On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling. (15th October 2018)

• Record Type: Journal Article
• Authors: Seis, Wolfgang; Zamzow, Malte; Caradot, Nicolas; Rouault, Pascale
• Is Part Of: Water research. Volume 143(2018)
• Page Start: 301
• Page End: 312
• Publication Date: 2018-10-15
• Subjects: Early waring -- Bathing waters -- Bathing water directive -- Bayesian regression modelling; WWTP wastewater treatment plant -- LOO-IC Approximate leave-one-out information criterion -- PPD Posterior predictive distribution -- Q Flow in [m3/s] -- MLE Maximum likelihood estimate -- MPN most probable number -- FIB Fecal indicator bacteria -- P Precipitation in [mm/d] -- BWD Bathing water directive -- EU European Union -- PI / CI prediction interval/credible interval -- CSO combined sewer overflow; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2018.06.057
• Languages: English
• ISSNs: 0043-1354

Abstract

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. Highlights: A methodology for early warning at European bathing waters is proposed. Probabilistic alert levels and model consistency criteria are developed. Implementation of the methodology is demonstrated and health benefits assessed.