The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model

doi:10.1016/j.envint.2020.106168

	The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model
	Liu, Qianqian 2,3; Wu, Rong 4; Zhang, Wenzhong 5,6; Li, Wan 7; Wang, Shaojian 1
刊名	ENVIRONMENT INTERNATIONAL
	2020-12-01
卷号	145 页码:10
关键词	PM2.5 concentrations Geographically and temporally weighted regression Natural conditions Socioeconomic determinants Spatial heterogeneity
ISSN号	0160-4120
DOI	10.1016/j.envint.2020.106168
通讯作者	Wu, Rong(wurong@gdut.edu.cn) ; Zhang, Wenzhong(zhangwz@igsnrr.ac.cn)
英文摘要	Background: Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change. Objective: Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature. Method: In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015. Result: Cities with serious PM2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country's southeast, where enhanced precipitation was also shown to reduce PM2.5 concentrations; whilst in southern and central and western regions, precipitation and PM2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM2.5 concentration levels in southeastern cities than in other cities in China. Conclusion: PM2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.
资助项目	Second Tibetan Plateau Scientific Expedition and Research[2019QZKK0406] ; Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
WOS关键词	PARTICULATE MATTER PM2.5 ; AIR-POLLUTION ; METEOROLOGICAL CONDITIONS ; SPATIOTEMPORAL VARIATION ; SPATIAL REGRESSION ; EMPIRICAL-EVIDENCE ; QUALITY ; URBANIZATION ; IMPACT ; PM10
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000580632000074
资助机构	Second Tibetan Plateau Scientific Expedition and Research ; Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156411]
专题	中国科学院地理科学与资源研究所
通讯作者	Wu, Rong; Zhang, Wenzhong
作者单位	1.Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China 2.Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 4.Guangdong Univ Technol, Sch Architecture & Urban Planning, 729 East Dongfeng Rd, Guangzhou 510090, Guangdong, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 7.East China Normal Univ, Ctr Modern Chinese City Studies, Shanghai 200241, Peoples R China
推荐引用方式 GB/T 7714	Liu, Qianqian,Wu, Rong,Zhang, Wenzhong,et al. The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model[J]. ENVIRONMENT INTERNATIONAL,2020,145:10.
APA	Liu, Qianqian,Wu, Rong,Zhang, Wenzhong,Li, Wan,&Wang, Shaojian.(2020).The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model.ENVIRONMENT INTERNATIONAL,145,10.
MLA	Liu, Qianqian,et al."The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model".ENVIRONMENT INTERNATIONAL 145(2020):10.