(566f) Creating Socially Minded Engineers through Student-Led Air Pollution Assessments of Local Towns

Wagstrom, K., University of Connecticut
This presentation will introduce a novel methodology to introduce chemical engineering students to service-learning through a directed student project. The overarching objective of this project is to create engineers with higher levels of community and policy-awareness while training them on many of the technical skills required in engineering practice. The specific course is an elective titled Transport and Transformation of Air Pollutants with an approximate class size of 20-25 predominantly senior-level chemical engineering students. The course is intended as either an overview of air pollution and atmospheric chemistry for non-specialist graduate and undergraduate students or an introduction to these topics for graduate students specializing in air quality. I cover the source and impacts of air pollutants, physical and chemical processes governing air pollutant concentrations, and prediction of concentrations using computational models. The major topics include: kinetics of chemistry in the atmosphere, pollutant transport, pollutant removal, atmospheric science, aerosol microphysics, and estimation methods for exposure.

For this specific project, a team of undergraduate and graduate students will carry out an air pollution assessment in a local town. This assessment includes (1) estimating pollutant emission amounts and locations, (2) selected monitoring at anticipated hotspots, (3) surveying the impacted community, and (4) compiling existing air pollution data in the area. Students are asked to identify hotspots, assess the current state of air pollution, and provide recommendations for the community moving forward. They share their findings with the partner town via a final report, a brief policy memo, and a presentation to the town (including community members).

This course aims to (1) create more community-minded professionals, (2) provide experience with open-ended problems using incomplete/flawed data, (3) teach communication skills, and (4) train students in handling uncertainty.