Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California
WashU affiliated authors: Aaron van Donkelaar, Randall V. Martin (Dept. of Energy, Environmental and Chemical Engineering)
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 (NO2), black carbon (BC), and fine particulate matter [PM ≤2:5 lm in aerodynamic diameter (PM2:5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution- attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2:5 con- centrations 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-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean = 30 (95% CI: 9, 50)], BC [mean = 2 (95% CI: 1, 2)], and PM2:5 , [mean = 49 (95% CI: 33, 64)]. Applying concentrations from mo- bile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR 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 NO2 and PM2:5, with more spatial heterogeneity at the grid-cell–level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean = 38 (95% CI: 11, 64); PM2:5 CBG mean = 59 (95% CI: 40, 77); and PM2:5 county mean = 55 (95% CI: 37, 71)].