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Short period PM2.5 prediction based on multivariate linear regression model
Zhao, Rui1; Gu, Xinxin1; Xue, Bing2; Zhang, Jianqiang1; Ren, Wanxia3
Corresponding AuthorXue, Bing(bing.xue@iass-potsdam.de)
2018-07-26
Source PublicationPLOS ONE
ISSN1932-6203
Volume13Issue:7Pages:15
AbstractA 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.
Funding OrganizationNational 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
DOI10.1371/journal.pone.0201011
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000439952400039
PublisherPUBLIC LIBRARY SCIENCE
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/128820
Collection中国科学院金属研究所
Corresponding AuthorXue, Bing
Affiliation1.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Sichuan, Peoples R China
2.Inst Adv Sustainabil Studies eV, Potsdam, Germany
3.Chinese Acad Sci, Inst Appl Ecol, Key Lab Pollut Ecol & Environm Engn, Shenyang, Liaoning, Peoples R China
Recommended Citation
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.
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