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Source water protection (SWP) is an important step in the implementation of a multi-barrier approach that ensures the delivery of safe drinking water. Available decision-making models for SWP primarily use complex mathematical formulations that require large data sets to perform analysis, which limit their use. Moreover, most of them cannot handle interconnection and redundancy among the parameters, or missing information. A fuzzy-based model is proposed in this study to overcome the above limitations. This model can estimate a reduction in the pollutant loads based on selected SWP strategies (e.g., storm water management ponds, vegetated filter strips). The proposed model employs an export coefficient approach and account for the number of animals to estimate the pollutant loads generated by different land usages (e.g., agriculture, forests, highways, livestock, and pasture land). Water quality index is used for the assessment of water quality once these pollutant loads are discharged into the receiving waters. To demonstrate the application of the proposed model, a case study of Page Creek was performed in the Clayburn watershed (British Columbia, Canada). The results show that increasing urban development and poorly managed agricultural areas have the most adverse effects on source water quality. The proposed model can help decision makers to make informed decisions related to the land use and resource allocation., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The goal of a water distribution system (WDS) is to deliver safe water with desirable quality, quantity and continuity to the consumers. In some cases, a WDS fails to deliver safe water due to the compromise/ failure of water quality which may have devastating consequences. The frequency and consequence of a water quality failure (WQF) can be reduced if prognostic analysis and necessary remedial measures are taken on time. This study developed a prognostic model to predict WQF potential in a WDS. The study identifies important factors (parameters) which can directly and/or indirectly linked to WQFs. These factors are classified into two groups-the causes of WQF such as lack of free residual chlorine, or excess of total organic carbon, and the symptoms of WQF such as taste & odor, color which are in fact the effects of certain causes of WQF. The interrelationships among the symptoms and the causes have been established based on extensive literature review and elicited expert opinion. A fuzzy-TOPSIS-OWA-based model has been developed to identify the impacts of different influencing parameters on the overall WQF potential. The developed model has been implemented for a WDS in Quebec City (Canada). To study the impacts of uncertainties of the influencing factors, a Monte Carlo simulation-based sensitivity analysis has been carried out. It is anticipated that the developed model can help water utilities to understand the role of different factors on WQF. [ABSTRACT FROM AUTHOR]

This paper explores the application of the quality function deployment (QFD) approach to identify and prioritize factors to reconcile 'actual' risk with 'perceived' risk for drinking water in a distribution network. Consumers' complaints (regarding water odour, taste, colour and other problems) and consumers' perception about water safety are used to define customers' requirements (aka 'whats'). Twelve water quality parameters and distribution network properties (e.g. turbidity, pipe breaks) are used as indicators for drinking water quality -- the product characteristics (aka 'hows'). Correlations between 'whats' and 'hows' are established using data obtained from a case study. Opinions of experts in drinking water and consumers are used to define correlations between the 'what' 'microbial safety' and the 'hows'. The analytic hierarchy process allows prioritizing customer requirements. Three QFD-based methods are applied to prioritize factors affecting water quality in a distribution network. The proposed approach is demonstrated through a case study of a water distribution network in Quebec City (Canada). Results show that the factors that primarily affect customers' requirements include free residual chlorine, turbidity and trihalomethanes. Sensitivity analyses using three scenarios confirm the robustness of the proposed approach. Utility managers must take action, especially concerning these product characteristics, to satisfy customers' requirements. [ABSTRACT FROM AUTHOR]

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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]