Environmental Earth Sciences最新文献

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.

The selection of appropriate inter-particle parameters in discrete element method (DEM) simulations is crucial, and the commonly used trial-and-error method has been criticised for its uncontrollability and high computational cost. Therefore, this study proposes a new framework based on convolutional neural networks (CNN) as an alternative method for calibrating inter-particle parameters in calcareous sand materials. Firstly, a biaxial test dataset for calcareous sand was generated using DEM simulations. This data set was then used to train a CNN to capture the primary underlying correlation between macroscopic mechanical properties and the inter-particle parameters of the contact model. To demonstrate the powerful performance of CNN, this paper also established a back-propagation neural network (BP) and gated recurrent units (GRUs) as control experiments. The results showed that the CNN had higher prediction accuracy compared to the BP and GRU models. After DEM simulation using the parameters predicted by the CNN, it was found that the stress–strain curves and failure patterns closely matched the results of the laboratory tests. This confirms that the CNN can quickly and accurately determine the inter-particle parameters for DEM simulation and verifies the robustness of the CNN model in predicting laboratory test results, this method provides a reference for the calibration of DEM parameters for calcareous sand, thereby offering strong support for the use of calcareous sand in marine development and construction projects.

The Himalayan Mountain ranges are very sensitive to geohazards like landslides, earthquakes, cloudbursts, and flash floods because they are known for their neotectonics activity. The geomechanical behaviour of road cut slopes along National Highway 7 (NH-7), from Rudraprayag to Joshimath in the Garhwal Himalayas, is significantly influenced by lithological diversity, tectonic discontinuities, and external triggering variables like seismic activity and precipitation. This study conducts a comprehensive comparative analysis of multiple rock mass and slope mass classification systems, specifically Rock Mass Rating (RMRbasic), Slope Mass Rating (SMR), Continuous Slope Mass Rating (CoSMR), Chinese Slope Mass Rating (CSMR), Geological Strength Index (GSI), and Q-slope, across 18 selected slope sections with diverse lithologies. Kinematic analysis via stereographic projection discovered failure modes, revealing planar, wedge, and toppling mechanisms associated with joint orientations and slope geometries. CoSMR, which incorporates continuous functions for adjustment factors (F1, F2, and F3), offered superior resolution for stability classification relative to conventional SMR and CSMR techniques. The RMRbasic values varied from 33 to 73, with quartzite and gneissic slopes demonstrating elevated stability indices, whereas phyllite, dolostone, and slate slopes were categorized as severely unstable due to diminished RMRbasic, GSI, and Q-slope values. The Q-slope approach, which integrates environmental influences and stress reduction factors (SRF), yielded Q-values ranging from 0.01 to 0.25, facilitating the calculation of critical slope angles by Barton’s empirical formula. The results indicate that CoSMR provides enhanced accuracy in intricate terrains through its continuous parameter scaling, whereas Q-slope yields reliable forecasts for slope angle stability across different probabilities of failure (PoF). This work offers improved geomechanical understanding for optimizing slope design, excavation techniques, and stabilization strategies in seismically active and rainfall-prone areas of the Himalayas.

The western mining area is an essential coal mining base in China. Because of the presence of the super-thick weakly cemented overburden, deep mining in this area faces problems such as high energy events and serious dynamic disasters, and the surface subsidence is slight in the initial stage of mining. To solve the above problems, this paper takes Ordos mine as research object, systematically studies the linkage mechanism between surface subsidence and large energy events in the full period mining. In the initial stage of mining, the two large energy mining seismic in the mining process of 1208 working face both occurred near the maximum subsidence point and were accompanied by sudden increase of surface subsidence. With the increase of mining area, there is an obvious interactive response between the subsidence incremental of surface and the energy release in high position super-thick rock strata. Moreover, based on the simulation of overburden breaking and the energy evolution of key horizon, the mechanism of action of super-thick overburden failure on surface subsidence and energy release is clarified. Based on this, the mining seismic activity for the full period of mining is predicted by the mechanical analysis. The results can provide theoretical support for large-scale continuous mining in weakly cemented mining areas in western China.

The spatial and temporal deformation features are important representations for stability analysis and assessment of the underground powerhouse. However, due to the excavation disturbance and sensors failure, the in-situ monitoring data may be of low quality. To reconstruct the actual project and analyze the full deformation characteristics of the underground powerhouse located in the Suki Kinari hydropower station, a BIM-based parametric geometry model of the caverns and the refined geological model are combined. An elaborate numerical analysis is implemented to discuss the spatial and temporal deformation features, and then, the deformation and failure mechanism has also been revealed. Results indicate that the deformation is time-dependent in a ladder form with the excavation process, and the deformation of rock mass is affected severely by the excavation of the corresponding and the following layers. Besides, the deformation values descend with the monitoring depth increasing. The excavation disturbed area enlarges with the unloading process, and the deformation at the corresponding position keeps deteriorating until new stability. For rock mass at the arch crown, nearly 90% of the total deformation occurs at the first layer excavation, indicating that immediate support is crucial. Besides, the deformation of rock mass at various depths is generally divided into three zones according to the decrement rate and magnitude. Furthermore, the failure of the surrounding rock mass is induced by the radial stress reduction and tangential stress increment, leading to excavation damage. Therefore, supporting strategies can be imposed to prevent stress variation.

This is the first study that coupled the detailed stratigraphic information with petrography and whole-rock geochemistry data on the Merbabu volcano (Central Java, Indonesia). A total of 22 pyroclastic layers originating from magmatic and phreatomagmatic eruptions were identified; among these, the massive orange lapilli (mLo) layer occurs as the key layer due to its widely dispersed characteristic and possibly originates from a VEI 4-scale eruption. Basaltic andesite pumice was identified as the main juvenile phase in pyroclastic deposits and is characteristically amphibole-rich. Five lava flows and three lava domes were identified. All lavas are porphyritic and pyroxene-rich; however, the composition of lava flows varies from basalt to andesite, while lava domes are exclusive to andesite. We also distinguished three layers of the lahar deposit; two represent the debris flow type, and one represents the hyper-concentrated flow type. Interestingly, all pumices typically have a high Zr/Nb value, while all lavas are characterized by a low Zr/Nb value. This evidence, coupled with the mineralogical differences between pumice and lava (amphibole-rich for pumice and pyroxene-rich for lava) strongly suggests the presence of two magma reservoirs beneath the volcano. All of these deposits successfully construct the present volcano landforms, which are further divided into upper cones, middle cones, and lower cones. Pyroclastic deposits and lava primarily constructed the upper and middle cones, while lahars and some pyroclastics built the lower cones. Landforms associated with lava typically have small drainage density values (3.0 – 3.4 km/km²); whereas, landforms associated with pyroclastic and lahar deposits have a characteristically high drainage density value (4.5 – 6.4 km/km²). This evidence suggests that the difference in material types strongly controls the erosion intensity. Moreover, the occurrence of three horseshoe escarpment landforms implies that Merbabu can produce explosive flank collapse eruptions.

The research was carried out between 2021 and 2023 in zones A‒D, 400, 600, 1000, and 1500 m away from the magnesite plant in Lubeník (central Slovakia), which are polluted by magnesite immissions. Due to the fall of magnesite dust, the Organic horizons became neutral to weakly acidic, the mineral layers of soils in zones A and B strongly to weakly alkaline, and neutral in zones C and D. Ca, Mg, and Na in the Organic horizon and 0–5 cm soil layers decreased from zone A toward zone D. Mn and K increased from zone A to zone D. Cd generally increased from zone A to zone C. A total of 107 species of macromycetes from the phyla Ascomycota (15) and Basidiomycota (92) were detected. The absolute best accumulator of Ca and Mg was Hymenochaete rubiginosa and Ca, Mg, and Mn Geastrum rufescens. The best K accumulator was Inosperma erubescens together with Russula cyanoxantha, and the absolute best Na accumulator was Clitocybe phyllophila. Cd was best accumulated by Hymenochaete rubiginosa and Inosperma erubescens. PCA showed that soil properties depended on pH values and C, N, K, and Na content. The mentioned variables were significantly correlated with PC1 (share 46%), while the contents of Ca, Mg, Mn, and Cd were correlated with PC2 (share 35%). Ca and Cd were the most important environmental variables accumulated in macromycete fruiting bodies. PC1 represents a significant axis for the prediction of Ca content (40.2%) and PC2 for the prediction of Cd content in macromycetes (19%). Environmental pollution by Mg emissions significantly contributed to soil degradation and the composition of macromycete communities. The knowledge gained can help in the implementation of corrective measures in areas affected by alkaline immissions.