太阳成8722(中国)有限公司-GREEN NO.1

标题: Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach

作者: He, JH (He, Jianhua); Ding, S (Ding, Su); Liu, DF (Liu, Dianfeng)

来源出版物: SCIENCE OF THE TOTAL ENVIRONMENT  卷: 659  页: 1513-1525  DOI: 10.1016/j.scitotenv.2018.12.402  出版年: APR 1 2019  

摘要: China has been under threat of severe haze in recent years, particularly that caused by fine particulatematter (PM2.5). Exploring the determinants of PM2.5 concentration is critical for improving air quality. The influencing mechanism of smog pollution is a comprehensive and systematic process affected bymultiple driving factors. In this research, we collected PM2.5 monitoring data from 292 cities across China in 2015 and employed multilevel regression models constructed using three levels to detect the physical and socioeconomic driving forces behind the PM2.5 concentration at monthly, seasonal and spatial scales, which captured random effects both varied by season and region. The results indicated significant spatiotemporal heterogeneity in the PM2.5 distribution, with the pollution core located in central China and northern China. The most severely haze episodes occurred in winter. Multilevel models showed that 46.40% of the variance was derived from the seasonal and spatial levels, and the models could explain a maximum of 90.7% of the PM2.5 concentration variance. The multilevel model identified more determinant influences varying by time and region. The outcomes suggested that the impacts of temperature and relative humidity on PM2.5 were of significant spatiotemporal heterogeneity due to the influencing mechanism differing from season and station. The variation of anthropogenic activities led to the socioeconomic influences featured a significant spatiotemporal heterogeneity. This research revealed the spatiotemporal characteristic of PM2.5 pollution influencingmechanism fromphysical and perspective and provided effective strategies for restricting air pollution. (c) 2018 Elsevier B.V. All rights reserved.

入藏号: WOS:000457293700144

语言: English

文献类型: Article

作者关键词: PM2.5; Multilevel regression analysis; Physical and anthropogenic factors; China

地址: [He, Jianhua; Ding, Su; Liu, Dianfeng] Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[He, Jianhua; Liu, Dianfeng] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

通讯作者地址: Ding, S; Liu, DF (通讯作者)，Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Ding, S; Liu, DF (通讯作者)，Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: hjianh@whu.edu.cn; suding@whu.edu.cn; liudianfeng@whu.edu.cn

影响因子：4.61