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Abstract Achieving net-zero greenhouse gas emissions by 2050 requires a step-change in resource management, and the utilisation of organic waste is currently an untapped opportunity in Latin America. This study carries out a quantitative and qualitative assessment of organic waste-to-energy potentials for the Chilean context. First, it produces a comprehensive quantification of organic waste, including annual crop residues, horticulture residues, livestock manure and OFMSW by region; then it estimates the energy potential of these bioresources; and finally, it conducts a series of stakeholder interviews determining barriers to greater waste-to-energy utilisation. The results show that the total bioenergy potential from waste is estimated at 78 PJ/yr (3.3% of annual energy demand), being livestock manure (41%) and annual crop residues (28%) the main sources, arising mostly from three regions. The stakeholder elicitation concluded that financial, technical, and institutional barriers prevent waste utilisation, highlighting the needs to address elevated investment costs and high reliance on landfilling practices, which together with public policies could enable the full exploitation of these resources to ensure energy security and resource efficiency.

Internet of Things (IoT) technologies will play an important role in enabling the smart grid achieving its goals in monitoring, protecting, and controlling by incorporating sensors, actuators, and metering devices while supporting various network functions and system automation. In this regard, home energy management systems (HEMS) enable customers efficiently use energy by managing their consumption, providing feedback information and improving control of major appliances. This work proposes a novel framework for IoT based appliance recognition in smart homes. It consists of two parts: training framework and inference framework. The proposed framework allows incorporating different loads in the monitoring system and enables selecting and testing specific parameters related to dataset configuration, feature extraction, and classifier model setting. The work contributes by developing an easy-to-use tool that allows customization of the training/prediction parameters according to the user criterion. Once the data and all its parameters are loaded, a novel feature extraction algorithm is used to obtain a total of ten statistical features. For the classification task, three machine learning models are included: a feed-forward neural network, a long short-term memory and a support vector machine. In addition, the user can apply a set of techniques to handle imbalanced classes, and also measure the influence of the selected features in the classifiers’ prediction by performing a feature importance analysis.

Abstract A large number of non‐native trees (NNTs) have been introduced globally and widely planted, contributing significantly to the world's economy. Although some of these species present a limited risk of spreading beyond their planting sites, a growing number of NNTs are spreading and becoming invasive leading to diverse negative impacts on biodiversity, ecosystem functions and human well‐being. To help minimize the negative impacts and maximize the economic benefits of NNTs, Brundu et al. developed eight guidelines for the sustainable use of NNTs globally—the Global Guidelines for the Use of NNTs (GG‐NNTs). Here, we used an online survey to assess perceptions of key stakeholders towards NNTs, and explore their knowledge of and compliance with the GG‐NNTs. Our results show that stakeholders are generally aware that NNTs can provide benefits and cause negative impacts, often simultaneously and they consider that their organization complies with existing regulations and voluntary agreements concerning NNTs. However, they are not aware of or do not apply most of the eight recommendations included in the GG‐NNTs. We conclude that effectively managing invasions linked to NNTs requires both more communication efforts using an array of channels for improving stakeholder awareness and implementation of simple measures to reduce NNT impacts (e.g. via GG‐NNTs), and a deeper understanding of the barriers and reluctance of stakeholders to manage NNT invasions. Read the free Plain Language Summary for this article on the Journal blog.

• Premise of the study: The growth limitation hypothesis (GLH) is the most accepted mechanistic explanation for treeline formation, although it is still uncertain whether it applies across taxa. The successful establishment of Pinus contorta––an exotic conifer species in the southern hemisphere––above the Nothofagus treeline in New Zealand may suggest a different mechanism. We tested the GLH in Nothofagus pumilio and Pinus contorta by comparing seedling performance and carbon (C) balance in response to low temperatures.• Methods: At a southern Chilean treeline, we grew seedlings of both species 2 m above ground level, to simulate coupling between temperatures at the meristem and in the air (colder), and at ground level, i.e., decoupling air temperature (relatively milder). We recorded soil and air temperatures as well. After 3 yr, we measured seedling survival and biomass (as a surrogate of growth) and determined nonstructural carbohydrates (NSC).• Key results: Nothofagus and Pinus did not differ in survival, which, as a whole, was higher at ground level than at the 2‐m height. The root‐zone temperature for the growing season was 6.6°C. While biomass and NSC decreased significantly for Nothofagus at the 2‐m height compared with ground level (C limitation), these trends were not significant for Pinus.• Conclusions: The treeline for Nothofagus pumilio is located at an isotherm that fully matches global patterns; however, its physiological responses to low temperatures differed from those of other treeline species. Support for C limitation in N. pumilio but not in P. contorta indicates that the physiological mechanism explaining their survival and growth at treeline may be taxon‐dependent.

Anaerobic digestion of microalgal biomass for biogas production may be limited due to the cell wall resulting in an inefficient bioconversion. Enzymatic pretreatments are applied for inducing cell damage/lysis and organic matter solubilisation and this way increasing biogas production. We evaluated enzymatic pretreatments in different conditions for comparing in relation to cell wall rupture, increase of soluble material and increase in biogas production through anaerobic digestion performance in BMP assay. Chlorella sorokiniana cultures were subjected to three different enzymatic pretreatments, each under four different conditions of enzyme/substrate ratio, pH and application time. The results showed increases over 21% in biogas productions for all enzymatic pretreatments. Enzymatic pretreatment was effective at damaging microalgae cell wall, releasing organic compounds and increasing the rate and final methane yield in BMP tests. We observed a synergistic activity between the mixtures enzymes, which would depend on operational conditions used for each pretreatment.

Volatile organic compounds (VOCs) are ubiquitous contaminants that can be found both in outdoor and indoor air, posing risks to human health and the ecosystems. The treatment of air contaminated with VOCs in low concentrations can be effectively performed using biofiltration, especially when VOCs are hydrophilic. However, the performance of biofilters inoculated with bacteria has been found to be low with sparsely water soluble molecules when compared to biofilters where fungi develop. Using conceptual and mathematical models, this review presents an overview of the physical, chemical and biological mechanisms that explain the differences in the performance of fungal and bacterial biofilters. Moreover, future research needs are proposed, with an emphasis on integrated models describing the biological and chemical reactions with the mass transfer using high-resolution descriptions of the packing material.

Cyanobacterial blooms imperil the use of freshwater around the globe and present challenges for water management. Studies have suggested that blooms are trigged by high temperatures and nutrient concentrations. While the roles of nitrogen and phosphorus have long been debated, cyanobacterial dominance in phytoplankton has widely been associated with climate warming. However, studies at large geographical scales, covering diverse climate regions and lake depths, are still needed to clarify the drivers of cyanobacterial success. Here, we analyzed data from 464 lakes covering a 14,000 km north-south gradient in the Americas and three lake depth categories. We show that there were no clear trends in cyanobacterial biomass (as biovolume) along latitude or climate gradients, with the exception of lower biomass in polar climates. Phosphorus was the primary resource explaining cyanobacterial biomass in the Americas, while nitrogen was also significant but particularly relevant in very shallow lakes (< 3 m depth). Despite the assessed climatic gradient water temperature was only weakly related to cyanobacterial biomass, suggesting it is overemphasized in current discussions. Depth was critical for predicting cyanobacterial biomass, and shallow lakes proved more vulnerable to eutrophication. Among other variables analyzed, only pH was significantly related to cyanobacteria biomass, likely due to a biologically mediated positive feedback under high nutrient conditions. Solutions toward managing harmful cyanobacteria should thus consider lake morphometric characteristics and emphasize nutrient control, independently of temperature gradients, since local factors are more critical - and more amenable to controls - than global external forces.

The Intergovernmental Panel on Climate Change (IPCC) provides the reference for national greenhouse gas emission (GHG) inventories towards standardized, accurate, measurable, and comparable National Inventory Reports (NIR). For compliance with the 1.5⁰C commitments under the Paris Agreement, most countries have made efforts to improve their inventory methods to tier 2 or 3. However, some relevant activities within Latin American and the Caribbean (LAC) countries, such as enteric methane emissions and methane and nitrous oxide emissions from cattle manure management are still estimated using tier 1 methods, which leads to a high uncertainty due to the importance of livestock emissions in the national totals for these countries. In this context, reducing the uncertainty in GHG inventories would not only improve the accuracy of national reports but it would also provide solid baselines for national mitigation initiatives e.g., Nationally Determined Contributions under the Paris Agreement, and accurate tools to venture into carbon bonds or payments for ecosystem services. The aim of this study was to review the status of national GHG inventories specifically for these three cattle emission categories in 11 LAC countries. We conducted a survey of GHG inventory experts in the 11 LAC countries, to identify the potential for improvement and the main barriers to achieving this. Despite some initiatives, there is still a large potential for reducing the uncertainty in LAC national GHG emission estimates, the barriers to or solutions can be categorized as technical, policy, and institutional issues. However, improving the GHG inventories of LAC countries, specifically for cattle emissions, is feasible in the medium term, as long as multilateral actions are considered, coherently linked under a comparable and verifiable methodology and including a commitment by countries to invest public funds in relevant research and innovations.