Geo-referenced simulations of E. coli in a sub-catchment of the Vecht River using a probabilistic approach

The proportion of wild swimmers at non-official bathing sites has increased during the Covid-19 pandemic. Bathing water quality at designated sites is monitored through analysis of the concentration of fecal indicator bacteria such as E. coli. However, non-official sites are generally not monitored. In a previous work, steady state modelling of E. coli was achieved at catchment scale, enabling a comparison of expected concentrations along an entire catchment for longtime average. However, E. coli concentrations can vary over several orders of magnitude at the same monitoring site throughout the year. To capture the temporal variability of E. coli concentrations on the catchment scale, we extended the existing deterministic E. coli sub-module of the GREAT-ER (Geo-referenced Exposure Assessment tool for European Rivers) model for probabilistic Monte-Carlo simulations. Here, selected model parameters are represented by probability distributions instead of fixed values. Wastewater treatment plant (WWTP) emissions and diffuse emissions were parameterized using selected data from a previous monitoring campaign (calibration data set) and in-stream processes were modeled using literature data. Comparison of simulation results with monitoring data (evaluation data set) indicates that predicted E. coli concentrations well-represent median measured concentrations, although the range of predicted concentrations is slightly larger than the observed concentration variability. The parameters with the largest influence on the range of predicted concentrations are flow rate and E. coli removal efficiency in WWTPs. A comparison of predicted 90th percentiles with the threshold for sufficient bathing water quality (according to the EU Bathing Water Directive) indicates that year-round swimming at sites influenced by WWTP effluents is advisable almost nowhere in the study area. A refinement of the model can be achieved if quantitative relationships between the WWTP removal efficiency and both, the treatment technologies as well as the operating parameters are further established.

Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany

The geo-referenced regional exposure assessment tool for European rivers (GREAT-ER) is designed to support river basin management or the implementation process within the EU Water Framework Directive by predicting spatially resolved exposure concentrations in whole watersheds. The usefulness of the complimentary application of targeted monitoring and GREAT-ER simulations is demonstrated with case studies for three pharmaceuticals in selected German watersheds. Comparison with monitoring data corroborates the capability of the probabilistic model approach to predict the expected range of spatial surface water concentrations. Explicit consideration of local pharmaceutical emissions from hospitals or private doctor's offices (e.g., for X-ray contrast agents) can improve predictions on the local scale without compromising regional exposure assessment. Pharmaceuticals exhibiting low concentrations hardly detectable with established analytical methods (e.g., EE2) can be evaluated with model simulations. Management scenarios allow for a priori assessment of risk reduction measures. In combination with targeted monitoring approaches, the GREAT-ER model can serve as valuable support tool for exposure and risk assessment of pharmaceuticals in whole watersheds.

Georeferenced regional simulation and aquatic exposure assessment

A simulation model system of the waste water pathways and the aquatic fate assessment is coupled to a discrete digitized river network. Temporal concentration distributions of down-the-drain chemicals are calculated from variable and uncertain input data and compared to monitoring data. The accuracy of the predictions are within a factor of three for the studied pilot catchments. Spatial distributions of the whole catchment give stochastic information on a regional basis for probabilistic risk assessments.

The German heavy metal survey by means of mosses

This is the first attempt to determine pollution with metals throughout the Federal Republic of Germany by analysing moss samples. Samples of Pleurozium schreberi, Scleropodium purum, Hypnum cupessiforme and Hylocomium splendens were collected at 593 sites and analysed by ICP-AES and AAS for the elements As, Cd, Cr, Cu, Fe, Ni, Pb, V and Zn. Citrus leaves and pine needles were used as reference materials to ensure the quality of the results. In many cases it was possible to trace the areas affected by known sources of heavy-metal emissions in addition to isolated local increases in the values. The moss monitoring programme showed up the highly industrialized and urban locations such as the Ruhr, parts of the Saarland and Baden-Wurttemberg and large areas of eastern Germany. Lower levels of many elements were found in wide stretches of Lower Saxony and Bavaria. The results largely reflect the pollution patterns found in these areas. On the other hand, expected correlations between the effects of traffic (e.g. Pb) and concentrations in moss could not be demonstrated with certainty. The element data yielded by this project are Germany's contribution to the European project 'Atmospheric Heavy Metal Deposition in Europe -- Estimations based on Moss Analysis'.