%A LIU Jie, YANG Peng, LYU Wensheng, LIU Agudamu, LIU Junxiu %T Prediction models of PM2.5 mass concentration based on meteorological factors %0 Journal Article %D 2015 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2014.214 %P 76-83 %V 45 %N 6 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1387.shtml} %8 2015-12-20 %X In order to get the optimal prediction model, the prediction models of PM2.5 mass concentration based on multiple linear regression and machine learning were developed. Basic values of pollutants mass concentrations and periodical factors were introduced as predictive inputs based on meteorological factors. Then four prediction models were developed for comparison. Results showed that goodness of fit of multiple linear regression model based on improved predictive inputs was increased from 0.52 to 0.64. The selected meteorological factors, basic values of pollutants mass concentrations and periodical factors could accurately describe daily variation of PM2.5. BP neural network and support vector machine models could be trained to model the highly non-linear relationships between PM2.5 mass concentration and predictive inputs. They provided satisfactory results with goodness of fit of 0.69 and 0.74, respectively. Support vector machine model was proved to be optimal prediction model of PM2.5 mass concentration.