Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3623-2025
© 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/gmd-18-3623-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
Hong Liao
CORRESPONDING AUTHOR
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
Dantong Liu
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Jianbing Jin
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
Lei Chen
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
Siyuan Li
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Yangzhou Wu
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
Changhao Wu
Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China
Shitong Zhao
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Xiaotong Jiang
College of Biological and Environmental Engineering, Shandong University of Aeronautics, Binzhou, 256600, China
Ping Tian
Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, China
Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, China
Kai Bi
Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, China
Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, China
Ye Wang
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing 210044, China
Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
Delong Zhao
Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, China
Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, China
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Cited
7 citations as recorded by crossref.
- Columnar optical properties of locally emitted light-absorbing carbonaceous aerosols K. Hu et al.
- Optical properties of carbonaceous aerosols during wintertime pollution events in Beijing X. Liu et al.
- Source-Specific PM2.5 Exposure and Associated Health Risks During Beijing Winter X. Liu et al.
- Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration X. Liu et al.
- Transboundary smog dynamics: A spatiotemporal assessment of brick kiln emission impacts in Central Punjab, Amritsar, and Ferozepur A. Naz et al.
- Unveiling the drivers of PM2.5 pollution in an industrial inland city in China during heating seasons (2021–2024) by an integrated machine learning method G. Wang et al.
- Decoding ozone pollution in Beijing-Tianjin-Hebei: A machine learning approach to disentangling meteorological and anthropogenic drivers X. Liu et al.
7 citations as recorded by crossref.
- Columnar optical properties of locally emitted light-absorbing carbonaceous aerosols K. Hu et al.
- Optical properties of carbonaceous aerosols during wintertime pollution events in Beijing X. Liu et al.
- Source-Specific PM2.5 Exposure and Associated Health Risks During Beijing Winter X. Liu et al.
- Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration X. Liu et al.
- Transboundary smog dynamics: A spatiotemporal assessment of brick kiln emission impacts in Central Punjab, Amritsar, and Ferozepur A. Naz et al.
- Unveiling the drivers of PM2.5 pollution in an industrial inland city in China during heating seasons (2021–2024) by an integrated machine learning method G. Wang et al.
- Decoding ozone pollution in Beijing-Tianjin-Hebei: A machine learning approach to disentangling meteorological and anthropogenic drivers X. Liu et al.
Saved (final revised paper)
Latest update: 19 May 2026
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
This study combines machine learning with concentration-weighted trajectory analysis to quantify...