DOI QR코드

DOI QR Code

The prediction of atmospheric concentrations of toluene using artificial neural network methods in Tehran

  • Received : 2015.05.19
  • Accepted : 2015.12.01
  • Published : 2015.12.25

Abstract

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

Keywords

References

  1. Asadollahfardi, G. Taklify, A. and Ghanbari, A. (2012), "Application of artificial neural network to predict TDS in Talkheh Rud river", J. Irrig. Drain. Eng., 138(3), 363-370. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000402
  2. Asadollahfardi, G., Mehdinejad, M., Mirmohammadi, M. and Asadollahfardi, R. (2015), "Predicting atmospheric concentrations of benzene in the southeast of Tehran using artificial neural network", Asian J. Atmosp. Environ., 9(1), 12-21. https://doi.org/10.5572/ajae.2015.9.1.012
  3. Bloemen, H.H. and Burn, J. (1993), "Volatile organic compounds in the environmental", Butch Institute of Public Health and Environmental protection; Blackie Academic Professional, 98, 125-156.
  4. Chattopadhay, S. and Chattopadhay, G. (2012), "Modeling and prediction of monthly total Ozone concentrations by use of an artificial neural network based on principal component analysis", Pure Appl. Geophys., 169(10), 1891-1908. https://doi.org/10.1007/s00024-011-0437-5
  5. Dayhoff, J.E. (1990), Neural network architectures: An introduction; Van Nostrand Reinhold Co.
  6. Gardner, M.W. and Dorling, S.R. (1999), "Neural network modelling and prediction of hourly $NO_x$ and $NO_2$ concentrations in urban air in London", Atmosp. Environ., 33(5), 709-719. https://doi.org/10.1016/S1352-2310(98)00230-1
  7. Grivas, G. and Chaloulakou, A. (2006), "Artificial neural network models for prediction of $PM_{10}$ hourly concentrations, in the Greater Area of Athens, Greece", Atmosp. Environ., 40(7), 1216-1229. https://doi.org/10.1016/j.atmosenv.2005.10.036
  8. Haiming, Z. and Xiaoxiao, S. (2013), "Study on prediction of atmospheric $PM_{2.5}$ based on RBF neural network", Proceedings of the 4th International Conference on Digital Manufacturing and Automation, Qindao, Shandong, China, June.
  9. Kennedy, J.B. and Neville, A.D. (1964), Basic Statistical Methods for Engineers and Scientists, (2nd Ed.), Harper and Row, New York, NY, USA.
  10. Kolehmainen, M., Martikainen, H. and Ruuskanen, J. (2001), "Neural networks and periodic components used in air quality forecasting", Atmosp. Environ., 35(5), 815-825. https://doi.org/10.1016/S1352-2310(00)00385-X
  11. Lu, W.Z., Wang, W.J., Wang, X.K., Yan, S.H. and Lam, J. (2004), "Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong", Environ. Res. J., 96(1), 79-87. https://doi.org/10.1016/j.envres.2003.11.003
  12. Menhaj, M. (1998), Computational Intelligence, Fundamentals of Artificial Neural Networks, Volume 1, Amirkabir University Publisher, Tehran, Iran.
  13. Moustris, K.P., Ziomas, I.C. and Paliatsos, A.G. (2010), "3-day-ahead forecasting of regional pollution index for the pollutants $NO_2$, CO, $SO_2$, and $O_3$3 using a neural networks in Athens, Greece", Water Air Soil Pollut., 209(1), 29-43. https://doi.org/10.1007/s11270-009-0179-5
  14. Mysteries, K.P., Larissi, I.K., Nastos, P.T., Koukouletsos, K.V. and Paliatsos, A.G. (2013), "Development and application of artificial neural network modeling in forecasting $PM_{10}$ Levels in a Mediterranean City", Water Air Soil Pollute., 224(1634), 3-11.
  15. Niskaa, H., Hiltunena, T., Karppinenb, A., Ruuskanena, J. and Kolehmainen, M. (2004), "Evolving the neural network model for forecasting air pollution time series", Eng. Appl. Artif. Intel. J., 17(2), 159-167. https://doi.org/10.1016/j.engappai.2004.02.002
  16. Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E. and Fila, M. (2006), "Identification of long-range aerosol transport patterns in Toronto via classification of back trajectories by cluster analysis and neural network techniques", Chemometrics and Intelligent Laboratory Systems, 83(1), 26-33. https://doi.org/10.1016/j.chemolab.2005.12.009
  17. Ozcan, H.K., Ucan, O.N., Sahin, U., Borat, M. and Bayat, C. (2006), "Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz-Odayeri landfill site", J. Sci. Ind. Res., 65(2), 128 p.
  18. Ruiz-Suarez, J.C., Mayora-Ibarra, O.A., Torres-Jimenez, J. and Ruiz-Suarez, L.G. (1995), "Short-term ozone forecasting by artificial neural networks", Adv. Eng. Software, 23(3), 143-149. https://doi.org/10.1016/0965-9978(95)00076-3
  19. Tasadduq, I., Rehman, S. and Bubshait, K. (2002), "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia", Renew. Energy, 25(4), 545-554. https://doi.org/10.1016/S0960-1481(01)00082-9
  20. US.EPA (1993), Integrated Risk Information System on Ethylbenzene, Cincinnati, OH, USA, P 36, 99, 124.
  21. Viotti, P., Liuti, G. and Genova, P.Di. (2002), "Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia", Ecol. Model., 148(1), 27-46. https://doi.org/10.1016/S0304-3800(01)00434-3
  22. Voukantsis, D., Karatzas, K., Kukkonen, J., Rasanen, T., Karppinen, T. and Kolehmainen, M. (2011), "Inter comparison of air quality data using principal component analysis, and forecasting of $PM_{10}$ and $PM_{2.5}$ concentrations using artificial neural networks, in Thessaloniki and Helsinki", Sci. Total Environ., 409(7), 1266-1276. https://doi.org/10.1016/j.scitotenv.2010.12.039
  23. Wu, W., Dandy, G.C. and Maier, H.R. (2014), "Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling", Environ. Model. Software J., 54, 108-127. https://doi.org/10.1016/j.envsoft.2013.12.016
  24. Yi, J. and Prybutok, V.R. (1996), "A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area", Environ. Pollut., 92(3), 349-357. https://doi.org/10.1016/0269-7491(95)00078-X

Cited by

  1. Air quality forecasting based on cloud model granulation vol.2018, pp.1, 2018, https://doi.org/10.1186/s13638-018-1116-3