Drinking water distribution systems contamination management to reduce public health impacts and system service interruptions

• Published in: Environmental modelling & software : with environment data news Vol. 51; pp. 12 - 25
• Main Authors: Rasekh, Amin, Brumbelow, Kelly
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-01-2014; Elsevier
• Subjects: Air. Soil. Water. Waste. Feeding; Animal, plant and microbial ecology; Biological and medical sciences; Contamination; Emergency management; Environment. Living conditions; Evolutionary computation; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Medical sciences; Methods and techniques (sampling, tagging, trapping, modelling...); Multiobjective; Pressure-dependant demand analysis; Public health. Hygiene; Public health. Hygiene-occupational medicine; Water distribution system; Multiobjective; Emergency management; Water distribution system; Contamination; Pressure-dependant demand analysis; Evolutionary computation; Drinking water; Analysis; Water distribution; Public health
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2013.09.019

Decisions on protecting public health against drinking water systems contamination threats should be made with careful consideration of credibility of threat observations and adverse impacts of response on system serviceability. Decision support models are developed in this study to prepare water utility operators for making these critical decisions during the intense course of an emergency. A pressure-dependent demand model is developed to simulate the system hydraulics and contaminant propagation under pressure-deficit conditions that emerge after the response actions are executed. Contrary to conventional demand-driven models, this hydraulic analysis approach prevents potential occurrence of negative pressures during the simulation and may identify better response protocols through exploring a larger search space. Response mechanisms of contaminant containment and discharge are optimized using evolutionary algorithms to achieve public health protection with minimum service interruption. Sensitivity analyses are conducted to assess optimal response performance for varying response delay, number of hydrants, and intrusion characteristics. Different methods for quantifying impacts on public health and system serviceability are explored and the sensitivity of the optimal response plan to these different formulations is investigated. The simulation-optimization schemes are demonstrated and discussed using a virtual water distribution system.
•Hydraulic and exposure models are developed to simulate water contamination events.•Response mechanisms are optimized to reduce contamination consequences.•Contaminant containment and flushing are effective for public health protection.•Effectiveness of emergency response actions drastically decreases as time passes.•Impacts are better quantified as ingested contaminant mass than number of sicknesses.