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Open AccessArticle

Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

1
Department of Engineering, University of Palermo, Viale delle Scienze, Ed. 8, 90100 Palermo, Italy
2
School of Engineering and Architecture, University of Enna “Kore”, Cittadella Universitaria, 94100 Enna, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Aizhong Ye
Water 2021, 13(7), 934; https://doi.org/10.3390/w13070934
Received: 3 March 2021 / Revised: 24 March 2021 / Accepted: 25 March 2021 / Published: 29 March 2021
(This article belongs to the Special Issue Urban Water Networks Modelling and Monitoring)
In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal ‘grey’ information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality. View Full-Text
Keywords: Bayesian approach; illicit intrusion; optimal positioning; urban drainage system; water quality sensors Bayesian approach; illicit intrusion; optimal positioning; urban drainage system; water quality sensors
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MDPI and ACS Style

Sambito, M.; Freni, G. Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants. Water 2021, 13, 934. https://doi.org/10.3390/w13070934

AMA Style

Sambito M, Freni G. Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants. Water. 2021; 13(7):934. https://doi.org/10.3390/w13070934

Chicago/Turabian Style

Sambito, Mariacrocetta; Freni, Gabriele. 2021. "Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants" Water 13, no. 7: 934. https://doi.org/10.3390/w13070934

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