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Scalable multi-pollution exposure assessment using routine mobile monitoring platforms

Principal Investigator: 

University of Texas–Austin

This New Investigator Award study will measure air pollutants in intensive campaigns with Google Street View cars in Oakland and Delhi and compare exposure estimates to conventional methods.

Funded under

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Sarah E. Chambliss1, Carlos P.R. Pinon1, Kyle P. Messier2, Brian LaFranchi3, Crystal Romeo Upperman3, Melissa M. Lunden3, Allen L. Robinson4, Julian D. Marshall5, and Joshua S. Apte6,7

1Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712; 2National Toxicology Program, National Institute of Environmental Health Sciences, Durham, NC 27713; 3Aclima, Inc., 10 Lombard Street, San Francisco, California 94111; 4Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213; 5Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195; 6Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720; 7School of Public Health, University of California, Berkeley, Berkeley, CA, 94720

Background. Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. While it is known to researchers and heavily impacted communities, that people of color face a higher average burden of air pollution, it is unknown whether racial/ethnic disparities are caused by spatial heterogeneities at the level of city blocks, neighborhoods, or urban regions.

Methods. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for the outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially-variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2, 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars.

Results. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 for Hispanic and Black populations are 8%-30% higher than the population average; for white populations, average exposures to the same pollutants are 9%-14% lower than the population average. Systematic racial/ethnic disparities are strongly affected by regional differences in background concentrations due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks.

Conclusions. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale. Even for pollutants with steep localized gradients, differences in average outdoor concentrations among racial/ethnic groups are driven by between-city variability. However, localized peaks indicate opportunities to reduce extremes within groups. The methods and findings of this study can inform strategies to reduce disparities in urban air pollution exposure.