Disparities in the Health Burden of Air Pollution on the Hyperlocal Scale: Case Study for the California Bay Area

Estimates of the health impacts associated with ambient air pollution in the United States are typically reported at the state or county level, which masks potential heterogeneity within these geographic units. The spatial distribution of air pollution health impacts at finer scales can reveal neighborhoods and population sub-groups that may be experiencing greater than average exposure and impacts and reveal inequalities. Estimating air pollution health impacts at the hyper-local scale (i.e. 100m x 100m) is now possible with concentrations derived from satellite remote sensing and mobile monitoring. In 2015, the Environmental Defense Fund and Google Earth conducted mobile monitoring of black carbon and nitrogen dioxide (NO₂) in the Bay Area using Google Street View cars outfitted with fast response air pollution monitors. Here, we estimate health impacts at 100m resolution throughout the Bay Area using satellite-derived fine particulate matter (PM_2.5) estimates, black carbon and NO₂ measurements from mobile monitoring, and NO₂ estimates from a land use regression model. We explore how estimated health impacts differ when using higher resolution versus county level baseline disease rates for asthma and mortality. Initial results find that mortality impacts at the county were not markedly affected by using county rates. But in Oakland using county average mortality rates underestimated the impact of air pollution by 25-50% in comparison to using census block group rates. Further we demonstrate that using highly resolved baseline disease rates and the mobile monitoring concentration datasets reveals almost 20-fold difference in air pollution risks that is obscured when more coarsely resolved data inputs are used. We conclude that the improved ascertainment of spatial distribution of results may be more impactful for local decision makers in understanding how air pollution affects different neighborhoods and populations as well as where to target interventions to maximize health benefits and reduce disparities.