Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
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Articles | Volume 25, issue 24
Articles | Volume 25, issue 24
https://doi.org/10.5194/acp-25-18509-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-25-18509-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 24
Research article
 | Highlight paper
 | 
19 Dec 2025
Research article | Highlight paper |  | 19 Dec 2025

Constraining urban fossil fuel CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling

Sojung Sim and Sujong Jeong

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Cited articles

Ahn, D. Y., Goldberg, D. L., Coombes, T., Kleiman, G., and Anenberg, S. C.: CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets, Environ. Res. Lett., 18, https://doi.org/10.1088/1748-9326/acbb91, 2023. 
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Executive editor
This study presents a new inverse modeling framework that assimilates information from both ground-based and satellite CO2 observations to constrain urban carbon emissions over Seoul. While multiple studies have looked at using either ground-based or satellite observations to constrain urban emissions, this approach aims to optimally combine the information from the two within a common inverse modeling framework. This framework, if robust and scalable, can be deployed for multiple other urban areas and megacities. In that sense, this study sets an important benchmark for future studies aiming to combine ground-based and satellite data for constraining urban emissions, and highly relevant and timely for the global carbon cycle community.
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Short summary
This study develops a high-resolution inverse modeling framework that combines ground-based and satellite CO2 observations to improve urban emission estimates in Seoul. By integrating atmospheric data and transport models, the research reduces uncertainties in CO2 emissions and reveals spatial and temporal patterns. The method offers a valuable tool for supporting climate policies and can be applied to other cities for better emission verification.
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