Articles | Volume 25, issue 9
https://doi.org/10.5194/acp-25-4965-2025
© Author(s) 2025. 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-25-4965-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Ning Zeng
Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Bo Yao
Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China
Wen Zhang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Weijun Quan
Institute of Urban Meteorology (Key Laboratory of Urban Meteorology), China Meteorological Administration, Beijing, China
Pucai Wang
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Ting Wang
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Minqiang Zhou
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Qixiang Cai
CORRESPONDING AUTHOR
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Qiluzhongke Institute of Carbon Neutrality, Jinan, China
Yuzhong Zhang
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang, China
Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
Ruosi Liang
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang, China
Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
Wanqi Sun
CORRESPONDING AUTHOR
Meteorological Observation Centre, China Meteorological Administration, Beijing, China
Shengxiang Liu
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Tourism and Geography, Jiujiang University, Jiujiang, Jiangxi, China
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Cited
6 citations as recorded by crossref.
- Sustainable Dairy Systems Through Resource Management, Climate Change Mitigation, and Smart Technological Innovation M. Abbas et al. https://doi.org/10.3390/agriculture16192105
- Assessing the impact of natural factors on CH₄ concentrations in Chinese cities from 2009 to 2023 Z. Linjing et al. https://doi.org/10.1080/01431161.2025.2526004
- Real-time atmospheric CO2 and CH4 monitoring via a novel mobile sensing system J. Chen et al. https://doi.org/10.1016/j.rineng.2025.107676
- Spatiotemporal heterogeneity relationships between atmospheric methane and various driving factors across China Y. Chen et al. https://doi.org/10.1016/j.atmosres.2026.109042
- Global daily TROPOMI XCH₄ reconstruction and methane emission hotspot identification using machine learning Q. Xiao et al. https://doi.org/10.1080/17538947.2026.2677964
- High-resolution inversion of urban methane emissions in the Chengdu–Chongqing economic circle using ground-based observations and a dynamic error Bayesian framework Z. Xia et al. https://doi.org/10.1016/j.jclepro.2026.148229
6 citations as recorded by crossref.
- Sustainable Dairy Systems Through Resource Management, Climate Change Mitigation, and Smart Technological Innovation M. Abbas et al. https://doi.org/10.3390/agriculture16192105
- Assessing the impact of natural factors on CH₄ concentrations in Chinese cities from 2009 to 2023 Z. Linjing et al. https://doi.org/10.1080/01431161.2025.2526004
- Real-time atmospheric CO2 and CH4 monitoring via a novel mobile sensing system J. Chen et al. https://doi.org/10.1016/j.rineng.2025.107676
- Spatiotemporal heterogeneity relationships between atmospheric methane and various driving factors across China Y. Chen et al. https://doi.org/10.1016/j.atmosres.2026.109042
- Global daily TROPOMI XCH₄ reconstruction and methane emission hotspot identification using machine learning Q. Xiao et al. https://doi.org/10.1080/17538947.2026.2677964
- High-resolution inversion of urban methane emissions in the Chengdu–Chongqing economic circle using ground-based observations and a dynamic error Bayesian framework Z. Xia et al. https://doi.org/10.1016/j.jclepro.2026.148229
Saved (final revised paper)
Latest update: 08 Oct 2026
Short summary
Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4 emissions, yet large observation gaps exist. Here we compiled a comprehensive dataset, which is publicly available, that includes ground-based, satellite-based, inventory, and modeling results to show the CH4 concentrations, enhancements, and spatial–temporal variations. The data can benefit the research community and policy-makers for future observations, atmospheric inversions, and policy-making.
Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4...
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