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Real-time water quality (WQ) sensors in water distribution networks (WDN)have the potential to enable network-wide observability of water qualityindicators, contamination event detection, and closed-loop feedback control ofWQ dynamics. To that end, prior research has investigated a wide range ofmethods that guide the geographic placement of WQ sensors. These methods assigna metric for fixed sensor placement (SP) followed by\textit{metric-optimization} to obtain optimal SP. These metrics includeminimizing intrusion detection time, minimizing the expected population andamount of contaminated water affected by an intrusion event. In contrast to theliterature, the objective of this paper is to provide a computational methodthat considers the overlooked metric of state estimation and network-wideobservability of the WQ dynamics. This metric finds the optimal WQ sensorplacement that minimizes the state estimation error via the Kalman filter fornoisy WQ dynamics -- a metric that quantifies WDN observability. To that end,the state-space dynamics of WQ states for an entire WDN are given and theobservability-driven sensor placement algorithm is presented. The algorithmtakes into account the time-varying nature of WQ dynamics due to changes in thehydraulic profile -- a collection of hydraulic states including heads(pressures) at nodes and flow rates in links which are caused by a demandprofile over a certain period of time. Thorough case studies are given,highlighting key findings, observations, and recommendations for WDN operators.Github codes are included for reproducibility.

Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics