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
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
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
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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Total article views: 3,281 (including HTML, PDF, and XML)
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Total article views: 2,487 (including HTML, PDF, and XML)
Thereof 2,487 with geography defined
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Total article views: 794 (including HTML, PDF, and XML)
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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...