Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective
文献类型：期刊论文
| 作者 | Wang, Shaojian1; Liu, Xiaoping1; Yang, Xue2; Zou, Bin3; Wang, Jieyu1 |
| 刊名 | JOURNAL OF CLEANER PRODUCTION
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 203页码:143-152 |
| 关键词 | PM2.5 Human activities Topographic and meteorological conditions Geographically weighted regression |
| ISSN号 | 0959-6526 |
| DOI | 10.1016/j.jclepro.2018.08.249 |
| 通讯作者 | Liu, Xiaoping(liuxp3@mail.sysu.edu.cn) |
| 英文摘要 | Fine particulate matter (PM2.5) concentrations are mainly influenced by human activities and natural conditions, yet how these impacts are driven under these two circumstances is not well understood. Identifying the spatial characteristics and the potential determinants of PM2.5 variations from the joint perspectives, can provide insights into particulate pollution control. Due to the limited observations for PM2.5, here a timely structure adaptive modeling method was employed in order to estimate PM2.5 concentration levels in China in 2014. Then global and local regression approaches were combined in order to identify the joint impacts of natural conditions (elevation, vegetation, precipitation, and temperature) and eight anthropogenic factors (including urban sprawl and economic structure) on PM2.5 concentrations. Results indicated an annual mean concentration of 69.7 mu g/m(3) of PM2.5 in China, with significant differences being observed across space. More than 70% of Chinese cities were found to exceed Grade II of the Chinese National Ambient Air Quality Standard, with the highest levels in the Sichuan Basin and the North China Plain. Global regression results, showing the relative influence of the twelve factors on variations in PM2.5 levels, indicate that elevation, vegetation, precipitation, temperature, and per capita GDP all have potentially offsetting effects in relation to increasing PM2.5 levels that are being driven by industrial structure and energy-related carbon emissions. If China persists in its development pattern, industrialization and fossil energy consumption will inevitably increase a development which will lead to higher PM2.5 concentrations according to the results of the analysis. The geographically weighted regression results showed that the relationship between the factors studied and PM2.5 concentrations is spatially heterogeneous at the local geographic level. Elevation (77.3%) and urban sprawl (57.4%) had the most significant impacts on PM2.5 concentrations. Findings from this research can shed new light on the multiple mechanisms underlying spatial variations in PM2.5 throughout China and provide insights useful to particulate pollution control in China. (C) 2018 Elsevier Ltd. All rights reserved. |
| WOS关键词 | AIR-POLLUTION ; AMBIENT PM2.5 ; SOCIOECONOMIC-FACTORS ; SPATIOTEMPORAL PATTERNS ; ECONOMIC-GROWTH ; ENVIRONMENT ; MEGACITIES ; PROXIMITY ; MORTALITY ; EMISSIONS |
| 资助项目 | National Natural Science Foundation of China[41601151] ; Natural Science Foundation of Guangdong Province[2016A030310149] ; Pearl River S&T Nova Program of Guangzhou[201806010187] |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000447568700012 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province ; Pearl River S&T Nova Program of Guangzhou |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52705]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Xiaoping |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Cent S Univ, Sch Geosci & Infophys, Changsha 410083, Hunan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Shaojian,Liu, Xiaoping,Yang, Xue,et al. Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective[J]. JOURNAL OF CLEANER PRODUCTION,2018,203:143-152. |
| APA | Wang, Shaojian,Liu, Xiaoping,Yang, Xue,Zou, Bin,&Wang, Jieyu.(2018).Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective.JOURNAL OF CLEANER PRODUCTION,203,143-152. |
| MLA | Wang, Shaojian,et al."Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective".JOURNAL OF CLEANER PRODUCTION 203(2018):143-152. |
入库方式： OAI收割
来源：地理科学与资源研究所
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