Optimal Deployment of Moving Sensors for Water Quality Monitoring of Sewer Systems

For a long time, scientific research has concentrated on identifying the sources of contamination in drinking water, certainly because it constitutes a problem for public health, however the illicit spillage of pollutants also has important consequences in wastewater. In fact, a contamination in the sewer has effects both on the drainage system and on the treatment plant, and on the receiving water body in case of a combined sewer overflow CSO. In this study, optimal strategies for deploying floating water quality sensors in sewers with or without the combination of fixed monitoring stations were investigated to identify xenobiotic-type contaminants. The proposed case study is a real network, the Mondello network, a seaside tourist village adjacent to the center of Palermo Italy. A Bayesian artificial intelligence AI solver was employed for likelihood estimation and probability update; Bayesian logic was also employed for the optimal positioning of the water quality sensors through the implementation of a decision support system DSS while the EPA SWMM model was employed to perform the hydraulic and water quality simulations. The results obtained revealed advantages in the combined use of mobile sensors and fixed monitoring stations.