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Extensive building closures due to the unprecedented COVID-19 pandemic resulted in long-term water stagnation within the plumbing of large buildings. This study examined water chemical quality deterioration in ten large buildings after prolonged stagnation caused by the closure of a university campus in response to the COVID-19 pandemic. Volume-based and constant-duration flushing protocols were implemented to replace stagnant water with fresh drinking water. The effectiveness of the developed water flushing protocols was examined by monitoring the disinfectant residuals, heavy metal concentrations and temperature for water samples collected from the buildings' point of entry (POE) and select water fixtures. More than 14 m3of water were flushed in all ten large buildings. The results demonstrated a significantly greater average total chlorine residual concentration in POE water samples collected after flushing (1.1 mg L−1) compared to the stagnant condition (0.6 mg L−1). For water samples collected from fixtures during the extended stagnation, chlorine was absent in 71% of samples from academic buildings and 69% of samples from athletic buildings. The effectiveness of flushing practices is underscored by increasing the median total chlorine concentration from <0.1 to 1.0 mg L−1in academic buildings and from <0.1 to 0.75 mg L−1in athletic buildings. Furthermore, the concentrations of Pb, Zn, and Cu had decreased following the water flushing, but the concentration of Fe had increased in some buildings. This study could be beneficial to prepare for prolonged water stagnation events including but not limited to pandemics.

AbstractDespite significant advances in Clinical Decision Support Systems, they have not been extensively used in nursing practice to date. One key problem is the failure of these systems to fully support actionable nursing practices that guide nurse decision-making. In addition, current workflow-related systems have failed to consider the specific workflow challenges associated with acute-care nursing. In response to these challenges, we describe a novel three-stage approach that builds and evaluates a meta-model that addresses key requirements of multi-level guideline-based clinical nursing-specific decision support. This research-in-progress presents the first two stages of this approach, highlighting the importance of meta-modelling as a tool to identify the essential system-centric information that underpins acute-care nursing practice.