Machine Learning and the Hydroxyl Radical for Air Quality and Climate: Qindan Zhu (MIT)
Paul G. Allen School Paul G. Allen School
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 Published On Feb 8, 2024

Allen School Colloquia Series
Title: The Radical Solution: Machine Learning and the Hydroxyl Radical for Air Quality and Climate
Speaker: Qindan Zhu (MIT)

Abstract: The hydroxyl radical (OH) lies at the nexus of climate and air quality as the primary oxidant for both reactive greenhouse gases and many hazardous air pollutants. Lacking direct observations, interannual trends of OH either in urban areas or at continental-to-global scale are not well understood due to the short lifetime and high spatial heterogeneity of OH. This talk will describe two examples from my research predicting OH using a synthesis of chemistry/climate model, satellite observations and machine learning (ML). At the urban scale, I will present OH predictions for 49 North American cities between 2005 and 2014 and show how these OH predictions can be used to inform the best control strategy of ozone pollution in each city. I will also present the ML predicted OH trends at continental-to-global scale. As the primary loss pathway for methane is its reaction with OH, I will utilize these OH predictions to interpret observed methane trends. Finally, I will provide an overview of OH sensitivity to factors modulated by climate and discuss how OH is expected to respond to future climate change.

Bio: Qindan is a NOAA Climate & Global Change Postdoc Fellow working with Arlene Fiore at MIT, focusing on fully coupled chemistry-climate models and chemistry-climate interaction. She earned her bachelor's degree with dual majors in environmental science and mathematics from Peking University in 2017 and completed her PhD in Earth and Planetary Science at the University of California, Berkeley, under the guidance of Ron Cohen. Her research encompasses satellite observations of air pollutants, airborne flux measurements, weather sensitive emissions and hydroxyl radical chemistry. A special emphasis in her work is the synthesis of machine learning, models, and observations to advance the understanding of the interplay between air quality and climate change.

This video is closed captioned.

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