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Title: Improving the identification of the source of faecal pollution in water using a modelling approach: from multi-source to aged and diluted samples
Author: Ballesté Pau, Elisenda
Belanche-Muñoz, Luis A.
Farnleitner, Andreas H.
Linke, Rita
Sommer, Regina
Santos, Ricardo
Monteiro, Silvia
Maunula, Leena
Oristo, Satu
Tiehm, Andraeas
Stange, Claudia
Blanch i Gisbert, Anicet
Keywords: Contaminació de l'aigua
Water pollution
Issue Date: 15-Mar-2020
Publisher: Elsevier Ltd
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.
Note: Versió postprint del document publicat a:
It is part of: Water Research, 2020, vol. 171, p. 115392
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ISSN: 0043-1354
Appears in Collections:Articles publicats en revistes (Genètica, Microbiologia i Estadística)

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