Air Quality Mapping

High-resolution air pollution mapping with Google Street View cars:
exploiting big data

Multi-Institution Collaboration
We conducted this study in collaboration with several organizations including academic, governmental, non-profit, local community groups, and private sector. The scientific team included researchers from UT-Austin, Utrecht University, University of British Columbia, University of Washington, Lawrence Berkeley National Laboratory, Environmental Defense Fund, and Aclima, Inc and Google. Funding for the research was provided by the Environmental Defense Fund. Google supported the operation of their Google Street View cars, which Aclima equipped with their Ei sensing platform. The local community group,  West Oakland Environmental Indicators Project, helped interpret and disseminate the results to the community. Finally, the Bay Area Air Quality Management district provided us with access to their ambient monitoring datasets.

EDF Website: 

The Environmental Defense Fund has prepared an interactive website that allows users to explore all aspects related to the project: Air Quality Maps Main Page & Methods.


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, USA, developing the largest urban air quality dataset 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.