Environmental and Earth Sciences

Abstract: The accelerating accumulation of atmospheric carbon dioxide (CO₂) from fossil fuel combustion represents one of the foremost environmental challenges of the twenty-first century. This paper presents the design, theoretical basis, and experimental framework of a novel artificial photosynthesis system capable of capturing CO₂ from combustion flue gases and converting it into oxygen (O₂) and energy-rich compounds, directly mimicking the biochemical process performed by trees. The proposed system integrates a sodium carbonate (Na₂CO₃) absorption tower for CO₂ capture, a thermal desorption unit for solvent regeneration, and a cobalt oxide-catalyzed photosynthetic reactor for CO₂-to-O₂ conversion. System performance is quantified using non-dispersive infrared (NDIR) sensors for CO₂ measurement and electrochemical oxygen sensors for O₂ detection. Stoichiometric analysis indicates that 1 kg of captured CO₂ yields approximately 0.73 kg of O₂, and national-scale deployment projections suggest energy savings of approximately $200 billion per year by 2030 alongside a potential reduction of 302,600 million metric tons of CO₂ emissions. Comparative analysis with existing decarbonization approaches—including carbon capture and storage (CCS), hydrogen production, and enhanced oil recovery (EOR)—demonstrates that artificial photosynthesis offers a fundamentally superior outcome by permanently transforming CO₂ into life-sustaining O₂ rather than merely sequestering or displacing it. This work establishes a laboratory-scale proof of concept and a systematic experimental roadmap for scaling the technology to industrial application.

Abstract: We propose a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the simultaneous bias correction of five hydroclimatic variables derived from the NASA POWER satellite product: minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (HR), solar radiation (Rad), and precipitation occurrence (Pbin). The GMFAMM extends the univariate functional framework by incorporating a shared latent Gaussian process Λ0i(t) that captures cross-variable thermodynamic dependence. A systematic experimental grid of more than 200 model configurations across four distributional families (Gaussian, Gamma, Poisson, Binomial), two effect structures (linear and smooth P-splines), and four nested covariate sets is evaluated on a strict chronological 70/30 hold-out – seven training years (2016–2022) and three hold-out years (2023–2025) – to identify the optimal marginal specification for each variable. The value of joint modelling is quantified through a two-stage cross-residual approximation to the GMFAMM shared latent process, which constitutes a conservative lower bound on the gains achievable by the full simultaneous model: out-of-sample RMSE is reduced by 53% for Tmin, 38% for Tmax, and 51% for relative humidity relative to the independent GAMM baseline. These gains are physically interpretable through the Clausius-Clapeyron thermodynamic coupling documented in the residual cross-correlation analysis. The trained model artefacts are deployed in ColClim, an open-access R Shiny web application that queries the NASA POWER API and the Open-Meteo forecast service for any user-selected location in Colombia, applies the GMFAMM correction pipeline, and delivers both historical bias-corrected time series and short-range (1–16 day) forecasts across the five variables.

Abstract: Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates CYGNSS Level 3 soil moisture in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023. Extended Triple Collocation was applied to CYGNSS, SMAP Enhanced Level 3, and ERA5-Land anomalies. Quadruple Collocation then used ESA CCI ACTIVE as a fourth product to quantify CYGNSS-SMAP error dependence. The standard SMAP-inclusive configuration gives CYGNSS a correlation with unknown true soil moisture of r = 0.425, an error standard deviation of 0.036m³m⁻³, and a signal-to-noise ratio of −6.56 dB. Quadruple Collocation identifies a CYGNSS-SMAP cross-error correlation of 0.325 and reduces the SMAP-independent CYGNSS estimate to r = 0.386, indicating that SMAP-inclusive validation overstates retrieval skill. Performance is weakest under dry soils (r = 0.331), where drought detection is most important, and location-level ETC convergence fails during Harmattan conditions as anomaly variance collapses. Skill is higher over cropland (r = 0.447), shrubland or grassland (r = 0.455), and moderate precipitation conditions (r = 0.630), but lower over tree cover (r = 0.342). These findings show that uncorrected CYGNSS Level 3 soil moisture is not sufficient for standalone year-round drought monitoring in Guinea savanna agriculture. Its value is strongest in bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.

Abstract: Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false positive rates across modalities, and perform a cost-benefit analysis fitting contamination scenario with the most cost effective detection method. All datasets are publicly released (https://zenodo.org/records/19100554) along with an interactive web-map (https://staging.dmsamfvv1bhon.amplifyapp.com/) to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection.

Abstract: Environmental Education (EE) and Education for Sustainable Development (ESD) play a crucial role in fostering environmentally responsible citizens and supporting the achievement of sustainability goals. This study aims to investigate primary school teachers’ knowledge, attitudes, and perceptions regarding EE/ESD, as well as the factors influencing their implementation in the educational process. A quantitative research design was employed using a structured questionnaire distributed to a sample of 500 teachers across Greece. Data were analyzed using descriptive statistics, content analysis, exploratory factor analysis, reliability testing, correlation analysis, and multiple regression analysis with the use of SPSS software. The results indicate that teachers generally demonstrate positive attitudes toward EE/ESD and recognize its importance in promoting environmental awareness, behavioral change, and students’ social development. Content analysis revealed that key environmental concerns identified by participants include pollution, climate change, and waste management, while EE/ESD is mainly associated with environmental practices and awareness. Factor analysis identified five core dimensions shaping teachers’ attitudes: (i) perceived value and impact, (ii) social and personal development outcomes, (iii) pedagogical design and evaluation understanding, (iv) institutional and structural barriers, and (v) practical implementation challenges. Significant correlations were found among these factors, particularly between perceived value and pedagogical understanding, as well as between institutional barriers and implementation challenges. Regression analysis showed that demographic and experiential variables have a modest but significant effect on perceived challenges, with age and participation in EE/ESD programs negatively associated with difficulties, while years of involvement increased awareness of implementation constraints. Overall, the findings highlight that although teachers possess a satisfactory level of awareness and positive attitudes toward EE/ESD, limited training, insufficient institutional support, and structural barriers hinder effective implementation. The study underscores the need for enhanced training opportunities, stronger policy support, and systematic integration of EE/ESD into school curricula to promote sustainable education practices.

Abstract: Accurate prediction of significant wave height (SWH) is essential for fisheries management, coastal socio-economic activities, and marine ecological conservation. In recent years, deep learning-based bias correction has shown considerable potential for improving numerical wave forecasts. However, many existing approaches are still constrained by limited receptive fields and often struggle to capture long-range spatiotemporal dependencies in wave forecast errors. To deal with this issue, we adapt and improve a video prediction framework, namely the Vision Mamba Recurrent Neural Network (VMRNN), to model and correct the spatiotemporal patterns of SWH prediction biases. Comprehensive evaluations show that the multi-channel VMRNN achieves consistently high predictive accuracy across different forecast lead times and sea-state conditions. When validated against reanalysis data, the proposed model reduces the root mean square error (RMSE) of WAVEWATCH III forecasts by 28.2%, 26.1%, and 24.7% at lead times of 24, 48, and 72 hours, respectively. It also preserves the spatial structure of SWH fields quite well, with the spatial structural similarity index remaining as high as 0.945 even at the 72-hour lead time. Regional assessments over high-wave areas further indicate that VMRNN can effectively reduce both the mean error and the systematic overestimation commonly found in numerical wave models. Additional validation using in-situ buoy observations confirms that the model has a robust ability to correct systematic positive biases, especially for wave heights ranging from 0.5 m to 2 m. Taken together, these results suggest that VMRNN has strong spatiotemporal modeling capability and can serve as a promising post-processing framework for improving operational physics-based wave forecasting systems.

Abstract:

It is known that snowcover properties change rapidly due to effect of weather and radiation, detailed models mapping effect of weather and radiation processes to evolution of snowpack have been developed. These models are capable of accurately simulating entire evolution of snowpack at a specific point if a sufficiently detailed time-series of weather and radiation parameters affecting the point is known. In this study we consider the reverse problem of finding the weather and radiation parameters that lead to changes in snowpack parameters, we have used a simulation approach to study the feasibility of finding this reverse map. We mapped a time-series of snowcover states to their corresponding time-series of weather and radiation states using a machine learning model. The data of snowcover states was generated using a well known and rigorously validated snowcover simulation model (SNOWPACK). The results of our experiments show that snow surface time-series contains important information about the meteorological time-series affecting it. We were able to find the meteorological parameters from the simulated data under certain conditions, we expect these results to generalize with actual data. There maybe important applications of these results in optimization of weather data collection systems, weather interpolation algorithms and downscaling algorithms, combining the snowpack data with weather observations can lead to improvements in these algorithms. This study makes a preliminary feasibility study of the reverse problem, our results are positive and encourage further field work using actual data.

Abstract: The transition to a circular economy requires the safe management of sewage sludge through nutrient and energy recovery. However, the presence of pharmaceuticals and personal care products (PPCPs) poses a critical obstacle, as these compounds tend to accumulate in the sludge matrix through sorption processes, thereby shifting the environmental problem from the water stream to the sludge stream. This manuscript provides a comprehensive review of the scientific literature on technical alternatives for valorizing sewage sludge and removing emerging contaminants. The study evaluates the limitations of conventional biological methods, such as anaerobic digestion and composting, which exhibit variable efficacy and are often insufficient to degrade recalcitrant molecules, including some commonly used pharmaceuticals. On the contrary, thermal treatments (pyrolysis, gasification, and hydrothermal processes) are considered robust alternatives capable of achieving removals exceeding 90-99% thanks to the thermal degradation of contaminants. Furthermore, the article emphasizes the innovative potential of utilizing carbon-based byproducts (biochar and hydrochar) as adsorbents or catalysts to enhance the removal of PPCPs within the treatment infrastructure itself. The integration of advanced thermal technologies is essential to mitigate the risks of contaminant transfer to the food chain and ensure a safe and sustainable nutrient cycle.

Abstract: This paper presents the results of research aimed at identifying deep-seated natural sealed reservoirs for the isolation of chemically active gases, including CO₂. A comprehensive analysis of geological, geophysical, and hydrogeological data was conducted to identify potential structures within the aquifers of Almaty and the Almaty region (Southern Kazakhstan) capable of capturing and storing carbon dioxide under geographically favorable and economically viable conditions. The study utilizes seismic survey results and drilling data from the eastern part of the Ili Basin, demonstrating the efficacy of seismic exploration in identifying stratigraphic horizons and their structural-tectonic settings. Based on an integrated analysis of available geo-physic information, the lithological and stratigraphic characteristics of the sedimentary cover in the Ili Basin are substantiated. Key caprock sequences and reservoir units for potential storage sites are identified, and recommendations for further geological exploration are provided. Five reflecting horizons were identified within the geological section of the troughs. It was established that the Miocene-Paleogene and Jurassic horizons contain sandstone reservoirs with a thickness exceeding 10 m and enhanced filtration properties. Clay complexes are prevalent in the Upper Jurassic deposits, which can serve as a caprock for these reservoir rocks. Furthermore, the Upper Cretaceous clay sequence may act as a fluid seal for the Neogene-Paleogene sandy horizons. Such conditions meet the requirements for sealed reservoirs for the isolation of chemically active gases, including CO₂. According to hydrogeological studies, seven aquifer complexes are distinguished: Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. The novelty and practical significance of this research lie in obtaining new information on the geological structure of deep horizons in poorly studied areas of the Ili Basin and establishing favorable geological factors for identifying potential sites suitable for carbon dioxide sequestration.

Abstract: This study characterized standard biochars produced at 300, 400, and 500 °C alongside a locally made biochar (LBC, drum kiln method with newly devised method of Bababe) to assess fertilizer value and toxicity against IBI thresholds. Pyrolysis temperature strongly influenced properties: electrical conductivity and salt content increased with tempera-ture (BC300 and BC500 highest; LBC lowest). All standard biochars were highly alkaline (pH 10.26–10.57), while LBC was near-neutral (7.84). Maximum carbon content occurred at 300–400 °C (56.8–56.9 %). At 10 kg ha⁻¹, standard biochars supplied 308–331 kg ha⁻¹ K, with BC400 providing the highest Ca and Mg. LBC had the highest volatile micronu-trients (B, Cu, Fe, Mn), which decreased with rising temperature. It can be particularly well suited to fertilizer coating or blending systems, especially for salt-sensitive soils where application rates are kept low (< 10 t ha⁻¹), thereby limiting agronomic risks such as Mo contaminant loading. Nevertheless, molybdenum levels in all biochars were 5–8 times above IBI safe limits (5–75 mg kg⁻¹), posing toxicity risk at 10 t ha⁻¹ application. Cd was undetectable, reduced Pb by 90 % at 400–500 °C, and kept Ni and Pd within limits. SEM revealed BC400 had optimal honeycomb porosity and homogeneous mineral dis-tribution. BC400 is most suitable for agricultural fertilizer value, BC500 for carbon se-questration, BC300 for potassium supply, and LBC as a low-cost, low-salinity material. However, excessive molybdenum across all biochars relates feedstock composition as the paramount safety factor. The weakness and limitation of this studies lies in the resource constraints from use of one feedstock, absence of direct measurement of surface area and phosphorus, and absence of measurement of biochar stability.

Abstract: Ammonia-oxidizing bacteria (AOB) are vital for the nitrogen cycle and wastewater treatment, yet their recalcitrance to isolation and cultivation hampers industrial application. To isolate an efficient strain and optimize its culture conditions for high-ammonia wastewater treatment, we collected water samples from a polluted river in Zhongshan City. After enrichment, a strain was isolated via gradient dilution and silica gel plating, identified by scanning electron microscopy and 16S rDNA sequencing as Nitrosomonas europaea W4 (99.93% similarity to the type strain). Single-factor medium optimization examined CaCO₃ and Fe²⁺/Fe³⁺, while temperature and initial ammonia nitrogen effects were tested, and landfill leachate was used for verification. CaCO₃ shortened the lag phase without affecting maximum specific growth rate; replacing Fe³⁺ with Fe²⁺ further reduced lag and enhanced the ammonia oxidation rate. Optimal growth occurred at 25–30 °C and an initial ammonia nitrogen concentration of ~2000 mg/L. In landfill leachate, the strain increased the ammonia degradation rate 6.3-fold. Overall, N. europaea W4 exhibits high ammonia oxidation efficiency, and the optimized medium and conditions improve its proliferation and metabolic stability, providing a basis for cultivation and application in treating high-strength ammonia nitrogen wastewater.

Abstract: This research examines how climate change intensifies urban heat stress, particularly in public spaces where mechanical cooling is impractical. A climate-driven, systems-level numerical model is developed to evaluate the pre-installation feasibility of portable, solar-powered misting canopies. Hourly Typical Meteorological Year data (TMYx, 2009–2023) are analyzed for each city to estimate photovoltaic (PV) energy yield, electrical load, potential misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. Misting operation is governed by an adaptive, rule-based control strategy based on air temperature, relative humidity, and solar radiation. To enable systematic comparison, K-means clustering is applied to classify the cities into six archetypal summer climate zones. Results indicate that evaporative cooling feasibility is driven more by ambient humidity than by air temperature. Hot-dry interior cities achieve the longest average misting duration (502.65 hours) and highest water consumption (30,486 L per module), but exhibit the lowest PV-to-load autonomy ratio (1.53) due to high energy demand for pumping. In contrast, humid Black Sea cities show minimal misting duration (13.11 hours) and water use (478 L) yet achieve the highest autonomy (40.91) because of limited system operation. It is important to note that the autonomy ratio reflects a seasonal energy balance rather than continuous off-grid capability. Overall, the adaptive control approach effectively aligns water and energy use with climatic suitability across all clusters. The proposed framework offers a scalable and quantitative screening tool to inform the design and deployment of PV-powered outdoor cooling systems across diverse urban environments.

Abstract: Remote sensing has become a key non-invasive tool in archaeological prospection, partic-ularly in regions where logistical constraints limit sustained fieldwork. This study pre-sents the results from Zar Tepe (1st–5th centuries AD), in the Surkhandarya province of southern Uzbekistan, within northwestern Bactria. The research aimed to document the site’s urban layout, accurately relocate Soviet-era excavation sectors within the pre-sent-day topography, and refine the interpretation of earlier interventions that were only partially documented and lacked precise georeferencing. A multiscale and multitemporal methodology was applied, integrating CORONA and WorldView-3 satellite imagery, UAV and terrestrial photogrammetry, GNSS Precise Point Positioning, magnetic prospection, and targeted archaeological verification. The workflow followed an iterative laboratory–field sequence, combining remote-sensing analysis, field checks, data refinement, and sys-tematic ground-truth validation. Fieldwork was conducted during two contrasting phe-nological periods, in June 2024 and December 2025, to assess seasonal variability in sur-face and subsurface visibility. The integrated approach enabled accurate spatial fitting of legacy excavation sectors and cross-validation of optical and salt-efflorescence-related anomalies with geophysical evidence. These results strengthen the interpretation of buried architectural features and provide a robust basis for reconstructing Zar Tepe’s urban or-ganization and occupational dynamics.

Abstract: The increasing presence of microplastics (MPs) in freshwater ecosystems poses significant threats to aquatic biota; yet, species-level information on the presence of MPs in Indian riverine ecosystems is scarce. This study assessed 220 fish samples from twelve species and various trophic levels for MP ingestion, organ-level accumulation, polymer type, and ecological risk at four locations along the River Yamuna in India. MPs were detected in all the studied species and organs, indicating their widespread distribution across various ecological habitats and trophic levels. A total of 1,678 MPs were quantified, which were significantly higher in fish from urban Delhi compared to upstream regions. The gastrointestinal tract had the highest MP concentrations (751), followed by gills (605) and muscle tissues (322), thus confirming ingestion as the primary route of MP uptake and their subsequent translocation into internal organs. Fibers dominated the MP community (>78%), with transparent (44%) and blue (19.5%) particles being the most abundant. ATR-FTIR analysis revealed the presence of ten different polymers, with polyethylene (≈24%) and polypropylene (≈21%) contributing to approximately 45% of MPs. Significant organ-level correlations (r/ρ = 0.635-0.958) and spatial variability (Kruskal-Wallis, H = 11.03, p = 0.011) indicated coordinated MP accumulation influenced by urban pollution. The Polymer Hazard Index analysis revealed a high PHI value (Category IV), mainly contributed by the widespread distribution of highly toxic polymers such as polycarbonate and polyimide.

Abstract: China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns: Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management.

Abstract: This paper examines how the body of research and innovation on electric cooking for low and middle income countries has evolved to the extent that electric cooking is now influencing energy system performance. Methods: The paper synthesises recent evidence from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly acting as a system-strengthening demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption improves utility and mini-grid viability and supports further investment; and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which improves load factors and aligns demand with generation. However, a persistent planning blind spot remains, with cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential.

Abstract: Soil health and irrigation water quality are fundamental to sustainable agricultural productivity, particularly in semi-arid environments. This study evaluated the influence of irrigation water quality on soil physical and chemical properties within the Kagitumba Irrigation Scheme in Eastern Rwanda. An observational analytical design integrated field sampling, laboratory analysis, and statistical evaluation. Soil samples (n = 20) were col-lected at depths of 0–30 cm and 30–60 cm, alongside irrigation water samples (n = 5) from intake and distribution points. Soil parameters analyzed included texture, bulk density, pH, electrical conductivity (EC), organic matter, and nutrient content, while water quality assessment focused on pH, EC, turbidity, dissolved oxygen (DO), and oxidation–reduction potential (ORP). Data were subjected to descriptive statistics, Pearson correlation, and ANOVA at a 95% confidence level. Findings revealed predominantly sandy loam soils with low bulk density, moderate water-holding capacity, and near-neutral pH. Soil salin-ity remained low, indicating limited risk of degradation. Irrigation water was generally suitable for agricultural use in terms of pH and salinity; however, elevated turbidity showed a strong negative correlation with infiltration rate (r = −0.73). Additionally, low soil nitrogen levels were significantly associated with water quality, suggesting nutrient leaching. These results underscore the critical role of irrigation water quality in shaping soil health and emphasize the need for improved water filtration and integrated nutrient management to enhance long-term sustainability.

Abstract: Swelling soil landslides pose severe challenges in geotechnical engineering due to non-linear deformation and strength degradation. Accurate characterisation of pore structure parameters remains the core difficulty. This study proposes a Physics-Informed Neural Network (PINNs) framework that utilises Mercury Intrusion Porosimetry (MIP) data to simultaneously invert three key physical parameters: pore fractal dimension (Ds), surface tension (γ), and contact angle (θ). By embedding the Washburn equation and fractal pore theory into the neural network loss function, the framework achieves high-precision inversion without requiring complete prior information. Validated on three expansive soil samples, the inverted Ds values were 2.47, 2.53, and 2.58, showing excellent agreement with classical models (R² > 0.99) and an average relative error below 2.3%. The inverted γ ranged from 0.476 to 0.480 N/m and θ from 142.3° to 144.2°, both satisfying physical plausibility requirements. Five-fold cross-validation confirmed the absence of overfitting (ΔR² < 0.001). Sensitivity analysis identified Ds as the dominant parameter controlling pore volume distribution; Ds exceeding 2.55 indicates elevated landslide susceptibility. This framework provides a rapid, automated approach for extracting pore structure parameters, offering parametric support for preliminary risk assessment of expansive soil slopes.

Abstract: Marginal ice zone (MIZ) is a transitional region between dense pack ice and open water. It is a highly dynamic zone under strong interactions between the atmosphere, ocean, sea ice and waves, playing a crucial role in the polar climate and ecosystem. Accurate determination of MIZ is therefore essential for advancing our understanding, modeling and prediction of the polar climate system. In this paper, we introduce and apply a suite of MIZ-related metrics to evaluate the performance of four satellite-derived high-resolution operational sea ice concentration (SIC) products in determination of the MIZs around Svalbard, using the Norwegian ice chart as reference. The metrics used for evaluation include sea ice extent (SIE), MIZ extent (MIZE), length of ice edge (LIE), integrated ice edge error (IIEE), integrated MIZ error (IME), ice edge distance error (IEDE), and MIZ width error (MWE). The evaluation is based on three years of daily SIC data (2023-2025) from four products, including the Bremen AMSR2 SIC data from the University of Bremen (Bremen SIC), the Resolution-enhanced AMSR2 SIC (RE SIC) and Multisensor SIC products (Multisensor SIC) from the Norwegian Meteorological Institute, and the Automated Sea Ice Product (ASIP) from the Copernicus Marine Environment and Monitoring Service (CMEMS) (ASIP SIC). To be consistent with the Norwegian ice chart, the MIZ is defined as MIZ70 and MIZ90, corresponding to SIC thresholds of up to 0.7 and 0.9, respectively. IEDE and MWE are calculated using two types of LIE, the reference LIE (LIEr) from the Norwegian ice chart and the average LIE (LIEa) by averaging the ice chart LIE and the concerned LIE from the four satellite products. The results demonstrate that all four satellite SIC products generally capture the evolution of the sea ice conditions around Svalbard well, but differ in their accuracy when determining the ice edge and MIZs. The Bremen SIC product tends to overlook areas with low SIC, leading to a significant underestimation of SIE and a large IIEE. However, it provides an overall close agreement with the ice chart for the MIZ90 metrics (MIZE, IME and MWE). The RE and ASIP SIC products exhibit strong performance in capturing the ice edge and total SIE, with the ASIP product particularly excelling in accurately representing the ice edge and MIZ70. The Multisensor provides the closest agreement with the ice chart for the IME90, MWE90 and MWEa70, and ranks as the second-best product for the IIEE, IME70 and MWEr70. These results suggest that SAR and Low-frequency AMSR2 channels are especially effective for capturing the lower bounds of the MIZ, while high-frequency channels are more suitable for defining the upper bounds. Despite these strengths, the complex summer surface conditions pose significant challenges for satellite sensors in determining the ice edge and the MIZ, resulting in higher IEDE and MWE values during this period. These results highlight both the capabilities and limitations of satellite-based data in determining the MIZ, particularly under challenging summer conditions. Accurate determination of MIZs may require significant advancements in satellite observation technologies, retrieval algorithms, and more robust methods for integrating multiple sources.

Abstract: The sealing behavior of fracture-filling minerals in the near-field of the deep geological repository (DGR) is critical for the safe disposal of high-level radioactive waste (HLW). In granite host rocks, natural fractures are often filled with polymineralic assemblages of calcite, quartz, and clay minerals; however, their coupled compaction–pressure solution mechanisms under thermal–hydraulic–mechanical–chemical (THMC) conditions remain poorly understood. In this study, 12 fracture sealing experiments were conducted on Beishan granite and its typical fracture fillings at 90 °C and 15 MPa effective stress, using different pore fluids and systematically varying grain size (75–250 μm), mineral proportions, and clay content. The results demonstrate that pressure solution–precipitation of calcite in saturated CaCO3 solution is the key mechanism for long-term fracture sealing, achieving a compaction strain of 24.6%—substantially higher than those obtained in deionized water (20.6%) and under dry conditions (14.8%). Fine-grained calcite compacts more effectively than its coarse-grained counterpart, reaching a porosity as low as 4.8%; rigid quartz accelerates calcite pressure solution via stress concentration at grain contacts; and a moderate amount of clay minerals (~20 wt%) further reduces porosity to 2.1% through lubrication and micropore filling. The study reveals a multi-stage process transitioning from mechanical compaction to pressure solution–precipitation, and a synergistic sealing mechanism dominated by calcite compaction–pressure solution, augmented by quartz stress transfer and clay lubrication. These findings revise the traditional monomineralic understanding and provide a scientific basis for safety assessment of HLW disposal and the design of natural sealing materials.