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Lane Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States More by Haley M. Lane , * Rachel Morello-Frosch Rachel Morello-Frosch School of Public Health, University of California, Berkeley, California 94720, United States Department of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, United States More by Rachel Morello-Frosch , * Julian D. Marshall Julian D. Marshall Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States More by Julian D. Marshall Orcidhttps://orcid.org/0000-0003-4087-1209 , and * Joshua S. Apte* Joshua S. Apte Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States School of Public Health, University of California, Berkeley, California 94720, United States *Email: [email protected] More by Joshua S. Apte Orcidhttps://orcid.org/0000-0002-2796-3478 Cite this: Environ. Sci. Technol. Lett. 2022, XXXX, XXX, XXX-XXX Publication Date (Web):March 9, 2022 Publication History * Received22 December 2021 * Accepted22 February 2022 * Revised18 February 2022 * Published online9 March 2022 https://doi.org/10.1021/acs.estlett.1c01012 (c) 2022 The Authors. 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Share Add toView In * Add Full Text with Reference * Add Description ExportRIS * Citation * Citation and abstract * Citation and references * More Options Share on * Facebook * Twitter * Wechat * Linked In * Reddit PDF (1 MB) Get e-Alerts Supporting Info (1)>>Supporting Information Supporting Information SUBJECTS: * Color, * Air pollution, * Environmental pollution, * Mathematical methods, * Particulate matter Go to Environmental Science & Technology Letters Get e-Alerts Abstract [ez1c01012_] High Resolution Image Download MS PowerPoint Slide Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO[2] and PM[2.5] air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (> 50%) increments in NO[2] levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO[2] and PM[2.5] are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States. KEYWORDS: * air pollution * redlining * NO[2] * PM[2.5] * * * Introduction ARTICLE SECTIONS Jump To --------------------------------------------------------------------- In the United States, communities of color are exposed to higher levels of air pollution at every income level. (1-4) As with other environmental justice (EJ) issues, the causes of systemic racial/ ethnic air pollution exposure disparities are complex and rooted in part in historical patterns of exclusion and discrimination. While air quality has improved in the United States over the past several decades, (5-7) people of color (POC), particularly Black and Hispanic Americans, are still exposed to higher-than-average levels of air pollution. (8-11) We examine here how redlining, a historical, racially discriminatory 1930s federal mortgage appraisal policy, is associated with present-day air pollution disparities in 202 U.S. cities. Racial/ethnic air pollution exposure disparities persist in part because the underlying sociological, economic, and policy drivers typically evolve on generational time scales. Multiple legacies of discrimination, including redlining and land use decision-making, have shaped the current spatial distributions of pollution sources among diverse communities. (12-18) The resulting locations of emissions infrastructure, including roads, rail lines, industrial facilities, ports, and other major sources of pollution, are typically long-lived. Similarly, while housing discrimination was deemed unconstitutional more than 50 years ago, many areas in the United States remain racially segregated. (19-22) Redlining has emerged as an area of interest because it is well documented and was explicit in its discriminatory implementation, widespread, and carried out by the federal government. Beginning in the 1930s, the federally sponsored Home Owners' Loan Corporation (HOLC) drew maps characterizing neighborhood security for emergency home lending for several hundred U.S. cities in the wake of the Great Depression. (23,24) These maps, which are digitized for 202 U.S. cities, (25) graded neighborhoods on a four-point scale: A (most desirable), B (still desirable), C (definitely declining), and D (hazardous, i.e., redlined). Many neighborhoods received the worst grade due to the presence of Black and immigrant communities and/or known environmental pollution sources. (25,26) For example, racist language provided to HOLC agents describes "infiltration of foreign-born, Negro, or lower-grade population" as cause for a lower neighborhood grade. (25) Homes in D neighborhoods were typically ineligible for federally backed loans or favorable mortgage terms. This practice isolated communities of color, restricting their ability to build wealth through home ownership, and informed later local government land use decisions that placed hazardous industries in and near D neighborhoods. (24) The discriminatory practices captured by the HOLC maps continued until 1968, when the Fair Housing Act banned racial discrimination in housing, yet the legacy of explicit racial discrimination still shapes patterns of racial residential segregation today. (27) A growing body of scholarship finds associations between redlining and present-day environmental health disparities in U.S. cities. For example, in 64% of grade D neighborhoods, a majority (>50%) of the population is POC (i.e., not non-Hispanic White); in 74% of grade D neighborhoods, the median income is low to moderate. (27) Redlining designations are associated with a variety of exposures, including greenspace prevalence, (28) tree canopy, (29-31) urban-heat exposure disparities, (29,32,33) and health effects, including asthma, (34) cancer, (35,36) adverse birth outcomes, (37,38) and overall urban health. (39) To date, limited research has investigated air pollution exposure and redlining, (31,34) despite its importance as an environmental risk factor. We focus here on two key air pollutants that are significant causes of ill health and premature mortality, nitrogen dioxide (NO[2]) and fine particulate matter (PM[2.5]), and have distinct sources, atmospheric behavior, and spatial patterns. NO[2] is a relatively short-lived, localized pollutant emitted by traffic, industry, power generation, and other high-temperature combustion processes. Urban areas tend to exhibit spatially sharp NO[2] gradients because primary traffic emissions are a major source of NO[2]. (40-43) In contrast, PM[2.5] varies more on a regional scale because it has an atmospheric lifetime of days to weeks and is influenced strongly by both a broad array of emission sectors and multiple secondary formation processes. (44-47) This paper explores associations between historical redlining and year-2010 air pollution levels and census demographics for 202 U.S. cities home to 65% of the U.S. urban population. We find monotonic and highly consistent associations between pollution levels and HOLC grades for both pollutants, with larger intraurban disparities associated with NO[2]. To the best of our knowledge, this study is the first full-scale examination of air pollution disparities relative to historical redlining and advances our understanding of the origins and persistence of inequities in air pollution exposures in the United States. Materials and Methods ARTICLE SECTIONS Jump To --------------------------------------------------------------------- Demographic and HOLC Data We used georeferenced 1930s era HOLC maps developed by the University of Richmond's Mapping Inequality project to identify HOLC codes in 202 cities (148 U.S. census urbanized areas) across the United States, shown in Figure S1. (25) Mapped neighborhoods were categorized by HOLC into one of four grades: A, best; B, still desirable; C, definitely declining; or D, hazardous for mortgage appraisal. We linked HOLC maps to individual U.S. Census blocks from the most recent available decennial census (2010); (48) census blocks provide a spatial resolution at approximately the scale of a city block in urban areas (geospatial procedures are described in the Supporting Information). The resulting data set incorporates 45 million people in 202 U.S. cities (n = 562,078 census blocks; average population of 80 people per block). Because of urban expansion post-1930, the HOLC areas represent only a subset of the overall present-day urban footprint in most metropolitan areas: the present-day urban core. To provide context and comparison, we also separately extend our analysis to the full U.S. Census urbanized areas (CUA; n = 148) that contain the HOLC-mapped neighborhoods. These 148 CUAs had a year-2010 population of 161 million people (~65% of the full U.S. population residing in urbanized areas in 2010). We combine race/ethnicity data to develop four aggregate groupings for analysis: people who are Hispanic of any race [24% of HOLC population (Table S1)], non-Hispanic White (henceforth White, 43%), non-Hispanic Black (Black, 23%), and non-Hispanic Asian (Asian, 7%). The remaining 3% of the HOLC population (Other) includes Pacific Islander, Native American, and populations self-identifying as belonging to two or more races. The broader CUA population demographics are as follows: 56% White, 15% Black, 7% Asian, and 19% Hispanic. Air Pollution Data We characterized NO[2] and PM[2.5] levels using empirical (i.e., land-use regression) models developed by the Center for Air, Climate and Energy Solutions (CACES; www.caces.us/data). (5) This data set provides annual ambient concentration predictions for census blocks for 1979-2015. We employ year-2010 pollution data here to align with the most recent available (2010) decennial census. This model surface and its predecessors are commonly used for disparity analyses (1,2,49) and predict NO[2] and PM[2.5] at U.S. EPA monitoring sites with high fidelity (R^2 = 0.81 and 0.84, respectively). (1) Our core results are expressed as population-weighted statistics [i.e., population-weighted mean (PWM) and other percentiles from the population distribution of exposures]. We first aggregate data in terms of unadjusted statistics (e.g., the national PWM concentration for all blocks in the D grade). Next, to isolate associations between redlining and intraurban gradients, we present adjusted statistics that hold constant for city-to-city differences in air pollution and therefore reveal only within-urban disparities. This adjusted statistic is computed as the national PWM of the intraurban concentration difference, i.e., the difference between census block levels and the corresponding urban PWM across all HOLC areas in a CUA (see section S1.2 of the Supporting Information). An example of the input data sets for Atlanta, GA, is included in Figure S2, and population demographics are outlined in Table S1 and Figure S3. Results and Discussion ARTICLE SECTIONS Jump To --------------------------------------------------------------------- Associations between Concentration and HOLC Category Because HOLC-mapped areas tend to cover only city centers and exclude suburban areas, air pollution levels in the HOLC-mapped areas tend to be higher than in the corresponding overall CUAs (Figure S4). Year-2010 PWM concentrations were 15.0 ppb (NO[2]) and 10.6 mg m^-3 (PM[2.5]) for the 45 million people residing in HOLC-mapped areas, versus 10.9 ppb (NO[2]) and 9.9 mg m^-3 (PM[2.5]) for the corresponding CUAs. Unadjusted national statistics show that redlining is strongly associated with NO[2] and more weakly but detectably associated with PM[2.5] (Figure 1a,b). PWM NO[2] pollution levels are 6.0 ppb (56%) higher in the D-grade ("hazardous") than in the A-grade census blocks (16.8 ppb vs 10.8 ppb). PWM concentrations increase monotonically across HOLC grades. For PM[2.5], this monotonic association also holds, but the PWM difference between A and D groups is smaller, 0.4 mg m^-3 (4%; 10.7 mg m^-3 vs 10.3 mg m^-3). The smaller difference for PM[2.5] aligns with existing research showing comparatively smaller intraurban pollution variations that are superimposed on a larger regional (mostly secondary) background. (50,51) Figure 1 [ez1c01012_] Figure 1. Population-weighted distributions of NO[2] and PM[2.5] levels within HOLC-mapped areas at the census block level. Bars represent 25th and 75th percentiles. Medians are indicated with horizontal lines, and means by the dot marker; the overall mean is indicated by the dotted line. Unadjusted national distributions are presented for (a) NO[2] and (b) PM[2.5]. Adjusted distributions (c and d) report the national distributions of intraurban differences for census blocks within a given HOLC grade relative to the PWM level within each city. In each panel, pollution level distributions are reported by both HOLC grade (left cluster) and race/ethnicity (right cluster). Vertical lines between these clusters reflect the pollution range of the group means: the difference in the population-weighted mean between groups A and D (left line) and between the highest-exposed and lowest-exposed racial/ethnic group. Panels c and d illustrate how intraurban disparities are consistently higher by historical HOLC grade than by race/ethnicity. High Resolution Image Download MS PowerPoint Slide Redlining is also associated with intraurban pollution gradients. PWM NO[2] pollution levels for each HOLC zone, relative to that city's average level (Figure 1), are 1.0 and 0.1 ppb higher for D and C areas, respectively, and 0.8 and 2.0 ppb lower for B and A areas, respectively (Figure 1c). Therefore, the PWM intraurban difference between the D and A grades is ~3 ppb NO[2]. Intraurban differences are smaller for PM[2.5] than for NO[2] (Figure 1d): maximum of 0.1 mg m^-3 (D grade) and minimum of -0.3 mg m^-3 (A grade), for a net 0.4 mg m^-3 difference. We find a high degree of city-to-city consistency in intraurban disparities. PWM NO[2] levels are higher in D neighborhoods than overall (i.e., considering all HOLC-mapped areas) in 80% of the 202 cities and are lower in A neighborhoods than overall in 84% of cities. Disparities exist not only for the average (PWM) but also throughout the distribution. Indeed, in most (52%) cities, the interquartile ranges (IQRs) for NO[2] exhibited no overlap for the A and D neighborhoods (i.e., the A group 75th percentile was lower than the D group 25th percentile). For PM[2.5], disparities are again in the same direction though more modest. PWM PM[2.5] levels were higher than average for D neighborhoods in 55% of cities and lower than average for A neighborhoods in 68% of the cities, and the A and D IQRs exhibit no overlap in 20% of cities. Overall, trends associated with redlining hold across city size (Figure S5), across geographical region (Figure S6), and for the most recent-year (2015) CACES model predictions (Figure S7). HOLC security maps were drawn on the basis of the demographic makeup of neighborhoods, reflecting preexisting racial residential segregation. However, redlining further solidified and accelerated those patterns that exist today. In addition, areas graded as C or D often hosted industrial facilities, railroads, and other pollution sources. We find that, within HOLC-mapped areas, D-grade neighborhoods are more likely to be near industrial sources and that the average number of sources nearby increases from A to D (Figure S8 ). Additionally, the portion of people living near railroads and primary roadways increases monotonically by HOLC grade from A to D ( Figure S9). While U.S. rail infrastructure was largely constructed before the 1930s, limited-access highways were constructed almost entirely after the 1930s and were preferentially constructed through Black and brown communities in U.S. cities. This comparison using rail lines and highways emphasizes that racial disparities in air pollution exposure reported here reflect infrastructure placement that occurred both before and after HOLC redlining. (52,53) Disparities by Race/Ethnicity We further stratified our results by comparing each HOLC-grade PWM concentration for individual racial/ethnic groups. Consistent with the substantial literature on racial/ethnic disparities for air pollution, we find that people of color experience higher-than-average NO[2] and PM[2.5] levels and are overrepresented within C and D neighborhoods, consistent with prior redlining research (Figure 1). For example, on average, PWM intraurban pollution differences for NO[2] (Figure 1c) are greater than average for Hispanic, Asian, and Black populations (0.8, 0.4, and 0.2 ppb higher than the urban average, respectively) and below average for the White population (-0.6 ppb). Differences for PM[2.5] are proportionally smaller (Figure 1d) but reflect similar racial disparities (PWMs of -0.1 mg m^-3 for White and Asian populations and 0.1 mg m^-3 for Black and Hispanic populations). Overall, intraurban PWM differences by HOLC grade are larger than by race/ethnicity ( Figure 1). We find a substantially larger PWM differences between D and A HOLC grades (3.0 ppb NO[2] and 0.4 mg m^-3 PM[2.5]) than between the most- and least-exposed racial/ethnic groups [1.3 ppb NO [2] and 0.26 mg m^-3 PM[2.5] (see Figure 1c,d)]. Next, we examined how racial/ethnic disparities interact with historical HOLC grade. Figure 2 illustrates PWM intraurban disparities that exist by race/ethnicity along the A-D HOLC grade gradient. Smaller, but still substantial, intraurban racial/ethnic disparities exist for PM[2.5] and NO[2] within each historical HOLC grade. On average, the within-grade white population experiences lower than average levels of NO[2] and PM[2.5] while the Hispanic population experiences above average levels. The Black population experiences consistently above HOLC-grade-average PM[2.5] levels while the Asian population experiences above HOLC-grade-average NO[2] levels. These within-grade disparities are nearly as large as the overall racial/ethnic disparity for the HOLC-mapped areas, implying that a substantial portion of the racial/ethnic exposure disparity within the study areas exists independently of historical HOLC status. Figure 2 [ez1c01012_] Figure 2. Population-weighted mean annual intraurban PWM levels by HOLC grade and race/ethnicity for (a) NO[2] and (b) PM[2.5]. All race /ethnicity groups demonstrate monotonic increases by HOLC grade. Disparities by HOLC grade were larger than those associated with differences between racial/ethnic groups (100% higher for NO[2] and 50% higher for PM[2.5]). High Resolution Image Download MS PowerPoint Slide Racial/ethnic air pollution disparities reported here are subdivided next into two distinct effects: those that are associated with historical HOLC redlining and those that are not. To explore the sensitivity of our overall results to racial/ethnic segregation (i) between and (ii) within each HOLC grade, we used stylized demographic scaling factors to mathematically redistribute the populations in every city to (as a counterfactual approach) eliminate intraurban racial/ethnic segregation first between, and then within, HOLC grades (details in section S1.3). The reduction in racial/ethnic disparity from removing between-grade segregation was larger for NO[2] than for PM[2.5]. However, both results were modest relative to the reductions produced by removing within-grade segregation (Figure S10). These findings may reflect various factors, including changes in demographics since the 1930s (e.g., gentrification), within-grade gradients of proximity to undesirable/polluting land uses (potentially preceding redlining), and later emission source placement (e.g., highways). Figure S11 offers a complementary insight. Intraurban air pollution disparities show distinct relationships with demographics, but there is also a stratified gradient from HOLC grade A to D for nearly any level of demographic composition. This suggests redlining disparity effects are one of multiple factors that contribute to intraurban racial/ethnic disparities in pollution exposure. Importantly, if one could remove all between-grade disparities, that would only modestly change the overall, because within-grade disparities are the larger contributor to overall racial/ethnic disparities. Broader Implications Converging lines of evidence from our analysis suggest the following key points. First, redlining is associated with substantial intraurban air pollution disparities for NO[2] and PM[2.5]. These findings are consistent with a broad body of evidence that adverse historical HOLC designations are associated with worse present-day local environmental quality and health outcomes, including air pollution, green space, (28) tree canopy, (29-31) COVID risk, (54) and urban heat. (29,32,33) Second, for the 45 million Americans who live in HOLC-mapped areas, NO[2] and PM[2.5] disparities by grade are larger than those by race/ethnicity. Third, despite the substantial association between HOLC redlining and aggregate pollution disparities, we find that intraurban racial/ethnic disparities in NO [2] and PM[2.5] are only moderately correlated with historical HOLC status; most of the disparities we observe are within grade rather than between grade. This finding likely reflects that historical redlining is only one of many racially discriminatory policies that have contributed to disparate environmental exposures for people of color. Findings here highlight that present-day disparities in U.S. urban pollution levels reflect a legacy of structural racism in federal policy-making-and resulting investment flows and land use decisions-apparent in maps drawn more than 80 years ago. NO[2] and PM [2.5] are considered "short-lived" pollutants (atmospheric lifetimes of approximately hours and days, respectively), yet the systems that created these disparities span more than a human lifetime. Results from this work (55) can support decision-makers in their efforts to improve air pollution policy in ways that address exposure inequities. Future work should propose, evaluate, and implement solutions that can benefit disparately impacted communities. Fully addressing exposure inequities will require transformations sustained across generations. Supporting Information ARTICLE SECTIONS Jump To --------------------------------------------------------------------- The Supporting Information is available free of charge at https:// pubs.acs.org/doi/10.1021/acs.estlett.1c01012. * Detailed description of materials and methods, supporting demographic tables, and supporting figures S1-S11 (PDF) * ez1c01012_si_001.pdf (2.24 MB) Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/ permissions.html. Author Information ARTICLE SECTIONS Jump To --------------------------------------------------------------------- * Corresponding Author + Joshua S. Apte - Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States; School of Public Health, University of California, Berkeley, California 94720, United States; Orcid https://orcid.org/0000-0002-2796-3478; Email: [email protected] * Authors + Haley M. Lane - Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States + Rachel Morello-Frosch - School of Public Health, University of California, Berkeley, California 94720, United States; Department of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, United States + Julian D. Marshall - Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States; Orcidhttps://orcid.org/ 0000-0003-4087-1209 * * * Notes The authors declare no competing financial interest. Extended data (55) are available at doi:10.6084/ m9.figshare.19193243. Acknowledgments ARTICLE SECTIONS Jump To --------------------------------------------------------------------- This publication was developed as part of the Center for Air, Climate and Energy Solutions (CACES), which was supported under Assistance Agreement No. R835873 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The views expressed in this document are solely those of authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. References ARTICLE SECTIONS Jump To --------------------------------------------------------------------- This article references 55 other publications. 1. 1 Liu, J.; Clark, L. P.; Bechle, M. J.; Hajat, A.; Kim, S.-Y.; Robinson, A. L.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010. Environ. Health Perspect. 2021, 129 (12), 127005, DOI: 10.1289/EHP8584 [Crossref], [PubMed], [CAS], Google Scholar 1 Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010 Liu Jiawen; Clark Lara P; Bechle Matthew J; Marshall Julian D; Hajat Anjum; Kim Sun-Young; Robinson Allen L; Sheppard Lianne; Szpiro Adam A; Sheppard Lianne Environmental health perspectives (2021), 129 (12), 127005 ISSN:. BACKGROUND: Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time. OBJECTIVE: The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010. METHODS: We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide ([Formula: see text]), ozone ([Formula: see text]), particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]; excluding year-1990), particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), and sulfur dioxide ([Formula: see text]). We used census data for demographic information and a national empirical model for ambient air pollution levels. RESULTS: For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for [Formula: see text] [54% ([Formula: see text])], smallest for [Formula: see text] [3.6% ([Formula: see text])], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for [Formula: see text] in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% ([Formula: see text]; [Formula: see text]) and 88% ([Formula: see text]; CO); relative disparities declined to between [Formula: see text] ([Formula: see text]; i.e., nearly zero change) and [Formula: see text] (CO; i.e., a [Formula: see text] reduction). DISCUSSION: As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB2cbmtFSgtg%253D%253D&md5= c931118843d9a947baf6b945b345cf84 2. 2 Clark, L. P.; Millet, D. B.; Marshall, J. D. National Patterns in Environmental Injustice and Inequality: Outdoor NO[2] Air Pollution in the United States. PLoS One 2014, 9 (4), e94431, DOI: 10.1371/journal.pone.0094431 [Crossref], [PubMed], [CAS], Google Scholar 2 National patterns in environmental injustice and inequality: outdoor NO2 air pollution in the United States Clark, Lara P.; Millet, Dylan B.; Marshall, Julian D. PLoS One (2014), 9 (4), e94431/1-e94431/8, 8 pp.CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science) We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO2) concns. in the contiguous United States. Our approach employs Census demog. data and a recently published high-resoln. dataset of outdoor NO2 concns. Nationally, population-weighted mean NO2 concns. are 4.6 ppb (38%, p<0.01) higher for nonwhites than for whites. The environmental health implications of that concn. disparity are compelling. For example, we est. that reducing nonwhites' NO2 concns. to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ~7,000 deaths per yr, which is equiv. to 16 million people increasing their phys. activity level from inactive (0 h/wk of phys. activity) to sufficiently active (>2.5 h/wk of phys. activity). Inequality for NO2 concn. is greater than inequality for income (Atkinson Index: 0.11 vs. 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO2. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO2 include New York, Michigan, and Wisconsin. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsFemtL%252FI&md5= c0fcf9529125069e69259fe779ace3d8 3. 3 Clark, L. P.; Millet, D. B.; Marshall, J. D. Changes in Transportation-Related Air Pollution Exposures by Race-Ethnicity and Socioeconomic Status: Outdoor Nitrogen Dioxide in the United States in 2000 and 2010. Environ. Health Perspect 2017, 125 (9), 097012, DOI: 10.1289/EHP959 [Crossref], [PubMed], [CAS], Google Scholar 3 Changes in transportation-related air pollution exposures by race-ethnicity and socioeconomic status: outdoor nitrogen dioxide in the United States in 2000 and 2010 Clark, Lara P.; Millet, Dylan B.; Marshall, Julian D. Environmental Health Perspectives (2017), 125 (9), 097012/ 1-097012/10CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health) BACKGROUND: Disparities in exposure to air pollution by race-ethnicity and by socioeconomic status have been documented in the United States, but the impacts of declining transportation-related air pollutant emissions on disparities in exposure have not been studied in detail. OBJECTIVE: This study was designed to est. changes over time (2000 to 2010) in disparities in exposure to outdoor concns. of a transportation-related air pollutant, nitrogen dioxide (NO2), in the United States. METHODS: We combined annual av. NO2 concn. ests. from a temporal land use regression model with Census demog. data to est. outdoor exposures by race-ethnicity, socioeconomic characteristics (income, age, education), and by location (region, state, county, urban area) for the contiguous United States in 2000 and 2010. RESULTS: Estd. annual av. NO2 concns. decreased from 2000 to 2010 for all of the race-ethnicity and socioeconomic status groups, including a decrease from 17.6 ppb to 10.7 ppb (-6:9 ppb) in nonwhite [non-(white alone, non-Hispanic)] populations, and 12.6 ppb to 7.8 ppb (-4.7 ppb) in white (white alone, non-Hispanic) populations. In 2000 and 2010, disparities in NO2 concns. were larger by race-ethnicity than by income. Although the national nonwhite-white mean NO2 concn. disparity decreased from a difference of 5.0 ppb in 2000 to 2.9 ppb in 2010, estd. mean NO2 concns. remained 37% higher for nonwhites than whites in 2010 (40% higher in 2000), and nonwhites were 2.5 times more likely than whites to live in a block group with an av. NO2 concn. above the WHO annual guideline in 2010 (3.0 times more likely in 2000). CONCLUSIONS: Findings suggest that abs. NO2 exposure disparities by race-ethnicity decreased from 2000 to 2010, but relative NO2 exposure disparities persisted, with higher NO2 concns. for nonwhites than whites in 2010. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC1MXlsFymsLs%253D&md5= d73f4ba27ba2a1765436a32587352648 4. 4 Tessum, C. W.; Paolella, D. A.; Chambliss, S. E.; Apte, J. S.; Hill, J. D.; Marshall, J. D. PM[2.5] Polluters Disproportionately and Systemically Affect People of Color in the United States. Sci. Adv. 2021, 7 (18), eabf4491, DOI: 10.1126/sciadv.abf4491 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 5. 5 Kim, S.-Y.; Bechle, M.; Hankey, S.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. Concentrations of Criteria Pollutants in the Contiguous U.S., 1979 - 2015: Role of Prediction Model Parsimony in Integrated Empirical Geographic Regression. PLoS One 2020, 15 (2), e0228535, DOI: 10.1371/journal.pone.0228535 [Crossref], [PubMed], [CAS], Google Scholar 5 Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression Kim, Sun-Young; Bechle, Matthew; Hankey, Steve; Sheppard, Lianne; Szpiro, Adam A.; Marshall, Julian D. PLoS One (2020), 15 (2), e0228535CODEN: POLNCL; ISSN:1932-6203. ( Public Library of Science) The impact of model parsimony (i.e., how model performance differs for a large vs. small no. of covariates) has not been systematically explored. We aim to (1) build annual-av. integrated empirical geog. (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979-2015; (2) explore systematically the impact on model performance of the no. of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-av. concns. from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979-2015. We also use ~350 geog. characteristics at each location including measures of traffic, land use, land cover, and satellite-based ests. of air pollution. We then develop IEG models, employing universal kriging and summary factors estd. by partial least squares (PLS) of geog. variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited no. of variables selected from the full set by forward selection, and (3) all variables. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BB3cXks1Orurc%253D&md5= cddf38697601371cfd380e022227e0c1 6. 6 Fann, N.; Kim, S.-Y.; Olives, C.; Sheppard, L. Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM[2.5] Exposures in the Contiguous United States: 1980-2010. Environ. Health Perspect 2017, 125 (9), 097003 DOI: 10.1289/ EHP507 [Crossref], [PubMed], [CAS], Google Scholar 6 Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM2.5 Exposures in the Contiguous United States: 1980-2010 Fann Neal; Kim Sun-Young; Kim Sun-Young; Olives Casey; Sheppard Lianne; Sheppard Lianne Environmental health perspectives (2017), 125 (9), 097003 ISSN:. BACKGROUND: PM2.5 precursor emissions have declined over the course of several decades, following the implementation of local, state, and federal air quality policies. Estimating the corresponding change in population exposure and PM2.5-attributable risk of death prior to the year 2000 is made difficult by the lack of PM2.5 monitoring data. OBJECTIVES: We used a new technique to estimate historical PM2.5 concentrations, and estimated the effects of changes in PM2.5 population exposures on mortality in adults (age >=30y), and on life expectancy at birth, in the contiguous United States during 1980-2010. METHODS: We estimated annual mean county-level PM2.5 concentrations in 1980, 1990, 2000, and 2010 using universal kriging incorporating geographic variables. County-level death rates and national life tables for each year were obtained from the U.S. Census and Centers for Disease Control and Prevention. We used log-linear and nonlinear concentration-response coefficients from previous studies to estimate changes in the numbers of deaths and in life years and life expectancy at birth, attributable to changes in PM2.5. RESULTS: Between 1980 and 2010, population-weighted PM2.5 exposures fell by about half, and the estimated number of excess deaths declined by about a third. The States of California, Virginia, New Jersey, and Georgia had some of the largest estimated reductions in PM2.5-attributable deaths. Relative to a counterfactual population with exposures held constant at 1980 levels, we estimated that people born in 2050 would experience an ~1-y increase in life expectancy at birth, and that there would be a cumulative gain of 4.4 million life years among adults >=30y of age. CONCLUSIONS: Our estimates suggest that declines in PM2.5 exposures between 1980 and 2010 have benefitted public health. https://doi.org/10.1289/EHP507. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BC1M%252FgvFantQ%253D%253D&md5= 5e918a6d3543ffe15cf08b829a728220 7. 7 McDonald, B. C.; Dallmann, T. R.; Martin, E. W.; Harley, R. A. Long-Term Trends in Nitrogen Oxide Emissions from Motor Vehicles at National, State, and Air Basin Scales. J. Geophys. Res.: Atmos. 2012, 117 (D21), D00V18, DOI: 10.1029/2012JD018304 [Crossref], Google Scholar There is no corresponding record for this reference. 8. 8 Ard, K. Trends in Exposure to Industrial Air Toxins for Different Racial and Socioeconomic Groups: A Spatial and Temporal Examination of Environmental Inequality in the U.S. from 1995 to 2004. Soc. Sci. Res. 2015, 53, 375- 390, DOI: 10.1016/ j.ssresearch.2015.06.019 [Crossref], [PubMed], [CAS], Google Scholar 8 Trends in exposure to industrial air toxins for different racial and socioeconomic groups: A spatial and temporal examination of environmental inequality in the U.S. from 1995 to 2004 Ard Kerry Social science research (2015), 53 (), 375-90 ISSN:. In recent decades there have been dramatic declines in industrial air toxins. However, there has yet to be a national study investigating if the drop has mitigated the unequal exposure to industrial toxins by race and social class. This paper addresses this by developing a unique dataset of air pollution exposure estimates, by aggregating the annual fall-out location of 415 air toxins, from 17,604 facilities, for the years 1995 to 2004 up to census block groups (N=216,159/year). These annual estimates of exposure were matched with census data to calculate trends in exposure for different racial and socioeconomic groups. Results show that exposure to air toxins has decreased for everyone, but African-Americans are consistently more exposed than Whites and Hispanics and socioeconomic status is not as protective for African-Americans. These results by race were further explored using spatially specified multilevel models which examine trends over time and across institutional boundaries. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BC28%252FkslWhuw%253D%253D&md5= d81447042a16ee588f7d5eee5951965b 9. 9 Kravitz-Wirtz, N.; Crowder, K.; Hajat, A.; Sass, V. The Long-Term Dynamics of Racial/Ethnic Inequality in Neighborhood Air Pollution Exposure, 1990-2009. Bois Rev. Soc. Sci. Res. Race 2016 , 13 (2), 237- 259, DOI: 10.1017/S1742058X16000205 [Crossref], [PubMed], [CAS], Google Scholar 9 THE LONG-TERM DYNAMICS OF RACIAL/ETHNIC INEQUALITY IN NEIGHBORHOOD AIR POLLUTION EXPOSURE, 1990-2009 Kravitz-Wirtz Nicole; Crowder Kyle; Sass Victoria; Hajat Anjum Du Bois review : social science research on race (2016), 13 (2), 237-259 ISSN:1742-058X. Research examining racial/ethnic disparities in pollution exposure often relies on cross-sectional data. These analyses are largely insensitive to exposure trends and rarely account for broader contextual dynamics. To provide a more comprehensive assessment of racial-environmental inequality over time, we combine the 1990 to 2009 waves of the Panel Study of Income Dynamics (PSID) with spatially- and temporally-resolved measures of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) in respondents' neighborhoods, as well as census data on the characteristics of respondents' metropolitan areas. Results based on multilevel repeated measures models indicate that Blacks and Latinos are, on average, more likely to be exposed to higher levels of NO2, PM2.5, and PM10 than Whites. Despite nationwide declines in levels of pollution over time, racial and ethnic disparities persist and cannot be fully explained by individual-, household-, or metropolitan-level factors. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BC1M%252FmsVektw%253D%253D&md5= 84992e75ab835c7aa42ee7b7b4dff750 10. 10 Post, E. S.; Belova, A.; Huang, J. Distributional Benefit Analysis of a National Air Quality Rule. Int. J. Environ. Res. Public. Health 2011, 8 (6), 1872- 1892, DOI: 10.3390/ ijerph8061872 [Crossref], [PubMed], [CAS], Google Scholar 10 Distributional benefit analysis of a national air quality rule Post Ellen S; Belova Anna; Huang Jin International journal of environmental research and public health (2011), 8 (6), 1872-92 ISSN:. Under Executive Order 12898, the U.S. Environmental Protection Agency (EPA) must perform environmental justice (EJ) reviews of its rules and regulations. EJ analyses address the hypothesis that environmental disamenities are experienced disproportionately by poor and/or minority subgroups. Such analyses typically use communities as the unit of analysis. While community-based approaches make sense when considering where polluting sources locate, they are less appropriate for national air quality rules affecting many sources and pollutants that can travel thousands of miles. We compare exposures and health risks of EJ-identified individuals rather than communities to analyze EPA's Heavy Duty Diesel (HDD) rule as an example national air quality rule. Air pollutant exposures are estimated within grid cells by air quality models; all individuals in the same grid cell are assigned the same exposure. Using an inequality index, we find that inequality within racial/ethnic subgroups far outweighs inequality between them. We find, moreover, that the HDD rule leaves between-subgroup inequality essentially unchanged. Changes in health risks depend also on subgroups' baseline incidence rates, which differ across subgroups. Thus, health risk reductions may not follow the same pattern as reductions in exposure. These results are likely representative of other national air quality rules as well. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BC3MnpvVGjtA%253D%253D&md5= 914106467991da782fea33004af6741d 11. 11 Demetillo, M. A. G.; Harkins, C.; McDonald, B. C.; Chodrow, P. S. ; Sun, K.; Pusede, S. E. Space-Based Observational Constraints on NO[2] Air Pollution Inequality From Diesel Traffic in Major US Cities. Geophys. Res. Lett. 2021, 48 (17), e2021GL094333, DOI: 10.1029/2021GL094333 [Crossref], Google Scholar There is no corresponding record for this reference. 12. 12 Schell, C. J.; Dyson, K.; Fuentes, T. L.; Des Roches, S.; Harris, N. C.; Miller, D. S.; Woelfle-Erskine, C. A.; Lambert, M. R. The Ecological and Evolutionary Consequences of Systemic Racism in Urban Environments. Science 2020, 369, aay4497, DOI: 10.1126/ science.aay4497 [Crossref], Google Scholar There is no corresponding record for this reference. 13. 13 Morello-Frosch, R. A. Discrimination and the Political Economy of Environmental Inequality. Environ. Plan. C Gov. Policy 2002, 20 ( 4), 477- 496, DOI: 10.1068/c03r [Crossref], Google Scholar There is no corresponding record for this reference. 14. 14 Morello-Frosch, R.; Lopez, R. The Riskscape and the Color Line: Examining the Role of Segregation in Environmental Health Disparities. Environ. Res. 2006, 102 (2), 181- 196, DOI: 10.1016 /j.envres.2006.05.007 [Crossref], [PubMed], [CAS], Google Scholar 14 The riskscape and the color line: Examining the role of segregation in environmental health disparities Morello-Frosch, Rachel; Lopez, Russ Environmental Research (2006), 102 (2), 181-196CODEN: ENVRAL; ISSN:0013-9351. (Elsevier) Environmental health researchers, sociologists, policy-makers, and activists concerned about environmental justice argue that communities of color who are segregated in neighborhoods with high levels of poverty and material deprivation are also disproportionately exposed to phys. environments that adversely affect their health and well-being. Examg. these issues through the lens of racial residential segregation can offer new insights into the junctures of the political economy of social inequality with discrimination, environmental degrdn., and health. More importantly, this line of inquiry may highlight whether obsd. pollution-health outcome relationships are modified by segregation and whether segregation patterns impact diverse communities differently. This paper examines theor. and methodol. questions related to racial residential segregation and environmental health disparities. We begin with an overview of race-based segregation in the United States and propose a framework for understanding its implications for environmental health disparities. We then discuss applications of segregation measures for assessing disparities in ambient air pollution burdens across racial groups and go on to discuss the applicability of these methods for other environmental exposures and health outcomes. We conclude with a discussion of the research and policy implications of understanding how racial residential segregation impacts environmental health disparities. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BD28XptFSqtL4%253D&md5= df64466940c1c1c3fa67187f53c624fe 15. 15 Heblich, S.; Trew, A.; Zylberberg, Y. East-Side Story: Historical Pollution and Persistent Neighborhood Sorting. J. Polit. Econ. 2021, 129 (5), 1508- 1552, DOI: 10.1086/713101 [Crossref], Google Scholar There is no corresponding record for this reference. 16. 16 Pastor, M.; Sadd, J.; Hipp, J. Which Came First? Toxic Facilities, Minority Move-In, and Environmental Justice. J. Urban Aff. 2001, 23 (1), 1- 21, DOI: 10.1111/0735-2166.00072 [Crossref], Google Scholar There is no corresponding record for this reference. 17. 17 Mohai, P.; Lantz, P. M.; Morenoff, J.; House, J. S.; Mero, R. P. Racial and Socioeconomic Disparities in Residential Proximity to Polluting Industrial Facilities: Evidence From the Americans' Changing Lives Study. Am. J. Public Health 2009, 99 (S3), S649- S656, DOI: 10.2105/AJPH.2007.131383 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 18. 18 Houston, D.; Wu, J.; Ong, P.; Winer, A. Structural Disparities of Urban Traffic in Southern California: Implications for Vehicle-Related Air Pollution Exposure in Minority and High-Poverty Neighborhoods. J. Urban Aff. 2004, 26 (5), 565- 592, DOI: 10.1111/j.0735-2166.2004.00215.x [Crossref], Google Scholar There is no corresponding record for this reference. 19. 19 Massey, D. S. Still the Linchpin: Segregation and Stratification in the USA. Race Soc. Probl. 2020, 12 (1), 1- 12, DOI: 10.1007/ s12552-019-09280-1 [Crossref], Google Scholar There is no corresponding record for this reference. 20. 20 Hall, M.; Iceland, J.; Yi, Y. Racial Separation at Home and Work: Segregation in Residential and Workplace Settings. Popul. Res. Policy Rev. 2019, 38 (5), 671- 694, DOI: 10.1007/ s11113-019-09510-9 [Crossref], Google Scholar There is no corresponding record for this reference. 21. 21 Morello-Frosch, R.; Jesdale, B. M. Separate and Unequal: Residential Segregation and Estimated Cancer Risks Associated with Ambient Air Toxics in U.S. Metropolitan Areas. Environ. Health Perspect. 2006, 114 (3), 386- 393, DOI: 10.1289/ehp.8500 [Crossref], [PubMed], [CAS], Google Scholar 21 Separate and unequal: residential segregation and estimated cancer risks associated with ambient air toxins in U.S. metropolitan areas Morello-Frosch, Rachel; Jesdale, Bill M. Environmental Health Perspectives (2006), 114 (3), 386-393CODEN: EVHPAZ; ISSN:0091-6765. (U. S. Department of Health and Human Services, Public Health Services) This study examines links between racial residential segregation and estd. ambient air toxics exposures and their assocd. cancer risks using modeled concn. ests. from the U.S. Environmental Protection Agency's National Air Toxics Assessment. We combined pollutant concn. ests. with potencies to calc. cancer risks by census tract for 309 metropolitan areas in the United States. This information was combined with socioeconomic status (SES) measures from the 1990 Census. Estd. cancer risks assocd. with ambient air toxics were highest in tracts located in metropolitan areas that were highly segregated. Disparities between racial/ ethnic groups were also wider in more segregated metropolitan areas. Multivariate modeling showed that, after controlling for tract-level SES measures, increasing segregation amplified the cancer risks assocd. with ambient air toxics for all racial groups combined [highly segregated areas: relative cancer risk (RCR) = 1.04; 95% confidence interval (CI), 1.01-107; extremely segregated areas: RCR = 1.32; 95% CI, 1.28-1.36]. This segregation effect was strongest for Hispanics (highly segregated areas: RCR = 1.09; 95% CI, 1.01-1.17; extremely segregated areas: RCR = 1.74; 95% CI, 1.61-1.88) and weaker among whites (highly segregated areas: RCR = 1.04; 95% CI, 1.01-1.08; extremely segregated areas: RCR = 1.28; 95% CI, 1.24-1.33), African Americans (highly segregated areas: RCR = 1.09; 95% CI, 0.98-1.21; extremely segregated areas: RCR = 1.38; 95% CI, 1.24-1.53), and Asians (highly segregated areas: RCR = 1.10; 95% CI, 0.97-1.24; extremely segregated areas: RCR = 1.32; 95% CI, 1.16-1.51). Results suggest that disparities assocd. with ambient air toxics are affected by segregation and that these exposures may have health significance for populations across racial lines. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BD28Xjt1yqurc%253D&md5= d2d397b072d703f0fe716b9db70647e8 22. 22 Bravo, M. A.; Anthopolos, R.; Bell, M. L.; Miranda, M. L. Racial Isolation and Exposure to Airborne Particulate Matter and Ozone in Understudied US Populations: Environmental Justice Applications of Downscaled Numerical Model Output. Environ. Int. 2016, 92-93, 247- 255, DOI: 10.1016/j.envint.2016.04.008 [Crossref], [PubMed], [CAS], Google Scholar 22 Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output Bravo, Mercedes A.; Anthopolos, Rebecca; Bell, Michelle L.; Miranda, Marie Lynn Environment International (2016), 92-93 (), 247-255CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.) Researchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, esp. given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations. To est. relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diam. of < 2.5 m (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US. Long-term (5 yr av.) census tract-level PM2.5 and O3 concns. were calcd. using output from a downscaler model (2002-2006). The downscaler uses a linear regression with additive and multiplicative bias coeffs. to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calcd. at the tract level, and tracts were classified by urbanicity, RI, and geog. region. We examd. differences in estd. pollutant exposures by RI, urbanicity, and demog. subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to est. assocns. between RI and air pollution levels in urban, suburban, and rural tracts. High RI tracts (>= 80th percentile) had higher av. PM2.5 levels in each category of urbanicity compared to low RI tracts (< 20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concns. were significantly and pos. assocd. with RI. The largest assocn. between PM2.5 and RI was obsd. in the rural Midwest, where a one quintile increase in RI was assocd. with a 0.90 mg/m3 (95% confidence interval: 0.83, 0.99 mg/m3) increase in PM2.5 concn. Assocns. between O3 and RI in the Northeast, Midwest and West were pos. and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty. RI is assocd. with higher 5 yr estd. PM2.5 concns. in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly assocd. with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC28XmvVKrtLo%253D&md5= 16d892f9ab284e7f5492876f3972b59a 23. 23 Hillier, A. Who Received Loans? Home Owners' Loan Corporation Lending and Discrimination in Philadelphia in the 1930s. J. Plan. Hist. 2003, 2 (1), 3- 24, DOI: 10.1177/1538513202239694 [Crossref], Google Scholar There is no corresponding record for this reference. 24. 24 Rothstein, R. The Color of Law; Liveright Publishing Corp.: New York, 2017. Google Scholar There is no corresponding record for this reference. 25. 25 Nelson, R. K.; Winling, L.; Marciano, R.; Connolly, N., et al. Mapping Inequality. American Panorama, ed. Robert K. Nelson and Edward L. Ayers; https://dsl.richmond.edu/panorama/redlining (accessed 28 Feb 2022). Google Scholar There is no corresponding record for this reference. 26. 26 Nelson, R.; Winling, L. Mapping Inequality. U.S. EPA Environmental Justice and Systemic Racism Session #1; 2021. Google Scholar There is no corresponding record for this reference. 27. 27 Mitchell, B.; Franco, J. HOLC "Redlining" Maps: The Persistent Structure of Segregation and Economic Inequality. National Community Reinvestment Coalition, 2018. Google Scholar There is no corresponding record for this reference. 28. 28 Nardone, A.; Rudolph, K. E.; Morello-Frosch, R.; Casey, J. A. Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States. Environ. Health Perspect. 2021, 129 (1), 017006 DOI: 10.1289/EHP7495 [Crossref], [PubMed], [CAS], Google Scholar 28 Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States Nardone Anthony; Rudolph Kara E; Morello-Frosch Rachel; Casey Joan A Environmental health perspectives (2021), 129 (1), 17006 ISSN:. INTRODUCTION: Redlining, a racist mortgage appraisal practice of the 1930s, established and exacerbated racial residential segregation boundaries in the United States. Investment risk grades assigned [Formula: see text] ago through security maps from the Home Owners' Loan Corporation (HOLC) are associated with current sociodemographics and adverse health outcomes. We assessed whether historical HOLC investment grades are associated with 2010 greenspace, a health-promoting neighborhood resource. OBJECTIVES: We compared 2010 normalized difference vegetation index (NDVI) across previous HOLC neighborhood grades using propensity score restriction and matching. METHODS: Security map shapefiles were downloaded from the Mapping Inequality Project. Neighborhood investment risk grades included A (best, green), B (blue), C (yellow), and D (hazardous, red, i.e., redlined). We used 2010 satellite imagery to calculate the average NDVI for each HOLC neighborhood. Our main outcomes were 2010 annual average NDVI and summer NDVI. We assigned areal-apportioned 1940 census measures to each HOLC neighborhood. We used propensity score restriction, matching, and targeted maximum likelihood estimation to limit model extrapolation, reduce confounding, and estimate the association between HOLC grade and NDVI for the following comparisons: Grades B vs. A, C vs. B, and D vs. C. RESULTS: Across 102 urban areas (4,141 HOLC polygons), annual average [Formula: see text] 2010 NDVI was 0.47 ([Formula: see text]), 0.43 ([Formula: see text]), 0.39 ([Formula: see text]), and 0.36 ([Formula: see text]) in Grades A-D, respectively. In analyses adjusted for current ecoregion and census region, 1940s census measures, and 1940s population density, annual average NDVI values in 2010 were estimated at [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]), [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]), and [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]) for Grades B vs. A, C vs. B, and D vs. C, respectively, in the 1930s. DISCUSSION: Estimates adjusted for historical characteristics indicate that neighborhoods assigned worse HOLC grades in the 1930s are associated with reduced present-day greenspace. https://doi.org/10.1289/EHP7495. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB3srlvVOluw%253D%253D&md5= c046339d5cdd76527da85853684935b6 29. 29 Hoffman, J. S.; Shandas, V.; Pendleton, N. The Effects of Historical Housing Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas. Climate 2020, 8 (1), 12, DOI: 10.3390/cli8010012 [Crossref], Google Scholar There is no corresponding record for this reference. 30. 30 Locke, D. H.; Hall, B.; Grove, J. M.; Pickett, S. T. A.; Ogden, L. A.; Aoki, C.; Boone, C. G.; O'Neil-Dunne, J. P. M. Residential Housing Segregation and Urban Tree Canopy in 37 US Cities. Npj Urban Sustain. 2021, 1 (1), 1- 9, DOI: 10.1038/ s42949-021-00022-0 [Crossref], Google Scholar There is no corresponding record for this reference. 31. 31 Namin, S.; Xu, W.; Zhou, Y.; Beyer, K. The Legacy of the Home Owners' Loan Corporation and the Political Ecology of Urban Trees and Air Pollution in the United States. Soc. Sci. Med. 2020, 246, 112758, DOI: 10.1016/j.socscimed.2019.112758 [Crossref], [PubMed], [CAS], Google Scholar 31 The legacy of the Home Owners' Loan Corporation and the political ecology of urban trees and air pollution in the United States Namin S; Xu W; Zhou Y; Beyer K Social science & medicine (1982) (2020), 246 (), 112758 ISSN:. This study examines the persistent impacts of historical racebased discriminatory housing policies on contemporary urban environments in the United States. Specifically, we examine the relationships between Home Owners' Loan Corporation (HOLC) grades assigned to neighborhoods in the 1930s and the current distribution of tree canopy and level of exposure to air pollution hazards. Our results indicate a clear gradient in tree canopy by HOLC grade, with better neighborhood grades associated with significantly higher percentage of tree canopy coverage. The pattern also exists for airborne carcinogens and respiratory hazards, with worse neighborhood grades associated with significantly higher hazards exposure. Our findings indicate that early 20th century discriminatory housing policies exert a contemporary influence on patterns of green space exposure in American cities, with implications for health and health inequities. Our findings suggest that, in order to achieve equitable access to the benefits of urban greenspace, we must acknowledge these historical influences and consider policies and practices that directly counter these influences, for example, through targeted greenspace development in areas historically identified as unfit for investment. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB3MbkslyntA%253D%253D&md5= 48724c8116eabc452a26b316cc488a49 32. 32 Wilson, B. Urban Heat Management and the Legacy of Redlining. J. Am. Plann. Assoc. 2020, 86 (4), 443- 457, DOI: 10.1080/ 01944363.2020.1759127 [Crossref], Google Scholar There is no corresponding record for this reference. 33. 33 Saverino, K. C.; Routman, E.; Lookingbill, T. R.; Eanes, A. M.; Hoffman, J. S.; Bao, R. Thermal Inequity in Richmond, VA: The Effect of an Unjust Evolution of the Urban Landscape on Urban Heat Islands. Sustainability 2021, 13 (3), 1511, DOI: 10.3390/ su13031511 [Crossref], Google Scholar There is no corresponding record for this reference. 34. 34 Nardone, A.; Casey, J. A.; Morello-Frosch, R.; Mujahid, M.; Balmes, J. R.; Thakur, N. Associations between Historical Residential Redlining and Current Age-Adjusted Rates of Emergency Department Visits Due to Asthma across Eight Cities in California: An Ecological Study. Lancet Planet. Health 2020, 4 (1 ), e24- e31, DOI: 10.1016/S2542-5196(19)30241-4 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 35. 35 Collin, L. J.; Gaglioti, A. H.; Beyer, K. M.; Zhou, Y.; Moore, M. A.; Nash, R.; Switchenko, J. M.; Miller-Kleinhenz, J. M.; Ward, K. C.; McCullough, L. E. Neighborhood-Level Redlining and Lending Bias Are Associated with Breast Cancer Mortality in a Large and Diverse Metropolitan Area. Cancer Epidemiol. Prev. Biomark. 2021, 30 (1), 53- 60, DOI: 10.1158/1055-9965.EPI-20-1038 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 36. 36 Krieger, N.; Wright, E.; Chen, J. T.; Waterman, P. D.; Huntley, E. R.; Arcaya, M. Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015. Am. J. Epidemiol. 2020, 189 (10), 1065- 1075, DOI: 10.1093/aje/ kwaa045 [Crossref], [PubMed], [CAS], Google Scholar 36 Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015 Krieger Nancy; Wright Emily; Chen Jarvis T; Waterman Pamela D; Huntley Eric R; Arcaya Mariana American journal of epidemiology (2020), 189 (10), 1065-1075 ISSN:. In the 1930s, maps created by the federal Home Owners' Loan Corporation (HOLC) nationalized residential racial segregation via "redlining," whereby HOLC designated and colored in red areas they deemed to be unsuitable for mortgage lending on account of their Black, foreign-born, or low-income residents. We used the recently digitized HOLC redlining maps for 28 municipalities in Massachusetts to analyze Massachusetts Cancer Registry data for late stage at diagnosis for cervical, breast, lung, and colorectal cancer (2001-2015). Multivariable analyses indicated that, net of age, sex/gender, and race/ethnicity, residing in a previously HOLC-redlined area imposed an elevated risk for late stage at diagnosis, even for residents of census tracts with present-day economic and racial privilege, whereas the best historical HOLC grade was not protective for residents of census tracts without such current privilege. For example, a substantially elevated risk of late stage at diagnosis occurred among men with lung cancer residing in currently privileged areas that had been redlined (risk ratio = 1.17, 95% confidence interval: 1.06, 1.29), whereas such risk was attenuated among men residing in census tracts lacking such current privilege (risk ratio = 1.01, 95% confidence interval: 0.94, 1.08). Research on historical redlining as a structural driver of health inequities is warranted. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB383otFCltA%253D%253D&md5= d063bd031ced96f47759500c421769c3 37. 37 Nardone, A. L.; Casey, J. A.; Rudolph, K. E.; Karasek, D.; Mujahid, M.; Morello-Frosch, R. Associations between Historical Redlining and Birth Outcomes from 2006 through 2015 in California . PLoS One 2020, 15 (8), e0237241 DOI: 10.1371/ journal.pone.0237241 [Crossref], [PubMed], [CAS], Google Scholar 37 Associations between historical redlining and birth outcomes from 2006 through 2015 in California Nardone, Anthony L.; Casey, Joan A.; Rudolph, Kara E.; Karasek, Deborah; Mujahid, Mahasin; Morello-Frosch, Rachel PLoS One (2020), 15 (8), e0237241CODEN: POLNCL; ISSN:1932-6203. ( Public Library of Science) Background: Despite being one of the wealthiest nations, disparities in adverse birth outcomes persist across racial and ethnic lines in the United States. We studied the assocn. between historical redlining and preterm birth, low birth wt. (LBW), small-for-gestational age (SGA), and perinatal mortality over a ten-year period (2006-2015) in Los Angeles, Oakland, and San Francisco, California. Methods: We used birth outcomes data from the California Office of Statewide Health Planning and Development between Jan. 1, 2006 and Dec. 31, 2015. Home Owners' Loan Corporation (HOLC) Security Maps developed in the 1930s assigned neighborhoods one of four grades that pertained to perceived investment risk of borrowers from that neighborhood: green (grade A) were considered "Best", blue (grade B) "Still Desirable", yellow (grade C) "Definitely Declining", and red (grade D, hence the term "redlining") "Hazardous". Geocoded residential addresses at the time of birth were superimposed on HOLC Security Maps to assign each birth a HOLC grade. We adjusted for potential confounders present at the time of Security Map creation by assigning HOLC polygons areal-weighted 1940s Census measures. We then employed propensity score matching methods to est. the assocn. of historical HOLC grades on current birth outcomes. Because tracts graded A had almost no propensity of receiving grade C or D and because grade B tracts had low propensity of receiving grade D, we examd. birth outcomes in the three following comparisons: B vs. B, and D vs. Results: The prevalence of preterm birth, SGA and mortality tended to be higher in worse HOLC grades, while the prevalence of LBW varied across grades. Overall odds of mortality and preterm birth increased as HOLC grade worsened. Propensity score matching balanced 1940s census measures across contrasting groups. Logistic regression models revealed significantly elevated odds of preterm birth (odds ratio (OR): 1.02, 95% confidence interval (CI): 1.00-1.05), and SGA (OR: 1.03, 95% CI: 1.00-1.05) in the C vs. B comparison and significantly reduced odds of preterm birth (OR: 0.93, 95% CI: 0.91-0.95), LBW (OR: 0.94-95% CI: 0.92-0.97), and SGA (OR: 0.94, 95% CI: 0.92-0.96) in the D vs. C comparison. Results differed by metropolitan area and maternal race. Conclusion: Similar to prior studies on redlining, we found that worsening HOLC grade was assocd. with adverse birth outcomes, although this relationship was less clear after propensity score matching and stratifying by metropolitan area. Higher odds of preterm birth and SGA in grade C vs. grade B neighborhoods may be caused by higher-stress environments, racial segregation, and lack of access to resources, while lower odds of preterm birth, SGA, and LBW in grade D vs. grade C neighborhoods may due to population shifts in those neighborhoods related to gentrification. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsF2ksLrK&md5= 7bc4eef1097590c36436eee600383ce5 38. 38 Krieger, N.; Van Wye, G.; Huynh, M.; Waterman, P. D.; Maduro, G.; Li, W.; Gwynn, R. C.; Barbot, O.; Bassett, M. T. Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017. Am. J. Public Health 2020, 110 (7), 1046- 1053, DOI: 10.2105/AJPH.2020.305656 [Crossref], [PubMed], [CAS], Google Scholar 38 Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017 Krieger Nancy; Van Wye Gretchen; Huynh Mary; Waterman Pamela D; Maduro Gil; Li Wenhui; Gwynn R Charon; Barbot Oxiris; Bassett Mary T American journal of public health (2020), 110 (7), 1046-1053 ISSN:. Objectives. To assess if historical redlining, the US government's 1930s racially discriminatory grading of neighborhoods' mortgage credit-worthiness, implemented via the federally sponsored Home Owners' Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation).Methods. We analyzed 2013-2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics.Results. The proportion of preterm births ranged from 5.0% in grade A ("best"-green) to 7.3% in grade D ("hazardous"-red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation.Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth.Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining's impacts on present-day residential segregation and health outcomes. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB38vpvVGisw%253D%253D&md5= 7dd6fceeed1f38ede96ec1ea9ad940b7 39. 39 Nardone, A.; Chiang, J.; Corburn, J. Historic Redlining and Urban Health Today in U.S. Cities. Environ. Justice 2020, 13 (4), 109- 119, DOI: 10.1089/env.2020.0011 [Crossref], Google Scholar There is no corresponding record for this reference. 40. 40 Nicholas Hewitt, C. Spatial Variations in Nitrogen Dioxide Concentrations in an Urban Area. Atmospheric Environ. Part B Urban Atmosphere 1991, 25 (3), 429- 434, DOI: 10.1016/0957-1272 (91)90014-6 [Crossref], Google Scholar There is no corresponding record for this reference. 41. 41 Mead, M. I.; Popoola, O. A. M.; Stewart, G. B.; Landshoff, P.; Calleja, M.; Hayes, M.; Baldovi, J. J.; McLeod, M. W.; Hodgson, T. F.; Dicks, J.; Lewis, A.; Cohen, J.; Baron, R.; Saffell, J. R. ; Jones, R. L. The Use of Electrochemical Sensors for Monitoring Urban Air Quality in Low-Cost, High-Density Networks. Atmos. Environ. 2013, 70, 186- 203, DOI: 10.1016/j.atmosenv.2012.11.060 [Crossref], [CAS], Google Scholar 41 Use of electrochemical sensors for monitoring urban air quality in low-cost, high-density network Mead, M. I.; Popoola, O. A. M.; Stewart, G. B.; Landshoff, P.; Calleja, M.; Hayes, M.; Baldovi, J. J.; McLeod, M. W.; Hodgson, T. F.; Dicks, J.; Lewis, A.; Cohen, J.; Baron, R.; Saffell, J. R.; Jones, R. L. Atmospheric Environment (2013), 70 (), 186-203CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) Measurements at appropriate spatial and temporal scales are essential for understanding and monitoring spatially heterogeneous environments with complex and highly variable emission sources, such as in urban areas. However, the costs and complexity of conventional air quality measurement methods means that measurement networks are generally extremely sparse. In this paper we show that miniature, low-cost electrochem. gas sensors, traditionally used for sensing at parts-per-million (ppm) mixing ratios can, when suitably configured and operated, be used for parts-per-billion (ppb) level studies for gases relevant to urban air quality. Sensor nodes, in this case consisting of multiple individual electrochem. sensors, can be low-cost and highly portable, thus allowing the deployment of scalable high-d. air quality sensor networks at fine spatial and temporal scales, and in both static and mobile configurations. In this paper we provide evidence for the performance of electrochem. sensors at the parts-per-billion level, and then outline results obtained from deployments of networks of sensor nodes in both an autonomous, high-d., static network in the wider Cambridge (UK) area, and as mobile networks for quantification of personal exposure. Examples are presented of measurements obtained with both highly portable devices held by pedestrians and cyclists, and static devices attached to street furniture. The widely varying mixing ratios reported by this study confirm that the urban environment cannot be fully characterised using sparse, static networks, and that measurement networks with higher resoln. (both spatially and temporally) are required to quantify air quality at the scales which are present in the urban environment. We conclude that the instruments described here, and the low-cost/high-d. measurement philosophy which underpins it, have the potential to provide a far more complete assessment of the high-granularity air quality structure generally obsd. in the urban environment, and could ultimately be used for quantification of human exposure as well as for monitoring and legislative purposes. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC3sXjslKmurg%253D&md5= fac1c1d89e2f94af9117fc584a522ae8 42. 42 Apte, J. S.; Messier, K. P.; Gani, S.; Brauer, M.; Kirchstetter, T. W.; Lunden, M. M.; Marshall, J. D.; Portier, C. J.; Vermeulen, R. C. H.; Hamburg, S. P. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ. Sci. Technol. 2017, 51 (12), 6999- 7008, DOI: 10.1021/acs.est.7b00891 [ACS Full Text ACS Full Text], [CAS], Google Scholar 42 High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data Apte, Joshua S.; Messier, Kyle P.; Gani, Shahzad; Brauer, Michael; Kirchstetter, Thomas W.; Lunden, Melissa M.; Marshall, Julian D.; Portier, Christopher J.; Vermeulen, Roel C. H.; Hamburg, Steven P. Environmental Science & Technology (2017), 51 (12), 6999-7008 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concns. vary sharply over short distances (<< 1 km) owing to unevenly distributed emission sources, diln., and physicochem. transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resoln. needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, USA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2 and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8x within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC2sXptVCku78%253D&md5= 3a2b5c8e04b3be0065b1e4524adae9ee 43. 43 Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data. Environ. Sci. Technol. 2010, 44 (14), 5334- 5344, DOI: 10.1021/es100008x [ACS Full Text ACS Full Text], [CAS], Google Scholar 43 Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data Karner, Alex A.; Eisinger, Douglas S.; Niemeier, Deb A. Environmental Science & Technology (2010), 44 (14), 5334-5344 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concn. measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the obsd. concns. Local regression models were specified to the concn.-distance relationship and anal. of variance was used to det. the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more std. background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to under-predict (be lower than) actual background. Changes in pollutant concns. with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concn. by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter no. concns.); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concns.). >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC3cXns1Wrtb4%253D&md5= ac6e9c38c90964168688cc4094594073 44. 44 Eeftens, M.; Tsai, M.-Y.; Ampe, C.; Anwander, B.; Beelen, R.; Bellander, T.; Cesaroni, G.; Cirach, M.; Cyrys, J.; de Hoogh, K.; De Nazelle, A.; de Vocht, F.; Declercq, C.; Dedele, A.; Eriksen, K.; Galassi, C.; Grazuleviciene, R.; Grivas, G.; Heinrich, J.; Hoffmann, B.; Iakovides, M.; Ineichen, A.; Katsouyanni, K.; Korek, M.; Kramer, U.; Kuhlbusch, T.; Lanki, T.; Madsen, C.; Meliefste, K.; Molter, A.; Mosler, G.; Nieuwenhuijsen, M.; Oldenwening, M.; Pennanen, A.; Probst-Hensch, N.; Quass, U.; Raaschou-Nielsen, O.; Ranzi, A.; Stephanou, E.; Sugiri, D.; Udvardy, O.; Vaskovi, E.; Weinmayr, G.; Brunekreef, B.; Hoek, G. Spatial Variation of PM[2.5], PM[10], PM[2.5] Absorbance and PM [coarse] Concentrations between and within 20 European Study Areas and the Relationship with NO[2] - Results of the ESCAPE Project. Atmos. Environ. 2012, 62, 303- 317, DOI: 10.1016/ j.atmosenv.2012.08.038 [Crossref], [CAS], Google Scholar 44 Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 - Results of the ESCAPE project Eeftens, Marloes; Tsai, Ming-Yi; Ampe, Christophe; Anwander, Bernhard; Beelen, Rob; Bellander, Tom; Cesaroni, Giulia; Cirach, Marta; Cyrys, Josef; de Hoogh, Kees; De Nazelle, Audrey; de Vocht, Frank; Declercq, Christophe; Dedele, Audrius; Eriksen, Kirsten; Galassi, Claudia; Grazuleviciene, Regina; Grivas, Georgios; Heinrich, Joachim; Hoffmann, Barbara; Iakovides, Minas; Ineichen, Alex; Katsouyanni, Klea; Korek, Michal; Kraemer, Ursula; Kuhlbusch, Thomas; Lanki, Timo; Madsen, Christian; Meliefste, Kees; Moelter, Anna; Mosler, Gioia; Nieuwenhuijsen, Mark; Oldenwening, Marieke; Pennanen, Arto; Probst-Hensch, Nicole; Quass, Ulrich; Raaschou-Nielsen, Ole; Ranzi, Andrea; Stephanou, Euripides; Sugiri, Dorothee; Udvardy, Orsolya; Vaskoevi, Eva; Weinmayr, Gudrun; Brunekreef, Bert; Hoek, Gerard Atmospheric Environment (2012), 62 (), 303-317CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concns. between and within 20 study areas across Europe. We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between Oct. 2008 and Apr. 2011 using standardized methods. PMcoarse was detd. as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population d., traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a ref. site, which was operated year-round. Substantial concn. differences were obsd. between and within study areas. Concns. for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest av. PM2.5 concns. found in Turin and the highest PMcoarse in Heraklion. Street/urban background concn. ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (av. R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (av. R2 = 0.39). Despite high correlations, concn. ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06. In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concns. across Europe will contribute to a consistent assessment of health effects across Europe. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFersbjK&md5= bb9604f323ccc65d1ea6b2a897d0207d 45. 45 Thakrar, S. K.; Balasubramanian, S.; Adams, P. J.; Azevedo, I. M. L.; Muller, N. Z.; Pandis, S. N.; Polasky, S.; Pope, C. A.; Robinson, A. L.; Apte, J. S.; Tessum, C. W.; Marshall, J. D.; Hill, J. D. Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources. Environ. Sci. Technol. Lett. 2020, 7 (9), 639- 645, DOI: 10.1021/ acs.estlett.0c00424 [ACS Full Text ACS Full Text], [CAS], Google Scholar 45 Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources Thakrar, Sumil K.; Balasubramanian, Srinidhi; Adams, Peter J.; Azevedo, Ines M. L.; Muller, Nicholas Z.; Pandis, Spyros N.; Polasky, Stephen; Pope, C. Arden; Robinson, Allen L.; Apte, Joshua S.; Tessum, Christopher W.; Marshall, Julian D.; Hill, Jason D. Environmental Science & Technology Letters (2020), 7 (9), 639-645 CODEN: ESTLCU; ISSN:2328-8930. (American Chemical Society) Air quality in the United States has dramatically improved, yet exposure to air pollution is still assocd. with 100000-200000 deaths annually. Reducing the no. of deaths effectively, efficiently, and equitably relies on attributing them to specific emission sources, but so far, this was done for only highly aggregated groups of sources, or a select few sources of interest. Here, we est. mortality in the United States attributable to all domestic, human-caused emissions of primary PM2.5 and secondary PM2.5 precursors. We present detailed source-specific attributions in four alternate groupings relevant for identifying promising ways to reduce mortality. We find that nearly half of the deaths can be attributed to just five activities, all in different sectors. Around half of the deaths can be attributed to fossil fuel combustion, with the remainder attributable to combustion of nonfossil fuels, agricultural processes, and other noncombustion processes. Both primary and secondary PM2.5 are important, including PM2.5 from currently unregulated precursor pollutants such as ammonia. We suggest improvements in air quality can be realized by continued redns. of emissions from traditionally important sources and by novel strategies for reducing emissions from sources of emerging relative importance and research focus. Such changes can contribute to improved health outcomes and other environmental goals. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlOgt7vI&md5= 867756324ad142beed3d5c2d1f45c395 46. 46 Kroll, J. H.; Seinfeld, J. H. Chemistry of Secondary Organic Aerosol: Formation and Evolution of Low-Volatility Organics in the Atmosphere. Atmos. Environ. 2008, 42 (16), 3593- 3624, DOI: 10.1016/j.atmosenv.2008.01.003 [Crossref], [CAS], Google Scholar 46 Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere Kroll, Jesse H.; Seinfeld, John H. Atmospheric Environment (2008), 42 (16), 3593-3624CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) A review is given. Secondary org. aerosol (SOA), particulate matter composed of compds. formed from the atm. transformation of org. species, accounts for a substantial fraction of tropospheric aerosol. The formation of low-volatility (semivolatile and possibly nonvolatile) compds. that make up SOA is governed by a complex series of reactions of a large no. of org. species, so the exptl. characterization and theor. description of SOA formation presents a substantial challenge. We outline what is known about the chem. of formation and continuing transformation of low-volatility species in the atm. The primary focus is chem. processes that can change the volatility of org. compds.: (1) oxidn. reactions in the gas phase, (2) reactions in the particle phase, and (3) continuing chem. (in either phase) over several generations. Gas-phase oxidn. reactions can reduce volatility by the addn. of polar functional groups or increase it by the cleavage of carbon-carbon bonds; key branch points that control volatility are the initial attack of the oxidant, reactions of alkylperoxy (RO2) radicals, and reactions of alkoxy (RO) radicals. Reactions in the particle phase include oxidn. reactions as well as accretion reactions, non-oxidative processes leading to the formation of high-mol.-wt. species. Org. C in the atm. is continually subject to reactions in the gas and particle phases throughout its atm. lifetime (until lost by phys. deposition or oxidized to CO or CO2), implying continual changes in volatility over the timescales of several days. The volatility changes arising from these chem. reactions must be parameterized and included in models in order to gain a quant. and predictive understanding of SOA formation. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BD1cXls1Kksbs%253D&md5= bcb3d4d2605d9a029941fe4d1d5d5ee9 47. 47 Gentner, D. R.; Jathar, S. H.; Gordon, T. D.; Bahreini, R.; Day, D. A.; El Haddad, I.; Hayes, P. L.; Pieber, S. M.; Platt, S. M.; de Gouw, J.; Goldstein, A. H.; Harley, R. A.; Jimenez, J. L.; Prevot, A. S. H.; Robinson, A. L. Review of Urban Secondary Organic Aerosol Formation from Gasoline and Diesel Motor Vehicle Emissions. Environ. Sci. Technol. 2017, 51 (3), 1074- 1093, DOI: 10.1021/acs.est.6b04509 [ACS Full Text ACS Full Text], [CAS], Google Scholar 47 Review of Urban Secondary Organic Aerosol Formation from Gasoline and Diesel Motor Vehicle Emissions Gentner, Drew R.; Jathar, Shantanu H.; Gordon, Timothy D.; Bahreini, Roya; Day, Douglas A.; El Haddad, Imad; Hayes, Patrick L.; Pieber, Simone M.; Platt, Stephen M.; de Gouw, Joost; Goldstein, Allen H.; Harley, Robert A.; Jimenez, Jose L.; Prevot, Andre S. H.; Robinson, Allen L. Environmental Science & Technology (2017), 51 (3), 1074-1093 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) A review summarizing evidence, research needs, and discrepancies between top-down and bottom-up approaches to est. secondary org. aerosols (SOA) formed from gasoline- and diesel-fueled motor vehicle gas-phase org. precursor compds., focusing on inconsistencies between mol.-level understanding and regional observations, is given. Topics discussed include: gas- and particle-phase org. compds. in urban areas; concise history of knowledge on urban SOA; motor vehicle emission: diversity in vehicle classes and org. compd. emissions; motor vehicle contributions to urban SOA; synthesis of approaches: looking from top-down and bottom-up; bottom-up methods 1 and 2: understanding SOA formation potential using unburned gasoline; diesel fuel as emission surrogates and oxidn. chamber expts. with dil. vehicle emission (overview, method results, advantages, key uncertainties and standing questions); top-down methods 1, 2, and 3: chem. compn. of ambient OA; day of week analyses using intra-week variability in diesel fuel use and total OA or SOA concn. data from factor anal.; comparing OA compn. across urban areas with different relative gasoline-diesel fuel use (overview, method results, advantages, key uncertainties and standing questions); reconciling evidence across methods (synthesizing bottom-up methods 1 and 2, uncertainties and considerations across all methods); implications and challenges for the developed and developing world; future research priorities; and supporting information. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC28XitFanu7bN&md5= 3f9ff9b69d0d1a17ad8ceedfad6597ea 48. 48 U.S. Census Bureau. 2010 Census Summary File 2 - United States; 2011. Google Scholar There is no corresponding record for this reference. 49. 49 Chambliss, S. E.; Pinon, C. P. R.; Messier, K. P.; LaFranchi, B.; Upperman, C. R.; Lunden, M. M.; Robinson, A. L.; Marshall, J. D.; Apte, J. S. Local- and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring. Proc. Natl. Acad. Sci. U. S. A. 2021, 118 (37), e2109249118, DOI: 10.1073/pnas.2109249118 [Crossref], [PubMed], [CAS], Google Scholar 49 Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring Chambliss, Sarah E.; Pinon, Carlos P. R.; Messier, Kyle P.; LaFranchi, Brian; Upperman, Crystal Romeo; Lunden, Melissa M.; Robinson, Allen L.; Marshall, Julian D.; Apte, Joshua S. Proceedings of the National Academy of Sciences of the United States of America (2021), 118 (37), e2109249118CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences) Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (> 10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resoln. observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concns. of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population av.; for White populations, av. exposures to these pollutants are 9 to 14% lower than the population av. Systematic racial/ethnic disparities are influenced by regional concn. gradients due to sharp contrasts in demog. compn. among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BB3MXitVCnu7vN&md5= 8f34706fe8b71d123f922ad1fcc326c6 50. 50 Lal, R. M.; Ramaswami, A.; Russell, A. G. Assessment of the Near-Road (Monitoring) Network Including Comparison with Nearby Monitors within U.S. Cities. Environ. Res. Lett. 2020, 15 (11), 114026, DOI: 10.1088/1748-9326/ab8156 [Crossref], [CAS], Google Scholar 50 Assessment of the Near-Road (monitoring) Network including comparison with nearby monitors within U.S. cities Lal, Raj M.; Ramaswami, Anu; Russell, Armistead G. Environmental Research Letters (2020), 15 (11), 114026CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.) Emissions from on-road mobile sources have historically been an important anthropogenic contributor to ambient air pollution leading to high levels of air pollution near major roadways. EPA recently implemented the Near-Road (monitoring) Network to measure NO2 concns. by high-traffic roadways in urban centers throughout the U.S., as these locations were believed to characterize worst-case human exposures to traffic-related air pollutants. Many near-road sites also include PM2.5 and CO measurements, which along with the NO2 observations, were compared in a pairwise manner against non-near-road monitors located within the city-scale boundary. After controlling for primary emissions from the target highways, we found the PM2.5 concn. difference (i.e. near-road concn. minus non-near-road site concn.) between the near-road and non-near-road urban sites to be d = 0.42 mg m -3 (H0: mdiff = 0; Ha: mdiff > 0 (mnon-near-road > mnear-road); p = 0.051; a= 0.05, 95% CI: -0.08-0.90 mg m -3, n = 35 comparisons). NO2 and CO levels were on av. higher at the near-road sites compared to the non-near-road urban sites by 5.0 (95% CI: 3.4-6.5) ppb (n = 44 comparisons) and 9.2 x 10 -2 (95% CI: 0.04-0.14) ppm (n = 42 comparisons), resp. The av. PM2.5 difference found here is 5%, and at 14 of the 35 (~40%) urban monitor comparisons and 28 of the 72 (~39%) overall comparisons, PM2.5 is actually higher at the non-near-road site relative to its near-road pair. Cleaner vehicle fleets, formation of secondary PM from on-road emissions occurring downwind (i.e. away from the road), decreased secondary org. aerosol (SOA) formation rates in the near-road environment, the prevalence of other low-vol. vehicular and local, non-vehicular sources of emissions at the non-near-road sites (e.g. railyards, truck yards, ports, biomass-fueled heating, backyard barbecuing, and com. cooking, etc) and local meteorol. (e.g. wind speed and wind direction) explain this finding. The wintertime PM2.5 concn. difference was higher than the other seasons, likely a result of higher primary PM2.5 tailpipe emissions and lower temps. that both reduced near-road PM volatility and decreased photochem. activity resulting in lower SOA prodn. at the urban scale. Further, all near-road NO2 and CO concns. were below the annual and hourly NAAQS, while eight (most of which were in wildfire-prone locations) of the 94 PM2.5 sites used in this study were above the annual National Ambient Air Quality Stds. In addn., strong agreement with both annual av. daily traffic and fleet-equiv. AADT were found for near-road NO2 and CO concns., while weaker, but still pos. relationships were found for near-road PM2.5 levels. Lastly, same observational data was used to assess on-road mobile source emission ests. from the EPA National Emission Inventory, and anal. of the observations are in rough agreement with the current ratio of NO x to CO emissions from on-road mobile sources. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BB3cXis1ylsr7E&md5= 4475ab9b2fc5ed23ff363330e0c18b35 51. 51 Gu, P.; Li, H. Z.; Ye, Q.; Robinson, E. S.; Apte, J. S.; Robinson, A. L.; Presto, A. A. Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables. Environ. Sci. Technol. 2018, 52 (20), 11545- 11554, DOI: 10.1021/acs.est.8b03833 [ACS Full Text ACS Full Text], [CAS], Google Scholar 51 Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables Gu, Peishi; Li, Hugh Z.; Ye, Qing; Robinson, Ellis S.; Apte, Joshua S.; Robinson, Allen L.; Presto, Albert A. Environmental Science & Technology (2018), 52 (20), 11545-11554 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Localized primary emissions of carbonaceous aerosols are major drivers of intra-city variability of submicron particulate matter (PM1) concns. This work assessed spatial variations of PM1 compn. by mobile sampling in Pittsburgh, Pennsylvania,and performed source-apportionment anal. to attribute primary org. aerosol (OA) to traffic (HOA) and cooking OA (COA). In high source-impact areas, PM1 concns. were, on av., 2 mg/m3 (40%) higher than urban background locations. Traffic emissions were the largest source contributing to population-weighted exposure to primary PM. Vehicle-miles traveled can be used to reliably predict HOA and localized black carbon (BC) concns. in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concn., likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28 and 9% of the total population are exposed to >1 mg/m3 traffic- and cooking-related primary emissions; some populations are impacted by both sources. The source mix in many US cities is similar; hence, the authors expect similar PM spatial patterns and increased exposure in high-source areas in other cities. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A528%3ADC%252BC1cXhslOrurfF&md5= ff6f3968cba8f7590589c71f3b9575d2 52. 52 Ananat, E. O. The Wrong Side(s) of the Tracks: The Causal Effects of Racial Segregation on Urban Poverty and Inequality. Am. Econ. J. Appl. Econ. 2011, 3 (2), 34- 66, DOI: 10.1257/app.3.2.34 [Crossref], Google Scholar There is no corresponding record for this reference. 53. 53 Archer, D. N. Transportation Policy and the Underdevelopment of Black Communities. Iowa Law Review 2021, 106 (2125), 21-12 Google Scholar There is no corresponding record for this reference. 54. 54 Li, M.; Yuan, F. Historical Redlining and Resident Exposure to COVID-19: A Study of New York City. Race and Social Problems 2021 , 1- 16, DOI: 10.1007/s12552-021-09338-z [Crossref], [PubMed], [CAS], Google Scholar 54 Historical Redlining and Resident Exposure to COVID-19: A Study of New York City Li Min; Yuan Faxi Race and social problems (2021), (), 1-16 ISSN:1867-1748. The Coronavirus Disease 2019 (COVID-19) has been reported to disproportionately impact racial/ethnic minorities in the USA, both in terms of infections and deaths. This racial disparity in the COVID-19 outcomes may result from the segregation of minorities in neighborhoods with health-compromising conditions. We, thus, anticipate that neighborhoods would be especially vulnerable to COVID-19 if they are of present-day economic and racial disadvantage and were redlined historically. To test this expectation, we examined the change of both confirmed COVID-19 cases and deaths from April to July, 2020, in zip code tabulation areas (ZCTAs) in the New York City using multilevel regression analysis. The results indicate that ZCTAs with a higher proportion of black and Hispanic populations are associated with a higher percentage of COVID-19 infection. Historically low-graded neighborhoods show a higher risk for COVID-19 infection, even for ZCTAs with present-day economic and racial privilege. These associations change over time as the pandemic unfolds. Racial/ethnic minorities are bearing the brunt of the COVID-19 pandemic's health impact. The current evidence shows that the pre-existing social structure in the form of racial residential segregation could be partially responsible for the disparities observed, highlighting an urgent need to stress historical segregation and to build a less segregated and more equal society. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS&resolution= options&coi=1%3ACAS%3A280%3ADC%252BB2c3jt1GnsA%253D%253D&md5= ebc4bbf275741c8f94c018e2429f38ae 55. 55 Lane, H. M.; Morello-Frosch, R.; Marshall, J. D.; Apte, J. S. Historical Redlining is Associated with Present-Day Air Pollution Disparities in U.S. Cities - Extended Data Files, 2022. DOI: 10.6084/m9.figshare.19193243 [Crossref], Google Scholar There is no corresponding record for this reference. Cited By --------------------------------------------------------------------- This article has not yet been cited by other publications. * Figures * References * Support Info * Abstract [ez1c01012_] High Resolution Image Download MS PowerPoint Slide Figure 1 [ez1c01012_] Figure 1. Population-weighted distributions of NO[2] and PM[2.5] levels within HOLC-mapped areas at the census block level. Bars represent 25th and 75th percentiles. Medians are indicated with horizontal lines, and means by the dot marker; the overall mean is indicated by the dotted line. Unadjusted national distributions are presented for (a) NO[2] and (b) PM[2.5]. Adjusted distributions (c and d) report the national distributions of intraurban differences for census blocks within a given HOLC grade relative to the PWM level within each city. In each panel, pollution level distributions are reported by both HOLC grade (left cluster) and race/ethnicity (right cluster). Vertical lines between these clusters reflect the pollution range of the group means: the difference in the population-weighted mean between groups A and D (left line) and between the highest-exposed and lowest-exposed racial/ethnic group. Panels c and d illustrate how intraurban disparities are consistently higher by historical HOLC grade than by race/ethnicity. High Resolution Image Download MS PowerPoint Slide Figure 2 [ez1c01012_] Figure 2. Population-weighted mean annual intraurban PWM levels by HOLC grade and race/ethnicity for (a) NO[2] and (b) PM[2.5]. All race/ethnicity groups demonstrate monotonic increases by HOLC grade. Disparities by HOLC grade were larger than those associated with differences between racial/ethnic groups (100% higher for NO[2] and 50% higher for PM[2.5]). High Resolution Image Download MS PowerPoint Slide * References ARTICLE SECTIONS Jump To ----------------------------------------------------------------- This article references 55 other publications. 1. 1 Liu, J.; Clark, L. P.; Bechle, M. J.; Hajat, A.; Kim, S.-Y.; Robinson, A. L.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010. Environ. Health Perspect. 2021, 129 (12), 127005, DOI: 10.1289/EHP8584 [Crossref], [PubMed], [CAS], Google Scholar 1 Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010 Liu Jiawen; Clark Lara P; Bechle Matthew J; Marshall Julian D; Hajat Anjum; Kim Sun-Young; Robinson Allen L; Sheppard Lianne; Szpiro Adam A; Sheppard Lianne Environmental health perspectives (2021), 129 (12), 127005 ISSN:. BACKGROUND: Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time. OBJECTIVE: The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010. METHODS: We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide ([Formula: see text]), ozone ([Formula: see text]), particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]; excluding year-1990), particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), and sulfur dioxide ([Formula: see text]). We used census data for demographic information and a national empirical model for ambient air pollution levels. RESULTS: For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ ethnic group with the highest vs. lowest national-average exposure was largest for [Formula: see text] [54% ([Formula: see text])], smallest for [Formula: see text] [3.6% ([Formula: see text])], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for [Formula: see text] in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% ([Formula: see text]; [Formula: see text]) and 88% ([Formula: see text]; CO); relative disparities declined to between [Formula: see text] ([Formula: see text]; i.e., nearly zero change) and [Formula: see text] (CO; i.e., a [Formula: see text] reduction). DISCUSSION: As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/ EHP8584. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB2cbmtFSgtg%253D%253D&md5= c931118843d9a947baf6b945b345cf84 2. 2 Clark, L. P.; Millet, D. B.; Marshall, J. D. National Patterns in Environmental Injustice and Inequality: Outdoor NO[2] Air Pollution in the United States. PLoS One 2014, 9 (4 ), e94431, DOI: 10.1371/journal.pone.0094431 [Crossref], [PubMed], [CAS], Google Scholar 2 National patterns in environmental injustice and inequality: outdoor NO2 air pollution in the United States Clark, Lara P.; Millet, Dylan B.; Marshall, Julian D. PLoS One (2014), 9 (4), e94431/1-e94431/8, 8 pp.CODEN: POLNCL ; ISSN:1932-6203. (Public Library of Science) We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO2) concns. in the contiguous United States. Our approach employs Census demog. data and a recently published high-resoln. dataset of outdoor NO2 concns. Nationally, population-weighted mean NO2 concns. are 4.6 ppb (38%, p <0.01) higher for nonwhites than for whites. The environmental health implications of that concn. disparity are compelling. For example, we est. that reducing nonwhites' NO2 concns. to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ~7,000 deaths per yr, which is equiv. to 16 million people increasing their phys. activity level from inactive (0 h/wk of phys. activity) to sufficiently active (>2.5 h/wk of phys. activity). Inequality for NO2 concn. is greater than inequality for income (Atkinson Index: 0.11 vs. 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO2. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO2 include New York, Michigan, and Wisconsin. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC2cXhsFemtL%252FI&md5= c0fcf9529125069e69259fe779ace3d8 3. 3 Clark, L. P.; Millet, D. B.; Marshall, J. D. Changes in Transportation-Related Air Pollution Exposures by Race-Ethnicity and Socioeconomic Status: Outdoor Nitrogen Dioxide in the United States in 2000 and 2010. Environ. Health Perspect 2017, 125 (9), 097012, DOI: 10.1289/EHP959 [Crossref], [PubMed], [CAS], Google Scholar 3 Changes in transportation-related air pollution exposures by race-ethnicity and socioeconomic status: outdoor nitrogen dioxide in the United States in 2000 and 2010 Clark, Lara P.; Millet, Dylan B.; Marshall, Julian D. Environmental Health Perspectives (2017), 125 (9), 097012/ 1-097012/10CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health) BACKGROUND: Disparities in exposure to air pollution by race-ethnicity and by socioeconomic status have been documented in the United States, but the impacts of declining transportation-related air pollutant emissions on disparities in exposure have not been studied in detail. OBJECTIVE: This study was designed to est. changes over time (2000 to 2010) in disparities in exposure to outdoor concns. of a transportation-related air pollutant, nitrogen dioxide (NO2), in the United States. METHODS: We combined annual av. NO2 concn. ests. from a temporal land use regression model with Census demog. data to est. outdoor exposures by race-ethnicity, socioeconomic characteristics (income, age, education), and by location (region, state, county, urban area) for the contiguous United States in 2000 and 2010. RESULTS: Estd. annual av. NO2 concns. decreased from 2000 to 2010 for all of the race-ethnicity and socioeconomic status groups, including a decrease from 17.6 ppb to 10.7 ppb (-6:9 ppb) in nonwhite [non-(white alone, non-Hispanic)] populations, and 12.6 ppb to 7.8 ppb (-4.7 ppb) in white (white alone, non-Hispanic) populations. In 2000 and 2010, disparities in NO2 concns. were larger by race-ethnicity than by income. Although the national nonwhite-white mean NO2 concn. disparity decreased from a difference of 5.0 ppb in 2000 to 2.9 ppb in 2010, estd. mean NO2 concns. remained 37% higher for nonwhites than whites in 2010 (40% higher in 2000), and nonwhites were 2.5 times more likely than whites to live in a block group with an av. NO2 concn. above the WHO annual guideline in 2010 (3.0 times more likely in 2000). CONCLUSIONS: Findings suggest that abs. NO2 exposure disparities by race-ethnicity decreased from 2000 to 2010, but relative NO2 exposure disparities persisted, with higher NO2 concns. for nonwhites than whites in 2010. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC1MXlsFymsLs%253D&md5= d73f4ba27ba2a1765436a32587352648 4. 4 Tessum, C. W.; Paolella, D. A.; Chambliss, S. E.; Apte, J. S. ; Hill, J. D.; Marshall, J. D. PM[2.5] Polluters Disproportionately and Systemically Affect People of Color in the United States. Sci. Adv. 2021, 7 (18), eabf4491, DOI: 10.1126/sciadv.abf4491 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 5. 5 Kim, S.-Y.; Bechle, M.; Hankey, S.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. Concentrations of Criteria Pollutants in the Contiguous U.S., 1979 - 2015: Role of Prediction Model Parsimony in Integrated Empirical Geographic Regression. PLoS One 2020, 15 (2), e0228535, DOI: 10.1371/ journal.pone.0228535 [Crossref], [PubMed], [CAS], Google Scholar 5 Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression Kim, Sun-Young; Bechle, Matthew; Hankey, Steve; Sheppard, Lianne; Szpiro, Adam A.; Marshall, Julian D. PLoS One (2020), 15 (2), e0228535CODEN: POLNCL; ISSN: 1932-6203. (Public Library of Science) The impact of model parsimony (i.e., how model performance differs for a large vs. small no. of covariates) has not been systematically explored. We aim to (1) build annual-av. integrated empirical geog. (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979-2015; (2) explore systematically the impact on model performance of the no. of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-av. concns. from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979-2015. We also use ~350 geog. characteristics at each location including measures of traffic, land use, land cover, and satellite-based ests. of air pollution. We then develop IEG models, employing universal kriging and summary factors estd. by partial least squares (PLS) of geog. variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited no. of variables selected from the full set by forward selection, and (3) all variables. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BB3cXks1Orurc%253D&md5= cddf38697601371cfd380e022227e0c1 6. 6 Fann, N.; Kim, S.-Y.; Olives, C.; Sheppard, L. Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM[2.5] Exposures in the Contiguous United States: 1980-2010. Environ. Health Perspect 2017, 125 (9), 097003 DOI: 10.1289/EHP507 [Crossref], [PubMed], [CAS], Google Scholar 6 Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM2.5 Exposures in the Contiguous United States: 1980-2010 Fann Neal; Kim Sun-Young; Kim Sun-Young; Olives Casey; Sheppard Lianne; Sheppard Lianne Environmental health perspectives (2017), 125 (9), 097003 ISSN:. BACKGROUND: PM2.5 precursor emissions have declined over the course of several decades, following the implementation of local, state, and federal air quality policies. Estimating the corresponding change in population exposure and PM2.5-attributable risk of death prior to the year 2000 is made difficult by the lack of PM2.5 monitoring data. OBJECTIVES: We used a new technique to estimate historical PM2.5 concentrations, and estimated the effects of changes in PM2.5 population exposures on mortality in adults (age >=30y), and on life expectancy at birth, in the contiguous United States during 1980-2010. METHODS: We estimated annual mean county-level PM2.5 concentrations in 1980, 1990, 2000, and 2010 using universal kriging incorporating geographic variables. County-level death rates and national life tables for each year were obtained from the U.S. Census and Centers for Disease Control and Prevention. We used log-linear and nonlinear concentration-response coefficients from previous studies to estimate changes in the numbers of deaths and in life years and life expectancy at birth, attributable to changes in PM2.5. RESULTS: Between 1980 and 2010, population-weighted PM2.5 exposures fell by about half, and the estimated number of excess deaths declined by about a third. The States of California, Virginia, New Jersey, and Georgia had some of the largest estimated reductions in PM2.5-attributable deaths. Relative to a counterfactual population with exposures held constant at 1980 levels, we estimated that people born in 2050 would experience an ~1-y increase in life expectancy at birth, and that there would be a cumulative gain of 4.4 million life years among adults >=30y of age. CONCLUSIONS: Our estimates suggest that declines in PM2.5 exposures between 1980 and 2010 have benefitted public health. https://doi.org/10.1289/EHP507. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BC1M%252FgvFantQ%253D%253D&md5= 5e918a6d3543ffe15cf08b829a728220 7. 7 McDonald, B. C.; Dallmann, T. R.; Martin, E. W.; Harley, R. A. Long-Term Trends in Nitrogen Oxide Emissions from Motor Vehicles at National, State, and Air Basin Scales. J. Geophys. Res.: Atmos. 2012, 117 (D21), D00V18, DOI: 10.1029/ 2012JD018304 [Crossref], Google Scholar There is no corresponding record for this reference. 8. 8 Ard, K. Trends in Exposure to Industrial Air Toxins for Different Racial and Socioeconomic Groups: A Spatial and Temporal Examination of Environmental Inequality in the U.S. from 1995 to 2004. Soc. Sci. Res. 2015, 53, 375- 390, DOI: 10.1016/j.ssresearch.2015.06.019 [Crossref], [PubMed], [CAS], Google Scholar 8 Trends in exposure to industrial air toxins for different racial and socioeconomic groups: A spatial and temporal examination of environmental inequality in the U.S. from 1995 to 2004 Ard Kerry Social science research (2015), 53 (), 375-90 ISSN:. In recent decades there have been dramatic declines in industrial air toxins. However, there has yet to be a national study investigating if the drop has mitigated the unequal exposure to industrial toxins by race and social class. This paper addresses this by developing a unique dataset of air pollution exposure estimates, by aggregating the annual fall-out location of 415 air toxins, from 17,604 facilities, for the years 1995 to 2004 up to census block groups (N=216,159/year). These annual estimates of exposure were matched with census data to calculate trends in exposure for different racial and socioeconomic groups. Results show that exposure to air toxins has decreased for everyone, but African-Americans are consistently more exposed than Whites and Hispanics and socioeconomic status is not as protective for African-Americans. These results by race were further explored using spatially specified multilevel models which examine trends over time and across institutional boundaries. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BC28%252FkslWhuw%253D%253D&md5= d81447042a16ee588f7d5eee5951965b 9. 9 Kravitz-Wirtz, N.; Crowder, K.; Hajat, A.; Sass, V. The Long-Term Dynamics of Racial/Ethnic Inequality in Neighborhood Air Pollution Exposure, 1990-2009. Bois Rev. Soc. Sci. Res. Race 2016, 13 (2), 237- 259, DOI: 10.1017/ S1742058X16000205 [Crossref], [PubMed], [CAS], Google Scholar 9 THE LONG-TERM DYNAMICS OF RACIAL/ETHNIC INEQUALITY IN NEIGHBORHOOD AIR POLLUTION EXPOSURE, 1990-2009 Kravitz-Wirtz Nicole; Crowder Kyle; Sass Victoria; Hajat Anjum Du Bois review : social science research on race (2016), 13 ( 2), 237-259 ISSN:1742-058X. Research examining racial/ethnic disparities in pollution exposure often relies on cross-sectional data. These analyses are largely insensitive to exposure trends and rarely account for broader contextual dynamics. To provide a more comprehensive assessment of racial-environmental inequality over time, we combine the 1990 to 2009 waves of the Panel Study of Income Dynamics (PSID) with spatially- and temporally-resolved measures of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) in respondents' neighborhoods, as well as census data on the characteristics of respondents' metropolitan areas. Results based on multilevel repeated measures models indicate that Blacks and Latinos are, on average, more likely to be exposed to higher levels of NO2, PM2.5, and PM10 than Whites. Despite nationwide declines in levels of pollution over time, racial and ethnic disparities persist and cannot be fully explained by individual-, household-, or metropolitan-level factors. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BC1M%252FmsVektw%253D%253D&md5= 84992e75ab835c7aa42ee7b7b4dff750 10. 10 Post, E. S.; Belova, A.; Huang, J. Distributional Benefit Analysis of a National Air Quality Rule. Int. J. Environ. Res. Public. Health 2011, 8 (6), 1872- 1892, DOI: 10.3390/ ijerph8061872 [Crossref], [PubMed], [CAS], Google Scholar 10 Distributional benefit analysis of a national air quality rule Post Ellen S; Belova Anna; Huang Jin International journal of environmental research and public health (2011), 8 (6), 1872-92 ISSN:. Under Executive Order 12898, the U.S. Environmental Protection Agency (EPA) must perform environmental justice (EJ) reviews of its rules and regulations. EJ analyses address the hypothesis that environmental disamenities are experienced disproportionately by poor and/or minority subgroups. Such analyses typically use communities as the unit of analysis. While community-based approaches make sense when considering where polluting sources locate, they are less appropriate for national air quality rules affecting many sources and pollutants that can travel thousands of miles. We compare exposures and health risks of EJ-identified individuals rather than communities to analyze EPA's Heavy Duty Diesel (HDD) rule as an example national air quality rule. Air pollutant exposures are estimated within grid cells by air quality models; all individuals in the same grid cell are assigned the same exposure. Using an inequality index, we find that inequality within racial/ethnic subgroups far outweighs inequality between them. We find, moreover, that the HDD rule leaves between-subgroup inequality essentially unchanged. Changes in health risks depend also on subgroups' baseline incidence rates, which differ across subgroups. Thus, health risk reductions may not follow the same pattern as reductions in exposure. These results are likely representative of other national air quality rules as well. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BC3MnpvVGjtA%253D%253D&md5= 914106467991da782fea33004af6741d 11. 11 Demetillo, M. A. G.; Harkins, C.; McDonald, B. C.; Chodrow, P. S.; Sun, K.; Pusede, S. E. Space-Based Observational Constraints on NO[2] Air Pollution Inequality From Diesel Traffic in Major US Cities. Geophys. Res. Lett. 2021, 48 (17 ), e2021GL094333, DOI: 10.1029/2021GL094333 [Crossref], Google Scholar There is no corresponding record for this reference. 12. 12 Schell, C. J.; Dyson, K.; Fuentes, T. L.; Des Roches, S.; Harris, N. C.; Miller, D. S.; Woelfle-Erskine, C. A.; Lambert, M. R. The Ecological and Evolutionary Consequences of Systemic Racism in Urban Environments. Science 2020, 369, aay4497, DOI: 10.1126/science.aay4497 [Crossref], Google Scholar There is no corresponding record for this reference. 13. 13 Morello-Frosch, R. A. Discrimination and the Political Economy of Environmental Inequality. Environ. Plan. C Gov. Policy 2002, 20 (4), 477- 496, DOI: 10.1068/c03r [Crossref], Google Scholar There is no corresponding record for this reference. 14. 14 Morello-Frosch, R.; Lopez, R. The Riskscape and the Color Line: Examining the Role of Segregation in Environmental Health Disparities. Environ. Res. 2006, 102 (2), 181- 196, DOI: 10.1016/j.envres.2006.05.007 [Crossref], [PubMed], [CAS], Google Scholar 14 The riskscape and the color line: Examining the role of segregation in environmental health disparities Morello-Frosch, Rachel; Lopez, Russ Environmental Research (2006), 102 (2), 181-196CODEN: ENVRAL; ISSN:0013-9351. (Elsevier) Environmental health researchers, sociologists, policy-makers, and activists concerned about environmental justice argue that communities of color who are segregated in neighborhoods with high levels of poverty and material deprivation are also disproportionately exposed to phys. environments that adversely affect their health and well-being. Examg. these issues through the lens of racial residential segregation can offer new insights into the junctures of the political economy of social inequality with discrimination, environmental degrdn., and health. More importantly, this line of inquiry may highlight whether obsd. pollution-health outcome relationships are modified by segregation and whether segregation patterns impact diverse communities differently. This paper examines theor. and methodol. questions related to racial residential segregation and environmental health disparities. We begin with an overview of race-based segregation in the United States and propose a framework for understanding its implications for environmental health disparities. We then discuss applications of segregation measures for assessing disparities in ambient air pollution burdens across racial groups and go on to discuss the applicability of these methods for other environmental exposures and health outcomes. We conclude with a discussion of the research and policy implications of understanding how racial residential segregation impacts environmental health disparities. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BD28XptFSqtL4%253D&md5= df64466940c1c1c3fa67187f53c624fe 15. 15 Heblich, S.; Trew, A.; Zylberberg, Y. East-Side Story: Historical Pollution and Persistent Neighborhood Sorting. J. Polit. Econ. 2021, 129 (5), 1508- 1552, DOI: 10.1086/713101 [Crossref], Google Scholar There is no corresponding record for this reference. 16. 16 Pastor, M.; Sadd, J.; Hipp, J. Which Came First? Toxic Facilities, Minority Move-In, and Environmental Justice. J. Urban Aff. 2001, 23 (1), 1- 21, DOI: 10.1111/0735-2166.00072 [Crossref], Google Scholar There is no corresponding record for this reference. 17. 17 Mohai, P.; Lantz, P. M.; Morenoff, J.; House, J. S.; Mero, R. P. Racial and Socioeconomic Disparities in Residential Proximity to Polluting Industrial Facilities: Evidence From the Americans' Changing Lives Study. Am. J. Public Health 2009, 99 (S3), S649- S656, DOI: 10.2105/AJPH.2007.131383 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 18. 18 Houston, D.; Wu, J.; Ong, P.; Winer, A. Structural Disparities of Urban Traffic in Southern California: Implications for Vehicle-Related Air Pollution Exposure in Minority and High-Poverty Neighborhoods. J. Urban Aff. 2004, 26 (5), 565- 592, DOI: 10.1111/j.0735-2166.2004.00215.x [Crossref], Google Scholar There is no corresponding record for this reference. 19. 19 Massey, D. S. Still the Linchpin: Segregation and Stratification in the USA. Race Soc. Probl. 2020, 12 (1), 1- 12, DOI: 10.1007/s12552-019-09280-1 [Crossref], Google Scholar There is no corresponding record for this reference. 20. 20 Hall, M.; Iceland, J.; Yi, Y. Racial Separation at Home and Work: Segregation in Residential and Workplace Settings. Popul. Res. Policy Rev. 2019, 38 (5), 671- 694, DOI: 10.1007 /s11113-019-09510-9 [Crossref], Google Scholar There is no corresponding record for this reference. 21. 21 Morello-Frosch, R.; Jesdale, B. M. Separate and Unequal: Residential Segregation and Estimated Cancer Risks Associated with Ambient Air Toxics in U.S. Metropolitan Areas. Environ. Health Perspect. 2006, 114 (3), 386- 393, DOI: 10.1289/ ehp.8500 [Crossref], [PubMed], [CAS], Google Scholar 21 Separate and unequal: residential segregation and estimated cancer risks associated with ambient air toxins in U.S. metropolitan areas Morello-Frosch, Rachel; Jesdale, Bill M. Environmental Health Perspectives (2006), 114 (3), 386-393 CODEN: EVHPAZ; ISSN:0091-6765. (U. S. Department of Health and Human Services, Public Health Services) This study examines links between racial residential segregation and estd. ambient air toxics exposures and their assocd. cancer risks using modeled concn. ests. from the U.S. Environmental Protection Agency's National Air Toxics Assessment. We combined pollutant concn. ests. with potencies to calc. cancer risks by census tract for 309 metropolitan areas in the United States. This information was combined with socioeconomic status (SES) measures from the 1990 Census. Estd. cancer risks assocd. with ambient air toxics were highest in tracts located in metropolitan areas that were highly segregated. Disparities between racial/ethnic groups were also wider in more segregated metropolitan areas. Multivariate modeling showed that, after controlling for tract-level SES measures, increasing segregation amplified the cancer risks assocd. with ambient air toxics for all racial groups combined [highly segregated areas: relative cancer risk (RCR) = 1.04; 95% confidence interval (CI), 1.01-107; extremely segregated areas: RCR = 1.32; 95% CI, 1.28-1.36]. This segregation effect was strongest for Hispanics (highly segregated areas: RCR = 1.09; 95% CI, 1.01-1.17; extremely segregated areas: RCR = 1.74; 95% CI, 1.61-1.88) and weaker among whites (highly segregated areas: RCR = 1.04; 95% CI, 1.01-1.08; extremely segregated areas: RCR = 1.28; 95% CI, 1.24-1.33), African Americans (highly segregated areas: RCR = 1.09; 95% CI, 0.98-1.21; extremely segregated areas: RCR = 1.38; 95% CI, 1.24-1.53), and Asians (highly segregated areas: RCR = 1.10; 95% CI, 0.97-1.24; extremely segregated areas: RCR = 1.32; 95% CI, 1.16-1.51). Results suggest that disparities assocd. with ambient air toxics are affected by segregation and that these exposures may have health significance for populations across racial lines. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BD28Xjt1yqurc%253D&md5= d2d397b072d703f0fe716b9db70647e8 22. 22 Bravo, M. A.; Anthopolos, R.; Bell, M. L.; Miranda, M. L. Racial Isolation and Exposure to Airborne Particulate Matter and Ozone in Understudied US Populations: Environmental Justice Applications of Downscaled Numerical Model Output. Environ. Int. 2016, 92-93, 247- 255, DOI: 10.1016/ j.envint.2016.04.008 [Crossref], [PubMed], [CAS], Google Scholar 22 Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output Bravo, Mercedes A.; Anthopolos, Rebecca; Bell, Michelle L.; Miranda, Marie Lynn Environment International (2016), 92-93 (), 247-255CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.) Researchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, esp. given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations. To est. relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diam. of < 2.5 m (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US. Long-term (5 yr av.) census tract-level PM2.5 and O3 concns. were calcd. using output from a downscaler model (2002-2006). The downscaler uses a linear regression with additive and multiplicative bias coeffs. to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calcd. at the tract level, and tracts were classified by urbanicity, RI, and geog. region. We examd. differences in estd. pollutant exposures by RI, urbanicity, and demog. subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to est. assocns. between RI and air pollution levels in urban, suburban, and rural tracts. High RI tracts (>= 80th percentile) had higher av. PM2.5 levels in each category of urbanicity compared to low RI tracts (< 20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concns. were significantly and pos. assocd. with RI. The largest assocn. between PM2.5 and RI was obsd. in the rural Midwest, where a one quintile increase in RI was assocd. with a 0.90 mg/m3 (95% confidence interval: 0.83, 0.99 mg/m3) increase in PM2.5 concn. Assocns. between O3 and RI in the Northeast, Midwest and West were pos. and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty. RI is assocd. with higher 5 yr estd. PM2.5 concns. in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly assocd. with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC28XmvVKrtLo%253D&md5= 16d892f9ab284e7f5492876f3972b59a 23. 23 Hillier, A. Who Received Loans? Home Owners' Loan Corporation Lending and Discrimination in Philadelphia in the 1930s. J. Plan. Hist. 2003, 2 (1), 3- 24, DOI: 10.1177/ 1538513202239694 [Crossref], Google Scholar There is no corresponding record for this reference. 24. 24 Rothstein, R. The Color of Law; Liveright Publishing Corp.: New York, 2017. Google Scholar There is no corresponding record for this reference. 25. 25 Nelson, R. K.; Winling, L.; Marciano, R.; Connolly, N., et al. Mapping Inequality. American Panorama, ed. Robert K. Nelson and Edward L. Ayers; https://dsl.richmond.edu/panorama /redlining (accessed 28 Feb 2022). Google Scholar There is no corresponding record for this reference. 26. 26 Nelson, R.; Winling, L. Mapping Inequality. U.S. EPA Environmental Justice and Systemic Racism Session #1; 2021. Google Scholar There is no corresponding record for this reference. 27. 27 Mitchell, B.; Franco, J. HOLC "Redlining" Maps: The Persistent Structure of Segregation and Economic Inequality. National Community Reinvestment Coalition, 2018. Google Scholar There is no corresponding record for this reference. 28. 28 Nardone, A.; Rudolph, K. E.; Morello-Frosch, R.; Casey, J. A. Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States. Environ. Health Perspect. 2021, 129 (1), 017006 DOI: 10.1289 /EHP7495 [Crossref], [PubMed], [CAS], Google Scholar 28 Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States Nardone Anthony; Rudolph Kara E; Morello-Frosch Rachel; Casey Joan A Environmental health perspectives (2021), 129 (1), 17006 ISSN:. INTRODUCTION: Redlining, a racist mortgage appraisal practice of the 1930s, established and exacerbated racial residential segregation boundaries in the United States. Investment risk grades assigned [Formula: see text] ago through security maps from the Home Owners' Loan Corporation (HOLC) are associated with current sociodemographics and adverse health outcomes. We assessed whether historical HOLC investment grades are associated with 2010 greenspace, a health-promoting neighborhood resource. OBJECTIVES: We compared 2010 normalized difference vegetation index (NDVI) across previous HOLC neighborhood grades using propensity score restriction and matching. METHODS: Security map shapefiles were downloaded from the Mapping Inequality Project. Neighborhood investment risk grades included A (best, green), B (blue), C (yellow), and D (hazardous, red, i.e., redlined). We used 2010 satellite imagery to calculate the average NDVI for each HOLC neighborhood. Our main outcomes were 2010 annual average NDVI and summer NDVI. We assigned areal-apportioned 1940 census measures to each HOLC neighborhood. We used propensity score restriction, matching, and targeted maximum likelihood estimation to limit model extrapolation, reduce confounding, and estimate the association between HOLC grade and NDVI for the following comparisons: Grades B vs. A, C vs. B, and D vs. C. RESULTS: Across 102 urban areas (4,141 HOLC polygons), annual average [Formula: see text] 2010 NDVI was 0.47 ([Formula: see text]), 0.43 ([Formula: see text]), 0.39 ([Formula: see text]), and 0.36 ([Formula: see text]) in Grades A-D, respectively. In analyses adjusted for current ecoregion and census region, 1940s census measures, and 1940s population density, annual average NDVI values in 2010 were estimated at [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]), [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]), and [Formula: see text] (95% CI: [Formula: see text], [Formula: see text]) for Grades B vs. A, C vs. B, and D vs. C, respectively, in the 1930s. DISCUSSION: Estimates adjusted for historical characteristics indicate that neighborhoods assigned worse HOLC grades in the 1930s are associated with reduced present-day greenspace. https://doi.org/10.1289/EHP7495. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB3srlvVOluw%253D%253D&md5= c046339d5cdd76527da85853684935b6 29. 29 Hoffman, J. S.; Shandas, V.; Pendleton, N. The Effects of Historical Housing Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas. Climate 2020 , 8 (1), 12, DOI: 10.3390/cli8010012 [Crossref], Google Scholar There is no corresponding record for this reference. 30. 30 Locke, D. H.; Hall, B.; Grove, J. M.; Pickett, S. T. A.; Ogden, L. A.; Aoki, C.; Boone, C. G.; O'Neil-Dunne, J. P. M. Residential Housing Segregation and Urban Tree Canopy in 37 US Cities. Npj Urban Sustain. 2021, 1 (1), 1- 9, DOI: 10.1038/s42949-021-00022-0 [Crossref], Google Scholar There is no corresponding record for this reference. 31. 31 Namin, S.; Xu, W.; Zhou, Y.; Beyer, K. The Legacy of the Home Owners' Loan Corporation and the Political Ecology of Urban Trees and Air Pollution in the United States. Soc. Sci. Med. 2020, 246, 112758, DOI: 10.1016/j.socscimed.2019.112758 [Crossref], [PubMed], [CAS], Google Scholar 31 The legacy of the Home Owners' Loan Corporation and the political ecology of urban trees and air pollution in the United States Namin S; Xu W; Zhou Y; Beyer K Social science & medicine (1982) (2020), 246 (), 112758 ISSN: . This study examines the persistent impacts of historical racebased discriminatory housing policies on contemporary urban environments in the United States. Specifically, we examine the relationships between Home Owners' Loan Corporation (HOLC) grades assigned to neighborhoods in the 1930s and the current distribution of tree canopy and level of exposure to air pollution hazards. Our results indicate a clear gradient in tree canopy by HOLC grade, with better neighborhood grades associated with significantly higher percentage of tree canopy coverage. The pattern also exists for airborne carcinogens and respiratory hazards, with worse neighborhood grades associated with significantly higher hazards exposure. Our findings indicate that early 20th century discriminatory housing policies exert a contemporary influence on patterns of green space exposure in American cities, with implications for health and health inequities. Our findings suggest that, in order to achieve equitable access to the benefits of urban greenspace, we must acknowledge these historical influences and consider policies and practices that directly counter these influences, for example, through targeted greenspace development in areas historically identified as unfit for investment. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB3MbkslyntA%253D%253D&md5= 48724c8116eabc452a26b316cc488a49 32. 32 Wilson, B. Urban Heat Management and the Legacy of Redlining. J. Am. Plann. Assoc. 2020, 86 (4), 443- 457, DOI: 10.1080/ 01944363.2020.1759127 [Crossref], Google Scholar There is no corresponding record for this reference. 33. 33 Saverino, K. C.; Routman, E.; Lookingbill, T. R.; Eanes, A. M.; Hoffman, J. S.; Bao, R. Thermal Inequity in Richmond, VA: The Effect of an Unjust Evolution of the Urban Landscape on Urban Heat Islands. Sustainability 2021, 13 (3), 1511, DOI: 10.3390/su13031511 [Crossref], Google Scholar There is no corresponding record for this reference. 34. 34 Nardone, A.; Casey, J. A.; Morello-Frosch, R.; Mujahid, M.; Balmes, J. R.; Thakur, N. Associations between Historical Residential Redlining and Current Age-Adjusted Rates of Emergency Department Visits Due to Asthma across Eight Cities in California: An Ecological Study. Lancet Planet. Health 2020, 4 (1), e24- e31, DOI: 10.1016/S2542-5196(19)30241-4 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 35. 35 Collin, L. J.; Gaglioti, A. H.; Beyer, K. M.; Zhou, Y.; Moore, M. A.; Nash, R.; Switchenko, J. M.; Miller-Kleinhenz, J. M.; Ward, K. C.; McCullough, L. E. Neighborhood-Level Redlining and Lending Bias Are Associated with Breast Cancer Mortality in a Large and Diverse Metropolitan Area. Cancer Epidemiol. Prev. Biomark. 2021, 30 (1), 53- 60, DOI: 10.1158 /1055-9965.EPI-20-1038 [Crossref], [PubMed], Google Scholar There is no corresponding record for this reference. 36. 36 Krieger, N.; Wright, E.; Chen, J. T.; Waterman, P. D.; Huntley, E. R.; Arcaya, M. Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015. Am. J. Epidemiol. 2020, 189 (10), 1065- 1075, DOI: 10.1093/aje/kwaa045 [Crossref], [PubMed], [CAS], Google Scholar 36 Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015 Krieger Nancy; Wright Emily; Chen Jarvis T; Waterman Pamela D; Huntley Eric R; Arcaya Mariana American journal of epidemiology (2020), 189 (10), 1065-1075 ISSN:. In the 1930s, maps created by the federal Home Owners' Loan Corporation (HOLC) nationalized residential racial segregation via "redlining," whereby HOLC designated and colored in red areas they deemed to be unsuitable for mortgage lending on account of their Black, foreign-born, or low-income residents. We used the recently digitized HOLC redlining maps for 28 municipalities in Massachusetts to analyze Massachusetts Cancer Registry data for late stage at diagnosis for cervical, breast, lung, and colorectal cancer (2001-2015). Multivariable analyses indicated that, net of age, sex/gender, and race/ethnicity, residing in a previously HOLC-redlined area imposed an elevated risk for late stage at diagnosis, even for residents of census tracts with present-day economic and racial privilege, whereas the best historical HOLC grade was not protective for residents of census tracts without such current privilege. For example, a substantially elevated risk of late stage at diagnosis occurred among men with lung cancer residing in currently privileged areas that had been redlined (risk ratio = 1.17, 95% confidence interval: 1.06, 1.29), whereas such risk was attenuated among men residing in census tracts lacking such current privilege (risk ratio = 1.01, 95% confidence interval: 0.94, 1.08). Research on historical redlining as a structural driver of health inequities is warranted. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB383otFCltA%253D%253D&md5= d063bd031ced96f47759500c421769c3 37. 37 Nardone, A. L.; Casey, J. A.; Rudolph, K. E.; Karasek, D.; Mujahid, M.; Morello-Frosch, R. Associations between Historical Redlining and Birth Outcomes from 2006 through 2015 in California. PLoS One 2020, 15 (8), e0237241 DOI: 10.1371/journal.pone.0237241 [Crossref], [PubMed], [CAS], Google Scholar 37 Associations between historical redlining and birth outcomes from 2006 through 2015 in California Nardone, Anthony L.; Casey, Joan A.; Rudolph, Kara E.; Karasek, Deborah; Mujahid, Mahasin; Morello-Frosch, Rachel PLoS One (2020), 15 (8), e0237241CODEN: POLNCL; ISSN: 1932-6203. (Public Library of Science) Background: Despite being one of the wealthiest nations, disparities in adverse birth outcomes persist across racial and ethnic lines in the United States. We studied the assocn. between historical redlining and preterm birth, low birth wt. (LBW), small-for-gestational age (SGA), and perinatal mortality over a ten-year period (2006-2015) in Los Angeles, Oakland, and San Francisco, California. Methods: We used birth outcomes data from the California Office of Statewide Health Planning and Development between Jan. 1, 2006 and Dec. 31, 2015. Home Owners' Loan Corporation (HOLC) Security Maps developed in the 1930s assigned neighborhoods one of four grades that pertained to perceived investment risk of borrowers from that neighborhood: green (grade A) were considered "Best", blue (grade B) "Still Desirable", yellow (grade C) "Definitely Declining", and red (grade D, hence the term "redlining") "Hazardous". Geocoded residential addresses at the time of birth were superimposed on HOLC Security Maps to assign each birth a HOLC grade. We adjusted for potential confounders present at the time of Security Map creation by assigning HOLC polygons areal-weighted 1940s Census measures. We then employed propensity score matching methods to est. the assocn. of historical HOLC grades on current birth outcomes. Because tracts graded A had almost no propensity of receiving grade C or D and because grade B tracts had low propensity of receiving grade D, we examd. birth outcomes in the three following comparisons: B vs. B, and D vs. Results: The prevalence of preterm birth, SGA and mortality tended to be higher in worse HOLC grades, while the prevalence of LBW varied across grades. Overall odds of mortality and preterm birth increased as HOLC grade worsened. Propensity score matching balanced 1940s census measures across contrasting groups. Logistic regression models revealed significantly elevated odds of preterm birth (odds ratio (OR): 1.02, 95% confidence interval (CI): 1.00-1.05), and SGA (OR: 1.03, 95% CI: 1.00-1.05) in the C vs. B comparison and significantly reduced odds of preterm birth (OR: 0.93, 95% CI: 0.91-0.95), LBW (OR: 0.94-95% CI: 0.92-0.97), and SGA (OR: 0.94, 95% CI: 0.92-0.96) in the D vs. C comparison. Results differed by metropolitan area and maternal race. Conclusion: Similar to prior studies on redlining, we found that worsening HOLC grade was assocd. with adverse birth outcomes, although this relationship was less clear after propensity score matching and stratifying by metropolitan area. Higher odds of preterm birth and SGA in grade C vs. grade B neighborhoods may be caused by higher-stress environments, racial segregation, and lack of access to resources, while lower odds of preterm birth, SGA, and LBW in grade D vs. grade C neighborhoods may due to population shifts in those neighborhoods related to gentrification. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsF2ksLrK& md5=7bc4eef1097590c36436eee600383ce5 38. 38 Krieger, N.; Van Wye, G.; Huynh, M.; Waterman, P. D.; Maduro, G.; Li, W.; Gwynn, R. C.; Barbot, O.; Bassett, M. T. Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017. Am. J. Public Health 2020, 110 (7), 1046- 1053, DOI: 10.2105/AJPH.2020.305656 [Crossref], [PubMed], [CAS], Google Scholar 38 Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017 Krieger Nancy; Van Wye Gretchen; Huynh Mary; Waterman Pamela D; Maduro Gil; Li Wenhui; Gwynn R Charon; Barbot Oxiris; Bassett Mary T American journal of public health (2020), 110 (7), 1046-1053 ISSN:. Objectives. To assess if historical redlining, the US government's 1930s racially discriminatory grading of neighborhoods' mortgage credit-worthiness, implemented via the federally sponsored Home Owners' Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation).Methods. We analyzed 2013-2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics.Results. The proportion of preterm births ranged from 5.0% in grade A ("best"-green) to 7.3% in grade D ("hazardous"-red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation.Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth.Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining's impacts on present-day residential segregation and health outcomes. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB38vpvVGisw%253D%253D&md5= 7dd6fceeed1f38ede96ec1ea9ad940b7 39. 39 Nardone, A.; Chiang, J.; Corburn, J. Historic Redlining and Urban Health Today in U.S. Cities. Environ. Justice 2020, 13 (4), 109- 119, DOI: 10.1089/env.2020.0011 [Crossref], Google Scholar There is no corresponding record for this reference. 40. 40 Nicholas Hewitt, C. Spatial Variations in Nitrogen Dioxide Concentrations in an Urban Area. Atmospheric Environ. Part B Urban Atmosphere 1991, 25 (3), 429- 434, DOI: 10.1016/ 0957-1272(91)90014-6 [Crossref], Google Scholar There is no corresponding record for this reference. 41. 41 Mead, M. I.; Popoola, O. A. M.; Stewart, G. B.; Landshoff, P. ; Calleja, M.; Hayes, M.; Baldovi, J. J.; McLeod, M. W.; Hodgson, T. F.; Dicks, J.; Lewis, A.; Cohen, J.; Baron, R.; Saffell, J. R.; Jones, R. L. The Use of Electrochemical Sensors for Monitoring Urban Air Quality in Low-Cost, High-Density Networks. Atmos. Environ. 2013, 70, 186- 203, DOI: 10.1016/j.atmosenv.2012.11.060 [Crossref], [CAS], Google Scholar 41 Use of electrochemical sensors for monitoring urban air quality in low-cost, high-density network Mead, M. I.; Popoola, O. A. M.; Stewart, G. B.; Landshoff, P.; Calleja, M.; Hayes, M.; Baldovi, J. J.; McLeod, M. W.; Hodgson, T. F.; Dicks, J.; Lewis, A.; Cohen, J.; Baron, R.; Saffell, J. R.; Jones, R. L. Atmospheric Environment (2013), 70 (), 186-203CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) Measurements at appropriate spatial and temporal scales are essential for understanding and monitoring spatially heterogeneous environments with complex and highly variable emission sources, such as in urban areas. However, the costs and complexity of conventional air quality measurement methods means that measurement networks are generally extremely sparse. In this paper we show that miniature, low-cost electrochem. gas sensors, traditionally used for sensing at parts-per-million (ppm) mixing ratios can, when suitably configured and operated, be used for parts-per-billion (ppb) level studies for gases relevant to urban air quality. Sensor nodes, in this case consisting of multiple individual electrochem. sensors, can be low-cost and highly portable, thus allowing the deployment of scalable high-d. air quality sensor networks at fine spatial and temporal scales, and in both static and mobile configurations. In this paper we provide evidence for the performance of electrochem. sensors at the parts-per-billion level, and then outline results obtained from deployments of networks of sensor nodes in both an autonomous, high-d., static network in the wider Cambridge (UK) area, and as mobile networks for quantification of personal exposure. Examples are presented of measurements obtained with both highly portable devices held by pedestrians and cyclists, and static devices attached to street furniture. The widely varying mixing ratios reported by this study confirm that the urban environment cannot be fully characterised using sparse, static networks, and that measurement networks with higher resoln. (both spatially and temporally) are required to quantify air quality at the scales which are present in the urban environment. We conclude that the instruments described here, and the low-cost/high-d. measurement philosophy which underpins it, have the potential to provide a far more complete assessment of the high-granularity air quality structure generally obsd. in the urban environment, and could ultimately be used for quantification of human exposure as well as for monitoring and legislative purposes. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC3sXjslKmurg%253D&md5= fac1c1d89e2f94af9117fc584a522ae8 42. 42 Apte, J. S.; Messier, K. P.; Gani, S.; Brauer, M.; Kirchstetter, T. W.; Lunden, M. M.; Marshall, J. D.; Portier, C. J.; Vermeulen, R. C. H.; Hamburg, S. P. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ. Sci. Technol. 2017, 51 (12), 6999- 7008, DOI: 10.1021/acs.est.7b00891 [ACS Full Text ACS Full Text], [CAS], Google Scholar 42 High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data Apte, Joshua S.; Messier, Kyle P.; Gani, Shahzad; Brauer, Michael; Kirchstetter, Thomas W.; Lunden, Melissa M.; Marshall, Julian D.; Portier, Christopher J.; Vermeulen, Roel C. H.; Hamburg, Steven P. Environmental Science & Technology (2017), 51 (12), 6999-7008 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concns. vary sharply over short distances (<< 1 km) owing to unevenly distributed emission sources, diln., and physicochem. transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resoln. needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, USA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2 and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8x within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC2sXptVCku78%253D&md5= 3a2b5c8e04b3be0065b1e4524adae9ee 43. 43 Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data. Environ. Sci. Technol. 2010, 44 (14), 5334- 5344, DOI: 10.1021/es100008x [ACS Full Text ACS Full Text], [CAS], Google Scholar 43 Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data Karner, Alex A.; Eisinger, Douglas S.; Niemeier, Deb A. Environmental Science & Technology (2010), 44 (14), 5334-5344 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concn. measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the obsd. concns. Local regression models were specified to the concn.-distance relationship and anal. of variance was used to det. the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more std. background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to under-predict (be lower than) actual background. Changes in pollutant concns. with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concn. by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter no. concns.); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concns.). >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BC3cXns1Wrtb4%253D&md5= ac6e9c38c90964168688cc4094594073 44. 44 Eeftens, M.; Tsai, M.-Y.; Ampe, C.; Anwander, B.; Beelen, R.; Bellander, T.; Cesaroni, G.; Cirach, M.; Cyrys, J.; de Hoogh, K.; De Nazelle, A.; de Vocht, F.; Declercq, C.; Dedele, A.; Eriksen, K.; Galassi, C.; Grazuleviciene, R.; Grivas, G.; Heinrich, J.; Hoffmann, B.; Iakovides, M.; Ineichen, A.; Katsouyanni, K.; Korek, M.; Kramer, U.; Kuhlbusch, T.; Lanki, T.; Madsen, C.; Meliefste, K.; Molter, A.; Mosler, G.; Nieuwenhuijsen, M.; Oldenwening, M.; Pennanen, A.; Probst-Hensch, N.; Quass, U.; Raaschou-Nielsen, O.; Ranzi, A. ; Stephanou, E.; Sugiri, D.; Udvardy, O.; Vaskovi, E.; Weinmayr, G.; Brunekreef, B.; Hoek, G. Spatial Variation of PM[2.5], PM[10], PM[2.5] Absorbance and PM[coarse] Concentrations between and within 20 European Study Areas and the Relationship with NO[2] - Results of the ESCAPE Project. Atmos. Environ. 2012, 62, 303- 317, DOI: 10.1016/ j.atmosenv.2012.08.038 [Crossref], [CAS], Google Scholar 44 Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 - Results of the ESCAPE project Eeftens, Marloes; Tsai, Ming-Yi; Ampe, Christophe; Anwander, Bernhard; Beelen, Rob; Bellander, Tom; Cesaroni, Giulia; Cirach, Marta; Cyrys, Josef; de Hoogh, Kees; De Nazelle, Audrey; de Vocht, Frank; Declercq, Christophe; Dedele, Audrius; Eriksen, Kirsten; Galassi, Claudia; Grazuleviciene, Regina; Grivas, Georgios; Heinrich, Joachim; Hoffmann, Barbara; Iakovides, Minas; Ineichen, Alex; Katsouyanni, Klea; Korek, Michal; Kraemer, Ursula; Kuhlbusch, Thomas; Lanki, Timo; Madsen, Christian; Meliefste, Kees; Moelter, Anna; Mosler, Gioia; Nieuwenhuijsen, Mark; Oldenwening, Marieke; Pennanen, Arto; Probst-Hensch, Nicole; Quass, Ulrich; Raaschou-Nielsen, Ole; Ranzi, Andrea; Stephanou, Euripides; Sugiri, Dorothee; Udvardy, Orsolya; Vaskoevi, Eva; Weinmayr, Gudrun; Brunekreef, Bert; Hoek, Gerard Atmospheric Environment (2012), 62 (), 303-317CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concns. between and within 20 study areas across Europe. We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between Oct. 2008 and Apr. 2011 using standardized methods. PMcoarse was detd. as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population d., traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a ref. site, which was operated year-round. Substantial concn. differences were obsd. between and within study areas. Concns. for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest av. PM2.5 concns. found in Turin and the highest PMcoarse in Heraklion. Street/urban background concn. ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (av. R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (av. R2 = 0.39). Despite high correlations, concn. ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06. In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concns. across Europe will contribute to a consistent assessment of health effects across Europe. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFersbjK& md5=bb9604f323ccc65d1ea6b2a897d0207d 45. 45 Thakrar, S. K.; Balasubramanian, S.; Adams, P. J.; Azevedo, I. M. L.; Muller, N. Z.; Pandis, S. N.; Polasky, S.; Pope, C. A.; Robinson, A. L.; Apte, J. S.; Tessum, C. W.; Marshall, J. D.; Hill, J. D. Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources. Environ. Sci. Technol. Lett. 2020, 7 (9), 639- 645, DOI: 10.1021/acs.estlett.0c00424 [ACS Full Text ACS Full Text], [CAS], Google Scholar 45 Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources Thakrar, Sumil K.; Balasubramanian, Srinidhi; Adams, Peter J.; Azevedo, Ines M. L.; Muller, Nicholas Z.; Pandis, Spyros N.; Polasky, Stephen; Pope, C. Arden; Robinson, Allen L.; Apte, Joshua S.; Tessum, Christopher W.; Marshall, Julian D.; Hill, Jason D. Environmental Science & Technology Letters (2020), 7 (9), 639-645CODEN: ESTLCU; ISSN:2328-8930. (American Chemical Society) Air quality in the United States has dramatically improved, yet exposure to air pollution is still assocd. with 100000-200000 deaths annually. Reducing the no. of deaths effectively, efficiently, and equitably relies on attributing them to specific emission sources, but so far, this was done for only highly aggregated groups of sources, or a select few sources of interest. Here, we est. mortality in the United States attributable to all domestic, human-caused emissions of primary PM2.5 and secondary PM2.5 precursors. We present detailed source-specific attributions in four alternate groupings relevant for identifying promising ways to reduce mortality. We find that nearly half of the deaths can be attributed to just five activities, all in different sectors. Around half of the deaths can be attributed to fossil fuel combustion, with the remainder attributable to combustion of nonfossil fuels, agricultural processes, and other noncombustion processes. Both primary and secondary PM2.5 are important, including PM2.5 from currently unregulated precursor pollutants such as ammonia. We suggest improvements in air quality can be realized by continued redns. of emissions from traditionally important sources and by novel strategies for reducing emissions from sources of emerging relative importance and research focus. Such changes can contribute to improved health outcomes and other environmental goals. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlOgt7vI& md5=867756324ad142beed3d5c2d1f45c395 46. 46 Kroll, J. H.; Seinfeld, J. H. Chemistry of Secondary Organic Aerosol: Formation and Evolution of Low-Volatility Organics in the Atmosphere. Atmos. Environ. 2008, 42 (16), 3593- 3624, DOI: 10.1016/j.atmosenv.2008.01.003 [Crossref], [CAS], Google Scholar 46 Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere Kroll, Jesse H.; Seinfeld, John H. Atmospheric Environment (2008), 42 (16), 3593-3624CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.) A review is given. Secondary org. aerosol (SOA), particulate matter composed of compds. formed from the atm. transformation of org. species, accounts for a substantial fraction of tropospheric aerosol. The formation of low-volatility (semivolatile and possibly nonvolatile) compds. that make up SOA is governed by a complex series of reactions of a large no. of org. species, so the exptl. characterization and theor. description of SOA formation presents a substantial challenge. We outline what is known about the chem. of formation and continuing transformation of low-volatility species in the atm. The primary focus is chem. processes that can change the volatility of org. compds.: (1) oxidn. reactions in the gas phase, (2) reactions in the particle phase, and (3) continuing chem. (in either phase) over several generations. Gas-phase oxidn. reactions can reduce volatility by the addn. of polar functional groups or increase it by the cleavage of carbon-carbon bonds; key branch points that control volatility are the initial attack of the oxidant, reactions of alkylperoxy (RO2) radicals, and reactions of alkoxy (RO) radicals. Reactions in the particle phase include oxidn. reactions as well as accretion reactions, non-oxidative processes leading to the formation of high-mol.-wt. species. Org. C in the atm. is continually subject to reactions in the gas and particle phases throughout its atm. lifetime (until lost by phys. deposition or oxidized to CO or CO2), implying continual changes in volatility over the timescales of several days. The volatility changes arising from these chem. reactions must be parameterized and included in models in order to gain a quant. and predictive understanding of SOA formation. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A528%3ADC%252BD1cXls1Kksbs%253D&md5= bcb3d4d2605d9a029941fe4d1d5d5ee9 47. 47 Gentner, D. R.; Jathar, S. H.; Gordon, T. D.; Bahreini, R.; Day, D. A.; El Haddad, I.; Hayes, P. L.; Pieber, S. M.; Platt, S. M.; de Gouw, J.; Goldstein, A. H.; Harley, R. A.; Jimenez, J. L.; Prevot, A. S. H.; Robinson, A. L. Review of Urban Secondary Organic Aerosol Formation from Gasoline and Diesel Motor Vehicle Emissions. Environ. Sci. Technol. 2017, 51 (3), 1074- 1093, DOI: 10.1021/acs.est.6b04509 [ACS Full Text ACS Full Text], [CAS], Google Scholar 47 Review of Urban Secondary Organic Aerosol Formation from Gasoline and Diesel Motor Vehicle Emissions Gentner, Drew R.; Jathar, Shantanu H.; Gordon, Timothy D.; Bahreini, Roya; Day, Douglas A.; El Haddad, Imad; Hayes, Patrick L.; Pieber, Simone M.; Platt, Stephen M.; de Gouw, Joost; Goldstein, Allen H.; Harley, Robert A.; Jimenez, Jose L.; Prevot, Andre S. H.; Robinson, Allen L. Environmental Science & Technology (2017), 51 (3), 1074-1093 CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) A review summarizing evidence, research needs, and discrepancies between top-down and bottom-up approaches to est. secondary org. aerosols (SOA) formed from gasoline- and diesel-fueled motor vehicle gas-phase org. precursor compds., focusing on inconsistencies between mol.-level understanding and regional observations, is given. Topics discussed include: gas- and particle-phase org. compds. in urban areas; concise history of knowledge on urban SOA; motor vehicle emission: diversity in vehicle classes and org. compd. emissions; motor vehicle contributions to urban SOA; synthesis of approaches: looking from top-down and bottom-up; bottom-up methods 1 and 2: understanding SOA formation potential using unburned gasoline; diesel fuel as emission surrogates and oxidn. chamber expts. with dil. vehicle emission (overview, method results, advantages, key uncertainties and standing questions); top-down methods 1, 2, and 3: chem. compn. of ambient OA; day of week analyses using intra-week variability in diesel fuel use and total OA or SOA concn. data from factor anal.; comparing OA compn. across urban areas with different relative gasoline-diesel fuel use (overview, method results, advantages, key uncertainties and standing questions); reconciling evidence across methods (synthesizing bottom-up methods 1 and 2, uncertainties and considerations across all methods); implications and challenges for the developed and developing world; future research priorities; and supporting information. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFanu7bN& md5=3f9ff9b69d0d1a17ad8ceedfad6597ea 48. 48 U.S. Census Bureau. 2010 Census Summary File 2 - United States; 2011. Google Scholar There is no corresponding record for this reference. 49. 49 Chambliss, S. E.; Pinon, C. P. R.; Messier, K. P.; LaFranchi, B.; Upperman, C. R.; Lunden, M. M.; Robinson, A. L.; Marshall, J. D.; Apte, J. S. Local- and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring. Proc. Natl. Acad. Sci. U. S. A. 2021, 118 (37), e2109249118, DOI: 10.1073/pnas.2109249118 [Crossref], [PubMed], [CAS], Google Scholar 49 Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring Chambliss, Sarah E.; Pinon, Carlos P. R.; Messier, Kyle P.; LaFranchi, Brian; Upperman, Crystal Romeo; Lunden, Melissa M.; Robinson, Allen L.; Marshall, Julian D.; Apte, Joshua S. Proceedings of the National Academy of Sciences of the United States of America (2021), 118 (37), e2109249118CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences) Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (& lt;100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resoln. observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concns. of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population av.; for White populations, av. exposures to these pollutants are 9 to 14% lower than the population av. Systematic racial/ethnic disparities are influenced by regional concn. gradients due to sharp contrasts in demog. compn. among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXitVCnu7vN& md5=8f34706fe8b71d123f922ad1fcc326c6 50. 50 Lal, R. M.; Ramaswami, A.; Russell, A. G. Assessment of the Near-Road (Monitoring) Network Including Comparison with Nearby Monitors within U.S. Cities. Environ. Res. Lett. 2020, 15 (11), 114026, DOI: 10.1088/1748-9326/ab8156 [Crossref], [CAS], Google Scholar 50 Assessment of the Near-Road (monitoring) Network including comparison with nearby monitors within U.S. cities Lal, Raj M.; Ramaswami, Anu; Russell, Armistead G. Environmental Research Letters (2020), 15 (11), 114026CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.) Emissions from on-road mobile sources have historically been an important anthropogenic contributor to ambient air pollution leading to high levels of air pollution near major roadways. EPA recently implemented the Near-Road (monitoring) Network to measure NO2 concns. by high-traffic roadways in urban centers throughout the U.S., as these locations were believed to characterize worst-case human exposures to traffic-related air pollutants. Many near-road sites also include PM2.5 and CO measurements, which along with the NO2 observations, were compared in a pairwise manner against non-near-road monitors located within the city-scale boundary. After controlling for primary emissions from the target highways, we found the PM2.5 concn. difference (i.e. near-road concn. minus non-near-road site concn.) between the near-road and non-near-road urban sites to be d = 0.42 mg m -3 (H0: mdiff = 0; Ha: mdiff > 0 (mnon-near-road > mnear-road); p = 0.051; a= 0.05, 95% CI: -0.08-0.90 mg m -3, n = 35 comparisons). NO2 and CO levels were on av. higher at the near-road sites compared to the non-near-road urban sites by 5.0 (95% CI: 3.4-6.5) ppb (n = 44 comparisons) and 9.2 x 10 -2 (95% CI: 0.04-0.14) ppm (n = 42 comparisons), resp. The av. PM2.5 difference found here is 5%, and at 14 of the 35 (~40%) urban monitor comparisons and 28 of the 72 (~39%) overall comparisons, PM2.5 is actually higher at the non-near-road site relative to its near-road pair. Cleaner vehicle fleets, formation of secondary PM from on-road emissions occurring downwind (i.e. away from the road), decreased secondary org. aerosol (SOA) formation rates in the near-road environment, the prevalence of other low-vol. vehicular and local, non-vehicular sources of emissions at the non-near-road sites (e.g. railyards, truck yards, ports, biomass-fueled heating, backyard barbecuing, and com. cooking, etc) and local meteorol. (e.g. wind speed and wind direction) explain this finding. The wintertime PM2.5 concn. difference was higher than the other seasons, likely a result of higher primary PM2.5 tailpipe emissions and lower temps. that both reduced near-road PM volatility and decreased photochem. activity resulting in lower SOA prodn. at the urban scale. Further, all near-road NO2 and CO concns. were below the annual and hourly NAAQS, while eight (most of which were in wildfire-prone locations) of the 94 PM2.5 sites used in this study were above the annual National Ambient Air Quality Stds. In addn., strong agreement with both annual av. daily traffic and fleet-equiv. AADT were found for near-road NO2 and CO concns., while weaker, but still pos. relationships were found for near-road PM2.5 levels. Lastly, same observational data was used to assess on-road mobile source emission ests. from the EPA National Emission Inventory, and anal. of the observations are in rough agreement with the current ratio of NO x to CO emissions from on-road mobile sources. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXis1ylsr7E& md5=4475ab9b2fc5ed23ff363330e0c18b35 51. 51 Gu, P.; Li, H. Z.; Ye, Q.; Robinson, E. S.; Apte, J. S.; Robinson, A. L.; Presto, A. A. Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables. Environ. Sci. Technol. 2018, 52 (20), 11545- 11554, DOI: 10.1021/ acs.est.8b03833 [ACS Full Text ACS Full Text], [CAS], Google Scholar 51 Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables Gu, Peishi; Li, Hugh Z.; Ye, Qing; Robinson, Ellis S.; Apte, Joshua S.; Robinson, Allen L.; Presto, Albert A. Environmental Science & Technology (2018), 52 (20), 11545-11554CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society) Localized primary emissions of carbonaceous aerosols are major drivers of intra-city variability of submicron particulate matter (PM1) concns. This work assessed spatial variations of PM1 compn. by mobile sampling in Pittsburgh, Pennsylvania,and performed source-apportionment anal. to attribute primary org. aerosol (OA) to traffic (HOA) and cooking OA (COA). In high source-impact areas, PM1 concns. were, on av., 2 mg/m3 (40%) higher than urban background locations. Traffic emissions were the largest source contributing to population-weighted exposure to primary PM. Vehicle-miles traveled can be used to reliably predict HOA and localized black carbon (BC) concns. in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concn., likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28 and 9% of the total population are exposed to >1 mg/m3 traffic- and cooking-related primary emissions; some populations are impacted by both sources. The source mix in many US cities is similar; hence, the authors expect similar PM spatial patterns and increased exposure in high-source areas in other cities. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhslOrurfF& md5=ff6f3968cba8f7590589c71f3b9575d2 52. 52 Ananat, E. O. The Wrong Side(s) of the Tracks: The Causal Effects of Racial Segregation on Urban Poverty and Inequality . Am. Econ. J. Appl. Econ. 2011, 3 (2), 34- 66, DOI: 10.1257 /app.3.2.34 [Crossref], Google Scholar There is no corresponding record for this reference. 53. 53 Archer, D. N. Transportation Policy and the Underdevelopment of Black Communities. Iowa Law Review 2021, 106 (2125), 21-12 Google Scholar There is no corresponding record for this reference. 54. 54 Li, M.; Yuan, F. Historical Redlining and Resident Exposure to COVID-19: A Study of New York City. Race and Social Problems 2021, 1- 16, DOI: 10.1007/s12552-021-09338-z [Crossref], [PubMed], [CAS], Google Scholar 54 Historical Redlining and Resident Exposure to COVID-19: A Study of New York City Li Min; Yuan Faxi Race and social problems (2021), (), 1-16 ISSN:1867-1748. The Coronavirus Disease 2019 (COVID-19) has been reported to disproportionately impact racial/ethnic minorities in the USA, both in terms of infections and deaths. This racial disparity in the COVID-19 outcomes may result from the segregation of minorities in neighborhoods with health-compromising conditions. We, thus, anticipate that neighborhoods would be especially vulnerable to COVID-19 if they are of present-day economic and racial disadvantage and were redlined historically. To test this expectation, we examined the change of both confirmed COVID-19 cases and deaths from April to July, 2020, in zip code tabulation areas (ZCTAs) in the New York City using multilevel regression analysis. The results indicate that ZCTAs with a higher proportion of black and Hispanic populations are associated with a higher percentage of COVID-19 infection. Historically low-graded neighborhoods show a higher risk for COVID-19 infection, even for ZCTAs with present-day economic and racial privilege. These associations change over time as the pandemic unfolds. Racial/ethnic minorities are bearing the brunt of the COVID-19 pandemic's health impact. The current evidence shows that the pre-existing social structure in the form of racial residential segregation could be partially responsible for the disparities observed, highlighting an urgent need to stress historical segregation and to build a less segregated and more equal society. >> More from SciFinder ^(r) https://chemport.cas.org/services/resolver?origin=ACS& resolution=options&coi= 1%3ACAS%3A280%3ADC%252BB2c3jt1GnsA%253D%253D&md5= ebc4bbf275741c8f94c018e2429f38ae 55. 55 Lane, H. M.; Morello-Frosch, R.; Marshall, J. D.; Apte, J. S. Historical Redlining is Associated with Present-Day Air Pollution Disparities in U.S. Cities - Extended Data Files, 2022. DOI: 10.6084/m9.figshare.19193243 [Crossref], Google Scholar There is no corresponding record for this reference. * Supporting Information Supporting Information ARTICLE SECTIONS Jump To ----------------------------------------------------------------- The Supporting Information is available free of charge at https:/ /pubs.acs.org/doi/10.1021/acs.estlett.1c01012. + Detailed description of materials and methods, supporting demographic tables, and supporting figures S1-S11 (PDF) + ez1c01012_si_001.pdf (2.24 MB) Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). 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