Key Laboratory of Beijing On Regional Air Pollution Control, Department of Environmental Science, College of Environmental Science & Engineering, Beijing University of Technology, Beijing, 100124, China.
School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China.
Environ Monit Assess. 2024 Jun 19;196(7):632. doi: 10.1007/s10661-024-12795-9.
In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM concentration within Shanxi Province, with PM concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m overall. The Hurst exponent indicated a projected decrease in PM concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM and provided a reference for similar studies.
在中国，尽管近年来大气环境质量持续改善，但 PM 污染仍未得到根本控制，其驱动机制复杂，有待进一步探索。本研究基于 2000-2020 年中国 1 公里地面 PM 数据集，结合空间自相关、趋势分析、地理探测器和多尺度地理加权回归（MGWR）模型，探讨了山西省 PM 的时空演变，并揭示了其背后的复杂驱动机制。结果表明：（1）山西省内 PM 浓度存在显著的空间集聚，从西南向东北逐渐降低。2000-2020 年，PM 污染水平呈时间递减趋势，整体浓度下降 9.15µg/m。Hurst 指数表明，山西省中部和北部地区 PM 浓度预计将下降，而其他地区预计将上升。（2）地理探测器表明，所有驱动因素对 PM 浓度均有显著影响，气象因素影响最大，其次是人类活动，植被覆盖影响最小。（3）国内生产总值和人口密度均与 PM 浓度呈正相关，而植被分数覆盖度、风速、降水和海拔对整个空间的 PM 浓度均有负向影响。本研究丰富了 PM 驱动机制的研究内容和思路，为类似研究提供了参考。
