Improving the identification of the source of faecal pollution in water using a modelling approach: From multi-source to aged and diluted samples. (15th March 2020) - Ballesté, Elisenda; Belanche-Muñoz, Luis A.; Farnleitner, Andreas H.; Linke, Rita; Sommer, Regina; Santos, Ricardo; Monteiro, Silvia; Maunula, Leena; Oristo, Satu; Tiehm A, Andreas; Stange, Claudia; Blanch, Anicet R.
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. Graphical abstract: Image 1 Highlights: Samples from 5 geographical sources were analysed with 30 faecal markers and indicators. A machine learning software was used to develop faecal source discriminant models. An in-silico matrix was generated using faecal samples, adding dilution and inactivation. LDA models' output was a combination of markers able to improve the accuracy of classification. Models using between 2 and 5 source tracking markers can achieve LOOCV accuracies of over 95%.