A Novel Modeling Framework to Support Community Preferences in Air Quality Decision-making

The Climate Justice & Numerical Modeling project assembles a collaborative team of researchers and nonprofit advocates for a project aimed at changing how community organizations interact with the air quality decision-making process. The project's objective is to create a chemical transport/machine learning model framework that informs and incorporates community preferences in a timely manner and with evidence from a policy-relevant chemical transport model for participation in public comment periods and other form of decision-making. We aim to ensure the relevance of air quality and climate policies to environmental justice.

The project’s core objectives include: 

• To demonstrate the capabilities of a state-of-the-science chemical transport model in replicating neighborhood-level health-relevant pollutant disparities resulting from changes in emissions. 
• To develop a machine learning model that replicates key features predicted by the chemical transport model in response to new policy interventions. 
• To plan the engineering infrastructure required for a fast and accessible community chemical transport/machine learning model accessible via a web application on a laptop.