scholarly journals Topological clustering as a tool for planning water quality monitoring in water distribution networks

2015 ◽  
Vol 15 (5) ◽  
pp. 1011-1018 ◽  
Author(s):  
Jonas Kjeld Kirstein ◽  
Hans-Jørgen Albrechtsen ◽  
Martin Rygaard
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Network Model ◽  
Water Distribution ◽  
Water Movement ◽  
Decision Support Tool ◽  
Water Quality Monitoring ◽  
Distribution Networks ◽  
Quality Monitoring ◽  
Support Tool

Topological clustering was explored as a tool for water supply utilities in preparation of monitoring and contamination contingency plans. A complex water distribution network model of Copenhagen, Denmark, was simplified by topological clustering into recognizable water movement patterns to: (1) identify steady clusters for a part of the network where an actual contamination has occurred; (2) analyze this event by the use of mesh diagrams; and (3) analyze the use of mesh diagrams as a decision support tool for planning water quality monitoring. Initially, the network model was divided into strongly and weakly connected clusters for selected time periods and mesh diagrams were used for analysing cluster connections in the Nørrebro district. Here, areas of particular interest for water quality monitoring were identified by including user-information about consumption rates and consumers particular sensitive towards water quality deterioration. The analysis revealed sampling locations within steady clusters, which increased samples' comparability over time. Furthermore, the method provided a simplified overview of water movement in complex distribution networks, and could assist identification of potential contamination and affected consumers in contamination cases. Although still in development, the method shows potential for assisting utilities during planning of monitoring programs and as decision support tool during emergency contingency situations.

scholarly journals Overview of microbial risks in water distribution networks and their health consequences: quantification, modelling, trends, and future implications

2019 ◽  
Vol 46 (3) ◽  
pp. 149-159 ◽  
Author(s):  
Victor Viñas ◽  
Annika Malm ◽  
Thomas J.R. Pettersson
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Water Distribution ◽  
Risk Mitigation ◽  
Distribution Network ◽  
Water Quality Monitoring ◽  
Distribution Networks ◽  
Quantitative Microbial Risk Assessment ◽  
Support Tool ◽  
Microbial Risks

The water distribution network (WDN) is usually the final physical barrier preventing contamination of the drinking water before it reaches consumers. Because the WDN is at the end of the supply chain, and often with limited online water quality monitoring, the probability of an incident to be detected and remediated in time is low. Microbial risks that can affect the distribution network are: intrusion, cross-connections and backflows, inadequate management of reservoirs, improper main pipe repair and (or) maintenance work, and biofilms. Epidemiological investigations have proven that these risks have been sources of waterborne outbreaks. Increasingly since the 1990s, studies have also indicated that the contribution of these risks to the endemic level of disease is not negligible. To address the increasing health risks associated to WDNs, researchers have developed tools for risk quantification and risk management. This review aims to present the recent advancements in the field involving epidemiological investigations, use of quantitative microbial risk assessment (QMRA) for modelling, risk mitigation, and decision-support. Increasing the awareness of the progress achieved, but also of the limitations and challenges faced, will aid in accelerating the implementation of QMRA tools for WDN risk management and as a decision-support tool.

scholarly journals Framework for water quality monitoring system in water distribution networks based on vulnerability and population sensitivity risks

2016 ◽  
Vol 17 (3) ◽  
pp. 811-824 ◽  
Author(s):  
Amin Abo-Monasar ◽  
Muhammad Al-Zahrani
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Water Demand ◽  
Water Distribution ◽  
Water Quality Monitoring ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Quality Monitoring ◽  
Demand Distribution ◽  
Main Challenge

Delivering water in sufficient quantity and acceptable quality is the main objective of water distribution networks (WDN) and at the same time is the main challenge. Many factors affect the delivery of water through distribution networks. Some of these factors are relevant to water quality, quantity and the condition of the infrastructure itself. The deterioration of water quality in the WDN leads to failure at the water quality level, which can be critical because it is closest to the point of delivery and there are virtually no safety barriers before consumption. Accordingly, developing a powerful monitoring system that takes into consideration water demand distribution, the vulnerability of the distribution system and the sensitivity of the population to the deterioration of water quality can be very beneficial and, more importantly, could save lives if there was any deterioration of water quality due to operational failure or cross-contamination events. In this paper, a framework for a water quality monitoring system that considers water demand distribution, the vulnerability of the system and the sensitivity of the population using fuzzy synthetic evaluation and optimization algorithms is developed. The proposed approach has been applied to develop a monitoring system for a real WDN in Saudi Arabia.

Improving water age estimation in a drinking water distribution network by field study and digital modeling

2021 ◽  
Author(s):  
Jon Kristian Rakstang ◽  
Michael B. Waak ◽  
Marius M. Rokstad ◽  
Cynthia Hallé
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Network Model ◽  
Age Estimation ◽  
Water Distribution ◽  
Distribution Network ◽  
Distribution Networks ◽  
Plate Count ◽  
Water Age ◽  
Drinking Water Distribution

<p>Municipal drinking water distribution networks are complex and dynamic systems often spanning many hundreds of kilometers and serving thousands of consumers. Degradation of water quality within a distribution network can be associated to water age (i.e., time elapsed after treatment). Norwegian distribution networks often consist of an intricate combination of pressure zones, in which the transport path(s) between source and consumer is not easily ascertained. Water age is therefore poorly understood in many Norwegian distribution networks. In this study, simulations obtained from a water network model were used to estimate water age in a Norwegian municipal distribution network. A full-scale tracer study using sodium chloride salt was conducted to assess simulation accuracy. Water conductivity provided empirical estimates of salt arrival time at five monitoring stations. These estimates were consistently higher than simulated peak arrival times. Nevertheless, empirical and simulated water age correlated well, indicating that additional network model calibration will improve accuracy. Subsequently, simulated mean water age also correlated strongly with heterotrophic plate count (HPC) monitoring data from the distribution network (Pearson’s R= 0.78, P= 0.00046), indicating biomass accumulation during distribution—perhaps due to bacterial growth or biofilm interactions—and illustrating the importance of water age for water quality. This study demonstrates that Norwegian network models can be calibrated with simple and cost-effective salt tracer studies to improve water age estimates. Improved water age estimation will increase our understanding of water quality dynamics in distribution networks. This can, through digital tools, be used to monitor and control water age, and its impact on biogrowth in the network.</p>

Smart Grid Vendée project: a decision-support tool for the multi-year planning of active distribution networks

2017 ◽  
Vol 2017 (1) ◽  
pp. 1988-1992 ◽  
Author(s):  
Héloïse Dutrieux Baraffe ◽  
Aländji Bouorakima ◽  
Gilles Malarange ◽  
Marie-Anne Lafittau ◽  
Guillaume Pelton
Keyword(s):  
Decision Support ◽  
Smart Grid ◽  
Decision Support Tool ◽  
Distribution Networks ◽  
Support Tool ◽  
Active Distribution Networks
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