Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

Abstract

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.

A predictive model for microbial counts on beaches where intertidal sand is the primary source

“…The water sample analysis is useful in terms of guiding beach warnings and advisories; however, due to the minimum one-day laboratory time requirement by the culture method and the high spatiotemporal variability associated with fecal indicator bacteria (FIB) in the nearshore water (Boehm, 2007; Ge et al, 2012a; Enns et al, 2012), this method may not be timely and sufficient for decision making, thereby potentially causing unnecessary beach closures or human health risks for beaches that remain open. Recently, many beach managers have begun to utilize predictive tools, of which the most widely applied are models developed through multivariable linear regression (e.g., Olyphant, 2005; Nevers and Whitman, 2005; Frick et al, 2008). In addition, process-based models, which couple hydrodynamic models with a microbe transport-fate model involving microbial loading, transport and fate processes (e.g., Sanders et al, 2005; Hipsey et al, 2008; Feng et al, 2013; Thupaki et al, 2013) can in principle be used to make predictions.…”

Evaluating a microbial water quality prediction model for beach management under the revised EU Bathing Water Directive

“…at four beaches in Hong Kong. MLR models have been also successfully applied as beach management tools in many parts of the US (see for examples USEPA, 2010b, Thoe et al, 2014;Frick et al, 2008;Francy, 2009;Olyphant and Whitman, 2004;Nevers and Whitman, 2005). ANN approaches have also been widely used in the US for beach water quality management (see for examples He and He, 2008;Zhang et al, 2012;Thoe et al, 2014).…”

“…The results of these modelling studies indicate that predictive models have generally out-performed traditional beach monitoring methods to capture beach pollution as beach monitoring relies only on outdated/previous-day measurements of FIB (Frick et al, 2008;Nevers and Whitman, 2011;Hou et al, 2006). While reasonable results are reported in the literature using the above MLR, ANN, and decision tree approaches, the performance of such-data driven approaches will continue to be questionable if they are utilised to extrapolate water quality predictions outside the range of data that was used in their development and training ( see USEPA, 2010a).…”

Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany

“…Nevers and Whitman (2005) applied a statistical model based on correlations between bacteria and other water quality parameters to predict bathing water quality. A similar approach was applied by Frick et al (2005) for Lake Erie, U.S.A. Viegas et al (2012) developed a hygienie early warning system for a beach at the Portuguese coast based on a deterministic modeling approach, taking into account tides, currents, and wind. Another method to predict bathing water quality was proposed by Lin et al (2008) and Kashefipour et al (2005): artificial neural networks (ANNs), which received input data from a hydrodynamic and water quality model.…”