Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany.

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.

Pharmaceuticals in the environment--Global occurrences and perspectives.

Pharmaceuticals are known to occur widely in the environment of industrialized countries. In developing countries, more monitoring results have recently become available, but a concise picture of measured environmental concentrations (MECs) is still elusive. Through a comprehensive literature review of 1016 original publications and 150 review articles, the authors collected MECs for human and veterinary pharmaceutical substances reported worldwide in surface water, groundwater, tap/drinking water, manure, soil, and other environmental matrices in a comprehensive database. Due to the heterogeneity of the data sources, a simplified data quality assessment was conducted. The database reveals that pharmaceuticals or their transformation products have been detected in the environment of 71 countries covering all continents. These countries were then grouped into the 5 regions recognized by the United Nations (UN). In total, 631 different pharmaceutical substances were found at MECs above the detection limit of the respective analytical methods employed, revealing distinct regional patterns. Sixteen substances were detected in each of the 5 UN regions. For example, the anti-inflammatory drug diclofenac has been detected in environmental matrices in 50 countries, and concentrations found in several locations exceeded predicted no-effect concentrations. Urban wastewater seems to be the dominant emission pathway for pharmaceuticals globally, although emissions from industrial production, hospitals, agriculture, and aquaculture are important locally. The authors conclude that pharmaceuticals are a global challenge calling for multistakeholder approaches to prevent, reduce, and manage their entry into and presence in the environment, such as those being discussed under the Strategic Approach to International Chemicals Management, a UN Environment Program.