Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics
文献类型：期刊论文
| 作者 | Li, Junming1,2; Wang, Nannan3; Wang, Jinfeng2; Li, Honglin4 |
| 刊名 | ENVIRONMENTAL POLLUTION
 |
| 出版日期 | 2018-07-01 |
| 卷号 | 238页码:471-481 |
| 关键词 | Bayesian statistics Health risk PM2.5 pollution Spatiotemporal evolution |
| ISSN号 | 0269-7491 |
| DOI | 10.1016/j.envpol.2018.03.050 |
| 通讯作者 | Wang, Jinfeng(Wangjf@lreis.ac.cn) |
| 英文摘要 | PM2.5 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. |
| WOS关键词 | FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; AMBIENT AIR-POLLUTION ; LUNG-CANCER ; MORTALITY ; DISEASE ; EVENTS ; HEALTH ; COHORT ; CHINA |
| 资助项目 | National Science Foundation of China[41531170] ; National Science Foundation of China[41421001] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000434754600050 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/54680]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Jinfeng |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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