Modelling SO2 concentration at a point with statistical approaches

Nunnari, Giuseppe; Dorling, Stephen; Schlink, Uwe; Cawley, Gavin; Foxall, Rob; Chatterton, Tim

Abstract

In this paper, the results obtained by inter-comparing several statistical techniques for modelling SO2 concentration at a point such as neural networks, fuzzy logic, generalised additive techniques and other recently proposed statistical approaches are reported. The results of the inter-comparison are the fruits of collaboration between some of the partners of the APPETISE project funded under the Framework V Information Societies and Technologies (IST) programme. Two different cases for study were selected: the Siracusa industrial area, in Italy, where the pollution is dominated by industrial emissions and the Belfast urban area, in the UK, where domestic heating makes an important contribution. The different kinds of pollution (industrial/urban) and different locations of the areas considered make the results more general and interesting. In order to make the inter-comparison more objective, all the modellers considered the same datasets. Missing data in the original time series was filled by using appropriate techniques. The inter-comparison work was carried out on a rigorous basis according to the performance indices recommended by the European Topic Centre on Air and Climate Change (ETC/ACC). The targets for the implemented prediction models were defined according to the EC normative relating to limit values for sulphur dioxide. According to this normative, three different kinds of targets were considered namely daily mean values, daily maximum values and hourly mean values. The inter-compared models were tested on real cases of poor air quality. In the paper, the inter-compared techniques are ranked in terms of their capability to predict critical episodes. A ranking in terms of their predictability of the three different targets considered is also proposed. Several key issues are illustrated and discussed such as the role of input variable selection, the use of meteorological data, and the use of interpolated time series. Moreover, a novel approach referred to as the technique of balancing the training pattern set, which was successfully applied to improve the capability of ANN models to predict exceedences is introduced. The results show that there is no single modelling approach, which generates optimum results in terms of the full range of performance indices considered. In view of the implementation of a warning system for air quality control, approaches that are able to work better in the prediction of critical episodes must be preferred. Therefore, the artificial neural network prediction models can be recommended for this purpose. The best forecasts were achieved for daily averages of SO2 while daily maximum and hourly mean values are difficult to predict with acceptable accuracy. © 2003 Elsevier Ltd. All rights reserved.

Citation

Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R., & Chatterton, T. (2004). Modelling SO2 concentration at a point with statistical approaches. Environmental Modelling and Software, 19(10), 887-905. https://doi.org/10.1016/j.envsoft.2003.10.003

• Journal Article Type: Article
• Publication Date: Aug 1, 2004
• Journal: Environmental Modelling and Software
• Print ISSN: 1364-8152
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 10
• Pages: 887-905
• DOI: https://doi.org/10.1016/j.envsoft.2003.10.003
• Keywords: air pollution, neural models, neuro-fuzzy model, linear model, phase-space model, generalised additive models
• Public URL: https://uwe-repository.worktribe.com/output/1062670
• Publisher URL: http://dx.doi.org/10.1016/j.envsoft.2003.10.003
• Additional Information: Additional Information : Equal co-author. Chatterton, researcher on IST RTD project 2000-2002 involving 9 institutions in 5 countries developing a variety of modelling tools for improved smog management. This paper contributed to air quality managers' understanding and decision making for managing point source sulphur dioxide emissions.