Open AccessShort period PM2.5 prediction based on multivariate linear regression model
Author(s) -
Rui Zhao,
Xinxin Gu,
Bing Xue,
Jianqiang Zhang,
Wanxia Ren
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0201011
Subject(s) - goodness of fit , multivariate statistics , linear regression , environmental science , regression analysis , bayesian multivariate linear regression , relative humidity , wind speed , statistics , regression , atmospheric sciences , aerosol , meteorology , linear model , mathematics , geography , geology
A multivariate linear regression model was proposed to achieve short period prediction of PM 2.5 (fine particles with an aerodynamic diameter of 2.5 μm 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 (SO 2 , NO 2 , 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.