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Water scarcity is one of the problems in water management that hinders European rivers in reaching a good ecological status as defined in the European Water Framework Directive. Water scarcity often coincides with high water temperature and low water quality. High water temperatures decrease the oxygen supply and often coincide with low flows that tend to increase the load of various compounds that affect the equilibrium in the ecosystem. The river Meuse regularly encounters situations of low water flow. In these situations, the river Rur, an important tributary of the river Meuse in Germany, contributes significantly to the Meuse discharge. Climate change can lead to more frequent periods of water scarcity. Moreover, plans exist to divert water from the Rur to former brown coal quarries in Germany. This exploratory study examines the relationships between discharge, water temperature and water quality under future climate change and water diversion scenarios in low-flow situations for the rivers Meuse and Rur. The results of the study confirm that rising air temperatures as a result of climate change will lead to higher water temperatures which will negatively impact the water quality of the Meuse in particular. Despite the fact that the contribution of the Rur has a positive impact on the water quality of the Meuse, the results suggest that effects of plans to divert water from the Rur may be small on average. However, the impact of the diversion may be stronger on individual hot summer days with low water levels when the Rur contributes significantly to the discharge of the Meuse.

In this study, electrochemical characterisation of glucose oxidation has been carried out in solution and using enzyme polymer electrodes prepared by mutant glucose oxidase (B11-GOx) obtained from directed protein evolution and wild-type enzymes. Higher glucose oxidation currents were obtained from B11-GOx both in solution and polymer electrodes compared to wt-GOx. This demonstrates an improved electrocatalytic activity towards electrochemical oxidation of glucose from the mutant enzyme. The enzyme electrode with B11-GOx also showed a faster electron transfer indicating a better electronic interaction with the polymer mediator. These encouraging results have shown a promising application of enzymes developed by directed evolution tailored for the applications of biosensors and biofuel cells.

Abstract Lignocellulose, the most abundant and renewable resource on Earth is an important raw material, which can be converted into high value products. However, to this end, it needs to be pretreated physically, chemically, or biologically. Its complex structure and recalcitrance against physical, chemical, or biological degradation render its breakdown an important target of study. The understanding of the enzymatic processes of lignocellulose breakdown and the changes in its chemistry are thus essential. Here, we review the current analytical challenges in the analysis of lignocellulose composition, lignocelluloytic pretreatment, analysis of enzymatic hydrolysis catalyzed by cellulases or hemicellulases and their biotechnological applications. Complex techniques including biochemical, genomic, and metagenomics methods such as high performance anion exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD), Respiration Activity Monitoring System (RAMOS), and next-generation sequencing are described. HPAEC-PAD is a promising, rapid, and reliable analytical technique for sugar quantification following lignocellulose breakdown. RAMOS is an effective technique for monitoring the growth of microorganisms during the different phases of enzyme production, enzymatic hydrolysis, and fermentation. The emergence of high throughput, next-generation sequencing techniques has enriched the databases of genes encoding glycoside hydrolase classes commonly involved in lignocellulose decomposition, and this knowledge can be readily used to analyse the involved processes. Still, novel analytical methods are highly welcome to understand the complete process of lignocelluloytic breakdown. In order to decrease environmental pollution and to save energy, lignocellulose conversion needs to be promoted in order to effectively compete with fossil resources on a global scale in future.

Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, there is no simple and reliable tool that simultaneously solves the energy and environmental balance of buildings. In this work, the authors address this challenge by proposing the application of an Artificial Neural Network. Due to the high reliability of learning algorithms in the resolution of complex and non-linear problems, it was possible to simultaneously solve two different but strongly dependent aspects after a deep training phase. In previous researches, the authors applied several topologies of neural networks, which were trained on a large and representative database and developed for the Italian building stock. The database, characterised by several building models simulated in different climatic conditions, collects 29 inputs (13 energy data and 16 environmental data) and provides 7 outputs, 1 for heating energy demand and 6 of the most used indicators in life cycle assessment of buildings. A statistical analysis of the results confirmed that the proposed method is appropriate to achieve the goal of the study. The best artificial neural network for each output presented low Root Mean Square Error, Mean Absolute Error lower than 5%, and determination coefficient close to 1. The excellent results confirmed that this methodology can be extended in any context and to any condition (other countries and building stocks). Furthermore, the implementation of this solution algorithm in a software program can enable the development of a suitable decision support tool, which is simple, reliable, and easy to use even for a non-expert user. The possibility to use an instrument to predict a building's performance in its design and planning phase, represent an important result to support decision-making processes toward more sustainable choices.

Abstract This case study of the Society of Environmental Toxicology and Chemistry (SETAC) workshop MODELINK demonstrates the potential use of mechanistic effects models for macrophytes to extrapolate from effects of a plant protection product observed in laboratory tests to effects resulting from dynamic exposure on macrophyte populations in edge-of-field water bodies. A standard European Union (EU) risk assessment for an example herbicide based on macrophyte laboratory tests indicated risks for several exposure scenarios. Three of these scenarios are further analyzed using effect models for 2 aquatic macrophytes, the free-floating standard test species Lemna sp., and the sediment-rooted submerged additional standard test species Myriophyllum spicatum. Both models include a toxicokinetic (TK) part, describing uptake and elimination of the toxicant, a toxicodynamic (TD) part, describing the internal concentration-response function for growth inhibition, and a description of biomass growth as a function of environmental factors to allow simulating seasonal dynamics. The TK–TD models are calibrated and tested using laboratory tests, whereas the growth models were assumed to be fit for purpose based on comparisons of predictions with typical growth patterns observed in the field. For the risk assessment, biomass dynamics are predicted for the control situation and for several exposure levels. Based on specific protection goals for macrophytes, preliminary example decision criteria are suggested for evaluating the model outputs. The models refined the risk indicated by lower tier testing for 2 exposure scenarios, while confirming the risk associated for the third. Uncertainties related to the experimental and the modeling approaches and their application in the risk assessment are discussed. Based on this case study and the assumption that the models prove suitable for risk assessment once fully evaluated, we recommend that 1) ecological scenarios be developed that are also linked to the exposure scenarios, and 2) quantitative protection goals be set to facilitate the interpretation of model results for risk assessment. Integr Environ Assess Manag 2016;12:82–95. ©2015 SETAC Key Points We use an herbicide case study to demonstrate how toxicodynamics-toxicokinetics models coupled with macrophyte population models can link dynamic exposure patterns to expected biomass dynamics in the field. We introduce models for 2 macrophyte species, Lemna sp. and Myriophyllum spicatum to refine a risk assessment based on laboratory experiments. Based on specific protection goals, we propose example criteria for duration and magnitude of effects and show how the modeling results could be used as risk refinement option. We recommend further refining and testing the models to develop ecological scenarios linked to the exposure scenarios and to set quantitative protection goals to facilitate the interpretation of model results for risk assessment.

Abstract Occupant behavior has a significant impact on building systems’ operations and efficiency. As a result, several innovative approaches have been introduced to quantify the dynamics of occupants within indoor environments, such as interactions with different building systems and the impact of various feedback and interventions to reduce the building energy consumption. To achieve this, researchers have highlighted the importance of reducing energy consumption without impacting occupant comfort. As a result, there is an increasing body of research evaluating how different theories of behavior across a variety of disciplines can explain occupant interactions with building systems. Future progress in this area calls for an in-depth understanding of behavioral theories in explaining occupant interactions with different building systems. In this paper, we have used a structured literature review approach to investigate how different psychological, sociological, and economic theories have been applied to explain occupant interactions with heating and cooling (HVAC systems), opening windows and ventilation, lighting and shading, electronic appliances, domestic hot water, as well as energy conservation behaviors. Throughout the paper, we identify the most common theories and methodologies applied within the existing research, general findings related to how occupants interact with different building systems, as well as a number of identified gaps within the literature. Finally, we provide a discussion on directions for future research studies in this area under each building system.