Search Results (143)

Flood susceptibility maps are essential tools for identifying high-risk areas. However, traditional approaches often face limitations in spatial resolution and adaptability under changing climatic conditions, particularly in data-scarce regions. This study addresses these limitations through a data-driven geospatial approach that integrates high-resolution Earth Observation and Geographic Information Systems (GIS) data to improve flood susceptibility assessment in a small river basin in Romania. Ten flood conditioning factors were analyzed, including Elevation, Slope, Topographic Wetness Index (TWI), Topographic Position Index (TPI), Profile Curvature, Aspect, Soil Texture, Distance to the River, Normalized Difference Vegetation Index (NDVI), and Soil Moisture. Historical flood extent data extracted from PlanetScope imagery were used for model training and validation. Two statistical methods, Frequency Ratio (FR) and Weight of Evidence (WoE), were applied to map flood susceptibility at a 12.5 m resolution. Results indicate that both models captured the spatial variability of flood-prone areas, but WoE achieved higher predictive performance (AUC = 0.945) than FR (AUC = 0.876), while FR tended to underestimate flood-prone zones. Half of the basin falls within low to very low susceptibility classes, whereas high and very high susceptibility together occupy about 25–29% of the basin and concentrate along river corridors in the central and southern sectors, overlapping with built-up areas. Consequently, about 38% (WoE) and 30% (FR) of the total built-up area fall within high and very high susceptibility classes. The results demonstrate that integrating high-resolution open-source Earth Observation data with statistical modeling provides a reliable, transferable framework for flood susceptibility assessment and land-use planning in data-scarce environments. Full article

The operationalization of the One Health (OH) approach remains a major challenge due to persistent fragmentation across human, animal, and environmental data systems. This gap is exacerbated by climate change, which acts as a risk multiplier for pathogen transmission and agri-food system vulnerability. Drawing on more than a decade of research, including the re-emergence of brucellosis in Italy and the 2024 Salmonella Umbilo outbreak, this perspective discusses key weaknesses in current data management, particularly the lack of real-time, interoperable data sharing. To address these challenges, we propose an AI-enabled One Health Information System (OH-IS), grounded in FAIR data principles and privacy-preserving architectures. The proposed conceptual framework integrates multi-matrix data streams, combining Earth observation data, genomic surveillance through whole-genome sequencing (WGS), and livestock mobility within a geospatially integrated architecture to support timely decision-making in vulnerable settings. By analyzing the constraints of siloed databases, we discuss how automated semantic harmonization could conceptually support improved risk assessment and outbreak reconstruction in recent zoonotic events. This approach may facilitate a transition from descriptive to anticipatory surveillance, providing a scalable model to move One Health from a conceptual paradigm toward a more integrated and data-driven surveillance framework aligned with EU digital health policies and global health security priorities. Full article

With the increase in operation time of DC traction systems due to the environment of tunnel and stress rupture, the insulation between the rail and ground inevitably decreases, causing increased stray current leakage. In view of this, we present an analytical and electrochemical study of stray current behavior and its corrosion impact arising from local rail-to-ground insulation damage in DC urban rail systems. A two-layer rail–earth continuous model of stray current distribution is developed (unilateral and bilateral supply cases) using Kirchhoff network formulations with insulation damage boundary conditions. Numerical simulations quantify the effects of damage location and grounding resistance on rail potential shifts, abrupt changes in rail and stray currents, and total leakage. To assess electrochemical consequences for nearby buried pipelines, the electrical model is proposed in this work with an impedance-informed corrosion model and Monte Carlo sampling of operational and electrical uncertainties to estimate dynamic corrosion rates and pitting evolution. The results show that single–point insulation faults shift the rail zero potential toward the fault, leading to instantaneous jumps in leakage and rail currents whose magnitude grows as damaged-point resistance decreases, markedly increasing pipeline corrosion risk. The integrated electrical-electrochemical framework provides a tool for detection, risk assessment, and mitigation planning for stray current-induced pipeline corrosion. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

Environmental contamination by rare earth elements (REEs) is increasing globally due to their extensive use in modern technologies, medicine, agriculture, and aquaculture. Their release into aquatic systems via wastewater discharge, industrial emissions, surface runoff, and atmospheric deposition has raised concerns regarding their environmental fate and potential ecotoxicological effects. Despite this, information on REE accumulation in marine predators remains limited. This study provides a multi-species assessment of REE bioaccumulation in elasmobranchs. Concentrations of 14 REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, and Yb) were quantified in liver and muscle tissues of six elasmobranch species collected from demersal and deep-sea habitats along the Portuguese continental shelf. Generalized linear models (GLMs) were used to evaluate differences in REE concentrations among species and tissues, and to explore potential patterns associated with ecological traits. Results indicated that REE concentrations varied significantly across tissues and species, with muscle generally exhibiting higher accumulation than liver. Overall, this study provides the first comprehensive baseline of REE bioaccumulation in elasmobranchs from the Portuguese coast, contributing to a better understanding of emerging contaminants in marine food webs. These findings have important implications for environmental biomonitoring and highlight potential risks associated with seafood consumption. Full article

To reveal the overtopping dam-break mechanism under extreme rainfall conditions and assess downstream flood risk, a series of dam-break flume tests, flood routing simulations and inundation risk assessments were conducted. Using the Hubuling Reservoir in Rizhao City, Shandong Province as a case study, a circulating extreme rainfall dam-break flume system with a controllable reservoir water level was constructed at a geometric similarity scale of 1:70. Four test conditions were designed: no rainfall and 50-year, 100-year and 2000-year rainfall return periods. Pore water pressure, earth pressure and water content sensors were embedded in critical dam sections to monitor real-time internal dynamic responses. The results show that, due to the combined effect of the highest rainfall intensity, rapid reservoir water-level rise, progressive infiltration-induced weakening and concentrated surface erosion, a dam-break occurs only under the 2000-year rainfall return period. The failure process is divided into four stages: initial infiltration, slope surface scour, overtopping initiation and rapid breach development. Based on dam-break parameters obtained by physical model tests, a two-dimensional numerical using HEC-RAS was conducted. The results show that, under the 2000-year rainfall return period, the flood reaches the downstream area at 80 min after dam failure. The maximum inundation area reaches 15.20 km² at 200 min, with a maximum inundation depth of 11.80 m and a maximum inundation duration of 144 h. By integrating the maximum inundation depth, inundation duration and land use conditions, the expected economic loss is estimated to be 690 million yuan. The results provide important support for dam-break early warnings, emergency management and disaster mitigation of similar small- and medium-sized reservoirs. Full article

With the rapid development of the world economy in recent years, the mineral resources in the shallow part of the Earth can no longer meet the needs of mankind, and resource development is continually moving into the deep part of the Earth. The filling method, in particular, has gradually been employed for the treatment of mining goaves in response to the requirements of environmentally sustainable development. As an important part of the filling system, the wear and tear of the deep filling pipeline is of great significance to the normal operation of the system, the safe production of the mine and the reasonable protection of the environment. In this paper, the wear situation of the deep well vertical filling pipeline is selected as the analysis angle to carry out the research, and the risk assessment model is constructed by combining the distance discriminant weighting method and the cloud model for the uncertainty of the wear risk grading assessment. Firstly, 11 quantitative factors and three qualitative factors were selected as evaluation indicators and employed as cloud model variables. Appropriate cloud model digital features were picked based on cloud model theory, to construct the cloud model of each indicator. Secondly, the distance discriminant weighting method is introduced to obtain the weight of each indicator, and the risk level of the vertical filling pipeline in deep wells is obtained by calculating the comprehensive certainty of the risk evaluation object belonging to different levels. Finally, the model was applied to Dongguashan Copper Mine, and the model calculation results were analyzed and compared with the actual wear situation. The results show that the model transforms the ambiguity and randomness into a certain quantitative value of certainty, completing the map from qualitative to quantitative. This method introduces a new idea and method for dealing with similar problems of vertical filling pipes in deep wells. Full article

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Water electrolysis is a key technology for sustainable hydrogen production and a cornerstone of future low-carbon energy systems. However, large-scale deployment is constrained not only by efficiency and cost, but increasingly by the sustainability and availability of materials used in electrocatalysts and membranes. This review provides a materials-centric assessment of state-of-the-art and emerging electrocatalysts for alkaline (AEL), proton exchange membrane (PEM), and solid oxide electrolysis (SOEC) technologies, emphasizing the interdependence of performance, durability, cost, and sustainability. Electrocatalyst activity and stability are linked to cell- and stack-level efficiency, energy demand, and the levelized cost of hydrogen. Life cycle assessment (LCA) and resource criticality analyses are integrated to quantify environmental impacts, supply risks, and recycling potential of key materials, including platinum group metals, nickel, rare earth elements, and ceramic oxides. Particular attention is given to recycling and circularity strategies, which are essential for mitigating material scarcity and reducing upstream emissions, especially in PEM electrolyzers. Emerging catalyst concepts such as single-atom catalysts, high-entropy alloys, and noble-metal-free systems are discussed as promising pathways to reduce critical material dependence. The review concludes by highlighting the need for integrated material–technology–system approaches to enable efficient, scalable, and truly sustainable hydrogen production. Full article

Urban flood risk management in southern Chile is often constrained by fragmented territorial information, discontinuous hydrological records, and weak integration between hazard assessment and formal land-administration systems. These limitations are particularly evident in persistently cloudy cities such as Temuco, where optical satellite imagery is frequently unusable. This study examines how satellite-derived flood observations can be incorporated into municipal land-administration practices. Flood-prone areas were identified using multitemporal Sentinel-1 SAR imagery (2018–2025) and integrated into a municipal multipurpose cadastre structured according to the ISO 19152 Land Administration Domain Model (LADM). Rather than remaining as standalone GIS maps, detected inundation areas were translated into standardized cadastral entities representing spatial units and hazard-related planning constraints. The analysis identified recurrent flooding along the Cautín River floodplain, characterized by strong winter seasonality and increasing exposure linked to urban expansion. More importantly, the results demonstrate that satellite-based hazard observations can be structured as interoperable administrative information with defined semantics, temporal validity, and traceable data sources. The proposed framework enables flood information to support territorial planning, emergency preparedness, and municipal risk management without altering property legal status. By linking Earth observation data with cadastral information infrastructures, the study provides a replicable approach for integrating environmental observations into land-administration systems in regions affected by institutional fragmentation and recurring hydrometeorological hazards. Full article

Rare earth elements (REEs) hold great importance in the transition to a low-carbon economy. However, their increased exploitation, supply risks, low recyclability, and limited substitution by other elements have led to their classification as critical and strategic materials. The extraction of REEs from primary mining sources generates several negative environmental impacts, with greenhouse gas emissions being among the most significant. These emissions are quantified through Life Cycle Assessment (LCA) under the Global Warming Potential (GWP) category. Recycling REEs from secondary sources has emerged as a promising alternative to reduce mining dependence and environmental impacts. Nickel–metal hydride (NiMH) batteries contain approximately 5–10% REEs and represent a potential secondary source through urban mining. Our literature review presents a comparative analysis of the carbon footprint associated with the extraction of REEs from primary sources (bastnäsite and monazite), expressed per tonne of rare earth oxides (REO) produced, and with industrial-scale recycling processes of NiMH batteries, expressed per tonne of recovered REE mixture. The analysis indicates that CO₂ emissions associated with recycling processes (85–179 kg CO₂-eq per tonne of REO) are approximately 4 to 9 times higher than those reported for primary extraction routes; however, this comparison should be interpreted with cautiously, as recycling systems are multifunctional and involve the simultaneous recovery of additional metals such as Ni and Co, whereas primary mining operations are typically focused exclusively on REEs. Furthermore, differences in functional units, energy mixes, and geographical contexts limit the strict comparability of the results. Accordingly, a direct comparison based solely on REEs may overestimate the environmental burden of recycling. Consequently, the reported emission ranges provide an indicative perspective on relative magnitudes under current technological and regional conditions rather than a definitive comparative assessment. Despite the higher reported emissions, recycling should not be regarded as environmentally detrimental; it also plays a vital role in mitigating supply risks and reducing dependence on primary extraction. By diversifying supply sources, recycling enhances resource security and resilience. Full article

Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this research gap in the Bonoumin-Palmeraie watershed (Abidjan, Côte d’Ivoire) by developing an integrated approach leveraging remote sensing, Geographic Information Systems (GIS), and the Random Forest algorithm to assess and map flood susceptibility. Twelve conditioning factors related to topography, hydrology, land use, and climate were derived from Sentinel-1, ALOS PALSAR, and multi-source earth observation datasets. Historical flood extents were mapped in Google Earth Engine to train the Random Forest model in a Google Colab environment. The model demonstrated high discriminatory power, yielding an Area Under the Curve of 0.94 and Overall Accuracy of 0.83. Drainage density, rainfall, and altitude were identified as the primary explanatory drivers. The resulting flood susceptibility map indicates that 39% of the watershed exhibits medium to very high susceptibility, with critical hotspots in the neighborhoods of Palmeraie, Attoban, Akouedo, Djorogobité, and Riviera-Sogefiha. While limited by the exclusion of certain anthropogenic variables and ground truth constraints, the study provides a reproducible, data-driven framework for flood risk assessment in tropical urban environments. These findings offer essential scientific support for urban planners and decision-makers to enhance territorial planning and sustainable flood management in Abidjan. Full article

The Z.D. granite weathering crust rare earth deposit in Ganzhou, China is a world-class resource. In situ leaching extraction may mobilize potentially toxic elements (PTEs) into surrounding soils. This study analyzed nine PTEs (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) in top soils within and around the mining area. A multi-method approach was employed, integrating geochemical assessment, pollution and ecological risk indices, and probabilistic health risk evaluation via Monte Carlo simulation and source apportionment using Positive Matrix Factorization (PMF) combined with Geographic Information System (GIS) analysis. Results indicated generally low background levels, though with localized Pb enrichment, and an overall low level of pollution and ecological risk. However, for children in nearby areas with prolonged exposure, there was a 9.11% probability of non-carcinogenic risk and a 13.82% probability of carcinogenic risk. PMF-GIS analysis traced PTEs to four sources: natural parent material, industrial emissions, mining/riverine transport, and agriculture. In conclusion, while current soil environmental risks in the Z.D. mining area remain manageable, the study underscores the need to monitor progressive PTE accumulation and children’s health risks. This work provides a scientific basis for targeted soil management and ecological restoration in rare earth mining regions. Full article