An Effective and Efficient Method for Identification of Contamination Sources in Water Distribution Systems Based on Manual Grab-Sampling

“Most of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi-parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab-sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre-specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub-network as equal as possible, where sub-networks are determined by the selected hydrants and current flow pipe directions. The CS’s size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real-world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade-off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors.”

(Citation: Ji, Y., Zheng, F., Du, J., et.al. – An Effective and Efficient Method for Identification of Contamination Sources in Water Distribution Systems Based on Manual Grab-Sampling – Water Resources Research 58(2022)11, art. no. 032784- DOI: 10.1029/2022WR032784 – (Open Access))

© 2022 Yiran Ji, Feifei Zheng, Jiawen Du, Yuan Huang, Weiwei Bi, Huan Feng Duan, Dragan Savic, Z. Kapelan – Embargo date 2023-07-01