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Digestates from low-tech digesters need to be post-treated to ensure their safe agricultural reuse. This study evaluated, for the first time, vermifiltration as a post-treatment for the digestate from a low-tech digester implemented in a small-scale farm, treating cattle manure and cheese whey under psychrophilic conditions. Vermifiltration performance was monitored in terms of solids, organic matter, nutrients, and pathogens removal efficiency. In addition, the growth of earthworms (Eisenia foetida) and their role in the process was evaluated. Finally, the vermicompost and the effluent of the vermifilter were characterized in order to assess their potential reuse in agriculture. Vermifilters showed high removal efficiency of chemical oxygen demand (55-90%), total solids (60-80%), ammonium nitrogen (83-97%), and phosphate-P (28-49%). Earthworms effectively grew and reproduced on digestate (i.e. earthworms number increased by 183%), enhancing the vermifiltration performance, while reducing clogging and odour-related issues. Both the vermicompost and effluent produced complied with legislation limits established for soil improvers and wastewater for fertigation, respectively. Indeed, there was an absence of pathogens and non-detectable heavy metals concentrations. Vermifiltration may be thus considered a suitable post-treatment option for the digestate from low-tech digesters, allowing for its safe agricultural reuse and boosting the circular bioeconomy in small-scale farms.

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Aqueous lithium-ion batteries (ALiBs) present a promising avenue for safer and more sustainable energy storage solutions compared to traditional non-aqueous lithium-ion batteries. LiNiO2 (LNO) has emerged as a potential cathode material for ALiBs due to its high capacity potential and ability to facilitate Li⁺ intercalation over H⁺ intercalation in aqueous media. However, challenges remain regarding its stability and performance in aqueous electrolytes. In this study, we employ periodic density functional theory simulations to investigate the interactions between LNO surfaces and aqueous electrolytes, evaluating its viability as a cathode material for ALiBs. We have systematically and exhaustively studied the surface energetics, shedding light on the formation of NiOOHx species, one of the common issues associated with this material. We have evaluated the oxygen evolution reaction on LNO surfaces, revealing that they decompose water molecules into hydroxide and other intermediate species, thereby degrading the electrolyte. Our findings suggest that, despite their promising abilities for Li+ ion intercalation, the tendency to boost the generation of NiOOHx and its facility to decompose water at potentials lower than 1.23 V are important limitations for the battery performance S.P.-P. appreciates the economic support of Marie Curie fellowship (H2020-MSCA-IF- 2020–101020330). A.P. is a Serra Húnter Fellow and received the ICREA Academia Prize 2019. M.S. and A.P. thank the Spanish MINECO for projects PID2020-13711GB-I00, PID2023-147424NB-I00, and PID2021-127423NB-I00, and the Generalitat de Catalunya for project 2021SGR623. Computational time at the MARENOSTRUM supercomputer has been provided by the Barcelona Supercomputing Centre through a grant from Red Española de Supercomputación, project (QHS-2022-3-0002)

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (ηEX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and ηEX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the ηEX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and ηEX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal ηEX,tot mainly varies in the range of 10.40–11.40%.

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

Abstract Level(s) is a common European Union framework of core sustainability indicators for measuring the performance of buildings along their life cycle, enabling emissions reductions and circular resource flows. A fundamental tool for the development of European policies to boost the market for sustainable, resilient and climate change adapted buildings. The objective of this study is to contribute to the existing body of knowledge in the field of sustainable building research, through the definition of strategies to adopt Level(s) for bringing buildings into the Circular Economy. For this reason, a triple SWOT-Analytical Hierarchy Process (AHP)-TOWS analysis was applied. The strengths, weaknesses, opportunities and threats (SWOT) of the Level(s) have been identified in relation to the availability of resources, product quality, internal and market structure, consumer perception, among others. The results obtained are conclusive in terms of the experts’ positive assessment of the tool; highlighting factors such as its response to the need to adapt buildings to climate change, its a standard reference language, and its use in multiple situations. However, several barriers have also been identified, which may affect its development, including its complexity of use, its lack of self-sufficiency, and its dependence the criteria used in each evaluation. Finally, the key strategies to be carried out for the implementation of the Levels have been established.