Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Abstract

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 ( ${\mathrm{NO}}_2$ ), black carbon (BC), and fine particulate matter [PM $\le 2.5\mathrm{\mu }\mathrm{m}$ in aerodynamic diameter ( ${\mathrm{PM}}_{2.5}$ )] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of ${\mathrm{NO}}_2$ and BC; and b) models predicting annual ${\mathrm{NO}}_2$ , BC and ${\mathrm{PM}}_{2.5}$ 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 $100\text{-m}$ grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for ${\mathrm{NO}}_2$ [ $\text{mean}=30$ (95% CI: 9, 50)], BC [ $\text{mean}=2$ (95% CI: 1, 2)], and ${\mathrm{PM}}_{2.5}$ , [ $\text{mean}=49$ (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 ${\mathrm{NO}}_2\text{-attributable}$ mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring $\text{mean}=64$ (95% CI: 19, 107) deaths per 100,000 people; $\text{LUR mean}=101$ (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both ${\mathrm{NO}}_2$ and ${\mathrm{PM}}_{2.5}$ , with more spatial heterogeneity at the grid-cell–level [ ${\mathrm{NO}}_2$ CBG $\text{mean}=41$ deaths per 100,000 people (95% CI: 12, 68); ${\mathrm{NO}}_2$ $\text{county mean}=38$ (95% CI: 11, 64); ${\mathrm{PM}}_{2.5}$ $\text{CBG mean}=59$ (95% CI: 40, 77); and ${\mathrm{PM}}_{2.5}$ $\text{county mean}=55$ (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