Machine Learning and the Hydroxyl Radical for Air Quality and Climate

Explore the intersection of machine learning, atmospheric chemistry, and climate science in this Allen School Colloquia Series lecture. Delve into the crucial role of the hydroxyl radical (OH) in air quality and climate change, and learn how machine learning techniques are being applied to predict OH trends in urban areas and on a global scale. Discover how these predictions can inform ozone pollution control strategies and help interpret observed methane trends. Gain insights into the sensitivity of OH to climate factors and its expected response to future climate change. Presented by Qindan Zhu, a NOAA Climate & Global Change Postdoc Fellow at MIT, this talk synthesizes chemistry/climate models, satellite observations, and machine learning to advance our understanding of the complex interplay between air quality and climate change.