Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods.

Autor: Casanovas-Massana A; Department of Microbiology, University of Barcelona, Av. Diagonal 643, Barcelona, Catalonia, Spain., Gómez-Doñate M; Department of Microbiology, University of Barcelona, Av. Diagonal 643, Barcelona, Catalonia, Spain., Sánchez D; Department of Software, Technical University of Catalonia, Jordi Girona 1-3, Barcelona, Catalonia, Spain., Belanche-Muñoz LA; Department of Software, Technical University of Catalonia, Jordi Girona 1-3, Barcelona, Catalonia, Spain., Muniesa M; Department of Microbiology, University of Barcelona, Av. Diagonal 643, Barcelona, Catalonia, Spain., Blanch AR; Department of Microbiology, University of Barcelona, Av. Diagonal 643, Barcelona, Catalonia, Spain. Electronic address: ablanch@ub.edu.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2015 Mar 15; Vol. 151, pp. 317-25. Date of Electronic Publication: 2015 Jan 10.
DOI: 10.1016/j.jenvman.2015.01.002
Abstrakt: 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 coli, 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 log10 CFU 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.
(Copyright © 2015 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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