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Nonlinear models for ground-level ozone forecasting

Author

Listed:
  • Silvano Bordignon

    (Università di Padova)

  • Carlo Gaetan

    (Università di Padova)

  • Francesco Lisi

    (Università di Padova)

Abstract

One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop statistical models giving shortterm forecasts of future ground-level ozone concentrations. Since there are few physical insights about the dynamic relationship between ozone, precursor emissions and/or meteorological factors, a nonparametric and nonlinear approach seems promising in order to specify the forecast models. First, we apply four nonparametric procedures to forecast daily maximum 1-hour and maximum 8-hour averages of ozone concentrations in an urban area. Then, in order to improve the forecast performances, we combine the time series of the forecasts. This idea seems to give encouraging results.

Suggested Citation

  • Silvano Bordignon & Carlo Gaetan & Francesco Lisi, 2002. "Nonlinear models for ground-level ozone forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 227-245, June.
    • Handle: RePEc:spr:stmapp:v:11:y:2002:i:2:d:10.1007_bf02511489
    DOI: 10.1007/BF02511489
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    References listed on IDEAS

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    1. Wolfgang Härdle & Helmut Lütkepohl & Rong Chen, 1997. "A Review of Nonparametric Time Series Analysis," International Statistical Review, International Statistical Institute, vol. 65(1), pages 49-72, April.
    2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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