Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 12
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.
https://doi.org/10.5194/gmd-18-3623-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 12
Development and technical paper
 | 
19 Jun 2025
Development and technical paper |  | 19 Jun 2025

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

Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao

Supplement

https://doi.org/10.5194/gmd-18-3623-2025-supplement

Data sets

The meteorology and PM2.5 data of "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'' Kang Hu https://doi.org/10.17632/bhfktx3kz8.2

The ECMWF data of "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" Kang Hu https://doi.org/10.5281/zenodo.14353871

The GDAS data of "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" Kang Hu https://doi.org/10.5281/zenodo.14347277

The HYSPLIT trajectory ensemble data of "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" Kang Hu https://doi.org/10.5281/zenodo.14375567

Model code and software

The XGBoost model system Kang Hu https://doi.org/10.5281/zenodo.14677125

The CWT model combined with the HYSPLIT trajectory ensemble Kang Hu https://doi.org/10.5281/zenodo.13994400

The PySPLIT model was used in the paper titled "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" Kang Hu https://doi.org/10.5281/zenodo.14354765

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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.
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