JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (6): 76-83.doi: 10.6040/j.issn.1672-3961.0.2014.214

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Prediction models of PM2.5 mass concentration based on meteorological factors

LIU Jie1, YANG Peng2, LYU Wensheng1, LIU Agudamu1, LIU Junxiu2   

  1. 1. School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
  • Received:2014-08-05 Revised:2015-06-02 Online:2015-12-20 Published:2014-08-05

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Abstract: 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.

Key words: PM2.5, multiple linear regression, support vector machine, machine learning, BP neural network

CLC Number: 

  • X831
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