Black carbon (BC) is a major short-lived climate pollutant (SLCP) with significant climate and environmentalhealth impacts. This review synthesizes critical advancements in the identification of emerging anthropogenic BC sources, updates to global warming potential (GWP) and global temperature potential (GTP) metrics, technical progress in characterization techniques, improvements in global-regional monitoring networks, emission inventory, and impact assessment methods. Notably, gas flaring, shipping, and urban waste burning have slowly emerged as dominant emission sources, especially in Asia, Eastern Europe, and Arctic regions. The updated GWP over 100 years for BC is estimated at 342 CO2-eq, compared to 658 CO2-eq in IPCC AR5. Recent CMIP6-based Earth System Models (ESMs) have improved attribution of BC's microphysics, identifying a 22 % increase in radiative forcing (RF) over hotspots like East Asia and Sub-Saharan Africa. Despite progress, challenges persist in monitoring network inter-comparability, emission inventory uncertainty, and underrepresentation of BC processes in ESMs. Future efforts could benefit from the integration of satellite data, artificial intelligence (AI)assisted methods, and harmonized protocols to improve BC assessment. Targeted mitigation strategies could avert up to four million premature deaths globally by 2030, albeit at a 17 % additional cost. These findings highlight BC's pivotal roles in near-term climate and sustainability policy.

Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

This computational study focuses on the thermo-hydro-mechanical simulations of the behaviors of freezing soils used for artificial ground freezing (AGF) in a metro project. Leveraging the experimental and field data available in the literature, we simulate the sequential freezing and excavation of a twin tunneling that occurred in months during the actual construction of the tunnel. A thermo-hydro-mechanical model is developed to capture the multi-physical rate-dependent behaviors triggered by phase transitions, as well as the creeping and secondary consolidation of the soil skeleton and the ice crystals. We then calibrate the material models and establish the THM finite element model coupled with the rate-dependent multi-physical models, which may accurately predict the surface heave induced by ground freezing throughout the project. To showcase the potential of using simulations to guide the AGF, we simulate the scenario where a simultaneous freezing scheme is employed as an alternative to the actual sequential scheme design. We then compared the simulated performance with the recorded results obtained from the sequential scheme. Finally, parametric studies on the effect of ground temperature, the porosity of the frozen soil, and the intrinsic elastic modulus of the solid skeleton are conducted. The maximum surface heave is inferred from finite element simulations to quantify the sensitivity and the impact on the safety of AGF operations.

Biological soil crusts (BSCs; biocrusts) are well developed in drylands, which are crucial to the stability and resilience of dryland ecosystems. In the southeastern Gurbantunggut Desert, a typical sandy desert in the middle part of central Asia, engineering development has an increasing negative impact on ecosystems. Fortunately, ecological restoration measures are being implemented, but the exact effect on soil quality is still unclear. In artificial sand-fixing sites on reshaped dunes along the west-east desert road, a total of 80 quadrats (1 m x 1 m) of reed checkerboards after the implementation of sand-fixing measures for 10 years were investigated to determine the BSC development status and soil properties. The algal and lichen crusts accounted for 48.75 % and 26.25 % of the total quadrat number, respectively, indicating an obvious recovery effect of BSC (only 25 % for bare sand). The developmental level of BSC gradually increased from the top to the bottom of the dunes (Li 0 -> Li 6),which was consistent with the distribution pattern of BSCs on natural dunes. Compared with bare sand, the soil organic carbon (13.85 % and 23.07 % increases), total nitrogen (12.55 % and 23.95 % increases), total potassium (9.30 % and 8.24 % increases), and available nitrogen (23.97 % and 61.41 % increases) contents of algal and lichen crusts were significantly increased, and lichen crusts had markedly higher increase effect than algal crusts. The BSC development markedly reduced soil pH (0.49 % and 0.50 % decreased) and increased electrical conductivity(11.99 % and 10.68 % increases), resulting in improved soil microenvironment. Soil properties showed significant linear relationships with BSC development level, and an optimal fitting (R2 = 0.770 or 0.780) was detected for the soil fertility index. Based on the soil property matrix, the bare sands, algal, and lichen crusts were markedly separated along the first axis in the PCA biplot, which once again confirmed the significant positive effect of BSC recovery on soil fertility improvement. Consequently, in the early stage of sand-fixation (e.g., < = 10 years) by reed checkerboards on the damaged desert surface, BSC recovery can well promote and predict soil fertility in this area. The results provide a reliable theoretical basis for the restoration technology and scientific management of degraded sandy desert ecosystems.

Freeze-thaw cycles pose a serious threat to the protection and preservation of earthen sites. To investigate the effects of freeze-thaw cycles on the shear strength and permeability of site soil, this study took artificially prepared site soil as the research object. Through triaxial shear tests and permeability tests, the strength and permeability characteristics of site soil under different sticky rice slurry content, sticky rice slurry density and freeze-thaw cycles were analyzed. In addition, the mineral composition, chemical structure, and microstructural characteristics of the samples were investigated by combining X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) tests. The results showed that the addition of sticky rice slurry could increase the shear strength and decrease the permeability coefficient of the soil, while the opposite effect was exhibited after freeze-thaw cycle. The optimum ratio of loess to sticky rice slurry was 95:5, and the optimum density of sticky rice slurry was 1.04 g/cm3. The addition of sticky rice slurry and the increase in the number of freeze-thaw cycles did not significantly change the mineral composition of the soil. The SEM results showed that the morphology and arrangement of soil particles became complex after freeze-thaw cycle, the inter-particle connections became loose, and the pore morphology became irregular. The surface porosity of the soil increased, and the proportion of large and medium pores increased. The directionality of the pores was enhanced, and the complexity of the pores increased. The pore arrangement became relatively stable after 15 freeze-thaw cycles. These findings can provide a reference for the restoration of ancient sites in loess areas.

True triaxial tests were conducted on artificially frozen sand. The effects of the intermediate principal stress coefficient, temperature and confining pressure on the strength of frozen sand were studied. The stress-strain curves under different initial conditions indicated a strain hardening. In response to increases of either the intermediate principal stress coefficient or the confining pressure or to a decrease of temperature, the strength typically increased. Furthermore, a new strength criterion was proposed to describe the strength of artificially frozen sand under a constant b-value stress path, combining the strength function in the p-q and pi planes. Considering the low confining pressure, the strength criterion in the p-q plane fitted the linear relationship in the parabolic strength criterion well. The strength criterion in the pi plane was combined with stress invariants, and a new strength criterion was established. This criterion considers unequal tension and compression strength, and integrates temperature. Test results indicated its validity. All parameters of the strength criterion could be easily determined from the triaxial compression and triaxial tensile tests.

The deterioration of soft rocks caused by freeze-thaw (F-T) climatic cycles results in huge structural and financial loss for foundation systems placed on soft rocks prone to F-T actions. In this study, cementtreated sand (CTS) and natural soft shale were subjected to unconfined compression and splitting tensile strength tests for evaluation of unconfined compressive strength (UCS, qu), initial small-strain Young's modulus (Eo) using linear displacement transducers (LDT) up to a small strain of 0.001%, and secant elastic modulus (E50) using linear variable differential transducers (LVDTs) up to a large strain of 6% before and after reproduced laboratory weathering (RLW) cycles (-20 degrees C-110 degrees C). The results showed that eight F-T cycles caused a reduction in qu, E50 and Eo, which was 8.6, 15.1, and 14.5 times for the CTS, and 2.2, 3.5, and 5.3 times for the natural shale, respectively. The tensile strength of the CTS and natural rock samples exhibited a degradation of 5.4 times (after the 8th RLW cycle) and 2.7 times (after the 15th RLW cycle), respectively. Novel correlations have been developed to predict Eo (response) from the parameters quand E50 (predictors) using MATLAB software's curve fitter. The findings of this study will assist in the design of foundations in soft rocks subjected to freezing and thawing. The analysis of variance (ANOVA) indicated 95% confidence in data health for the design of retaining walls, building foundations, excavation in soft rock, large-diameter borehole stability, and transportation tunnels in rocks for an operational strain range of 0.1%-0.01% (using LVDT) and a reference strain of less than 0.001% (using LDT). (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

Artificial ground freezing (AGF), widely employed in subway tunnel construction, significantly alters the microstructure of surrounding soils through freeze-thaw processes. These changes become critical under subway operation, where traffic-induced dynamic loading can lead to progressive soil deformation. Understanding the dynamic behavior of freeze-thaw-affected soils is therefore essential for predicting and mitigating deformation risks. This study investigates the microstructural evolution of soil subjected to a single freeze-thaw cycle-representative of AGF practice-and subsequent dynamic loading. Dynamic triaxial tests were conducted under a fixed dynamic stress amplitude of 10 kPa and loading frequencies of 0.5 Hz, 1.5 Hz, and 2.5 Hz, simulating typical subway traffic conditions. Microstructural analyses were performed using mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM). Results show that the freeze-thaw cycle leads to a denser yet more disordered particle arrangement, with sharper and more angular particles, as reflected by increased probability entropy and reductions in surface porosity, form factor, and uniformity coefficient. Dynamic loading further causes particles to flatten and align in a more directional manner, accompanied by decreased surface porosity and form factor, and an increased uniformity coefficient. Pore structures become more uniform and less complex. Among various microstructural indicators, total intrusion volume from MIP displays a strong correlation with cumulative plastic strain, suggesting its potential as a micro-scale predictor of soil deformation. These findings enhance our understanding of the coupled effects of freeze-thaw and dynamic loading on soil behavior and offer valuable insights for improving the safety and durability of subway tunnel systems constructed using AGF.

This study utilizes a combined approach of Finite Element Method (FEM) simulation and Artificial Neural Network (ANN) modeling to analyze and predict the load-displacement relationship of bored piles in clayey sand. FEM is applied to simulate the nonlinear relationship between load and vertical displacement, with input parameters including load and the mechanical properties of the soil. The results obtained from FEM are used as input data for the ANN model, enabling accurate predictions of vertical displacement based on these parameters. The findings of this study show that the predicted ultimate bearing capacity of the bored piles is highly accurate, with negligible error when compared to field experiments. The ANN model achieved a high level of accuracy, as reflected by an R2 value of 0.9992, demonstrating the feasibility of applying machine learning in pile load analysis. This research provides a novel, efficient, and feasible approach for analyzing and predicting the bearing capacity of bored piles, while also paving the way for the application of machine learning in geotechnical engineering and foundation design. The integration of FEM and ANN not only minimizes errors compared to traditional methods but also significantly reduces time and costs when compared to field experiments.