|Title||Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Southerland, VA, Anenberg, SC, Harris, M, Apte, J, Hystad, P, van Donkelaar, A, Martin, RV, Beyers, M, Roy, A|
|Journal||Environmental Health Perspectives|
Background: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.
Objectives: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.
Methods: We estimated mortality and morbidity attributable to nitrogen dioxide (NO2NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm≤2.5μm in aerodynamic diameter (PM2.5PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2NO2 and BC; and b) models predicting annual NO2NO2, BC and PM2.5PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.
Results: Estimated pollution-attributable deaths per 100,000 people at the 100-m100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2NO2 [mean=30mean=30 (95% CI: 9, 50)], BC [mean=2mean=2 (95% CI: 1, 2)], and PM2.5PM2.5, [mean=49mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributableNO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2NO2 and PM2.5PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2NO2 CBG mean=41mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2NO2 county mean=38county mean=38 (95% CI: 11, 64); PM2.5PM2.5 CBG mean=59CBG mean=59 (95% CI: 40, 77); and PM2.5PM2.5 county mean=55county mean=55 (95% CI: 37, 71)].
Discussion: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.
|Short Title||Environ Health Perspect|