A physical descriptive model for predicting bacteria level variation at a dynamic beach

Abstract

A rational-based physical descriptive model (PDM) has been developed to predict the levels of Escherichia coli in water at a beach with dynamic conditions in the Greater Toronto Area (GTA), Ontario, Canada. Bacteria loadings in the water were affected not only by multiple physical factors (precipitation, discharge, wind, etc.), but also by cumulative effects, intensity, duration and timing of storm events. These may not be linearly related to the observed variations in bacteria levels, and are unlikely to be properly represented by a widely used multiple linear regression model. In order to account for these complex relationships, the amounts of precipitation and nearby creek discharge, the impact of various time-related factors, lag time between events and sample collection, and threshold for different parameters were used in determining bacteria levels. This new comprehensive PDM approach improved the accuracy of the E. coli level predictions in the studied beach water compared to the previously developed statistical predictive and presently used geometric mean models. In spite of the complexity and dynamic conditions at the studied beach, the PDM achieved 75% accuracy overall for the five case years examined.