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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.

Indoor air quality is crucial for human health due to the significant time people spend at home, and it is mainly affected by internal sources such as solid fuel combustion for heating. This study investigated the indoor air quality and health implications associated with residential coal burning covering gaseous pollutants (CO, CO2 and total volatile organic compounds), particulate matter, and toxicity. The PM10 chemical composition was obtained by ICP-MS/OES (elements), ion chromatography (water-soluble ions) and thermal-optical analysis (organic and elemental carbon). During coal combustion, PM10 levels were higher (up to 8.8 times) than background levels and the indoor-to-outdoor ratios were, on average, greater than unity, confirming the existence of a significant indoor source. The chemical characterisation of PM10 revealed increased concentrations of organic carbon and elemental carbon during coal combustion as well as arsenic, cadmium and lead. Carcinogenic risks associated with exposure to arsenic exceeded safety thresholds. Indoor air quality fluctuated during the study, with varying toxicity levels assessed using the Aliivibrio fischeri bioluminescence inhibition assay. These findings underscore the importance of mitigating indoor air pollution associated with coal burning and highlight the potential health risks from long-term exposure. Effective interventions are needed to improve indoor air quality and reduce health risks in coal-burning households.

Abstract Smart agriculture aims at generating high harvest yields with an efficient resource management, such as the estimation of crop irrigation. One of the factors on which a productive crop irrigation depends on is evapotranspiration, defined as the water loss process from the soil. This is mainly measured by empirical equations, even though they are conditioned by the specific climatological variables they require. In recent years, data mining techniques are proposed as a powerful alternative to predict evapotranspiration. Among them, ensembles are notable in that they provide accurate estimators in different scenarios. Stacking is an ensemble-building technique aimed at strengthening the prediction capabilities of the system by the combined learning from the original features in the data and synthetic features created from the predictions of multiple models. This research proposes the usage of stacking for evapotranspiration prediction, which has been overlooked in the specialized literature, with the aim of a more sustainable management of water resources. The proposal is compared to other state-of-the-art empirical equations and data mining methods over several real-world climatological datasets of different agricultural areas in Spain. This comparison is performed considering separate datasets with features based on temperature, mass transfer, radiation and, finally, using the main meteorological variables together. The results obtained show that stacking is the best approach in all datasets and each group of features evaluated, running as good alternative to predict evapotranspiration when using data of a different nature and under different conditions.

Embargado hasta 01/11/2026 Agriculture faces a major challenge in meeting the world’s growing demand for food in a sustainable manner in the face of increasing environmental pressures, in particular the growing impact of climate change. Agriculture is also a major contributor to climate change. Digital technologies in agriculture can contribute to climate change adaptation and mitigation. This paper examines the interactions between climate change and agriculture, reviews adaptation and mitigation strategies, explores the application of digital technologies in this context, and discusses future challenges and opportunities for sustainable and resilient agriculture. The final aim is to provide a comprehensive overview of the current state and future prospects of digital agriculture in the context of climate change. A comprehensive literature review was conducted on adaptation and mitigation strategies in agriculture, and on the current state and future prospects of digital agriculture in the context of climate change adaptation and mitigation. The identified applications of digital technologies in agriculture include Remote Sensing for crop monitoring, Big Data for predictive modelling of water shortages and pest outbreaks, Artificial Intelligence for pest identification and tracking, the Internet of Things for precision fertiliser management, nanotechnology for soil improvement, robots for targeted spraying, and blockchain for improved soil management and supply chain transparency, among others. These technologies facilitate the precise management of resources, improve decision-making processes and enable more efficient agricultural practices. Digital technologies also help mitigate climate change by optimising inputs such as water and fertiliser, thereby reducing greenhouse gas emissions and promoting carbon sequestration. However, there are significant barriers to the adoption of these technologies, including the digital divide, high up-front costs and complexity, as well as privacy and security concerns and the environmental impact of technology use. Future action must address these barriers by investing in infrastructure and training, ensuring financial incentives, developing scalable digital solutions tailored to local agricultural conditions, increasing digital literacy among farmers, developing comprehensive governance frameworks, and exploring the integration of multiple digital technologies. The paper contributes to advancing scientific understanding and guiding practice and policy towards sustainable agriculture in the face of climate change. It provides a call to action for a more sustainable future in the context of climate change and highlights the urgency of multi-stakeholder collaboration to create an enabling environment for the widespread adoption of these innovations, ensuring that they are accessible, cost-effective and suitable for different farming environments.

Rapeseed meal (RSM) is a candidate for biopolymer production due to its abundance, low cost and potential integration with other rapeseed-derived products. However, existing studies pursuing such schemes are limited. The feasibility of different strategies for RSM valorization via protein extraction and polyhydroxyalkanoate production were evaluated. Nitrogen-limited RSM media was produced from hydrolysis of residues which had undergone extensive protein extraction using sodium hydroxide. A study of oxygen-limited fermentation was also performed on hydrolysate of untreated RSM via batch feeding. The typical strategy of using a high carbon-to-nitrogen ratio may not be the most suitable route for polyhydroxyalkanoate (PHA) production using nitrogen-rich biomass as a feedstock. Central composite design-based experiments show that due to mass transfer limitations protein extraction at 1-L scale could only achieve yields around 50% and 69%, at room temperature and 60 °C, respectively. Protein extraction yields reduced with successive extractions, meaning that whilst the RSM hydrolysate is viable for growth, designing a valorization scheme which has the fermentation step dictated by the protein extraction may not be practical/economical. A better route which utilizes oxygen-limitation to initially induce stationary phase was identified, giving accumulation of polyhydroxyalkanoate once the oxygen levels began to recover; 8.93% and 1.75% PHA accumulation in fed-batch cultures of synthetic and RSM media, respectively. The findings demonstrate that decoupling of protein extraction performance from PHA synthesis is feasible. This study provides important insight into the degrees of freedom available in the design of a holistic valorization scheme of rapeseed meal, and high protein lignocellulosic biomass in general.

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.

This paper analyses the probabilistic future behaviour of heat-waves (HWs) in the city of Madrid in the twenty-first century, using maximum daily temperatures from twenty-one climate circulation models under two representative concentration pathways (RCP 8.5 & RCP 4.5). HWs are modelled considering three factors: number per annum, duration and intensity, characterised by three stochastic processes: Poisson, Gamma and truncated Gaussian, respectively. Potential correlations between these processes are also considered. The probabilistic temperature behaviour is combined with an epidemiological model with stochastic mortality risk following a generalized extreme value distribution (gev). The objective of this study is to obtain probability distributions of mortality and risk measures such as the mean value of the 5% of worst cases in the 21st century, in particular from 2025 to 2100. Estimates from stochastic models for characterising HWs and epidemiological impacts on human health can vary from one climate model to another, so relying on a single climate model can be problematic. For this reason, the calculations are carried out for 21 models and the average of the results is obtained. A sensitivity adaptation analysis is also performed. Under RCP 8.5 for 2100 for Madrid city a mean excess of 3.6 °C over the 38 °C temperature threshold is expected as the average of all models, with an expected attributable mortality of 1614 people, but these figures may be substantially exceeded in some cases if the highest-risk cases occur.