On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling

Wolfgang Seis, Malte Zamzow, Nicolas Caradot, Pascale Rouault

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)301-312
Number of pages12
JournalWater Research
Volume143
DOIs
Publication statusPublished - 15 Oct 2018

Keywords

  • Bathing water directive
  • Bathing waters
  • Bayesian regression modelling
  • Early waring

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