Evolving Gaussian process models for prediction of ozone concentration in the air

Ozone is one of the main air pollutants with harmful influence to human health. Therefore, predicting the ozone concentration and informing the population when the air-quality standards are not being met is an important task. In this paper, various first- and high-order Gaussian process models for prediction of the ozone concentration in the air of Bourgas, Bulgaria are identified off-line based on the hourly measurements of the concentrations of ozone, sulphur dioxide, nitrogen dioxide, phenol and benzene in the air and the meteorological parameters, collected at the automatic measurement stations in Bourgas. Further, as an alternative approach an on-line updating (evolving) Gaussian process model is proposed and evaluated. Such an approach is needed when the training data is not available through the whole period of interest and consequently not all characteristics of the period can be trained or when the environment, that is to be modelled, is constantly changing.