Environmental Earth Sciences最新文献

Geothermal resources, as a renewable and clean source of energy, are attracting widespread attention globally. In China, most medium to high enthalpy geothermal resources are developed in the Tibetan Plateau, especially in the rift zone of Southern Tibet. To further investigate the genesis mechanisms of geothermal resources, this study collected geothermal spring samples from the Cuona-Woka rift zone in Southern Tibet. Hydrochemical and isotopic characteristics were analyzed to reveal the origin, evolution, reservoir temperature, and circulation mechanisms of the geothermal waters. The exposed temperature of the geothermal spring ranges from 34 to 67 °C. Compared with HCO₃-Ca·Na and HCO₃-Na type samples, HCO₃·Cl-Na and HCO₃·SO₄-Na type samples have higher concentrations of Cl⁻ and trace elements. The geothermal springs are recharged by a mixture of meteoric water, snow-melt water, and magmatic water. The recharge areas had an elevation range from 5091 to 6087 m, with temperatures from −5 to −10 °C. The hydrochemical processes are dominated by silicate and carbonate dissolution, and positive cation exchange, with local gypsum dissolution. Solute geothermometers, silica-enthalpy mixing models, and geothermal conceptual model indicate that there exist shallow geothermal reservoirs (temperature = 137–162 °C) mixed by surficial cold groundwater and initial deep geothermal reservoirs (temperature = 196–212 °C), respectively. Finally, two genesis models of geothermal waters are proposed: the deep melt mixing and heating model (Type A) and the high-temperature steam heating model (Type B). The achievements of this study would provide valuable insight into geothermal research and exploitation in the Tibetan Plateau.

In this study, the spatial distribution of heavy minerals (HMs) in Miri beach sediments was assessed to quantify the abundance and determine the provenance. Surface sediment samples were collected in Miri beaches during low tide with respect to longitudinal, lateral and depth samplings. The collected samples were processed for grain size analysis by using sieving method. Then the HMs separation at different grain size fractions was carried out by using gravity and magnetic separation methods. Polarising Microscope was used to identify various HMs present in the sediment samples. In addition, selected sediment samples were analysed by using X-ray Fluorescence (XRF) to determine the elemental composition and major oxides for the geochemistry and provenance. From the results, HMs found in Miri beach sediments were zircon, magnetite, tourmaline, and rutile. Magnetite was abundant in 600–250 μm size fraction, zircon in 125–63 μm size fraction, tourmaline in 125–63 μm size fraction, and rutile in 250–125 μm size fraction. Longitudinally, magnetite was found to have higher abundance towards the southern part of the coastline. However, the abundance of rutile and tourmaline was found in the northern part of the coastline. Zircon was more abundant in the middle and southern parts of the coastline. Laterally, the concentration of HMs was higher towards the landward side. In terms of depth, the distribution of HMs varies and abundant in the surface sample. Geochemically, the beach sediments were found to be highly weathered in the north beaches (CIA = 78.9), moderately weathered in the middle (CIA = 63.8 to 66.5) and poorly weathered in the southern beaches (CIA = 40.4–49.6). The high content of quartz in the sediments are derived from the quartzose sedimentary origin, which undergone recycling process. Environmental indices such as contamination factor (CF), geoaccumulation index (Igeo), and enrichment factor (EF) indicate that the Miri beach sediments were highly concentrated with Cr, Ni, and Ta.

Heavy metal contamination in soil poses significant environmental and geotechnical challenges, requiring effective stabilization to limit contaminant mobility, enhance soil stability, and reduce deformation. This study investigates the dynamic response and microstructural changes in heavy metal-contaminated clayey sand, emphasizing the effects of clay type (kaolin and bentonite) and zeolite stabilization at varying contents (5%, 10%, and 15%). Laboratory tests, including cyclic triaxial, bender element, adsorption, sedimentation, pH measurements, Atterberg limits, and SEM analyses, were performed. Results reveal that contamination significantly reduces liquefaction resistance, with kaolin-based mixtures more susceptible than bentonite-based ones due to differences in plasticity, specific surface area, and swelling capacity. Zeolite stabilization, especially at 10% content, improves resistance by strengthening the soil structure and mitigating pore pressure under cyclic loading. Contamination affects shear modulus and damping ratio differently for kaolin and bentonite mixtures, with zeolite amplifying these impacts at higher contents through enhanced particle dispersion. Heavy metal adsorption increases with bentonite and zeolite addition, with bentonite exhibiting 180% greater lead adsorption than kaolin. Optimal adsorption performance is achieved with 10% zeolite. Microstructural analysis indicates contamination disrupts hydrogen bonding of kaolin, induces flocculation in bentonite, and has minimal effect on the stable structure of zeolite. These findings highlight the importance of clay type, zeolite content, and soil composition in mitigating contamination effects, providing insights into effective soil stabilization strategies.

Backbreak is an undesirable outcome in blasting operations caused by factors such as equipment failure, improper fragmentation, unstable mine walls, reduced drilling efficiency, and other issues that contribute to increased mining operation costs. To overcome these problems effectively, this study developed a least square support vector machine (LSSVM) model and optimized it using metaheuristic algorithms, including genetic algorithm (GA)-LSSVM, particle swarm optimization (PSO)-LSSVM, and grey wolf optimization (GWO)-LSSVM, to predict the efficiency and accuracy of backbreak due to blasting in surface mines using burden (m), spacing (m), stemming (m), powder factor (kg/m^s), and stiffness ratio (m/m) as input parameters. Among the models evaluated, the GWO-LSSVM model demonstrated superior performance compared to the LSSVM, GA-LSSVM, and PSO-LSSVM models, achieving a coefficient of determination of 0.998 and 0.997, mean absolute error of 0.0068 and 0.1209, root mean squared error of 0.0825 and 0.1936, and SI of 0.021 and 0.044 on the training and testing datasets, respectively. Sensitivity analysis of the GWO-LSSVM model revealed that the powder factor exerted the most significant influence, while the burden had the least impact on backbreak. This developed method has proven to significantly enhance the performance evaluation of backbreak in surface mines and offers valuable engineering applications in mining and other related fields.

The Grombalia Basin, located in Northern Tunisia, is facing significant challenges related to water scarcity. The cultivation of citrus fruits in this region, supported by the government, has become increasingly vulnerable to the impacts of climate change, including reduced rainfall and more frequent drought periods. The agricultural sector faces a crisis due not only to the lack of water resources but also to inadequate management (water losses in irrigation systems). This study aims to delineate the most suitable areas for implementing solar photovoltaic (PV) desalination farms utilizing abandoned brackish groundwater. A Fuzzy Analytical Hierarchy Process (FAHP), integrated with Geographic Information Systems (GIS), is employed as a Multi-Criteria Decision Analysis (MCDA) approach. This paper evaluates potential sites based on climatic, socioeconomic, and environmental factors. The FAHP framework determines criteria weights through pairwise comparisons, ensuring robust and systematic decision-making. The results indicate that the most suitable sites are located north of the Grombalia basin, which currently lacks access to external water resources for irrigation. The "Dependence of Farmers on Water Resources (DFWR)" is the most sensitive criterion, and the most suitable sites remain relatively the same despite variations in weighting. These findings will assist farmers in using solar energy to desalinate brackish groundwater, thus ensuring the sustainability of their crops and preserving their citrus heritage.

Overbank sediments are a significant sink for trace metals (TMs) and, thus, may represent an important secondary source of TMs in industrial environments. The current study (i) assessed the degree of TM contamination in overbank sediments along the Blesbokspruit River (located in the Witbank Coalfield in South Africa) using enrichment factors (EFs); and (ii) determined potential sources of TM contamination using log-ratio-transformed data as inputs to factor analysis (FA). Overbank sediments along the study site were characterized by no to minor enrichment of Al, Ni, Cu, Zn and Pb; no to moderate enrichment of Fe and Cr; and no to moderately severe enrichment of Mn and Cd. The FA revealed three main factors: F1 (loaded mainly by Zn and Ni), F2 (loaded mainly by Pb and Cu) and F3 (loaded mainly by Cr). With the exception of samples along the main roadway, Zn and Ni which loaded F1 appeared largely derived from acid mine drainage (AMD) linked to coal mining in the study site, while F2 (loaded by Cu and Pb) and F3 (loaded by Cr) could be linked to additional sources, such as industrial wastewater and ferrochrome processing, respectively. This study highlights the importance of monitoring AMD and industrial wastes and emissions in the study area to minimize the potential threat of TMs to environmental and human health.

In this study, the levels of metallic elements (Al, Cr, Cu, Fe, Mn, Ni, Pb, Sc, V, and Zn) and radioactive tracers (excess ²¹⁰Pb and ¹³⁷Cs) were determined in three sediment cores using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and gamma spectrometry, respectively. These samples were collected from the Santos-São Vicente Estuarine System (SSVES) on the western South Atlantic coast. This work, involving multivariate statistics and time-series analysis, discussed how anthropogenic pressures and climate-related processes impact the metal content in sediments deposited in this heavily industrialized coastal system. The sedimentation rate increased during the late 1960s, particularly during the 1970s and 1980s, corresponding to the period of heavy investments in industrial and urban development of the SSVES over the last seven decades. Principal component analysis generated two factors that explained between 57% and 87% of the variance in the elemental content of the sediments in each core. The first component, referred to as the natural component, showed a decreasing trend after 1970. Meanwhile, the second component, the anthropogenic component, correlated with Cu, Pb, and Zn, and increased during the same period. Time-series REDFIT analysis demonstrated that the natural component exhibits statistically significant (α = 5%) periodicities associated with local rainfall variability linked to the El Niño-Southern Oscillation (ENSO), changes in the South American Convergence Zone (SACZ), and solar activity. These forcings drive erosional processes and influence sediment production that contribute naturally to the metallic element content in this tropical humid region where chemical weathering prevails.

Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.

Accurate representation of inflow boundary conditions is critical for hydrodynamic simulations in natural and open channel systems, where irregular topographies often result in complex flow patterns. Traditional methods, such as uniform or simplified velocity distributions, fail to capture the variability of flow velocities and water depths along the channel cross-section. This limitation leads to inaccurate predictions, particularly in simulations involving pollutant transport, sediment movement, and flood risk assessments. To address these challenges, this study proposes a Modified Beta Distribution (MBD) tailored to account for varying water depths at the inflow boundary. Building upon the traditional Beta Distribution, the MBD introduces a depth-weighting factor, ensuring that inflow discharge and velocity profiles are accurately represented in channels with irregular topography. The model was validated through simulations on rectangular, triangular, parabolic, and asymmetric channel cross-sections, demonstrating improved accuracy and stability compared to existing methods. The results showed that MBD outperformed traditional methods in channels with non-uniform cross-sections, significantly reducing velocity prediction errors. This enhanced accuracy improves the simulation of flow characteristics, making the MBD an essential tool for environmental modeling, urban flood management, and water resource engineering.

This study examines hydraulic connectivity in Warna region of Maharashtra by analyzing rainfall, reservoir, and groundwater levels. Correlation analysis and entropy measures were employed to investigate this connectivity, which is notably influenced by rainfall and reservoir water level on groundwater. The findings reveal significant seasonal variability in rainfall, with peaks occurring during the monsoon season (June–September). The Warna Reservoir’s water levels respond significantly to monsoon rainfall, with notable increases of approximately 20 m during peak monsoon periods, indicating a strong interaction between rainfall and reservoir levels. Groundwater levels show variable correlations with both rainfall and reservoir water levels. The Marleswar well, for example, demonstrates a strong negative correlation with rainfall (− 0.82), indicating a rise in groundwater levels with increased rainfall, which persists during the monsoon, with a correlation of − 0.77. Correlations with reservoir water levels are more varied; the Ukalu well exhibits the strongest negative correlation, suggesting a significant relationship with reservoir water level fluctuations. Phase-segmented data analysis reveals strong cross-correlations between reservoir and groundwater levels in some wells, with the Ukalu well showing the highest connectivity during the peak monsoon period, which indicates effective reservoir recharge. Entropy and transinformation analysis for the Ukalu well indicate a substantial correlation between groundwater and reservoir levels, with transinformation averaging 54%, reflecting notable seasonal and phase variations. The variability in hydraulic connectivity appears to be influenced by geological conditions. The Ukalu well, located nearer to the Warna reservoir, shows better connectivity compared to wells situated in the Western Ghats. The basaltic terrain and associated fractures likely affect groundwater flow and connectivity, influencing well responses to variations in reservoir levels and rainfall. This study highlights the non-linear interactions and feedback mechanisms that traditional methods may not fully capture, presenting valuable insights for similar hydrogeological conditions.