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A Granular Characterization of Mobility-Related Air Pollution Exposure Disparity

Principal Investigator(s): Paolo Santi PhD, Carlo Ratti PhD, Simone Mora PhD

Project Partners: MIT Senseable City Lab, The Hong Kong Polytechnic University, New York City Department of Health and Mental Hygiene

Research Project Funding: $144,768

Project Start and End Date: Oct 1st, 2023 – September 30st, 2024

Project Description: Air pollution is disproportionately affecting racial minorities and economically-disadvantaged populations. Despite continuous improvement in ambient air quality across the United States, relative exposure disparities among different socioeconomic groups continue to persist, worsening health outcomes and the quality of life of disadvantaged groups. Previous studies have typically measured air pollution exposure based on people’s home locations without considering how individual mobility patterns might influence it. This project quantifies air pollution exposure using big mobility data on individual trips from more than 40 million mobile devices in the contiguous United States for the pre-pandemic year 2019. We combine these highly granular mobility data with national air pollution estimates, specifically PM2.5, to first calculate mobility-related exposure in major U.S. cities. In addition, we link anonymized personal mobility data with their demographics at the census tract level to characterize inequalities in particulate matter (PM2.5) exposure among different racial, ethnic, as well as other demographic groups. Methodologically, our approach explores a new paradigm to assessing short- and long-term individual-level exposure, serving as a reference for cross-sectional and cohort epidemiological studies. The study outcome reveals the spatial heterogeneity of air pollution exposure disparity and how it is linked to street design for cities in the United States. Our analysis can inform evidence-based environmental plans and public health strategies to mitigate air pollution’s disproportionate impacts on racial and economically disadvantaged communities.

US DOT Priorities: This project fits well within the US DOT strategic goals of Climate and Sustainability and Equity, aiming to quantify the spatial heterogeneity of air pollution exposure disparity and how it is linked to street design for cities in the United States. This project also contributes to CCST’s two focus areas including VMT & GHG Reduction via Modal Shift and Changes in Travel Behavior (Focus area 4) and Community-Centered Solutions to Environmental Justice (Focus Area 2).

Outputs: The products/outputs of this project are as following:

  1. Two Conference and one university lecture presentations
  2. A peer-review journal article focusing on PIs novel methodology for exposure calculation leveraging personal mobility patterns and big data
  3. A peer-review journal article focusing on the introduction of robust methodology for environmental justice, specifically, exposure disparity quantifications.
  4. Final profiles of exposure disparity in major U.S cities, revealing systemic inequalities
  5. Workshops and outreach activities for public engagement
  6. Final report to CCST and governmental agencies
  7. Interactive web visualization tool for exposure disparity mapping and screening and knowledge dissemination. To be directly used by local DOTs, environmental agencies, and public health agencies for detailed characterization of exposure disparities and evidence-based environmental justice policymaking.


Outcomes/Impacts: This study establishes a comprehensive framework for assessing air pollution exposure and its inequalities in the urban context by leveraging big mobility data and high-resolution air quality measurements. This research is the first to combine high resolution mobility and air pollution data, advancing the state-of-the-art of exposure science by providing granular exposure estimation associated with social inequality. It is instrumental for academics and practitioners working on environmental epidemiological and public health research as well as evidence-based and equitable environmental policymaking. The findings contextualize mobility pattern-aware air pollution exposure and its inequalities at a hyperlocal scale in a dense and diverse urban area, which can serve as a generalizable reference for environmentally just city planning and decision making.

EPA and local environmental protection agencies of the included major U.S. cities (10-15) can directly use the interactive mapping tool for short-term analysis of air pollution exposure and exposure disparity analysis and policymaking (within 5 years given no significant disruption in people’s mobility patterns). Long-term assessment (beyond 5 years) can be done using the same methodology by updating the air quality predictions and mobility data. The methodology is open-sourced and designed to be a plug-and-play module in Python language. The methodology can also be adopted by future research to evaluate environment justice across the globe, such as Hong Kong, with local air quality and mobility data. Given the open-sourced Python package, the implementation can be immediate. The public, especially socially disadvantaged communities, are encouraged to play with the interactive exposure disparity mapping tool for knowledge dissemination and environmental justice initiative advocation.