Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-26T01:32:12.442Z Has data issue: false hasContentIssue false

Mathematical model of biofilm-mediated pathogen persistence in a water distribution network with time-constant flows

Published online by Cambridge University Press:  06 June 2018

SADIQAH AL MARZOOQ
Affiliation:
Department of Mathematical Sciences, McMicken College of Arts and Sciences, University of Cincinnati, Cincinnati, Ohio 45221-0025, USA emails: s_almarzooq@yu.edu.sa, ortizlaa@mail.uc.edu, vaughabn@ucmail.uc.edu Al Yamamah University, P.O. Box 45180, Riyadh 11512, Saudi Arabia
ALVARO ORTIZ-LUGO
Affiliation:
Department of Mathematical Sciences, McMicken College of Arts and Sciences, University of Cincinnati, Cincinnati, Ohio 45221-0025, USA emails: s_almarzooq@yu.edu.sa, ortizlaa@mail.uc.edu, vaughabn@ucmail.uc.edu
BENJAMIN L. VAUGHAN Jr.
Affiliation:
Department of Mathematical Sciences, McMicken College of Arts and Sciences, University of Cincinnati, Cincinnati, Ohio 45221-0025, USA emails: s_almarzooq@yu.edu.sa, ortizlaa@mail.uc.edu, vaughabn@ucmail.uc.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In industrialized nations, potable water is often provided through sophisticated water distribution systems. If pathogenic bacteria are introduced into the water distribution network, the presence of a biofilm can lead to biofilm-assisted retention of the pathogens, affecting the potability of the water. To study the dynamics of planktonic and biofilm-bound pathogens within the large network of pipes in a water distribution system, we develop a network model governing the concentration of introduced pathogens within the bulk fluid and the biofilms lining the pipes. Under time-constant flow regimes within the network, we prove that the long-time behaviour of the entire network is dependent on the Lyapunov exponents for each connection in the network when viewed in isolation and the network connectivity. An efficient algorithm is developed for predicting the long-time behaviour of the pathogen population within large networks using the network's topological ordering. The algorithm's predictions are validated using numerical simulations of the full non-linear system on a range of water distribution network sizes.

Type
Papers
Copyright
Copyright © Cambridge University Press 2018 

References

Ashwin, P., Coombes, S. & Nicks, R. (2016) Mathematical frameworks for oscillatory network dynamics in neuroscience. J. Math. Neurosci. 6 (1), 2.Google Scholar
Barrat, A., Barthelemy, M. & Vespignani, A. (2008) Dynamical Processes on Complex Networks, Cambridge University Press, Cambridge, U.K.Google Scholar
Bartram, J. (2003) Heterotrophic Plate Counts and Drinking-Water Safety: The Significance of HPCs for Water Quality and Human Health, IWA Publishing, London, U.K.Google Scholar
Besner, M. C., Gauthier, V., Servais, P. & Camper, A. (2002) Explaining the occurrence of coliforms in distribution systems. J. Am. Water Works Ass. 94 (8), 95109.Google Scholar
Brauer, F., Castillo-Chavez, C. & Castillo-Chavez, C. (2012) Mathematical Models in Population Biology and Epidemiology, vol. 1, Springer, New York, USA.Google Scholar
Camper, A. K. (1998) Pathogens in Model Distribution System Biofilms, American Water Works Association, USA.Google Scholar
Camper, A. K. & McFeters, G. A. (2000) Industrial Biofouling: Detection, Prevention and Control, Walker, J., Surman, S. & Jass, J. (editors), Wiley, New York, USA.Google Scholar
Camper, A. K., Jones, W. L. & Hayes, J. T. (1996) Effect of growth conditions and substratum composition on the persistence of coliforms in mixed-population biofilms. Appl. Environ. Microbiol. 62 (11), 4014.Google Scholar
Carabeţ, A., Mirel, I., Florescu, C., Stăniloiu, C., Gīrbaciu, A. & Olaru, I. (2011) Drinking water quality in water-supply networks. Environ. Eng. Manag. J. 10 (11), 16591665.Google Scholar
Carter, J. T., Rice, E. W., Buchberger, S. G. & Lee, Y. (2000) Relationships between levels of heterotrophic bacteria and water quality parameters in a drinking water distribution system. Water Res. 34 (5), 14951502.Google Scholar
Castellano, C., Fortunato, S. & Loreto, V. (2009) Statistical physics of social dynamics. Rev. Mod. Phys. 81 (2), 591.Google Scholar
Costerton, J. W., Lewandowski, Z., Caldwell, D. E., Korber, D. R. & Lappin-Scott, H. M. (1995) Microbial biofilms. Ann. Rev. Microbiol. 49 (1), 711745.Google Scholar
Davis, M. L. (2011) Water and Wastewater Engineering, McGraw-Hill Higher Education, New York.Google Scholar
Eberl, H., Morgenroth, E., Noguera, D., Picioreanu, C., Rittmann, B., van Loosdrecht, M. & Wanner, O. (2006) Mathematical Modeling of Biofilms, IWA Publishing, London, U.K.Google Scholar
Eiger, G., Shamir, U. & Ben-Tal, A. (1994) Optimal design of water distribution networks. Water Resour. Res. 30 (9), 26372646.Google Scholar
Flemming, H. and Wingender, J. (2010) The biofilm matrix. Nat. Rev. Microbiol. 8 (9), 623633.Google Scholar
Kahn, A. B. (1962) Topological sorting of large networks. Commun. ACM 5 (11), 558562.Google Scholar
Kuramoto, Y. (1984) Chemical Oscillations, Waves and Turbulence, Springer, Berlin, Germany.Google Scholar
LeChevallier, M. W., Welch, N. J. & Smith, D. (1996) Full-scale studies of factors related to coliform regrowth in drinking water. Appl. Environ. Microbiol. 62 (7), 2201.Google Scholar
LeChevallier, M. W., Gullick, R., Karim, M., Friedman, M. & Funk, J. (2003) The potential for health risks from intrusion of contaminants into the distribution system from pressure transients. J. Water. Health 1 (1), 314.Google Scholar
Lehtola, M. J., Laxander, M., Miettinen, I. T., Hirvonen, A., Vartiainen, T. & Martikainen, P. J. (2006) The effects of changing water flow velocity on the formation of biofilms and water quality in pilot distribution system consisting of copper or polyethylene pipes. Water Res. 40 (11), 21512160.Google Scholar
Mays, L. W. (2010) Water Resources Engineering, John Wiley & Sons, New York, USA.Google Scholar
Momba, M. N. B., Kfir, R., Venter, S. N. & Cloete, T. E. (2000) An overview of biofilm formation in distribution systems and its impact on the deterioration of water quality. Water S.A. 26 (1), 5966.Google Scholar
Murray, R., Uber, J. & Janke, R. (2006) Model for estimating acute health impacts from consumption of contaminated drinking water. J. Water. Res. Pl.-ASCE 132 (4), 293299.Google Scholar
Porter, M. & Gleeson, J. (2016) Dynamical Systems on Networks: A Tutorial, vol. 4, Springer, Cham, Switzerland.Google Scholar
Power, K. N. & Nagy, L. A. (1999) Relationship between bacterial regrowth and some physical and chemical parameters within Sydney's drinking water distribution system. Water Res. 33 (3), 741750.Google Scholar
Rossman, L. A. (2000) EPANET 2: Users manual, US Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory.Google Scholar
Rossman, L. A. & Boulos, P. F. (1996) Numerical methods for modeling water quality in distribution systems: A comparison. J. Water Res. Pl.-ASCE 122 (2), 137146.Google Scholar
Shampine, L. F. & Reichelt, M. W. (1997) The MATLAB ODE suite. SIAM J. Sci. Comput. 18 (1), 122.Google Scholar
Skraber, S., Schijven, J., Gantzer, C. & de Roda Husman, A. M. (2005) Pathogenic viruses in drinking-water biofilms: A public health risk? Biofilms 2 (02), 105117.Google Scholar
Strogatz, S. H. (2000) From kuramoto to crawford: Exploring the onset of synchronization in populations of coupled oscillators. Physica D: Nonlinear Phenomena 143 (1–4), 120.Google Scholar
Szabo, J. G. (2006) Persistence of Microbiological Agents on Corroding Biofilm in a Model Drinking Water system Following Intentional Contamination, PhD thesis, University of Cincinnati.Google Scholar
Trulear, M. G. & Characklis, W. G. (1982) Dynamics of biofilm processes. J. Water Pollut. Con. F. 54 (9), 12881301.Google Scholar
Uber, J., Murray, R. & Janke, R. (2004) Use of systems analysis to assess and minimize water security risks. J. Contemp. Water Res. Edu. 129 (1), 3440.Google Scholar
van der Kooij, D. (1992) Assimilable organic carbon as an indicator of bacterial regrowth. J. Am. Water Works Ass. 84 (2), 5765.Google Scholar
van Loosdrecht, M., Heijnen, J., Eberl, H., Kreft, J. & Picioreanu, C. (2002) Mathematical modelling of biofilm structures. A. Van Leeuw. J. Microb. 81 (1), 245256.Google Scholar
Vespignani, A. (2012) Modelling dynamical processes in complex socio-technical systems. Nature Phys. 8 (1), 32.Google Scholar
World Health Organization. (2002) The world health report 2002: Reducing the risks, promoting healthy life. Geneva, Switzerland.Google Scholar
Zhang, W. & DiGiano, F. A. (2002) Comparison of bacterial regrowth in distribution systems using free chlorine and chloramine: A statistical study of causative factors. Water Res. 36 (6), 14691482.Google Scholar