Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods
Tipo de documentoArtículo
Fecha de publicación2015-03-15
Condiciones de accesoAcceso restringido por política de la editorial
In this study we use a machine learning software (Ichnaea) to generate predictive models for water samples with different concentrations of fecal contamination (point source, moderate and low). We applied several MST methods (host-specific Bacteroides phages, mitochondrial DNA genetic markers, Bifidobacterium adolescentis and Bifidobacterium dentium markers, and bifidobacterial host-specific qPCR), and general indicators (Escherichia colt, enterococci and somatic coliphages) to evaluate the source of contamination in the samples. The results provided data to the Ichnaea software, that evaluated the performance of each method in the different scenarios and determined the source of the contamination. Almost all MST methods in this study determined correctly the origin of fecal contamination at point source and in moderate concentration samples. When the dilution of the fecal pollution increased (below 3 log(10) CPU E. coli/100 ml) some of these indicators (bifidobacterial host-specific qPCR, some mitochondrial markers or B. dentium marker) were not suitable because their concentrations decreased below the detection limit. Using the data from source point samples, the software Ichnaea produced models for waters with low levels of fecal pollution. These models included some MST methods, on the basis of their best performance, that were used to determine the source of pollution in this area. Regardless the methods selected, that could vary depending on the scenario, inductive machine learning methods are a promising tool in MST studies and may represent a leap forward in solving MST cases.
CitaciónCasanovas, A. [et al.]. Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods. "Journal of environmental management", 15 Març 2015, vol. 151, p. 317-325.
Versión del editorhttp://www.sciencedirect.com/science/article/pii/S0301479715000031
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