Abstract
The paper describes the status of an on-going research program to develop a highly reliable operational public warning system for air pollution monitoring in Bordeaux, France. Experimental results are presented for ground-level ozone concentrations. Meteorological variables are used as input in order to obtain an estimate of the next day’s maximum ozone concentration. Moreover, the warning system provides additional information regarding the duration of a smog episode that is very important for assessments of human health hazards and negative environmental effects. The developed methodology is based on hard and soft computing techniques. The proposed approach combines new adaptive nonlinear state space modelling techniques, a gain scheduling strategy and multi-layer perceptron neural networks. A key characteristic of such a system is that its behaviour can be adapted to the short term changes of air pollution and consequently the model can handle the time-evolving nature of the phenomena and does not need frequent adjustments. The monitoring software is developed in a MATLAB ® environment.
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