Search Results (2,140)

Uranium exploration in southeastern Mongolia remains constrained by fragmented Soviet-era datasets and limited modern synthesis. This study addresses the problem of integrating historical geological records with contemporary exploration methods to evaluate uranium mineralization potential. A comprehensive GIS-based database was compiled from Soviet reports legally acquired from the Mineral Resources Authority of Mongolia and expanded with geological, geophysical, and drilling data collected between 2006 and 2011. Methodological advances included remote sensing detection of anomalous radioactivity in arid environments, stratigraphic modeling, and hydrogeochemical surveys. The dataset encompasses more than 1100 radioactive anomalies and approximately 300 mineralized zones, with emphasis on the Han Bogd granite massif, Ail Bayan coal deposit, Suujin Tal structural system, Zuunbayan depression, and Naarst structural complex. Results indicate that most anomalous zones are sub-economic, commonly associated with organic-rich facies such as coal seams, while the continuity of mineralized bodies remains uncertain. Nevertheless, the dual consideration of granitic source terrains and coal-bearing sedimentary traps provides new insights into uranium mobility and deposition. The significance of this work lies in its systematic integration of historical and modern data, offering a refined geological framework and highlighting key areas for future investigation, thereby contributing to ongoing discussions on sedimentary uranium resources in Mongolia. Results indicate that most anomalous zones are sub-economic, commonly associated with organic-rich facies such as coal seams, while the continuity of mineralized bodies remains uncertain. Importantly, the study highlights granitic intrusions and volcanic complexes as the primary uranium sources, with coal-bearing and sedimentary basins acting as secondary depositional environments. The dual consideration of source terrains and depositional traps provides new insights into uranium mobility and deposition. Full article

Sandy calcareous soils in arid regions suffer from low phosphorus (P) availability due to high fixation rates, limiting crop productivity. This study investigates a novel hybrid smart fertilizer (BN) composed of olive pomace biochar (BC) and nano-hydroxyapatite (nHAP). BN was synthesized and characterized using XRD, FTIR, SEM/TEM, and zeta potential analysis. Its P release kinetics were modeled, and its agronomic performance was assessed on faba bean (Vicia faba L.) in a pot experiment under sandy soil conditions with and without wood vinegar (WV). The 1:1 BC:nHAP formulation showed a two-stage release profile: a rapid initial burst (Higuchi model, R² = 0.86) followed by sustained zero-order release (R² = 0.80). In the pot experiment, BN combined with WV significantly increased plant height by 36%, shoot fresh weight by 232%, and available soil P by 39% compared to conventional SSP (p < 0.05). This synergistic treatment also improved root nodulation and nutrient (N, P, K) uptake. The BC-nHAP hybrid coupled with WV acts as an efficient P delivery system, improving soil fertility in arid environments based on circular economy principles, aligning with SDGs 2, 12, and 15. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

Aiming at the problems of uneven radiation distribution and difficult regulation in double-film multi-span greenhouses in the Gobi Desert, a spatiotemporal simulation model of the radiation environment based on the coupling of Rhino–Grasshopper and Radiance was constructed in this study. Parametric simulation and multi-objective optimization were adopted to significantly improve the solar radiation capture and distribution uniformity inside the greenhouse, providing a scientific basis for greenhouse design in the Gobi area. The results show that the model has high accuracy (R² > 0.98), and the radiation inside the greenhouse presents a distribution pattern of “higher in the northeast, lower in the southwest, higher in the upper layer and lower in the lower layer”. The optimal orientation is 1° west of south, and the optimal configuration is 8 m span, 5 m eave height, and 30° roof slope. This study can provide quantitative support for the structural design, planting layout and energy-saving regulation of double-film multi-span greenhouses in arid desert areas, and has important practical value for promoting the efficient and sustainable development of facility agriculture in the Gobi Desert. Full article

Soil erosion is a significant environmental concern in arid regions, particularly in dam-regulated watersheds, where intermittent flows from sprinkler irrigation can exacerbate land degradation. This study assesses soil erosion susceptibility in the Sidi Aich watershed using a combined approach of the Revised Universal Soil Loss Equation (RUSLE) and the Analytic Hierarchy Process (AHP), enabling the integration of both regional characteristics and expert-driven weighting. The RUSLE model accounts for natural and human-induced factors, whereas AHP provides a hierarchical weighting system that highlights rainfall erosivity and the local impacts of dam-regulated discharges. Results show that 26.12% of the area falls into the very high susceptibility category, 25.45% into high, 23.91% into moderate, and 24.51% into low susceptibility. Model validation demonstrates satisfactory predictive performance, with Area Under the Curve (AUC) values of 0.85 for AHP and 0.78 for RUSLE. Overall, the findings emphasize the critical role of dam-controlled releases in increasing soil vulnerability, a factor that may not be fully captured when using RUSLE alone. By combining RUSLE and AHP, this research provides a more realistic and regionally tailored assessment of erosion risk, offering valuable guidance for watershed management and erosion mitigation strategies in arid environments. Full article

Semantic segmentation of land use and land cover (LULC) in arid regions remains challenging due to severe class imbalance, fragmented spatial distributions, and high spectral similarity among different land cover types. These characteristics often lead to an information bottleneck in deep segmentation networks and hinder the extraction of discriminative semantic representations. To address these issues, we propose SDS-Former, a lightweight semantic segmentation network specifically designed for remote sensing imagery in arid environments. SDS-Former incorporates an SSM-inspired Lightweight Semantic Enhancement (LSE) module to strengthen contextual modeling and alleviate the loss of discriminative information in deep features. To tackle scale variations, a Dynamic Selective Feature Fusion (DSFF) module is employed in the decoder to adaptively weight and fuse high-level semantics with low-level spatial details. Furthermore, a Feature Refinement Head (FRH) is introduced to enhance boundary localization and improve the recognition of small-scale and sparsely distributed land cover objects. Extensive ablation and comparative experiments demonstrate that SDS-Former consistently outperforms representative semantic segmentation methods across multiple evaluation metrics. On the Tarim Basin dataset, the proposed network achieves a mean Intersection over Union (mIoU) of 82.51% and an F1 score of 86.47%, indicating its superior effectiveness and robustness. Qualitative results further verify that SDS-Former exhibits clear advantages in distinguishing spectrally similar land cover types and preserving the spatial continuity of ground objects in complex arid-region scenes. Full article

Camel breeding plays an important role in sustaining pastoral systems in the arid and desert regions of Southern Tunisia, yet the sector remains marginal and insufficiently documented. This study aimed to assess the sustainably production potential of the Tunisian camel herd and to project future meat and milk production. Given the limited availability of detailed statistical data, a demographic simulation model adapted to Tunisian conditions was used to analyze herd dynamics under equilibrium scenarios. Sustainable offtake rates were estimated, and sensitivity analyses were conducted to evaluate the influence of fertility and mortality on productivity. The results indicate that an average annual offtake rate of about 19.1 percent of the herd can be achieved without compromising demographic stability. Productivity gains were primarily driven by improvements in reproductive performance, while further reductions in mortality had a limited effect. Projections for the period 2022 to 2030 show moderate but consistent increases in herd size as well as meat and milk production. These findings suggest that sustainable development of camel production in arid environments relies on adaptive management strategies focused on reproduction, mobility, and market integration rather than conventional intensification. Full article

An integrated analysis incorporating total organic carbon (TOC) content measurement, X-ray diffraction (XRD), scanning electron microscopy (SEM), and gas adsorption experiments was performed on core samples from Well FY1-4 of the upper-fourth Shahejie Formation (Es₄) in the Minfeng Sag. To address the lack of systematic research on the pore and fractal characteristics of organic-rich low-maturity shales in the Minfeng Sag (against the preponderance of studies on high-maturity shales), this study characterized the lithofacies, reservoir space and pore fractal features of the target low-maturity shale interval and clarified the sedimentary controls on lithofacies and key factors regulating pore fractal heterogeneity. The results reveal that the shale in the Es₄ of the study area exhibits low thermal maturity, with six distinct lithofacies identified. Organic-rich laminated calcareous shale lithofacies (RL-1) and organic-rich laminated calcareous/argillaceous mixed shale lithofacies (RL-2) represent the most favorable lithofacies, which are dominated by large mesopores and macropores. Their reservoir spaces were primarily composed of intergranular pores, intragranular pores, and organic pores, whereas the other lithofacies are dominated by small mesopores. The pore surface fractal dimension (D) was calculated using the Frenkel–Halsey–Hill (FHH) model based on low-temperature N₂ adsorption (LTNA) data. The meso-macropore system shows higher heterogeneity than the micropore system (D₂ > D₁). Both D₁ and D₂ exhibit a weak negative correlation with TOC and carbonate content and a positive correlation with clay content. In the initial depositional stage of the Es₄, the arid climate, weak terrigenous input, shallow lake depth, and high salinity resulted in the strongly reducing saline depositional environment with relatively low organic matter enrichment. As the climate became progressively humid in the middle and late stages, hydrodynamic conditions intensified, leading to a lithofacies transition from mixed shales to argillaceous calcareous shales. Increased TOC and carbonate contents reduce the pore fractal dimension of shale. Smaller fractal dimensions directly indicate a simple pore structure and regular pore surface in the shale oil reservoir of the Minfeng Sag, where reservoir space is dominated by large pores such as intercrystalline pores and dissolved pores. Such pore characteristics are more favorable for the enrichment of shale oil. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Sandstone of the Lower Cretaceous Luohe Formation is the main water inrush source in the Binchang Mining Area in the southwestern Ordos Basin. Its sedimentary environment and provenance features are critical for local coal development and safe mining. The Luohe Formation at Xiaozhuang Coal Mine comprises three vertical members: the lower member dominated by coarse- to medium-grained sandstones, the middle member mainly composed of fine-grained sandstones, and the upper member characterized by interbedded fine- to medium-grained sandstones and sandy conglomerates. This subdivision newly identifies a complete hydrodynamic evolutionary cycle of depositional environments from high-energy to low-energy and back to high-energy conditions. Integrated petrographic observations and analyses of major and rare earth elements first confirm that the tectonic affinity of the Luohe Formation progressively shifted from a passive continental margin to an active continental margin, accompanied by a corresponding transition in sediment provenance from the North China Craton to a magmatic arc source region. Trace element compositions precisely indicate that the Luohe Formation was deposited in a fluvial freshwater environment under hot, arid, and oxidizing conditions, thus providing new constraints on the paleoenvironmental evolution of the region. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Designing sustainable pedestrian infrastructure in hyper-arid cultural landscapes requires balancing visitor experience, heritage protection, and environmental constraints. This study develops a statistically grounded model for planning sustainable walking trails in Al-Ula, Saudi Arabia, using multi-spectral remote sensing data integrated with expert-based evaluation. A GIS-based Multi-Criteria Decision-Making (MCDM) framework was applied to assess topographic slope, vegetation cover (NDVI), built-up density (NDBI), Land Surface Temperature (LST), and solar exposure. Indicator weights were validated through a three-round Delphi survey involving fifteen experts. The results indicate strong consensus among experts, identifying LST (21%) and slope (20%) as the most influential determinants of trail suitability in desert environments. These findings highlight the critical role of thermal stress in shaping safe and sustainable pedestrian mobility in hot climates. The optimized 44.5 km trail network, classified into three difficulty levels, improves energetic efficiency by reducing caloric expenditure by 24% compared to conventional routing. In addition, the proposed network has the potential to reduce carbon emissions associated with heritage-related travel by approximately 75% through modal shift from vehicles to walking. The framework provides a practical decision-support tool for planners seeking to develop low-carbon, climate-responsive tourism infrastructure aligned with the objectives of Saudi Arabia’s Vision 2030. Full article

Urban remote sensing provides an efficient and accessible way to monitor and assess the urban environment. However, the difficulty in classifying bare soil and built-up land is exacerbated in desert landscapes, due to the spectral confusion of bare soil and impervious surfaces. Therefore, urban remote sensing research in desert environments employs complex and time-consuming classification techniques, which cause difficulties in reliability when transferring these methods to other desert cities. This paper describes two new index-based approaches that can successfully detect and classify urban areas without the disruption of bare soil influences in desert environments using Landsat 8–9 satellite imagery. They are called the desert urban landscape index (DULI) and the isoline impervious surface index (IISI). The desert cities of Phoenix, Ciudad Juárez, and Riyadh were used as study areas for the development of these indices. The two proposed indices outperformed the dry built-up index (DBI), with overall accuracy rates of 85% in Phoenix using DULI, 87% in Ciudad Juárez using DULI, and 90% in Riyadh using IISI. DULI also demonstrates the ability to suppress landscape features such as bare soil, mountains, and canyons. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

This study aimed to characterize the spatiotemporal dynamics of soil respiration (CO₂ efflux), soil moisture, and soil temperature across different land-use systems in a semi-arid environment through in situ monthly monitoring and to evaluate the potential of UAV-based imagery combined with Random Forest modeling to spatialize these variables within the agroforestry system. The variables were monitored monthly using an Infrared Gas Analyzer (IRGA) over 9 months, and UAV imagery was acquired at two distinct time points. The 11-month experimental campaign enabled evaluation of seasonal and spatial variability and of soil physical and hydraulic properties. Soil CO₂ efflux ranged from 1.0 to 6.7 μmol m⁻² s⁻¹, with higher values observed during the rainy period, closely following soil moisture dynamics. Soil moisture and temperature exhibited clear seasonal patterns driven by rainfall variability. The pasture system showed higher CO₂ efflux in most months, while AFS2 presented more stable fluxes over time. In contrast, AFS1 exhibited lower CO₂ efflux, likely associated with its soil characteristics. Despite these patterns, no significant differences were observed among land-use systems for most soil physical properties. UAV-derived data combined with machine learning techniques proved effective for modeling soil CO₂ efflux, soil temperature, and soil moisture, demonstrating their potential for monitoring soil processes in semi-arid environments. Overall, agroforestry systems did not significantly differ from other land uses in terms of CO₂ efflux, likely due to their early stage of development. These findings indicate that the effects of agroforestry systems on soil processes occur gradually and highlight the importance of long-term monitoring to fully capture system dynamics. Full article