A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
<p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) is closely linked to human health, with its sources generally divided into local emissions and regional transport. This study combined concentration-weighted trajectory (CWT) ana...
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Copernicus Publications
2025-06-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/3623/2025/gmd-18-3623-2025.pdf |
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| author | K. Hu H. Liao D. Liu J. Jin L. Chen S. Li Y. Wu C. Wu S. Zhao X. Jiang P. Tian P. Tian K. Bi K. Bi Y. Wang Y. Wang Y. Wang D. Zhao D. Zhao |
| author_facet | K. Hu H. Liao D. Liu J. Jin L. Chen S. Li Y. Wu C. Wu S. Zhao X. Jiang P. Tian P. Tian K. Bi K. Bi Y. Wang Y. Wang Y. Wang D. Zhao D. Zhao |
| author_sort | K. Hu |
| collection | DOAJ |
| description | <p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) is closely linked to human health, with its sources generally divided into local emissions and regional transport. This study combined concentration-weighted trajectory (CWT) analysis with the HYSPLIT trajectory ensemble to obtain hourly resolution pollutant source results. The Extreme Gradient Boosting (XGBoost) model was then employed to simulate local emissions and ambient PM<span class="inline-formula"><sub>2.5</sub></span> in Beijing from 2013 to 2020. The results revealed that clean air masses influencing the Beijing area mainly originated from the north and east regions, exhibiting a strong winter and weak summer pattern. Following the implementation of the Air Pollution Prevention and Control Action Plan (Action Plan) by the Chinese government in 2017, pollution in Beijing decreased significantly, with the most substantial reduction in regional transport pollution events occurring in the west region during summer. Regional transport pollution events were most frequent in spring, up to 1.8 times higher than in winter. Pollutants mainly originated from the west and south regions, while polluted air masses from the east showed the least reduction, and the proportion of pollution sources from this region was gradually increasing. The COVID-19 restrictions might have reduced PM<span class="inline-formula"><sub>2.5</sub></span> concentrations in 2020. From 2013 to 2020, local emissions were the main contributors to pollution events in Beijing. The Action Plan has more effectively reduced pollution caused by regional transport, particularly during autumn and winter. This finding underscores the importance of Beijing prioritizing local emission reduction while also considering potential<span id="page3624"/> contributions from the east region to effectively mitigate pollution events.</p> |
| format | Article |
| id | doaj-art-ff51f617308042b480d951052bf80dee |
| institution | Kabale University |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-ff51f617308042b480d951052bf80dee2025-08-20T03:30:43ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-06-01183623363410.5194/gmd-18-3623-2025A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysisK. Hu0H. Liao1D. Liu2J. Jin3L. Chen4S. Li5Y. Wu6C. Wu7S. Zhao8X. Jiang9P. Tian10P. Tian11K. Bi12K. Bi13Y. Wang14Y. Wang15Y. Wang16D. Zhao17D. Zhao18Jiangsu 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, ChinaJiangsu 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, ChinaDepartment of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaJiangsu 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, ChinaJiangsu 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, ChinaDepartment of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaGuangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, ChinaInstitute of International Rivers and Eco-security, Yunnan University, Kunming 650091, ChinaDepartment of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Biological and Environmental Engineering, Shandong University of Aeronautics, Binzhou, 256600, ChinaBeijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, ChinaField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, ChinaBeijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, ChinaField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, ChinaKey Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJoint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaBeijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, ChinaField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 100089, China<p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) is closely linked to human health, with its sources generally divided into local emissions and regional transport. This study combined concentration-weighted trajectory (CWT) analysis with the HYSPLIT trajectory ensemble to obtain hourly resolution pollutant source results. The Extreme Gradient Boosting (XGBoost) model was then employed to simulate local emissions and ambient PM<span class="inline-formula"><sub>2.5</sub></span> in Beijing from 2013 to 2020. The results revealed that clean air masses influencing the Beijing area mainly originated from the north and east regions, exhibiting a strong winter and weak summer pattern. Following the implementation of the Air Pollution Prevention and Control Action Plan (Action Plan) by the Chinese government in 2017, pollution in Beijing decreased significantly, with the most substantial reduction in regional transport pollution events occurring in the west region during summer. Regional transport pollution events were most frequent in spring, up to 1.8 times higher than in winter. Pollutants mainly originated from the west and south regions, while polluted air masses from the east showed the least reduction, and the proportion of pollution sources from this region was gradually increasing. The COVID-19 restrictions might have reduced PM<span class="inline-formula"><sub>2.5</sub></span> concentrations in 2020. From 2013 to 2020, local emissions were the main contributors to pollution events in Beijing. The Action Plan has more effectively reduced pollution caused by regional transport, particularly during autumn and winter. This finding underscores the importance of Beijing prioritizing local emission reduction while also considering potential<span id="page3624"/> contributions from the east region to effectively mitigate pollution events.</p>https://gmd.copernicus.org/articles/18/3623/2025/gmd-18-3623-2025.pdf |
| spellingShingle | K. Hu H. Liao D. Liu J. Jin L. Chen S. Li Y. Wu C. Wu S. Zhao X. Jiang P. Tian P. Tian K. Bi K. Bi Y. Wang Y. Wang Y. Wang D. Zhao D. Zhao A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis Geoscientific Model Development |
| title | A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis |
| title_full | A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis |
| title_fullStr | A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis |
| title_full_unstemmed | A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis |
| title_short | A novel method for quantifying the contribution of regional transport to PM<sub>2.5</sub> in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis |
| title_sort | novel method for quantifying the contribution of regional transport to pm sub 2 5 sub in beijing 2013 2020 combining machine learning with concentration weighted trajectory analysis |
| url | https://gmd.copernicus.org/articles/18/3623/2025/gmd-18-3623-2025.pdf |
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