Joint Assimilation of Multi‐Source Observations for Optimized Global Anthropogenic NO _x Emission Estimates

Abstract Accurate quantification of anthropogenic emissions of NO x is crucial for improving air quality. Top‐down inversion methods using satellite observations have emerged as an effective method for evaluating the spatiotemporal evolution of global anthropogenic NO x emissions. However, the potential enhancement from incorporating high‐precision surface observations remains insufficiently quantified. This study develops a global inversion model using GEOS‐Chem and ensemble Kalman filter that integrates TROPOMI tropospheric NO 2 columns with surface NO 2 measurements from China, the U.S., and Europe to quantify the impact of multi‐source compared to satellite‐only inversion on global anthropogenic NO x emission estimates in January 2022. The results indicate that multi‐source inversion significantly reduces the posterior uncertainty in China, the U.S., and Europe. Compared to satellite‐only inversion, multi‐source inversion enables better characterization of emission spatial patterns and more accurate reconstruction of temporal variations. These findings are of significant importance for enhancing the accuracy of global anthropogenic NO x emission estimates. Conducting global‐scale emission assimilation and inversion by coupling coarse‐resolution models with surface NO 2 observations represents not only a significant advance in the field, but also demonstrates the feasibility of using surface measurements to support global emission inversion. Furthermore, the paper discusses the uncertainties and limitations inherent in using the current coarse‐resolution approach to assimilate surface NO 2 observations for constraining NO x emissions.