Small-Particle Pollution Modeling Using Fuzzy Approaches

  • Àngela Nebot
  • Francisco Mugica
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 256)


Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick layer of smog that float over the city. Modeling air pollution is a political and administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM2.5). In this work two different fuzzy approaches for modeling PM2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.


Air Pollution Modeling PM2.5 Pollution Fuzzy Inductive Reasoning ANFIS Persistence Time Series Analysis 


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  1. 1.
    WHO World healt oranization. Air quality guidelines: the global update 2005 (2006) Google Scholar
  2. 2.
    van Donkelaar, A., Martin, R., Verduzco, C., Brauer, M., Kahn, R., Levy, R., Villeneuve, P.: 2010. A Hybrid Approach for Predicting PM2.5 Exposure: van Donkelaar et al. Respond. Environ. Health Perspect. 118(10), 425 (2010)CrossRefGoogle Scholar
  3. 3.
    NWM: National Weather Service of Mexico (2012),
  4. 4.
    Mintz, R., Young, B.R., Svrcek, W.Y.: Fuzzy logic modeling of surface ozone con-centrations. Computers & Chemical Engineering 29, 2049–2059 (2005)CrossRefGoogle Scholar
  5. 5.
    Ghiaus, C.: Linear fuzzy-discriminant analysis applied to forecast ozone concentration classes in sea-breeze regime. Atmospheric Environment 39, 4691–4702 (2005)CrossRefGoogle Scholar
  6. 6.
    Morabito, F.C., Versaci, M.: Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data. Neural Networks 16, 493–506 (2003)CrossRefGoogle Scholar
  7. 7.
    Heo, J.S., Kim, D.S.: A new method of ozone forecasting using fuzzy expert and neural network system. Sicence of the Total Environment 325, 221–237 (2004)CrossRefGoogle Scholar
  8. 8.
    Yildirim, Y., Bayramoglu, M.: Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. Chemosphere 63, 1575–1582 (2006)CrossRefGoogle Scholar
  9. 9.
    Peton, N., Dray, G., Pearson, D., Mesbah, M., Vuillot, B.: Modelling and analysis of ozone episodes. Environmental Modelling & Software 15, 647–652 (2000)CrossRefGoogle Scholar
  10. 10.
    Onkal-Engin, G., Demir, I., Hiz, H.: Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation. Atmospheric Environment 38, 3809–3815 (2004)CrossRefGoogle Scholar
  11. 11.
    Klir, G., Elias, D.: Architecture of Systems Problem Solving, 2nd edn. Plenum Press, New York (2002)Google Scholar
  12. 12.
    Nebot, A., Mugica, F., Cellier, F., Vallverdú, M.: Modeling and Simulation of the Central Nervous System Control with Generic Fuzzy Models. Simulation 79(11), 648–669 (2003)CrossRefGoogle Scholar
  13. 13.
    Carvajal, R., Nebot, A.: Growth Model for White Shrimp in Semi-intensive Farming using Inductive Reasoning Methodology. Computers and Electronics in Agriculture 19, 187–210 (1998)CrossRefGoogle Scholar
  14. 14.
    Escobet, A., Nebot, A., Cellier, F.E.: Visual-FIR: A tool for model identification and prediction of dynamical complex systems. Simulation Modelling Practice and Theory 16, 76–92 (2008)CrossRefGoogle Scholar
  15. 15.
    Nauck, D., Klawonn, F., Kruse, R.: Neuro-Fuzzy Systems. John Wiley & Sons (1997)Google Scholar
  16. 16.
  17. 17.
    Muñoz, R., Carmona, M.R., Pedroza, J.L., Granados, M.G.: Data analysis of PM2.5 registered with TEOM equipment in Azcapotzalco (AZC) and St. Ursula (SUR) stations of the automatic air quality monitoring network (RAMA). In: National Congress of Medicine Engineering and Ambient Sciences, pp. 21–24 (2000) (in Spanish)Google Scholar
  18. 18.
    Pérez, P., Trier, A., Reyes, A.: Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environment 34, 1189–1196 (2000)CrossRefGoogle Scholar
  19. 19.
    Cobourn, W.G.: An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmospheric Environment 44, 3015–3023 (2010)CrossRefGoogle Scholar
  20. 20.
    Salini, G., Perez-Jara, P.: Time series analysis of atmosphere pollution data using artificial neural networks technique. Revista Chilena de Ingeniería 14(3), 284–290 (2006)Google Scholar
  21. 21.
    Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., Kenski, D.: PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining. Expert Systems with Applications 36, 9046–9055 (2009)CrossRefGoogle Scholar
  22. 22.
    Kang, D., Mathur, R., Trivikrama Rao, S.: Assessment of bias-adjusted PM2.5 air quality forecast over the continental United States during 2007. Geoscience Model Dev. 3, 309–320 (2010)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Soft Computing Research GroupTechnical University of CataloniaBarcelonaSpain

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