Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies.

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2-4h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

A silicon-based microelectrode array with a microdrive for monitoring brainstem regions of freely moving rats.

OBJECTIVE: Exploring neural activity behind synchronization and time locking in brain circuits is one of the most important tasks in neuroscience. Our goal was to design and characterize a microelectrode array (MEA) system specifically for obtaining in vivo extracellular recordings from three deep-brain areas of freely moving rats, simultaneously. The target areas, the deep mesencephalic reticular-, pedunculopontine tegmental-and pontine reticular nuclei are related to the regulation of sleep-wake cycles. APPROACH: The three targeted nuclei are collinear, therefore a single-shank MEA was designed in order to contact them. The silicon-based device was equipped with 3 × 4 recording sites, located according to the geometry of the brain regions. Furthermore, a microdrive was developed to allow fine actuation and post-implantation relocation of the probe. The probe was attached to a rigid printed circuit board, which was fastened to the microdrive. A flexible cable was designed in order to provide not only electronic connection between the probe and the amplifier system, but sufficient freedom for the movements of the probe as well. MAIN RESULTS: The microdrive was stable enough to allow precise electrode targeting into the tissue via a single track. The microelectrodes on the probe were suitable for recording neural activity from the three targeted brainstem areas. SIGNIFICANCE: The system offers a robust solution to provide long-term interface between an array of precisely defined microelectrodes and deep-brain areas of a behaving rodent. The microdrive allowed us to fine-tune the probe location and easily scan through the regions of interest.

Modelling micro-pollutant fate in wastewater collection and treatment systems: status and challenges.

This paper provides a comprehensive summary on modelling of micro-pollutants' (MPs) fate and transport in wastewater. It indicates the motivations of MP modelling and summarises and illustrates the current status. Finally, some recommendations are provided to improve and diffuse the use of such models. In brief, we conclude that, in order to predict the contaminant removal in centralised treatment works, considering the dramatic improvement in monitoring and detecting MPs in wastewater, more mechanistic approaches should be used to complement conventional, heuristic and other fate models. This is crucial, as regional risk assessments and model-based evaluations of pollution discharge from urban areas can potentially be used by decision makers to evaluate effluent quality regulation, and assess upgrading requirements, in the future.