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Abstract Commercial and institutional buildings now experience weeks and even months with below‐normal occupancy due to remote work/learning, which results in reduced water use and has the potential to adversely impact water quality. This study monitored the variations in water quality in multiple university buildings during several months of below‐normal occupancy followed by several months of normal occupancy. The levels of free chlorine, copper, and cellular ATP in water varied within buildings and between buildings. Using Wi‐Fi activity as a surrogate for building occupancy, the free chlorine concentration in water increased as Wi‐Fi counts increased. The copper concentration in building water was higher when the occupancy was below‐normal compared with normal occupancy, and the copper concentration decreased as Wi‐Fi counts increased. Throughout the study, flushing a fixture at the time of use decreased the concentrations of copper and cellular ATP and increased the concentration of free chlorine. 

Research and practice suggest markers of drinking water quality such as trihalomethanes (THMs), can change during treatment and distribution, potentially elevating health risk of end users. Models have been developed to predict THM formation at drinking water treatment plants (DWTP), in drinking water distribution systems (DWDS), and to a lesser extent, building premise plumbing (PP). The goal of this research was to evaluate the performance of published THM models and their development methodology, with the purpose of improving future THM model development. Water quality variable data were collected from literature and used as inputs for collected models. Mean and variance of model prediction values were used to measure THM model performance compared to THM data trends from literature. The research found differences in model formulation, water quality variable selection, and model development practices, despite evaluated models being statistical in nature. These differences lead to substantial inconsistencies in model output behavior. Diversity of data used for model development was found to be the most important factor for generalizable model prediction capabilities. Following these findings, a new framework was proposed to encourage novel strategies, data sharing, and collaboration among researchers and practitioners to improve THM model development, application, and performance. Potential use of machine learning techniques for future model development was also discussed based on findings.