This study examines links between racial residential segregation and estimated ambient air toxics exposures and their associated cancer risks using modeled concentration estimates from the U.S. Environmental Protection Agency’s National Air Toxics Assessment. We combined pollutant concentration estimates with potencies to calculate 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. Estimated cancer risks associated 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 associated 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 associated with ambient air toxics are affected by segregation and that these exposures may have health significance for populations across racial lines.

Background: A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent.Objectives: We aimed to quantify the association between maternal exposure to particulate air pollution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association.Methods: Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continuous birth weight associated with PM10 and PM2.5 (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates.Results: In random-effects meta-analyses, term LBW was positively associated with a 10-μg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM2.5 (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-μg/m3 increase in PM10 exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (–8.9 g; 95% CI: –13.2, –4.6 g). Meta-regressions revealed that centers with higher median PM2.5 levels and PM2.5:PM10 ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations.Conclusion: Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment methodology that appeared to contribute to the variation in associations reported by centers.

Background: In late October 2003, a series of wildfires exposed urban populations in Southern California to elevated levels of air pollution over several weeks. Previous research suggests that short-term hospital admissions for respiratory outcomes increased specifically as a result of these fires.Objective: We assessed the impact of a wildfire event during pregnancy on birth weight among term infants.Methods: Using records for singleton term births delivered to mothers residing in California’s South Coast Air Basin (SoCAB) during 2001–2005 (n = 886,034), we compared birth weights from pregnancies that took place entirely before or after the wildfire event (n = 747,590) with those where wildfires occurred during the first (n = 60,270), second (n = 39,435), or third (n = 38,739) trimester. The trimester-specific effects of wildfire exposure were estimated using a fixed-effects regression model with several maternal characteristics included as covariates.Results: Compared with pregnancies before and after the wildfires, mean birth weight was estimated to be 7.0 g lower [95% confidence interval (CI): –11.8, –2.2] when the wildfire occurred during the third trimester, 9.7 g lower when it occurred during the second trimester (95% CI: –14.5, –4.8), and 3.3 g lower when it occurred during the first trimester (95% CI: –7.2, 0.6).Conclusions: Pregnancy during the 2003 Southern California wildfires was associated with slightly reduced average birth weight among infants exposed in utero. The extent and increasing frequency of wildfire events may have implications for infant health and development.

Objective: We examined the distribution of heat risk–related land cover (HRRLC) characteristics across racial/ethnic groups and degrees of residential segregation.Methods: Block group–level tree canopy and impervious surface estimates were derived from the 2001 National Land Cover Dataset for densely populated urban areas of the United States and Puerto Rico, and linked to demographic characteristics from the 2000 Census. Racial/ethnic groups in a given block group were considered to live in HRRLC if at least half their population experienced the absence of tree canopy and at least half of the ground was covered by impervious surface (roofs, driveways, sidewalks, roads). Residential segregation was characterized for metropolitan areas in the United States and Puerto Rico using the multigroup dissimilarity index.Results: After adjustment for ecoregion and precipitation, holding segregation level constant, non-Hispanic blacks were 52% more likely (95% CI: 37%, 69%), non-Hispanic Asians 32% more likely (95% CI: 18%, 47%), and Hispanics 21% more likely (95% CI: 8%, 35%) to live in HRRLC conditions compared with non-Hispanic whites. Within each racial/ethnic group, HRRLC conditions increased with increasing degrees of metropolitan area-level segregation. Further adjustment for home ownership and poverty did not substantially alter these results, but adjustment for population density and metropolitan area population attenuated the segregation effects, suggesting a mediating or confounding role.Conclusions: Land cover was associated with segregation within each racial/ethnic group, which may be explained partly by the concentration of racial/ethnic minorities into densely populated neighborhoods within larger, more segregated cities. In anticipation of greater frequency and duration of extreme heat events, climate change adaptation strategies, such as planting trees in urban areas, should explicitly incorporate an environmental justice framework that addresses racial/ethnic disparities in HRRLC.

Regulatory agencies, including the U.S. Environmental Protection Agency (US EPA) and state authorities like the California Air Resources Board (CARB), have sought to address the concerns of environmental justice (EJ) advocates who argue that chemical-by-chemical and source-specific assessments of potential health risks of environmental hazards do not reflect the multiple environmental and social stressors faced by vulnerable communities. We propose an Environmental Justice Screening Method (EJSM) as a relatively simple, flexible and transparent way to examine the relative rank of cumulative impacts and social vulnerability within metropolitan regions and determine environmental justice areas based on more than simply the demographics of income and race. We specifically organize 23 indicator metrics into three categories: (1) hazard proximity and land use; (2) air pollution exposure and estimated health risk; and (3) social and health vulnerability. For hazard proximity, the EJSM uses GIS analysis to create a base map by intersecting land use data with census block polygons, and calculates hazard proximity measures based on locations within various buffer distances. These proximity metrics are then summarized to the census tract level where they are combined with tract centroid-based estimates of pollution exposure and health risk and socio-economic status (SES) measures. The result is a cumulative impacts (CI) score for ranking neighborhoods within regions that can inform diverse stakeholders seeking to identify local areas that might need targeted regulatory strategies to address environmental justice concerns.