title: Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany.
abstract: The lower Ruhr River is located in a densely populated and industrialized area in Northrhine-Westphalia (NRW) in western Germany. Due to upgrades of sanitary infrastructure, such as wastewater treatment plants (WWTPs) and combined sewer overflows (CSOs), and a decline of industrial production, water quality of Ruhr River has been constantly increasing over the past decades. One effect is a growing attractiveness of the Ruhr for bathing and water sports. In order to enable future bathing in the lower Ruhr, this study investigates methods for predicting the permissibility of bathing, according to the microbial water quality regulations of the Bathing Water Ordinance of Northrhine-Westphalia (NRW-BWO). On basis of the European Commission Bathing Water Directive, the NRW-BWO defines methods for the assessment of bathing water quality on basis of bacterial threshold concentrations of Escherichia coli (E. coli) and intestinal enterococci (Int. Ent.). Furthermore, if the bathing water is subject to short-term pollution, the NRW-BWO requires the installation of an early warning system to prevent bathers' exposure. Laboratory detections of both bacteria species from water samples are not suitable to be used in an early warning system. Online measurement devices for bacteria showed to be not sensitive and accurate enough to reliably indicate an exceedance of the threshold values. Thus, the application of a prediction model is appropriate. In total, four different modeling approaches were developed and compared to provide short-term predictions of bacterial concentrations: (i) statistical modeling based on linear correlations between hydro-chemical parameters, such as ammonia and turbidity, and bacteria, (ii) modeling based on artificial neural networks (ANNs), which consider non-linear correlations between hydro-chemical and climate parameters and bacteria concentrations, (iii) a balance model, which considers all in- and outflows, both in terms of water quality and quantity, along a stretch of the lower Ruhr River, and (iv) binary modeling based on precipitation rates, as rainfall is assumed to trigger high bacteria loads in the river. It could be shown that ANNs allow the most accurate prediction of bacterial concentrations in the lower Ruhr River. However, the model performance varies among different stretches along the Ruhr River. This indicates that local conditions, e.g. distance to next upstream WWTP or CSO, are essential and need to be further investigated. The binary model which considered rainfall effects also provided acceptable short-term predictions. For example, at all potential bathing spots, after two days following substantial precipitation amounts, bathing would have been allowed. The balance model showed the weakest results, which is mainly due to data gaps, as time series of bacterial loads from tributaries, WWTPs and CSOs had to be estimated. As a next step, high resolution bacterial measurements following CSO discharge events are planned in order to develop a concise picture of processes determining bacterial concentrations at the Ruhr River.
authors.affiliation: IWW Water Centre, Mülheim an der Ruhr, Germany. Electronic address: a.maelzer@iww-online.de.
authors.forename: Hans-Joachim
authors.initials: HJ
authors.lastname: Mälzer
authors.affiliation: IWW Water Centre, Mülheim an der Ruhr, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Aus der Beek
authors.affiliation: aquatune - Dr. Gebhardt & Co GmbH, Hahnstätten, Germany.
authors.forename: Silke
authors.initials: S
authors.lastname: Müller
authors.affiliation: aquatune - Dr. Gebhardt & Co GmbH, Hahnstätten, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Gebhardt
mesh_terms: D016983:Enterococcus / Q000302:isolation & purification; D004784:Environmental Monitoring / Q000379:methods*; D004926:Escherichia coli / Q000302:isolation & purification; D005858:Germany; D006920:Hygiene; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011998:Recreation; D045483:Rivers / Q000382:microbiology*; D014871:Water Microbiology; D014873:Water Pollutants / Q000302:isolation & purification; D060753:Water Quality
chemical_list: D014873:Water Pollutants
keywords: Bacteria; Bathing water quality; Surface waters; Water quality modeling