Riano-Briceno Gerardo, Abokifa Ahmed, Taha Ahmad, Sela Lina
Fariborz Maseeh Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, Chicago, Illinois 60607, United States.
ACS ES T Water. 2025 Feb 25;5(3):1099-1111. doi: 10.1021/acsestwater.4c00671. eCollection 2025 Mar 14.
Ensuring water security and enabling timely responses to contamination events in water distribution systems (WDSs) rely heavily on the accurate and timely localization of contamination sources. Despite advances in water quality monitoring technologies, such as continuous sensing and grab-sampling, the coverage of monitoring remains sparse in most WDSs, making it difficult to accurately pinpoint the source of contamination. This paper introduces a novel source localization methodology designed to overcome these challenges by integrating sparse continuous sensing with targeted manual grab-sampling. The proposed approach iteratively narrows down the set of probable contamination sources by applying heuristics that account for the timing and signals from sensor measurements. To further address the uncertainty inherent in source localization, the methodology generates a probabilistic distribution over potential source locations. This distribution highlights areas requiring closer attention and guides where subsequent samples should be collected, effectively reducing uncertainty in the localization process. The methodology's performance is validated through extensive analysis, demonstrating that combining fixed sensors with adaptive sampling significantly improves precision, accuracy, and localization speed, particularly in sparse sensor networks. The proposed approach advances the use of water quality sensing technology for source localization, with further research needed to optimize its effectiveness in improving WDS security and maximizing public health protection.
确保供水安全并能及时应对配水系统（WDS）中的污染事件，在很大程度上依赖于对污染源的准确及时定位。尽管水质监测技术取得了进展，如连续传感和抓取采样，但在大多数配水系统中，监测覆盖范围仍然稀疏，难以准确确定污染源。本文介绍了一种新颖的源定位方法，旨在通过将稀疏连续传感与有针对性的手动抓取采样相结合来克服这些挑战。所提出的方法通过应用考虑传感器测量时间和信号的启发式算法，迭代缩小可能污染源的集合。为了进一步解决源定位中固有的不确定性，该方法在潜在源位置上生成概率分布。这种分布突出了需要密切关注的区域，并指导后续样本的采集位置，有效降低了定位过程中的不确定性。通过广泛分析验证了该方法的性能，表明将固定传感器与自适应采样相结合可显著提高精度、准确性和定位速度，特别是在稀疏传感器网络中。所提出的方法推动了水质传感技术在源定位中的应用，还需要进一步研究以优化其在提高配水系统安全性和最大化公共卫生保护方面的有效性。
