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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

Abstract for the 2018 HEI Annual Conference

Scalable Multi-Pollutant Exposure Assessment Using Mobile Monitoring Platforms

Joshua S. Apte1; Adam A. Szpiro2; Michael Brauer3

1 University of Texas at Austin, Austin, TX, USA; 2 University of Washington, Seattle, WA, USA; 3 University of British Columbia, Vancouver, Canada

Background. Urban air pollution concentrations can vary sharply over short distances. High spatial resolution surfaces of urban air quality data are needed for a variety of purposes, including exposure assessment for health studies, identification of emissions sources, and characterization of exposure. However, conventional techniques are generally unable to routinely provide data on intraurban exposure gradients.

Objectives. This project investigates the potential for mobile monitoring using fleet vehicles to fill current gaps in fine-scale air pollution exposure assessment. The project builds on recent work to develop very large (multi-year, > 107 observations) mobile monitoring datasets using Google Street View cars equipped with fast-response gas and particle analyzers. The emphasis here is on developing, validating, and challenging this method with a view to scalability: to addressing persistent air quality data gaps at large scale.

Study Design. The following investigations are planned for this three-year study. First, we will externally validate the mobile measurement technique by comparing Google Street View pollution observations against a dense network of 100 fixed-site air pollution monitors in Oakland, California. Second, we will collect a new set of mobile measurements in New Delhi, India, to test the extensibility of this mobile measurement technique to developing-world settings. Third, we will compare and contrast the information provided by the mobile monitoring approach relative to what can be detected by other conventional exposure assessment techniques, including satellite remote sensing, land-use regression, and chemical transport model simulations. Fourth, we will probe mobile measurements with data mining techniques to understand how sources influence population exposures, and compare these understandings with what can be learned by high-resolution receptor modeling. Finally, we plan to evaluate how this monitoring approach could be scaled up to address large scale data gaps in low- and high-income regions of the world.


Poster by Apte et al, 2018 HEI Annual Conference