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Inferring air pollutant emission using satellites (NC State EWC departmental presentation)
Citation:
East, J., B. Henderson, S. Napelenok, S. Koplitz, R. Pierce, A. Lenzen, R. Gilliam, G. Sarwar, D. Tong, AND F. Garcia-Menendez. Inferring air pollutant emission using satellites (NC State EWC departmental presentation). Environmental, Water Resources, and Coastal Engineering Symposium at North Carolina State University, Raleigh, NC, March 04, 2022.
Impact/Purpose:
Presentation at departmental research symposium showcasing graduate student research in the Environmental, Water Resources, and Coastal Engineering group in the Department of Civil, Construction, and Environmental Engineering at NC State University.
Description:
The long record of nitrogen dioxide (NO2) viewed from space by the Ozone Monitoring Instrument (OMI) has aided advances in the understanding of global emissions of oxides of nitrogen (NOx) through emissions inversion experiments. The more recent overlap of the data record between OMI and the TROPOspheric Monitoring Instrument (TropOMI) since the launch of TROPOMI provides the opportunity to compare emissions inferred using NO2 columns from each instrument. To compare the capabilities of OMI and TropOMI NO2 observations in NOx emissions estimations, we introduce a satellite 3D-variational data assimilation system for inverse modeling of northern hemispheric NOx emissions. The system can provide estimates of NOx emissions in China, India, Europe, US, and the Middle East based on a finite-difference mass balance inversion. We assimilate data from OMI and TropOMI separately in the Community Multiscale Air Quality (CMAQ) model for 2019 over the northern hemisphere and perform NOx emissions inversions for each assimilation. We compare NOx emissions estimated with observations from each satellite instrument and analyze model performance: (1) without any satellite derived information, (2) with assimilated NO2, and (3) with satellite derived emissions updates, for each satellite. Results show large NOx emissions biases in India and China, and smaller biases in the US and Europe compared to satellite-inferred estimates. Assimilating NO2 and using satellite-inferred emissions both improve ozone and NOx model performance.