A database of threat statuses and life-history traits of Red List species in Flanders (northern Belgium).

BACKGROUND: Red Lists estimate the extinction risk of species at global or regional levels and are important instruments in conservation policies. Global Red List assessments are readily available via the IUCN website (https://www.iucnredlist.org) and are regularly updated by (taxonomic) experts. Regional Red Lists, however, are not always easy to find and often use local criteria to assess the local extinction risk of species. NEW INFORMATION: Here, we publish a database with the outcome of 38 Red List assessments in Flanders (northern Belgium) between 1994 and 2018. In total, the database contains 6,224 records of 5,039 unique taxa pertaining to 24 different taxonomic groups. Using a quality control procedure, we evaluated the criteria used, the number of records, the temporal and spatial distribution of the data and the up-to-dateness of the Red Lists. This way, nineteen Red Lists were approved as being of sufficient high quality (i.e. validated) and nineteen others were not. Once validated, Red Lists are approved by the regional Minister of Environment and published in the Belgian Official Gazette acquiring legal status. For the validated Red Lists, we additionally compiled (life-history) traits that are applicable to a wide variety of species groups (taxonomic kingdom, environment, biotope, nutrient level, dispersal capacity, lifespan and cuddliness). The publication of this dataset allows comparison of Red List statuses with other European regions and countries and permits analyses about how certain (life-history) traits can explain the Red List status of species. The dataset will be regularly updated by adding new Red List (re)assessments and/or additional (life-history) traits.

Tanks in series versus compartmental model configuration: considering hydrodynamics helps in parameter estimation for an N2O model.

The choice of the spatial submodel of a water resource recovery facility (WRRF) model should be one of the primary concerns in WRRF modelling. However, currently used mechanistic models are limited by an over-simplified representation of local conditions. This is illustrated by the general difficulties in calibrating the latest N2O models and the large variability in parameter values reported in the literature. The use of compartmental model (CM) developed on the basis of accurate hydrodynamic studies using computational fluid dynamics (CFD) can take into account local conditions and recirculation patterns in the activated sludge tanks that are important with respect to the modelling objective. The conventional tanks in series (TIS) configuration does not allow this. The aim of the present work is to compare the capabilities of two model layouts (CM and TIS) in defining a realistic domain of parameter values representing the same full-scale plant. A model performance evaluation method is proposed to identify the good operational domain of each parameter in the two layouts. Already when evaluating for steady state, the CM was found to provide better defined parameter ranges than TIS. Dynamic simulations further confirmed the CM's capability to work in a more realistic parameter domain, avoiding unnecessary calibration to compensate for flaws in the spatial submodel.

From the affinity constant to the half-saturation index: understanding conventional modeling concepts in novel wastewater treatment processes.

The "affinity constant" (KS) concept is applied in wastewater treatment models to incorporate the effect of substrate limitation on process performance. As an increasing number of wastewater treatment processes rely on low substrate concentrations, a proper understanding of these so-called constants is critical in order to soundly model and evaluate emerging treatment systems. In this paper, an in-depth analysis of the KS concept has been carried out, focusing on the different physical and biological phenomena that affect its observed value. By structuring the factors influencing half-saturation indices (newly proposed nomenclature) into advectional, diffusional and biological, light has been shed onto some of the apparent inconsistencies present in the literature. Particularly, the importance of non-ideal mixing as a source of variability between observed KS values in different systems has been illustrated. Additionally, discussion on the differences existent between substrates that affect half-saturation indices has been carried out; it has been shown that the observed KS for some substrates will reflect transport or biological limitations more than others. Finally, potential modeling strategies that could alleviate the shortcomings of the KS concept have been provided. These could be of special importance when considering the evaluation and design of emerging wastewater treatment processes.

A GLUE uncertainty analysis of a drying model of pharmaceutical granules.

A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma™, GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries.