Bayesian Inversion of NO x Emissions Over Northern Egypt Using TROPOMI‐NO 2 Satellite Observations

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Abstract The assessment of NO x emissions plays a key role in air quality monitoring, but national emission inventories are often outdated and incomplete, leading to inaccurate emission estimates. This study presents an optimal estimation of daily NO x emissions over northern Egypt, subdivided into 12 subregions for seven main sectors of activity using TROPOMI NO 2 tropospheric column retrievals assimilated in a Bayesian atmospheric inversion framework. Performed over two contrasted climatic conditions, our Bayesian inversion system uses NO 2 concentrations simulated from the WRF‐Chem model and a modified version of the EDGAR HTAP v3 air pollutants 2000–2018 inventory as prior. New sources were added manually based on missing plumes in simulated NO 2 concentrations (from recent developments), increasing by 14% the original EDGAR national emissions. Our results show that the total inverse NOx emissions (35 ktNOx) are higher than our prior estimate (20.8 ktNOx) in January 2022. However, inverted NOx emissions from energy production are significantly lower (from 16.1 ktNOx down to 3.1 ktNOx), corresponding to the deployment of large, combined cycle gas turbines across Egypt. Our inversion results were then compared with previous emissions estimates, resulting in a close agreement for July 2022 (26 kt NOx for both estimates) and a lower estimate for January (35 and 46 kt Nox , respectively). We conclude that while our nonlinear inversion system remains uncertain at the sectoral level, it can produce reliable NO x emissions estimates while attributing major NO 2 plumes to specific industrial and urban areas across Egypt.

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