Articles | Volume 26, issue 11
https://doi.org/10.5194/acp-26-7789-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-26-7789-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2026
Research article |
| 02 Jun 2026
| 02 Jun 2026
Quantifying meteorological impacts on local landfill methane emissions by using field measurements and machine learning
Donghee Kim
Department of Environmental Management, Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea
Department of Environmental Management, Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea
Dong Yeong Chang
Department of Environmental Management, Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea
Jaewon Joo
Department of Environmental Management, Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea
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Short summary
This study uses data and machine learning to better estimate methane emissions from a major landfill in South Korea. By considering local weather conditions like temperature and rain, the research improves how landfill methane is tracked over time. The results help us understand how climate affects emissions and provide tools that can be used worldwide to improve greenhouse gas monitoring and climate action planning.
This study uses data and machine learning to better estimate methane emissions from a major...
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