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http://hdl.handle.net/2445/154131
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DC Field | Value | Language |
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dc.contributor.author | Ballesté Pau, Elisenda | - |
dc.contributor.author | Belanche-Muñoz, Luis A. | - |
dc.contributor.author | Farnleitner, Andreas H. | - |
dc.contributor.author | Linke, Rita | - |
dc.contributor.author | Sommer, Regina | - |
dc.contributor.author | Santos, Ricardo | - |
dc.contributor.author | Monteiro, Silvia | - |
dc.contributor.author | Maunula, Leena | - |
dc.contributor.author | Oristo, Satu | - |
dc.contributor.author | Tiehm, Andraeas | - |
dc.contributor.author | Stange, Claudia | - |
dc.contributor.author | Blanch i Gisbert, Anicet | - |
dc.date.accessioned | 2020-03-27T08:50:55Z | - |
dc.date.available | 2022-03-15T06:10:16Z | - |
dc.date.issued | 2020-03-15 | - |
dc.identifier.issn | 0043-1354 | - |
dc.identifier.uri | http://hdl.handle.net/2445/154131 | - |
dc.description.abstract | The last decades have seen the development of several source tracking (ST) markers to determine the source of pollution in water, but none of them show 100% specificity and sensitivity. Thus, a combination of several markers might provide a more accurate classification. In this study Ichnaea® software was improved to generate predictive models, taking into account ST marker decay rates and dilution factors to reflect the complexity of ecosystems. A total of 106 samples from 4 sources were collected in 5 European regions and 30 faecal indicators and ST markers were evaluated, including E. coli, enterococci, clostridia, bifidobacteria, somatic coliphages, host-specific bacteria, human viruses, host mitochondrial DNA, host-specific bacteriophages and artificial sweeteners. Models based on linear discriminant analysis (LDA) able to distinguish between human and non-human faecal pollution and identify faecal pollution of several origins were developed and tested with 36 additional laboratory-made samples. Almost all the ST markers showed the potential to correctly target their host in the 5 areas, although some were equivalent and redundant. The LDA-based models developed with fresh faecal samples were able to differentiate between human and non-human pollution with 98.1% accuracy in leave-one-out cross-validation (LOOCV) when using 2 molecular human ST markers (HF183 and HMBif), whereas 3 variables resulted in 100% correct classification. With 5 variables the model correctly classified all the fresh faecal samples from 4 different sources. Ichnaea® is a machine-learning software developed to improve the classification of the faecal pollution source in water, including in complex samples. In this project the models were developed using samples from a broad geographical area, but they can be tailored to determine the source of faecal pollution for any user. | - |
dc.format.extent | 12 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier Ltd | - |
dc.relation.isformatof | Versió postprint del document publicat a: https://doi.org/10.1016/j.watres.2019.115392 | - |
dc.relation.ispartof | Water Research, 2020, vol. 171, p. 115392 | - |
dc.relation.uri | https://doi.org/10.1016/j.watres.2019.115392 | - |
dc.rights | cc-by-nc-nd (c) Elsevier Ltd, 2020 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es | - |
dc.source | Articles publicats en revistes (Genètica, Microbiologia i Estadística) | - |
dc.subject.classification | Contaminació de l'aigua | - |
dc.subject.other | Water pollution | - |
dc.title | Improving the identification of the source of faecal pollution in water using a modelling approach: from multi-source to aged and diluted samples | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/acceptedVersion | - |
dc.identifier.idgrec | 695132 | - |
dc.date.updated | 2020-03-27T08:50:55Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Appears in Collections: | Articles publicats en revistes (Genètica, Microbiologia i Estadística) |
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File | Description | Size | Format | |
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695132.pdf | 1.31 MB | Adobe PDF | View/Open |
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