Modeling Air Pollution-Related Health Benefits of Transportation Scenarios: A Collaboration Between Academic Researchers and Environmental Justice Organizations

Abstract

Transportation is a leading contributor to greenhouse gas emissions and has become a focus for climate policies. Traffic-related air pollution disproportionately affects environmental justice (EJ) communities—neighborhoods that have disproportionate exposure to environmental hazards, but health impact assessments rarely center EJ issues or prioritize the concerns of EJ communities. One explanation for the lack of focus on EJ communities is that both policymakers and academia have often failed to engage these communities. In this paper, academic researchers collaborate with seven EJ organizations in the northeastern US, working with collaboration advisors and facilitators, to design and evaluate potential transportation emissions reduction scenarios using air quality and health benefits modeling tools. We model and estimate the benefits of these scenarios, while working to build collaborative relationships between academic researchers and EJ organizations. The two primary outputs from this process are: quantification of health benefits attributable to emission reduction scenarios of interest to EJ organizations, and enhanced trust and community building between academic researchers and EJ organizations, with reflections on strengths, challenges, and opportunities for future work. We find the largest improvements to health result from scenarios that reduce car and truck traffic. Dialog between academic researchers and EJ organizations reinforce the disconnect between regional-scale models and local community concerns as well as the more general gaps between statistical models and lived experience. Despite these challenges, the collaboration led to more meaningful models and valued insight for community organizations, and we recommend comparable collaborations in other settings where pollution control is being planned and evaluated in EJ communities.