Accidental contamination and malicious attacks can degrade the water quality in water distribution networks and threat the human health. Therefore, a rapid detection of water contamination is required to prevent waterborne diseases. The use of water quality sensors allows a real-time monitoring of several physical and chemical parameters. The aim of this paper is to combine the smart monitoring with the risk assessment approach to ensure early detection of water contamination. Within the European Project “SmartWater4Europe,” S::CAN sensor was implemented, on the water supply system of the Campus of Lille University, France, since 2016. The campus is a representative field study with 15 km of water distribution network. In this paper, Turbidity and Chlorine, recorded online by S::CAN, were analyzed continuously to define the risk assessment parameters (the severity of consequences and the likelihood of an event). The application of the proposed approach indicates that the magnitude of deviation from thresholds limits and the duration of events are the two essential parameters to be considered in risk assessment approach. The paper shows that this new approach provides promising perspective for the early detection of the water contamination. It allows to identify the risk level and the priority required in real time, without resorting laboratory analyses.

中文翻译：

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使用智能水质监测的水意外污染风险评估

意外污染和恶意攻击会降低配水网络中的水质，并威胁人类健康。因此，需要快速检测水污染以预防水传播疾病。使用水质传感器可以实时监测几个物理和化学参数。本文的目的是将智能监控与风险评估方法相结合，以确保及早发现水污染。自2016年以来，在欧洲项目“ SmartWater4Europe”中，在法国里尔大学校园的供水系统上实施了S :: CAN传感器。该校园是具有15公里水分配网络的代表性现场研究。本文由S :: CAN在线记录了浊度和氯，进行连续分析以定义风险评估参数（后果的严重性和事件的可能性）。拟议方法的应用表明，偏离阈值极限的幅度和事件的持续时间是风险评估方法中要考虑的两个基本参数。本文表明，这种新方法为水污染的早期检测提供了广阔的前景。它允许实时识别风险级别和优先级，而无需借助实验室分析。本文表明，这种新方法为水污染的早期检测提供了广阔的前景。它允许实时识别风险级别和优先级，而无需借助实验室分析。本文表明，这种新方法为水污染的早期检测提供了广阔的前景。它允许实时识别风险级别和优先级，而无需借助实验室分析。