Prof. Kang Sun, Accurate and timely quantification ofair pollution sources by satellites

Описание: How can we more precisely identify and predict the sources of atmospheric pollution? In this talk, Prof. Kang Sun from the University of Buffalo describes a new streamlined, physics‑based and AI framework that links emissions to satellite‑observed column amounts. This capability could transform early fire‑weather and air pollution prediction—shifting us from reacting to disasters to anticipating them.

Abstract:
Anthropogenic emissions have substantially altered the Earth's
atmospheric composition, leading to air pollution and climate change. A
unified framework connecting emissions with satellite-observed column
amounts is derived from the first principles. The emission information
originates from the inner product of the horizontal wind and the gradient of
column amount, which is more accurate than the horizontal flux divergence
as used in previous studies. Additionally, the topographical and chemical
effects are accounted for through fitted scale height and chemical
reactivity. This framework derives emissions of multiple key atmospheric
species observed by the new generation satellite instruments and enables
deep-learning based emission source attribution. Preliminary results from
these updates show the potential for significant improvements in fire
weather forecasting.

Bio: Kang Sun is an associate professor of Environmental Engineering at
University at Buffalo (UB). He received his B.S. in Environmental Sciences
from Peking University in Beijing, China and a Ph.D. in Environmental
Engineering from Princeton University in Princeton, NJ. He worked as a
physicist at Smithsonian Astrophysical Observatory before joining UB in
2018. His research focuses on remote sensing of the Earth's atmospheric
composition.