Understanding Traffic-Related Air Pollution Exposures through Mobile Monitoring

Abstract

Human exposures to air pollution can vary sharply in space and time. Carefully designed mobile sampling campaigns are now able to reveal patterns of long-term ambient air pollution concentrations at fine scales (<< 100 m). Here, we report on a campaign where two specially equipped Google Street View cars mapped spatial patterns of air quality in the San Francisco Bay Area between May 2015 and December 2017. Cars were outfitted with reference-grade instruments to measure NO, NO2, black carbon (BC) and ultrafine particle number count at ~ 1 Hz. During a 30-month campaign, measurements occured on most weekdays during daytime hours, resulting in a large dataset: ~10M samples collected during > 4000 h, encompassing 100k km of driving. First, we sampled every road 20-50× within three neighborhoods (~30 km2) in Oakland, CA, during the first year of measurements. We found persistent fine-scale variability in pollution exists within many neighborhoods. In some neighborhoods, prominent ~50-200 m sized hotspots of elevated primary pollutants were ubiquitous, with pollutant levels varying by 5-8× within many city blocks. Next, during 1.5 years of follow-up measurements, we mapped pollutant concentrations in rural, suburban, and dense urban neighborhoods throughout the SF Bay Area. These measurements reveal how within-neighborhood pollution structure is overlaid on top of regional spatial gradients in air quality. Repeated measurements over the 2.5-year period demonstrate persistent spatial variability over time. Patterns of NO, NO2 and BC in Oakland had high correlation (r2 > 0.85) between the first and second years of measurement. Short-term measurement periods (~1-2 months) were sufficient to reproduce overall spatial patterns, albeit with ±30% bias in mean concentrations relative to annual-average conditions. This presentation summarizes how routine mobile air pollution monitoring reveals new information about spatial variability in population exposure to air pollution.