TY - JOUR
T1 - Improving the identification of the source of faecal pollution in water using a modelling approach
T2 - From multi-source to aged and diluted samples
AU - Ballesté, Elisenda
AU - Belanche-Muñoz, Luis A
AU - Farnleitner, Andreas H
AU - Linke, Rita
AU - Sommer, Regina
AU - Santos, Ricardo
AU - Monteiro, Silvia
AU - Maunula, Leena
AU - Oristo, Satu
AU - Tiehm A, Andreas
AU - Stange, Claudia
AU - Blanch, Anicet R
N1 - Funding Information:
This study was supported by the European Project FP7 KBBE AQUAVALENS , Grant agreement no: 311846 , Spanish Government research project CGL2011-25401 and the 2017SGR170 project by the Catalan Government . We thank Kirsi Söderberg for technical support at UH, Nathalie Schuster for her laboratory expertise and Gerhard Lindner for providing lab support at Institute of Chemical, Environmental and Bioscience Engineering (Vienna); Laura Sala and Marta Gómez for her lab support in the UB. Appendix A
Funding Information:
This study was supported by the European Project FP7 KBBE AQUAVALENS, Grant agreement no: 311846, Spanish Government research project CGL2011-25401 and the 2017SGR170 project by the Catalan Government. We thank Kirsi S?derberg for technical support at UH, Nathalie Schuster for her laboratory expertise and Gerhard Lindner for providing lab support at Institute of Chemical, Environmental and Bioscience Engineering (Vienna); Laura Sala and Marta G?mez for her lab support in the UB.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/3/15
Y1 - 2020/3/15
N2 - 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.
AB - 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.
KW - Ecosystem
KW - Environmental Monitoring
KW - Escherichia coli
KW - Feces
KW - Humans
KW - Water
KW - Water Microbiology
KW - Water Pollution
UR - http://www.scopus.com/inward/record.url?scp=85076531162&partnerID=8YFLogxK
U2 - 10.1016/j.watres.2019.115392
DO - 10.1016/j.watres.2019.115392
M3 - Journal article
C2 - 31865126
SN - 0043-1354
VL - 171
SP - 115392
JO - Water Research
JF - Water Research
M1 - 115392
ER -