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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.
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Data‐mining methods predict chlorine residuals in premise plumbing using low‐cost sensors
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.
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- Award ID(s):
- 1804229
- PAR ID:
- 10453466
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AWWA Water Science
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2577-8161
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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