Lian-Hua Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,3, Ze-Hong Deng1, Wen-Bo Wang2,3 

1 School of Literature, Law and Economics, Wuhan University of Science and Technology, Wuhan 430065, China
2 Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology), Wuhan 430081, China
3 College of Science, Wuhan University of Science and Technology, Wuhan 430065, China

Received: June 22, 2020
Revised: January 28, 2021
Accepted: January 29, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Zhang, L.H., Deng, Z.H., Wang, W.B. (2021). PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization. Aerosol Air Qual. Res. 21, 200144.


  • The approximate Markov blanket based nMRMR algorithm is used.
  • A hybrid kernel (HK) was created.
  • A support vector regression model (nMRMR-PSO-HK-SVR) was established and applied.


This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. In addition, a hybrid kernel (HK) was created to improve upon the traditional support vector regression (SVR) model. Particle swarm optimization (PSO) was used to calculate the optimal parameters of hybrid kernel (HK) SVR, which were then used to establish the nMRMR-PSO-HK-SVR model for PM2.5 concentration prediction. The 2016–2019 year air quality and weather data of Wuhan and Tianjin were employed to test the proposed method. The experimental results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil’s inequality coefficient (TIC) of nMRMR-PSO-HK-SVR model are lower than those of SVR, PSO-SVR, nMRMR-SVR and PSO-HK-SVR model. But also, the proposed model could more precisely track moments of sudden PM2.5 concentration change. Thus, the nMRMR-PSO-HK-SVR model has more satisfactory generalizability and can predict PM2.5 concentration more precisely.

Keywords: PM2.5, Maximum relevance minimum redundancy (MRMR), Hybrid kernel, Support vector regression, Prediction model

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