Asian Journal of Atmospheric Environment

  • 제9권1호
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  • Pages.12-21
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  • 2015
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  • 1976-6912(pISSN)
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  • 2287-1160(eISSN)
한국대기환경학회 (Korean Society for Atmospheric Environment)
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Predicting Atmospheric Concentrations of Benzene in the Southeast of Tehran using Artificial Neural Network

  • 투고 : 2014.07.08
  • 심사 : 2014.09.24
  • 발행 : 2015.03.31
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초록

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

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참고문헌

  1. Al-Alawi, S.M., Abdul-Wahab, S.A., Bakheit, C.S. (2008) Combining Principal Component Regression and Artificial Neural Networks for More Accurate Predictions Of Ground-Level Ozone. Environmental Modelling & Software 23, 396-403. https://doi.org/10.1016/j.envsoft.2006.08.007
  2. Asadollahfardi, G., Taklify, A., Ghanbari, A. (2012) Application of Artificial Neural Network to Predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering 138(4), 363-370. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000402
  3. Bodaghpour, S., Abrahemi, M., Javadi, S. (2011) Prediction of Air Quality in District 12 in Tehran using the ANN. The 6th National Congress of Civil Engineering, University of Semnan, Semnan, Iran.
  4. Charkhastani, A., Bodaghpour, S. (2008) Prediction of Air Pollution Concentration in Tehran using Neural Network. 2nd Environment Conference, University of Tehran, Tehran.
  5. Chattopadhay, S., Chattopadhay, G. (2012) Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis. Pure and Applied Geophyscis 169, 1891-1908. https://doi.org/10.1007/s00024-011-0437-5
  6. Cruz-Nunez, X., Hernandez-Solis, J.M., Ruiz-Suarez, L.G. (2003) Evaluation of Vapor Recovery System's Efficiency and Personal Exposure in Service Stations in Mexico City. Science of the Total Environment 309, 59-68. https://doi.org/10.1016/S0048-9697(03)00048-2
  7. Cybenko, G. (1989) Approximate by Superpositions of a Sigmoid Function. Mathematics of Control. Signals, and System 2(4), 303-314. https://doi.org/10.1007/BF02551274
  8. Dawson, C.W., Wibly, R.L. (2001) Hydrological Modeling using Artificial Neural Networks. Progress in Physical Geography 25(80), 81-108.
  9. Dean, B.J. (1985) Recent Findings On The Genetic Toxicology of Benzene, Toluene, Xylenes and Phenols. Mutation Research/Review in Genetic Toxicology 154, 153-181. https://doi.org/10.1016/0165-1110(85)90016-8
  10. Grivas, G., Chaloulakou, A. (2006) Artificial Neural Network Models for Prediction of $PM_{10}$ Hourly Concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment 40, 1216-1229. https://doi.org/10.1016/j.atmosenv.2005.10.036
  11. Haiming, Z., Xiaoxiao, S. (2013) Study on prediction of Atmospheric $PM_{2.5}$ based on RBF Neural Network. 4th International Conference on Digital Manufacturing and Automation, June, Qindao, Shandong, China.
  12. Heckman, J.J. (1979) Sample Selection Bias as a Specification Error. Econometrica: Journal of the Econometric Society 153-161.
  13. Hornik, K.M. (1991) Approximation Capabilities of Multilayer Feed Forward Networks. Neural Networks 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
  14. Hornik, K.M. (1993) Some new Results on Neural Network Approximation. Neural Networks 6(8), 1069-1072. https://doi.org/10.1016/S0893-6080(09)80018-X
  15. Hornik, K.M., Stinchocombe, M., White, H. (1989) Multilayer Feed Forward Networks are Universal Approximators. Neural Networks 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  16. International Agency for Research on Cancer (1988) Overall Evaluation of Carcinogenicity: An Update of IARC Monographs [IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Suppl 7] IARC, Lyon France. Vol 1-44.
  17. Jo, W.K., Song, K.B. (2001) Exposure to Volatile Organic Compounds for Individuals with Occupations Associated with Potential Exposure to Motor Vehicle Exhaust and/or Gasoline Vapor Emissions. Science of the Total Environment 269(1-3), 25-37. https://doi.org/10.1016/S0048-9697(00)00774-9
  18. Kennedy, J.B., Neville, A.D. (1964) Basic Statistical Methods for Engineers and Scientists. 2nd Ed. Harper and Row, New York.
  19. Kohohen, T. (1984) Self-Organization and Associative Memory. New York: Springer-Verlag.
  20. Kurt, A., Gulbagci, B., Karaca, F., Alagha, O. (2008) An Online Air Pollution Forecasting System using Neural Networks. Environment International 34, 592-598. https://doi.org/10.1016/j.envint.2007.12.020
  21. Leshno, M., Lin, V.Y., Pinkus, A., Schocken, S. (1993) Multilayer Feed Forward Networks with a Nonpolynomial Activation Function can approximate any Function. Neural Networks 6(6), 861-867. https://doi.org/10.1016/S0893-6080(05)80131-5
  22. Maltoni, C., Ciliberti, A., Pinto, C., Soffritti, M., Belpoggi, F., Menarini, L. (1990) Results of Long Term Experimental Carcinogenicity Studies of the Effects of Gasoline, Correlated Fuels and Major Gasoline Aromatic in Rats, Ann. Clean Air Act Amendments: Part A, Section 112, pp. 101-549.
  23. Menhaj, M. (1998) Computational Intelligence, Fundamentals of Artificial Neural Networks, Vol. 1 Amirkabir University publisher, Tehran, Iran.
  24. Menhaj, M., Safpour (1998) Computational Intelligence, Application of Artificial Neural Networks, Vol. 2, professor Hasabi publisher, Tehran, Iran.
  25. Moustris, K.P., Ziomas, I.C., Paliatsos, A.G. (2010) 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants $NO_2$, CO, $SO_2$, and $O_3$ using a neural Networks in Athens, Greece. Water, Air and Soil Pollution 209, 29-43. https://doi.org/10.1007/s11270-009-0179-5
  26. Mysteries, K.P., Larissi, I.K., Nastos, P.T., Koukouletsos, K.V., Paliatsos, A.G. (2013) Development and Application of Artificial Neural Network Modeling in Forecasting $PM_{10}$ Levels in a Mediterranean City. Water, Air and Soil Pollution 224(1634), 3-11.
  27. Noori, A., Eshraghi, A., Ajdarpour, A. (2013) Comparison of the ANN Neural Network with Multivariate Linear Regression Analysis for Perdiction of Daily Carbon Monoxide: A Case Study, Tehran. 7th national congress of civil engineering, Sistan and Zahedan University, Zahedan, Iran.
  28. Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E., 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
  29. Ozcan, H.K., Ucan, O.N., Sahin, U., Borat, M., Bayat, C. (2006) Artificial Neural Network Modeling of Methane Emissions at Istanbul Kemerburgaz-Odayeri Landfill site. Journal of Scientific and Industrial Research 65(2), 128-134.
  30. Ruiz-Suarez, J.C., Mayora-Ibarra, O.A., Torres-Jimenez, J., Ruiz-Suarez, L.G. (1995) Short-term Ozone Forecasting with Artificial Neural Networks. Advances in Engineering Software 23(3), 43-149.
  31. Song, X.M. (1996) Radial Basis Function Networks for Empirical Modeling of Chemical Process. MSc Thesis, University of Helsinki.
  32. Sousa, S.I.V., Martins, F.G., Pereira, M.C., Alvim-Ferraz, M.C.M. (2006) Prediction of Ozone Concentrations in Oporto City with Statistical Approaches. Chemosphere 64, 1141-1149. https://doi.org/10.1016/j.chemosphere.2005.11.051
  33. Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., Pereira, M.C. (2007) Multiple Linear Regression and Artificial Neural Networks based on Principal Components to Predict Ozone Concentrations. Environmental Modelling and Software 22, 97-103. https://doi.org/10.1016/j.envsoft.2005.12.002
  34. Sun, G., Hoff, S.J., Zelle, B.C., Nelson, M.A. (2008) Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis function network. Journal of Air and Waste Management 58, 1571-1576. https://doi.org/10.3155/1047-3289.58.12.1571
  35. Tasadduq, I., Rehman, S., Bubshait, K. (2002) Application of Neural Networks for the Prediction of Hourly Mean Surface Temperatures in Saudi Arabia. Renewable Energy 25(4), 545-554. https://doi.org/10.1016/S0960-1481(01)00082-9
  36. Wallace, L. (1990) Major Sources of Exposure to Benzene and other Volatile Organic Chemicals. Risk Anal 10, 59-64. https://doi.org/10.1111/j.1539-6924.1990.tb01020.x
  37. Willmott, C.J., Robeson, S.M., Matsuura, K. (2012). A refined Index of Model Performance. International Journal of Climatology 32(13), 2088-2094. https://doi.org/10.1002/joc.2419
  38. World Health Organization Regional Office for Europe (1989) Indoor Air Quality Organic Pollutants, Report on a WHO meeting. p. 56-71.

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