Human exposures to air pollution can vary sharply in space and time. Recent advances in measurement technology enable a rich understanding of the spatiotemporal patterns of outdoor air pollution that influence population exposures and environmental inequities. For example, carefully designed mobile sampling campaigns are now able to reveal patterns of long-term ambient air pollution concentrations at very fine scales (&lt;&lt; 100 m). Here, we present an overview of the most extensive mobile air pollution measurement campaign conducted to date in a single urban area. Using two specially equipped Google Street View cars, we mapped spatial patterns of air quality in the San Francisco Bay Area between May 2015 and December 2017. The cars were outfitted with reference-grade instruments to measure NO, NO2, black carbon (BC) and ultrafine particle number count at high time resolution (~ 1 Hz). During a 32-month campaign, measurements took place on most weekdays during daytime hours, resulting in a large dataset: ~10 million samples collected during &gt; 4000 h, encompassing 120,000 km of driving. Additional sampling incorporated a dense network of low-cost samplers and a mobile aerosol mass spectrometer (AMS). The overall campaign consisted of several targeted investigations. During an initial year of measurements, we sampled every road 20-50× within three neighborhoods (~30 km2) in Oakland, CA. Next, during 1.5 years of follow-up, we mapped pollutant concentrations in rural, suburban, and dense urban neighborhoods throughout the SF Bay Area. These measurements reveal how the within-neighborhood pollution structure is overlaid on top of regional spatial gradients in air quality. Within Oakland, spatial patterns of BC determined by mobile monitoring generally agree well with the spatial patterns that are revealed with a dense fixed-site BC network. Using mobile AMS data, we illustrate how localized hotspots of NO, NO2 and BC are largely driven by traffic emissions. This presentation (i) summarizes how routine mobile air pollution monitoring can reveal new information about spatial variability in population exposure to air quality, (ii) explores the methodological advantages and tradeoffs of alternative sampling approaches, and (iii) illustrates how established measurement techniques from the atmospheric sciences can add powerful new insights to studies of human exposures in urban areas.

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

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.