Environmental predictors of <i>Escherichia coli</i> concentration at marine beaches in Vancouver, Canada: a Bayesian mixed-effects modelling analysis

Abstract

Understanding historical environmental determinants associated with the risk of elevated marine water contamination could enhance monitoring marine beaches in a Canadian setting, which can also inform predictive marine water quality models and ongoing climate change preparedness efforts. This study aimed to assess the combination of environmental factors that best predicts <i>Escherichia coli</i> (<i>E. coli)</i> concentration at public beaches in Metro Vancouver, British Columbia, by combining the region's microbial water quality data and publicly available environmental data from 2013 to 2021. We developed a Bayesian log-normal mixed-effects regression model to evaluate predictors of geometric <i>E. coli</i> concentrations at 15 beaches in the Metro Vancouver Region. We identified that higher levels of geometric mean <i>E. coli</i> levels were predicted by higher previous sample day <i>E. coli</i> concentrations, higher rainfall in the preceding 48 h, and higher 24-h average air temperature at the median or higher levels of the 24-h mean ultraviolet (UV) index. In contrast, higher levels of mean salinity were predicted to result in lower levels of <i>E. coli.</i> Finally, we determined that the average effects of the predictors varied highly by beach. Our findings could form the basis for building real-time predictive marine water quality models to enable more timely beach management decision-making.