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Understanding physiological mechanisms of tolerance to heat exposure, and potential ways to improve such tolerance, is increasingly important in the context of ongoing climate change. We discuss the concept of heat tolerance in humans and experimental models (primarily rodents), including intracellular mechanisms and improvements in tolerance with heat acclimation.

To facilitate effective decarbonization of the electric power sector, this paper introduces the generic Carbon-aware Optimal Power Flow (C-OPF) method for power system decision-making that considers demand-side carbon accounting and emission management. Built upon the classic optimal power flow (OPF) model, the C-OPF method incorporates carbon emission flow equations and constraints, as well as carbon-related objectives, to jointly optimize power flow and carbon flow. In particular, this paper establishes the feasibility and solution uniqueness of the carbon emission flow equations, and proposes modeling and linearization techniques to address the issues of undetermined power flow directions and bilinear terms in the C-OPF model. Additionally, two novel carbon emission models, together with the carbon accounting schemes, for energy storage systems are developed and integrated into the C-OPF model. Numerical simulations demonstrate the characteristics and effectiveness of the C-OPF method, in comparison with OPF solutions.

Tropical forests are of great ecological and climatological importance. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainties in the local land carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests: more than 98% of the carbon flux generated by changes in land-use may be due to tropical deforestation, which converts carbon stored as biomass into emissions.In this context, the AfriSAR 2015/16 campaign, supported by ESA, was carried out over four forest sites in Gabon by ONERA (July 2015) during the dry season and by DLR (February 2016) during the wet season. From the data collected the innovative techniques applied to estimate forest height and biomass could be improved significantly and are summarized in a special issue ‘Forest Structure Estimation in Remote Sensing’ of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.The motivation of the AfriSAR campaign was to acquire demonstration data for the soon to be launched ESA BIOMASS mission, that was selected as the 7th Earth Explorer mission in May 2013 in order to meet the pressing need for information on tropical carbon sinks and sources by providing estimates of forest height and biomass. AfriSAR focused on African tropical and savannah forest types (with biomass in the 100-300 t/ha range) and complements previous ESA campaigns over Indonesian and Amazonian forest types in 2004 (INDREX-II) and 2009 (TropiSAR).The present contribution concerns the GABONX campaign, the ESA supported successor to AfriSAR, which took place in May to July 2023. GABONX aims to detect and quantify changes that have occurred since the DLR acquisitions in February 2016. To this end, DLR’s F-SAR sensor acquired interferometric stacks of fully polarimetric L- and P-Band data over the same forest sites in the same flight geometry as in 2016. The results presented give an overview of campaign activities with particular emphasis on the calibration of the SAR instrument as well as the validation of forest parameters derived from polarimetric interferometry. The SAR sensor calibration is based on an innovative approach that leverages state-of-the-art EM simulation to accurately characterize the 5m trihedral reference target deployed for the campaign in Gabon. The validation of derived forest parameters uses lidar measurements obtained in the time frame of the GABONX campaign by NASA’s LVIS sensor. As an outlook, further collaborative calibration and validation activities will hopefully include the cross-calibration of DLR’s F-SAR and NASA’s UAVSAR, which is set to acquire L- and P-Band data over the GABONX sites in 2024.

DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016Q1 until 2025Q1. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations.The dataset continues the work of 10.5194/essd-14-4251-2022. File metadata File Time Geometry Spatial extent DeepOWT.geojson (Dataset) 2016Q1-2025Q1 points Global gt_2021Q2_nsb.geojson (Ground Truth Location) 2021Q2 polygons North Sea Basin gt_2021Q2_ecs.geojson (Ground Truth Location) 2021Q2 polygons East China Sea gt_2021Q2_vtn.geojson (Ground Truth Location) 2021Q2 polygons Southeast Vietnamese Coast gt_nsb_gridded.geojson (Ground Truth Region) - polygon North Sea Basin gt_ecs_gridded.geojson (Ground Truth Region) - polygon East China Sea gt_ecs_gridded.geojson (Ground Truth Region) - polygon Southeast Vietnamese Coast Used semantic label open sea under construction offshore wind turbine offshore wind farm substation

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

We are facing an energy crisis because of the limitation of the fossil fuel and the pollution caused by burning it. Clean energy technologies, such as fuel cells and metal-air batteries, are studied extensively because of this high efficiency and less pollution. Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are essential in the process of energy storage and conversion, and noble metals (e.g. Pt) are needed to catalyze the critical chemical reactions in these devices. Functionalized carbon nanomaterials such as heteroatom-doped and molecule-adsorbed graphene can be used as metal-free catalysts to replace the expensive and scarce platinum-based catalysts for the energy storage and conversion. Traditionally, experimental studies on the catalytic performance of carbon nanomaterials have been conducted extensively, however, there is a lack of computational studies to guide the experiments for rapid search for the best catalysts. In addition, theoretical mechanism and the rational design principle towards ORR and OER also need to be fully understood. In this dissertation, density functional theory calculations are performed to calculate the thermodynamic and electrochemical properties of heteroatom-doped graphene and molecule-adsorbed graphene for ORR and OER. Gibb's free energy, overpotential, charge transfer and edge effect are evaluated. The charge transfer analysis show the positive charges on the graphene surface caused by the heteroatom, hetero-edges and the adsorbed organic molecules play an essential role in improving the electrochemical properties of the carbon nanomaterials. Based on the calculations, design principles are introduced to rationally design and predict the electrochemical properties of doped graphene and molecule-adsorbed graphene as metal-free catalysts for ORR and OER. An intrinsic descriptor is discovered for the first time, which can be used as a materials parameter for rational design of the metal-free catalysts with carbon nanomaterials for energy storage and conversion. The success of the design principle provides a better understanding of the mechanism behind ORR and OER and a screening approach for the best catalyst for energy storage and conversion.

The climate crisis is the biggest threat to the health, development, and wellbeing of the current and future generations. While there is extensive evidence on the direct impacts of climate change on human livelihood, there is little evidence on how children and young people are affected, and even less discussion and evidence on how the climate crisis could affect violence against children.In this commentary, we review selected research to assess the links between the climate crisis and violence against children.We employ a social-ecological perspective as an overarching framework to organize findings from the literature and call attention to increased violence against children as a specific, yet under-examined, direct and indirect consequence of the climate crisis.Using such a perspective, we examine how the climate crisis exacerbates the risk of violence against children at the continually intersecting and interacting levels of society, community, family, and the individual levels. We propose increased risk of armed conflict, forced displacement, poverty, income inequality, disruptions in critical health and social services, and mental health problems as key mechanisms linking the climate crisis and heightened risk of violence against children. Furthermore, we posit that the climate crisis serves as a threat multiplier, compounding existing vulnerabilities and inequities within populations and having harsher consequences in settings, communities, households, and for children already experiencing adversities.We conclude with a call for urgent efforts from researchers, practitioners, and policymakers to further investigate the specific empirical links between the climate crisis and violence against children and to design, test, implement, fund, and scale evidence-based, rights-based, and child friendly prevention, support, and response strategies to address violence against children.

Improving our comprehension of infiltration processes in karst systems is crucial for a better adaptation to the global change regarding water resources availability and management. In this work, the effective recharge under different meteorological conditions and its transfer along the vertically distributed compartments of a geologically complex karst aquifer in southern Spain have been evaluated. Continuous records of soil moisture and temperature values (at 5 and 10 cm depth and the soil-rock transition -average depth of 28 cm-) have been combined with hourly hydrodynamic and hydrothermal responses recorded at two springs with a marked influence of the unsaturated zone (UZ) and the saturated zone (SZ), respectively.Most rainfalls generate soil moisture signal in the shallowest probes. However, a mean increase of soil water content of 10.5% in summer (from background values of 2.5%) and 6.1% in autumn-winter (from 9.6%) at the soil-rock interface were needed to produce hydrodynamic responses in the system: first in the spring related to the UZ, with a time delay of 4-9 hours after moisture peaks, and then (14-18 hours) in the spring draining the SZ, but only during autumn-winter recharge events. In addition, recharge caused decreases (up to 0.9°C) in the temperature of the water drained by the first spring, while lagged rises (up to 0.6°C) occurred in the second outlet.Transmission of the input signal would be favoured by stronger karstification, but the presence of inter-bedded detrital formations in the lithological sequence of the aquifer (partially confined in the SE border) filter and buffer groundwater flows before being drained by the spring related to the SZ. These findings will help to assess thresholds for effective infiltration and to predict groundwater recharge in karst aquifers under different climate change scenarios.

Sub-Saharan Africa (SSA) faces significant food security risks, primarily due to low soil fertility leading to low crop yields. Climate change is expected to worsen food security issues in SSA due to a combined negative impact on crop yield and soil fertility. A common omission from climate change impact studies in SSA is the interaction between change in soil fertility and crop yield. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, is expected to increase crop yield but it is uncertain how this advantage is maintained with climate change.   We explored the impact of scenarios of change in soil fertility and climate variables (temperature, rainfall, and CO2) on rainfed maize yield in four representative sites in SSA with no input and ISFM management. To do so, we used an ensemble of 15 calibrated soil-crop models. Reset and continuous simulations were performed to assess the impact of soil fertility vs climate change on crop yield. In reset simulations, SOC, soil N and soil water were reinitialized each year with the same initial conditions. In continuous simulations, SOC, soil N and soil water values of a given year were obtained from the simulation of the previous year, allowing cumulative effects on SOC and crop yields.Most models agreed that with current baseline (no input) management, yield changed by a much larger order of magnitude when considering declining soil fertility with baseline climate (-39%), compared with considering constant soil fertility but changes in temperature, rainfall and CO2 (from -12% to +5% depending on the climate variable considered). The interaction between change in soil fertility and climate variables only marginally influenced maize yield (high agreement between models). The model ensemble indicated that when accounting for soil fertility change, the benefits of ISFM systems over no-input systems increased over time (+190%). This increase in ISFM benefits was greater in sites with low initial soil fertility. We advocate for the urgent need to account for soil-crop long-term feedback in climate change studies to avoid large underestimations of climate change and ISFM impact on food production in SSA.