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AbstractThe ClimApp smartphone application was developed to merge meteorological forecast data with personal information for individualized and improved thermal warning during heat and cold stress and for indoor comfort in buildings. For cold environments, ClimApp predicts the personal thermal stress and strain by the use of the Insulation REQuired model that combines weather and personal physiological data with additional consideration of the Wind Chill index based on the local weather forecast. In this study, we validated the individualized ClimApp index relative to measurements and compared it with the Universal Temperature Climate Index (UTCI). To this aim, 55 participants (27 females) were exposed to at least 1 h in an outdoor environment of 10 °C or below (average 1.4 °C air temperature, 74.9% relative humidity, and 4.7 m/s air velocity) inputting their activity level and clothing insulation as instructed by ClimApp. The UTCI and ClimApp indices were calculated and compared to the participants’ perceived thermal sensation. The ClimApp index root mean square deviation (RMSD) was below the standard deviation of the perceived thermal sensation which indicates a valid prediction and the UTCI RMSD was higher than the standard deviation which indicates an invalid prediction. The correlation of ClimApp and UTCI to the perceived thermal sensation was statistically significant for both models.

Fine particles were sampled at the 800 MWth Avedøre Power Station Unit 2 (AVV2) as well as at the 900 MWth Studstrup Power Station Unit 3 (SSV3), both utilizing wood pellets together with coal fly ash (CFA), for alkali capturing. In parallel, fine particle formation was quantified in the BoCTeR pilot-scale pulverized-fuel biomass combustion test-rig, at the Technical University of Munich, using the same wood pellets and CFA-additive as was applied in full-scale, to compare the impact of the CFA-addition. A comparison between fine particle formation and concentration in the BoCTeR and in full-scale, revealed a higher quantity of submicron particles (PM1) at AVV2, compared to SSV3, i.e. differences between the two full-scale boilers, while the PM1-values at BoCTeR was found to be substantially higher than what was measured in full-scale, mainly due to differences in temperature–time history between these two combustor scales.Ash deposition during wood pellets firing with CFA-addition, was quantified by an advanced deposit probe at SSV3. The results showed that with a probe surface temperature of 550 °C, and 2 %wt or 3 %wt CFA-addition, no significant amount of deposit was formed. When increasing the probe surface temperature to 650 °C, at 3 %wt CFA-addition, deposit was formed on the upstream side of the probe. No Cl was detected in the deposit samples, indicating that Cl-related corrosion can be effectively suppressed by 2 %wt or 3 %wt coal fly ash addition.Finally, deposit formation when firing different pre-treated biomasses; K- and Cl-rich wheat straw, milled wood pellets, hydrothermally carbonized (HTC)-leaves and steam exploded (SE)-bark, with and without a K-capturing additives, was also investigated in the entrained flow reactor at Technical University of Denmark (DTU-EFR). In terms of deposit formation, the K- and Cl-rich wheat straw proved to be the most problematic fuel, while milled wood pellets, HTC-leaves and SE-bark, had significantly lower deposition propensities. When applying kaolin and two types of CFA (both rich in Al and Si), as K-capturing additive, the deposition propensity of wheat straw decreased significantly, when applying a molar ratio of fuel-K/additive-Al of 1.0.This paper was originally presented at the recent 28th International Conference on Impacts of Fuel Quality on Power Production, the, Aare, Sweden, September 19–23, 2022

This work aims to generate the data needed to set epidemiological cut-off values for minimum inhibitory concentration (MIC) and disc-diffusion zone measurements of Vibrio anguillarum. A total of 261 unique isolates were tested applying standard methods specifying incubation at 28 °C for 24-28h. Aggregated MIC distributions for a total of 247 isolates were determined in nine laboratories for eleven agents. Data aggregations of the disc zone for the ten agents analysed contained between 157 and 218 observations made by four to seven laboratories. Acceptable ranges for quality control (QC) reference strains were available for seven agents and the related multi-laboratory aggregated data were censored excluding the data of a laboratory that failed to meet QC requirements. Statistical methods were applied to calculate epidemiological cut-off values. Cut-off values for MIC data were calculated for florfenicol (≤1 µg/ml), gentamicin (≤4 µg/ml), oxytetracycline (≤0.25 µg/ml) and trimethoprim/sulfamethoxazole (≤0.125/2.38 µg/ml). The cut-off values for disc zone data were calculated for enrofloxacin (≥29 mm), florfenicol (≥27 mm), gentamicin (≥19 mm), oxolinic acid (≥24 mm), oxytetracycline (≥24 mm) and trimethoprim/sulfamethoxazole (≥26 mm). MIC and disc-diffusion zone data for the other agents where not supported by QC, thus yielding only provisional cut-off values (meropenem, ceftazidime). Regardless of whether QC is available or not, some of the aggregated MIC distributions (enrofloxacin, oxolinic acid), disc zone (sulfamethoxazole) and MIC and disc-diffusion distributions (ampicillin, chloramphenicol) did not meet the statistical requirements. The data produced will be submitted to CLSI for their consideration in setting international consensus epidemiological cut-off values. International audience

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.

International audience; Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i.e., datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.e., missing standards for data formats; second, data owners having a negative perception of sharing data. To make data ready for data science exploitation, one of the necessary steps to map the existing data and their availability to facilitate their access is to create a taxonomy for the field’s topics. For this, a group of experts in different renewable technologies such as photovoltaics, wind and concentrated solar power and in transversal fields such as life cycle assessment and the EU taxonomy for sustainable activities have been gathered to propose a coherent and detailed taxonomy for renewable energy-related data. The result is a coherent classification of relevant data sources, considering both the general aspects applicable to electricity generation from selected renewable energy technologies and the specific aspects of each of them. It is based on previous relevant work and can be easily extended to other renewable resources not considered in this work and conventional energy technology.

AbstractThe optimal management of scarce transboundary water resources among competitive users is expected to be challenged by the effects of climate change on water availability. The multiple economic and social implications, including conflicts between neighbouring countries, as well as competitive sectors within each country are difficult to estimate and predict, to inform policy-making. In this paper, this problem is approached as a stochastic multistage dynamic game: we develop and apply a novel framework for assessing and evaluating different international strategies regarding transboundary water resources use, under conditions of hydrological uncertainty. The Omo-Turkana transboundary basin in Africa is used as a case study application, since it increasingly faces the above challenges, including the international tension between Kenya and Ethiopia and each individual country’s multi-sectoral competition for water use. The mathematical framework combines a hydro-economic model (water balance, water costs and benefits), and an econometric model (production functions and water demand curves) which are tested under cooperative and non-cooperative conditions (Stackelberg “leader–follower” game). The results show the cross-country and cross-sectoral water use—economic trade-offs, the future water availability for every game case, the sector-specific production function estimations (including residential, agriculture, energy, mining, tourism sectors), with nonparametric treatment, allowing for technical inefficiency in production and autocorrelated Total Factor Productivity, providing thus a more realistic simulation. Cooperation between the two countries is the most beneficial case for future water availability and economic growth. The study presents a replicable, sophisticated modelling framework, for holistic transboundary water management.

The scope of this paper was to examine the environmental and economic performance of alternative household fermentable waste (HFW) management scenarios. In Greece, the business-as-usual scheme for the management of HFW is its disposal in landfills as part of mixed waste. Within a HORIZON2020 called Waste4think a series of alternative approaches based on the benefits of source separation was developed. Specifically, source separated HFW is led to a drying/shredding plant, located in the municipality, for the production of a high-quality biomass product, which is called FORBI (Food Residue Biomass). Alternative approaches have been examined for the exploitation of FORBI: a simple alternative consists of the transportation of food waste (without drying/shredding) to the landfill, composting and covering the landfill’s layers with the produced compost. On the other hand, a set of technological alternatives examined are: one- and two-stage anaerobic digestion for the production of biogenic compressed natural gas (bio-CNG) and bio-hythane, composting and utilization of compost in the municipality, bio-ethanol production and pelletization. The alternatives have been assessed using Life Cycle Assessment and Life Cycle Costing tools. The results show that both the simple and the innovative alternatives proposed perform better than the baseline scenario both in economic and environmental terms.