Journals / Journal of International Environmental Application and Science / 2016 / Cilt: 11 - Sayı: 4

Used Some Modelling Applications in Air Pollution Estimates

Pages
418–425
Publication Date
2016-12-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results were compared and try to select the most suitable modelling program.

📌 3 Key Takeaways

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Objective & Hypothesis:Air Pollution is produced by airborne Sulphur dioxide (SO2), particulate matter (PM), nitrogen oxides (NOx) and ozone (O3) of pollutants in the environment and defined as the level that will have a negative impact on human health.
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Key Findings & Data:This pollution disrupts natural processes in the atmosphere and affects public health and comfort.
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Conclusion & Impact:The results were compared and try to select the most suitable modelling program.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Air pollution · modelling · ANN

Abstract

Air Pollution is produced by airborne Sulphur dioxide (SO2), particulate matter (PM), nitrogen oxides (NOx) and ozone (O3) of pollutants in the environment and defined as the level that will have a negative impact on human health. This pollution disrupts natural processes in the atmosphere and affects public health and comfort. In the developing world, industry and human population growth poses a risk in terms of environmental pollution. Therefore, it is important to estimate air pollution and measures taken in advance. Some modelling applications used for this purpose include the commonly used Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System models. In this study; compared different modelling programs with some gases which cause air pollution were estimated. The results were compared and try to select the most suitable modelling program.

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Cite This Article

KUNT, F., AYTURAN, Z. C., DURSUN, S. (2016). Used Some Modelling Applications in Air Pollution Estimates. Journal of International Environmental Application and Science, Cilt: 11 - Sayı: 4, 418–425. https://ajindex.com/articles/376717

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References (12)

  1. 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. & Pollution, 40, 301-321.
  2. Asadollahfardi G., Zangooei H, Aria SH., (2016) Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City, Asian J. Atmos. Environ., 10, 67-79.
  3. Basheer IA, Hajmeer M, (2000) Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol.l Meth., 43, 3-31. Battiti R, (1992) First and second order methods for learning between steepest descent and Newton’s method, Neural Comput. 4, 141–166. Bonissone P, (2002), Adaptive Neural Fuzzy Inference Systems (ANFIS): Analysis and Applications, Online avalible from https://www.researchgate.net/file.PostFileLoader.html?id.
  4. Boube.R, Fox DL, Turner DB, Stern AC, (1994) Fundamentals of Air Pollution, 3rd Edition,
  5. Elsevier, USA, 3-150,.
  6. Chelani AB, Chalapati Rao CV, Phadke KM, (2001)
  7. Hasan M. Z, Prediction of sulphur dioxide concentration using artificial neural networks, Environ. Model. & Software, 17, 161–168.
  8. Curtis LW, Rea P, Smith-Willis E, Fenyves PY, (2006) Adverse health effects of outdoor air pollutants. Environ Int., 32, 815–830. Dehkordi MB, (2012) Compressive Sensing Based Compressed Neural Network for Sound Source Localization, Am. J. Intel. Syst., 2, 35-39.
  9. Demir G, Altay GC, Sakar O, Albayrak S, Ozdemir H, Yalcin S, (2008) Prediction and evaluation of tropospheric ozone concentration in Istanbul using artificial neural network modeling according to time parameter, J. Sci. & Ind. Res., 67, 674-679.
  10. Dib S, Ferdi B, Benachaiba C, (2011) Adaptive Neuro-Fuzzy Inference System based DVR Controller Design, Online avalible from (http://lejpt.academicdirect.org/A18/get_htm.php?htm=049_064).
  11. 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, Int. J. Environ. Sci. & Tech., 12, 3915-3928. Fuller AD, (1995) Neural fuzzy systems. Abo Akademi University, Abo. Gardner MW, Dorling SR, (1998) Artificial neural networks: the multilayer perceptron: a review of applications in atmospheric sciences, Atmos. Environ. 32, 2627–2636.
  12. Hanafy TOS, Al-Osaimy AS, Merabtine N, (2015) Identification and Modeling of Air Pollutions using Adaptive Nero Fuzzy Systems (ANFIS), Int. J. Comp. & Inform. Tech. 4, 2. Haykin S, (2001) Neural N