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

A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (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 (SO2, NO2, CO, and O3). 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 (R2 = 0.766) goodness-of-fit and (R2 = 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.

Publication Year
2018
Publication Type
Academic Articles
Citation

Zhao, R., Gu, X., Xue, B., Zhang, J., Ren, W. (2018): Short period PM2.5 prediction based on multivariate linear regression model. - Plos One, 13, 7, e0201011.DOI: http://doi.org/10.1371/journal.pone.0201011

DOI
10.1371/journal.pone.0201011
Links
http://publications.iass-potsdam.de/pubman/item/escidoc:3418890:2/component/esc…
Staff involved
Projects involved
Kopernikus Project Energy Transition Navigation System (Enavi) Mobilising the Multiple Opportunities of Renewable Energies