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Phosphorus (P) is a finite resource and necessary nutrient for agriculture. Urine contains a higher concentration of P than domestic wastewater, which can be recovered by source separation and treatment (hereafter urine diversion). Commercial and institutional (CI) buildings are a logical location for urine diversion since restrooms account for a substantial fraction of water use and wastewater generation. This study estimated the potential for P recovery from human urine and water savings from reduced flushing in CI buildings, and proposed an approach to identify building types and community layouts that are amenable to implementing urine diversion. The results showed that urine diversion is most advantageous in CI buildings with either high daily occupancy counts or times, such as hospitals, schools, office buildings, and airports. Per occupant P recovery benchmarks were estimated to be between 0.04–0.68 g/cap·d. Per building P recovery rates were estimated to be between 0.002–5.1 kg/d, and per building water savings were estimated to be between 3 and 23 % by volume. Recovered P in the form of phosphate fertilizer and potable water savings could accrue profits and cost reductions that could offset the capital costs of new urine diversion systems within 5 y of operation. Finally, urine diversion systems can be implemented at different levels of decentralization based on community layout and organizational structure, which will require socioeconomic and policy acceptance for wider adoption. 

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. 

Abstract Human urine is a readily available nutrient source that can complement commercial fertilizer production, which relies on finite mineral resources and global supply chains. This study evaluated the effectiveness of a simplified solar distillation process for urine to recover phosphorus (P) and nitrogen for agricultural use and water for non-potable purposes. Synthetic fresh, synthetic hydrolyzed, real fresh, and real hydrolyzed urine were exposed to direct sunlight for 6 h in a simple distillation apparatus, which produced distillation bottoms and distillate. Metal phosphate precipitation in the distillation bottoms was evaluated to recover P. The non-potable water was recovered as distillate. Hydrolyzed urine recovered more metal phosphate solid in the distillation bottoms and had a higher conductivity in the distillate than fresh urine. Hydrolyzed urine also achieved greater distillate volume recovery than fresh urine. Hydrolyzed urine had a greater presence of UV-absorbing organics in the distillate than fresh urine and therefore produced a lower-quality product water. There was no significant correlation between the daily high air temperature and the volume of distillate recovered. This study provides a comprehensive data set on simplified solar distillation of human urine considering the fate of nutrients and water for different types of urine. 

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. 

Abstract Variable water quality within buildings is of increasing concern due to public health impacts (e.g., lead,Legionella pneumophila,Naegleria fowleri, disinfection byproducts). Advances in data acquisition and analytics provide the opportunity to monitor real‐time building‐wide water quality variability. Accordingly, the goal of this research was to create a water quality sensor platform including data acquisition, storage, and mining methods able to monitor, and ultimately improve, water quality within buildings. The platform was used to monitor water temperature, pH, conductivity, oxidation–reduction potential, dissolved oxygen, and chlorine using sensors only. Other building data infrastructure, specifically Wi‐Fi logins by occupants, were used to approximate activity rates and associated water use. An advanced machine‐learning technique, gradient boosting machines, predicted the chlorine residuals throughout the building plumbing network better than multivariate linear regression models. Finally, the implications of water quality monitoring on costs, scalability, reliability, human dimensions, regulatory compliance, and future green building designs are considered.