Short period PM2.5 prediction based on multivariate linear regression model
文献类型：期刊论文
| 作者 | Zhao, Rui2; Gu, Xinxin2; Xue, Bing3; Zhang, Jianqiang2; Ren, Wanxia1 |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2018-07-26 |
| 卷号 | 13期号:7页码:15 |
| ISSN号 | 1932-6203 |
| DOI | 10.1371/journal.pone.0201011 |
| 通讯作者 | Xue, Bing(bing.xue@iass-potsdam.de) |
| 英文摘要 | A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (fine particles with an aerodynamic diameter of 2.5 mu m or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO2, NO2, CO, and O-3). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R-2 = 0.766) goodness-of-fit and (R-2 = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study. |
| 资助项目 | National Natural Science Foundation of China[41571520] ; National Natural Science Foundation of China[41471116] ; Sichuan Provincial Key Technology Support[2014GZ0168] ; Fundamental Research Funds for the Central Universities[A0920502051408] ; BMBF Kopernikus Project for the Energy Transition-Thematic Field No. 4 System Integration and Networks for the Energy Supply (ENavi) ; Youth Innovation Promotion Association CAS[2016181] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000439952400039 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 资助机构 | National Natural Science Foundation of China ; Sichuan Provincial Key Technology Support ; Fundamental Research Funds for the Central Universities ; BMBF Kopernikus Project for the Energy Transition-Thematic Field No. 4 System Integration and Networks for the Energy Supply (ENavi) ; Youth Innovation Promotion Association CAS |
| 源URL | [http://ir.imr.ac.cn/handle/321006/128819]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Xue, Bing |
| 作者单位 | 1.Chinese Acad Sci, Inst Appl Ecol, Key Lab Pollut Ecol & Environm Engn, Shenyang, Liaoning, Peoples R China 2.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Sichuan, Peoples R China 3.Inst Adv Sustainabil Studies eV, Potsdam, Germany |
| 推荐引用方式 GB/T 7714 | Zhao, Rui,Gu, Xinxin,Xue, Bing,et al. Short period PM2.5 prediction based on multivariate linear regression model[J]. PLOS ONE,2018,13(7):15. |
| APA | Zhao, Rui,Gu, Xinxin,Xue, Bing,Zhang, Jianqiang,&Ren, Wanxia.(2018).Short period PM2.5 prediction based on multivariate linear regression model.PLOS ONE,13(7),15. |
| MLA | Zhao, Rui,et al."Short period PM2.5 prediction based on multivariate linear regression model".PLOS ONE 13.7(2018):15. |
入库方式： OAI收割
来源：金属研究所
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