Spatio-temporal Variation of PM2.5 Related Relationships in China from the Perspective of Air Pollution Regional Linkage Control and Prevention

被引：0

作者：

Yang W.-T. ^{[1
,2
]}

Huang H.-K. ^{[1
,3
]}

Wei D.-S. ^{[4
]}

Zhao B. ^{[3
]}

Peng H.-H. ^{[2
]}

机构：

[1] Department of Geographical Information Science, Hunan University of Science and Technology, Xiangtan

[2] National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan

[3] Department of Geological Engineering, Central South University, Changsha

[4] Department of Surveying and Mapping Engineering, Central South University of Forest and Technology, Changsha

来源：

Huanjing Kexue/Environmental Science | 2020年 / 41卷 / 05期

关键词：

Geographically and temporally weighted regression; PM[!sub]2.5[!/sub; Regional linkage control and prevention; Remote sensing data; Spatio-temporal variation;

D O I：

10.13227/j.hjkx.201908125

中图分类号：

学科分类号：

摘要：

Identification of spatio-temporal variation of PM2.5 related relationships under joint management zones is of great significance for scientifically conducting joint control of air pollution in China. Based on the PM2.5 concentration data of 334 prefecture-level cities in China from 2000 to 2016, from the perspective of air pollution regional linkage control and prevention, this paper systematically analyzes the spatio-temporal variation of PM2.5 related relationships in China using a spatial unit aggregation strategy and geographically and temporally weighted regression. The results show that: ① With PM2.5 as the primary pollutant, ten air pollution joint management areas are obtained by considering the degree of pollution, geographical location, meteorology, topography, and economy. ② Geographically and temporally weighted regression can effectively reveal the spatio-temporal non-stationarity of the relationships between PM2.5 concentration and related factors. Meanwhile, population size, secondary industry gross domestic product, SO2 emissions, annual average temperature, annual precipitation, and annual relative humidity are identified as having a significant effect on changes in PM2.5 concentration. ③ The population impacts on PM2.5 concentration in the Beijing-Tianjin-Yunmeng region are the largest of all regions during the period. The influence of the secondary industry's gross domestic product on the PM2.5 concentration in the Sichuan-Yunnan District is the most variable. Apart from these values in the northeast of China, the regression coefficient values of SO2 emissions first decrease with time, then increase, and then decrease again. The time variability of the average annual temperature of each treatment area to PM2.5 is small. The influences of annual precipitation and annual average relative humidity on PM2.5 present different variability characteristics in each region. © 2020, Science Press. All right reserved.

引用

页码：2066 / 2074

页数：8

共 26 条

[1] Liu J., Han Y.Q., Tang X., Et al., Estimating adult mortality attributable to PM2.5exposure in China with assimilated PM2.5 Concentrations based on a ground monitoring network, Science of the Total Environment, 568, pp. 1253-1262, (2016)
[2] Zeng X.G., Ruan F.F., Peng Y.Y., Health effects' spatial distribution analysis of PM2.5 pollution in China based on spatial grid scale, China Environmental Science, 39, 6, pp. 2624-2632, (2019)
[3] Liu M.D., Shadbegian R., Zhang B., Does environmental regulation affect labor demand in China? Evidence from the textile printing and dyeing industry, Journal of Environmental Economics and Management, 86, pp. 277-294, (2017)
[4] Luo J.Q., Du P.J., Samat A., Et al., Spatiotemporal pattern of PM2.5 concentrations in mainland China and analysis of its influencing factors using geographically weighted regression, Scientific Reports, 7, (2017)
[5] He Z.J., Deng M., Cai J.N., Et al., Mining spatiotemporal association patterns from complex geographic phenomena, International Journal of Geographical Information Science, (2019)
[6] Liu K.K., Zhang H., Liu G.J., Elemental composition characteristics of PM2.5 and PM10, and heavy pollution analysis in Hefei, Environmental Science, 40, 8, pp. 3415-3420, (2019)
[7] Lv Z., Wei W., Zhou Y., Et al., Cause and effect evaluation of PM2.5 during three red alerts in Beijing from 2015 to 2016, Environmental Science, 40, 1, pp. 1-10, (2019)
[8] Duan J.X., Zhai W.X., Cheng C.Q., Et al., Socio-economic factors influencing the spatial distribution of PM2.5 concentrations in China: an exploratory analysis, Environmental Science, 39, 5, pp. 2498-2504, (2018)
[9] Han L.J., Zhou W.Q., Li W.F., Et al., Impact of urbanization level on urban air quality: a case of fine particles (PM2.5) in Chinese cities, Environmental Pollution, 194, pp. 163-170, (2014)
[10] Liu H.M., Fang C.L., Huang J.J., Et al., The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration, Acta Geographica Sinica, 73, 1, pp. 177-191, (2018)

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