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

Abstract

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.