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Abstract: Disinfection byproducts (DBPs) in municipal supply water are a concern because of their possible risks to human health. Risk assessment studies often use DBP data in water distribution systems (WDS). However, DBPs in tap water may be different because of stagnation of the water in plumbing pipes (PP) and heating in hot water tanks (HWT). This study investigated occurrences and developed predictive models for DBPs in the PP and the HWT of six houses from three municipal water systems in Quebec (Canada) in a year-round study. Trihalomethanes (THMs) in PP and HWT were observed to be 1.4–1.8 and 1.9–2.7 times the THMs in the WDS, respectively. Haloacetic acid (HAAs) in PP and HWT were observed to be variable (PP/WDS = 0.23–2.24; HWT/WDS = 0.53–2.61). Using DBPs occurrence data from these systems, three types of linear models (main factors; main factors, interactions and higher orders; logarithmic) and two types of nonlinear models (three parameters Logistic and four parameters Weibull) were investigated to predict DBPs in the PP and HWT. Significant factors affecting DBPs formation in the PP and HWT were identified through numerical and graphical techniques. The R 2 values of the models varied between 0.77 and 0.96, indicating excellent predictive ability for THMs and HAAs in the PP and the HWT. The models were found to be statistically significant. The models were validated using additional data. These models can be used to predict DBPs increase from WDS (water entry point of house) to the PP and HWT, and could thereby help gain a better understanding of human exposure to DBPs and their associated risks. [ABSTRACT FROM AUTHOR]

Heterotrophic plate count (HPC) constitutes a common indicator for monitoring of microbiological water quality in distribution systems (DS). This paper aims to identify factors explaining the spatiotemporal distribution of heterotrophic bacteria and model their occurrence in the distribution system. The case under study is the DS of Quebec City, Canada. The study is based on a robust database resulting from a sampling campaign carried out in about 50 DS locations, monitored bi-weekly over a three-year period. Models for explaining and predicting HPC levels were based on both one-level and multi-level Poisson regression techniques. The latter take into account the nested structure of data, the possible spatiotemporal correlation among HPC observations and the fact that sampling points, months and/or distribution sub-systems may represent clusters. Models show that the best predictors for spatiotemporal occurrence of HPC in the DS are: free residual chlorine that has an inverse relation with the HPC levels, water temperature and water ultraviolet absorbance, both having a positive impact on HPC levels. A sensitivity analysis based on the best performing model (two-level model) allowed for the identification of seasonal-based strategies to reduce HPC levels. [Copyright &y& Elsevier]