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The severity of drought is predicted to increase across Europe due to climate change. Droughts can substantially impact terrestrial nitrogen (N) cycling and the corresponding microbial communities. Here, we investigated how ammonia-oxidizing bacteria (AOB), archaea (AOA), and complete ammonia oxidizers (comammox) as well as inorganic N pools and N2O fluxes respond to simulated drought under different cropping systems. A rain-out shelter experiment was conducted as part of a long-term field experiment comparing cropping systems that differed mainly in fertilization strategy (organic, mineral, or mixed mineral and organic) and plant protection management (biodynamic versus conventional pesticide use). We found that the effect of drought varied depending on the specific ammonia-oxidizing (AO) groups and the type of cropping system. Drought had the greatest impact on the structure of the AOA community compared to the other AO groups. The abundance of ammonia oxidizers was also affected by drought, with comammox clade B exhibiting the highest sensitivity. Additionally, drought had, overall, a stronger impact on the AO community structure in the biodynamic cropping system than in the mixed and mineral-fertilized conventional systems. The responses of ammonia-oxidizing communities to drought were comparable between bulk soil and rhizosphere. We observed a significant increase in NH4+ and NO3− pools during the drought period, which then decreased after rewetting, indicating a strong resilience. We further found that drought altered the complex relationships between AO communities and mineral N pools, as well as N2O fluxes. These results highlight the importance of agricultural management practices in influencing the response of nitrogen cycling guilds and their processes to drought. Soil Biology and Biochemistry, 201 ISSN:0038-0717 ISSN:1879-3428

In the quest for effective solutions to address Environ. Pollut. and meet the escalating energy demands, heterojunction photocatalysts have emerged as a captivating and versatile technology. These photocatalysts have garnered significant interest due to their wide-ranging applications, including wastewater treatment, air purification, CO2 capture, and hydrogen generation via water splitting. This technique harnesses the power of semiconductors, which are activated under light illumination, providing the necessary energy for catalytic reactions. With visible light constituting a substantial portion (46%) of the solar spectrum, the development of visible-light-driven semiconductors has become imperative. Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light. In this comprehensive review, we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media, as well as the remarkable progress made in renewable energy production. Moreover, we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems. Finally, we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain. By unraveling the potential of heterojunction photocatalysts, this review contributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.

The key to this study is to explore the relation between renewable energy and trade openness under climate change impacts on international trade. This study is based on the analyses of trade openness index and the Notre Dame-Global Adaptation Index in measures of research of the transfer of renewable energy technologies globally, promotion of the growth of renewable energy share, and the country's current vulnerability to climate disruptions. Research is focused on evaluation of low-carbon trade, analysis of renewable heat, and estimation of the impact of renewable energy on trade openness under climate disruptions. Research verified that development of renewable energy can reduce the human impact on climate change, but such technologies are not available to all countries; because of this, different countries may be affected by climate change to varying degrees. The implementation of renewable energy technologies should help countries become more resilient to climate change through the availability of appropriate means of producing renewable energy; however, this is possible subject to the country's openness to the arrival of goods and technologies from outside-increasing trade openness level. Countries with open and competitive markets are more inclusive to adopt and support the renewable energy technologies and their development. From another position, development of trade and deep integration of economy can create external dependence on imported energy sources and block the development of domestic renewable energy sectors. The economic challenges in the field of renewable energy need the focus of governments and policy makers on supporting the stability of renewable generation facilities, increasing the reliability of energy supply, protection from seasonal changes in demand for electricity, support of the energy cooperatives and private consumers of renewable energy, and transition the public transport sector for renewable energy sources. Further research in this direction has prospects, because globalization is a stable trend of recent decades, and allows for an even distribution of efforts to produce various products and provide them to the population, but decarbonization potential of renewable energy production is untapped as fossil fuels dominate in largest world markets.

The importance of climate change for driving adverse climate impacts has motivated substantial effort to understand the rate and magnitude of regional climate change in different parts of the world. However, despite decades of research, there is substantial uncertainty in the time remaining until specific regional temperature thresholds are reached, with climate models often disagreeing both on the warming that has occurred to-date, as well as the warming that might be experienced in the next few decades. Here, we adapt a recent machine learning approach to train a convolutional neural network to predict the time (and its uncertainty) until different regional warming thresholds are reached based on the current state of the climate system. In addition to predicting regional rather than global warming thresholds, we include a transfer learning step in which the climate-model-trained network is fine-tuned with limited observations, which further improves predictions of the real world. Using observed 2023 temperature anomalies to define the current climate state, our method yields a central estimate of 2040 or earlier for reaching the 1.5 degrees C threshold for all regions where transfer learning is possible, and a central estimate of 2040 or earlier for reaching the 2.0 degrees C threshold for 31 out of 34 regions. For 3.0 degrees C, 26 degrees C out of 34 regions are predicted to reach the threshold by 2060. Our results highlight the power of transfer learning as a tool to combine a suite of climate model projections with observations to produce constrained predictions of future temperatures based on the current climate. Environmental Research Letters, 20 (1) ISSN:1748-9326 ISSN:1748-9318

| openaire: EC/H2020/963646/EU//HELIOS Lithium titanate oxide (LTO) batteries have been under intensive research due to their stability, safety, and rapid charging characteristics. Nevertheless, uncertainties as to LTO-batteries behavior when used as a raw material in battery recycling still exist. This study provides a grave-to-gate life cycle inventory for a hydrometallurgical battery recycling process in which Li-battery waste materials nickel manganese cobalt (NMC), LTO, and graphite were used as feed. The simulation showed that NMC cathode materials and lithium from both battery waste fractions could be recovered. In contrast, the titanium present within LTO cannot be recovered by the recycling process. Nevertheless, the life cycle assessment (LCA) of the process demonstrated clear benefits of recycling battery materials, highlighted by the decrease in global warming potential, acidification, eutrophication, and ozone depletion potential. Additionally, two routes for hazardous waste management were simulated to ascertain the environmental impacts of hazardous waste management within the recycling process. Peer reviewed

This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the coordination of residential energy flexibility. Particularly, we focus on achieving cooperation between homes in a way that is fully privacy-preserving, scalable, and that allows for the management of distribution network voltage constraints. Previous work demonstrated that MARL-based distributed control can be achieved with no sharing of personal data required during execution. However, previous cooperative MARL-based approaches impose an ever greater training computational burden as the size of the system increases, limiting scalability. Moreover, they do not manage their impact on distribution network constraints. We therefore adopt a deep multi-agent actor-critic method that uses a centralised but factored critic to rehearse coordination ahead of execution, such that homes can successfully cooperate at scale, with only first-order growth in computational time as the system size increases. Training times are thus 34 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes. Moreover, experiments show that the cooperation of agents allows for a decrease of 47.2% in the likelihood of under-voltages. The results indicate that there is significant potential value for management of energy user bills, battery depreciation, and distribution network voltage management, with minimal information and communication infrastructure requirements, no interference with daily activities, and no sharing of personal data. Applied Energy, 377, Part A ISSN:0306-2619 ISSN:1872-9118