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<p>Atmospheric self-cleaning ability reflects the atmosphere’s capacity to dilute and remove air pollutants through meteorological processes. In this study, we quantified the atmospheric self-cleaning capacity across China from 1998 to 2020 by calculating the Atmospheric Self-cleaning Capacity Index (ASI). We analyzed the temporal variations in ASI and PM<sub>2.5</sub> concentrations and examined their spatial patterns and dynamics using the Mann-Kendall trend test. To further investigate the impacts of ASI and anthropogenic emissions on PM<sub>2.5</sub> concentrations, we developed a Light Gradient Boosting Machine (LightGBM) model integrated with SHapley Additive exPlanations (SHAP). The results indicated that ASI generally exhibited a decreasing trend followed by an increase, whereas PM<sub>2.5</sub> concentrations showed the opposite pattern, with an initial increase followed by a subsequent decline. At the national scale and in southeastern China, ASI and PM<sub>2.5</sub> concentrations exhibited a significant negative correlation. In the North China Plain, the Fenwei Plain, and the Taklimakan Desert in Xinjiang, ASI remained low, while PM<sub>2.5</sub> concentrations were high. Moreover, some areas within these regions showed a significant decline in ASI. The LightGBM-SHAP analysis revealed that ASI contributed the most (43.73%) to the spatiotemporal variation in PM<sub>2.5</sub> concentrations, highlighting its dominant role in regulating air quality. And, the role of ASI in modulating PM<sub>2.5</sub> concentrations became increasingly influential in recent years. These findings provide new insights into the dynamic relationship between atmospheric self-cleaning capacity and air pollution, offering a scientific basis for more targeted air quality management strategies.</p>

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Spatio-temporal variation of atmospheric self-cleaning capacity and its impact on PM2.5 concentrations in China

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Dongyang Yang,
  • Menglin Li,
  • Guanpeng Dong,
  • Yang Gao,
  • Hang Zhang

摘要

Atmospheric self-cleaning ability reflects the atmosphere’s capacity to dilute and remove air pollutants through meteorological processes. In this study, we quantified the atmospheric self-cleaning capacity across China from 1998 to 2020 by calculating the Atmospheric Self-cleaning Capacity Index (ASI). We analyzed the temporal variations in ASI and PM2.5 concentrations and examined their spatial patterns and dynamics using the Mann-Kendall trend test. To further investigate the impacts of ASI and anthropogenic emissions on PM2.5 concentrations, we developed a Light Gradient Boosting Machine (LightGBM) model integrated with SHapley Additive exPlanations (SHAP). The results indicated that ASI generally exhibited a decreasing trend followed by an increase, whereas PM2.5 concentrations showed the opposite pattern, with an initial increase followed by a subsequent decline. At the national scale and in southeastern China, ASI and PM2.5 concentrations exhibited a significant negative correlation. In the North China Plain, the Fenwei Plain, and the Taklimakan Desert in Xinjiang, ASI remained low, while PM2.5 concentrations were high. Moreover, some areas within these regions showed a significant decline in ASI. The LightGBM-SHAP analysis revealed that ASI contributed the most (43.73%) to the spatiotemporal variation in PM2.5 concentrations, highlighting its dominant role in regulating air quality. And, the role of ASI in modulating PM2.5 concentrations became increasingly influential in recent years. These findings provide new insights into the dynamic relationship between atmospheric self-cleaning capacity and air pollution, offering a scientific basis for more targeted air quality management strategies.