Abstract
Tropospheric Ozone has been recognised as a harmful pollutant since it accumulates when the natural equilibrium between ozone and nitrogen dioxide is altered due to the presence of air pollutants (e.g. volatile compounds) in presence of high values of the solar radiation and with the contribution of not favourable meteo-climatic conditions (low wind speed, opaque cloud cover etc) [1]. In this paper we present some results concerning the modelling of Ozone time series in an area characterised by a high density of petrochemical plants. In order to handle the difficulties which arise in identifying non-linear systems with traditional techniques, neural networks are used to build 1-day-ahead prediction models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Chu, Meteorological considerations in siting photochemical pollutants monitors, Atmosperic Environment, 29, pp 2905–2913.
G. Nunnari, A. Nucifora, C. Randieri, “The Application of Neural Techniques to the Modelling of Time Series of Atmospheric Pollution Data”, Ecological Modelling, Vol. III, pp. 187–205, 1998.
E. Pelikan, K. Eben, J. Vondracek, P. Krejcir, Ground level ozone peak forecasts using neural networks and Kaiman filter, Submitted to Meteorological Journal, ISSN 1335- 339X, Slovakia
R. M. van Aalst, F. A. A. M. de Leeuw (editors), National Ozone Forecasting System and International Data Exchange in Northwest Europe, European Topic Centre on Air Quality, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nunnari, G., Nucifora, A. (2002). A Neural Prediction Model for the Maximum Daily Ozone Concentration. In: Sportisse, B. (eds) Air Pollution Modelling and Simulation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04956-3_54
Download citation
DOI: https://doi.org/10.1007/978-3-662-04956-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07637-4
Online ISBN: 978-3-662-04956-3
eBook Packages: Springer Book Archive