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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 goal of this study was to investigate changes in water chemistry and microbiology at a green building following a transition between municipal water and rainwater. We monitored water chemistry (metals, alkalinity, and 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 those in municipal water. Because of 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.

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. [ABSTRACT FROM AUTHOR]

Significant seasonal changes in chemical and microbiological water quality can occur in buildings at different fixture locations due to temperature and time dependent reaction rates. Here, a series of calibrated plumbing hydraulic‐water quality models were developed for the extensively monitored Retrofitted Net‐zero Energy, Water & Waste (ReNEWW) house in West Lafayette, Indiana, USA. The eight new models predict the absolute level of free chlorine, total trihalomethanes, copper, iron, lead, NO3− (nitrate‐nitrogen), heterotrophic plate count (HPC), and Legionella spp. concentration at each fixture for plumbing use, operational characteristics, and design layouts of the plumbing system. Model development revealed that the carrying capacity to describe Legionella spp. growth (and other organisms) under water usage and plumbing design conditions is lacking in the literature. Reducing simulated building water use by 25% prompted increased absolute concentrations of HPC and Legionella spp., each increasing by a factor of about 105. When the simulated service line length was increased, Legionella spp. concentrations increased by up to 106 gene copies /L in the Summer season. The proposed modeling framework can be used to support better planning, design, analysis, and operational decision‐making. [ABSTRACT FROM AUTHOR]

Quantitative microbial risk assessment (QMRA) is a computational science leveraged to optimize infectious disease controls at both population and individual levels. Often, diverse populations will have different health risks based on a population's susceptibility or outcome severity due to heterogeneity within the host. Unfortunately, due to a host homogeneity assumption in the microbial dose-response models' derivation, the current QMRA method of modeling exposure volume heterogeneity is not an accurate method for pathogens such as Legionella pneumophila. Therefore, a new method to model within-group heterogeneity is needed. The method developed in this research uses USA national incidence rates from the Centers for Disease Control and Prevention (CDC) to calculate proxies for the morbidity ratio that are descriptive of the within-group variability. From these proxies, an example QMRA model is developed to demonstrate their use. This method makes the QMRA results more representative of clinical outcomes and increases population-specific precision. Further, the risks estimated demonstrate a significant difference between demographic groups known to have heterogeneous health outcomes after infection. The method both improves fidelity to the real health impacts resulting from L. pneumophila infection and allows for the estimation of severe disability-adjusted life years (DALYs) for Legionnaires' disease, moderate DALYs for Pontiac fever, and post-acute DALYs for sequela after recovering from Legionnaires' disease. [ABSTRACT FROM AUTHOR]