Environmental Earth Sciences最新文献

To study the influence of pipe-jacking construction on adjacent existing pipelines, take a project where a pipe-jacking tunnel closely crossed over the existing Metro Line 5 in Xi’an as the background. The laws of surface settlement, earth pressure, and the force and deformation of the existing tunnel caused by the construction of the new tunnel crossing over were obtained by combining centrifugal model tests and numerical simulations. The results show that: (1) The surface settlement caused by tunnel excavation in sandy stratum was divided into three stages: severe deformation, slow deformation, and stable deformation. For any position on the surface, the disturbance caused by the approaching of the excavation face was greater than that caused by the leaving. (2) The upward crossing construction increased the earth pressure on the near side of the existing tunnel near the new tunnel axis and reduced the earth pressure on the far side. The range of the earth pressure reduction area was positively correlated with the net distance between the tunnels. (3) After the upward crossing construction, the change of the additional bending moment in the longitudinal direction of the existing tunnel was much greater than that in the circumferential direction. The maximum additional bending moment in the longitudinal took place at the intersection of the tunnels. After passing the inflection point, the negative bending moment increased sharply. The curve of bending moment conformed to the Gaussian distribution. The larger the net distance between the tunnels and the smaller the overall burial depth, the smaller the maximum additional bending moment in the longitudinal. The change of the net distance changed the position of the inflection point. (4) The construction of pipe-jacking caused the existing tunnel to rise and deform, with the shape of an inverted “V”. The smaller the overall burial depth of the tunnel, the smaller the range of the existing tunnel rise, and the shape of the inverted “V” is lower and wider.

Water immersion in the gas extraction borehole will reduce the stability of the borehole, lead to borehole deformation and collapse, and reduce the efficiency of gas extraction. In order to study the failure characteristics and deformation law of coal and rock bodies around boreholes with different water content, the digital image observation system of coal and rock deformation and failure was used to carry out the surface deformation observation experiment of coal samples with boreholes in dry, natural and saturated states under uniaxial compression. The time series speckle images of the surface deformation of the samples under different stress states were obtained, and the surface deformation of the samples was qualitatively and quantitatively analyzed. The results show that: (1) As the water content increases, the peak strength and modulus of elasticity of the porous specimens gradually deteriorate and decrease, with a maximum deterioration of 39.53% and 17.39%, respectively, and the peak strain gradually increases, with a maximum increase of 40%. (2) The deformation localization phenomenon of the water-containing samples started earlier than that of the dry sample. The deformation localization zones of the dry samples had a faster displacement opening speed and a smaller displacement dislocation amplitude. (3) From dry to water-saturated conditions, the borehole contracted inward by 64.1% overall in the vertical direction and expanded outward by 87.8% overall in the horizontal direction. The higher the water content, the greater the deformation and flattening of the borehole, and the greater the amount of radial displacement and circumferential displacement. (4) Under the action of water-force coupling, the bonding force between particles is reduced, the internal transformation of the specimen to loose and porous, the tensile stress in the pore-fracture stress concentration area is enhanced, and the pores and microcracks develop and expand rapidly, which weakens the bearing capacity of the coal body.

In the first article, entitled “Hydrochemical stratigraphic analysis of the filling of the Meirama open pit mine I: Monitoring and filling” (Juncosa et al. Environ Sci Pollut Res 20(11):7520–7533, 2013), the filling process of the old Meirama mining pit (NW Spain), as well as the methodology used in the sample collection and analysis, was described. Likewise, the evolution of the temperature, pH, dissolved oxygen, iron, and manganese in the filling and postfilling processes are shown. This second article presents the temporal evolution of other major components and nutrients during the filling period (2008–2016) and postfilling period (2016–2019). The continuation of the analysis initiated in the aforementioned article is done at certain heights of the vertical profiles monitored at the midpoint of the lake (the surface, the first 2 m of depth with respect to the surface (2 mbs), at 59 masl, and at the bottom (2 masl)). As explained in the filling process, an invariant chemocline and a seasonal thermocline near the water surface are formed. Therefore, the analysis encompasses not only the bottom and surface of the lake but also includes an intermediate point where the chemocline is found. Based on the analysis carried out, it has been possible to verify that the most superficial waters (80 m) are in line with the geological and fluvial environment of the basin, so that the stored waters do not need a special physicochemical treatment. However, at deeper levels, anoxization processes are developing, a step prior to the methanization of the lake bottom.

The mineralogical compositions and distributions of trace and rare earth elements (REEs) in carbonaceous sediments provide valuable insights into the paleoenvironmental conditions during peat formation. Given the increasing demand for REEs in modern technologies, understanding their occurrence and economic potential is critical. However, the precise mineral hosts of trace and REEs in these sediments and their implications for the paleoenvironment during the Eocene epoch in India remain less explored. Therefore, this study examines Eocene carbonaceous sediments from the Saurashtra Basin in western India to uncover mineralogical controls on trace and REEs distribution and to infer paleoenvironmental conditions during peat accumulation. A total of 15 samples were collected from two mines (Surkha-lignite and Khadsaliya-shale). X-ray Diffraction (XRD), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and Inductively Coupled Plasma Optical Emission Spectroscopy ICP-OES analysis were utilized to know the mineralogy, REEs, and trace elements distribution. The total REE concentrations in shale and lignite were low, while for in shale (avg. 195.89 ppm) was relatively higher than lignite (avg. 177.32 ppm), with cerium (Ce) being the most abundant element in both rock types. The concentrations of REEs in the studied lignite samples followed the order Ce˃Nd˃La˃Y˃Sc˃Gd˃Pr˃Sm˃Dy˃Er˃Yb˃Eu˃Ho˃Tb˃Tm˃Lu, while in shale samples the order was Ce˃Y˃Nd˃La˃Sc˃Gd˃Pr˃Dy˃Sm˃Er˃Yb˃Eu˃Ho˃Tb˃Tm˃Lu. The REE concentrations in the studied samples are notably lower than global averages, yet the presence of critical REEs suggests potential economic value. The outlook coefficient (C_outl) values ranging from 0.82 to 2.59 indicate promising REE sources within the basin. XRD studies revealed the presence of various mineral phases in the analyzed samples, including quartz, kaolinite, dickite, zeolite, coesite, anatase, pyrite, gypsum, calcite, biotite, clinopyroxene, montmorillonite, and magnetite. The dominance of kaolinite and quartz indicates that felsic rocks are the primary source of inorganic sediments in the paleomire. The major and trace element ratios suggest that the deposition of the studied lignite and shales occurred under conditions of increased oxygen deficiency, ranging from dysoxic-suboxic to anoxic environments. The transition from lake water to brackish water conditions was also evident with limited terrestrial influx in the basin. Furthermore, the Ce anomalies observed in the samples, ranging from 3.51 to 5.05 in shales and 3.92 to 4.13 in lignite, suggest shales were formed under oxidizing conditions and lignites in more restricted, potentially freshwater environments.

The presence of complex karst caves poses significant challenges to construction safety and progress in tunnel engineering. Undetected karst caves in front of tunnel faces pose a risks of sudden water influx disasters. We employed an advanced semi-airborne transient electromagnetic method (SATEM) to detect potential karst caves located in areas with undulating karst terrain. Additionally, we utilized a handheld Simultaneous Localization and Mapping (SLAM)-based LiDAR device to accurately and efficiently scan large exposed caves, providing support for the design of karst cave treatment plans. For different karst cave characteristics, we introduce a series of effective comprehensive treatment methods, including sealing, crossing, and filling strategies. We demonstrate, for the first time, the outstanding effectiveness of SATEM surveys in detecting highly resistive caves, with multiple successful predictions of dried-up caves in Linlan tunnel. The proposed SATEM detection and comprehensive treatment technical system not only ensures the safety of the construction of the Tian’e-Bama expressway but also provides profound insights and practical guidance for the field of karst tunnel engineering.

In this study, image processing has been applied to ASTER and Sentinel-2A satellite images, and obtained data is used to reveal Fe-bearing rocks in the vicinity of Hasançelebi (Malatya), close to Divriği (Sivas) which is one of the most important iron provenances in the Central-Eastern Anatolia region of Türkiye. Remote sensing images, particularly the visible-near-infrared (VNIR) and partially shortwave infrared (SWIR) bands, have been employed to identify Fe-bearing minerals and rocks. With the purpose of identifying Fe-bearing minerals and rocks, various band rationing processes have been applied. Supervised classification which utilizes a parallelepiped algorithm has been employed on the resulting ratio images to create classification distributions for Fe-bearing minerals. According to the classification results; ferrous iron (Fe²⁺) and ferric oxides are more associated with ophiolitic rocks, ferrous silicates and ferric iron (Fe³⁺). The distributions are generally associated with clastic lithologies, and laterite and gossan appear to be associated with volcanic and plutonic rocks. Because of the different band widths in the VNIR range, Sentinel-2A classifications have the highest pixel count when compared to ASTER classifications for the same surface areas. During fieldwork, rock samples representing the lithologies and Fe-bearing minerals in the region have been collected and mineralogical-petrographic, geochemical, and XRD analyses have been conducted on these samples. Additionally, for spectral mineral identification and to compare Fe-bearing minerals with other analysis results, spectral signatures have also been obtained from the same samples via Analytical Spectral Device (ASD). In extracting features such as lineaments and faults, which play a crucial role in the development of ore deposits along the structural discontinuities, digital elevation models (DEM) have been preferred instead of optical images. When lineament analysis results and iron deposits, which had been identified in previous studies, were overlapped, it has been detected that revealed iron deposits are predominantly associated with the Ciritbelen-Otmangölü Fault (COF) which is an approximately east-west trending strike-slip fault located in the study area, along with other related fault systems. They are generally distributed within an ophiolitic slice and the surrounding magmatic intrusions.

Runoff refers to the quantity of water that flows over the surface of the ground from precipitation, snowmelt, or other sources, playing a crucial role in water resource management. Accurate runoff prediction in water resource modeling aids in managing water resources, forecasting floods and droughts, optimizing reservoir operations, and formulating reasonable water use policies. Advanced modeling techniques, enable more precise capture of the temporal characteristics of runoff, thereby improving the accuracy and reliability of predictions and playing a significant role in ensuring the sustainable use of water resources. To enhance the precision of runoff forecasts, a novel approach has been introduced. This methodology integrates the Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN) with a Bidirectional Long Short-Term Memory (BiLSTM) model, further optimized through the application of the Sparrow Search Algorithm (SSA). The coupling of the SSA-BiLSTM model has led to substantial optimization of several parameters, including the number of iterations, the quantity of hidden layer nodes, and the learning rate. The resulting model, termed the CEEMDAN-SSA-BiLSTM, offers an advanced and integrated solution for predicting both short-term and long-term runoff scenarios, thereby facilitating more effective water resource management and environmental preservation within the basin. Daily runoff data from 2016 to 2022 were analyzed at four hydrological stations—Huayuankou, Jiahetan, Gaocun, and Lijin. The approach involved using 80% of the daily runoff data for training and 20% for prediction. The performance of the CEEMDAN-SSA-BiLSTM model was compared against several other models, including LSTM, BiLSTM, and CEEMDAN-BiLSTM, using various evaluation indices. The error results for the CEEMDAN-SSA-BiLSTM model compared to the aforementioned models are as follows: For the HuaYuankou station, the RMSE is 97.42, the MAPE is 5.46%, the MAE is 56.9, and the NSE is 0.96. At the JiaHetan station, the RMSE is 950.36, the MAPE is 6.76%, the MAE is 59.33, and the NSE is 0.96. For the GaoCun station, the RMSE is 92.38, the MAPE is 5.53%, the MAE is 54.85, and the NSE is 0.97. Finally, for the LiJin station, the RMSE is 88.31, the MAPE is 6.49%, the MAE is 52.68, and the NSE is 0.95. The ultimate results indicate that the CEEMDAN-SSA-BiLSTM model demonstrates superior accuracy in forecasting daily runoff, with fewer errors relative to the other models.

Predicting groundwater drawdown is crucial to the Duhok Governorate’s sustainable management of its water resources. To ensure long-term water availability as extraction from population growth and development intensifies, predicting drawdown helps to prevent overuse, provide a continuous supply of water, and enable effective planning for urbanization, agriculture, and industrial needs. In this work, a novel approach based on Multi-layer perceptron neural network (MLP), support vector regression (SVR), k-nearest neighbor algorithm (KNN), and extreme learning Machine (ELM) optimized by whale optimization algorithm (WOA) were proposed for estimating the total drawdown at Zakho region, Duhok Governorate, Northern Iraq for the first time. The input variables of the models include the rate of water extraction from the well (Q), well depth (D), and various meteorological parameters such as rainfall (R), evapotranspiration (E), Maximum Temperature (Tmax), and Minimum Temperature (Tmin). It is found that ELM showed the highest performance in modeling groundwater drawdown (R² = 0.911, RMSE = 5.674 m, and MAE = 4.937 m). Moreover, the novelty of the research work is to enhance the accuracy of the individual models using two ensemble techniques including simple averaging ensemble (SAE) and weighted average ensemble (WAE). Based on the findings, the WAE technique increased the performance of individual models by up to 20%, proving the reliability of the WAE technique for groundwater drawdown prediction.

This study evaluates the efficiency of some open source optical remote sensing data (Landsat OLI, Sentinel-2 A, ASTER and DEM) in automatic lineaments extraction over Lokoja region of Central Nigeria. Various image processing techniques involving principal component analysis (PCA), directional filtering and shaded relief were employed followed by a robust lineament extraction technique and novel false lineaments filtration method. The result indicated significant variation in the number, length and accuracy of extracted lineaments across the datasets. Comparison of results by way of accuracy assessment was achieved after superimposition of extracted lineaments on geological map and shading map of the study area as well as by comparing their orientations with field obtained fracture data. Landsat OLI and Sentinel-2 A demonstrated better performance by extracting longer and more numerous lineaments many of which correlated with lithological boundaries and boundaries between shaded and unshaded areas. Orientations of the extracted lineaments indicated a majorly ENE-WSW and NE-SW trend while the field obtained fracture orientations showed a majorly NNW-SSE and NE-SW directions. All the images failed to identify lineaments along the NW-SE directions. The findings of this study underscored the importance of selecting appropriate datasets for regional geological investigations.

Understanding the geotechnical characteristics of subsoil is important for safety and efficiency in design and construction processes. In particular, the subsoil in the Bangkok Metropolitan area has accumulated soft marine clay over a long period, resulting in a thick layer of soft clay, which poses challenges for engineers. This study presents an approach to modelling the subsoil of the Bangkok Metropolitan region by utilising a large dataset of borehole data, enhanced with a Deep Neural Network (DNN) model, to develop a 3D geotechnical map. The hyperparameters of the DNN were tuned to fit the dataset for classifying the soil layers and the regression models were generated to predict the geotechnical engineering properties of the Bangkok subsoil, including the bulk unit weight, water content, plasticity index, undrained shear strength, and SPT-N values. The DNN model performance has been evaluated to ensure the accuracy and reliability of its predictions. The generated 3D geotechnical map was compared with the map obtained from the traditional kriging method to verify the map accuracy and differences in results between these two approaches. This study demonstrates the potential of machine learning techniques for improving geotechnical mapping and geotechnical engineering information. The outcomes of this research also support Sustainable Development Goals (SDGs), particularly SDG 9, by providing accurate geotechnical data to enhance sustainable infrastructure planning, and SDG 11, by refining the subsoil model in urban areas, which contributes to safer and more sustainable urban development while reducing environmental risks in construction.