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<p>Air pollution remains a major global environmental health risk, and clarifying how the Air Quality Index (AQI) evolves and responds to coupled meteorological and human drivers is essential for effective governance, especially in high-altitude and ecologically sensitive regions such as Qinghai Province. Here we propose and apply “a multi-evidence framework for interpreting regional air quality dynamics in Qinghai Province”, integrating spatiotemporal characterization, Morlet wavelet–based periodicity diagnosis, machine-learning–based machine-learning prediction, and interpretability-based mechanism inference, together with CMIP6-SSP scenario projection. The results show: (1) Qinghai’s AQI exhibits pronounced spatial heterogeneity during 2014–2023, with persistently higher levels in the eastern urban corridor, lower levels in the southern plateau, and marked interannual variability in the Haixi region. (2) Wavelet analysis confirms a dominant annual-scale periodicity for AQI and major pollutants, indicating strong seasonal forcing with episodic strengthening in some years. (3) Among multiple candidate models, the best-performing boosting model provides robust prediction and serves as the basis for interpretation. (4) By jointly using SHAP attribution and interaction interpretation with two-dimensional partial dependence analysis, we identify temperature as the primary driver, with wind speed and precipitation acting mainly as conditional regulators; the responses are distinctly nonlinear and reveal clear “high-risk windows” under specific joint meteorological ranges. (5) SSP-based projections suggest that spatial hotspots are likely to persist; particulate matter and primary gaseous pollutants tend to decline or stabilize under mitigation-oriented pathways, while ozone increases emerge as an increasingly important constraint, particularly under higher-forcing scenarios. These findings support climate-sensitive, region-targeted strategies combining clean-energy transition, coordinated multi-pollutant control with an ozone-oriented focus, and sustained management of persistent hotspot regions.</p>

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A multi-evidence framework extended for interpreting spatiotemporal AQI dynamics and future risks in Qinghai province, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • Yupeng Zhai,
  • Zhiyuan Song,
  • Ziyi Gao

摘要

Air pollution remains a major global environmental health risk, and clarifying how the Air Quality Index (AQI) evolves and responds to coupled meteorological and human drivers is essential for effective governance, especially in high-altitude and ecologically sensitive regions such as Qinghai Province. Here we propose and apply “a multi-evidence framework for interpreting regional air quality dynamics in Qinghai Province”, integrating spatiotemporal characterization, Morlet wavelet–based periodicity diagnosis, machine-learning–based machine-learning prediction, and interpretability-based mechanism inference, together with CMIP6-SSP scenario projection. The results show: (1) Qinghai’s AQI exhibits pronounced spatial heterogeneity during 2014–2023, with persistently higher levels in the eastern urban corridor, lower levels in the southern plateau, and marked interannual variability in the Haixi region. (2) Wavelet analysis confirms a dominant annual-scale periodicity for AQI and major pollutants, indicating strong seasonal forcing with episodic strengthening in some years. (3) Among multiple candidate models, the best-performing boosting model provides robust prediction and serves as the basis for interpretation. (4) By jointly using SHAP attribution and interaction interpretation with two-dimensional partial dependence analysis, we identify temperature as the primary driver, with wind speed and precipitation acting mainly as conditional regulators; the responses are distinctly nonlinear and reveal clear “high-risk windows” under specific joint meteorological ranges. (5) SSP-based projections suggest that spatial hotspots are likely to persist; particulate matter and primary gaseous pollutants tend to decline or stabilize under mitigation-oriented pathways, while ozone increases emerge as an increasingly important constraint, particularly under higher-forcing scenarios. These findings support climate-sensitive, region-targeted strategies combining clean-energy transition, coordinated multi-pollutant control with an ozone-oriented focus, and sustained management of persistent hotspot regions.