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.

Glacier shrinkage, a notable consequence of climate change, is expected to intensify, particularly in high-elevation areas. While plant diversity and soil microbial communities have been studied, research on soil organic matter (SOM) and soil protein function dynamics in glacier forefields is limited. This limited understanding, especially regarding the link between microbial protein functions and biogeochemical functions, hampers our knowledge of soil-ecosystem processes along chronosequences. This study aims to elucidate the mechanistic relationships among soil bacterial protein functions, SOM decomposition, and environmental factors such as plant density and soil pH to advance understanding of the processes driving ecosystem succession in glacier forefields over time. Proteomic analysis showed that as ecosystems matured, the dominant protein functions transition from primarily managing cellular and physiological processes (biological controllers) to orchestrating broader ecological processes (ecosystem regulators) and increasingly include proteins involved in the degradation and utilization of OM. This shift was driven by plant density and pH, leading to increased ecosystem complexity and stability. Our confirmatory path analysis findings indicate that plant density is the main driver of soil process evolution, with plant colonization directly affecting pH, which in turn influenced nutrient metabolizing protein abundance, and SOM decomposition rate. Nutrient availability was primarily influenced by plant density, nutrient metabolizing proteins, and SOM decomposition, with SOM decomposition increasing with site age. These results underscore the critical role of plant colonization and pH in guiding soil ecosystem trajectories, revealing complex mechanisms and emphasizing the need for ongoing research to understand long-term ecosystem resilience and carbon sequestration.

The alpine ecosystems of the Qinghai-Tibet Plateau (QTP) provide multiple ecosystem services. In recent decades, these ecosystem services have been profoundly affected by climate change, human activity, and frozen ground degradation. However, related research remains lacking to date in the QTP. To address this gap, the upper reaches of the Shule River, a typical cryospheric-dominated basin in the QTP, was selected. We simultaneously assessed the spatial-temporal patterns and driving factors of ecosystem services, including habitat quality (HQ), net primary productivity (NPP), water conservation (WC), carbon storage (CS), water yield (WY), green space recreation (GSR), and total ecosystem service (TES), by employing the InVEST, CASA, and Noah-MP land surface models in combination with remote sensing and field survey data. Our results showed that: (1) HQ, NPP, WC, CS, WY, and GSR all increased significantly from 2001 to 2020 at rates of 0.004 a(-1), 1.920 g Cm(-2)a(-1), 0.709 mma(-1), 0.237 Mg & sdot;ha(-1)a(-1), 0.212 x 10(8) m(3)a(-1), and 0.038 x 10(9) km(2)a(-1) (P < 0.05), respectively; (2) warm and humid climates, combined with shrinking of barren, contributed to the increases in HQ, NPP, WC, CS, WY, and GSR; (3) frozen ground degradation had promoting effects on HQ, NPP, CS, GSR, and TES, while inhibiting effects were observed on WY and WC (P < 0.05); (4) synergies among ecosystem services were prominent over the past 20 years; (5) the total ecosystem service value increased significantly at a rate of 1.18 x 10(9) CNYa(-1) from 2001 to 2020 (P < 0.05), primarily due to the increase in the provisioning service value.

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.

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.

The direct radiative impact of atmospheric aerosols remains more uncertain than that of greenhouse gases, largely due to the complex transformations' aerosols undergo during atmospheric aging. Sulfate aerosols have been the subject of considerable research, with a robust body of literature characterising their cooling effect. In contrast, the light-absorbing properties and warming potential of black carbon and related products remain less well understood, with limited research available to date. The present study examines the iron-catalyzed reaction of catechol in levitated microdroplets, tracked in situ using elastic light scattering spectroscopy. The reaction forms water-insoluble polycatechol aggregates, which drive a transition from homogeneous spheres to heterogeneous droplets with internal inclusions. To interpret the evolving optical behaviour, the Multiple Sphere T-Matrix (MSTM) model is employed, a method which overcomes the limitations of Mie theory by accounting for internal morphological complexity. The model provides realistic complex refractive indices and fractal parameters, though it should be noted that its solutions are not unique due to sensitivity to input assumptions and droplet variability. This underscores the necessity for supplementary measurements and more comprehensive models incorporating evaporation, chemical dynamics, and phase transitions. These findings emphasise the potential of elastic scattering spectroscopy for real-time monitoring of multiphase chemistry and offer new constraints for improving aerosol aging schemes in climate models, thereby contributing to reduced uncertainties in aerosol radiative forcing.

Seasonal freezing and thawing significantly influence the migration and distribution of soil hydrothermal salts. Understanding the dynamics of hydrothermal salt forces in canal foundation soils is crucial for effective canal disease control and optimization. However, the impact on rectangular canals remains poorly understood. Therefore, field-scale studies on water-heat-salt-force-displacement monitoring were conducted for the canal. The study analyzed the changes and interaction mechanisms of water-heat-salt-force in the soil beneath the canal, along with the damage mechanisms and preventive measures. The results indicate that the most rapid changes in temperature, moisture, and salt occur in the subsoil on the canal side, with the greatest depth of freezing. Heat transfer efficiency provides an intuitive explanation for the sensitivity of ground temperature at the junction of the canal wall and subsoil to air temperature fluctuations, as well as the minimal moisture migration in this region under the subcooling effect. The temperature-moisture curve suggests that current waterheat-force and water-heat-salt-force models exhibit a delay in accurately predicting water migration within the subsoil. Rectangular canals are more susceptible to damage under peak freezing conditions, requiring a combined approach of freezing restraint and frost-heaving force to mitigate damage. These findings offer valuable insights for canal design, maintenance, and further research.