This study assesses the stability of the Bei'an-Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from -35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by similar to 107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence 10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed 'past-present-future' framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

The reasonable value of good gradation characteristic parameters is key in designing and optimising soil-rock mixed high fill embankment materials. Firstly, the DJSZ-150 dynamic-static large-scale triaxial testing instrument was used for triaxial compression shear tests on compacted skeleton structure soil-rock mixture standard specimens. The changes in strength and deformation indicators under different gradation parameters and confining pressure were analysed. Then, based on the Janbu empirical formula, relationships between parameters K, n, and (sigma 1-sigma 3)ult and the coefficient of uniformity Cu and coefficient of curvature Cc were explored. Empirical fitting formulas for Duncan-Chang model constants a and b were proposed, establishing an improved Duncan-Chang model for soil-rock mixtures considering gradation characteristics and stress states. Finally, based on significant differences in particle spatial distribution caused by gradation changes, three generalised models of matrix-block stone motion from different particle aggregation forms were proposed. Results indicate the standard specimen's strength and deformation indicators exhibit significant gradation effects and stress-state correlations. The improved Duncan-Chang model effectively simulates the stress-strain relationship curve under different gradations and confining pressure, with its characteristics explainable based on the matrix block stone motion generalised model.

Bedrock-soil layer slopes (BSLSs) are widely distributed in nature. The existence of the interface between bedrock and soil layer (IBSL) affects the failure modes of the BSLSs, and the seismic action makes the failure modes more complex. In order to accurately evaluate the safety and its corresponding main failure modes of BSLSs under seismic action, a system reliability method combined with the upper bound limit analysis method and Monte Carlo simulation (MCS) is proposed. Four types of failure modes and their corresponding factors of safety (Fs) were calculated by MATLAB program coding and validated with case in existing literature. The results show that overburden layer soil's strength, the IBSL's strength and geometric characteristic, and seismic action have significant effects on BSLSs' system reliability, failure modes and failure ranges. In addition, as the cohesion of the inclination angle of the IBSL and the horizontal seismic action increase, the failure range of the BSLS gradually approaches the IBSL, which means that the damage range becomes larger. However, with the increase of overburden layer soil's friction angle, IBSL's depth and strength, and vertical seismic actions, the failure range gradually approaches the surface of the BSLS, which means that the failure range becomes smaller.

An analytical methodology was developed for the first time in this work enabling the simultaneous enantiomeric separation of the fungicide fenpropidin and its acid metabolite by Capillary Electrophoresis. A dual cyclodextrin system consisting of 4 % (w/v) captisol with 10 mM methyl-beta-cyclodextrin was employed in a 100 mM sodium acetate buffer at pH 4.0. Optimal experimental conditions (temperature 25 degrees C, separation voltage -25 kV, and hydrodynamic injection of 50 mbar x 10 s) allowed the simultaneous separation of the four enantiomers in <10.7 min with resolutions of 3.1 (fenpropidin) and 3.2 (its acid metabolite). Analytical characteristics of the method were evaluated and found adequate for the quantification of both chiral compounds with a linearity range from 0.75 to 70 mg L-1, good accuracy (trueness included 100 % recovery, precision with RSD<6 %), and limits of detection and quantification of 0.25 and 0.75 mg L-1, respectively, for the four enantiomers. No significant differences were found between the concentrations determined and labelled of fenpropidin in a commercial agrochemical formulation. The stability over time (0-42 days) of fenpropidin enantiomers using the commercial agrochemical formulation was evaluated in two sugar beet soils, revealing to be stable at any time in one sample, while in the other a decrease of 45 % was observed after 42 days. Individual and combined toxicity of fenpropidin and its metabolite was determined for the first time for marine organism Vibrio fischeri, demonstrating higher damage caused by parent compound. Synergistics and antagonists' interactions were observed at low and high effects levels of contaminants.

With global warming and the intensification of human activities, frozen soils continue to melt, leading to the formation of thermokarst collapses and thermokarst lakes. The thawing of permafrost results in the microbial decomposition of large amounts of frozen organic carbon (C), releasing greenhouse gases such as carbon dioxide (CO2) and methane (CH4). However, little research has been done on the thermo-water-vapor-carbon coupling process in permafrost, and the interactions among hydrothermal transport, organic matter decomposition, and CO2 transport processes in permafrost remain unclear. We considered the decomposition and release of organic C and established a coupled thermo-water-vapor-carbon model for permafrost based on the study area located in the Beiluhe region of the Qingzang Plateau, China. The model established accurately reflected changes in permafrost temperature, moisture, and C fluxes. Dramatic changes in temperature and precipitation in the warm season led to significant soil water and heat transport, CO2 transport, and organic matter decomposition. During the cold season, however, the soil froze, which weakened organic matter decomposition and CO2 transport. The sensitivity of soil layers to changes in the external environment varied with depth. Fluctuations in energy, water, and CO2 fluxes were greater in shallow soil layers than in deeper ones. The latent heat of water-vapor and water-ice phase changes played a crucial role in regulating the temperature of frozen soil. The low content of soil organic matter in the study area resulted in a smaller influence of the decomposition heat of soil organic matter on soil temperature, compared to the high organic matter content in other soil types (such as peatlands).

The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

Substantial nitrous oxide (N2O) emissions from permafrost-affected regions could accelerate climate warming, given that N2O exhibits approximately 300 times greater radiative forcing potential than carbon dioxide. Pronounced differences exist in N2O emissions between freeze and thaw periods (FP and TP), but the mechanisms by which environmental factors regulate the production and emission of N2O during these two periods have not been thoroughly examined. We therefore combined static chamber gas chromatography, in-situ soil temperature (ST) and moisture (SM) monitoring, and 16S rRNA sequencing to investigate seasonal N2O variations in the Qinghai-Tibet Plateau (QTP) alpine meadow ecosystem, and assess the relative contributions of environmental and microbial drivers. Our findings indicate that N2O fluxes (-3.15 to 6.10 mu g m-2 h-1) fluctuated between weak sources and sinks, peaking during FP, particularly at its late stage with initial surface soil thawing. Soil properties affect N2O emissions by regulating denitrification processes and altering microbial community diversity. During the FP, ST fluctuations control N2O release by modifying mineral nutrient availability. During TP, soil texture modulates denitrification-driven N2O production through its effect on SM. Spring N2O pulses likely originate from microbial reactivation in thawed soil. N2O accumulated in frozen soil may gradually release during vertical profile thawing. On the QTP, a warmer and wetter climate scenario may alter N2O emissions by modifying the duration of the FP and TP and phase-specific hydrothermal allocation. This study provides mechanistic insights for predicting climate change impacts on N2O flux in fragile alpine meadow ecosystems.

Ground subsidence resulting from underground coal mining poses significant challenges to urban safety, infrastructure stability, and environmental protection, particularly in regions extending beneath water bodies. This study investigates subsidence patterns in the Kozlu coal basin by integrating Interferometric Synthetic Aperture Radar (InSAR), numerical modelling, and machine learning techniques. The Kozlu coal basin, located in Zonguldak, Turkey, serves as a critical example, where extensive mining activities have led to complex deformation patterns. InSAR effectively captures terrestrial subsidence but is limited in underwater regions. Numerical modelling provides insights into geological behaviour but requires extensive input data. Machine learning, specifically Gaussian Process Regression (GPR), bridges this gap by predicting subsidence in unobservable underwater zones with high accuracy. The integrated approach reveals consistent deformation trends across terrestrial and marine environments, offering practical tools for risk mitigation and resource management. These findings underscore the importance of interdisciplinary methods in addressing complex geological challenges and pave the way for future advancements in subsidence monitoring and prediction.

Root-knot nematodes (RKN) severely reduce watermelon yields worldwide, despite its nutraceutical value. This study investigated the effects of rock dust (RD) and poultry manure (PM) amendments, applied singly or in combination, on RKN suppression and watermelon fruit yield enhancement. A two-trial field experiment was conducted utilizing a randomized complete block design with three replicates. The treatments included RD and PM each applied at 0, 2.5, or 5 t/ha and combined applications of RD and PM at 2.5 or 5 t/ha each. At 60-66 days post-inoculation, root galling and RKN population density were assessed alongside root-shoot weight. The results indicated that root galling in watermelons was reduced by 60-85 % and 67-89 % in the combined RD- and PMtreated plots across the 1st and 2nd trials, respectively, in contrast to the control plots. Likewise, the RKN population was suppressed by 94-99 % in treated plots in both trials, differing from the control plots. Notably, watermelon fruit yield was significantly higher (p < 0.05) in combined RD and PM treated plots, ranging from 24.7 to 33.7 t/ha and 34.6-46.5 t/ha in the 1st and 2nd trials, respectively, compared to control plots with 13.5 t/ha in the 1st trial compared to and 20.9 t/ha yield in the 2nd trial. In conclusion, our study indicates that coapplication of RD and PM effectively reduced RKN damage and enhanced watermelon fruit yield, providing a sustainable strategy for watermelon production.

Char and soot represent distinct types of elemental carbon (EC) with varying sources and physicochemical properties. However, quantitative studies in sources, atmospheric processes and light-absorbing capabilities between them remain scarce, greatly limiting the understanding of EC's climatic and environmental impacts. For in-depth analysis, concentrations, mass absorption efficiency (MAE) and stable carbon isotope were analyzed based on hourly samples collected during winter 2021 in Nanjing, China. Combining measurements, atmospheric transport model and radiative transfer model were employed to quantify the discrepancies between char-EC and soot-EC. The mass concentration ratio of char-EC to soot-EC (R-C/S) was 1.4 +/- 0.6 (mean +/- standard deviation), showing significant dependence on both source types and atmospheric processes. Case studies revealed that lower R-C/S may indicate enhanced fossil fuel contributions, and/or considerable proportions from long-range transport. Char-EC exhibited a stronger light-absorbing capability than soot-EC, as MAE(char) (7.8 +/- 6.7 m(2)g(-1)) was significantly higher than MAE(soot) (5.4 +/- 3.4 m(2)g(-1))(p < 0.001). Notably, MAE(char) was three times higher than MAE(soot) in fossil fuel emissions, while both were comparable in biomass burning emissions. Furthermore, MAE(soot) increased with aging processes, whereas MAE(char) exhibited a more complex trend due to combined effects of changes in coatings and morphology. Simulations of direct radiative forcing (DRF) for five sites indicated that neglecting the char-EC/soot-EC differentiation could cause a 10 % underestimation of EC's DRF, which further limit accurate assessments of regional air pollution and climate effects. This study underscores the necessity for separate parameterization of two types of EC for pollution mitigation and climate change evaluation.