| Autor: |
Desta BN; School of Occupational and Public Health, Toronto Metropolitan University, Toronto, ON, Canada., Tustin J; School of Occupational and Public Health, Toronto Metropolitan University, Toronto, ON, Canada., Sanchez JJ; School of Occupational and Public Health, Toronto Metropolitan University, Toronto, ON, Canada., Heasley C; School of Occupational and Public Health, Toronto Metropolitan University, Toronto, ON, Canada., Schwandt M; Vancouver Coastal Health, Vancouver, BC, Canada.; School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada., Bishay F; Metro Vancouver, Vancouver, BC, Canada., Chan B; Metro Vancouver, Vancouver, BC, Canada., Knezevic-Stevanovic A; Metro Vancouver, Vancouver, BC, Canada., Ash R; Vancouver Coastal Health, Vancouver, BC, Canada., Jantzen D; Vancouver Coastal Health, Vancouver, BC, Canada., Young I; School of Occupational and Public Health, Toronto Metropolitan University, Toronto, ON, Canada. |
| Abstrakt: |
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 Escherichia coli ( E. coli) 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 E. coli concentrations at 15 beaches in the Metro Vancouver Region. We identified that higher levels of geometric mean E. coli levels were predicted by higher previous sample day E. coli 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 E. coli. 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. |
