Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Israel; Mekorot, National Water Company of Israel, Israel.
Mekorot, National Water Company of Israel, Israel.
Water Res. 2019 May 15;155:333-342. doi: 10.1016/j.watres.2019.02.027. Epub 2019 Feb 25.
Water is a resource that affects every aspect of life. Intentional (terrorist or wartime events) or accidental water contamination events could have a tremendous impact on public health, behavior and morale. Quick detection of such events can mitigate their effects and reduce the potential damage. Continuous on-line monitoring is the first line of defense for reducing contamination associated damage. One of the available tools for such detection is the UV-absorbance spectrophotometry, where the absorbance spectra are compared against a set of normal and contaminated water fingerprints. However, as there are many factors at play that affect this comparison, it is an elusive and tedious task. Further, the comparison against a set of known fingerprints is futile when the water in the supply system are a mix, with varying proportions, of water from different sources, which differ significantly in their physicochemical characteristics. This study presents a new scheme for early detection of contamination events through UV absorbance under unknown routine conditions. The detection mechanism is based on a new affinity measure, Fitness, and a procedure similar to Gram based amplification, which result in a flexible mechanism to alert if a contamination is present. The method was shown to be most effective when applied to a set of comprehensive experiments, which examined the absorbance of various contaminants in drinking water in lab and real-life configurations. Four datasets, which contained real readings from either laboratory experiments or monitoring station of an operational water supply system were used. To extend the testbed even further, an artificial dataset, simulating a vast array of proportions between specific water sources is also presented. The results show, that for all datasets, high detection rates, while maintaining low levels of false alarms, were obtained by the algorithm.
水是一种影响生活方方面面的资源。有意（恐怖或战争时期的事件）或意外的水污染事件可能会对公众健康、行为和士气产生巨大影响。快速检测此类事件可以减轻其影响并降低潜在的破坏。连续在线监测是减少与污染相关的损害的第一道防线。用于此类检测的一种可用工具是紫外吸收分光光度法，其中吸收光谱与一组正常和污染水指纹进行比较。然而，由于有许多因素会影响这种比较，因此这是一项难以捉摸且繁琐的任务。此外，当供应系统中的水是不同来源的水的混合物，并且其物理化学特性有很大差异时，与一组已知指纹进行比较是徒劳的。本研究提出了一种新的方案，即在未知常规条件下通过紫外吸收进行早期检测污染事件。检测机制基于一种新的亲和力度量——适应性，以及类似于基于革兰氏染色的扩增程序，这导致了一种灵活的机制，可以在存在污染时发出警报。该方法在应用于一系列综合实验时最为有效，这些实验检查了饮用水中各种污染物在实验室和实际生活条件下的吸收情况。使用了四个数据集，其中包含来自实验室实验或运行中的供水系统监测站的实际读数。为了进一步扩展测试平台，还提出了一个模拟特定水源之间大量比例的人工数据集。结果表明，对于所有数据集，该算法都能以低误报率获得高检测率。
