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Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics
Li, Junming1,2; Wang, Nannan3; Wang, Jinfeng2; Li, Honglin4
2018-07-01
Source PublicationENVIRONMENTAL POLLUTION
ISSN0269-7491
Volume238Pages:471-481
Corresponding AuthorWang, Jinfeng(Wangjf@lreis.ac.cn)
AbstractPM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a 'high-value increasing and low value decreasing' trend. Areas with annual PM(2.)5 concentrations of more than 70 mu g/m(3) and less than 10 mu g/m(3) expanded, while areas with of an annual PM2.5 concentrations of 10-25 mu g/m(3) shrank. The most heavily PM2.5-polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5-polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5-polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5-polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordBayesian statistics Health risk PM2.5 pollution Spatiotemporal evolution
DOI10.1016/j.envpol.2018.03.050
WOS KeywordFINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; AMBIENT AIR-POLLUTION ; LUNG-CANCER ; MORTALITY ; DISEASE ; EVENTS ; HEALTH ; COHORT ; CHINA
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[41531170] ; National Science Foundation of China[41421001]
Funding OrganizationNational Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000434754600050
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54681
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Jinfeng
Affiliation1.Shanxi Univ Finance & Econ, Sch Stat, Wucheng Rd 696, Taiyuan 030006, Shanxi, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Datun Rd 11A, Beijing 10010, Peoples R China
3.Henan Univ, Sch Environm & Planning, Kaifeng 475004, Peoples R China
4.Prov Ctr Remote Sensing Shanxi, Yingze St 136, Taiyuan 030001, Shanxi, Peoples R China
Recommended Citation
GB/T 7714
Li, Junming,Wang, Nannan,Wang, Jinfeng,et al. Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics[J]. ENVIRONMENTAL POLLUTION,2018,238:471-481.
APA Li, Junming,Wang, Nannan,Wang, Jinfeng,&Li, Honglin.(2018).Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics.ENVIRONMENTAL POLLUTION,238,471-481.
MLA Li, Junming,et al."Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics".ENVIRONMENTAL POLLUTION 238(2018):471-481.
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