Evolution of the Distribution of PM2.5 Concentration in the Yangtze River Economic Belt and Its Influencing Factors

被引：5

作者：

Huang X.-G. ^{[1
,2
,3
]}

Zhao J.-B. ^{[2
,3
]}

Cao J.-J. ^{[2
]}

Xin W.-D. ^{[1
]}

机构：

[1] College of Geographical Sciences, Shanxi Normal University, Linfen

[2] Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an

[3] School of Geography and Tourism, Shaanxi Normal University, Xi'an

来源：

Zhao, Jing-Bo (zhaojb@snnu.edu.cn) | 1600年 / Science Press卷 / 41期

关键词：

Distribution; Evolution; Geographically weighted regression (GWR); Influencing factors; PM[!sub]2.5[!/sub] concentration; Yangtze River economic belt;

D O I：

10.13227/j.hjkx.201906158

中图分类号：

学科分类号：

摘要：

Intensive social and economic activity has led to serious pollution in the Yangtze River economic belt since 2000. It is urgent to study the evolution of the distribution of PM2.5 concentration and its influencing factors in this area, to adopt new ways of development into practice and promote comprehensive regional air pollution prevention and control. Based on PM2.5 concentration estimated by remote sensing retrieval, this paper studied the evolution of the distribution of PM2.5 concentration in the Yangtze River economic belt from 2000 to 2016, and analyzed spatial non-stationarity of the influence of natural and socio-economic factors on this evolution via a geographically weighted regression model. The results showed that: ①The general law of PM2.5 concentration presented as higher in the east and lower in the west, with a significant trait of the pollution agglomerations corresponding to urban agglomerations. ②Taking the year 2007 as a divide, annual concentration of PM2.5 went through a pattern of annually increasing from 2000 to 2007. and then wavelike decreasing from 2007 to 2016. The annual average concentration increased to 44.1 μg•m-3 in 2007 from the record of 27.2 μg•m-3 in 2000, and then decreased to 33.6 μg•m-3 in 2016. In terms of regions polluted, before 2007, it covered areas including the Yangtze River Delta urban agglomerations, the Yangtze River Middle Reaches urban agglomerations, and the Chengdu-Chongqing urban agglomerations, before quickly stretching to their neighboring areas; after 2007, the extent of areas covered shrank. ③Based on spatial auto-correlation analysis, PM2.5 concentration had a significant spatial auto-correlation with hot spots spread over Shanghai, Jiangsu, north-central Anhui, northern Zhejiang, and the central part of Hubei, while cool spots were located in Yunnan, the western and southern parts of Sichuan, and the western part of Guizhou. ④There is a space-time discrepancy by socio-economic and natural factors in the distribution of PM2.5 concentration. The socio-economic factors mainly have a positive influence on the concentration, whereas precipitation, one of the natural factors, has a negative influence. The remaining natural factors not only varied in their degree of influence, but also triggered the influence either in a positive or negative manner from time to time and space to space. © 2020, Science Press. All right reserved.

引用

页码：1013 / 1024

页数：11

共 35 条

[1] Yan X., Cheng C.C., Yi G.F., Et al., Economic threshold effect of urbanization on energy consumption: take the Yangtze River Economic Zone as an example, Economic Geography, 39, 1, pp. 73-81, (2019)
[2] Sofowote U.M., Su Y.S., Dabek-Zlotorzynska E., Et al., Constraining the factor analytical solutions obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in Ontario, Canada, Atmospheric Environment, 108, pp. 151-157, (2015)
[3] Li M.N., Zhang L.L., Haze in China: current and future challenges, Environmental Pollution, 189, pp. 85-86, (2014)
[4] 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)
[5] Turpin B.J., Cary R.A., Huntzicker J.J., An in situ, time-resolved analyzer for aerosol organic and elemental carbon, Aerosol Science and Technology, 12, 1, pp. 161-171, (1990)
[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] Zhao X.J., Zhao P.S., Xu J., Et al., Analysis of a winter regional haze event and its formation mechanism in the North China Plain, Atmospheric Chemistry and Physics, 13, 11, pp. 5685-5696, (2013)
[8] Chen G.L., Zhou Y., Cheng S.Y., Et al., Air pollutant emission inventory and impact of typical industries on PM2.5 in Chengde, Environmental Science, 37, 11, pp. 4069-4079, (2016)
[9] Jiang C., Wang H., Zhao T.L., Et al., Modeling study of PM2.5 pollutant transport across cities in China's Jing-Jin-Ji region during a severe haze episode in December 2013, Atmospheric Chemistry and Physics Discussions, 15, 3, pp. 3745-3776, (2015)
[10] Wang X.Q., Lang J.L., Cheng S.Y., Et al., Study on transportation of PM2.5 in Beijing-Tianjin-Hebei (BTH) and its surrounding area, China Environmental Science, 36, 11, pp. 3211-3217, (2016)

← 1 2 3 4 →