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Water quality was assessed at two marine beaches in California by measuring the concentrations of culturable fecal indicator bacteria (FIB) and by library-independent microbial source tracking (MST) methods targeting markers of human-associated microbes (human polyomavirus [HPyV] PCR and quantitative PCR, Methanobrevibacter smithii PCR, and Bacteroides sp. strain HF183 PCR) and a human pathogen (adenovirus by nested PCR). FIB levels periodically exceeded regulatory thresholds at Doheny and Avalon Beaches for enterococci (28.5% and 31.7% of samples, respectively) and fecal coliforms (20% and 5.8%, respectively). Adenoviruses were detected at four of five sites at Doheny Beach and were correlated with detection of HPyVs and human Bacteroides HF183; however, adenoviruses were not detected at Avalon Beach. The most frequently detected human source marker at both beaches was Bacteroides HF183, which was detected in 27% of samples. Correlations between FIBs and human markers were much more frequent at Doheny Beach than at Avalon Beach; e.g., adenovirus was correlated with HPyVs and HF183. Human sewage markers and adenoviruses were routinely detected in samples meeting FIB regulatory standards. The toolbox approach of FIB measurement coupled with analysis of several MST markers targeting human pathogens used here demonstrated that human sewage is at least partly responsible for the degradation of water quality, particularly at Doheny Beach, and resulted in a more definitive assessment of recreational water quality and human health risk than reliance on FIB concentrations alone could have provided.

Same-day microbial water quality assessments are not possible with standard methods, which increases the possibility of public exposure to fecal pathogens. This study examined the efficacy of high-volume hollow fibre ultrafiltration coupled to biosensor detection for enterococci in marine waters to allow same-day public notification of poor water quality. Fifty-six 1001 ultrafiltered samples and 100 ml grab samples were collected weekly from May to July 2007. Post-ultrafiltration processing included sonication and micron sieve passage to remove interfering particulates, followed by centrifugation for secondary concentration. Levels of enterococci in grab and ultrafiltration samples were determined by a standard method (EPA method 1600) for calculation of recovery efficiencies and concentration factors. Each final retentate was analysed with the RAPTOR evanescent wave biosensor. Enterococci levels increased over 26,000-fold in final retentates. Enterococci were detected when ambient concentrations exceeded the regulatory standard for a single sample (> or = 105 CFU/100 ml), and detection was highly correlated with breaches of the single-sample regulatory limit. The combined procedure required 2.5 h for detection compared with 24h for EPA method 1600. This field study achieved rapid detection of enterococci by ultrafiltration, secondary concentration and biosensor analysis, and demonstrates its potential usefulness for water quality monitoring.

As part of a larger microbial source tracking (MST) study, several laboratories used library-based, phenotypic subtyping techniques to analyse fecal samples from known sources (human, sewage, cattle, dogs and gulls) and blinded water samples that were contaminated with the fecal sources. The methods used included antibiotic resistance analysis (ARA) of fecal streptococci, enterococci, fecal coliforms and E. coli; multiple antibiotic resistance (MAR) and Kirby-Bauer antibiotic susceptibility testing of E. coli; and carbon source utilization for fecal streptococci and E. coli. Libraries comprising phenotypic patterns of indicator bacteria isolated from known fecal sources were used to predict the sources of isolates from water samples that had been seeded with fecal material from the same sources as those used to create the libraries. The accuracy of fecal source identification in the water samples was assessed both with and without a cut-off termed the minimum detectable percentage (MDP). The libraries (approximately 300 isolates) were not large enough to avoid the artefact of source-independent grouping, but some important conclusions could still be drawn. Use of a MDP decreased the percentage of false-positive source identifications, and had little effect on the high percentage of true-positives in the most accurate libraries. In general, the methods were more prone to false-positive than to false-negative errors. The most accurate method, with a true-positive rate of 100% and a false-positive rate of 39% when analysed with a MDP, was ARA of fecal streptococci. The internal accuracy of the libraries did not correlate with the accuracy of source prediction in water samples, showing that one should not rely solely on parameters such as the average rate of correct classification of a library to indicate its predictive capabilities.