Bacterial and viral fecal indicator predictive modeling at three Great Lakes recreational beach sites.

Coliphage are viruses that infect Escherichia coli (E. coli) and may indicate the presence of enteric viral pathogens in recreational waters. There is an increasing interest in using these viruses for water quality monitoring and forecasting; however, the ability to use statistical models to predict the concentrations of coliphage, as often done for cultured fecal indicator bacteria (FIB) such as enterococci and E. coli, has not been widely assessed. The same can be said for FIB genetic markers measured using quantitative polymerase chain reaction (qPCR) methods. Here we institute least-angle regression (LARS) modeling of previously published concentrations of cultured FIB (E. coli, enterococci) and coliphage (F+, somatic), along with newly reported genetic concentrations measured via qPCR for E. coli, enterococci, and general Bacteroidales. We develop site-specific models from measures taken at three beach sites on the Great Lakes (Grant Park, South Milwaukee, WI; Edgewater Beach, Cleveland, OH; Washington Park, Michigan City, IN) to investigate the efficacy of a statistical predictive modeling approach. Microbial indicator concentrations were measured in composite water samples collected five days per week over a beach season (∼15 weeks). Model predictive performance (cross-validated standardized root mean squared error of prediction [SRMSEP] and R2PRED) were examined for seven microbial indicators (using log10 concentrations) and water/beach parameters collected concurrently with water samples. Highest predictive performance was seen for qPCR-based enterococci and Bacteroidales models, with F+ coliphage consistently yielding poor performing models. Influential covariates varied by microbial indicator and site. Antecedent rainfall, bird abundance, wave height, and wind speed/direction were most influential across all models. Findings suggest that some fecal indicators may be more suitable for water quality forecasting than others at Great Lakes beaches.

Advancements in mitigating interference in quantitative polymerase chain reaction (qPCR) for microbial water quality monitoring.

The United States Environmental Protection Agency's (EPA)1 2012 Recreational Water Quality Criteria included an Enterococcus spp. quantitative polymerase chain reaction (qPCR) method as a supplemental indicator-method. In 2012, performance of qPCR for beach monitoring remained limited, specifically with addressing interference. A systematic literature search of peer-reviewed publications was conducted to identify where Enterococcus spp. and E. coli qPCR methods have been applied in ambient waters. In the present study, we evaluated interference rates, contributing factors resulting in increased interference in these methods, and method improvements that reduced interference. Information on qPCR methods of interest and interference controls were reported in 16 papers for Enterococcus spp. and 13 papers for E. coli. Of the Enterococcus spp. qPCR methods assessed in this effort, the lowest frequencies of interference were reported in samples using Method 1609. Low frequencies of sample interference were also reported EPA's modified E. coli qPCR method, which incorporates the same reagents and interference controls as Method 1609. The literature indicates that more work is needed to demonstrate the utility of E. coli qPCR for widespread beach monitoring purposes, whereas more broad use of Method 1609 for Enterococcus spp. is appropriate when the required and suggested controls are employed.

Modeling Enterococcus densities measured by quantitative polymerase chain reaction and membrane filtration using environmental conditions at four Great Lakes beaches.

Data collected by the US Environmental Protection Agency (EPA) during the summer months of 2003 and 2004 at four US Great Lakes beaches were analyzed using linear regression analysis to identify relationships between meteorological, physical water characteristics, and beach characteristics data and the fecal indicator bacteria, Enterococcus. Water samples were analyzed for Enterococcus densities by quantitative polymerase chain reaction (qPCR) and membrane filtration (MF). This paper investigates the ability of regression models to accurately predict Enterococcus densities above or below a threshold value, using environmental data on a beach-by-beach basis for both methods. The ability to create statistical models for real-time water quality analysis would allow beach managers to make more accurate decisions regarding beach safety. Results from linear regression models indicate that environmental factors explain more of the variability in Enterococcus densities measured by MF than Enterococcus densities measured by qPCR. Results also show that models for both methods did not perform well at predicting occurrences in which water quality levels exceeded a threshold.