Abstract: The soil at industrial sites is frequently characterized by very heterogeneous properties, which are often related to physical disturbance and contamination. A conventional approach to characterize the soil, with only a limited number of invasive observations, fails to capture the full extent of soil heterogeneity. Proximal soil sensing provides efficient tools to record spatially dense soil information. Nevertheless, because the output of most sensors is affected by more than one soil property, the simultaneous characterization of different soil properties requires the use of multiple sensors. Here, we apply multi-receiver electromagnetic induction (EMI) and stepped frequency ground penetrating radar (GPR) to survey a former gasworks site in a seaport area of Belgium. We used the EMI and GPR sensors in a motorized system to obtain densely sampled measurements of apparent electrical conductivity, apparent magnetic susceptibility and contrasts in relative dielectric permittivity. Our study shows that the sensors give detailed information on the variation in these electromagnetic soil properties. Interpretation of the variation in terms of the stratification of the soil was hampered by localized anthropogenic disturbances. However, the sensors provided complementary information that enabled the identification, discrimination and accurate location of several of these localized disturbances, including underground utility services such as electric cables, buried structures such as the remains of foundations and contamination by salts. Because these represent typical targets in industrial site investigation, we conclude that multi-receiver EMI and stepped frequency GPR provide a useful set of tools to expedite the investigation of industrial sites.

Abstract: When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed.

Abstract: The use of temperature-time series measured in streambed sediments as input to coupled water flow and heat transport models has become standard when quantifying vertical groundwater-surface water exchange fluxes. We develop a novel methodology, called LPML, to estimate the parameters for 1-D water flow and heat transport by combining a local polynomial (LP) signal processing technique with a maximum likelihood (ML) estimator. The LP method is used to estimate the frequency response functions (FRFs) and their uncertainties between the streambed top and several locations within the streambed from measured temperature-time series data. Additionally, we obtain the analytical expression of the FRFs assuming a pure sinusoidal input. The estimated and analytical FRFs are used in an ML estimator to deduce vertical groundwater-surface water exchange flux and its uncertainty as well as information regarding model quality. The LPML method is tested and verified with the heat transport models STRIVE and VFLUX. We demonstrate that the LPML method can correctly reproduce a priori known fluxes and thermal conductivities and also show that the LPML method can estimate averaged and time-variable fluxes from periodic and nonperiodic temperature records. The LPML method allows for a fast computation of exchange fluxes as well as model and parameter uncertainties from many temperature sensors. Moreover, it can utilize a broad frequency spectrum beyond the diel signal commonly used for flux calculations.

Abstract: In this study a numerical groundwater reactive transport model of a shallow groundwater aquifer contaminated with volatile organic compounds is developed. In addition to advective-dispersive transport, the model includes contaminant release from source areas, natural attenuation, abiotic degradation by a permeable reactive barrier at the site, and dilution by infiltrating rain. Aquifer heterogeneity is parameterized using pilot points for hydraulic conductivity, specific yield and groundwater recharge. A methodology is developed and applied to estimate the large number of parameters from the limited data at the field site (groundwater levels, groundwater concentrations of multiple chemical species, point-scale measurements of soil hydraulic conductivity, and lab-scale derived information on chemical and biochemical reactions). The proposed methodology relies on pilot point parameterization of hydraulic parameters and groundwater recharge, a regularization procedure to reconcile the large number of spatially distributed model parameters with the limited field data, a step-wise approach for integrating the different data sets into the model, and high performance computing. The methodology was proven to be effective in reproducing multiple contaminant plumes and in reducing the prior parameter uncertainty of hydraulic conductivity and groundwater recharge. Our results further indicate that contaminant transport predictions are strongly affected by the choice of the groundwater recharge model and flow parameters should be identified using both head and concentration measurements. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: The growing concern for the increase of the global warming effects due to anthropogenic activities raises the challenge of finding novel technological approaches to stabilize CO2 emissions in the atmosphere and counteract impinging interconnected issues such as desertification and loss of biodiversity. Biological-CO2 mitigation, triggered through biological fixation, is considered a promising and eco-sustainable method, mostly owing to its downstream benefits that can be exploited. Microorganisms such as cyanobacteria, green algae and some autotrophic bacteria could potentially fix CO2 more efficiently than higher plants, due to their faster growth. Some examples of the potential of biological-CO2 mitigation are reported and discussed in this paper. In arid and semiarid environments, soil carbon sequestration (CO2 fixation) by cyanobacteria and biological soil crusts is considered an eco-friendly and natural process to increase soil C content and a viable pathway to soil restoration after one disturbance event. Another way for biological-CO2 mitigation intensively studied in the last few years is related to the possibility to perform carbon dioxide sequestration using microalgae, obtaining at the same time bioproducts of industrial interest. Another possibility under study is the exploitation of specific chemotrophic bacteria, such as Ralstonia eutropha (or picketii) and related organisms, for CO2 fixation coupled with the production chemicals such as polyhydroxyalkanoates (PHAs). In spite of the potential of these processes, multiple factors still have to be optimized for maximum rate of CO2 fixation by these microorganisms. The optimization of culture conditions, including the optimal concentration of CO2 in the provided gas, the use of metabolic engineering and of dual purpose systems for the treatment of wastewater and production of biofuels and high value products within a biorefinery concept, the design of photobioreactors in the case of phototrophs are some of the issues that, among others, have to be addressed and tested for cost-effective CO2 sequestration.

Abstract: EU strategies for waste management have long recognized the key role of recycling to move towards sustainable consumption and production. This resulted in a range of regulatory measures, among which the Waste Electrical and Electronic Equipment (WEEE) directive, which sets weight-based targets for recovery, preparation for re-use and recycling. The increasing strategic relevance of the supply of raw materials has, however, spurred a more integrated approach towards resource efficiency. In addition to the prevention of disposal, recycling practices are now also meant to contribute to sustainable materials management by pursuing (i) a higher degree of material cycle closure, (ii) an improved recovery of strategically relevant materials, and (iii) the avoidance of environmental burdens associated with the extraction and refining of primary raw materials. In response to this evolution, this paper reports about the development of an indicator set that allows to quantitatively demonstrate these recycling benefits, hence going further than the weight-based objectives employed in the WEEE directive. The indicators can be calculated for WEEE recycling processes for which information is available on both input and output fractions. It offers a comprehensive framework that aims to support decision making processes on product design, to identify opportunities for the optimization of WEEE End-of-Life scenarios, and to assess the achieved (or expected) results of implemented (or planned) recycling optimization strategies. The paper is illustrated by a case study on the recycling of LCD televisions. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract: We report the atomic layer deposition of titanium dioxide on carbon nanosheet templates and investigate the effects of postdeposition annealing in a helium environment using different characterization techniques. The crystallization of the titanium dioxide coating upon annealing is observed using in situ X-ray diffraction. The (micro)structural characterization of the films is carried out by scanning electron microscopy and advanced transmission electron microscopy techniques. Our study shows that the annealing of the atomic layer deposition processed and carbon nanosheets templated titanium dioxide layers in helium environment resulting in the formation of a porous, nanocrystalline and photocatalytically active titanium dioxide-carbon nanosheet composite film. Such composites are suitable for photocatalysis and dye-sensitized solar cells applications.

Abstract: A spatially distributed model for leaching of Cd from the unsaturated zone was developed for the Belgian-Dutch transnational Kempen region. The model uses as input land-use maps, atmospheric deposition data, and soil data and is part of a larger regional model that simulates transport of Cd in soil, groundwater, and surface water. A new method for deriving deposition from multiple sites was validated using soil data in different wind directions. Leaching was calculated for the period 1890 to 2010 using a reconstruction of metal loads in the region. The model was able to reproduce spatial patterns of concentrations in soil and groundwater and predicted the concentration in shallow groundwater adequately well for the purpose of evaluating management options. For 42% of the data points, measurements and calculations were within the same concentration class. The model was used for forecasting under a reference scenario, an autonomous development scenario including climate change, and a scenario with implementation of remediation measures. The impact of autonomous development (under the most extreme scenario of climatic change) amounted to an increase of 10% in cumulative Cd flux after 100 yr as compared with the reference scenario. The impact of remediation measures was mainly local and is less pronounced (i.e., only 3% change in cumulative flux at the regional scale). The integrated model served as a tool to assist in developing management strategies and prioritization of remediation of the wide-spread heavy metal contamination in the region.

Abstract: In the commercialisation of photocatalytic air purifiers, the performance as well as the cost of the catalytic material plays an important role. Where most comparative studies only regard the photocatalytic activity as a decisive parameter, in this study both activity and cost are taken into account. Using a cost-effectiveness analysis, six different commercially available TiO2-based catalysts are evaluated in terms of their activities in photocatalytic degradation of acetaldehyde as a model reaction for indoor air purification.

Abstract: Immobilization of photocatalytic powder is crucial to obtain industrially relevant purification processes. To achieve this goal, self-supporting TiO2 foams were manufactured by a polyacrylamide gel process. These gels were calcined at different temperatures to study the effect of the calcination temperature on foam characteristics (rigidity, crystallinity, and porosity) and its influence on photocatalytic activity. The results show that an optimal degradation is achieved for those foams calcined between 700 and 800°C. Calcination at higher temperatures results in a steep decrease in activity, explained by stability issues of the material due to formation of Na2SO4 phases and a larger rutile fraction.

Abstract: Soot deposition has the negative ability to devalue the aesthetic appearance of buildings. Titanium dioxide applied on the building material is one way to counteract this problem as it provides air-purifying and self-cleaning properties due to its photocatalytic activity. In literature, photocatalytic soot oxidation was described, but until now, little information was available about the influence of process parameters on the photocatalytic degradation efficiency. The influence of three process parameters was tested in this study, namely TiO2 concentration, soot concentration and water-to-cement ratio (WIC-ratio) of the mortar substrates. The results revealed 50 mu gTiO(2) cm(-2) is better to use on the cementitious materials than 250 mu gTiO(2) cm(-2). The soot concentrations occurring in real-world situations will not inhibit the photocatalyst to be activated by light. Furthermore, the photonic efficiency increases slightly for lower WIC-ratios. This can be of interest for structural building applications, since a lower WIC-ratio results in a lower porosity of the samples and consequently in an increase in mortar strength. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: Stimulated anaerobic dechlorination is generally considered a valuable step for the remediation of aquifers polluted with chlorinated ethenes (CEs). Correct simulation and prediction of this process in situ, however, require good knowledge of the associated biological reactions. The aim of this study was to evaluate the dechlorination reaction in an aquifer contaminated with trichloroethene (TCE) and its daughter products, discharging into the Zenne River. Different carbon sources were used in batch cultures and these were related to the dechlorination reaction, together with the monitored biomarkers. Appropriate kinetic formulations were assessed. Reductive dechlorination of TCE took place only when external carbon sources were added to microcosms, and occurred concomitant with a pronounced increase in the Dehalococcoides mccartyi cell count as determined by 16S rRNA gene-targeted qPCR. This indicates that native dechlorinating bacteria are present in the aquifer of the Zenne site and that the oligotrophic nature of the aquifer prevents a complete degradation to ethene. The type of carbon source, the cell number of D. mccartyi or the reductive dehalogenase genes, however, did not unequivocally explain the observed differences in degradation rates or the extent of dechlorination. Neither first-order, Michaelis-Menten nor Monod kinetics could perfectly simulate the dechlorination reactions in TCE spiked microcosms. A sensitivity analysis indicated that the inclusion of donor limitation would not significantly enhance the simulations without a clear process understanding. Results point to the role of the supporting microbial community but it remains to be verified how the complexity of the microbial (inter)actions should be represented in a model framework. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: 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 (TM), 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. (C) 2013 Elsevier B.V. All rights reserved.