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Selecting and prioritizing monitoring locations (zones) in a water distribution network (DN) involves complex decision-making that warrants the use of risk-based decision-making. This paper couples the concept of risk with fuzzy sets and provides a simple approach to prioritizing monitoring locations in a DN. An index-based approach is proposed to estimate vulnerability, sensitivity and risk indices at a given DN location. The approach is based on fuzzy-based techniques that include fuzzy synthetic evaluation and fuzzy rule-base. Two types of analyses (deterministic and probabilistic) are used to demonstrate the proposed approach for a DN in Quebec City (Canada). Fifteen 'input factors' related to structural integrity, hydraulics, water quality and consumer sensitivity are used in the analyses. Based on the risk index results, the DN is divided into two parts. Sensitivity analysis results reveal that four factors, including two sensitivity factors (i.e. % population < 5 and > 75 yr) and two vulnerability factors (free residual chlorine and pipe breaks), explain 85% of the risk index variability. The usefulness of the proposed approach is demonstrated and strategies to improve the model are discussed. [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]