Pre-Conditioning Approach to Bayesian Decision Networks for Water Quality Sensors Positioning in Urban Drainage Systems

In the last decades, the growth of mini- and micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Such pollutants are usually characterized by low removal efficiencies in urban wastewater treatment plants and they may have an acute or cumulative impact on environment. In order to facilitate early detection and efficient containment of the illicit intrusions, the present work aims to develop a decision-support approach for positioning the water quality sensors. It is mainly based on the use of a decision-making support of the BDN type (Bayesian Decision Network), specifically looking soluble conservative pollutants, such as metals. In the application and result section the methodology is tested on two sewer systems, with increasing complexity: a literature scheme from the SWMM manual and a real combined sewer.