Fifty years after the passing of the Clean Air Act in the U.S., nearly half of the nation's population is estimated to live with and breathe polluted air in 2020. The recent acceleration in the number and intensity of wildfires driven by climate change and the devastating COVID-19 pandemic have made air pollution effects on public health an even more heightened concern. This Smart and Connected Communities project builds and enhances technological, economic, political, and social infrastructures in vulnerable communities to better sense and monitor air quality, prevent exposure to respiratory hazards, and raise awareness of and battle misinformation on air quality. The research aims to build smart and connected toxic-free communities, which resonates with NSF's mission 'to advance the national health, prosperity, and welfare.' The research team collaborates with local grass-root organizations, communities, research centers, and planning agencies in two communities, Roxbury, Boston, MA and St. John the Baptist Parish, LA. The new technological approaches and air quality data generated by the project along with the deep community engagement will potentially influence and enhance policies and benefit underserved communities with more equitable access to a healthy environment.

In this research project, the research team (1) develops data-driven approaches to seamlessly link human mobility (i.e., a person's footprints within and beyond the residential place) and respiratory hazards measured by air quality monitors; (2) designs real-time AI technologies to connect information and communities that support both network-based interventions; and (3) deploys the methodology in two representative local communities characterized with minority and diversity both burdened by air pollution. The research focuses on volatile organic compounds (VOCs) and particulate matter (e.g., PM2.5) for their known health impacts of causing lung cancers and cardiovascular and respiratory diseases. The key deliverables of work include: (1) a data-driven and real-time AI-based framework that links multi-model data streams (e.g., geolocations, air quality, social networks, etc.) to quantify and predict air pollution exposures associated with human footprints; (2) social network-based interventions strategies developed interactively with and tested by two communities burdened by air pollution (Roxbury, Boston, MA and St. John Parish, New Orleans, LA); and (3) real-time AI-powered mobile application (App), Toxic-Free Life, for mainstream smartphones that allows real-time estimations of air exposure and supports network-based interventions.

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.