Search Results (1,492)

To facilitate easy accessibility to the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) results for the geoscientific community, multiple institutions have successively developed mass anomaly grid products including mass concentration (mascon) grids; these were provided at the Gravity Information Service (GravIS) portal. However, an assessment of their consistency for studying large-scale mass redistribution and transport in Earth’s system is still not available. Here, we compare three major mascon solutions separately from the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the Goddard Space Flight Center (GSFC) and GravIS products based on the Combination Service for Time-variable Gravity fields (COST-G) by analyzing the Antarctic Ice Sheet (AIS) mass changes in four aspects. Our results demonstrate that: (1) the four datasets exhibit strong consistency on the entire AIS mass change time series, with the largest difference occurring in the Antarctic Peninsula; (2) mass trend estimates show better agreement over longer periods and larger regions, but differences with a percentage of 20–40 exist during the late stage of GRACE and the whole GRACE-FO timespan; (3) notable discrepancies arise in the annual statistics of the Eastern AIS in 2016, leading to inconsistency on the sign of annual AIS mass change; (4) good agreement can be seen among these interannual mass variations over the AIS and its three subregions during 2003–2023, excluding the period from mid-2016 to mid-2018. These findings may provide key insights into improving algorithms for mascon solutions and grid products towards refining their applications in ice mass balance studies. Full article

Energy efficiency is widely recognized as a critical strategy for reducing energy consumption in industrial systems. Improving energy efficiency has become a central point in industrial systems aiming to reduce energy consumption and operational costs. Industrial air compressors are among the most energy-intensive assets and often operate under static control policies that fail to adapt to real-time dynamics. This paper proposes a cognitive digital twin (CDT) framework that integrates reinforcement learning as, especially, a Proximal Policy Optimization (PPO) agent into the virtual replica of the air compressor system. CDT learns continuous from multidimensional telemetry which includes power, outlet pressure, air flow, and intake temperature, enabling autonomous decision-making, fault adaptation, and dynamic energy optimization. Simulation results demonstrate that PPO strategy reduces average SEC by 12.4%, yielding annual energy savings of approximately 70,800 kWh and a projected payback period of one year. These findings highlight the CDT potential to transform industrial asset management by bridging intelligent control. Full article

The study analyzed energy, environmental impact, and costs in intensive broiler production systems in the southeast of the state of Minas Gerais, Brazil, comparing scenarios with and without photovoltaic systems. Four configurations were evaluated, considering different ventilation types (positive and negative pressure) and photovoltaic generation. The Life Cycle Assessment (LCA), with a functional unit of 1 kg of live weight of chicken and a cradle-to-gate approach, indicated that photovoltaic systems reduce between 2.58 t and 4.96 t of CO₂-eq annually, in addition to offering better energy efficiency. Economically, sheds with positive pressure ventilation have the lowest cost–benefit ratios, while the feeding subsystem was the one that contributed the most to global warming, among the environmental impact categories evaluated in the LCA. Photovoltaic systems demonstrated the potential to reduce electricity costs between 19.4% and 26.5% per year. However, coffee husks used as chicken litter accounted for 36.5% of production costs, highlighting the need for more economical alternatives. It was concluded that photovoltaic systems are a viable solution to reduce environmental impacts and increase profitability, reinforcing the importance of resource-use optimization strategies in poultry farming. Full article

Remote island regions often depend on isolated power grids dominated by small-scale thermal power plants. Decarbonizing these systems is challenging due to limited interconnection capacity and variable renewable output, highlighting the need for flexible resource balance. This study develops an optimization model that minimizes system costs and CO₂ emissions by integrating variable renewable energy and Vehicle-to-Grid (V2G) while considering the minimum-output constraints of thermal power generation. The model is applied to Tanegashima Island, Japan. The results demonstrate that all optimized scenarios reduced the cost and emissions compared with the baseline. In the cost-minimizing scenario, the total annual cost decreased from 2.81 to 2.46 billion yen, while CO₂ emissions decreased from 56.5 to 44.4 kt. In the CO₂-minimizing scenario, V2G further reduced emissions to 43.8 kt at a lower cost (2.54 billion yen) than the system without V2G. However, renewable curtailment remained high due to the minimum-output constraint of thermal generators. These findings confirm that while V2G is a cost-effective, distributed flexibility resource, it cannot fully eliminate renewable curtailment under current operational limits. Enhanced coordination, behavioral engagement, and complementary measures—such as relaxing thermal constraints and expanding storage—are required to unlock its full potential in isolated power systems. Full article

The oil sector in Ecuador represents one of the largest national energy consumers, with significant contributions to greenhouse gas and thermal emissions due to reliance on diesel-based thermoelectric generation. This study assesses the feasibility of implementing waste heat recovery processes in upstream petroleum operations, aiming to improve energy efficiency and reduce the sector’s carbon footprint. Historical production and energy consumption data (2015–2020) from the main oil blocks (43-ITT, 57-Shushufindi, 57-Libertador, 58-Cuyabeno, 60-Sacha, and 61-Auca) were analyzed, alongside experimental parameters from thermoelectric equipment. Key energy indicators, including recoverable heat potential, energy intensity, and CO₂ emissions, were quantified to identify inefficiencies and opportunities for recovery. Results show that blocks with the highest crude production also exhibit the largest energy demand, with flue gas temperatures averaging 400 °C and an estimated recovery potential of up to 1.9 MWe through Rankine Cycle systems. Pre-feasibility analysis indicates a cost–benefit ratio of 1.03 under current conditions, which could reach 1.29 with higher load factors, while avoided emissions surpass 7000 tCO₂ annually. The findings highlight a strong correlation between energy intensity and CO₂ emissions, emphasizing the environmental relevance of recovery projects. Adoption of heat recovery technologies, coupled with regulatory incentives such as carbon pricing, offers a viable pathway to enhance energy efficiency, reduce operational costs, and strengthen sustainability in the Ecuadorian oil industry. Full article

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas, drives significant and potentially irreversible impacts on ecosystems, biodiversity, and human health. Achieving the Paris Agreement target of limiting global warming to well below 2 °C, ideally 1.5 °C, requires rapid and substantial global emission reductions. While recent decades have seen advances in clean energy technologies, carbon capture, utilization, and storage (CCUS) remain essential for deep decarbonization. Despite proven technical readiness, large-scale carbon capture and storage (CCS) deployment has lagged initial targets. This review evaluates CCS technologies and their contributions to net-zero objectives, with emphasis on sector-specific applications. We found that, in the iron and steel industry, post-combustion CCS and oxy-combustion demonstrate potential to achieve the highest CO₂ capture efficiencies, whereas cement decarbonization is best supported by oxy-fuel combustion, calcium looping, and emerging direct capture methods. For petrochemical and refining operations, oxy-combustion, post-combustion, and chemical looping offer effective process integration and energy efficiency gains. Direct air capture (DAC) stands out for its siting flexibility, low land-use conflict, and ability to remove atmospheric CO₂, but it’s hindered by high costs (~$100–1000/t CO₂). Conversely, post-combustion capture is more cost-effective (~$47–76/t CO₂) and compatible with existing infrastructure. CCUS could deliver ~8% of required emission reductions for net-zero by 2050, equivalent to ~6 Gt CO₂ annually. Scaling deployment will require overcoming challenges through material innovations aided by artificial intelligence (AI) and machine learning, improving capture efficiency, integrating CCS with renewable hybrid systems, and establishing strong, coordinated policy frameworks. Full article

This study evaluates integrated water-saving strategies in two school centres (SC1 and SC2) located in Bragança, Portugal, combining rainwater harvesting systems (RWHS), green roofs (GR), and the replacement of conventional taps with high-efficiency models. Water consumption patterns were analysed, and nine scenarios were simulated to assess their feasibility and economic performance. Scenario 1, which focuses on replacing conventional taps, achieved the highest short-term cost-effectiveness, reducing potable water consumption by approximately 30% and providing a payback period of about one year. Scenario 3, integrating RWHS into conventional roofs with efficient taps, demonstrated the greatest overall benefits, reducing potable water demand by up to 60% and generating annual savings exceeding €7000 + VAT, with payback periods of eight years for SC1 and seven years for SC2. In contrast, scenarios involving extensive GR significantly reduced stormwater runoff but required higher investments and presented longer payback periods, ranging from 17 to 42 years. Overall, the results indicate that combining low-cost efficiency measures with RWHS maximises potable water savings and supports sustainable water management, while GR implementation should be considered selectively, particularly when broader ecological and thermal benefits are prioritised. Full article

This paper addresses the simultaneous feeder routing and conductor sizing problem in unbalanced three-phase distribution systems, formulated as a nonconvex mixed-integer nonlinear program (MINLP) that minimizes the equivalent annualized expansion cost—combining investment and loss costs—under voltage, ampacity, and radiality constraints. The model captures nonconvex voltage–current–power couplings, $\Delta /Y$ load asymmetries, and discrete conductor selections, creating a large combinatorial design space that challenges heuristic methods. An exact MINLP formulation in complex variables is implemented in Julia/JuMP and solved with the Basic Open-source Nonlinear Mixed Integer programming (BONMIN) solver, which integrates branch-and-bound for discrete variables and interior-point methods for nonlinear subproblems. The main contributions are: (i) a rigorous, reproducible formulation that jointly optimizes routing and conductor sizing; (ii) a transparent, replicable implementation; and (iii) a benchmark against minimum spanning tree (MST)-based and metaheuristic approaches, clarifying the trade-off between computational time and global optimality. Tests on 10- and 30-node rural feeders show that, although metaheuristics converge faster, they often yield suboptimal solutions. The proposed MINLP achieves globally optimal, technically feasible results, reducing annualized cost by 14.6% versus MST and 2.1% versus metaheuristics in the 10-node system, and by 17.2% and 2.5%, respectively, in the 30-node system. These results highlight the advantages of exact optimization for rural network planning, providing reproducible and verifiable decisions in investment-intensive scenarios. Full article

This study evaluates the feasibility of fully renewable energy systems on El Hierro, the smallest and most isolated Canary Archipelago Island (Spain), contributing to the broader effort to decarbonize the European economy. By 2040, the island’s energy demand is projected to reach 80–110 GWh annually, assuming full economic decarbonization. Currently, El Hierro faces challenges due to its dependence on fossil fuels and inherent variability of renewable sources. To ensure system reliability, the study emphasizes the integration of renewable and storage technologies. Two scenarios are modeled using HOMER Pro 3.18.4 software with probabilistic methods to capture variability in generation and demand. The first scenario, BAU, represents the current system enhanced with electric vehicles. While the second, Efficiency, incorporates energy efficiency improvements and collective mobility policies. Both prioritize electrification and derive an optimal generation mix based on economic and technical constraints, to minimize Levelized Cost Of Energy (LCOE). The approach takes advantage of El Hierro’s abundant solar and wind resources, complemented by reversible pumped hydro storage and megabatteries. Fully renewable systems can meet demand reliably, producing about 30% energy surplus with an LCOE of roughly 10 c€/kWh. The final BAU scenario includes 53 MW of solar PV, 16 MW of wind, and a storage system of 40 MW–800 MWh. The Efficiency scenario has 42 MW of solar PV, 11.5 MW of wind, and 35 MW–550 MWh of storage. Uncertainty analysis indicates that maintaining system reliability requires an approximate 10% increase in both installed capacity and costs. This translates into an additional 7 MW of solar PV and 6 MW–23.5 MWh of batteries in the BAU, and 6 MW and 4 MW–16 MWh in the Efficiency. Full article

The performance of large-scale photovoltaic (PV) power plants is strongly influenced by array layout parameters including module tilt angle, azimuth angle, and row spacing. These geometric variables jointly determine solar irradiance geometry, shading losses, and land-use efficiency, affecting annual energy yield and levelized cost of electricity. To achieve multi-objective comprehensive optimization of array layout parameters for a PV power generation system, a collaborative optimization strategy for PV array layout based on the lemur optimization (LO) algorithm is proposed in this paper. The method couples the Perez anisotropic irradiance model with a dynamic shading irradiance geometric model to simulate the effective insolation, incorporating land availability, shading thresholds, and maintenance access requirements. In addition, the LO algorithm is employed to solve resulting nonlinear and constrained problems, enabling an efficient global search across large parameter spaces. The case studies in Lianyungang, Dalian, and Fuzhou City show that the proposed scheme based on the LO algorithm improves annual energy yield compared with the existing optimization schemes, providing new theoretical methods and engineering application paths for the optimal layout of PV arrays. Full article

The rural community of L’Anse au Loup in southern Labrador depends on a long-distance transmission link to Hydro-Québec for its electricity supply, with diesel generation as backup during outages. This dependence raises electricity costs, exposes the community to supply disruptions, and limits control over local energy security. This study evaluates the feasibility of a solar–wind hybrid energy system to reduce imported electricity and improve supply reliability. A detailed site assessment identified a 50-hectare area north of the community as suitable for system installation, offering adequate space and minimal land-use conflict. Using Hybrid Optimization of Multiple Energy Resources (HOMER Pro 3.18.3) software, the analysis modeled local load data, renewable resource profiles, and financial parameters to determine the optimal grid-connected configuration. The optimized design installs 19.25 MW of photovoltaic (PV) and 4.62 MW of wind capacity, supported by inverters and maximum power point tracking (MPPT) to ensure stable operation. Simulations show that the hybrid system supplies about 70% of annual demand, cuts greenhouse gas emissions by more than 95% compared with conventional generation, and lowers long-term energy costs. The results confirm that the proposed configuration can strengthen local energy security and provide a replicable framework for other remote and coastal communities in Newfoundland and Labrador pursuing decarbonization. Full article

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization. Full article

The rapid adoption of hydrogen fuel cell vehicles (HFCVs) in the Beijing–Tianjin–Hebei (BTH) hub accentuates the mismatch between renewable-based hydrogen supply in Hebei and concentrated demand in Beijing and Tianjin. We develop a mixed-integer linear model that co-configures a hydrogen pipeline network and optimizes hourly flow schedules to minimize annualized cost and CO₂ emissions simultaneously. For 15,000 HFCVs expected in 2025 (137 t d⁻¹ demand), the Pareto-optimal design consists of 13 production plants, 43 pipelines and 38 refueling stations, delivering 50 767 t yr⁻¹ at 68% pipeline utilization. Hebei provides 88% of the hydrogen, 70% of which is consumed in the two megacities. Hourly profiles reveal that 65% of electrolytic output coincides with local wind–solar peaks, whereas refueling surges arise during morning and evening rush hours; the proposed schedule offsets the 4–6 h mismatch without additional storage. Transport distances are 40% < 50 km, 35% 50–200 km, and 25% > 200 km. Raising the green hydrogen share from 10% to 70% increases total system cost from USD 1.56 bn to USD 2.73 bn but cuts annual CO₂ emissions from 142 kt to 51 kt, demonstrating the trade-off between cost and decarbonization. The model quantifies the value of sub-day pipeline scheduling in resolving spatial–temporal imbalances for large-scale low-carbon hydrogen supply. Full article

Viral zoonoses or viral pathogens transmitted from animals to humans—constitute a rapidly intensifying global health and economic challenge. They are responsible for an estimated 2.5 billion illnesses and 2.7 million deaths annually, representing nearly 60% of all infectious diseases and 75% of newly emerging infections. Recent outbreaks, including Coronavirus disease 2019 (COVID-19), Ebola, Nipah, and avian influenza, underscore their capacity to overwhelm health systems, with COVID-19 alone projected to reduce global Gross Domestic Product by USD 22 trillion by 2025 and impose annual healthcare costs of USD 2–3 trillion. Beyond mortality and morbidity, zoonotic events disrupt trade, depress rural livelihoods, and inflict agricultural losses exceeding USD 100 billion per outbreak, with impacts disproportionately borne by low- and middle-income countries. Hotspot regions across tropical North and South America, Asia, and Central Africa remain especially vulnerable due to accelerating land use change, climate variability, and intensified wildlife–human interfaces. While the Global One Health Index highlights high regional heterogeneity, with sub-Saharan Africa scoring lowest, a critical gap persists between the conceptual strength of One Health and its operationalization in resource-limited settings. This review synthesizes evidence on drivers, clinical manifestations, and socioeconomic burdens of viral zoonoses, while highlighting novel perspectives on equity gaps, co-infection dynamics, and limitations of global preparedness initiatives. We argue that current strategies remain over-reliant on donor-driven agendas and insufficiently integrated across sectors. Addressing future zoonotic threats requires prioritizing surveillance in high-risk geographies, integrating epidemiological and economic data for preparedness planning, and supporting context sensitive One Health approaches that confront political, financial, and structural barriers to implementation. Full article

Utility-scale energy storage can help improve grid reliability, reduce costs, and promote faster adoption of intermittent sources such as solar and wind. This paper analyzes the technical aspects and economics of standalone microgrids operating on intermittent power combined with hydrogen energy storage. It explores the feasibility of using dibenzyltoluene (DBT) as a liquid organic hydrogen carrier to absorb excess energy during periods of high supply and polymer electrolyte fuel cells to generate electrical energy during periods of low supply. A comparative analysis is conducted on three power demand scenarios (industrial, residential, and office), in conjunction with three alternative energy sources: solar, wind and wind–solar mix. A mixed system of solar and wind energy can maintain an annual average efficiency above 70%, except for residential power demand, which lowered the efficiency to 67%. A balanced combination of wind and solar power was the most cost-effective option. The current levelized cost of electricity (LCOE) for industrial power demand was estimated to 15 ¢/kWh, and it is projected to decrease to 9 ¢/kWh in the future. For residential power demand, the LCOE was 45% higher due to the demand profile. In comparison, battery storage is significantly more expensive than hydrogen storage, even with future cost projections, increasing the LCOE between 60 and 120 ¢/kWh. Full article