Uncertainty effects on Pollution Source Location in Water Networks.

Being the quality of drinking water a great concern in many countries, many researches have recently focused their attention to improve drinking water security threatened by deliberate or accidental intrusions in water distribution systems. In this contest, methods for detecting and controlling contaminations are required. If a contamination is detected by monitoring stations installed on the network or directly by users, the individuation of the pollutant source location is very important to stop the event as soon as possible and propose a control strategy. In this work is used a methodology to identify source location of an accidental pollution in a municipal water distribution network, using time-varying concentration measurements. Since source identification is an inverse problem, it may be ill-posed and then its results may depend strongly on the input data quality. This aspect can be crucial in a water quality inverse problem, in which, among the input data, nodal demands uncertainty and errors in concentration measurements determine a high level of uncertainty. In order to verify the robustness of the methodology respect to water demand variability and measurement errors, an analysis based on a Monte Carlo procedure is performed. The methodology effectiveness is demonstrated through an application to a midsize sample network and considering different levels of uncertainty. The results show a good frequency of identification of the right pollution source node also at high uncertainty levels. It is also observed that results depend on number and the location of water quality measurements. The maximum coverage criterion appears a good method for selecting measurement location.