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Assimilating satellite observations of NO2 pollution in an air quality model to identify emissions biases
Citation:
East, J., B. Henderson, S. Koplitz, S. Napelenok, F. Menendez, A. Lenzen, AND B. Pierce. Assimilating satellite observations of NO2 pollution in an air quality model to identify emissions biases. 2021 EWC Graduate Research Symposium, Raleigh, NC, February 26, 2021.
Impact/Purpose:
Accurate emissions estimates are necessary to properly model atmospheric formation and transport of air pollution. Satellites provide global coverage of observations of certain important atmospheric trace gases such as NO2. This work attempts to explore chemical data assimilation of satellite observations of NO2 for the purposes of exploring possible emissions biases in the inventories of this pollutant.
Description:
Air pollutant emissions inventories are a crucial part of air quality modeling endeavors which help determine the environmental, climate, and health impacts of human activities. However, inventories constructed with conventional data sources can be time and data intensive to build, can suffer from temporal lag, and can be inaccurate for low and middle-income countries. Observations from satellite instruments provide valuable opportunities for evaluating and improving emissions inventories at a global scale in near real time. In particular, the recently launched TROPOMI instrument provides daily global coverage of NO2 observations at unprecedented resolution. Here we utilize a 3-D variational data assimilation technique to fuse TROPOMI NO2 observations with an air quality model over the northern hemisphere. We use the assimilated results to identify NOx emissions biases in air quality simulations and to update the NOx emissions inventory. The presentation will describe the method for making emissions inferences from satellite data, present results from a case study over the northern hemisphere, and share current progress and results of ongoing simulations.
