基于地质探测器和GWR模型的PM2.5度空间时间模式的定量归因:来自中国三大城市聚集地的证据
Zeduo Zou1, Xiuyan Zhao1, Shuyuan Liu1
1School of Geography and Planning, Sun Yat-sen University, Guangzhou, China.
PloS one
|July 7, 2025
概括
从2005年到2020年,中国主要城市地区的PM2.5度显著下降,自然地理和社会经济因素推动了这些变化. 空间图案显示,在北京-天津-河北,长江三角洲和珠江三角洲地区有明显的区域集群和梯度.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 城市规划 城市规划
背景情况:
- 空气污染,特别是PM2.5,对城市发展和公共卫生构成重大挑战.
- 了解PM2.5的时空动态对于中国主要城市聚集区的有效环境管理至关重要.
研究的目的:
- 分析2005年至2020年中国北京-天津-河北 (BTH),长江三角洲 (YRD) 和珠江三角洲 (PRD) PM2.5度的时空演变模式.
- 使用先进的空间分析技术,确定影响PM2.5度模式的关键驱动因素.
主要方法:
- 利用遥感和PM2.5度的统计数据在BTH,YRD和PRD (2005-2020) 中.
- 采用空间分析方法,包括地质探测器和地理加权回归 (GWR) 模型,分析污染模式和影响因素.
主要成果:
- 在所有三个城市聚集区,PM2.5度每年显著下降,尽管YRD始终处于最高水平,其次是BTH,然后是PRD.
- 空间分析显示了PM2.5度的显著聚类,具有不同的模式,如BTH中的"高南",YRD中的"高西北",以及PRD中的西-东梯度.
- 地质探测器和GWR模型将自然地理和社会经济因素 (温度,降水,植被,城市扩张,工业结构,能源效率) 确定为关键驱动因素,自然因素的影响力更强.
结论:
- 中国主要城市聚集区的PM2.5污染量呈现下降趋势,但仍然存在显著的空间异质性.
- 自然和社会经济因素都在塑造PM2.5度模式中发挥着关键作用,需要综合环境管理战略.
- 这些发现为中国的有针对性的空气污染控制政策和可持续城市发展提供了宝贵的见解.
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