与加拿大马尼托巴省温尼伯湖淡水海滩大肠杆菌浓度有关的环境因素

B. Desta, Johanna Sanchez, Cole Heasley, Ian Young, J. Tustin
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摘要

许多公共海滩都会使用粪便指示细菌对海滩水质进行例行监测,以评估娱乐用水患病的风险。水样分析结果可能需要 24 小时以上的时间,这可能不再能准确反映当前的水质状况。本研究旨在通过将 2007 年至 2021 年的水质数据和公开环境数据联系起来,评估哪种环境因素组合最能预测马尼托巴省温尼伯湖两个最受欢迎的海滩(Gimli 和 Grand Beach)的粪便污染(大肠杆菌)水平。我们为每个海滩分别建立了线性(连续对数变换的大肠杆菌浓度)和分类(200 CFU/100 ml 阈值)两种结果的混合效应模型,以分别探索大肠杆菌浓度和超过省健康风险阈值的预测因素的差异。我们使用有向无环图(Directed Acyclic Graph)来选择将哪些预测变量纳入模型。对于这两个海滩,我们按年份确定了大肠埃希氏菌结果的聚类,这表明了特定年份的变化。我们还确定,极端天气日(前 48 小时降雨量、前一天平均气温和前一天大肠埃希氏菌浓度较高)可能导致水体中大肠埃希氏菌阈值超标或浓度较高。在大海滩,我们发现前 24 小时平均紫外线水平较低的日子和前一天干燥的日子会导致大肠杆菌阈值超标或浓度升高的概率较高。这些发现可以为其他淡水环境中可能出现的趋势提供信息,并可用于帮助开发实时娱乐水质预测模型,从而做出更准确的海滩管理决策,并确保加强海滩监测计划,以应对极端天气事件,作为气候变化准备工作的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Environmental factors associated with Escherichia coli concentration at freshwater beaches on Lake Winnipeg, Manitoba, Canada
At many public beaches, routine monitoring of beach water quality using fecal indicator bacteria is conducted to evaluate the risk of recreational water illness. Results from water sample analysis can take over 24-hr, which may no longer accurately reflect current water quality conditions. This study aimed to assess which combination of environmental factors best predicts fecal contamination (E. coli) levels at two of the most popular beaches on Lake Winnipeg, Manitoba (Gimli and Grand Beach), by linking water quality data and publicly available environmental data from 2007 to 2021. We developed separate mixed effects models for each beach for two outcomes, linear (continuous log-transformed E. coli concentration) and categorical (200 CFU/100 ml threshold), to explore differences in the predictors of E. coli concentrations and exceedances of the provincial health risk threshold, respectively. We used a Directed Acyclic Graph to choose which predictor variables to include in the models. For both beaches, we identified clustering of the E. coli outcomes by year, suggesting year-specific variation. We also determined that extreme weather days, with higher levels of rainfall in the preceding 48-hr, previous day average air temperature, and previous day E. coli concentration could result in a higher probability of E. coli threshold exceedances or higher concentrations in the water bodies. In Grand Beach, we identified that days with lower average UV levels in the previous 24-hr and antecedent dry days could result in a higher probability of E. coli threshold exceedances or higher concentrations. The findings can inform possible trends in other freshwater settings and be used to help develop real-time recreational water quality predictive models to allow more accurate beach management decisions and warrant enhancement of beach monitoring programs for extreme weather events as part of the climate change preparedness efforts.
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