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

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 pollution patterns with surprisingly sharp
small-scale variability attributable to local sources, up to 5–8×
within individual city blocks. Since local variation in air quality
profoundly impacts public health and environmental equity, our results
have important implications for how air pollution is measured and
managed. If validated elsewhere, this readily scalable measurement
approach could address major air quality data gaps worldwide.