The residence time of water in residential building water systems is a critical factor regarding water quality at end use. Published literature has highlighted the importance of water age in these systems and its relationship with pathogenic bacteria such as Legionella pneumophila. However, tools to measure water age in such plumbing systems are typically repurposed from other applications and include limitations that make them inappropriate for some plumbing systems. This work presents a novel means of estimating water age by assuming these systems operate without mixing. Data for this study was collected from a full‐scale home equipped with an extensive array of flow meters to monitor water use. Further, 408 individual water quality samples were collected to ascertain water quality changes that take place in the plumbing. Model results show weak correlation with EPANET 2.2 (ρ = 0.666), a commonly used hydraulic modeling software. The results of the water age model were also evaluated with several variable selection tools. These analyses indicate that this method's water age results are a statistically significant (p < .05) predictor of Legionella concentrations. Model results from this approach could be used in plumbing design and/or operation to assist in managing Legionella risks.

Understanding the end-use of water is essential to a plethora of critical research in premise plumbing. However, direct access to end-use data through physical sensors is prohibitively expensive for most researchers, building owners, operators, and practitioners. Therefore, machine learning models can alleviate these costs by predicting downstream end-use events (e.g., sink, shower, dishwasher, and washing machine) via an affordable subset of upstream sensors. Choosing which upstream sensors, as well as data preprocessing methods, are best for machine learning has historically been a manual process. This paper proposes a novel approach to systematically configure the machine platform automatically. The optima were determined through a Pareto analysis of the exhaustive combinations of upstream predictors and preprocessing methods. The model was trained and validated with real-world data obtained from a house that has been extensively monitored for over a year. Results from the analysis suggested that downstream events can be effectively predicted with minimum overfitting error for most categories, using as few as two to four upstream sensors. This study automatically implemented highly accurate machine learning models to predict downstream features within premise plumbing systems, significantly lowering the costs of researching residential plumbing best practices such as water conservation.

Opportunistic pathogens such as Legionella spp. (including Legionella pneumophila) and Mycobacterium avium complex (MAC) are common within building plumbing systems. These opportunistic pathogens can be amplified by conditions of premise plumbing, including diminished disinfectant residual, high surface area to volume ratios resulting from smaller-diameter pipes, and elevated water temperatures compared to distribution mains. Understanding the risk of infection from these pathogens through common exposure pathways is critical to addressing and mitigating human health risks. This study presents a quantitative microbial risk assessment (QMRA) framework for inhalation exposures to aerosolized Legionella spp. and MAC during three common residential water uses, showering, toilet flushing, and handwashing. Inhalation risks were quantified by Monte Carlo simulation of bulk water pathogen concentrations, size-specific aerosol generation, respiratory deposition, and dose–response assessment. Because co-located paired measurements for Legionella spp. and MAC were limited, organism-specific results reflect modeled concentration distributions and dose–response assumptions rather than direct field co-occurrence comparisons. Across modeled scenarios, Legionella spp. presents a higher risk of infection than MAC. Risks were greatest for shower exposures and lowest for handwashing. Correlation analysis shows that risks are most sensitive to exposure dose (ρ > 0.99) and the concentration of pathogen in bulk water (ρ > 0.96).