impacts associated with PM2.5 (Table 1) and O3 (Table 2) greater than the ATS recommended levels in monitored U.S. counties include 13,900 deaths (95% confidence interval [CI], 13,500–14,300) for PM2.5 and 7,400 deaths (95% CI, 2,650–11,900) for O3, 3,000 new cases of lung cancer (95% CI, 1,550–4,390) attributable to PM2.5, 748,660 (95% CI, 326,050–1,057,080) cardiovascular and respiratory morbidities combined for PM2.5 and O3, 52.4 million (95% CI, 7.9–92.4 million) adversely impacted days combined for PM2.5 and O3, and 10,660 (95% CI, 3,180–18,330) adverse birth outcomes. Because health estimates are based on the pollution increment between the ATS recommendations and the current county pollution levels (design values), these estimates represent the health outcomes that could have been prevented if all counties in the United States met the ATS recommendations between 2018 and 2020. Mortality totals reported here include all causes of death based on the work of Di and coworkers (40) for PM2.5 and that of Katsouyanni and coworkers (43) for O3; however, results from other mortality studies used in EPA assessments are also included in Tables 1 and 2. Individual county design values and health outcomes are provided in Table E2 in the data supplement. Because of the low 5-year survival rates of 26% for non–small-cell and 7% for small-cell lung cancer (44), these are grouped with the mortality health endpoints in the tables in this report.
| Health Endpoint | Annual Preventable Health Impacts |
|---|---|
| Mortality* | |
| All-cause (40) | 13,900 (13,500 to 14,300) |
| All-cause (41) | 20,400 (13,700 to 26,800) |
| Lung cancer diagnosis | 3,000 (1,550 to 4,390) |
| Morbidity | |
| Acute myocardial infarction | 1,120 (530 to 1,680) |
| Emergency room visits | |
| Cardiovascular | 3,320 (−1,280 to 7,740) |
| Respiratory | 7,630 (1,500 to 15,870) |
| Hospital admissions | |
| Cardiovascular | 1,210 (−8,390 to 6,010) |
| Neurological | 3,580 (2,680 to 4,350) |
| Respiratory | 220 (10 to 420) |
| New onset asthma and hay fever/rhinitis | 234,080 (66,410 to 347,250) |
| Out-of-hospital cardiac arrest, stroke | 780 (270 to 1,140) |
| Adversely impacted days | |
| Acute respiratory symptoms | 10,300,000 (8,350,000 to 12,100,000) |
| Asthma symptoms (albuterol use) | 4,720,000 (−2,310,000 to 11,400,000) |
| Work loss days | 1,760,000 (1,480,000 to 2,020,000) |
| Adverse birth outcomes | |
| Low birth weight | 1,880 (1,090 to 2,740) |
| Preterm birth | 3,150 (1,590 to 4,920) |
| Health Endpoint | Annual Preventable Health Impacts |
|---|---|
| Mortality* | |
| All-cause (43) | 7,400 (2,650 to 11,900) |
| Respiratory (41) | 6,730 (4,730 to 8,610) |
| Morbidity | |
| Emergency room visits, respiratory | 30,940 (8,550 to 64,210) |
| Hospital admissions, respiratory | 1,190 (−310 to 2,640) |
| New-onset asthma and hay fever/rhinitis | 464,600 (256,090 to 605,780) |
| Adversely impacted days | |
| Acute respiratory symptoms | 9,720,000 (3,930,000 to 15,200,000) |
| Asthma symptoms | 19,100,000 (−2,490,000 to 38,300,000) |
| School loss days | 6,800,000 (−1,030,000 to 13,400,000) |
| Adverse birth outcomes | |
| Low birth weight | 4,390 (500 to 8,190) |
| Preterm birth | 1,250 (0 to 2,490) |
Across monitored U.S. counties, 4,400 preterm (95% CI, 1,590–7,410) and 6,270 low-weight (95% CI, 1,590–10,930) births each year are associated with air pollution levels greater than the ATS recommendations (Tables 1 and 2). A total of 72% of air pollution–related preterm births are associated with PM2.5, and 70% of low-weight births are associated with O3. Comparing across monitored metropolitan areas, the highest annual adverse birth outcome counts associated with ambient air pollution occur in Los Angeles and Riverside, CA; Houston, TX; Chicago, IL; and Phoenix, AZ (Tables 3 and 4).
| Rank | City Region | Annual PM2.5-Attributable Health Impacts | ||||
|---|---|---|---|---|---|---|
| Mortality | Lung Cancer Diagnosis | Adverse Birth Outcomes | Morbidity | Adversely Impacted Days | ||
| 1 | Los Angeles (Long Beach–Glendale), CA | 2,086 (2,031–2,140) | 390 (203–567) | 740 (395–1,125) | 40,896 (9,770–62,279) | 2,945,090 (1,397,246–4,414,398) |
| 2 | Riverside (San Bernardino–Ontario), CA | 1,269 (1,235–1,302) | 232 (122–336) | 469 (251–711) | 26,032 (6,473–39,400) | 1,658,884 (705,749–2,559,216) |
| 3 | Chicago (Naperville–Arlington Heights), IL | 662 (644–679) | 182 (94–268) | 259 (138–396) | 11,424 (2,812–17,598) | 788,988 (372,568–1,187,332) |
| 4 | Phoenix (Mesa–Scottsdale), AZ | 480 (467–493) | 107 (55–158) | 151 (80–232) | 8,261 (2,022–12,676) | 536,149 (239,419–819,764) |
| 5 | Sacramento (Roseville–Arden–Arcade), CA | 465 (453–477) | 100 (52–146) | 137 (73–209) | 7,647 (1,856–11,625) | 512,416 (229,004–780,891) |
| 6 | Anaheim (Santa Ana–Irvine), CA | 460 (448–472) | 93 (48–136) | 127 (67–194) | 8,404 (2,125–12,770) | 592,643 (279,331–891,471) |
| 7 | Houston (The Woodlands–Sugar Land), TX | 408 (397–418) | 101 (52–148) | 310 (164–474) | 12,273 (3,131–18,825) | 764,772 (323,639–1,186,754) |
| 8 | Bakersfield, CA | 355 (345–364) | 66 (35–95) | 172 (93–258) | 8,800 (2,262–13,079) | 526,310 (201,753–829,415) |
| 9 | Fresno, CA | 349 (340–358) | 59 (31–85) | 155 (83–234) | 7,567 (1,900–11,298) | 455,677 (175,183–719,427) |
| 10 | Oakland (Hayward–Berkeley), CA | 338 (328–346) | 73 (37–108) | 111 (59–170) | 6,254 (1,549–9,543) | 451,175 (216,097–675,769) |
| 11 | Pittsburgh, PA | 333 (324–341) | 81 (41–119) | 63 (33–96) | 2,960 (659–4,596) | 216,336 (108,471–319,085) |
| 12 | Detroit (Dearborn–Livonia), MI | 327 (318–336) | 89 (46–131) | 126 (67–192) | 4,522 (995–6,998) | 286,704 (125,603–440,430) |
| 13 | San Diego (Carlsbad), CA | 263 (256–270) | 54 (28–80) | 85 (45–130) | 4,712 (1,162–7,227) | 334,597 (160,513–501,544) |
| 14 | San Jose (Sunnyvale–Santa Clara), CA | 262 (255–269) | 59 (30–86) | 103 (55–157) | 6,006 (1,490–9,123) | 434,913 (205,045–653,729) |
| 15 | Las Vegas (Henderson–Paradise), NV | 208 (202–214) | 0 | 80 (42–122) | 3,285 (788–5,059) | 228,973 (108,339–344,428) |
| 16 | Modesto, CA | 203 (198–209) | 39 (20–56) | 65 (35–98) | 3,753 (883–5,713) | 226,866 (92,308–352,906) |
| 17 | Stockton (Lodi), CA | 203 (198–209) | 41 (21–60) | 81 (43–123) | 4,249 (1,096–6,417) | 249,267 (99,916–389,573) |
| 18 | Cincinnati, OH/KY/IN | 185 (180–189) | 53 (27–78) | 63 (33–96) | 2,507 (607–3,841) | 162,533 (72,219–248,617) |
| 19 | Philadelphia, PA | 176 (171–181) | 50 (25–74) | 57 (30–87) | 2,456 (581–3,778) | 164,119 (76,477–248,001) |
| 20 | Visalia (Porterville), CA | 173 (169–178) | 26 (14–37) | 85 (46–128) | 4,423 (1,148–6,593) | 247,855 (87,165–398,422) |
| 21 | Redwood City–South San Francisco, CA | 173 (168–177) | 42 (21–61) | 48 (25–73) | 2,432 (564–3,753) | 224,466 (124,944–319,649) |
| 22 | Indianapolis (Carmel–Anderson), IN | 167 (163–171) | 55 (28–81) | 88 (47–134) | 2,985 (737–4,512) | 196,725 (85,693–302,578) |
| 23 | Medford, OR | 160 (155–164) | 31 (16–45) | 22 (12–33) | 1,322 (319–2,000) | 89,765 (40,556–135,709) |
| 24 | Portland (Vancouver–Hillsboro), OR/WA | 141 (137–145) | 31 (16–45) | 35 (18–54) | 2,427 (600–3,716) | 161,395 (72,768–245,911) |
| 25 | Seattle (Bellevue–Everett), WA | 138 (135–142) | 36 (18–54) | 45 (24–70) | 2,620 (665–4,023) | 190,926 (93,020–284,905) |
The top 25 cities with the most to gain by meeting ATS recommendations, shown in Tables 3 and 4, were determined by ranking cities by all-cause mortality counts. The cities with the greatest adverse health impacts from air pollution are Los Angeles, CA, with approximately 3,300 (95% CI, 2,470–4,080) mortalities per year, followed by Riverside, CA (1,900; 95% CI, 1,460–2,310); Chicago, IL (985; 95% CI, 759–1,200); and Phoenix, AZ (802; 95% CI, 581–1,200).
| Rank | City Region | Annual Ozone-Attributable Health Impacts | |||
|---|---|---|---|---|---|
| Mortality | Adverse Birth Outcomes | Morbidity | Adversely Impacted Days | ||
| 1 | Los Angeles (Long Beach–Glendale), CA | 1,208 (434–1,944) | 850 (76–1,577) | 81,183 (43,702–108,198) | 6,331,382 (113,802 to 11,406,036) |
| 2 | Riverside (San Bernardino–Ontario), CA | 628 (226–1,009) | 474 (42–876) | 45,474 (24,769–60,786) | 3,352,223 (−7,677 to 6,042,116) |
| 3 | Chicago (Naperville–Arlington Heights), IL | 323 (115–522) | 257 (22–489) | 20,747 (10,932–28,432) | 1,505,220 (27,042 to 2,852,944) |
| 4 | Phoenix (Mesa–Scottsdale), AZ | 322 (114–520) | 235 (20–445) | 22,415 (11,990–30,249) | 1,567,737 (12,824 to 2,950,264) |
| 5 | New York (Jersey City–White Plains), NY/NJ | 291 (103–471) | 237 (21–453) | 19,077 (9,972–26,249) | 1,395,885 (35,565 to 2,662,401) |
| 6 | Houston (The Woodlands–Sugar Land), TX | 204 (74–329) | 286 (24–545) | 20,985 (11,113–28,625) | 1,418,634 (−140 to 2,707,715) |
| 7 | San Diego (Carlsbad), CA | 200 (71–324) | 148 (13–281) | 14,215 (7,628–19,006) | 1,019,392 (20,699 to 1,911,679) |
| 8 | Anaheim (Santa Ana–Irvine), CA | 183 (65–297) | 121 (10–230) | 13,873 (7,520–18,622) | 958,164 (17,610 to 1,799,818) |
| 9 | Dallas (Plano–Irving), TX | 146 (53–236) | 164 (14–314) | 13,983 (7,376–19,157) | 999,046 (5,273 to 1,910,006) |
| 10 | Denver (Aurora–Lakewood), CO | 123 (44–199) | 115 (10–219) | 9,134 (4,885–12,345) | 689,207 (11,292 to 1,301,038) |
| 11 | Las Vegas (Henderson–Paradise), NV | 121 (43–195) | 100 (8–191) | 7,014 (3,693–9,506) | 534,748 (10,406 to 1,010,503) |
| 12 | Sacramento (Roseville–Arden–Arcade), CA | 105 (37–170) | 61 (5–117) | 6,449 (3,432–8,708) | 466,616 (5,863 to 887,625) |
| 13 | Warren (Troy–Farmington Hills), MI | 105 (37–170) | 61 (5–116) | 5,333 (2,792–7,319) | 360,121 (8,379 to 684,958) |
| 14 | St. Louis, MO/IL | 102 (36–165) | 70 (6–135) | 5,451 (2,870–7,463) | 366,653 (5,220 to 702,392) |
| 15 | Fort Worth (Arlington), TX | 98 (35–158) | 98 (8–187) | 8,044 (4,258–10,996) | 560,380 (1,018 to 1,070,248) |
| 16 | Bakersfield, CA | 92 (33–148) | 85 (7–160) | 7,864 (4,214–10,600) | 563,260 (−8,360 to 1,043,243) |
| 17 | Philadelphia, PA | 87 (31–141) | 84 (7–161) | 4,937 (2,582–6,770) | 372,582 (5,656 to 710,427) |
| 18 | Cincinnati, OH/KY/IN | 82 (29–133) | 61 (5–116) | 4,986 (2,627–6,795) | 327,427 (2,687 to 626,076) |
| 19 | Cleveland (Elyria), OH | 81 (28–132) | 51 (4–98) | 3,655 (1,916–5,011) | 243,614 (4,607 to 465,055) |
| 20 | Detroit (Dearborn–Livonia), MI | 77 (27–125) | 75 (6–144) | 4,345 (2,293–5,950) | 286,612 (1,599 to 550,257) |
| 21 | Montgomery County (Bucks County–Chester County), PA | 75 (26–122) | 28 (2–55) | 3,342 (1,744–4,595) | 252,881 (5,478 to 482,140) |
| 22 | Fresno, CA | 74 (26–119) | 66 (5–125) | 5,742 (3,102–7,644) | 431,641 (−5,955 to 814,848) |
| 23 | Baltimore (Columbia–Towson), MD | 72 (25–116) | 46 (4–88) | 3,848 (2,030–5,252) | 274,440 (4,594 to 524,556) |
| 24 | Nassau County (Suffolk County), NY | 63 (22–102) | 36 (3–69) | 3,455 (1,840–4,666) | 246,366 (5,349 to 468,961) |
| 25 | Pittsburgh, PA | 63 (22–102) | 25 (2–49) | 2,164 (1,121–2,996) | 167,860 (5,160 to 320,042) |
Daily PM2.5 levels from wildland fires are compared spatially in counties with and without federal ground monitors in Figure 2. PM2.5 from wildland fires is ubiquitous across the United States and may not be fully captured by existing ground monitors, with annual averages of exposures reaching ⩾1 μg/m3 in 32% of all counties (981 of 3,109). In the southern United States, 34% of counties (484 of 1,421) experience ⩾1 μg/m3 of fire PM2.5 on any given day, and the vast majority of these are unmonitored (86%), including all counties in the top 1% of exposure concentration. Half of the counties in the western United States (50 of 104) experiencing an average of >4 μg/m3 of fire PM2.5 per day are also unmonitored.
Figure 2. Annual means of daily fine particulate matter <2.5 µm in diameter from wildland fires in counties with and without federal ground monitors (2020). PM2.5 = fine particulate matter <2.5 µm in diameter.
[More] [Minimize]Table 5 summarizes regional fire PM2.5 concentrations and populations by count monitoring status. Across the nation, fire PM2.5 makes up approximately 16% of total ambient PM2.5 over the entire year, with a daily average of 1.6 μg/m3. This fraction varies by region and constitutes 44% in the western United States, averaging 4.6 μg/m3 each day. In the southern and midwestern regions, where approximately 60% of the U.S. population lives, nearly 10% of ambient PM2.5 comes from wildland fires year-round.
| Region | Population in Millions (% of U.S. Total) | Population-weighted 2020 Annual Fire PM2.5 | ||
|---|---|---|---|---|
| Monitored | Unmonitored | Daily Mean, μg/m3 | Percent of Total PM2.5 | |
| Midwest | 44.5 (14) | 24.5 (7) | 0.6 | 7% |
| Northeast | 45.3 (14) | 12.3 (4) | 0.3 | 4% |
| South | 77.0 (23) | 49.4 (15) | 0.8 | 10% |
| West | 68.6 (21) | 7.9 (2) | 4.6 | 44% |
| Nation | 235.3 (71) | 94.0 (29) | 1.6 | 16% |
Preventable national annual (2019) health impacts associated with wildland fire PM2.5 (Table 6) and O3 (Table 7) in all contiguous U.S. counties total approximately 28,000 deaths (95% CI, 27,300–28,700) for PM2.5 and 828 deaths (95% CI, 295–1,340) for O3, 5,910 new cases of lung cancer (95% CI, 3,080–8,600) attributable to PM2.5, 474,840 (95% CI, 149,870–700,140) cardiovascular and respiratory morbidities combined for PM2.5 and O3, 27.5 million (95% CI, 9.7–42.0 million) adversely impacted days combined for PM2.5 and O3, and 7,310 (95% CI, 3,730–11,260) adverse birth outcomes. Mortality totals reported here are based on all causes of death based on the work of Di and coworkers (40) for PM2.5 and the work of Katsouyanni and coworkers (43) for O3. However, the estimate of mortality impacts from wildland fire PM2.5 varies dramatically when using standard health functions versus wildland fire–specific health functions (4,080 deaths; 95% CI, 242–7,890), although these wildland fire–specific functions are relatively scarce and have yet to be fully articulated. State-level PM2.5-associated annual mortalities are compared between standard and wildland fire–specific health functions in Table 8.
| Health Endpoint | Impacts due to Wildland Fire PM2.5 |
|---|---|
| Mortality* | |
| All-cause (40) | 28,000 (27,300 to 28,700) |
| All-cause (41) | 39,700 (26,700 to 52,200) |
| All-cause (42)† | 4,080 (242 to 7,890) |
| Lung cancer diagnosis | 5,910 (3,080 to 8,600) |
| Morbidity | |
| Emergency room visits, respiratory | 13,250 (2,680 to 26,930) |
| Hospital admissions, neurological | 7,060 (5,310 to 8,600) |
| Hospital admissions, respiratory | 400 (20 to 760) |
| New onset asthma & hay fever/rhinitis | 395,560 (112,760 to 582,240) |
| Adversely impacted days | |
| Acute respiratory symptoms | 14,040,000 (11,800,000 to 16,200,000) |
| Asthma symptoms (albuterol use) | 7,310,000 (−4,330,000 to 16,100,000) |
| Work loss days | 2,550,000 (2,190,000 to 2,880,000) |
| Adverse birth outcomes | |
| Low birth weight | 2,570 (1,500 to 3,740) |
| Preterm birth | 4,330 (2,200 to 6,730) |
| Health Endpoint | Impacts due to Wildland Fire O3 |
|---|---|
| Mortality* | |
| All-cause (43) | 828 (295 to 1,340) |
| Respiratory (41) | 973 (675 to 1,260) |
| Morbidity | |
| Emergency room visits, respiratory | 3,180 (670 to 6,850) |
| Hospital admissions, respiratory | 130 (−30 to 280) |
| New onset asthma and hay fever/rhinitis | 55,260 (28,460 to 74,480) |
| Adversely impacted days | |
| Acute respiratory symptoms | 941,000 (379,000 to 1,480,000) |
| Asthma symptoms | 1,920,000 (−245,000 to 3,910,000) |
| School loss days | 692,000 (−102,000 to 1,390,000) |
| Adverse birth outcomes | |
| Low birth weight | 320 (40 to 610) |
| Preterm birth | 90 (0 to 180) |
| State | Mortalities due to Wildland Fire PM2.5 | |
|---|---|---|
| Standard Function* | Wildland Fire–Specific Function† | |
| Alabama | 583 (567–598) | 85 (5–164) |
| Arizona | 636 (619–653) | 91 (5–177) |
| Arkansas | 270 (263–277) | 38 (2–73) |
| California | 11,801 (11,492–12,099) | 1,768 (105–3,416) |
| Colorado | 578 (563–593) | 87 (5–169) |
| Connecticut | 64 (62–66) | 9 (1–17) |
| Delaware | 25 (24–26) | 4 (0–7) |
| District of Columbia | 12 (12–13) | 2 (0–5) |
| Florida | 958 (932–983) | 131 (8–255) |
| Georgia | 725 (706–744) | 111 (7–216) |
| Idaho | 341 (332–350) | 48 (3–92) |
| Illinois | 426 (415–438) | 61 (4–118) |
| Indiana | 237 (230–243) | 34 (2–66) |
| Iowa | 170 (165–174) | 23 (1–44) |
| Kansas | 210 (204–215) | 29 (2–57) |
| Kentucky | 197 (192–202) | 29 (2–56) |
| Louisiana | 331 (322–340) | 51 (3–98) |
| Maine | 17 (17–18) | 2 (0–4) |
| Maryland | 154 (150–158) | 23 (1–44) |
| Massachusetts | 104 (102–107) | 15 (1–28) |
| Michigan | 269 (261–276) | 37 (2–72) |
| Minnesota | 205 (200–211) | 28 (2–54) |
| Mississippi | 277 (270–284) | 41 (2–80) |
| Missouri | 389 (378–399) | 55 (3–108) |
| Montana | 175 (170–180) | 23 (1–45) |
| Nebraska | 115 (112–118) | 16 (1–30) |
| Nevada | 434 (423–445) | 63 (4–122) |
| New Hampshire | 19 (19–20) | 3 (0–5) |
| New Jersey | 219 (213–225) | 30 (2–59) |
| New Mexico | 131 (127–134) | 19 (1–36) |
| New York | 335 (326–344) | 48 (3–93) |
| North Carolina | 511 (497–525) | 74 (4–143) |
| North Dakota | 32 (31–33) | 5 (0–9) |
| Ohio | 396 (385–406) | 56 (3–109) |
| Oklahoma | 262 (255–268) | 38 (2–73) |
| Oregon | 2,196 (2,139–2,252) | 298 (18–576) |
| Pennsylvania | 400 (389–411) | 54 (3–104) |
| Rhode Island | 20 (20–21) | 3 (0–5) |
| South Carolina | 317 (308–325) | 46 (3–90) |
| South Dakota | 52 (51–53) | 7 (0–14) |
| Tennessee | 360 (350–369) | 53 (3–103) |
| Texas | 1,117 (1,087–1,146) | 169 (10–328) |
| Utah | 130 (127–134) | 20 (1–40) |
| Vermont | 9 (9–9) | 1 (0–2) |
| Virginia | 281 (273–288) | 40 (2–77) |
| Washington | 1,162 (1,131–1,192) | 165 (10–320) |
| West Virginia | 77 (75–79) | 11 (1–21) |
| Wisconsin | 195 (190–200) | 26 (2–51) |
| Wyoming | 59 (58–61) | 8 (0–16) |
The EPA is currently (as of 2023) reviewing the federal air quality standards for PM2.5 and O3. These reviews not only take into account the scientific evidence of the adverse health impacts of outdoor air pollution, but also include exposure and risk assessments that show the potential impacts of various policy decisions. In reviewing this evidence, the ATS has recommended that the EPA adopt revised standards for PM2.5 (8 μg/m3 annual; 25 μg/m3 daily) and O3 (60 ppb daily) (3). This report provides policy-relevant health estimates for ambient pollution concentrations greater than these recommended levels. These estimates are not only provided at the national level, showing the profound health benefits that would occur if these recommendations were adopted, but also at the local level to provide a meaningful context for air quality management decisions made by counties, cities, and states.
Air pollution–related health risks are not only driven by ambient pollution exposures and baseline health risks, but can also reflect the size and distribution of the exposed population. For example, the ranking of cities with the most to gain by meeting ATS recommendations (shown in Tables 3 and 4) shows that Houston, TX, has the sixth and seventh highest mortality impacts for O3 and PM2.5, respectively, but is ranked third for the most air pollution–related preterm birth outcomes. Overall, the cities with the most to gain by improving ambient air quality to the levels recommended by the ATS have high pollution concentrations and large exposed populations.
Although most attention is often focused on the mortality impacts of outdoor air pollution, other health endpoints are often more profoundly felt at the individual level. This report estimates that there are hundreds of thousands of cases of new-onset asthma and rhinitis due to PM2.5 and O3 exposures. This is in line with a recent ATS report that concluded that air pollution not only worsens the health conditions for those who already have respiratory disease, but can lead to the new onset of disease as well (45).
Specifically in regard to the results for adverse birth outcomes, based on current evidence, exposure to PM2.5 is more strongly associated with preterm birth, whereas exposure to ozone is more strongly associated with low birth weight. These differences could reflect different mechanisms of action. For example, placental-mediated toxicity may be greater for PM2.5 (46), whereas oxidative stress mechanisms leading to fetal growth restriction may be the primary mechanism associated with ozone-related low birth weight (47). Additionally, exposure duration and variation across the pregnancy period in the underlying epidemiological analysis could also influence the derived concentration–response relationships for these outcomes.
Our results are comparable to those of other studies that have investigated the associations of preterm births with particulate matter pollution in the United States (29–31). Overall, the magnitude of impact due to this health endpoint provides strong supporting evidence in favor of including preterm birth in future EPA health assessments of air pollution. The Health of the Air report has a history of including estimates for health endpoints with growing epidemiological evidence supporting their associations with air pollution, but these are not included in EPA regulatory impact assessments, such as a previous version of the report that provided estimates of the impact in terms of new cases of lung cancer (1). The EPA has since incorporated lung cancer health functions in its assessments, and may benefit from the inclusion of preterm births to provide a more comprehensive assessment of overall health burden.
This year’s report uses a novel dataset of satellite-based and modeled wildland fire pollutant concentrations to quantify the health impacts associated with smoke exposure across the contiguous United States (38). This is an improvement on much of the existing literature, which estimates health impacts from wildland fires based on binary temporal comparisons of time periods with and without smoke exposure or satellite-based imagery of plume areas to determine smoke locations (7, 48, 49). This analysis also provides additional insight into the nationwide impacts of wildland fires, not only in the western United States, where the impacts are greatest (36, 50). Overall, it is clear that the magnitude of adverse health impacts from wildland fires constitutes a serious, and likely increasing, problem for much of the United States. This is particularly true for the western and southern regions of the country, where wildland fires contribute a sizable portion of total PM2.5.
Estimates of mortality impacts attributable to wildland fires vary widely depending on the health impact function that is selected for use in the analysis. Other studies have provided estimates of mortality impacts from wildland fires (13, 51) but have not had access to wildland fire–specific health functions. Differences in estimates may also be attributable in part to the inclusion of agricultural burning as a type of wildland fire and the year-to-year variability that can define this important emission source (38). Perhaps more important than providing quantified nationwide estimates, the present study demonstrates local estimates that show that important health impacts are occurring not just in close proximity in time and location to large individual fires, but more so as the cumulative impact of fires burning throughout the year in many locations around the country.
There is a pressing need for more wildland fire–specific health functions for use in health impact assessments. At present, there is relatively little research on the toxicity of wildland fire PM2.5 relative to total ambient PM2.5 from all sources (52–54). Smoke components vary widely based on fuel type, including not only various biomass sources but also manmade structures inside the expanding wildland–urban interface (55). Because most fires in the United States originate in rural areas without air quality monitoring (39), existing PM2.5 health damage functions may be biased toward urban pollution exposures and unsuitable for smoke-specific epidemiological studies. Early evidence from observational and animal studies suggests that wildfire smoke affects respiratory health more than other PM2.5 sources (56, 57) but is less likely to impact cardiovascular outcomes, which drive mortality impacts (58, 59). The wildland fire–specific function used in this report contains only short-term exposure impact and does not account for any increased impact occurring from prolonged, long-term exposures to PM2.5 from wildland fires. These long-term exposures are associated with increased atherosclerotic activity (60), which may explain why the results of chronic exposures are so much greater than the sum of short-term mortalities. It is very likely that the best estimate for wildland fire PM2.5 lies somewhere between the fire-specific function for short-term effects and the standard function for long-term exposure from more typical ambient air pollution.
EPA regulations require air quality monitors be sited in counties with high populations, so, on a national scale, urban areas with larger proportions of racial and ethnic minorities have greater access to monitoring data because they are populous. At smaller spatial scales, however, insufficient spatial resolution of exposure data may mask health-relevant peaks in air pollution, preventing the identification of communities experiencing environmental injustice from a local regulator’s perspective (61). In this report, estimates of health impacts from air pollution exceeding the ATS recommended levels represent only counties with valid monitoring data, which is approximately one third of the total number of counties in the United States. This likely includes the majority of true health impacts in the entire United States because these counties have greater populations, but it is not informed by subcounty exposures because of the sparsity of existing ground networks. Additionally, because individual county-level results are more commonly available in urban areas (with greater populations), rural counties are left without information regarding their air pollution exposures and the resulting health impacts experienced by their occupants. Representatives from the EPA, local agencies, and stakeholders recognize the insufficiency of current monitoring networks and the need for higher spatiotemporal resolution of air quality data (62). Interviews of these individuals by the Government Accountability Office revealed a commonly expressed need for better information on air quality in rural areas, where monitor density is far lower than in urban centers, an issue that has also been emphasized by the World Health Organization (63).
Differences in underlying baseline incidences of adverse health outcomes can result in increased air pollution–related adverse health outcomes even for two locations with the same level of ambient pollution. For example, multiple studies have established differences in rates of preterm birth and low birth weight across socioeconomic groupings and race/ethnicity (64, 65), yet the relative contributions of inequalities in environmental exposures, healthcare quality or access, and other upstream determinants is unclear. Inequalities in exposures by race/ethnicity or income can result in additional air pollution–related health burdens as a result of large within-county differential exposures (66). Because this analysis is based on pollution exposures and baseline health statistics at the county level, it is unable to fully quantify the magnitude of these inequalities.
In the context of smoke exposures, disparities are highly dependent on regional population demographic characteristics and fire types. In the western United States, wildfires often originate near small, wealthy, White communities at or near the wildland–urban interface. However, large wildfire plumes can travel far. In California, smoke often settles at lower elevations, where many immigrant farmworkers live and work (67). For prescribed burns, Kondo and coworkers found disproportionate smoke exposures in racial/ethnic minorities and low-income counties across the United States but no additional health burdens in these groups due to the resulting wildfire prevention (50). Generally, much more investigation is needed to fully understand the impacts of fire PM2.5 on different demographic groups (68). This research is becoming increasingly important with changing meteorological conditions related to climate change, impacting not only wildfire activity but ambient air quality as a whole while increasing the health burdens in already vulnerable communities.
The policy-relevant estimates of adverse health impacts associated with ambient outdoor air pollution included in this report show that there are tremendous public health benefits that are achievable through the adoption and attainment of the ATS recommended levels for PM2.5 and O3. Outdoor air pollution in the United States impacts every city and region, but the magnitude of these impacts is dependent on ambient pollutant concentrations, baseline health risks, and the size and age distribution of the exposed populations.
This report emphasizes the impact that air pollution has across the full life spectrum, from exposures that occur before birth to the very end of life. This includes thousands of adverse birth outcomes due to exposures starting before birth, tens of thousands of major morbidities, and millions of adversely impacted days that occur as a result of exposures continuing through childhood and adulthood, culminating in thousands of new cases of lung cancer and tens of thousands of early deaths due to exposures that occur later in life.
Although we will never fully eliminate outdoor air pollution, the large magnitude of adverse health impacts estimated in this report can be avoided through the policy framework that already exists under the Clean Air Act if more health protective standards were adopted by the EPA. The need for improved federal air quality standards is heightened by the already large, and expected-to-increase, number of adverse health impacts attributable to air pollution from wildland fires, which presents an even more difficult air quality management challenge.
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Supported by the Office of STEM Engagement Rapid Response Research Award of the National Aeronautics and Space Administration, the Environmental Defense Fund, the Marron Institute of Urban Management at New York University, and the American Thoracic Society.
Author Contributions: K.C.: conceptualization; methodology; writing, original draft; writing, review and editing; supervision; project administration; funding acquisition. L.G.: methodology; software; formal analysis; writing, original draft; writing, review and editing; visualization. J.G.: conceptualization; methodology; writing, original draft; writing, review and editing; supervision; project administration; funding acquisition. Y.L.: methodology; software; formal analysis; writing, review and editing. D.T.: conceptualization; methodology; software; resources; writing, review and editing; supervision. G.E.: writing, review and editing; project administration.
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Author disclosures are available with the text of this article at www.atsjournals.org.
