For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD.

为确保微生物安全，当前的欧洲沐浴水指令（BWD）（76/160 / EEC 2006）要求对沐浴水实施可靠的预警系统，已知该系统会受到短期污染。但是，BWD没有提供明确定义的阈值水平，超过该阈值水平，预警系统应开始警告或通知人群。统计回归建模是预测粪便指示剂细菌浓度的常用方法。本研究提出了一种基于多元回归模型的预警系统实施方法，该方法考虑了欧洲沐浴水立法对于警报水平和模型验证标准的概率特征。我们的研究得出了方法论，