Search Results (1,482)

Selecting a reasonable mesoscopic contact model and corresponding contact parameters is a key problem in discrete element simulation. In order to characterize the mesoscopic contact characteristics between particles in cohesive soil–rock mixture (CSRM), a set of laboratory consolidated and undrained triaxial tests were conducted on remolded samples of clay and CSRM collected in situ. Based on the experiments, 2D discrete element models of clay and CSRM were established, respectively. Considering the difference in the mechanical characteristics between soil particles and between soil and rock particles, different types of contact model were applied. The effects of the contact stiffness, bond strength, and friction coefficient between soil particles and between soil and rock particles on the stress–strain curves of both clay and CSRM numerical samples were sequentially studied by parameter sensitivity analysis. Results show that the contact stiffness and friction coefficient between soil particles affect the initial tangent modulus, the peak stress and the post-peak residual stress of the clay sample, while the bonding strength only affects its peak stress and residual stress. However, the mesoscopic contact parameters between soil and rock particles have little effect on the initial tangent modulus of CSRM sample but have a certain impact on the development of stress in the plastic stage, among which the influences of normal bonding strength and friction coefficient between soil and rock particles are more obvious. Finally, according to the comparison between the laboratory test results and the corresponding numerical simulation results in both clay and CSRM samples, mesoscopic contact parameters in CSRM were calibrated. Full article

This research evaluates the stabilization of a clay collected from the northern expansion zone of Cartagena de Indias, Colombia. Laboratory analyses, including particle size distribution, Atterberg limits, compaction, specific gravity, and XRF/XRD, classified the soil as a highly plastic clay (CH) with moderate dispersivity, as confirmed by pinhole and crumb tests. The soil was treated with 3–9% lime, with and without the addition of NaCl (0% and 2%), and tested for unconfined compressive strength (q_u), small-strain stiffness (G_o), and microstructural properties under curing periods of 14 and 28 days at two compaction densities. Results showed that lime significantly improved mechanical behavior, while the inclusion of NaCl further enhanced q_u (up to 185%) and G_o (up to 3-fold), particularly at higher lime contents and curing times. Regression models demonstrated that both q_u and G_o follow power-type relationships with the porosity-to-lime index, with consistent exponents (−4.75 and −5.23, respectively) and high coefficients of determination (R² > 0.79). Normalization of the data yielded master curves with R² values above 0.90, confirming the robustness of the porosity-to-lime framework as a predictive tool. The G_o/q_u ratio obtained (3737.4) falls within the range reported for cemented geomaterials, reinforcing its relevance for comparative analysis. SEM observations revealed the transition from a porous, weakly aggregated structure to a dense matrix filled with C–S–H and C–A–H gels, corroborating the macro–micro correlation. Overall, the combined use of lime and NaCl effectively converts dispersive clays into non-dispersive, mechanically improved geomaterials, providing a practical and sustainable approach for stabilizing problematic coastal soils in tropical environments. Full article

Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R² values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63–98.28% for silt, 0.26–36.16% for clay, and 0.64–96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. Full article

Ultra-deep tight sandstone gas reservoirs are key targets for natural gas exploration, yet their pore structures under high temperature, pressure, and stress greatly affect gas occurrence and flow. This study investigates representative reservoirs in the Kelasu structural belt, Tarim Basin. Porosity–permeability were measured under in situ conditions, and multi-scale pore structures were analyzed using thin sections, a SEM, mercury intrusion, and nitrogen adsorption. The results show that (1) the median permeability of cores at an ambient temperature and a confining stress of 3 MPa is 13.33–29.63 times that under the in situ temperature and pressure conditions. When the core permeability is lower than 0.1 mD, the stress sensitivity effect is significantly enhanced; (2) nanopores and micron-fractures are well developed yet exhibit poor connectivity. The majority of a core’s porosity is derived from the intergranular pores in clay minerals; (3) the volume of nano-sized pores within the 100 nm diameter range is mainly composed of mesopores, with an average proportion of 73.37%, while the average proportions of macropores and micropores are 22.29% and 4.34%, respectively; (4) full-scale pore sizes show bimodal peaks at 100–1000 nm and >100 μm, which are poorly connected; (5) the pore structure exhibits distinct fractal characteristics. The fractal dimension D_f1 (2.65 on average) corresponds to the larger pore diameters of the primary intergranular pores, residual intergranular pores, and intragranular dissolution pores. The fractal dimension D_f2 (2.10 on average) corresponds to the grain margin fractures, micron-fractures and partial throats. The pore types corresponding to the fractal dimensions D_f3 (2.36 on average) and D_f4 (2.58 on average) are mainly intercrystalline pores of clay minerals and a small number of intraparticle dissolution pores. These findings clarify the pore structure of ultra-deep tight sandstones and provide insights into their gas occurrence and flow mechanisms. Full article

Coastal wetlands play a crucial role in carbon sequestration, yet the invasion of Spartina alterniflora (SA) significantly alters the cycling and sequestration of soil organic carbon (SOC) in coastal wetlands. Nevertheless, the potential underlying mechanisms governing the dynamics of SOC in coastal wetlands following SA invasion remain poorly understood. Here, we investigated the impacts of SA invasion on the dynamics and potential sequestration mechanisms of SOC in the Hangzhou Bay Estuary Wetland, China. Compared to the bare flat (BF), SOC and its fractions in 0–20 cm increased by 1.37–2.24 times after 8 years of SA invasion. Variance partitioning analysis indicated that the combined effects of soil physicochemical properties, soil carbon cycle-related enzymes, and vegetation type were the primary drivers of SOC and its fractions. Redundancy analysis revealed significant positive correlations between SOC and key soil physicochemical properties and enzymes, including sucrase, clay particles, total nitrogen, ammonium nitrogen, and β-glucosidase. Structural equation modeling demonstrated that SA invasion was associated with significant alterations in soil physicochemical properties and positively correlated with both stable and labile carbon fractions, or indirectly linked to these fractions through carbon cycle-related enzymes, thereby substantially positively contributing to SOC. This study supports the hypothesis that the invasion of SA affects the linkage pathway of SOC sequestration and offers valuable guidance for carbon sequestration strategies of coastal wetlands. Full article

In this work, zeolite LTA (Linde Type A) was synthesised from natural clay as a novel adsorbent for copper and lead ions removal from water effluents. The applied process allowed the reuse of kaolin, as natural clay, for the production of zeolite LTA through a stepwise process, which involved the formation of metakaolin. The results of characterisation showed the formation of crystalline cubic crystals of zeolite with a mean dimension of 2–3 microns, indicating the successful nucleation and development of the LTA zeolite phase. Batch adsorption studies were carried out to study the removal ability of zeolite LTA by testing Cu²⁺ and Pb²⁺ ions. Effects of contact time, pH, and adsorbent dosage were investigated. At pH > 5, the removal efficiency for both metals exceeded 95%. As the zeolite dosage increases from 2 to 10 g/L, the removal effectiveness for both metals markedly enhances (>95% at 10 g/L for lead ions and >90% at 10 g/L for copper ions). The adsorbent showed a higher adsorption capacity in removing lead compared to copper (Q_m = 81.5 mg/g for Pb²⁺ and 67.5 mg/g for Cu²⁺). The adsorption process was well described by the pseudo-second-order kinetic model, while the Langmuir isotherm adequately depicted the equilibrium behavior. Notably, the kinetics revealed distinct contributions from chemisorption and physisorption, with the AOAS model effectively quantifying their respective roles in metal ion removal. The findings revealed that prepared zeolite LTA acts as an efficient adsorbent to remove heavy metals. Full article

Soil moisture is a very important parameter influencing many hydrological and climatic processes. It is also a key factor in agriculture, determining crop yields and thus influencing food security. It is crucial to model this variable for large areas with high spatial and temporal resolution and good accuracy. The aim of this study is to develop a soil moisture model for bare soils from Sentinel-1 SAR data that would be characterized by high spatial resolution and would be universal enough to be applicable to large areas of various soil types, textures, and large ranges of roughness. Over 800 soil moisture measurements from five study areas located in different parts of Poland were used. The work was performed on Sentinel-1 data registered between March 2024 and March 2025 using both backscattering and polarimetric analysis. The soil data were obtained from a 1:5000 scale soil map available online for Poland through the soil-agricultural geoportal. The results of machine learning modeling of soil moisture based on backscattering were relatively poor, with R² = 0.49 and 6.65% accuracy of volumetric water content in the soils. In the case of polarimetric channels, results were more or less the same. The best results were obtained by taking the silt and clay content (particles < 0.02 mm) in the soil into account. Volumetric water content accuracy of 5.27% with R² = 0.69 was thus achieved. The proposed solution seems to be a good alternative to soil moisture studies that take soil roughness into account due to its simplicity, good accuracy, and relatively easy availability of data necessary for model inversion. The analyses carried out showed that it can be used for exposed soils of very diverse roughness. Full article

Soil organic carbon (SOC) constitutes the largest terrestrial carbon pool and plays a crucial role in climate regulation, soil fertility, and ecosystem functioning. Understanding its spatiotemporal dynamics is particularly important in semi-arid regions, where fragile environments and extensive ecological restoration may alter carbon cycling. The Loess Plateau, the world’s largest loess accumulation area with a history of severe erosion and large-scale vegetation restoration, provides a natural laboratory for examining how environmental gradients influence SOC storage over time. This study used a random forest model with multi-source environmental data to quantify soil organic carbon density (SOCD) dynamics in the 0–100 cm soil layer of the Loess Plateau from 2005 to 2020. SOCD showed strong spatial heterogeneity, decreasing from the humid southeast to the arid northwest. Over the 15-year period, total SOC storage increased from 4.84 to 5.23 Pg C (a 7.9% rise), while the annual sequestration rate declined from 0.046 to 0.020 kg·m⁻²·yr⁻¹, indicating that the regional carbon sink may be approaching saturation after two decades of restoration. Among soil types, Cambisols were the largest carbon pool, accounting for over 44% of total SOC storage. Vegetation productivity emerged as the dominant driver of SOC variability, with clay content as a secondary factor. These results indicate that although ecological restoration has substantially enhanced SOC storage, its marginal benefits are diminishing. Understanding the spatial and temporal patterns of SOC and their environmental drivers provides essential insights for evaluating long-term carbon sequestration potential and informing future land management strategies. Broader generalization requires multi-regional comparisons, long-term monitoring, and deeper soil investigations to capture ecosystem-scale carbon dynamics fully. Full article

The Lower Submember of the Second Member of the Lianggaoshan Formation (LGS2-LS) in the Fuling area, southeastern Sichuan Basin, represents the deepest lacustrine depositional stage of the formation and constitutes an important target for shale oil and gas exploration. Based on core observations, thin-section petrography, X-ray diffraction, geochemical analyses, and sedimentary facies interpretation from representative wells, this study characterizes the lithofacies types, sedimentary environments, and depositional evolution of the LGS2-LS. Results show that the LGS2-LS is dominated by clay–quartz assemblages, with average clay mineral and quartz contents of 44.6% and 38.8%, respectively, and can be subdivided into shallow and semi-deep lacustrine subfacies comprising eight microfacies. Geochemical proxies indicate alternating warm-humid and hot-arid paleoclimatic phases, predominantly freshwater conditions, variable redox states, and fluctuations in paleoproductivity. Sedimentary evolution reveals multiple transgressive–regressive cycles, with Sub-layer 6 recording the maximum water depth and deposition of thick organic-rich shales under strongly reducing conditions. The proposed sedimentary model outlines a terrigenous clastic lacustrine system controlled by lake-level fluctuations, transitioning from littoral to shallow-lake to semi-deep-lake environments. The distribution of high-quality organic-rich shales interbedded with sandstones highlights the LGS2-LS as a favorable interval for shale oil and gas accumulation, providing a geological basis for further hydrocarbon exploration in the southeastern Sichuan Basin. Full article

Tight sandstone reservoirs generally exhibit poor physical properties and characterization of microscopic pore structure is crucial for evaluating reservoir quality and fluid flow behavior. Fractal dimension provides an effective means to quantify the complexity and heterogeneity of pore structures in such reservoirs. This study investigates tight sandstone reservoirs of the Kongdian Formation in the Nanpi Slope, Cangdong Sag, using cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI), and constant-rate mercury injection (CRMI) experiments. We establish a full-range fractal model to characterize pore-throat distributions and elucidate the correlation between fractal dimensions and reservoir properties, alongside factors influencing pore-structure heterogeneity. Key findings include that (1) pore types are predominantly residual intergranular pores, intergranular dissolution pores, and clay mineral intercrystalline pores, with throats primarily consisting of sheet-like and curved sheet-like types, exhibiting strong pore-structure heterogeneity; (2) full-range fractal dimensions D₁, D₂ and D₄ effectively characterize the heterogeneity of pore structure, where higher D₁ and D₂ values correlate with increased macro–mega pore and micro-fine throat abundance, respectively, indicating enhanced pore connectivity and superior flow capacity, while elevated D₄ reflects greater nano throat complexity, degrading reservoir properties and impeding hydrocarbon flow; (3) compared to conventional methods splicing HPMI and CRMI data at 0.12 μm, the fractal-derived integration point more accurately resolves full-range pore-throat distributions, revealing significant disparities in pore-throat size populations; (4) the fractal dimensions D₁, D₂, and D₄ are collectively governed by clay mineral content, average throat radius, displacement pressure, and tortuosity. Full article

This study evaluated soil–atmosphere greenhouse gas (GHG) fluxes—including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—in cacao agroecosystems on São Tomé Island, Central Africa. The field campaign was conducted between April and May 2025, coinciding with the transition from the short rainy season to the onset of the dry period. The sampling design comprised two system types (biodiverse and conventional), two crop development stages (growing and productive), and two climatic zones (wet and dry). Gas fluxes were measured using the static chamber method and analyzed in relation to climatic, topographic, and edaphic variables. CO₂ fluxes were the dominant contributor to total emissions, accounting for approximately 97.4% of the global warming potential (GWP), while CH₄ and N₂O together contributed less than 3%. The highest CO₂ emissions occurred in conventional systems during the growing stage in the wet zone (125.5 ± 11.41 mg C m⁻² h⁻¹). CH₄ generally acted as a sink, particularly in conventional systems in the dry zone (−12.58 ± 2.35 μg C m⁻² h⁻¹), although net emissions were detected in biodiverse systems in the wet zone (5.08 ± 1.50 μg C m⁻² h⁻¹). The highest N₂O fluxes were observed in conventional growing systems (32.28 ± 5.76 μg N m⁻² h⁻¹). GHG dynamics were mainly regulated by climatic factors—such as air temperature, relative humidity, and precipitation—and by key edaphic properties, including soil pH, soil organic carbon, soil temperature, and clay content. Projected GWP values ranged from 9.05 ± 2.77 to 40.9 ± 6.23 Mg CO₂-eq ha⁻¹ year⁻¹, with the highest values recorded in conventional systems in the growing stage. Overall, our findings underscore the potential of biodiversity-based agroforestry as a climate-smart practice to mitigate net GHG emissions in tropical cacao landscapes. Full article

In the Amazonian piedmont, cacao-based agroforestry systems (cAFSs) were significantly influenced by the soil’s physical, hydraulic, and structural characteristics, which largely determined agricultural productivity. A total of 122 plots with cocoa-based agroforestry systems measuring 1000 m² were randomly selected from different farms located in the Amazonian foothills in the department of Caquetá. Different variables related to soil physics and hydrology, as well as production, were determined for each plot. Soil characteristics explain 33% of the total variance in cocoa yield. Sand content (71.2%) correlated positively with yield, while clay (22.62%) and silt (23.99%) correlated negatively. Three soil types were identified: sandy loam (high productivity, yield 1129.07 g) and two variants of sandy clay loam (lower yield, 323.97 g). Hydraulic properties were important, with total porosity of 56.04% and hydraulic conductivity of 20.45 mm h⁻¹. The CCN-51 and ICS-60 clones performed better in sandy loam soils, while ICS-95 and TSH-565 adapted better to sandy clay loam soils with medium stability. The physical and hydric soil properties are crucial factors that directly influence cocoa productivity in agroforestry systems of the Amazon piedmont, where the appropriate selection of clones according to soil characteristics is fundamental to optimize crop productivity and sustainability. Full article

The dynamic shear modulus ratios and dynamic damping ratios of soil are critical parameters for soil seismic response analyses and seismic safety evaluation of engineering sites. This study utilized dynamic triaxial test and resonant column test data of 5208 soil samples collected from more than 2500 boreholes across the Beijing-Tianjin-Hebei (BTH) region. Statistical analyses were conducted for five typical soil types (silty clay, clay, silt, silty sand, and fine sand), focusing on their dynamic shear modulus ratios and dynamic damping ratios. Key parameters representing the characteristics of soil dynamics, including the reference strain, the maximum damping ratio, and the damping ratio nonlinearity coefficient, were statistically evaluated. Median values, as well as the values corresponding to 84% and 16% exceedance probabilities, were provided. The median values of the reference strain, the maximum damping ratio, and the damping ratio nonlinearity coefficient were 13.43 × 10⁻⁴, 0.2155, and 0.7799 for silty clay; 16.47 × 10⁻⁴, 0.2266, and 0.7722 for clay; 10.64 × 10⁻⁴, 0.2012, and 0.7856 for silt; 11.98 × 10⁻⁴, 0.1842, and 0.7911 for silty sand; and 12.73 × 10⁻⁴, 0.1803, and 0.8064 for fine sand. Based on these statistics, the influence of various factors on the reference shear strain, maximum damping ratio, and damping ratio nonlinearity coefficient were investigated. The results showed considerable variability, and weak correlations were observed between these parameters and site-related factors such as sampling depth, shear wave velocity at sampling depth, overburden thickness, 30 m average shear wave velocity (V_S30), and 20 m equivalent shear wave velocity (V_se). The coefficients of determination for the linear regressions considering each factor were between 0.001 and 0.274, which were sufficiently close to 0 and indicated a weak predictive ability of the model considering only one factor. Furthermore, multivariate linear regression models incorporating all five influencing factors also achieved a slight reduction in standard deviation compared with directly adopting the mean values—by <5.5% for the reference shear strain, <3.9% for the maximum damping ratio, and <7.3% for the damping ratio nonlinearity coefficient. A case study was conducted to demonstrate the impact of the variability in soil dynamic parameters on both site seismic response and structural seismic response. For the selected ground motion inputs, site model, and structural model, differences in soil dynamic parameters led to variations in structural seismic response up to 54.5%. Comparative analyses with recommended values from existing studies indicate that the dynamic parameters of the five typical soil types in the BTH region investigated exhibited distinct regional characteristics: the dynamic shear modulus ratios were significantly lower, while the dynamic damping ratios were significantly higher. Comparisons with results from other studies on soil dynamic parameters in China showed that the dynamic shear modulus ratios derived from this study were noticeably smaller, while the dynamic damping ratios were significantly larger. At least one of the three soil dynamic parameters for each soil type failed to pass two-side t-tests, which indicated that the statistical data were from two distributions, that is, soil dynamic properties were intrinsically linked to sedimentary environments, exhibiting distinct regional specificity. Therefore, for boreholes lacking laboratory dynamic test data of soil in the BTH region, it was recommended to use the median values of reference shear strains, maximum damping ratios, and damping ratio nonlinearity coefficients provided in this study for the estimation of dynamic shear modulus ratios and dynamic damping ratios, while their variability must be taken into consideration. Full article

The potential of thin-section petrography for the analysis of ancient ceramic materials, such as pottery vessels, figurative objects and building materials made of fired clay, was already recognized during the 19th century, but its use has become more intensive during the past 80 years. Since pottery is the most common and typologically datable artifact in archaeological excavations from the pottery Neolithic period onwards (some 7000–8000 years ago), the analysis of pottery, including its composition, is a central component of archaeological research. As ceramic materials are made of fired clay, which in turn is procured from soils, weathered rocks and geological formations, the mineralogical composition of the ceramic artifacts represents the clay sources. The study of the mineralogical and rock fragment composition of thin sections of ancient ceramic artifacts can yield the characterization of the clay and soil type and thus the geographic location or area of the clay source. Since in antiquity we assume clay was not precured from a distance of more than one day’s walk from the production site (‘site catchment area’), the production location can be detected as well. Thus, petrographic analysis can identify the trade of artifacts and commodities (if the ceramics are containers) in antiquity, which can shed light on political and cultural links and trade between ancient societies and their economic and social structure. In addition, since clay was often treated by ancient potters to improve its quality (levigation, clay mixing, addition of temper), technological aspects of the production sequence (chaîne opératoire) can also be acquired by petrographic analysis. Today, petrographic analysis is part of many standard studies of ancient pottery. While it is an old and relatively ‘low tech’ method, the accessibility of the equipment needed and its high analytic potential maintains its important and common position in archaeological research. This article describes the method and its analytical potential from the archaeological point of view and briefly mentions several archaeological case studies exemplifying its wide and diversified potential in the study of ancient ceramics in past decades. Full article

Soil health and fertility are essential components of sustainable land management. In Bhutan, where agricultural practices range from organic to conventional systems, and natural vegetation areas persist across varied elevations. Understanding how these factors influence soil properties is essential for advancing sustainable agriculture and fostering environmental stewardship. Thus, the objectives of this study were to evaluate some soil chemical properties across land use practices and their relationship to soil texture. Soil organic carbon (SOC) and macro-nutrients in three land use types (organic fields—OrgF; conventional fields—ConF; and natural vegetation—NatV) were studied across high-, mid-, and low-altitude sites in the Wangdue Phodrang, Chhukha, and Dagana districts of Bhutan. The effects of land use practices on soil properties varied with altitude. While available P responded significantly at both high- and mid-altitude locations (p < 0.01), SOC content was influenced only at high altitude (p < 0.001). In contrast, soil pH (p < 0.01) and available K (p < 0.001) showed clear sensitivity to land use at low altitude but were unaffected at higher elevations. Total N content and C:N ratios remained relatively stable across management practices within each altitude category. Silt and clay content had positive relationship with SOC (R² ≥ 0.13), whereas sand content had a significant negative relationship (R² = 0.23, p < 0.001). These findings are pertinent to providing guidelines for sustainable land management, improving agricultural practices, and shaping policies to protect and restore soil health across varied agro-ecological zones. Full article