Spatiotemporal mapping and multiple driving forces identifying of PM2.5 variation and its joint management strategies across China

• Published in: Journal of cleaner production Vol. 250; p. 119534
• Main Authors: Chen, Xiyao, Li, Fei, Zhang, Jingdong, Zhou, Wei, Wang, Xiaoying, Fu, Huijuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20.03.2020
• Subjects: Geographic weighted regression; Joint management strategies; Meteorological factors; PM2.5; Spatial autocorrelation analysis; Geographic weighted regression; Meteorological factors; Spatial autocorrelation analysis; Joint management strategies; PM2.5
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2019.119534

Facing the challengeable PM2.5 pollution management across China, it is of significance to identify key pollution driving factors with a nation-region-city perspective and establish targeted joint management strategies on the trans-provincial scale. To identity hot study regions, the spatiotemporal pollution variation of PM2.5 in 2016 was explored using multi-scale spatial autocorrelation analysis based on the ground monitoring data for Chinese 366 cities. In the identified study regions, the relationship of natural factors and PM2.5 was analyzed on annual and daily scales via the correlation analyses, and the multiple socioeconomic driving forces of PM2.5 were identified utilizing geographic weighted regression (GWR) and principal component analysis. Consequently, 111 cities were identified as the hot regions, where their annual means of PM2.5 (AM) were 60.40 ± 14.23 μg·m−3 exceeding the Chinese level II standard and WHO Air Quality Guideline IT-1 (35 μg·m−3). The elevation and annual accumulative rainfall were found to be negatively correlated with the AM (p < 0.01). The increase of temperature and wind speed mainly helped daily PM2.5 reduce. Furthermore, the sensitive socioeconomic factors, mainly including the added value of the industry, the per capita gross domestic product and the per capital annual disposable income, were identified due to GWR. Then, based on the found spatial heterogeneity of sensitive factors, the selected 111 cities were identified into four types (Q1, Q2, Q3, Q4) and two sub-regions, Q1+Q2 (13+49 cities) and Q3+Q4 (27+22 cities). Finally, for multi-scale environmental sustainability and equity, the joint management strategies were formulated for each cities pattern based on their feature of sensitive pollution driving forces and city role positioning. [Display omitted] •Spatiotemporal patterns of PM2.5 in 366 Chinese cities were explored on multi-scale.•Increase of elevation, rainfall/wind, temperature help annual/daily PM2.5 reduce.•PM2.5 was sensitive to social factors like Added value of industry, Per Capita GDP.•111 hot cities were identified into four types on spatially heterogeneous features.•Joint management strategies were formulated for each kind based on driving factors.