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Prediction and analysis of PM2.5 in Fuling District of Chongqing by artificial neural network

  • S.I.: DPTA Conference 2019
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Abstract

The meteorological data, measurements of aerosol optical depth (AOD) and PM2.5 concentration from 2016 to 2017 in Fuling District of Chongqing were selected to study their correlation. The back propagation (BP) artificial neural network (ANN) was used to build a PM2.5 prediction model with the meteorological factors as input, and the predicted PM2.5 values were compared with the measured ones. The results show that PM2.5 concentration has a piecewise linear relationship with temperature attributed to diffusion rate and premise conversion rate, a positive correlation with relative humidity, and a significant inverse correlation with wind speed, but no apparent linear relationship with rainfall, although rainfall has a significant purification effect on PM2.5. The similarity in the influence mechanism of AOD and PM2.5 concentration leads to a certain positive correlation between them. The predicted PM2.5 by the BP ANN model shows a similar trend with the measured one, but has some significant differences in numerical values. Therefore, it is feasible to establish BP artificial neural network to predict PM2.5 by using meteorological data.

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References

  1. Liu G, Li X, Hu F (2000) Neural network prediction of air pollutant concentration. China Environ Sci 20(05):45–47

    Google Scholar 

  2. Li J, Luan S, You MH (2010) Introduction to the principle of artificial neural network. Mod Educ Sci S1:98–99

    Google Scholar 

  3. Chin CS, Ji X, Woo WL et al (2019) Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform. Neural Comput Appl 31:1127–1142

    Article  Google Scholar 

  4. Zheng H, Shang X (2013) Study on prediction of atmospheric PM2.5 based on RBF neural network. In: Fourth international conference on digital manufacturing and automation. IEEE, pp 1287–1289

  5. Yang Y, Yan-Li FU (2015) The prediction of mass concentration of PM2.5 based on T–S fuzzy neural network. J Shaanxi Univ Sci Technol 33(6):162–166

    Google Scholar 

  6. Tian-Cheng MA, Liu DM, Xue-Jie LI et al (2014) Improved particle swarm optimization based fuzzy neural network for PM_(2.5) concentration prediction. Comput Eng Des 35(9):3258–3262

    Google Scholar 

  7. Hu Z, Li W, Qiao J (2016) Prediction of PM2.5 based on Elman neural network with chaos theory. In: Control conference. IEEE, pp 3573–3578

  8. Zhu H, Lu X (2016) The prediction of PM2.5 value based on ARMA and improved BP neural network model. In: International conference on intelligent networking and collaborative systems. IEEE, pp 515–517

  9. Zhou S, Li W, Qiao J (2017) Prediction of PM2.5 concentration based on recurrent fuzzy neural network. In: Control conference. IEEE, pp 3920–3924

  10. Papadopoulos H, Haralambous H (2011) Reliable prediction intervals with regression neural networks. Neural Netw 24(8):842–851

    Article  Google Scholar 

  11. Fu YL (2016) Prediction of PM2.5 mass concentration based on neural network. Shaanxi University of Science and Technology, Xi’an

    Google Scholar 

  12. Tang G, Ma XG (2016) The influence of meteorological factors on the concentration of PM2.5 and other air pollutants in Shanghai. Energy Res Inf 32(02):71–74

    Google Scholar 

  13. Zhou TX, Wang GQ, Kuang HY et al (2017) Analysis of the relationship between PM2.5 and wind direction and speed in Changzhou. Agric Technol 37(09):140–141

    Google Scholar 

  14. Pan BF, Zhao YL, Li J et al (2012) Analysis of the removal effect of meteorological factors on PM2.5 in the atmosphere. Environ Sci Technol 25(06):41–44

    Google Scholar 

  15. Chen Y, Zeng Y, Zhang Q et al (2014) Analysis of the influence of meteorological factors on the periodic change of PM2.5 in Changsha City. Sichuan Environ 33(06):81–87

    Google Scholar 

  16. Liang Y Fi, Zang Z L, You W et al (2018) Correlation analysis of MODIS AOD and PM2.5-hour mass concentration in China. China Powder Technol 24(03):63–68 + 75

  17. Han DW, Liu WQ, Zhang YJ et al (2007) Effect of temperature and relative humidity on vertical distribution of aerosol mass concentration. J Grad Sch Chin Acad Sci 05:619–624

    Google Scholar 

  18. Ma TC, Liu DM, Li XJ et al (2014) Prediction of PM2.5 concentration based on improved PSO. Comput Eng Des 35(09):3258–3262

    Google Scholar 

  19. Feng J, Liu G, Huang Y et al (2016) Prediction of PM2.5 concentration in Tianjin based on BP neural network. Environ Sci Manag 41(06):121–125

    Google Scholar 

  20. He QB (2004) BP neural network and application research. Chongqing Institute of Transportation, Chongqing

    Google Scholar 

Download references

Acknowledgements

The research work was supported by Chongqing Municipal Commission of Natural Science Foundation Projects (Grant No. cstc2019jcyj-msxmX0860).

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Authors and Affiliations

  1. The Green Intelligent Environmental School, Yangtze Normal University, Fuling, Chongqing, 408100, China

    Xianghong Wang, Jing Yuan & Baozhen Wang

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  1. Xianghong Wang
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  2. Jing Yuan
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  3. Baozhen Wang
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Correspondence to Xianghong Wang.

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Wang, X., Yuan, J. & Wang, B. Prediction and analysis of PM2.5 in Fuling District of Chongqing by artificial neural network. Neural Comput & Applic 33, 517–524 (2021). https://doi.org/10.1007/s00521-020-04962-z

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  • DOI: https://doi.org/10.1007/s00521-020-04962-z

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