High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data

TLDR: This work equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type, revealing stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources.

Abstract: Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollut