Leveraging mobile monitoring and satellite remote sensing to estimate the health burden of air pollution on the hyper-local scale: case study for the California Bay Area.
Abstract
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 in impacts at finer spatial scales. 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 (PM2.5) 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 mobile monitoring versus satellite and land use regression concentration estimates, and when using higher resolution, such as census block groups, versus county level baseline disease rates. Initial results demonstrate that estimates of air pollution-attributable disease burdens for the Bay area are similar across models using varying inputs, when aggregated to the county level. However, using highly resolved baseline disease rates and the mobile monitoring concentration datasets reveals spatial variability that is obscured when more coarsely resolved data inputs are used. We conclude that the improved characterization 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.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMGH21B1210S
- Keywords:
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- 0230 Impacts of climate change: human health;
- GEOHEALTH;
- 0240 Public health;
- GEOHEALTH;
- 0245 Vector born diseases;
- GEOHEALTH;
- 0299 General or miscellaneous;
- GEOHEALTH