CAREER: Engaging Communities to Bridge the Local to Regional Gap in Air Pollution Exposure Assessment

University Of Connecticut, Storrs CT

Investigators

Abstract

Over 19% of the United States population lives near major roads. This can negatively impact health and lead to lower life expectancy. This project will equip communities to obtain and understand information on local air pollution and advocate for solutions to local, near-road air quality concerns. Student involvement will lead to an increase in participation of underrepresented groups in engineering. The team will achieve these goals by combining air quality measurements, modeling, and community engagement. If successful, the results of this research will transform air pollution exposure assessment modeling and highlight the potential for productive collaborations between researchers and community members to solve air quality problems. The central research question for this project is how accounting for spatial and temporal variation in air pollutant concentrations impacts exposure estimates for socioeconomically disadvantaged populations and active individuals. The primary hypothesis is that the proposed hybrid modeling approach will yield better estimates of air pollutant concentrations than each model individually. The central education question is whether combining undergraduate service learning projects and community engagement increases retention and recruitment of underrepresented students in engineering and empowers community members around local air pollution concerns. This project pursues the following aims to address these questions: (1) Create a dataset of extensive, detailed, and accessible modeled air pollution concentrations and exposures needed for community planning, advocacy, and empowerment. (2) Partner with neighborhood associations to create a robust local dataset of measured air pollutant concentrations in Hartford, CT. (3) Engage engineering students in service learning projects using local air pollution assessment projects. The coupling of computational modeling, low-cost monitoring, community engagement, and service learning will transform the way air pollution researchers and impacted communities interact. This project moves beyond standard concentration estimates for exposure by addressing a large number of chemical species from all spatial scales. This project tests the effectiveness of high-resolution networks of low-cost (<$1000) air pollution monitors for use in model evaluation. This contribution is significant because it uses an unprecedented combination of model development, community engaged research (citizen science), and service learning and will transform how policy makers, environmental justice researchers, urban planners, and epidemiologists estimate human exposures to air pollution and develop solutions to local concerns in a project that highlights engineering as an agent for societal change. Community engagement during this project increases the impacted public's literacy in areas pertaining to environmental science and air pollution. This work positively impacts society by facilitating the development of more effective and equitable air pollution policies, providing improved estimates of concentrations and exposures for epidemiological studies, informing urban planning, educating and empowering local communities and individuals around environmental concerns, and increasing retention and recruitment of underrepresented students in engineering. 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.