  Real-time water quality (WQ) sensors in water distribution networks (WDN)
have the potential to enable network-wide observability of water quality
indicators, contamination event detection, and closed-loop feedback control of
WQ dynamics. To that end, prior research has investigated a wide range of
methods that guide the geographic placement of WQ sensors. These methods assign
a metric for fixed sensor placement (SP) followed by
\textit{metric-optimization} to obtain optimal SP. These metrics include
minimizing intrusion detection time, minimizing the expected population and
amount of contaminated water affected by an intrusion event. In contrast to the
literature, the objective of this paper is to provide a computational method
that considers the overlooked metric of state estimation and network-wide
observability of the WQ dynamics. This metric finds the optimal WQ sensor
placement that minimizes the state estimation error via the Kalman filter for
noisy 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 the
observability-driven sensor placement algorithm is presented. The algorithm
takes into account the time-varying nature of WQ dynamics due to changes in the
hydraulic profile -- a collection of hydraulic states including heads
(pressures) at nodes and flow rates in links which are caused by a demand
profile 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.
