气候变化领域集成服务门户(CCPortal): Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data

CCPortal

DOI	10.1088/1748-9326/abbd62
	Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data
	Cao Z.; Wu Z.; Li S.; Ma W.; Deng Y.; Sun H.; Guan W.
发表日期	2020
ISSN	17489318
卷号	15 期号:11
英文摘要	Air pollution in the form of PM2.5 decreases life expectancy considering its contribution to morbidity and mortality. Therefore, scientific and accurate PM2.5 exposure risk assessment is essential. However, the considering daily/hourly mean PM2.5 concentration and overlooking population mobility in exposure risk assessments result in underestimation of its adverse effects. Thus, using social media data and exceedance PM2.5 concentration, two novel indicators named hourly exceedance PM2.5 exposure (HEPE) and daily cumulative variation of exceedance PM2.5 exposure (DCEPE) are developed in our study. Spatiotemporal variation analysis of HEPE showed that the first exceedance PM2.5 exposure risk was observed at 10:00; this lasted till the end of the day. According to the standard deviation ellipse (SDE) method analysis results, at first, the major spatial tendency direction was northeast-southwest, with the average center (AE) located in the Yuangang Township. Then, the HEPE in the western study area increased dramatically. The major spatial tendency direction shifted from northeast-southwest to east-west. Consequently, the AE of HEPE shifted to central study area. The spatiotemporal variation characteristics led us to investigate the mechanisms. A bivariate LISA was applied to detect the spatial association between DCEPE and city functional zones (CFZs). Results showed that highly spatial associations were found between the DCEPE and CFZs in the southwest of the study area. Residential neighborhoods and transportation services showed a closer relationship with the spatial distribution of DCEPE. Based on these results, we found increasing public health threats posed by PM2.5. Thus, HEPE is an essential factor to assess air pollution exposure risk. Moreover, more attention should be paid to the spatial association between DCEPE and city functions, which is important for the development of air pollution mitigation strategies. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	daily cumulative variation; PM2.5; population mobility; risk assessment; TUD data
语种	英语
scopus关键词	Air pollution; Big data; Health risks; Social networking (online); Air pollution exposures; PM2.5 concentration; Pollution mitigation; Residential neighborhoods; Social media datum; Spatial associations; Spatio-temporal variation; Transportation services; Risk assessment; Venustaconcha ellipsiformis
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153473
作者单位	School of Geographical Sciences, Guangzhou University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China; Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China; Ecological Meteorological Center of Guangdong Province, Guangzhou, 510080, China
推荐引用方式 GB/T 7714	Cao Z.,Wu Z.,Li S.,et al. Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data[J],2020,15(11).
APA	Cao Z..,Wu Z..,Li S..,Ma W..,Deng Y..,...&Guan W..(2020).Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data.Environmental Research Letters,15(11).
MLA	Cao Z.,et al."Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data".Environmental Research Letters 15.11(2020).