Several microbes and chemicals have been considered as potential tracers to identify fecal sources in the environment. However, to date, no one approach has been shown to accurately identify the origins of fecal pollution in aquatic environments. In this multilaboratory study, different microbial and chemical indicators were analyzed in order to distinguish human fecal sources from nonhuman fecal sources using wastewaters and slurries from diverse geographical areas within Europe. Twenty-six parameters, which were later combined to form derived variables for statistical analyses, were obtained by performing methods that were achievable in all the participant laboratories: enumeration of fecal coliform bacteria, enterococci, clostridia, somatic coliphages, F-specific RNA phages, bacteriophages infecting Bacteroides fragilis RYC2056 and Bacteroides thetaiotaomicron GA17, and total and sorbitol-fermenting bifidobacteria; genotyping of F-specific RNA phages; biochemical phenotyping of fecal coliform bacteria and enterococci using miniaturized tests; specific detection of Bifidobacterium adolescentis and Bifidobacterium dentium; and measurement of four fecal sterols. A number of potentially useful source indicators were detected (bacteriophages infecting B. thetaiotaomicron, certain genotypes of F-specific bacteriophages, sorbitol-fermenting bifidobacteria, 24-ethylcoprostanol, and epycoprostanol), although no one source identifier alone provided 100% correct classification of the fecal source. Subsequently, 38 variables (both single and derived) were defined from the measured microbial and chemical parameters in order to find the best subset of variables to develop predictive models using the lowest possible number of measured parameters. To this end, several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatic coliphages and the ratio of the densities of fecal coliform bacteria and phages infecting Bacteroides thetaiotaomicron to the density of fecal coliform bacteria. Other models with high rates of correct classification were developed, but in these cases, higher numbers of variables were required.Determining the source of fecal contamination in aquatic environments is essential for estimating the health risks associated with pollution, facilitating measures to remediate polluted waterways, and resolving legal responsibility for remediation. Source tracking methods should enable investigators to uncover the sources of fecal pollution in a particular water body (40). Candidate microbes and chemicals have been investigated and reviewed (15,54,55) as potential tools for the identification of human fecal sources. More recently, new approaches using eukaryotic mitochondrial DNA to differentiate fecal sources in feces-contaminated surface waters have been explored (43). However, field ...

A number of chemical, microbial, and eukaryotic indicators have been proposed as indicators of fecal pollution sources in water bodies. No single one of the indicators tested to date has been able to determine the source of fecal pollution in water. However, the combined use of different indicators has been demonstrated to be the best way of defining predictive models suitable for determining fecal pollution sources. Molecular methods are promising tools that could complement standard microbiological water analysis. In this study, the feasibility of some proposed molecular indicators for microbial source tracking (MST) was compared (names of markers are in parentheses): host-specific Bacteroidetes (HF134, HF183, CF128, and CF193), Bifidobacterium adolescentis (ADO), Bifidobacterium dentium (DEN), the gene esp of Enterococcus faecium, and host-specific mitochondrial DNA associated with humans, cattle, and pigs (Humito, Bomito, and Pomito, respectively). None of the individual molecular markers tested enabled 100% source identification. They should be combined with other markers to raise sensitivity and specificity and increase the number of sources that are identified. MST predictive models using only these molecular markers were developed. The models were evaluated by considering the lowest number of molecular indicators needed to obtain the highest rate of identification of fecal sources. The combined use of three molecular markers (ADO, Bomito, and Pomito) enabled correct identification of 75.7% of the samples, with differentiation between human, swine, bovine, and poultry sources. Discrimination between human and nonhuman fecal pollution was possible using two markers: ADO and Pomito (84.6% correct identification). The percentage of correct identification increased with the number of markers analyzed. The best predictive model for distinguishing human from nonhuman fecal sources was based on 5 molecular markers (HF134, ADO, DEN, Bomito, and Pomito) and provided 90.1% correct classification.

Bifidobacteria are one of the most common bacterial types found in the intestines of humans and other animals and may be used as indicators of human fecal pollution. The presence of nine human-related Bifidobacterium species was analyzed in human and animal wastewater samples of different origins by using species-specific primers based on 16S rRNA sequences. Only B. adolescentis and B. dentium were found exclusively in human sewage. A multiplex PCR approach with strain-specific primers was developed. The method showed a sensitivity threshold of 10 cells/ml. This new molecular method could provide useful information for the characterization of fecal pollution sources.

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A new, simple, and specific protocol to discriminate between human and animal fecal pollution is described. The procedure is based on the detection of certain Bifidobacterium species in the samples. Two 16S rRNA gene-targeted probes are described. One of these probes (BDE) has as its target a region of the 16S rRNA gene of Bifidobacterium dentium, a Bifidobacterium species of exclusively human origin. The other probe (BAN) is based on the sequence of a region of 16S rRNA gene for several Bifidobacterium species related with animal origins. The specificity of both probes was evaluated by using 24 Bifidobacterium species, and their threshold detection limit was established by DNA-DNA hybridization. DNA-DNA hybridization with the BDE probe showed it to be specific for B. dentium, whereas that with the BAN probe showed it to be specific for B. animalis, B. asteroides, B. coryneforme, B. cuniculi, B. globosum, B. magnum, B. minimum, and B. subtile. A simple and specific protocol was also developed for the detection of their target species in environmental samples (sewage and feces). DNA-DNA hybridization with the BAN probe was only positive for samples from cattle and goats. Thus, this probe is not suitable for the identification of any animal fecal pollution. Whereas all samples with human fecal pollution showed a positive DNA-DNA hybridization result with the BDE probe, none of those with animal fecal pollution did. Therefore, this finding supports the potential use of this probe in detecting fecal pollution of human origin.

The objectives of this study are to generate knowledge about methods to track the sources of faecal pollution in surface waters, with the aim of having one or a few easy procedures applicable to different geographic areas in Europe. For this, a first field study using already proposed methods (genotypes of F-specific RNA bacteriophages, bacteriophages infecting Bacteroides fragilis, phenotypes of faecal coliforms and enterococci, and sterols) has been done in five areas representing a wide array of conditions in Europe. The present faecal indicators (faecal coliforms, enterococci, sulfite reducing clostridia and somatic coliphages) have also been included in this first field study. At the same time some emerging methods have been settled or adapted to water samples and assayed in a limited number of samples. The results of this first field study indicate that no single parameter alone is able to discriminate the sources, human or non-human, of faecal pollution, but that a 'basket' of 4 or 5 parameters, which includes one of the present faecal indicators, will do so. In addition, numerical analysis of the data shows that this 'basket' will allow the successful building of predictive models. Both the statistical analyses and the studied predictive models indicate that genotype II of F-specific RNA bacteriophages, the coprostanol and the ratio coprostanol: coprostanol+epicoprostanol are, out of the studied parameters, those with a greater discriminating power. Either because unsuccessful adaptation of the methods to water samples or because the preliminary assays in water samples indicated low discriminating capability, only three (sorbitol-fermenting bifidobacteria, some species of bifidobacteria detected by PCR with specific primers and phages infecting Bacteroides tethaiotaomicron) of the newly assayed methods have been considered for a second field study, which is currently underway. Expectations are that these new tools will minimize the number of parameters in the 'basket', or at least minimize the difficulty in assaying them.

Aims:  To determine relative to faecal coliforms (FC) and sulfite‐reducing clostridia (SRC), the environmental persistence of natural populations of Bifidobacterium spp. enumerated by culturing and quantitative polymerase chain reaction (q‐PCR). Methods and Results:  Dialysis tubing containing river supplemented with overnight cultures of Bifidobacterium adolescentis (BA) and Bifidobacterium dentium (BD) or urban wastewater were suspended in a river for up to 10 days. At intervals, the contents of each dialysis tube were assayed using q‐PCR assays for BA and BD, and selective culture media for FC, SRC, total bifidobacteria (TB), sorbitol‐fermenting bifidobacteria (SFB) and cultivable BA. Mean summer T90 values were 251 h for SRC, 92 h for FC, 48 h for BA and BD by q‐PCR, and 9 h for TB. Conclusions:  Bifidobacterium spp. was the population with the lowest persistence, showing seasonal differences in T90 when measured by culture techniques or by q‐PCR. This difference in relative persistence is because of a longer persistence of molecular targets than cultivable cells. Significance and Impact of the Study:  The persistence of a viable bifidobacteria cells is shorter, but the longest persistence of molecular targets. This factor could be used for origin the faecal pollution in water for the development of microbial source tracking (MST).

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