Arabian Journal of Geosciences最新文献

The subject of this research deals with the study of inscriptions and rock drawings found in Jabal At-Tais Mountain located at the type locality of the Saq Formation west of Al-Qassim. In this research, two inscriptions in the Thamudic language, an elaborate drawing of a lion in a clicked manner, and drawings of wild goats and hounds were identified. These inscriptions and drawings indicate the early development of the Arabic alphabetic and the possibility of the passage of ancient trade routes near Jabal At-Tais Mountain.

Soils are exposed to various environmental pressures resulting from human activities, which can impact their physical properties, chemical composition, and microbial populations. Soil samples from cassava processing mills (Double Crown, Ekueme, Olorungbogo, Olalandu, and Akewe) in Ilaro metropolis, Ogun State, Nigeria, were assessed through physicochemical, heavy metal, and microbial analyses, as well as evaluations of contamination indices and human health risks. Twenty-two soil samples were collected from four points at a depth of 0.5 m on each of the five selected mills, and two control samples were obtained from unpolluted sites. About 5 g post-sieved samples was prepared and taken for the contamination assessments. The increased soil pH, textural content, cations, cation exchange capacity, organic matter, and organic carbon altered the toxic metal levels and microbial populations in the study samples. High mean heavy metal concentrations (Ni, Pb, Cd, Mn, Cu, Fe, and Zn) were observed in all the samples except Mn in Double Crown samples. The contamination indices indicated anthropogenic origins of the toxins, except for Mn in Double Crown, which revealed crustal sources. These suggested that the cassava mills significantly contributed to bioaccumulating toxins in the soil. The Double Crown, Ekueme, and Olorungbogo samples indicated moderate contamination, while the Akewe and Olanlandu samples showed severe contamination. The microbial counts of the soils (total coliforms, E. coli, fungi, and total bacteria) were relatively high and play crucial roles in soil and crop health. The total potential non-carcinogenic and carcinogenic health risks for both children and adults through inhalation, ingestion, and dermal pathways were within the permissible limits.

This research paper presents a scientific analysis of the land use and land cover changes in Old Dhaka over a 20-year period from 2003 to 2023. Data have been collected from the United States Geological Survey (USGS) for utilizing Landsat satellite imagery by using Remote sensing (RS) and GIS techniques. Despite numerous studies on urban expansion in Dhaka, limited research has focused specifically on the historic core of Old Dhaka, which is uniquely constrained by heritage structures, high population density, and informal land use practices. The research aims to fill this gap by systematically analyzing spatial and temporal LULC dynamics to understand how urban growth has impacted green spaces, bare land, and water bodies within this context. The study reveals a dramatic expansion of built-up areas rising from 41.89% in 2003 to 63.16% in 2023 accompanied by a sharp decline in vegetation and water bodies. In 2003, 33.81% of the land was covered with vegetation; by 2023, that percentage had dropped to 12.63%. The water body’s proportion decreased to 0.95% in 2023 from 3.56% in 2003. These changes highlight urgent environmental challenges and underscore the need for integrated, heritage-sensitive urban planning. The objective of this research is to generate evidence-based insights that can inform sustainable urban management strategies tailored to the unique characteristics of Old Dhaka. This research highlights the need for sustainable urban planning and conservation efforts to safeguard the city’s green spaces and maintain ecological balance amid rapid urbanization trends.

This study presents a comprehensive assessment of groundwater quality in Madurai North Taluk, Tamil Nadu, with a focus on its seasonal variation and irrigation suitability. Groundwater samples were collected during the pre- and post-monsoon seasons of 2022 and analyzed for physicochemical parameters including pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and anions (Cl⁻, HCO₃⁻, SO₄²⁻, NO₃⁻), as well as Sodium Adsorption Ratio (SAR) and Sodium Percentage (Na%). The pre-monsoon season exhibited higher concentrations of salts, TDS, and Hardness, attributed to limited recharge and evaporation. Post-monsoon samples showed increased variability due to dilution, leaching, and anthropogenic return flows. Piper Trilinear Diagram interpretations revealed that CaHCO₃ and mixed CaMgCl types were dominant, indicating rock-water interactions and the influence of domestic and agricultural effluents. The USSL Diagram showed that a majority of the samples fall within C2–S1 and C3–S1 fields, reflecting medium to high Salinity with low Sodium hazard. However, a few samples shifted toward C4 salinity classes, particularly in the post-monsoon season, indicates that the need for caution in irrigation usage. The Wilcox classification further supported these findings, with most samples falling into the “excellent to good” and “good to permissible” categories, though several were “doubtful to unsuitable” due to elevated Na%. Overall, the findings suggest that groundwater in the region is generally suitable for irrigation, but localized management strategies like adopting micro-irrigation, introducing salt-tolerant crops, and improving recharge through percolation ponds are required to mitigate increasing salinity and sodium hazards.

Slopes under conditions such as seismic loading or rainfall are highly susceptible to instability phenomena including landslides, rockfalls, debris flows and so on. Traditional methods such as the limit equilibrium method and numerical simulation offer simplicity and intuitive application but often struggle to accurately identify the most critical slip surface and compute the accurate and effective factor of safety (FOS) when dealing with heterogeneous soil layers, dynamic loads, and complex geological conditions. These limitations may lead to discrepancies between predicted results and actual engineering performance. To address these challenges, researchers have increasingly introduced intelligent algorithms such as machine learning and neural networks, aiming to improve the accuracy and efficiency of slope stability analysis through data-driven approaches and intelligent optimization techniques. This review presents: (1) A comprehensive review of advances in slope stability analysis over the past decade is presented, covering both conventional and intelligent methods. Key limitations of intelligent approaches are identified, including sensitivity to parameter selection, limited model generalization, data sparsity, challenges in multi-source data integration, and a lack of extensive field validation and standardized frameworks. (2) a critical analysis of existing challenges in intelligent methods, including parameter sensitivity, model generalization, data sparsity, and multi-source data fusion, along with potential solutions; (3) prospects for future development, including multi-physics coupling modeling, adaptive learning systems, explainable artificial intelligence techniques, and standardized data platform construction, to provide theoretical support and practical insights for solving complex slope stability problems in engineering practice.

This study investigates hybrid deep learning approaches for predicting human thermal comfort indices in urban environments, based on feels-like temperature—a metric that captures physiological responses to weather conditions by human beings more effectively than conventional temperature readings. This parameter is determined based on the 'BiLSTM-GRU-Attention’ model, developed to forecast heat index and wind chill and serve as critical indicators of perceived thermal stress. The model was trained on meteorological data from Patna City, using air temperature, specific humidity, and wind speed as input features. For enhancing performance evaluation, five statistical metrics were employed: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Results demonstrated high predictive accuracies. Heat index forecasts achieved MAE between 55.55 and 51.5, MSE of 4.809, RMSE between 7.453 and 7.17, MAPE of 0.044, and R² of 0.9649. Wind chill predictions performed even better, with MAE of 1.491, MSE of 3.88, RMSE of 1.97, MAPE of 0.0195, and R² of 0.9739. Individual parameter forecasts showed excellent correlation for air temperature (R² = 0.9748) and specific humidity (R² = 0.9424), while wind speed predictions were less accurate (R² = 0.2396), likely due to urban atmospheric variability. The study of GRU-Attention, BiLSTM-GRU-Attention, and CNN-BiLSTM-Transformer models using three parameters: temperature, wind speed, and humidity and extracting two derived parameters, heat index and wind chill, with five statistical matrices: R², MSE, RMSE, MAE, and MAPE establishes a reliable framework for predicting human-centered thermal indices, offering valuable insights for urban weather forecasting systems. These findings support public health strategies and urban climate resilience planning by emphasising thermal comfort over traditional meteorological metrics in the context of increasing climate variability.

This research focuses on the evaluation of physiochemical characteristics and technological properties of illitic-rich clays endowed in the Okposi-Uburu region, to assess their suitability as raw materials for structural ceramic and brick production. To achieve this, mineralogical compositions and elemental analyses were investigated via X-ray diffraction (XRD) and X-ray fluorescence spectroscopy, respectively. Evaluations of physical characteristics were performed by particle-size analysis, Atterberg limits, and total organic content tests. As part of the ceramic evaluation process, technological tests were performed on clays fired at a conventional temperature range of 850–1050 °C. In terms of mechanical strength, the compressive resistances were investigated via three-point loading strength tests. From the results, XRD revealed the predominance of illite, quartz, flux-inducing minerals (K-feldspar, carbonate), and accessory phases. The illite, which promotes glassy phase formation, accompanied by significant percentages of SiO₂ (42.6–56.4%), Al₂O₃ (21.9–30.3%), and K₂O (2.3–6.9%), signifies good materials for the production of ceramic and terracotta building materials. Low amounts of Fe₂O₃ with high alkali contents positively influenced the ceramic properties. Using ternary diagrams, particle-size analysis revealed adequate characteristics with good plasticity. The extruded specimen revealed red coloration attributable to hematite present in mineral compositions. At sintering temperatures above 850 °C, mineral transformations occurred with the crystallization of new phases. The specimen revealed the development of earlier densification, good linear shrinkage, reduced water absorption, and enhanced mechanical performance, with compressive strength ranges of 8.5–9.9 MPa, attributable to the sintering process. The silicon–aluminum compositions, based on technological characteristics, indicate that the clays satisfy favorably as raw materials according to ASTM standards and specifications for structural ceramic and building brick production.

Remote sensing (RS) technologies play a pivotal role in acquiring massive volumes of spatial data across various domains. To augment the capabilities of RS systems such as scene classification and change detection, machine learning (ML), especially deep learning (DL) networks, has achieved great success, but many of them often rely on a large number of labeled samples for supervision. As sufficient labeled training data are not always ready due to, e.g., continuously emerging RS datasets and costly sample annotation in real-world applications. In order to address the issue of sample scarcity, many studies prefer to utilize auxiliary information including those in the form of knowledge graph (KG) and zero-shot learning (ZSL) to reduce the reliance on labeled samples. To exclusively focus on discussing related technologies, this paper thoroughly reviews zero-shot learning (ZSL) and knowledge graphs (KG) with remote sensing (RS), highlighting their individual benefits and combined potential. Additionally, it discussed constructing methods of specific KGs for RS applications, emphasizing semantic relationships for contextualized information retrieval. The paper discusses the advantages of integrating ZSL & KG methods in RS applications, particularly in scene classification, and then presents emerging approaches based on ZSLKG across various domains which quotes the potentiality of ZSLKG and finally, suggests potential RS applications complying ZSL & KG. Overall, it highlights the transformative impact of ZSLKG convergence on enhancing the intelligence and efficiency of RS applications.

The excavation of deep rock masses by blasting is influenced by both the static and dynamic stresses produced by the detonation of explosives. Based on the analysis of explosive effects, formulas for explosive stress and energy have been derived. A computational model for blasting in a large cavity with four boreholes has been established. Numerical simulations were performed using the finite element analysis software ANSYS-LS/DYNA, exploring eight distinct in situ stress scenarios, which encompassed both bidirectional equal pressure and bidirectional unequal pressure conditions. The results show that explosive cracks in the four boreholes facing the cavity are denser, and the rocks in the excavation area are more fragmented. This confirms that the presence of a large cavity enhances the fragmentation effect of the blasting excavation. Given that the in situ stress is significantly lower than the pressure produced by the shock wave during detonation, its influence on the development of the fractured zone is minimal. However, it significantly influences the length and morphology of the cracks. In the blasting of rock masses with high in situ stress, the distance of crack propagation between boreholes diminishes with increasing levels of in situ stress. The cracks predominantly extend in the direction of the maximum principal stress. Therefore, arranging the boreholes along the direction of the maximum principal stress and reducing the spacing between boreholes is beneficial for connecting and penetrating the cracks between boreholes, resulting in a better blasting excavation surface.