Bayesian Techniques are attractive and viable tools for modeling complex stochastic processes in general and the groundwater contamination process in particular. This is mainly because these techniques do not only emphasize the stochastic nature of this process but also the precision and the accuracy of the tested methods used by environmental laboratories. In this work, we describe the development and application of a prototype Dynamic Bayesian Network (DBN) that models groundwater quality in order to assess and predict the impact of pollutants on the water column.