Prof. Kang Sun, Accurate and timely quantification ofair pollution sources by satellites скачать в хорошем качестве

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

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.