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Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks

Year 2016, Volume: 11 Issue: 1, 1 - 7, 30.03.2016

Z. Cansu Ozturk

Abstract

Air
pollution has become a major environmental problem since last century because
of the effects of fast population growth and industrial developments. Sulphur
dioxide is considered as one of the major and most common air pollutant with
using fossil fuels causing severe health problems such as disrupting tissues
and mucous membranes of the eyes, disturbing nose and throat because of the
irritating toxic odour, and affecting badly to upper part of respiratory system
and bronchi. Seydişehir town of Konya was selected as working area for this
study because heavy industrial activities are very wide in many fields such as
mining and manufacturing industry. Also, usage of fossil fuels for heating
system in winter period is other important atmospheric pollutants source. Eti
Aluminium facility is the biggest industrial unite for SO2 pollution
source in Seydişehir town. In this study, SO2 pollution in
Seydişehir town was modelled with Artificial Neural Networks (ANN) which uses
characteristics of biological neurons and capable of solving highly complex
problems constructing parallel computations. Meteorological factors and
previous day’s SO2 concentrations were integrated to model as input parameters
and next day’s SO2 concentration was tried to be predicted. Two
seasons were selected for model development namely winter and summer.
Prediction performances of develop models are 67% for winter season and 81% for
summer season. These values are compatible compared with previous studies using
ANN modelling and can be improved with larger data sets.

Keywords

SO2 ANN Air Pollution Modelling Prediction

References

  • Akkoyunlu A, Yetilmezsoy K, Erturk F,Oztemel E, (2010) A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area, Int. J. Environ. & Pollut., 40, 301-321.
  • Basheer IA, Hajmeer M, (2000) Artificial neural networks: fundamentals, computing, design, and application, J. Microb. Methods, 43, 3-31.
  • Chelani AB, Chalapati Rao CV, Phadke KM, Hasan MZ, (2001) Prediction of sulphur dioxide concentration using artificial neural networks, Environ. Model. & Software 17, 161–168.
  • Dursun S, Kunt, F, Taylan O, (2015) Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors, International J. Environ. Sci.& Tech., 12, 3915-3928.
  • Gardner MW, Dorling SR, (1998) Artificial neural networks (the multilayer perceptron) A review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627-2636.
  • Hornik K, Stinchcombe M, White H, (1989) Multilayer feedforward networks are universal approximators, Neural Netw. 2, 359–366.
  • Hussain ST, (2011) Sulfur Dioxide: Properties, Applications and Hazards, Nova Science Publishers, Inc., Chapter 3, 49-68.
  • M100E Operation Manual, (2011) UV Fluorescence SO2Analyzer, Teledyne Advanced Pollution Instrumentation 9480, Carroll Park Drive San Diego, CA 92121-5201, USA, http://www.teledyne-api.com/manuals/04515F_100E.pdf, retrieval date: 02.08.2015.
  • Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC, (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environ. Model. & Software,22, 97-103.
  • URL 1, 2014, Seydişehir İlçe Raporu, Mevlana Kalkınma Ajansı (MEVKA), Konya, http://www.mevka.org.tr/Download.aspx? Retrieval date: 15.07.2015.
  • URL 2, Seydişehir Eti Alüminyum Tesisi, http://www.etialuminyum.com/tr/Tesisler/Sayfalar/ Seydisehir-Eti-Aluminyum-Tesisi.aspx, retrieval date: 15.07.2015.
  • URL 3, 2007, Bart Determination for Alcoa Intalco Works Ferndale, Washington
  • http://www.ecy.wa.gov/programs/air/globalwarm_reghaze/BART/IntalcoBARTDeterminationFINAL.pdf, ENVIRON Corporation, retrieval date: 15.07.2015.
  • Viotti P, Liuti G, Di Genova P, (2002) Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecol. Model, 148, 27-46.
  • Yüksek GA, Bircan H, Zontul M, Kaynar O, (2007) Sivas İlinde Yapay Sinir Ağları ile Hava Kalitesi Modelinin Oluşturulması Üzerine Bir Uygulama, C.Ü. İktisadi ve İdari Bil. Dergisi, 8, 97-112.
  • Zannetti P, (1990)Air Pollution Modelling Theories, Computational Methods and Available Software, Springer Science + Business Media, LLC, AeroVironment Inc. Monrovia, California, 3-20.
  • Zurada JM, (1997) Introduction to Artificial Neural Systems, WestPublish. Com., Mumbai, India.

Year 2016, Volume: 11 Issue: 1, 1 - 7, 30.03.2016

Z. Cansu Ozturk

Abstract

References

  • Akkoyunlu A, Yetilmezsoy K, Erturk F,Oztemel E, (2010) A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area, Int. J. Environ. & Pollut., 40, 301-321.
  • Basheer IA, Hajmeer M, (2000) Artificial neural networks: fundamentals, computing, design, and application, J. Microb. Methods, 43, 3-31.
  • Chelani AB, Chalapati Rao CV, Phadke KM, Hasan MZ, (2001) Prediction of sulphur dioxide concentration using artificial neural networks, Environ. Model. & Software 17, 161–168.
  • Dursun S, Kunt, F, Taylan O, (2015) Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors, International J. Environ. Sci.& Tech., 12, 3915-3928.
  • Gardner MW, Dorling SR, (1998) Artificial neural networks (the multilayer perceptron) A review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627-2636.
  • Hornik K, Stinchcombe M, White H, (1989) Multilayer feedforward networks are universal approximators, Neural Netw. 2, 359–366.
  • Hussain ST, (2011) Sulfur Dioxide: Properties, Applications and Hazards, Nova Science Publishers, Inc., Chapter 3, 49-68.
  • M100E Operation Manual, (2011) UV Fluorescence SO2Analyzer, Teledyne Advanced Pollution Instrumentation 9480, Carroll Park Drive San Diego, CA 92121-5201, USA, http://www.teledyne-api.com/manuals/04515F_100E.pdf, retrieval date: 02.08.2015.
  • Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC, (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environ. Model. & Software,22, 97-103.
  • URL 1, 2014, Seydişehir İlçe Raporu, Mevlana Kalkınma Ajansı (MEVKA), Konya, http://www.mevka.org.tr/Download.aspx? Retrieval date: 15.07.2015.
  • URL 2, Seydişehir Eti Alüminyum Tesisi, http://www.etialuminyum.com/tr/Tesisler/Sayfalar/ Seydisehir-Eti-Aluminyum-Tesisi.aspx, retrieval date: 15.07.2015.
  • URL 3, 2007, Bart Determination for Alcoa Intalco Works Ferndale, Washington
  • http://www.ecy.wa.gov/programs/air/globalwarm_reghaze/BART/IntalcoBARTDeterminationFINAL.pdf, ENVIRON Corporation, retrieval date: 15.07.2015.
  • Viotti P, Liuti G, Di Genova P, (2002) Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecol. Model, 148, 27-46.
  • Yüksek GA, Bircan H, Zontul M, Kaynar O, (2007) Sivas İlinde Yapay Sinir Ağları ile Hava Kalitesi Modelinin Oluşturulması Üzerine Bir Uygulama, C.Ü. İktisadi ve İdari Bil. Dergisi, 8, 97-112.
  • Zannetti P, (1990)Air Pollution Modelling Theories, Computational Methods and Available Software, Springer Science + Business Media, LLC, AeroVironment Inc. Monrovia, California, 3-20.
  • Zurada JM, (1997) Introduction to Artificial Neural Systems, WestPublish. Com., Mumbai, India.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Z. Cansu Ozturk

Publication Date March 30, 2016
Acceptance Date December 16, 2015
Published in Issue Year 2016 Volume: 11 Issue: 1

Cite

APA Ozturk, Z. C. (2016). Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. Journal of International Environmental Application and Science, 11(1), 1-7.
AMA Ozturk ZC. Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. J. Int. Environmental Application & Science. March 2016;11(1):1-7.
Chicago Ozturk, Z. Cansu. “Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks”. Journal of International Environmental Application and Science 11, no. 1 (March 2016): 1-7.
EndNote Ozturk ZC (March 1, 2016) Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. Journal of International Environmental Application and Science 11 1 1–7.
IEEE Z. C. Ozturk, “Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks”, J. Int. Environmental Application & Science, vol. 11, no. 1, pp. 1–7, 2016.
ISNAD Ozturk, Z. Cansu. “Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks”. Journal of International Environmental Application and Science 11/1 (March 2016), 1-7.
JAMA Ozturk ZC. Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. J. Int. Environmental Application & Science. 2016;11:1–7.
MLA Ozturk, Z. Cansu. “Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks”. Journal of International Environmental Application and Science, vol. 11, no. 1, 2016, pp. 1-7.
Vancouver Ozturk ZC. Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. J. Int. Environmental Application & Science. 2016;11(1):1-7.

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