Statistical Methods and Applications

, Volume 11, Issue 2, pp 227–245 | Cite as

Nonlinear models for ground-level ozone forecasting

  • Silvano BordignonEmail author
  • Carlo Gaetan
  • Francesco Lisi
Statistical Applications


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.

Key words

Ground-level ozone forecasting nonlinear time-series models combination of forecasts 


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  1. Billings SA, Voon WSF (1986) Correlation based model validity tests for non-linear models. International Journal of Control 44:235–244zbMATHGoogle Scholar
  2. Box GEP, Jenkins GM (1976) Time Series Analysis: Forecasting and Control (second edition). Holden Day, San FranciscoGoogle Scholar
  3. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees, Wadsworth, Belmont CAzbMATHGoogle Scholar
  4. Burrows WR, Benjamin M, Beauchamp S, Lord ER, McCollor D, Thomson B (1995) CART decisiontree statistical analysis and prediction of summer season maximum surface ozone for the Vancouver, Montreal and Atlantic regions of Canada. Journal of Applied Meteorology 34:1848–1862CrossRefGoogle Scholar
  5. Chen R, Tsay RS (1993) Nonlinear additive ARX models Journal of the American Statistical Association 88:955–967MathSciNetCrossRefGoogle Scholar
  6. Clemen RT (1989) Combining forecasts: a review and annotated bibliography. International Journal of Forecasting 5:559–583CrossRefGoogle Scholar
  7. Cobourn WG, Hubbard MC (1999) An enhanced ozone forecasting model using air mass trajectory analysis. Atmospheric Environment 33:4663–4674CrossRefGoogle Scholar
  8. Comrie AC (1997) Comparing neural networks and regression models for ozone forecasting. Journal of the Air & Waste Management Association 47:653–663Google Scholar
  9. Davis JM, Speckman P (1999) A model for predicting maximum and 8h average ozone in Houston. Atmospheric Environment 33:2487–2500CrossRefGoogle Scholar
  10. De Leeuwe FAAA (2000) Criteria for evaluation of smog forecast systems. Environmental Monitoring and Assessment 60:1–14CrossRefGoogle Scholar
  11. Finzi G, Bergoli D, Volta M (1999) Modelli per la previsione di episodi critici di ozono troposferico in accordo alle linee guida europee. Atti del Convegno SCO99:399–404Google Scholar
  12. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55:119–139zbMATHMathSciNetCrossRefGoogle Scholar
  13. Friedman JH (1991) Multivariate adaptive regression splines. The Annals of Statistics 19:1–50zbMATHMathSciNetGoogle Scholar
  14. Graf-Jaccottet M, Jaunin MH (1998) Predictive models for ground ozone and nitrogen dioxide time series. Environmetrics 9:393–406CrossRefGoogle Scholar
  15. Granger CWJ, Ramanathan R (1984) Improved methods of forecasting. Journal of Forecasting 3:197–204Google Scholar
  16. Härdle W, Lütkepohl H, Chen R (1997) A review of nonparametric time series analysis. International Statistical Review 65:49–72zbMATHGoogle Scholar
  17. Hastie TJ, Tibshirani RJ (1990) Generalized Additive Models. Chapman and Hall, New YorkzbMATHGoogle Scholar
  18. Hertz J, Krogh A, Palmer R (1991) Introduction to the Theory of Neural Computation. Addison-Wesley, Reading MAzbMATHGoogle Scholar
  19. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks. Neural Networks 4: 251–257CrossRefGoogle Scholar
  20. Jorquera H, Perez R, Cipriano A, Espejo A, Letelier MV, Acuna G (1988) Forecasting ozone daily maximum level at Santiago, Chile. Atmospheric Environment 32:3415–3424CrossRefGoogle Scholar
  21. Milionis AE, Davies TD (1994) Regression and stochastic models for air pollution—I Review, comments and suggestions. Atmospheric Environment 28:2801–2810CrossRefGoogle Scholar
  22. Niu X (1996) Nonlinear additive models for environmental time series with applications to ground-level ozone data analysis. Journal of the American Statistical Association 91:1310–1321zbMATHCrossRefGoogle Scholar
  23. Prybutok VR, Yi J, Mitchell A (2000) Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations. European Journal of Operational Research 122:31–40zbMATHCrossRefGoogle Scholar
  24. Ryan WF (1995) Forecasting severe ozone episodes in the Baltimore metropolitan area. Atmospheric Environment 29:2387–2398CrossRefGoogle Scholar
  25. Seinfeld JH (1986) Atmospheric Chemistry and Physics of Air Pollution. Wiley, New YorkGoogle Scholar
  26. Sjöberg J, Zhang Q, Ljung L, Benveniste A, Delyon B, Glorennec P, Hjalmarsson H, Juditsky A (1995) Nonlinear black-box modeling in system identification: a unified overview Automatica 31:1691–1724zbMATHGoogle Scholar
  27. Venables WN, Ripley BD (1997) Modern Applied Statistics with S-Plus (second edition). Springer, New YorkzbMATHGoogle Scholar
  28. Yang Y, Barron AR (1998) An asymptotic property of model selection criteria. IEEE Trans on Information Theory 44:95–116zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2002

Authors and Affiliations

  • Silvano Bordignon
    • 1
    Email author
  • Carlo Gaetan
    • 1
  • Francesco Lisi
    • 1
  1. 1.Dipartimento di Scienze StatisticheUniversità di PadovaPadovaItaly

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