Bioavailability of pharmaceuticals in waters close to wastewater treatment plants: use of fish bile for exposure assessment

Pharmaceuticals are ubiquitous in surface waters as a consequence of discharges from municipal wastewater treatment plants. However, few studies have assessed the bioavailability of pharmaceuticals to fish in natural waters. In the present study, passive samplers and rainbow trout were experimentally deployed next to three municipal wastewater treatment plants in Finland to evaluate the degree of animal exposure. Pharmaceuticals from several therapeutic classes (in total 15) were analyzed by liquid chromatography-tandem mass spectrometry in extracts of passive samplers and in bile and blood plasma of rainbow trout held at polluted sites for 10 d. Each approach indicated the highest exposure near wastewater treatment plant A and the lowest near that of plant C. Diclofenac, naproxen, and ibuprofen were found in rainbow trout, and their concentrations in bile were 10 to 400 times higher than in plasma. The phase I metabolite hydroxydiclofenac was also detected in bile. Hence, bile proved to be an excellent sample matrix for the exposure assessment of fish. Most of the monitored pharmaceuticals were found in passive samplers, implying that they may overestimate the actual exposure of fish in receiving waters. Two biomarkers, hepatic vitellogenin and cytochrome P4501A, did not reveal clear effects on fish, although a small induction of vitellogenin mRNA was observed in trout caged near wastewater treatment plants B and C.

Estimating Annual CO2 Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.

Status of the Zimmerwald SLR station

The Zimmerwald SLR station is operated in a monostatic mode with 532nm laser pulses emitted at adjustable frequencies of 90-110Hz with energies slightly less than 10mJ. A rotating shutter protects the CSPAD receiver from the backscatter of the transmit beam. These systems are located below the telescope in an operator room housed within the observatory building with the laser system located in a separated, air-conditioned part of the room. All hardware components may be automatically accessed by the control software and from remote if required. Thanks to the fully automatic and remotely controllable SLR operations, the Zimmerwald station is one of the most productive stations in the ILRS network. Key characteristics of the hardware are shown. Specialities like the tracking of the full GLONASS constellation, one-way ranging to the Lunar Reconnaissance Orbiter, and photon reception from bi-static experiments with the Graz SLR station are highlighted as well.

Sustainable Wastewater Management: Is it Possible to Regulate Micropollution in the Future by Learning from the Past? A Policy Analysis

This paper applies a policy analysis approach to the question of how to effectively regulate micropollution in a sustainable manner. Micropollution is a complex policy problem characterized by a huge number and diversity of chemical substances, as well as various entry paths into the aquatic environment. It challenges traditional water quality management by calling for new technologies in wastewater treatment and behavioral changes in industry, agriculture and civil society. In light of such challenges, the question arises as to how to regulate such a complex phenomenon to ensure water quality is maintained in the future? What can we learn from past experiences in water quality regulation? To answer these questions, policy analysis strongly focuses on the design and choice of policy instruments and the mix of such measures. In this paper, we review instruments commonly used in past water quality regulation. We evaluate their ability to respond to the characteristics of a more recent water quality problem, i.e., micropollution, in a sustainable way. This way, we develop a new framework that integrates both the problem dimension (i.e., causes and effects of a problem) as well as the sustainability dimension (e.g., long-term, cross-sectoral and multi-level) to assess which policy instruments are best suited to regulate micropollution. We thus conclude that sustainability criteria help to identify an appropriate instrument mix of end-of-pipe and source-directed measures to reduce aquatic micropollution.