GeoHealth Frontlines
In our third episode, we welcome Dr. Chris Tessum from the University of Illinois Urbana-Champaign, a leader in air quality modeling at the intersection of atmospheric science, environmental justice, and public health. Tune in as he shares his journey into GeoHealth, how a graduate class project grew into InMAP, one of the most widely used reduced-complexity air quality models. Also how his work revealed racial-ethnic inequities in air pollution exposure, and where machine learning is taking the field next. Guest: Dr. Christopher Tessum is an Assistant Professor of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. His research focuses on modeling air pollution and its health impacts, quantifying inequalities and inequities in exposure, and developing tools to evaluate solutions. He has received early career awards from the U.S. EPA, NASA, and NSF, and is the recipient of the AGU GeoHealth Early Career Award. More about Dr. Tessum's group: https://tessumlab.cee.illinois.edu/ (00:00) Introduction to GeoHealth Frontlines (02:49) From mechanical engineering to environmental science: a winding path (07:47) Postdoc years and arriving at the University of Illinois (09:30) What are reduced-complexity air quality models? (10:40) The design philosophy behind InMAP (15:22) Origin story: the class project that became InMAP (20:16) How the research community uses (and sometimes misuses) InMAP (24:07) The 2019 PNAS paper on racial-ethnic inequities in air pollution exposure (28:28) Key finding: people of color exposed to ~50% more pollution than they cause (30:46) Public reactions, media coverage, and pushback to equity research (33:19) Why clean-air policies haven't closed the disparity gap (38:23) Machine learning and the next generation of air quality models (41:07) Growing the GeoHealth community and looking ahead
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