Air pollution episodes: Modelling tools for improved smog management (APPETISE)

Most ambient air quality models are deterministic models or rely upon simple regression based statistics. Their success, however, is limited either by their failure to capture the non-linear behaviour of air pollutants, or the incomplete understanding of the physical and chemical processes involved. The APPETISE project aims to develop and test the suitability of novel non-linear statistical methods to improve the ability to accurately forecast variations in air quality. It also aims to develop methods for handling missing data, which will have generic applications for other real data situations. The work is being carried out over a period of 2 years by a consortium from 9 institutions from 5 different European countries and is funded under the European Union Fifth Framework Programme. The project concentrates on 4 key pollutants; nitrogen oxides, particulates, ground level ozone and sulphur dioxide. Since it is likely that different methods and models will work best under different situations an ensemble approach will be utilised to improve the confidence held in any given prediction. The project will work towards the construction of a prototype air quality prediction and warning system the performance of which will be tested against existing systems.