Ryan Julien’s research while affiliated with Michigan State University and other places

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. HIGHLIGHTS Physical end-use monitoring is prohibitively expensive for most practitioners.; Machine learning is a viable method of lowering the cost of end-use categorization.; Here we proposed a novel approach to systematically configure the machine platform automatically.; Effective end-use prediction is possible with a small subset of upstream features.;

Opportunistic pathogens, such as several species of Legionella, are a growing concern globally. This is especially true in building plumbing. Plumbing design guidance has not been updated to reflect changes in use, leading to increased hydraulic retention time and exacerbating pathogen risks. While the effects of several water quality variables on Legionella spp. concentrations are well‐studied at bench‐scale, little is known about the strength of, or interactions between, these relationships in full‐scale plumbing. The influence of water quality variables and water use metrics on Legionella spp. concentrations was evaluated with several statistical tools from a data‐driven perspective. The results add to the weight of evidence required to develop systematic risk management strategies like monitoring. Variables related to Legionella and logistically more feasible to measure in full scale buildings, such as dissolved oxygen, heterotrophic plate count, total organic carbon, and water age, were identified.

The rising trend in water conservation awareness has given rise to the use of water-efficient appliances and fixtures for residential potable water systems. This study characterizes the microbial dynamics at a water-efficient residential building over the course of one year (58 sampling events) and examines the effects of water stagnation, season, and changes in physicochemical properties on the occurrence of opportunistic pathogen markers. Mean heterotrophic plate counts (HPC) were typically lowest upon entering the building at the service line, but increased by several orders of magnitude at the furthest location in the building plumbing. Legionella spp. and Mycobacterium spp. were detected in the plumbing, with the highest detection occurring in the summer months. Log-transformed HPC were significantly correlated with total cell counts (TCC) (rs = 0.714, p<0.01), Legionella spp. (rs = 0.534, p<0.01), and Mycobacterium spp. occurrence (rs= 0.458, p<0.01). Reduced water usage induced longer stagnation times and longer stagnation times were correlated with an increase in Legionella spp. (rs = 0.356, p<0.001), Mycobacterium spp. (rs = 0.287, p<0.001), TCC (rs = 0.216, p<0.001) and HPC (rs = 0.395 , p<0.001). Interrelationships between seasonal shifts in water chemistry and genus-level genetic markers for opportunistic pathogens were revealed. This study highlights how drinking water microbiology varies seasonally and spatially throughout a low-flow plumbing building and highlights the possible unintended consequences associated with increases in stagnation time.

When rainwater harvesting is utilized as an alternative water resource in buildings, a combination of municipal water and rainwater is typically required to meet water demands. Altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. Still, it is unknown if similar reactions occur within building plumbing following a transition in source water quality. The study goal was to investigate changes in water chemistry and microbiology at a green building following a transition between municipal water and rainwater. We monitored the water chemistry (metals, alkalinity, disinfectant byproducts and microbiology (total cell counts, plate counts, and opportunistic pathogen gene markers) throughout two source water transitions. Several constituents including alkalinity and disinfectant byproducts served as indicators of municipal water remaining in the system since the rainwater source does not contain these constituents. In the treated rainwater, microbial proliferation, and Legionella spp. gene copy numbers were often three logs higher than in the municipal water. Due to differences in source water chemistry, rainwater and municipal water uniquely interacted with building plumbing and generated distinctively different drinking water chemical and microbial quality profiles.

Sustainability, water conservation, water efficiency, and green infrastructure have led to significant decreases in the quantity of water used in buildings. In addition, changes in water usage, building design, plumbing material selection, and high‐efficiency fixtures contribute to water age and potential chemical and microbiological contamination. Through a literature review and stakeholder workshop with representatives from the public and private sectors, this article explores the current state of the science on plumbing safety. Results indicate that links between water usage, design, and material selection and water quality must be established with data‐driven methods to support risk assessment and risk management decisions. Of particular interest is a better understanding of the contribution of water usage and piping materials to the proliferation of opportunistic pathogens. This article will help further efforts to evaluate these pathogenic risks, a critical need as drinking water regulations have primarily focused on exposure through ingestion.

Drinking water chemical quality can deteriorate after water enters building plumbing. This study aimed to better understand seasonal and spatial water quality differences in a highly monitored net-zero energy residential building. Water flow rate and temperature were monitored for one year at the service line and at every fixture throughout the crosslinked polyethylene plumbing. Discrete water sampling events (58) were conducted at the service line, 1st floor kitchen sink, 2nd floor bathroom sink, the water heater, and 2nd floor shower. More than 2.4 billion online monitoring records were collected for fixture flow and temperature. In-building water stagnation time varied seasonally and across fixtures. Significant spatial and temporal water chemical quality variations were found. Average seasonal variability was found for service line temperature (15–23 °C) for the total chlorine residual (0.4–0.9 mg/L-Cl2), NH3 (<0.01–0.4 mg/L-N), NO3⁻ (0.1–0.8 mg/L-N), and Cu (32–81 μg/L) concentrations. For 10.3% of the discrete water sampling events, service line water did not contain a detectable total chlorine residual. Water pH consistently and significantly increased in the plumbing system from 7.5 to 9.4, and total trihalomethane (TTHM) levels increased up to 89%. The service line total organic carbon level (0.5–0.6 mg/L) was consistent, but much greater in-building variability was found for cold (0.4–61.0 mg/L) and hot water (0.5–4.7 mg/L). Models are needed to predict chemical water quality at the faucet using service line water quality results and plumbing design and operational information. Building water sensor technology innovations are also needed.