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Students should check their experiments every day for signs of growth. Care should be employed when examining the sponges under a stereomicroscope since the gemmules may take more than a day to attach firmly to the bottom of the well. Magnification at 30 X or less should be sufficient for the visualization of growth. As the experiments progress, students will be able to detect major growth abnormalities immediately, but should also look for more subtle developmental abnormalities (such as the absence of a well defined water vascular system). As mentioned earlier, normal sponge growth. | 25 Artificial Neural Networks -a Useful Tool in Air Pollution and Meteorological Modelling Primoz Mlakar and Marija Zlata Boznar MEIS environmental consulting d.o.o. Slovenia 1. Introduction Artificial neural networks have become a widely used tool in several air pollution and meteorological applications. Yi and Prybutok 1996 used MPNN for surface ozone predictions as well as Comrie 1997 . Several prediction models were also made for other pollutants for instance for SO2 Boznar et al. 1993 and for CO Moseholm et al. 1996 . Marzban Stumpf 1996 used MPNN for predicting the existence of tornadoes. A review article by Gardner 1998 described a variety of applications mainly in the field of air pollution forecasting and pattern classification. Though the number of applications is growing especially in recent years no special attention has been paid to the principles of artificial neural network usage in environmental applications. Our group first established a method for short term forecasting of SO2 concentrations on the basis of a multilayer perceptron neural network Boznar et al 1993 but in the following years we use an artificial neural networks in several other applications that differ very much each another. In this article we intend to show examples of a variety of applications of artificial neural networks in air pollution and the meteorological field. Examples are taken from our past experience extending over a decade. Several applications in this field start from fundamentals and too much attention is paid to optimization and speeding up of the learning algorithms. From our experience this should be a minor problem for an environmental modeller and does not significantly affect the final model quality if modern tools are used. In the process of model construction other factors are much more crucial - such as feature determination pattern selection and learning process optimization. These are the methods that are derived from the basic principle of artificial .
