A study of PM_2.5 transport pathways in China from 2000 to 2021 with a novel spatiotemporal correlation method

Abstract

In the context of urbanization, air pollution has emerged as a significant environmental challenge. A thorough understanding of their transport pathways, especially at a national scale, is essential for environmental protection and policy-making. However, it remains partially elusive due to the constraints of available data and analytical methods. This study proposed a data-driven spatiotemporal correlation analysis method employing the Dynamic Time Warping (DTW). We represented the first comprehensive attempt to chart the long-term and nationwide transport pathways of PM_2.5 utilizing an extensive dataset spanning from 2000 to 2021 across China, which is crucial for understanding long-term air pollution trends. Compared with traditional chemical transport models (CTMs), this data-driven method can generate transport pathways of PM_2.5 without requiring extensive meteorological or emission data, and suggesting fundamentally consistent spatial distribution and trends. Our analysis reveals that China's transport pathways are notably pronounced in the Northwest (34% of the total pathways in China), Southwest (22%), and North (21%) regions, with less significant pathways in the Northeast (10%) region and isolated occurrences elsewhere. Additionally, a notable decrease in the number of China's PM_2.5 transport pathways, similar to annual average concentrations, was observed after 2013, aligning with stricter environmental regulations. Furthermore, we have demonstrated the feasibility of applying our method to the transport pathways of other gaseous pollutants. The approach is effective in detecting and quantifying air pollutants' transport pathways, even in regions like the Northwest with limited monitoring infrastructure, which may aid in environmental decision-making. The study will notably improve the current understanding of air pollutants' transport process, providing a new perspective for studying the large-scale spatiotemporal correlations.

Keywords

Air pollutants / Spatiotemporal correlation / Big Earth Data / Transport pathways / PM_2.5 / Sustainable development goals

CRediT authorship contribution statement

Yiming Liu: Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Software, Writing - review & editing, Writing - original draft, Visualization, Validation. Huadong Guo: Supervision, Project administration, Investigation, Writing - review & editing, Conceptualization. Lu Zhang: Writing - review & editing, Visualization, Supervision, Project administra-tion, Methodology, Investigation, Funding acquisition, Formal anal-ysis. Dong Liang: Writing - review & editing, Visualization, Supervision, Software, Resources, Investigation, Formal analysis. Qi Zhu: Methodology, Investigation, Formal analysis, Writing - review & editing, Visualization, Software. Zhuoran Lv: Writing - review & editing, Visualization, Software, Investigation. Xinyu Dou: Writing - review & editing, Visualization, Software, Method-ology, Formal analysis. Xiaobing Du: Writing - review & editing, Visualization, Investigation.

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was funded by the National Natural Science Founda-tion of China (grant No. 42376246), the Key Research and Develop-ment Project of Guangxi (grant No. GuikeAB24010046), and the Joint Funds of the National Natural Science Foundation of China (grant No. U2268217). The authors acknowledgement Dr. Jing Wei for his high-quality PM2.5 and other datasets like gaseous pol-lutants. They also thank Binhao Wang for his help and suggestions on the improvement of the paper.

Supplementary Data

Supplementary Data available at https://disk.pku.edu.cn/link/AA2855070F4F02467FB7A129F97506E569.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102116.

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