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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

From the application submitted to RFA 16-1:

Scalable Multi-pollutant Exposure Assessment using Routine Mobile Monitoring Platforms
Joshua S. Apte, Assistant Professor, University of Texas at Austin

High-resolution urban air quality data are needed for a variety of purposes, including exposure assessment for health studies, identification of emissions sources, characterization of exposure disparities, and a variety of other scientific and management applications. However, conventional measurement and modeling techniques are generally unable to routinely provide data on intraurban exposure gradients for the full suite of pollutants that may be relevant for air quality management and public health. This proposal investigates the potential of a new approach to mobile monitoring – routinely sampling pollution using reference instruments mounted on fleet vehicles – to fill some of the data gaps associated with conventional exposure
measurement techniques. The emphasis here is on developing, validating, and challenging this method with a view to scalability, i.e., to addressing persistent air quality data gaps at large scale. This proposal builds on recent research led by my research group, where we have collected an extremely large mobile monitoring dataset using Google Street View cars equipped with fast-response gas and particle analyzers to measure NO, NO2, BC, UFP, and PM2.5. In recently published work, we have demonstrated that this measurement approach can provide precise (± 10-20%) estimates of on-road concentrations at 30 meter scale. In the proposed work, we seek to:

(1) externally validate this routine mobile measurement technique by comparing Google Street View pollution observations against a dense network of fixed-site air quality monitors in Oakland, California;

(2) test the extensibility of this approach to a developing country locale, Delhi, India;

(3) evaluate, compare and contrast the information provided by this monitoring approach relative to what can be detected other conventional exposure assessment techniques, including satellite remote sensing, land-use regression, and chemical transport model simulations;

(4) probe our rich multipollutant dataset 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; and

(5) evaluate how this monitoring approach could be scaled up to address large scale data gaps in low- and high-income regions of the world.

Relative to my career development goals, this proposal will challenge me to more explicitly connect my interest in air quality measurement and data analysis to the needs of the air pollution health effects community. I will develop valuable professional and technical skills in collaboration with senior mentors with expertise in exposure assessment, statistical analysis for air pollution epidemiology, and air quality engineering.