CAREER: Leveraging mobile monitoring, low-cost sensors, and Google Street View imagery to identify and modify street-level determinants of exposure to particulate air pollution

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Commuting and other time spent in transport in urban areas is responsible for a large share of exposure to air pollution. The amount of exposure is influenced not only by the amount of vehicle traffic, but also in large part by the design of streets and neighborhoods in urban areas. The goal of this project is to improve air quality model prediction to address the role that physical geography plays in exposure. This will be achieved by using a novel combination Google Street View (GSV) images, low-cost sensing, and mobile measurements. Results from these models will provide new evidence on how best to modify streets and neighborhoods to reduce exposure to air pollution. An added societal benefit of this new approach will be the ability to cost-effectively measure air pollution in urban areas. A smartphone app will be created as an outreach tool to track exposure in the Washington DC metro area as a test case. The smartphone app will be used by community members, Virginia Tech students, and other stakeholders to facilitate design solutions in partner communities to cost effectively reduce air pollution and protect human health.Land use regression (LUR) was developed to provide high spatial resolution estimates of air quality at locations without measurements. Recent advances in mobile monitoring and low-cost sensing have enabled unprecedented spatial coverage of measurements in urban areas. Comparisons of these measurements to LUR estimates suggest that LUR models do not capture all concentration gradients in urban micro-environments. A potential reason for these differences is that street-level LUR covariates are not included in traditional databases. This project will test if emerging mobile monitoring and low-cost sensing measurement techniques can be used in conjunction with street-level metrics from GSV images to identify previously overlooked determinants of exposure that may be ripe for modification. The project has three objectives to address these gaps: 1) use mobile monitoring and a low-cost sensor network of particulate air pollution to develop previously unavailable real-time LUR models; 2) develop new GSV-derived measures of street-level features in urban areas to identify street-level determinants of exposure; and 3) integrate the LUR models into a smartphone app to create a real-time exposure tool for collaborative teaching activities with high school students and community partners. This project will provide new knowledge of importance to the public and policy-makers that could be readily applied to other cities, settings, and pollutants. The web- and phone-based exposure tools have the potential to transform how air quality models are disseminated and used by the public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.