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Decision-Making Tools to Manage the Microbiology of Drinking Water Distribution Systems

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Decision-Making Tools to Manage the Microbiology of Drinking Water Distribution Systems

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dc.contributor.author Carpitella, Silvia es_ES
dc.contributor.author Del Olmo, Gonzalo es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.contributor.author Husband, Stewart es_ES
dc.contributor.author Boxall, Joby es_ES
dc.contributor.author Douterelo, Isabel es_ES
dc.date.accessioned 2021-03-04T04:30:58Z
dc.date.available 2021-03-04T04:30:58Z
dc.date.issued 2020-05 es_ES
dc.identifier.issn 2073-4441 es_ES
dc.identifier.uri http://hdl.handle.net/10251/162955
dc.description.abstract [EN] This paper uses a two-fold multi-criteria decision-making (MCDM) approach applied for the first time to the field of microbial management of drinking water distribution systems (DWDS). Specifically, the decision-making trial and evaluation laboratory (DEMATEL) was applied removing the need for reliance on expert judgement, and analysed interdependencies among water quality parameters and microbiological characteristics of DWDS composed of different pipe materials. In addition, the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) ranked the most common bacteria identified during trials in a DWDS according to their relative abundance while managing vagueness affecting the measurements. The novel integrated approach presented and proven here for an initial real world data set provides new insights in the interdependence of environmental conditions and microbial populations. Specifically, the application shows as the bacteria having associated the most significant microbial impact may not be the most abundant. This offers the potential for integrated management strategies to promote favourable microbial conditions to help safeguard drinking water quality. es_ES
dc.description.sponsorship The research reported here was supported by the UK Engineering and Physical Sciences Research Council (EPSRC), EPSRC-LWEC Challenge Fellowship EP/N02950X/1. We also would like to thank United Utilities for sampling sites, field work and physicochemical sample analysis. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Water es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Drinking water distribution systems es_ES
dc.subject Water quality monitoring es_ES
dc.subject Microbiological assessment es_ES
dc.subject Multi-criteria system analysis es_ES
dc.subject DEMATEL es_ES
dc.subject FTOPSIS es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Decision-Making Tools to Manage the Microbiology of Drinking Water Distribution Systems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/w12051247 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//EP%2FN02950X%2F1/GB/Uncovering microbial tactics in drinking water supply systems: using advances in genetics for countering the effects of climate change/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Carpitella, S.; Del Olmo, G.; Izquierdo Sebastián, J.; Husband, S.; Boxall, J.; Douterelo, I. (2020). Decision-Making Tools to Manage the Microbiology of Drinking Water Distribution Systems. Water. 12(5):1-18. https://doi.org/10.3390/w12051247 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/w12051247 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela S\411412 es_ES
dc.contributor.funder Engineering and Physical Sciences Research Council, Reino Unido es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.description.references Guidelines for Drinking Water Quality, 4th Edition, Incorporating the 1st Addendum 2017 https://www.who.int/water_sanitation_health/publications/drinking-water-quality-guidelines-4-including-1st-addendum/en/ es_ES
dc.description.references Water Quality-Sampling-Part 5: Guidance on Sampling of Drinking Water from Treatment Works and Piped Distribution Systems https://www.iso.org/obp/ui/#iso:std:iso:5667:-5:ed-2:v1:en es_ES
dc.description.references Deng, W., & Wang, G. (2017). A novel water quality data analysis framework based on time-series data mining. Journal of Environmental Management, 196, 365-375. doi:10.1016/j.jenvman.2017.03.024 es_ES
dc.description.references McClymont, K., Keedwell, E., & Savic, D. (2015). An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design. Environmental Modelling & Software, 69, 414-424. doi:10.1016/j.envsoft.2014.12.023 es_ES
dc.description.references Izquierdo, J., Montalvo, I., Pérez-García, R., & Matías, A. (2012). On the Complexities of the Design of Water Distribution Networks. Mathematical Problems in Engineering, 2012, 1-25. doi:10.1155/2012/947961 es_ES
dc.description.references Fox, S., Shepherd, W., Collins, R., & Boxall, J. (2016). Experimental Quantification of Contaminant Ingress into a Buried Leaking Pipe during Transient Events. Journal of Hydraulic Engineering, 142(1), 04015036. doi:10.1061/(asce)hy.1943-7900.0001040 es_ES
dc.description.references Mounce, S. R., Mounce, R. B., & Boxall, J. B. (2010). Novelty detection for time series data analysis in water distribution systems using support vector machines. Journal of Hydroinformatics, 13(4), 672-686. doi:10.2166/hydro.2010.144 es_ES
dc.description.references Vališ, D., Hasilová, K., Forbelská, M., & Vintr, Z. (2020). Reliability modelling and analysis of water distribution network based on backpropagation recursive processes with real field data. Measurement, 149, 107026. doi:10.1016/j.measurement.2019.107026 es_ES
dc.description.references Liu, G., Bakker, G. L., Li, S., Vreeburg, J. H. G., Verberk, J. Q. J. C., Medema, G. J., … Van Dijk, J. C. (2014). Pyrosequencing Reveals Bacterial Communities in Unchlorinated Drinking Water Distribution System: An Integral Study of Bulk Water, Suspended Solids, Loose Deposits, and Pipe Wall Biofilm. Environmental Science & Technology, 48(10), 5467-5476. doi:10.1021/es5009467 es_ES
dc.description.references Donlan, R. M., & Costerton, J. W. (2002). Biofilms: Survival Mechanisms of Clinically Relevant Microorganisms. Clinical Microbiology Reviews, 15(2), 167-193. doi:10.1128/cmr.15.2.167-193.2002 es_ES
dc.description.references Douterelo, I., Husband, S., Loza, V., & Boxall, J. (2016). Dynamics of Biofilm Regrowth in Drinking Water Distribution Systems. Applied and Environmental Microbiology, 82(14), 4155-4168. doi:10.1128/aem.00109-16 es_ES
dc.description.references Wang, H., Hu, C., Hu, X., Yang, M., & Qu, J. (2012). Effects of disinfectant and biofilm on the corrosion of cast iron pipes in a reclaimed water distribution system. Water Research, 46(4), 1070-1078. doi:10.1016/j.watres.2011.12.001 es_ES
dc.description.references Husband, S., Fish, K. E., Douterelo, I., & Boxall, J. (2016). Linking discolouration modelling and biofilm behaviour within drinking water distribution systems. Water Supply, 16(4), 942-950. doi:10.2166/ws.2016.045 es_ES
dc.description.references Wingender, J., & Flemming, H.-C. (2011). Biofilms in drinking water and their role as reservoir for pathogens. International Journal of Hygiene and Environmental Health, 214(6), 417-423. doi:10.1016/j.ijheh.2011.05.009 es_ES
dc.description.references Douterelo, I., Boxall, J. B., Deines, P., Sekar, R., Fish, K. E., & Biggs, C. A. (2014). Methodological approaches for studying the microbial ecology of drinking water distribution systems. Water Research, 65, 134-156. doi:10.1016/j.watres.2014.07.008 es_ES
dc.description.references Douterelo, I., Sharpe, R. L., & Boxall, J. B. (2013). Influence of hydraulic regimes on bacterial community structure and composition in an experimental drinking water distribution system. Water Research, 47(2), 503-516. doi:10.1016/j.watres.2012.09.053 es_ES
dc.description.references Harwani, D. (2012). The Great Plate Count Anomaly and the Unculturable Bacteria. International Journal of Scientific Research, 2(9), 350-351. doi:10.15373/22778179/sep2013/122 es_ES
dc.description.references Ikonen, J., Pitkänen, T., Kosse, P., Ciszek, R., Kolehmainen, M., & Miettinen, I. T. (2017). On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system. Journal of Environmental Management, 198, 384-392. doi:10.1016/j.jenvman.2017.04.090 es_ES
dc.description.references Wang, Z., Chen, Q., Zhang, J., Dong, J., Yan, H., Chen, C., & Feng, R. (2019). Characterization and source identification of tetracycline antibiotics in the drinking water sources of the lower Yangtze River. Journal of Environmental Management, 244, 13-22. doi:10.1016/j.jenvman.2019.04.070 es_ES
dc.description.references Yu, J., Kim, D., & Lee, T. (2010). Microbial diversity in biofilms on water distribution pipes of different materials. Water Science and Technology, 61(1), 163-171. doi:10.2166/wst.2010.813 es_ES
dc.description.references Rogers, J. W., & Louis, G. E. (2008). Risk and opportunity in upgrading the US drinking water infrastructure system. Journal of Environmental Management, 87(1), 26-36. doi:10.1016/j.jenvman.2007.01.002 es_ES
dc.description.references Henriques, J. J., & Louis, G. E. (2011). A decision model for selecting sustainable drinking water supply and greywater reuse systems for developing communities with a case study in Cimahi, Indonesia. Journal of Environmental Management, 92(1), 214-222. doi:10.1016/j.jenvman.2010.09.016 es_ES
dc.description.references Ramos-Martínez, E., Herrera, M., Gutiérrez-Pérez, J., Izquierdo, J., & Pérez-García, R. (2014). Rehabilitation Actions in Water Supply Systems: Effects on Biofilm Susceptibility. Procedia Engineering, 89, 225-231. doi:10.1016/j.proeng.2014.11.181 es_ES
dc.description.references Dalvi-Esfahani, M., Niknafs, A., Kuss, D. J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250. doi:10.1016/j.tele.2019.101250 es_ES
dc.description.references Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018). DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications. Mathematical Problems in Engineering, 2018, 1-33. doi:10.1155/2018/3696457 es_ES
dc.description.references Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. doi:10.1016/s0165-0114(97)00377-1 es_ES
dc.description.references Gul, M., & Ak, M. F. (2018). A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. Journal of Cleaner Production, 196, 653-664. doi:10.1016/j.jclepro.2018.06.106 es_ES
dc.description.references Carpitella, S., Certa, A., Izquierdo, J., & La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering & System Safety, 169, 394-402. doi:10.1016/j.ress.2017.09.017 es_ES
dc.description.references Palczewski, K., & Sałabun, W. (2019). The fuzzy TOPSIS applications in the last decade. Procedia Computer Science, 159, 2294-2303. doi:10.1016/j.procs.2019.09.404 es_ES
dc.description.references Sarkar, S., Pratihar, D. K., & Sarkar, B. (2018). An integrated fuzzy multiple criteria supplier selection approach and its application in a welding company. Journal of Manufacturing Systems, 46, 163-178. doi:10.1016/j.jmsy.2017.12.004 es_ES
dc.description.references Dinçer, H., Yüksel, S., & Martínez, L. (2019). Interval type 2-based hybrid fuzzy evaluation of financial services in E7 economies with DEMATEL-ANP and MOORA methods. Applied Soft Computing, 79, 186-202. doi:10.1016/j.asoc.2019.03.018 es_ES
dc.description.references Chen, J.-K., & Chen, I.-S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981-1990. doi:10.1016/j.eswa.2009.06.079 es_ES
dc.description.references Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., … Hassan Ahmed, O. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. doi:10.1016/j.cie.2019.106005 es_ES
dc.description.references Li, G., Ma, X., Chen, R., Yu, Y., Tao, H., & Shi, B. (2019). Field studies of manganese deposition and release in drinking water distribution systems: Insight into deposit control. Water Research, 163, 114897. doi:10.1016/j.watres.2019.114897 es_ES
dc.description.references Zhu, Y., Wang, H., Li, X., Hu, C., Yang, M., & Qu, J. (2014). Characterization of biofilm and corrosion of cast iron pipes in drinking water distribution system with UV/Cl2 disinfection. Water Research, 60, 174-181. doi:10.1016/j.watres.2014.04.035 es_ES
dc.description.references Du, Y.-W., & Zhou, W. (2019). New improved DEMATEL method based on both subjective experience and objective data. Engineering Applications of Artificial Intelligence, 83, 57-71. doi:10.1016/j.engappai.2019.05.001 es_ES
dc.description.references Carpitella, S., Carpitella, F., Certa, A., Benítez, J., & Izquierdo, J. (2018). Managing Human Factors to Reduce Organisational Risk in Industry. Mathematical and Computational Applications, 23(4), 67. doi:10.3390/mca23040067 es_ES
dc.description.references Gerami Seresht, N., & Fayek, A. R. (2019). Computational method for fuzzy arithmetic operations on triangular fuzzy numbers by extension principle. International Journal of Approximate Reasoning, 106, 172-193. doi:10.1016/j.ijar.2019.01.005 es_ES
dc.description.references Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-x es_ES
dc.description.references Prokopowicz, P. (2019). The use of Ordered Fuzzy Numbers for modelling changes in dynamic processes. Information Sciences, 470, 1-14. doi:10.1016/j.ins.2018.08.045 es_ES
dc.description.references Okunuki, S., Kawaharasaki, M., Tanaka, H., & Kanagawa, T. (2004). Changes in phosphorus removing performance and bacterial community structure in an enhanced biological phosphorus removal reactor. Water Research, 38(9), 2433-2439. doi:10.1016/j.watres.2004.02.008 es_ES
dc.description.references Rofner, C., Sommaruga, R., & Teresa Pérez, M. (2016). Phosphate and ATP uptake by lake bacteria: does taxonomical identity matter? Environmental Microbiology, 18(12), 4782-4793. doi:10.1111/1462-2920.13368 es_ES
dc.description.references Ferro, P., Vaz-Moreira, I., & Manaia, C. M. (2019). Betaproteobacteria are predominant in drinking water: are there reasons for concern? Critical Reviews in Microbiology, 45(5-6), 649-667. doi:10.1080/1040841x.2019.1680602 es_ES
dc.description.references Ertekin, E., Hatt, J. K., Konstantinidis, K. T., & Tezel, U. (2016). Similar Microbial Consortia and Genes Are Involved in the Biodegradation of Benzalkonium Chlorides in Different Environments. Environmental Science & Technology, 50(8), 4304-4313. doi:10.1021/acs.est.5b05959 es_ES
dc.subject.ods 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


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