Cyclostationary Neural Networks for Air Pollutant Concentration Prediction

  • Monica Bianchini
  • Ernesto Di Iorio
  • Marco Maggini
  • Augusto Pucci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)

Abstract

There are many substances in the air which may impair the health of plants and animals, including humans, that arise both from natural processes and human activity. Nitrogen dioxide NO2 and particulate matter (PM10, PM2.5) emissions constitute a major concern in urban areas pollution. The state of the air is, in fact, an important factor in the quality of life in the cities, since it affects the health of the community and directly influences the sustainability of our lifestyles and production methods. In this paper we propose a cyclostationary neural network (CNN) model for the prediction of the NO2 and PM10 concentrations. The cyclostationary nature of the problem guides the construction of the CNN architecture, which is composed by a number of MLP blocks equal to the cyclostationary period in the analyzed phenomenon, and is independent from exogenous inputs. Some experiments are also reported in order to show how the CNN model significantly outperforms standard statistical tools and linear regressors usually employed in these tasks.

Keywords

Nitric Oxide Nitrogen Dioxide Prediction Task Photochemical Smog Hourly Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Cecchetti, M., Corani, G., Guariso, G.: Artificial neural networks prediction of PM10 in the Milan area. In: 2nd International Environmental Modelling and Software Society Conference (2004)Google Scholar
  2. 2.
    Collet, R.S., Oduyemi, K.: Air quality modelling: A technical review of mathematical approaches. Metereological Applications 4(3), 235–246 (1997)CrossRefGoogle Scholar
  3. 3.
    Corani, G., Barazzetta, S.: First results in the prediction of particulate matter in the Milan area. In: 9th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (2004)Google Scholar
  4. 4.
    Finzi, G., Volta, M., Nucifora, A., Nunnari, G.: Real time ozone episode forecast: A comparison between neural network and grey box models. In: Proceedings of International ICSC/IFAC Symposium of Neural Computation, pp. 854–860. ICSC Academic Press, London (1998)Google Scholar
  5. 5.
    Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron) – A review of applications in the atmospheric sciences. Atmospheric Environment 32(14–15), 2627–2636 (1998)CrossRefGoogle Scholar
  6. 6.
    Gardner, M.W., Dorling, S.R.: Neural network modelling and prediction of hourly NOx and NO2 concentration in urban air in London. Atmospheric Environment 33, 709–719 (1999)CrossRefGoogle Scholar
  7. 7.
    Goyal, P., Chanb, A.T., Jaiswa, N.: Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmospheric Environment 40(11), 2068–2077 (2006)CrossRefGoogle Scholar
  8. 8.
    Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O.: A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment 39(18), 3279–3289 (2005)CrossRefGoogle Scholar
  9. 9.
    Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S.R., Chatterton, T., Foxall, R., Cawley, G.: Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmospheric Environment 37, 4539–4550 (2003)CrossRefGoogle Scholar
  10. 10.
    Ljung, L.: System Identification — Theory for the User, 2nd edn. PTR Prentice Hall, Upple Saddle River (1999)Google Scholar
  11. 11.
    Lu, W.Z., Fan, H.Y., Lo, S.M.: Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing 51, 387–400 (2003)CrossRefGoogle Scholar
  12. 12.
    Lu, W.Z., Wang, W.J., Xu, Z.B., Leung, A.Y.: Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong. Environmental Monitoring and Assessment 87(3), 235–254 (2003)CrossRefGoogle Scholar
  13. 13.
    Morabito, F.C., Versaci, M.: Wavelet neural network processing of urban air pollution. In: Proceedings of IJCNN 2002, Honolulu (Hawaii), vol. 1, pp. 432–437. IEEE, Los Alamitos (2002)Google Scholar
  14. 14.
    Nunnari, G., Cannavò, F.: Modified cost functions for modelling air quality time series by using neural networks. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 723–728. Springer, Heidelberg (2003)Google Scholar
  15. 15.
    Ordieres, J.B., Vergara, E.P., Capuz, R.S., Salazar, R.E.: Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environmental Modelling and Software 20(5), 547–559 (2005)CrossRefGoogle Scholar
  16. 16.
    Ostro, B., Chestnut, L., Vichit-Vadakan, N., Laixuthai, A.: The impact of particulate matter on daily mortality in Bangkok, Thailand. Journal of Air and Waste Management Association 49, 100–107 (1999)Google Scholar
  17. 17.
    Papoulis, A.: Probability, Random Variables, and Stochastic Processes, 3rd edn. McGraw–Hill, New York (1991)Google Scholar
  18. 18.
    Perez, P., Reyes, J.: Prediction of maximum of 24–h average of PM10 concentrations 30 h in advance in Santiago, Chile. Atmospheric Environment 36, 4555–4561 (2002)CrossRefGoogle Scholar
  19. 19.
    Pope, C.A., Burnett, R., Thun, M.J., Calle, E.E., Krewskik, D., Ito, K., Thurston, G.D.: Lung cancer, cardiopulmonary mortality, and long term exposure to fine particulate air pollution. Journal of the American Medical Association 287, 1132–1141 (2002)CrossRefGoogle Scholar
  20. 20.
    Pope, C.A., Thun, M.J., Namboodiri, M.M., Dockery, D.W., Evans, J.S., Speizer, F.E., Heath, C.W.: Particulate air pollution as predictor of mortality in a prospective study of US adults. American Journal of Respiratory and Critical Care Medicine 151, 669–674 (1995)Google Scholar
  21. 21.
    Wang, J.Y., Lord, E., Cannon, A., Walters, G.: Statistical models for spot air quality forecasts (O3 and PM10) in British Columbia. In: Proceedings of the 2005 Puget Sound Georgia Basin Research Conference, Seattle (2005)Google Scholar
  22. 22.
    Zolghadri, A., Henry, D.: Minimax statistical models for air pollution time series, Application to ozone time series data measured in Bordeaux. Environmental Monitoring and Assessment 98(1–3), 275–294 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Monica Bianchini
    • 1
  • Ernesto Di Iorio
    • 1
  • Marco Maggini
    • 1
  • Augusto Pucci
    • 1
  1. 1.Dipartimento di Ingegneria dell’Informazione SienaItaly

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