Environmental Earth Sciences最新文献

A comprehensive evaluation of pore fluid properties, involves detailed analysis of various characteristics and behaviours relevant to its storage and management in subsurface reservoirs. The assessment includes variations in CO₂ density, bulk modulus, temperature, pressure, velocities, and interactions with reservoir fluids and rocks. The seismic response of porous rocks hosting pore fluids is influenced by these physical properties, crucial for understanding CO₂ behaviour in carbon capture and storage (CCS) initiatives. In this study, we first utilize the Batzle–Wang model to predict the behavior of common pore fluids, such as brine and gas, which are key to understanding the seismic response of the reservoir. This initial analysis provides the foundation for the next step: monitoring the behavior of injected CO₂ at the Sleipner field in Norway. To accurately track changes in the subsurface related to CO₂ injection, we employ seismic inversion using the simulated annealing (SA) technique. This global optimization approach offers significant advantages over traditional local optimization methods, yielding more reliable and near-optimal solutions for estimating the changes in acoustic impedance caused by CO₂ saturation. The study examines five sets of time-lapse seismic data from the Sleipner field, from 1994 to 2006. Acoustic impedances are computed for the pre-injection period and post-injection years, revealing a low impedance zone spanning from 2000 to 2500 m/s/g/cc. This inversion result predicts the injected CO₂ volume by calculating the CO₂ area from the uppermost time slice of different years, based on acoustic impedance seismic sections. To address inherent non-uniqueness in time-lapse analysis, the estimated volume is compared with the original production volume. The results indicate that the estimated volume closely resembles the original injected volume for different time-lapse seismic data.

The relevance of the study is related to the gap in the analysis of the impact of the gypsum mining industry on the environment in the specific conditions of the northern taiga, so an integrated approach was used, including a detailed study of the snow-water-soil-bottom sediment system within a radius of four kilometers from the gypsum quarry. Assessments of the dangers of using water for domestic drinking and fishery purposes were carried out, as well as quantitative assessments of the degree of contamination of sediments and soils and the degree of impact of pollution on biota. In order to clarify the sources of trace metals, a Principal Component Analysis of bottom sediments was performed. It has been established that bottom sediments are most contaminated with molybdenum and cadmium. For soils, the main contamination turned out to be associated with cadmium, zinc and lead. All these elements were found in increased quantities in snow samples, and zinc and lead in surface waters. The maximum contamination was determined from samples of bottom sediments with the maximum content of organic matter. According to the environmental risk index values, the level of pollution was characterized by a low potential environmental risk, but cadmium contamination was characterized by a moderate environmental risk. In sediments, the three principal components explained 91.5% of the total variance in the data set. The influence of the first component, is due to anthropogenic forcing. The second component is the natural processes of removing manganese, magnesium and strontium from rocks.

Soil erosion is one of the critical environmental issues in many places globally. It is significantly affecting in the lower Subansiri Basin of Assam, India, to environmental degradation, reduced soil quality, and declining agricultural productivity while exacerbating climate change vulnerability. This study identifies erosion-prone areas in the lower Subansiri basin, using a hybrid methodology combining fuzzy logic modeling with hydrological indices analysis, supported by mineralogical and granulometric assessments. Key factors influencing soil erosion, including rainfall, aspect, topographic variables, land use/land cover (LULC), normalized difference vegetation index (NDVI), and slope, were analysed in this study. Sediment composition and distribution patterns were further examined using X-Ray Diffraction (XRD) and grain size analysis. The results reveal that the north and northwest regions of the basin are most susceptible to erosion, with approximately 80% of the soil being sandy. Dominant minerals identified include quartz, montmorillonite, illite, calcite, and plagioclase feldspar albite. The erosion vulnerability map highlights five classes: low (11%), moderate (5%), high (20%), very high (26%), and severe (37%). These findings emphasize the need for targeted management and mitigation strategies in high-risk zones to address soil erosion effectively. This study offers valuable insights for sustainable land-use planning and soil conservation in the lower Subansiri Basin, promoting environmental resilience and agricultural sustainability.

Physics-informed machine learning (PIML) seeks to integrate scientific knowledge into conventional machine learning models to mitigate the black-box nature of the latter and prevent them from producing physically inconsistent results. Recently, Adombi et al. (2024) [a causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis] have shown that incorporating scientific knowledge into machine learning models is not enough to make them obey certain fundamental principles of physics, such as causality. They then derived certain constraints, called causal relationship constraints (CRC), to force PIML to obey the principle of causality. However, in some situations, CRC constraints in PIML prioritize the satisfaction of the principle of causality to the detriment of performance. In this study, we propose new CRC conditions and a new architecture for PIML, with the aim of testing the hypothesis that these conditions improve the performance of PIML models without transgressing the principle of causality. The models were tasked with simulating groundwater levels in six piezometers located in Quebec, Canada. A conventional machine learning model (convolutional neural network, 1D-CNN), a PIML model based on Adombi et al. (2024) (H-Lin) and a PIML model based on the architecture proposed in this work (H-LinC) were trained and subsequently compared. The results show that 1D-CNN outperforms H-LinC, which in turn outperforms H-Lin in terms of accuracy, with median NSE and KGE of 0.76 and 0.87 for 1D-CNN, 0.68 and 0.76 fir H-LinC, and 0.53 and 0.59 fir H-Lin. However, only H-LinC and H-Lin satisfy the principle of causality.

This study focuses on the temporal and spatial variations in water quality of Tigris River in Mosul, Iraq, from 2020 to 2023. It involved monthly monitoring of 17 water quality parameters at 10 different sampling stations along the river. The data were analyzed using different statistical methods, including one-way ANOVA, cluster analysis, and principal component analysis/factor analysis, to check the variation and identify potential pollution sources. The one-way ANOVA showed significant spatial and temporal differences for most of the water quality parameters, while BOD₅ and COD were relatively less variable. Cluster analysis classified the sampling sites into two distinct clusters, which were the upstream and downstream sites, indicating that urbanization significantly affects the water quality. PCA and FA revealed five key components that together comprised 87.9% of the overall variance in water quality. These components were associated with mineral content and salinity (30.5%), alkalinity and hardness (22.6%), oxygenation and organic matter (12.8%), nutrients (11.7%), and chemical oxygen demand (10.3%). The results indicate that the deterioration of water quality is mainly due to geological factors, urban runoff, agricultural activities, and domestic sewage discharge. This research provides the necessary knowledge regarding the processes that determine water quality in the Tigris River and gives a scientific basis for developing effective water resource management and pollution abatement strategies for the region.

Lake sediments are used as indicators of the condition of water bodies and the changes that have recently occurred in them and in their catchments. This work for the first time detailed examined two small lakes in Northwestern Russia (Arctic). Sediment cores were collected from the center of each lake and separated into 1 cm layers. An ICP-mass spectrometer was used to determine the chemical composition. It was found that both lakes have sediments containing organic matter (up to 61%). In both lakes, the content of rare earth elements, Th, V, Cr, and others, was found to be elevated relative to the background levels of the region and the average content in the Earth’s crust. It was established that in the sediments of Lake Aprelskoe, the total concentration of rare earth elements (1916 mg/kg) exceeds or is at the same level as the similar values of rare earth elements in lakes near cities and industrial enterprises. Such high concentrations of REE have not previously been found in pristine lakes. An analysis of the geological conditions of the study area and adjacent territories (Karelia, Finland) showed that the source of anomalies of rare earth and other elements in the studied lakes may be bedrock containing increased concentrations of Y, Ce, La, and other elements (Zr, Ta, Ba, Th). However, an analysis of the geochemistry of the studied sediments also indicated technogenic influence on the lakes: an increase in the concentrations of Pb, Sd, Cd, Ni, and Cu in the uppermost layers of the lakes’ sediments was established. Suspected sources of pollution are metallurgical plants in the neighboring region and the effects of long-range transport of metals due to coal burning around the world.

Vegetation is a crucial ecosystem component in the ecologically fragile and typically human-disturbed Loess Plateau. The Loess Plateau has undergone dramatic vegetation changes in the past few decades due to dramatic human activity and climate change. It is essential to clarify the characteristics and mechanism of vegetation variation for future ecosystem restoration and conservation. Based on the long-term data record (LTDR) NDVI dataset, this study employed scenario reconstruction and target pixel determination to explore a new insight and provide a clear finding on vegetation-climate interactions, and then give a reliable detection and assessment on vegetation variation, as well as the impact mode and intensity. The results show that NDVI of the three vegetation types was positively correlated with precipitation, especially cropland. The vegetation conversions significantly impact NDVI, particularly the conversions from cropland and grassland to woodland. Attribution analysis reveals that climate change and human activity jointly affect the variation of NDVI, but the leading role changed around 1999. During 1981–1999, 78% of the Loess Plateau experienced a declining NDVI, which was mainly caused by climate change. Conversely, NDVI increased in 47% of the area after 2000, particularly in the central and northern regions. Positive anthropogenic contribution was detected in over 49% of the area. This study is expected to provide the basis for developing effective and adaptive strategies to realize the economic and ecological stability of the Loess Plateau.

Acid mine drainage (AMD) is one of the challenging environmental issues in sulfidic mines. These hazardous solutions generally contain a mixture of indigenous iron- and sulfur-oxidizing microorganisms that could be used as a source for biotechnological purposes. In this study, the ability of an AMD from a sulfide-bearing gold mine to biooxidize its high-grade pyritic gold ore was investigated and its efficiency was compared with iron- and sulfur-oxidizing microorganisms from a microbial culture bank. Experiments were conducted at 35 and 45 ̊C, initial pH values of 1.5 and 2 in a Norris culture medium prepared from deionized and saline local waters. The effects of some critical parameters including the initial pH and the concentrations of ferrous or ferric sulfate were investigated on the efficiency of the biooxidation process and gold extraction. The results showed that the AMD microorganisms had a greater ability to oxidize the sulfide ore than the microorganisms from the microbial bank. The addition of ferrous and ferric sulfates increased the efficiency of biooxidation, while high concentrations of these ions caused the formation of inhibitory precipitates (jarosite) and decreased gold extraction. The results showed that biooxidation using the AMD medium in the saline local water increased the extraction of gold from 73 to 99%. It can be concluded that the application of AMD for the treatment of refractory gold sulfide ores could be an efficient solution for increasing gold extraction and reducing environmental problems.

To study the crack evolution patterns in expansive soils under wetting–drying cycles, a series tests were conducted on the expansive soil from a canal side slope in the South-to-North Water Diversion Middle Route Project. Six indoor wet–dry cycle tests were performed on the samples with compaction degrees of 97%, 88%, and 79%. The crack image processing system by using Python was developed for quantitative analysis of crack ratios the expansive soil samples. Furthermore, PIV (particle image velocimetry) technology was also utilized to monitor the entire process of crack development. Results show that the evolution of crack ratios over time in the expansive soil samples can be divided into four stages, crack formation, crack development, crack closing, and crack stabilization stages. The higher the compaction degree of an expansive soil sample, the shorter its duration of the crack formation stage, and the shorter the time required for the crack ratio to reach its peak. The stress and displacement field nephograms of the samples can effectively reflect the crack evolution process on their surfaces. In addition, closing ratio was proposed to studied the crack closing capacity in expansive soil samples. The crack closing ratio decrease with the increase of the number of wet-dry cycles, as well as the compaction degree decreases. The primary cause of crack closing in compacted expansive soil is uneven shrinkage in the vertical direction, which arises from differing evaporation rates between the upper and lower parts of the sample.

Appropriate support pressure is crucial for maintaining the face stability of tunnels constructed using the opposite-excavation method. In this study, two failure mechanisms are constructed to determine the appropriate support pressure on the faces of opposite-excavation tunnels, considering the effects of face distance and pore water pressure. Two theoretical models for the limit support pressure of tunnel faces are developed based on the limit equilibrium method, and then validated by simulation results and previous studies. The results show that as the face distance between the opposite-excavation tunnels decreases, the limit support pressure initially increases and then decreases. A critical distance of 20 m is identified, with the corresponding critical limit support pressures of 160 kPa and 325 kPa without and with the influence of pore water pressure, respectively. Additionally, the limit support pressure is found to decrease with cohesion but increase with unit weight. When the face distance exceeds 30 m and without the influence of pore water pressure, a larger support pressure is required to maintain the face stability of tunnel in strata with a larger internal friction angle; conversely, when the face distance is less than 30 m, an opposite trend is observed. Considering the influence of pore water pressure, maintaining the face stability of tunnel in strata with a larger internal friction angle requires a smaller limit support pressure.