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Diesel engine exhausts from a common rail 3.0 L F1C diesel engine were analyzed at two different load conditions of the WLTC testing cycle downstream of both the diesel particulate filter (DPF) and selective catalytic reactor (SCR) to verify their effect on the characteristics of carbon particulate matter. An array of chemical, physical and spectroscopic techniques (gas chromatography coupled with mass spectrometry (GC-MS), mobility analyzer, UV-Visible absorption and fluorescence spectroscopy) was applied for characterizing polycyclic aromatic hydrocarbons (PAH), heavy aromatic compounds and soot, constituting the particulate matter (PM) sampled from the exhaust. The engine was operated in half load (HL) (188 Nm, representing the more common condition for engine in urban traffic) and full load (FL) (452 Nm, representing the best performance of the engine operation) conditions, at the same engine speed (2000 rpm). Soot formation was enhanced in HL condition, with respect to FL, but, just because of the much lower soot amount, the after-treatment systems in this last condition resulted to be less efficient in the soot abatement. Indeed, the abatement through DPF was about 40% lower in the FL condition with respect to HL condition, and any significant further concentration decrease was found after SCR, in both conditions. By contrast, PAH concentration after DPF abatement was found to be higher in the HL with respect to FL condition. A further PAH concentration decrease of about 30% was found after the SCR in the HL condition whereas in FL the reduction was only about 5-6%. Also the heavy aromatic compounds having molecular weight above the GC-MS detection limit (300 u), were mitigated by SCR. Therefore, SCR did not cause a further soot reduction, whereas it was effective in largely reducing PAH and heavy aromatics emissions, especially in the lower temperature condition featuring the half-load condition, when combustion efficiency is worse. Moreover, SCR system reduced the emission of small particles probably due to an enhanced agglomeration of particles, with beneficial effect on the harmfulness to human health.

This paper presents results of an online stated choice experiment on preferences of Dutch private car owners for alternative fuel vehicles (AFVs) and their characteristics. Results show that negative preferences for alternative fuel vehicles are large, especially for the electric and fuel cell car, mostly as a result of their limited driving range and considerable refueling times. Preference for AFVs increases considerably with improvements on driving range, refueling time and fuel availability. Negative AFV preferences remain, however, also with substantial improvements in AFV characteristics; the remaining willingness to accept is on average € 10,000-€ 20,000 per AFV. Results from a mixed logit model show that consumer preferences for AFVs and AFV characteristics are heterogeneous to a large extent, in particular for the electric car, additional detour time and fuel time for the electric and fuel cell car. An interaction model reveals that annual mileage is by far the most important factor that determines heterogeneity in preferences for the electric and fuel cell car. When annual mileage increases, the preference for electric and fuel cell cars decreases substantially, whilst the willingness to pay for driving range increases substantially. Other variables such as using the car for holidays abroad and the daily commute also appear to be relevant for car choice. © 2014 Elsevier Ltd.

Climaps.eu is an online atlas providing data, visualizations and commentaries about climate adaptation debate. It contains 33 issue-maps and 5 issue-stories. Each of the maps focuses on one issue in the adaptation debate and provides.The atlas is addressed to climate experts (negotiators, NGOs and companies concerned by global warming, journalists…) and to citizens willing to engage with the issues of climate adaptation.It employs advanced digital methods to deploy the complexity of the issues related to climate adaptation and information design to make this complexity legible.

In this study we show that a chemical ferricyanide cathode can be replaced by a biological oxygen reducing cathode in a plant microbial fuel cell (PMFC) with a new record power output. A biocathode was successfully integrated in a PMFC and operated for 151 days. Plants growth continued and the power density increased reaching a maximum power output of 679 mW/m2 plant growth area (PGA) in a 10 min polarization. The two week record average power densities was 240 mW/m2 PGA. The new records were reached due to the high redox potential of oxygen reduction which was effectively catalyzed by microorganisms in the cathode. This resulted in a 127 mV higher cathode potential of the PMFC with a biocathode than a PMFC with a ferricyanide cathode. We also found that substrate availability in the anode likely limits the current generation. This work is crucial for PMFC application as it shows that PMFC can be a completely sustainable biotechnology with an improved power output.

Hot water heat stores with stratification are a common technology used in solar energy systems and reuse of waste heat. Adding a PCM module at the top of the water tank would give the system higher storage density, and compensate heat loss in the top layer. The work presented here includes experimental results and numerical simulation of the system using an explicit finite-difference method. Experiments and simulations were carried out using different cylindrical PCM modules. With only 1/16 of the volume of the store being PCM, 3/16 of water at the top of the store was held warm for 50% to 200% longer and the average energy density was increased by 20% to 45%. Furthermore, these 3/16 of water were reheated by the heat from the module after being cooled down in only 20 min.

Due to the increasing awareness of climate change, depletion of natural resources, and increasing world population, companies in the agri-food sector need to redesign their existing supply chains and take into account both the economic and environmental impact of their operations. In practice not all the required information is available in advance due to various sources of uncertainty in agri-food supply chains. In this research a multi-objective two-stage stochastic programming model is proposed to analyse and evaluate the economic and environmental impacts to account for uncertainty in agri-food supply chains. A mushroom supply chain in the Netherlands is presented as an illustrative case study. Optimal production planning decisions calculated with a two-stage stochastic programming model are compared with the results of an equivalent deterministic model. The results of the optimizations show that accounting for stochasticity in important model parameters can reduce the difference between expected and realized economic performance by approximately 4% on average. Moreover, this paper demonstrates that including stochastic model parameters can reduce the environmental impact without compromising the current economic performance. Given the assumptions in the setup of the case study and the available information, it is concluded that applying a 2-stage stochastic programming approach for production planning decisions can lead to improved economic and environmental performance in an agri-food supply chain. New findings in real-life case studies are needed to get profound insights and understanding on the impact of uncertainty on production planning decisions in sustainable agri-food supply chains.

Biomass is considered one of the most important options in the transition to a sustainable energy system with reduced greenhouse gas (GHG) emissions and increased security of enegry supply. In order to facilitate this transition with targeted policies and implementation strategies, it is of vital importance to understand the economic benefits, uncertainties and risks of this transition. This article presents a quantification of the economic impacts on value added, employment shares and the trade balance as well as required biomass and avoided primary energy and greenhouse gases related to large scale biomass deployment on a country level (the Netherlands) for different future scenarios to 2030. This is done by using the macro-economic computable general equilibrium (CGE) model LEITAP, capable of quantifying direct and indirect effects of a bio-based economy combined with a spread sheet tool to address underlying technological details. Although the combined approach has limitations, the results of the projections show that substitution of fossil energy carriers by biomass, could have positive economic effects, as well as reducing GHG emissions and fossil energy requirement. Key factors to achieve these targets are enhanced technological development and the import of sustainable biomass resources to the Netherlands. (C) 2013 Elsevier Ltd. All rights reserved.

Wood biomass is turned into industrial fuel through chipping. The efficiency of chipping depends on many factors, including chipper knife wear. Chipper knife wear was determined through a long-term follow-up study, conducted at a waste wood recycling yard. Knife wear determined a sharp drop of productivity (>20%) and a severe decay in product quality. Dry sharpening with a grinder mitigated this effect, but it could not replace proper wet sharpening. Increasing the frequency of wet sharpening sessions determined a moderate increase of knife depreciation cost, but it could drastically enhance machine performance and reduce biomass processing cost. Since benefits largely exceed costs, increasing the frequency of wet sharpening sessions may be an effective measure for reducing overall chipping cost. If the main goal of a chipper operator is to increase productivity and/or decrease fuel consumption, then managing knife wear should be a primary target. (C) 2014 Elsevier Ltd. All rights reserved.