Kang Sun (Associate Professor of Environmental Engineering at University at Buffalo)

Generations of satellites have revolutionized the observation of emissions from manmade and natural sources. A unified framework quantifying emission fluxes from satellite-observed column amounts is derived from mass conservation. 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 observation-based scale height and chemical reactivity. This framework derives emissions of multiple key atmospheric species observed by the new generation satellite instruments. Empowered by abundant measurement data, deep learning models disentangle spatiotemporal patterns of chemical loss from observation noise and separate weak emission signals, e.g., soil NOx, from stronger and intertwining interferences.

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 postdoc and physicist at Smithsonian Astrophysical Observatory before joining UB in 2018. His research focuses on remote sensing of the Earth's atmospheric composition.