Short period PM2.5 prediction based on multivariate linear regression model

doi:10.1371/journal.pone.0201011

IMR OpenIR

	Short period PM2.5 prediction based on multivariate linear regression model
	Zhao, Rui 1; Gu, Xinxin 1; Xue, Bing 2; Zhang, Jianqiang 1; Ren, Wanxia 3
通讯作者	Xue, Bing(bing.xue@iass-potsdam.de)
	2018-07-26
发表期刊	PLOS ONE
ISSN	1932-6203
卷号	13 期号:7 页码:15
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.
资助者	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
DOI	10.1371/journal.pone.0201011
收录类别	SCI
语种	英语
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
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000439952400039
出版者	PUBLIC LIBRARY SCIENCE
引用统计	被引频次：65[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/128820
专题	中国科学院金属研究所
通讯作者	Xue, Bing
作者单位	1.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
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.