Impacts of the ageing and rehabilitation of water pipes on residence times at the residential neighborhood scale

Abstract The impact of ageing and rehabilitation on water residence times was evaluated in a looped drinking water distribution network, using hydraulic modeling and flow characterization. A reference scenario representing the best state of knowledge of the studied area was created and realistic scenarios were applied to evaluate the impact of ageing and rehabilitation of water pipes on residence times. Results showed that the ageing of infrastructures, with consideration for pipe corrosion and a 20% leakage loss increase, lead to an overall 10% water residence time reduction. Considering a non-uniform ageing of water pipes results in localized impacts brought by changes in hydraulic patterns. Considering pipe rehabilitation scenarios coupled with a 20% flow reduction leads to small water residence time increases (2--8 h), with the local pipe rehabilitation having the least impact on water residence times.

[1]  Jonathan J. Cole,et al.  Synchronous variation of dissolved organic carbon and color in lakes , 2002 .

[2]  Manuel J. Rodriguez,et al.  Water residence time in a distribution system and its impact on disinfectant residuals and trihalomethanes , 2011 .

[3]  François-Julien Delisle.  Amélioration de la connaissance des temps de séjour en réseau de distribution en vue de l'amélioration de la qualité de l'eau potable , 2009 .

[4]  Manuel J. Rodríguez,et al.  Chlorinated disinfection by-products in drinking water according to source, treatment, season, and distribution location , 2007 .

[5]  R. Clark,et al.  Predicting Chlorine Residuals and Formation of TTHMs in Drinking Water , 1998 .

[6]  Frank Harary,et al.  Graph Theory , 2016 .

[7]  Joby Boxall,et al.  Modeling for Hydraulic Capacity , 2004 .

[8]  M. F K Pasha,et al.  Analysis of uncertainty on water distribution hydraulics and water quality , 2005 .

[9]  L. Kiene,et al.  Relative importance of the phenomena responsible for chlorine decay in drinking water distribution systems , 1998 .

[10]  R. Sadiq,et al.  Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review. , 2004, The Science of the total environment.

[11]  Dragutin Gereš.  Water demand management , 2003 .

[12]  L. Kiene,et al.  Chlorine demand of biofilms in water distribution systems , 1999 .

[13]  Ryan T. Christensen.  Age effects on iron -based pipes in water distribution systems , 2009 .

[14]  Manuel J. Rodriguez,et al.  Tracer study to verify hydraulic limits and determine water residence times in a distribution system: Part I , 2015 .

[15]  John D. Dietz,et al.  Combined chlorine dissipation: Pipe material, water quality, and hydraulic effects , 2007 .

[16]  Syed A. Imran,et al.  Red Water Release in Drinking Water Distribution Systems , 2005 .

[17]  Ram Kailash Prasad,et al.  Uncertainty Analysis of Looped Water Distribution Networks Using Linked EPANET-GA Method , 2015, Water Resources Management.

[18]  Othman Jaafar,et al.  Effects Residence Time to Water Quality in Large Water Distribution Systems , 2013 .

[19]  Emmanuel Ewa Ekeng.  The Effect of Pipe Ageing of Different Diameter And Pressure On Residual Chlorine , 2011 .

[20]  M. F K Pasha,et al.  Effect of parameter uncertainty on water quality predictions in distribution systems-case study , 2010 .

[21]  Québec,et al.  Règlement sur la qualité de l'eau potable : guide destiné aux établissements touristiques , 2003 .

[22]  Mitchell J. Small,et al.  Vulnerability Assessment of a Drinking Water Distribution System: Implications for Public Water Utilities , 2007 .

[23]  P. Singer.  Control of Disinfection By‐Products in Drinking Water , 1994 .

[24]  Manuel J. Rodriguez,et al.  Using a tracer to identify water supply zones in a distribution network , 2009 .

[25]  A. Grünwald,et al.  THM formation in water supply in South Bohemia, Czech republic , 2000 .

[26]  Antoine Montiel,et al.  Variations in trihalomethane levels in three French water distribution systems and the development of a predictive model. , 2010, Water research.

[27]  Guillaume Binet.  Mieux prédire la dégradation du chlore en réseau de distribution : cas d'étude : la ville de Québec , 2010 .

[28]  D. B. Smith,et al.  Full-scale studies of factors related to coliform regrowth in drinking water , 1996, Applied and environmental microbiology.