Public concern over microbial contamination of recreational waters has increased in recent years. A common approach to evaluating beach water quality has been to use the persistence model which assumes that day-old monitoring results provide accurate estimates of current concentrations. This model is frequently incorrect. Recent studies have shown that statistical regression models based on least-squares fitting often are more accurate. To make such models more generally available, the Virtual Beach (VB) tool was developed. VB is publicdomain software that prescribes site-specific predictive models. In this study we used VB as a tool to evaluate statistical modeling for predicting Escherichia coli (E. coli) levels at Huntington Beach, on Lake Erie. The models were based on readily available weather and environmental data, plus U.S. Geological Service onsite data. Although models for Great Lakes beaches have frequently been fitted to multiyear data sets, this work demonstrates that useful statistical models can be based on limited data sets collected over much shorter time periods, leading to dynamic models that are periodically refitted as new data become available. Comparisons of the resulting nowcasts (predictions of current, but yet unknown, bacterial levels) with observations verified the effectiveness of VB and showed that dynamic models are about as accurate as long-term static models. Finally, fitting models to forecasted explanatory variables, bacteria forecasts were found to compare favorably to nowcasts, yielding adjusted coefficients of determination (adjusted R 2 ) of about 0.40.

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The impact of river outfalls on beach water quality depends on numerous interacting factors. The delivery of contaminants by multiple creeks greatly complicates understanding of the source contributions, especially when pollution might originate up- or down-coast of beaches. We studied two beaches along Lake Michigan that are located between two creek outfalls to determine the hydrometeorologic factors influencing near-shore microbiologic water quality and the relative impact of the creeks. The creeks continuously delivered water with high concentrations of Escherichia coli to Lake Michigan, and the direction of transport of these bacteria was affected by current direction. Current direction reversals were associated with elevated E. coli concentrations at Central Avenue beach. Rainfall, barometric pressure, wave height, wave period, and creek specific conductance were significantly related to E. coli concentration at the beaches and were the parameters used in predictive models that best described E. coli variation at the two beaches. Multiple inputs to numerous beaches complicates the analysis and understanding of the relative relationship of sources but affords opportunities for showing how these complex creek inputs might interact to yield collective or individual effects on beach water quality.

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The US Environmental Protection Agency has a history of developing plume models and providing technical assistance. The Visual Plumes model (VP) is a recent addition to the public-domain models available on the EPA Center for Exposure Assessment Modeling (CEAM) web page. The Windows-based VP adapts, modifies, and enhances the earlier DOS-based PLUMES with a new interface, models, and capabilities. VP is a public platform for mixing zone models designed to encourage the continued improvement of plume theory and models by facilitating verification and inter-model comparison. Some examples are presented to illustrate VP's new capabilities. One demonstrates its ability, for reasonably one-dimensional estuaries, to estimate background concentrations due to tidal re-circulation of previously contaminated receiving water. This capability depends on the optional linkage to time-series input files that enables VP to simulate mixing zone and far-field parameters for long periods. Also described are the new bacterial decay models used to estimate depth changes in first-order decay rates based on environmental stressors, including solar insolation, salinity, and temperature. The nascent density phenomenon is briefly described as it is potentially important to Outer Continental Shelf (OCS) oil exploration discharges.