Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5113-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-5113-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
01 Oct 2025
Data description paper |
| 01 Oct 2025
| 01 Oct 2025
Tracking county-level cooking emissions and their drivers in China from 1990 to 2021 with ensemble machine learning
Zeqi Li
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Shengyue Li
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Zhezhe Shi
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Dejia Yin
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Qingru Wu
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Fenfen Zhang
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Department of Environment, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing 314006, China
Xiao Yun
China Energy Longyuan Environmental Protection Co., Ltd., Beijing 100039, China
National Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, China
Guanghan Huang
Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, China
Yun Zhu
Guangdong Provincial Key Laboratory of Atmos. Environ. and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou 510006, China
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Jia Xing, Siwei Li, Yueqi Jiang, Shuxiao Wang, Dian Ding, Zhaoxin Dong, Yun Zhu, and Jiming Hao
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Pengfei Han, Ning Zeng, Tom Oda, Xiaohui Lin, Monica Crippa, Dabo Guan, Greet Janssens-Maenhout, Xiaolin Ma, Zhu Liu, Yuli Shan, Shu Tao, Haikun Wang, Rong Wang, Lin Wu, Xiao Yun, Qiang Zhang, Fang Zhao, and Bo Zheng
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An accurate estimation of China’s fossil-fuel CO2 emissions (FFCO2) is significant for quantification of carbon budget and emissions reductions towards the Paris Agreement goals. Here we assessed 9 global and regional inventories. Our findings highlight the significance of using locally measured coal emission factors. We call on the enhancement of physical measurements for validation and provide comprehensive information for inventory, monitoring, modeling, assimilation, and reducing emissions.
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Short summary
This study uses an ensemble machine learning model to predict long-term, high-resolution cooking activity data, establishing China’s first county-level cooking emission inventory spanning 1990–2021. It covers key pollutants such as polycyclic aromatic hydrocarbons. It reveals emissions’ long-term spatiotemporal trends and driving factors, such as population migration and economic growth, offering efficient control strategies. This dataset is crucial for air pollution and health impact studies.
This study uses an ensemble machine learning model to predict long-term, high-resolution cooking...
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