Search Results (712)

In the Iberian Pyrite Belt (IPB), long-term persistence of mine waste piles poses environmental challenges. The present work studies the Trimpancho Mining Complex in northern IPB with exposed mine waste and acidic waters in the proximity to the Chança River, a tributary of the Guadiana international river. A multidisciplinary approach is proposed, using hyperspectral reflectance spectroscopy, portable X-ray fluorescence (pXRF), multispectral Unmanned Aerial Vehicle (UAV) and Sentinel-2 images. Spectroscopic, geochemical and remote sensing methods were applied to characterise the mining area. Comparison of hyperspectral data with spectral libraries were used to validate mineralogy. Multispectral UAV data is used for custom band-ratios and adapted to Sentinel-2 images. Results grouped the samples into four groups. Spectroscopy is indicative of clays (white mica and smectite group), hematite/goethite, jarosite, and arsenopyrite and pyrite (exclusive to the Group 2); iron-rich samples reach maximum reflectance earlier than iron-poor samples. Geochemical studies show an increase in content of heavy metal such as As, Cu, Fe, Pb, and Zn from Group 1 < Group 3 ≈ Group 4 < Group 2, but Group 4 showed elevated Pb and Zn. Custom false colour composition highlighted the groups in UAV and satellite, thus constituting cost-effective tools for finding contamination sources. Full article

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

Rice cultivation commonly employs the continuous flooding (CF) method, which depends heavily on water availability creating anaerobic conditions for methane (CH₄) emissions. Rainfed rice areas rely on precipitation for irrigation, making the system sensitive to climatic variability. This study examines associations between ENSO phases and satellite-observed atmospheric XCH₄ variability over Thailand using GOSAT as the primary long-term dataset from 2012 to 2022, with Sentinel-5P/TROPOMI used as a supporting dataset for recent spatial patterns. The analysis conducted covers three cropping seasons: (1) January–April, (2) May–August, and (3) September–December. The results indicate comparable average atmospheric methane concentrations of 1787.94 ± 11.50 XCH₄ (ppb) during El Niño, 1788.8 ± 11.22 XCH₄ (ppb) in neutral conditions, and 1793.45 ± 10.93 XCH₄ (ppb) during La Niña. The obtained data indicate a seasonal variability, with the highest satellite-observed XCH₄ values found during September–December, corresponding to the main growing period of wet-season rice. The results suggest that climate change amplifies these anomalies through altered precipitation patterns and water availability. Current rice cultivation practices warrant reconsideration, in particular the alternate wetting and drying (AWD) method, offering reduced CH₄ emissions while conserving water resources. This underscores the importance of water management strategies for sustainable rice production and resilience to climate variability. Full article

Olive (Olea europaea L.) orchards on the hyper-arid Peruvian coast (Tacna, ${18}^{\circ }$ S) suffered >70$\%$ yield collapses in the 2016 and 2024 El Niño seasons against a non-failure mean of $\sim 6$ t ha⁻¹ and a 2022 La Niña bumper harvest, raising the question of whether insufficient winter chilling is the binding climate constraint. We combined in situ daily meteorology (2015–2025) with yield records from eleven Sevillana–Ascolana parcels (88 parcel-years over eight seasons) and fitted a year-level log-OLS model with mean chill-window and fruit-growth temperatures, validated by year-block bootstrap, permutation, a closed-form Bayesian posterior, and a parcel-year mixed model. The model achieves $R_{log}^2=0.65$, and the chill slope ($\beta =-0.82$) is robust across three independent tests: one-sided permutation $p=0.036$; Bayesian posterior with 99.8% of mass below zero (Savage–Dickey BF₁₀ = 15.9); parcel-year mixed model $p<{10}^{-14}$. Counterfactual restoration of chill-window temperature to its non-failure climatology recovers the full collapse in both years, whereas restoring fruit-growth temperature recovers nothing. CMIP6 delta-method projections identify a chill-collapse threshold at $\Delta T_{\mathrm{winter}}\approx +{1.25 }^{\circ }$C; SSP1-2.6 alone reduces mid-century mean yield by $\sim 52\%$, and SSP5-8.5 reaches $-89\%$ by 2051–2070. Tacna emerges as a chill-sentinel system where winter warmth, not summer heat, is the binding constraint and the transition to the failure regime lies on a near-term adaptation horizon. Full article

Water hyacinth (Eichhornia crassipes) is among the world’s most invasive aquatic macrophytes, yet quantitative models of shoreline preference remain absent for Lake Victoria. This study developed a distance-based quantitative framework for spatial distribution and decay modelling to quantify seasonal nearshore accumulation dynamics in Winam Gulf, Kenya, using Sentinel-2 imagery. A Support Vector Machine classifier with polygon-mean feature extraction achieved 94–96% accuracy, supported by strong spectral separability (Jeffries–Matusita distance > 1.9 in six bands). During peak dry season, water hyacinth covered 405.81 km² (27.1% of gulf area) and occurred significantly closer to shore than open water (mean preference = 687.9 m; 95% CI: 616.6–753.7 m; p < 0.001). Water hyacinth was 3.10 times more likely than open water to occur within 100 m of shoreline, with 48% of biomass concentrated within 2 km. A power-law decay model of odds ratio with shoreline distance provided superior fit (R² = 0.870, F = 10.06, p = 0.047) compared to exponential decay (R² = 0.477, p = 0.378). Critically, pronounced nearshore preference occurred only during dry-season conditions (+687.9 m to +1946.6 m), while wet–dry transition periods showed no significant preference (−124.2 m; p = 1.00), supporting wind-driven Stokes drift as the dominant transport mechanism and enabling seasonal prioritization of nearshore management interventions. Full article

The purpose of this work is to investigate the use of high-resolution ($~100$ m) surface soil moisture (SSM) maps derived from Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify irrigation events occurring in the Riaza irrigation district (Castilla y León region, Spain) from 2017 to 2021. The proposed method is based on the application of the Constant False Alarm Rate (CFAR) algorithm, which is an adaptive and unsupervised thresholding algorithm traditionally used for target detection in SAR images. This algorithm uses a sliding window approach that allows an adaptive threshold estimate for each pixel of the image, depending on the distribution of the surrounding pixels. The analysis was carried out on fields cultivated with maize, sugar beet and sunflower. Results show that the Overall Accuracy (OA) of the detection mainly depends on the time span (TS) between the S-1 passage and the irrigation event, the acquisition timing and the development stage of the vegetation. Indeed, the OA reaches a mean of $78\%$ and $70\%$, respectively, for the $6$ a.m. and $6$ p.m. acquisitions, when the irrigation events occur within $36$ h before the S-1 passage, and it follows a downward trend as the TS increases. On the other hand, when the vegetation reaches the mature stage, the mean OA decreases respectively to $56\%$ and $52\%$. Stemming from the event detection, the study explored the estimation of the total irrigated area in the early growing season, showing promising agreement with in situ data, as evidenced by the low Relative Error ($E_r\approx 5.6\%$). Additionally, the analysis revealed a significant correlation between field-scale mean SSM and irrigation depths ($R=0.89$). Full article

Ground deformation monitoring is critical for safety and environmental management in modern mining. Active mining sites are highly exposed to terrain instabilities and subsidence, risking infrastructure integrity, disrupting operations, and posing hazards to communities. In this context, Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques provide an effective and non-invasive tool capable of detecting millimetric surface displacements. This study implements the Small Baseline Subset (SBAS) technique through an open-source workflow based on the Python package hyp3_sbas, enabling semi-automated and reproducible interferometric processing by combining HyP3 with MintPy. The workflow is applied to the Björkdal gold mine (Sweden), a pilot site of the Horizon Europe XTRACT project focused on enhancing resilience in critical raw material supply chains. Integrating Sentinel-1 viewing geometries resolves the true vertical deformation field, yielding an overall mean velocity of −3.99 mm/year across the mining complex, with significant displacement rates concentrated below the 25th percentile (Q1) at −11.07 mm/year. Sector-specific analysis reveals localised subsidence accelerating over underground footprints and tailings storage facilities (mean velocities of −6.56 and −3.98 mm/year; Q1 thresholds near −13.00 mm/year), contrasting with the geomechanical stability observed at the open-pit area (mean: −0.45 mm/year). The proposed open-source framework shows strong potential for operational satellite-based monitoring, supporting predictive maintenance and early-warning strategies for risk management in mining environments while simplifying and standardising the interferometric processing workflow. Full article

The large-scale mobility restrictions implemented worldwide in response to the COVID-19 (SARS-CoV-2) pandemic led to short-term reductions in anthropogenic emissions, providing an opportunity to explore atmospheric pollutant responses to large-scale changes in human activity and mobility patterns. Although numerous studies have reported air quality improvements during lockdowns, most rely on ground-based monitoring networks and focus on developed regions, leaving gaps in less-studied areas such as Central America. This study evaluates spatiotemporal changes in tropospheric nitrogen dioxide (NO₂) across Central America before, during, and after COVID-19 lockdowns using satellite-based remote sensing. High-resolution NO₂ vertical column density (VCD) data from the TROPOMI instrument onboard Sentinel-5P were processed using Google Earth Engine. Percentage variations were calculated using the March–May 2020 lockdown period as a reference within the 2019–2021 analysis period. Results indicate reductions in NO₂ across several high-density departments, particularly in Guatemala, El Salvador, and Honduras, with decreases of 20–30% and localized negative variations below −40%. In contrast, Nicaragua exhibited comparatively limited changes, while a gradual recovery in NO₂ concentrations was observed during 2021. The observed patterns suggest a potential association between NO₂ variability and changes in anthropogenic activity during the COVID-19 period, while also highlighting the importance of considering meteorological influences in regional atmospheric assessments. The results further demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring in data-scarce tropical regions. Full article

Digital soil mapping (DSM), based on multimodal satellite data, is a crucial tool for the transition to precision agriculture. However, systematic studies using this method and machine and deep learning techniques are lacking for the arid and semi-arid regions of Central Asia, where multimodal satellite data can provide valuable insights into soil conditions. This work provides, for the first time, benchmark metrics for the predictive ability of six soil agrochemical properties (pH, Soil Organic Carbon, NO₃, P₂O₅, K₂O, and S) in the dry steppe zone of Central Asia, with a quantitative assessment of the difference between “standard” and “fair” validation strategies. This has methodological significance for the entire field of DSM research. A comprehensive comparison of 11 machine learning (ML) models and four deep learning (DL) architectures was conducted to predict soil agrochemical properties using a set of 530 features extracted from various satellite datasets. These features were extracted from Sentinel-2, Landsat-8, Sentinel-1 SAR, SRTM DEM, and ERA 5-Land using Google Earth Engine (GEE) automated pipeline. All models were evaluated using three spatial validation strategies with increasing stringency: Leave-One-Field-Out CV (LOFO-CV), Leave-One-Farm-Out CV (Farm-LOFO), and an optimized spatial split. We propose a three-level hierarchical validation scheme that allows for the quantitative separation of spatial leakage and feature selection leakage, a methodology that can be applied to any spatial ML problem. Local models have been shown to outperform the global SoilGrids v2.0 product in terms of accuracy, demonstrating the need for high-resolution regional models for precision agriculture. Local models outperformed SoilGrids v2.0 by 3.6× in Spearman ρ for pH (0.750 vs. 0.208), quantitatively confirming the necessity of regional calibration over global soil products. Multi-season ConvNeXt with SE-blocks on 54-channel composites improved R² for NO₃ by 36% (0.422 → 0.575), confirming the value of temporal dynamics for mobile elements; however, it underperformed RF on tabular features for most properties at the available sample size (n = 1085). Full article

Atmospheric correction is a critical preprocessing step in optical remote sensing, but repeated high-fidelity radiative transfer simulations remain computationally expensive for dense look-up-table generation, sensitivity analysis, retrieval support, and operational preprocessing. This study presents a physics-guided multi-fidelity surrogate framework for emulating atmospheric correction coefficients using paired 6S and libRadtran simulations. Atmospheric and geometric states are sampled using Latin Hypercube Sampling, and both radiative transfer models are evaluated under matched conditions for Sentinel-2 bands using spectral-response-function-aware coefficient generation. The high-fidelity targets are path reflectance, total transmittance, and spherical albedo. A physics-guided Kolmogorov–Arnold Network, termed pKANrtm, receives the atmospheric state and low-fidelity 6S coefficients, predicts the residual relative to libRadtran, and reconstructs the high-fidelity coefficients. The pKANrtm model uses an Efficient-KAN architecture and is trained with a physics-guided penalty applied in the original coefficient space. The proposed model is evaluated against state-of-the-art regression-based RTM surrogates. Across both standard and out-of-distribution (OOD) evaluation settings, pKANrtm achieves the strongest overall predictive performance among the compared models. Band-wise analysis shows that most Sentinel-2 bands are accurately emulated, while absorption-sensitive bands remain comparatively challenging. Runtime benchmarking demonstrates substantial acceleration relative to libRadtran, with GPU inference providing approximately four orders of magnitude single-sample speedup and batched inference reaching tens of thousands of samples per second. As an initial real-scene validation, the trained pKANrtm correction was applied to a Sentinel-2A acquisition over the Gobabeb RadCalNet site, demonstrating that the learned residual correction improves downstream surface-reflectance retrieval beyond synthetic RTM-to-RTM coefficient emulation. These results indicate that physics-guided multi-fidelity pKANrtm emulation provides an accurate, physically structured, computationally efficient, and practically useful strategy for atmospheric correction coefficient generation. Full article

Titanium dioxide nanoparticles (TiO₂-NPs) are widely produced and persist in aquatic ecosystems, yet their indirect effects on host–microbe interactions remain poorly defined. By using zebrafish (Danio rerio) as a sentinel species, this study investigated the effects of subchronic 5 mg/L TiO₂-NP exposure. Dynamic light scattering was utilized to characterize the bimodal aggregates (peaks at 917 and 46,841 nm; surface charge: +22.08 mV) that define the environmental state of TiO₂-NPs. Parallel 16S rRNA metagenomic profiling on Day 6, prior to mortality, revealed profound gut dysbiosis. A marked increase in Chao1 richness (p < 0.01), alongside a catastrophic 333-fold reduction in beneficial Cetobacterium and an 856-fold enrichment of pathogenic Mycobacterium, was observed. Beta-diversity and hierarchical clustering analyses revealed a striking convergence between gut and gill microbial signatures, supporting a gut-to-gill translocation model. These results suggest that TiO₂-NPs exposure induces intestinal dysbiosis, facilitating opportunistic bacterial migration via internal (gut–blood–gill) or external (fecal–water–gill) pathways. This study identifies dysbiosis-driven secondary infection as a novel, overlooked mechanism of nanoparticle toxicity, necessitating a shift in ecological risk assessments toward host–microbe interactions. Full article

Background/Objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease in which alterations in the gut microbiota and dietary lipid composition play a central role; this study aimed to evaluate the effects of synbiotics, omega-3 polyunsaturated fatty acids, and their combination on clinical, macroscopic, microbiological, and histopathological outcomes in dextran sodium sulfate (DSS)-induced colitis in Wistar rats. Methods: Seventy-two male Wistar rats were randomly allocated to four groups (n = 18/group) and received 5% DSS in drinking water for eight days to induce colitis. Following DSS withdrawal and histological confirmation of colitis in sentinel animals, groups were treated for 8 days as follows: DSS (control), DSS-S (synbiotics, Ecologic^® 825), DSS-Ω3 (omega-3 fatty acid-enriched diet, ProSure^®), or DSS-S&Ω3 (combined therapy). Eight rats per group were sacrificed on days 4 and 8 post-DSS. Body weight, Disease Activity Index (DAI), distal colon length, hematologic parameters, bacterial translocation to the liver and mesenteric lymph nodes, histological colitis score, and myeloperoxidase (MPO)-positive cell counts were assessed. Results: DSS induced severe colitis characterized by diarrhea, rectal bleeding, and extensive mucosal erosions. After 8 days of treatment, the DSS-S&Ω3 group showed the greatest body-weight recovery (206.1→222.9 g, p < 0.05 vs. other groups), significantly preserved distal colon length, and the largest reduction in DAI (p < 0.05). Both the DSS-S and DSS-S&Ω3 groups demonstrated reduced bacterial translocation compared with DSS. The DSS-Ω3 group demonstrated persistent MPO-positive neutrophil infiltration compared with the DSS-S and DSS-S&Ω3 groups, whereas combined therapy was associated with lower MPO-positive cell counts. Histological colitis scores were significantly improved only in the DSS-S&Ω3 group (p < 0.05). Conclusions: In this DSS colitis model, the DSS-S&Ω3 group demonstrated superior clinical and histological outcomes compared with DSS-S or DSS-Ω3 alone, supporting further evaluation of combined synbiotic and omega-3 therapy as an adjunctive approach in ulcerative colitis. Full article

The Neoproterozoic Wadi Mahasin metavolcanics (WMVs) in the Central Eastern Desert, Egypt, were remapped using Landsat-8 and Sentinel-2 imagery and verified by field observations, and their petrogenesis was evaluated using petrography, whole-rock geochemistry, and Pb isotopes. The image processing techniques of decorrelation stretch (DS), band ratios (BR), principal component analysis (PCA), and Minimum Noise Fraction (MNF) were applied to three remotely sensed datasets from Landsat-8, Sentinel-2B, and Planet to produce an updated geologic map of the study area. Moreover, two robust supervised classification techniques, maximum likelihood (MLC) and the support vector machine (SVM), enhanced geological contacts, structural elements, and produced classified images by 95.68% and 96%, respectively. The WMV suite comprises metadacite and metarhyolite with SiO₂ contents of 61.8–66.5 and 77.8–79.8 wt.%, respectively, and belongs to a subalkaline calc–alkaline series with a transitional medium- to high-K character at the felsic end. Primitive mantle-normalized patterns show enrichment in LILEs (Rb, U, K, and Pb) and depletion in Nb, Ta, Ti, and P, consistent with subduction-related felsic magmatism. Chondrite-normalized REE patterns are characterized by enriched LREEs, flat to weakly fractionated HREEs ((Gd/Yb)_N ≈ 1.5), and negative Eu anomalies (Eu/Eu* = 0.30–0.81). The flat HREE segment suggests melting of a garnet-free source, most plausibly a plagioclase–amphibole-bearing crustal assemblage. Eu/Eu* correlates positively with Sr for the suite as a whole, indicating plagioclase control during differentiation. Metarhyolite samples form a tightly clustered evolved group, whereas metadacites show broader scatter that mainly reflects differentiation. Pb isotopes and crust-like trace-element ratios (high Y/Nb, low Ce/Pb, and low Nb/U) indicate strong crustal involvement. Although assimilation–fractional crystallization from a mantle-derived parent magma cannot be excluded completely, the available isotopic data do not define a simple mantle-to-crust differentiation trend, and the uniformly evolved major- and trace-element signatures favor direct partial melting of felsic continental crust, followed by limited fractional crystallization. The WMV suite is, therefore, interpreted as a mature continental-arc felsic assemblage within the Arabian–Nubian Shield. Full article

Background: Molecular classification has transformed risk stratification in endometrial cancer, providing prognostic information beyond traditional clinicopathologic features. However, the relationship between molecular subtype, nodal involvement, and recurrence risk remains incompletely defined. This study aimed to compare lymph node metastasis rates across molecular subgroups and evaluate survival outcomes and prognostic factors for recurrence. Methods: We conducted a retrospective study including 158 patients with a preoperative diagnosis of presumed early-stage endometrial carcinoma treated surgically between 2021 and 2024. Molecular classification was performed according to WHO criteria, including POLE-ultramutated, mismatch repair deficient (MMRd), p53-abnormal (p53-abn), and no specific molecular profile (NSMP). Sentinel lymph node biopsy (SLNB) was the primary method for nodal staging. Survival outcomes were assessed using a Kaplan–Meier analysis, and logistic regression was used to identify prognostic factors for recurrence. Results: NSMP was the most frequent molecular subtype (44.3%), followed by MMRd (29.1%), p53-abn (20.9%), and POLE-mutated tumors (5.7%). Overall, 11.4% of patients had nodal metastases, most commonly in the p53-abn subgroup, which showed significantly higher rates of positive sentinel lymph nodes (p = 0.010). Prognosis differed significantly across molecular subtypes. POLE-mutated and NSMP tumors demonstrated the most favorable outcomes, while p53-abn tumors showed the poorest overall survival and progression-free survival. In a univariate analysis, grade, lymphovascular space invasion (LVSI), myometrial invasion, FIGO stage, and molecular classification were associated with recurrence. Stratified analyses suggested LVSI as the most relevant prognostic factor within the MMRd subgroup. Conclusions: Molecular classification is strongly associated with nodal involvement and survival outcomes in early-stage endometrial cancer. Integrating molecular subtype with clinicopathologic factors may improve recurrence risk stratification and guide individualized surgical and adjuvant treatment strategies. Full article

We present the first results of polar mesospheric cloud (PMC) detection using ultraviolet observations from TROPOMI (TROPOspheric Monitoring Instrument). An improved retrieval algorithm, developed on the basis of the SBUV-type approach and adapted to TROPOMI UV1 (270–300 nm) measurements, combines spatial binning, iterative Rayleigh background modeling, and adaptive thresholding to extract PMC signals from the background atmosphere. The robustness of the TROPOMI retrievals is evaluated through multi-scale comparisons with PMC data from the Cloud Imaging and Particle Size experiment (CIPS) and the Ozone Mapping and Profiler Suite Nadir Profiler (OMPS-NP). Compared with CIPS, the two datasets show broadly consistent hemispheric-scale horizontal structures and a westward wave-like phase progression consistent with possible quasi-5-day planetary-wave modulation, despite local-time differences. Compared with OMPS-NP, residual albedo under matched spatiotemporal conditions shows strong agreement for bright PMCs, whereas differences in spatial resolution lead to discrepancies in the detection of faint clouds. Seasonal-scale comparisons of PMC occurrence frequency also show consistent variability among the datasets. These results demonstrate that TROPOMI can resolve PMC structures smaller than 250 km that are difficult to detect with current low-resolution instruments. TROPOMI therefore provides a bridge between long-term coarse-resolution records and high-resolution observations, offering valuable data for studies of mesospheric dynamics and climate change. Full article