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.

Light-absorbing carbonaceous aerosols, comprising black carbon (BC) and brown carbon (BrC), significantly influence air quality and radiative forcing. Unlike traditional approaches that use a fixed value of absorption & Aring;ngstrom exponent (AAE), this study investigated the absorption and optical properties of carbonaceous aerosols in Beijing for both local emission and regional transport events during a wintertime pollution event by using improved AAE results that employs wavelength-dependent AAE (WDA). By calculating the difference of BC AAE at different wavelengths using Mie theory and comparing the calculated results to actual measurements from an Aethalometer (AE31), a more accurate absorption coefficient of BrC can be derived. Through the analysis of air mass sources, local emission was found dominated the pollution events during this study, accounting for 81 % of all cases, while regional transport played a minor role. Carbonaceous aerosols exhibited a continuous increasing trend during midday, which may be attributed to the re-entrainment of nighttime-accumulated carbonaceous aerosols to the surface during the early planetary boundary layer (PBL) development phase, as the mixed layer rises, combined with the variation of PBL and anthropogenic activity. At night, variations in the PBL height, in addition to anthropogenic activities, effectively contributed to surface aerosol concentrations, leading to peak surface aerosol values during local pollution episodes. The diurnal variation of AAE470/880 exhibited a decreasing trend, with a total decrease of approximately 12 %. Furthermore, the BrC fraction showed a constant diurnal variation, suggesting that the declining AAE470/880 was primarily influenced by BC, possibly due to enhanced traffic contributions.

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.

Earthquakes are common geological disasters, and slopes under seismic loading can trigger coseismic landslides, while also becoming unstable due to accumulated damage caused by the seismic activity. Reinforced soil slopes are widely used as seismic-resistant geotechnical systems. However, traditional geosynthetics cannot sense internal damage in reinforced soil systems, and existing in-situ distributed monitoring technologies are not suitable for seismic conditions, thus limiting accurate post-earthquake stability assessments of slopes. This study presents, for the first time, the use of a batch molding process to fabricate self-sensing piezoelectric geogrids (SPGG) for distributed monitoring of soil behavior under seismic conditions. The SPGG's reinforcement and damage sensing abilities were verified through model experiments. Results show that SPGG significantly enhances soil seismic resistance and can detect soil failure locations through voltage distortions. Additionally, the tensile deformation of the reinforcement material can be quantified with sub-centimeter precision by tracking impedance changes, enabling high-precision distributed monitoring of reinforced soil under seismic conditions. Notably, when integrated with wireless transmission technology, the SPGG-based monitoring system offers a promising solution for real-time monitoring and early warning in road infrastructure, where rapid detection and response to seismic hazards are critical for mitigating catastrophic outcomes.

Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.

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 recent large reduction in anthropogenic aerosol emissions across China has improved China's air quality but has potential consequences for climate forcing. This sharp reduction in anthropogenic emissions has occurred against a background influenced by changing regional biomass burning emissions over a similar period of time. Here, we use the UK Earth System Model (UKESM) to estimate aerosol instantaneous radiative forcing (IRF) due to changes in emissions of aerosols and precursors from biomass burning and anthropogenic sources (separately and in combination) over 2008-2016, with a focus on China and regions downwind. We also separately quantify the IRF due to changes in anthropogenic aerosol emissions inside China (CHN) and the Rest Of the World (ROW). Reductions in Chinese anthropogenic emissions of BC, SO2 and OC contributed -0.30 +/- 0.01, +1.00 +/- 0.04, and +0.05 +/- 0.01 W m-2, respectively to IRF over China, accounting for similar to 97% of the total local anthropogenic aerosol IRF. These emission changes contributed a remote regional IRF of 0.22 +/- 0.04 W m-2 over the North Pacific Ocean. The reduction in SO2 emissions from China contributed a global IRF of equal magnitude to that from SO2 emissions from ROW (similar to 0.08 W m-2). Changes in global biomass burning emissions contributed 0.03 W m-2 (equivalent to over 20% of the magnitude of anthropogenic aerosol IRF), enhancing the global anthropogenic aerosol IRF, whereas they partly offset the anthropogenic IRF over China. Meanwhile, biomass burning emissions dominated the total IRF (around 98%) over the Arctic.

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.