Environmental Earth Sciences最新文献

Coal, traditionally a major fossil fuel, is now recognized as an unconventional potential resource for Trace Elements (TEs) and Rare Earth Elements (REEs). This article examines the occurrence, distribution, and geochemical significance of TEs and REEs in Egyptian El-Maghara coal, along with the associated environmental implications. The results indicated a medium-ash content (2.48–15.9%) of El-Maghara coal. The prevailing oxides in the studied coal samples are SO₃ (5.11%), Fe₂O₃ (1.44%) and SiO₂ (1.18%). The elevated SO₃ and Fe₂O₃ levels suggest a significant presence of sulfide minerals, primarily pyrite, which is consistent with mineralogical observations. X-ray diffraction (XRD) identified quartz as the principal silicate mineral (as the first phase), while the secondary phases such as gypsum and clay minerals contribute to the ash composition. The mean total REE content, ∑REE is 33.6 ppm, falls within the global range reported for many coal systems under investigation as unconventional REE sources. Although this value is below thresholds proposed for direct economic extraction, the outlook coefficient indicates relative enrichment in critical REEs, suggesting geochemical promising because this concentration can be reach 15-fold in coal ash. The coal is also significantly enrichment in certain trace elements (e.g., Tl, W, Cd, Sn, and Hf). The sequential extraction indicated the affinity of REEs and TEs mostly within the residual (mineral matter) fraction. Overall, this study provides a robust geochemical and mineralogical baseline for El-Maghara coal, supporting future assessments of both its environmental behavior and its long-term potential within the emerging field of coal-based unconventional critical-element resources.

Landslide disasters cause significant economic losses and casualties. Accurately identifying landslide failure modes is crucial for monitoring and early warning. This study proposes a general classification system of four failure modes. Based on 219 landslide cases collected from literature, the study uses slope angle, material, rock stratum structure, and dip angle as prediction indicators. Five machine learning algorithms are applied, with non-numerical indicators processed by one-hot encoding. A parameterization scheme with optimal effects is determined through comparisons. Adjusting neural network parameters shows that the CNN algorithm performs best, but it has limitations in distinguishing between buckling and toppling fracture plane sliding of cataclinal rock slopes due to overlapping data distribution and limited sample size. Overall, the research results landslide failure mode identification, providing a reference for enhancing monitoring and early warning capabilities. It offers practical significance and technical support for landslide disaster prevention and control.

Soil–rock mixture (S-RM) is a natural geomaterial characterized by multiple grain size grades and pronounced trans-scale hierarchical features. To investigate the multiscale coupling shear strength of S-RM, the material is decomposed into a soil matrix and rock blocks based on the deformation characteristics of mineral particles at different scales. A multiscale soil–rock cell element model is proposed. Moreover, three groups of in situ large-scale direct shear tests were conducted on S-RM samples with varying rock block contents to calibrate the microscale parameters of the model. The results show that rock blocks significantly affect the mechanical behavior of S-RM. The trans-scale, coordinated deformation between the soil matrix and rock blocks induces strain gradients at the mesoscale and microcrack coordination at the microscale, particularly at the matrix–block interfaces. This interaction leads to increased energy release at the macroscale compared with that of the pure soil matrix. This phenomenon constitutes the multiscale physical mechanism underlying the shear strength enhancement due to rock blocks. The proposed model accurately predicts the shear strength of S-RM, consistent with experimental observations.

An analysis of data from 339 groups of inorganic groundwater samples collected in a typical area of the Huai River Basin shows that shallow high-iodine groundwater is widely distributed in the northern and northeastern parts of the research area, followed by the region near Taikang. Deep high-iodine groundwater is distributed mainly in the southern part, followed by the area near Kaifeng. The main source of iodine in groundwater is iodine from Quaternary loose sediments. Climate, rock weathering, evaporation, crystallization, groundwater flow field, and pumping groundwater for irrigation all lead to the gradual concentration of iodine in groundwater and thus the formation of high-iodine groundwater.

Inert gases in CBM, such as helium (He) and argon (Ar), serve as important geochemical tracers that aid in studying the origin of CBM. In this study, 12 CBM samples were collected from the Luling coal mine in the Huaibei Coalfield. Gas composition analysis was conducted on these samples to measure the concentrations of methane, N₂, CO₂, He, and Ar. Additionally, the isotopic compositions of helium and argon in the gas samples were analyzed.Methane is the most prominent component in CBM, with concentrations ranging from 91.80% to 95.10%. Helium concentrations vary between 143.12 ppm and 580.23 ppm, falling short of the criteria for helium-rich gas. Argon concentrations lie between 62.73 ppm and 99.22 ppm. By examining the ratios of N₂/Ar_-a and He/Ar_-r, the average contents of atmospheric nitrogen (N_2-atm) and organic nitrogen (N_2-org) were calculated to be 0.11% and 4.23%, respectively, concentrations typical of the shallow crustal region in this area. This study aims to quantify the relative contributions of crustal, mantle and atmospheric components to CBM in the Luling coal mine by integrating conventional gas compositions with He-Ar isotopes, and to evaluate how these noble-gas tracers record the tectono-thermal evolution and deep fluid migration associated with the Tan-Lu Fault Zone.

Soil aggregates serve as habitats of soil microbiome and directly influence on soil microbial nutrient demand and stabilization of organic matter. Evaluating the long-term impact of different nutrient management strategies on soil enzymatic stoichiometry and microbial indices within aggregates is essential for environmental significance. Hence, this study was planned to investigate the long-term biochemical changes occurring in the soil aggregates isolated through sieving methods under five nutrient management approaches: absolute control (no fertilizer or manure), 100% recommended dose of mineral fertilizer (NPK), 50% NPK + 50% N from farm yard manure (FYM), 50% NPK + 50% N from wheat cut straw (WCS) and 50% NPK + 50% N from green manure (GM) for rice; whereas, 100% NPK was applied to wheat in all treatments except absolute control. After 40 cycles of continuous rice-wheat cropping, the application of organic amendments along with chemical fertilizers significantly increased the proportion of macro-aggregates (Macro-A; >2 mm) while reducing micro-aggregates (Micro-A; <0.25 mm). Activities of C, N and P-cycling enzymes viz. β-glucosidase, cellulase and laccase increased significantly in Macro-A after wet sieving by 7.8–87.8, 8.2–18.8 and 8.6–43.3%, respectively compared to dry sieving. Shannon’s diversity index and Simpson-Yule index was significantly higher in Macro-A than Micro-A by 2.5–5.3 and 8.6–14.8% respectively under wet sieving. It was therefore, concluded that wet sieving proved to be more sensitive in detecting variations in enzyme activity and microbial indices compared to dry sieving, making it a valuable method for assessing overall soil health and soil functionality through microbial processes under nutrient management practices.

The paper carries out an experimental investigation of strength properties of a composite geomaterial (CGM) composed of river dredged soils (RDS), ground granulated blast furnace slag (GGBFS), and waste fishing net (WFN). The experimental schemes are performed to examine the strength characteristics of CGM, encompassing unconfined compressive strength (UCS), California Bearing Ratio (CBR), and flexural strength. CGM specimens are prepared with various contents of GGBFS–RDS mixtures (5%, 10%, 15%, 20%, 30% and 40%) and two WFN contents (0.5% and 1%) for each GGBFS–RDS mixture. The experimental results indicate that the strength of CGM increases with an increase in curing time and influenced by content of each component in the mixture. The UCS and CBR of CGM increase as the GGBFS content increases from 0% to 15%, but decrease when the GGBFS content exceeds 15%. Although the CBR of mixtures with WFN inclusion is higher than that without WFN, the WFN does not significantly enhance the compressive strength in the UCS test. The WFN improves significantly the flexural strength and plays an important role in changes the brittle behavior into ductile response of CGM such as increase in axial strain at peak strength and maintain strength after failure state. An empirical relationship between UCS, CBR, and the secant modulus of CGM has been established, which is useful for practical engineering application.

The aquatic ecosystems of the alpine cryosphere have shown significant responses to climate change. While previous studies have primarily focused on long-term variations in alpine aquatic ecosystems, dramatic diurnal variations of nutrients and their relationships in alpine stream remain underexplored. This study employed high-frequency diurnal sampling along a glacier-fed alpine stream in the Qilian Mountains, from upstream to downstream, conducted monthly during the May–September melt period, to investigate the spatial and temporal variability of diurnal nutrient and environmental parameter variations. Turbidity increased sharply from 20 to over 2000 NTU in the afternoon as glacial melt intensified. Total phosphorus exhibited a similar trend to turbidity, whereas total nitrogen and dissolved organic carbon showed inverse trends. These coordinated variation patterns between nutrients and turbidity highlighted the role of diurnal glacial melt dynamics in driving nutrient variability. The influence of these variations diminished and delayed with increasing distance from the glacier. The diurnal variations of nutrients were intense in the early months of the melt and became weaker in the following months. This study emphasis the importance of examining the nutrient variations in glacier-fed streams at finer temporal scales and provide new insights into understanding the alpine aquatic ecosystems.

Graphical Abstract

Coal mining has significant impacts on groundwater environments, often causing changes in hydrogeological conditions and deterioration of water quality, which threaten ecosystem security and domestic water use. In the study area, acid mine drainage is discharged during mining, severely affecting groundwater quality and regional water safety. Conducting water quality research here helps to reveal the impacts of coal mining on groundwater chemistry and provides scientific support for environmental protection and public health. A combination of Self-Organizing Maps (SOM), K-means clustering, hydrogeochemical analysis, and GIS spatial analysis was applied to explore the mechanisms of groundwater quality deterioration and associated health risks. Results indicate that the dominant groundwater hydrochemical types are HCO₃-Ca and HCO₃-Na·Ca types, primarily controlled by water–rock interactions, particularly the dissolution of carbonate and evaporite minerals. Clustering analysis categorized groundwater samples into four distinct groups, Cluster analysis identified four distinct groundwater types. Cluster 1 showed the lowest mineralization but the highest ammonium (NH₄⁺), likely from agricultural or domestic inputs. Cluster 2 was characterized by elevated ion levels, with nitrate (NO₃⁻) contamination being most prominent. Cluster 3 had generally higher ion concentrations, with nitrite (NO₂⁻) as the key indicator. Cluster 4 exhibited the strongest mineralization, with high TDS, sodium (Na⁺), and bicarbonate (HCO₃⁻), reflecting water–rock interaction and evaporative concentration. The water quality assessment indicated that groundwater quality is generally good across most of the study area; however, approximately 2.45% of the region, covering 53.53 km² primarily within and around the mining zones, was classified as poor. Overall, the groundwater system in the Nalin River mining area exhibits a typical pattern of evolution controlled by multiple interacting factors. These findings elucidate the dominant mechanisms governing regional hydrochemical processes and provide scientific guidance and methodological support for groundwater resource protection, pollution prevention, and ecological management in mining regions.

Graphical abstract

Heavy metals in agricultural soils pose risks to ecosystems and public health, influenced by industrial emissions, atmospheric deposition, and agricultural practices. This study assesses contamination in reforested farmlands following unauthorized development removal in conservation areas of Baoqizhai and Dayangyu (Jiaodai Town, Lantian County, Xi’an City, northern Qinling foothills, China). Spatial distributions of chromium (Cr), arsenic (As), nickel (Ni), cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn) were quantified using inductively coupled plasma optical emission spectrometry (ICP-OES). Pollution levels were evaluated with the geo-accumulation index (Igeo) and potential ecological risk index (PERI). Mean concentrations were 65.4 mg/kg (Cr), 12.1 (As), 29.8 (Ni), 0.36 (Cd), 32.7 (Pb), 42.5 (Cu), and 110.3 mg/kg (Zn), generally below China’s Grade II standards except for Cd, which approached limits and contributed the highest risk. I_geo values indicated unpolluted to moderately polluted conditions (maximum Cd I_geo = 0.28), while PERI showed low to moderate overall risk (RI = 62.02), primarily from Cd (Er = 36.0). Atmospheric deposition, sewage irrigation, and industrial/agricultural activities were identified as the main sources through correlation and principal component analysis. The study evaluates reforestation’s effects on legacy pollution and recommends ongoing Cd monitoring and phytoremediation to protect soil health and biodiversity in this ecologically sensitive region.