Climate change is reshaping the risk landscape for natural gas pipelines, with landslides emerging as a major driver of technological accidents triggered by natural hazards (Natech events). Conventional Natech risk models rarely incorporate climate-sensitive parameters such as groundwater levels and soil moisture, limiting their capacity to capture evolving threats. This study develops a probabilistic model that explicitly links climate-driven landslide susceptibility to pipeline vulnerability, providing a quantitative basis for assessing pipeline failure probability under different emission projection scenarios. Using Monte Carlo simulations across five regions in China, the results show that under high-emission pathways (SSP5-8.5), pipeline failure probability in summer increases dramatically. For example, from 0.320 to 0.943 in Xinjiang, 0.112 to 0.220 in Sichuan, and 0.087 to 0.188 in Hainan. In cold regions, winter failure probability more than doubles, rising from 0.206 to 0.501 in Heilongjiang and from 0.235 to 0.488 in Beijing. These shifts reveal an overall increase in risk, intensification of seasonal contrasts, and, in some areas, a reconfiguration of high-risk periods. Sensitivity analysis highlights groundwater levels and soil moisture as the dominant drivers, with regional differences shaped by precipitation regimes, permafrost thaw, and typhoon impacts. Building on these insights, this study proposes an AI-based condition-monitoring framework that integrates real-time climate and geotechnical data to support adaptive early warning and safety management.

Black carbon (BC) mixed with non-BC components strongly absorbs visible light and leads to uncertainty in assessing the absorption enhancement (Eabs) and thus radiative forcing. Traditional Single-Particle Soot Photometer (SP2) combined with the leading-edge only fitting (the only-SP2 method) derives BC's mixing states through Mie scattering calculations. However, errors exist in retrieved optical diameter (Dopt) and MR due to the assumption of the ideal spherical core-shell structure and the selection of the calculation parameters like density and refractive index (RI) of the components. Here, we employed a custom-developed tandem CPMA-SP2 system, which classifies fixed-mass BC to characterize the mixing state, then compared with the only-SP2 method in quantifying the mixing state and Eabs. The field measurements show that the SP2 demonstrates variability in assessing the mixing state of BC in different aging states. The thickly-coated particles with small core approaching the internally mixed state are more sensitive to the change of calculated RI. The Dopt decreases with the RI increasing, indicating that this method accurately measures both Dopt and Eabs when a reasonable refractive index is selected for calculation. However, for thinly-coated particles with moderate or large core, this method results in significant deviations in the computed Eabs (errors up to 15 %). These deviations may be caused by the various shapes of BC and systematic errors. Our results provide valuable insights into the accuracy of the SP2-retrieved Dopt and MR based on Mie calculations and highlight the importance of employing advanced techniques for further assessment of BC's mixing state.

Slope failures resulting from thaw slumps in permafrost regions, have developed widely under the influence of climate change and engineering activities. The shear strength at the interface between the active layer and permafrost (IBALP) at maximum thawing depth is a critical factor to evaluate stability of permafrost slopes. Traditional direct shear, triaxial shear, and large-scale in-situ shear experiments are unsuitable for measuring the shear strength parameter of the IBALP. Based on the characteristics of thaw slumps in permafrost regions, this study proposes a novel test method of self-weight direct shear instrument (SWDSI), and its principle, structure, measurement system and test steps are described in detail. The shear strength of the IBALP under maximum thaw depth conditions is measured using this method. The results show that under the condition that the permafrost layer is thick underground ice and the active layer consists of silty clay with 20% water content, the test results are in good agreement with the results of field large-scale direct shear tests and are in accordance with previous understandings and natural laws. The above analysis indicates that the method of the SWDSI has a reliable theoretical basis and reasonable experimental procedures, and meets the needs of stability assessment of thaw slumps in permafrost regions. The experimental data obtained provide important parameter support for the evaluation of related geological hazards.

The thermal coupling between the atmosphere and the subsurface on the Qinghai-Tibetan Plateau (QTP) governs permafrost stability, surface energy balance, and ecosystem processes, yet its spatiotemporal dynamics under accelerated warming are poorly understood. This study quantifies soil-atmosphere thermal coupling ((3) at the critical 0.1 m root-zone depth using in-situ data from 99 sites (1980-2020) and a machine learning framework. Results show significantly weaker coupling in permafrost (PF) zones (mean (3 = 0.42) than in seasonal frost (SF) zones (mean (3 = 0.50), confirming the powerful thermal buffering of permafrost. Critically, we find a widespread trend of weakening coupling (decreasing (3) at 66.7 % of sites, a phenomenon most pronounced in SF zones. Our driver analysis reveals that the spatial patterns of (3 are primarily controlled by surface insulation from summer rainfall and soil moisture. The temporal trends, however, are driven by a complex and counter-intuitive interplay. Paradoxically, rapid atmospheric warming is the strongest driver of a strengthening of coupling, likely due to the loss of insulative snow cover, while trends toward wetter conditions drive a weakening of coupling by enhancing surface insulation. Spatially explicit maps derived from our models pinpoint hotspots of accelerated decoupling in the eastern and southern QTP, while also identifying high-elevation PF regions where coupling is strengthening, signaling a loss of protective insulation and increased vulnerability to degradation. These findings highlight a dynamic and non-uniform response of land-atmosphere interactions to climate change, with profound implications for the QTP's cryosphere, hydrology, and ecosystems.

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 Himalayan glacier valleys are encountering escalating environmental challenges. One of the contributing factors is thought to be the rising amounts of light-absorbing carbonaceous aerosols, particularly brown carbon (BrC) and black carbon (BC), that are reaching glacier valleys. The present study examines the optical and radiative characteristics of BC at Bhojbasa, near Gaumukh (similar to 3800amsl). Real-time in-situ BC data, optical characteristics, radiative forcing, heating rate, several meteorological parameters, and BC transport pathways to this high-altitude site are investigated. The daily mean concentration of equivalent black carbon (eBC) was 0.28 +/- 0.21 mu g/m(3) over the research period, and the eBC from fossil fuel (BCFF) is dominant with 78 % with a daily mean of 0.22 +/- 0.19 mu g/m(3)(,) and eBC from biomass burning (BCBB) is 22 % with a daily mean of 0.06 +/- 0.08 mu g/m(3). Meteorological data, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) imaging, and backward air-mass trajectory analysis demonstrate the presence of BC particles and their plausible transit pathways from multiple source locations to the pristine Gangotri Glacier Valley. The estimated daily mean BC radiative forcing values are +6.71 +/- 1.80 W/m(2) in the atmosphere, +1.87 +/- 1.16 W/m(2) at the top of the atmosphere, and -4.84 +/- 1.01 W/m(2) at the surface with a corresponding atmospheric heating rate of 0.19 +/- 0.05 K/day. These findings highlight the critical role of ground-based measurements in monitoring the fluctuations of BC over such varied Himalayan terrain, as they offer important information on the localized trends and effects. Long-term measurements of glacier valleys are essential for a comprehensive evaluation of the impact of BC particles on Himalayan ecology and climate.

This study presents the first high-resolution Regional Climate Model 5 (RegCM5) analysis of the unprecedented May-June 2024 heatwave in India, evaluating the role of absorbing aerosols-black carbon (BC) and dust-in amplifying extreme heat. Heatwaves have a severe impact on health, mortality, and agriculture, with absorbing aerosols exacerbating warming. MERRA-2 Aerosol Optical Depth (AOD) anomalies show that BC peaked at +0.027 in May, weakening in June, while dust remained higher (up to +0.36), intensifying over the Indo-Gangetic Plain (IGP) and northwestern India. RegCM5 simulations, validated against India Meteorological Department (IMD) observational data, indicate that these aerosols amplified surface temperature anomalies, with BC-induced warming exceeding +4 degrees C in northern India during May, while dust produced stronger anomalies, surpassing +5 degrees C in the IGP and Rajasthan in June. BC-induced warming was vertically distributed and more pronounced under clear skies, whereas dust-induced warming was surface-concentrated and persisted longer in regions with higher dust concentrations. Both aerosols increased net shortwave radiation (SWR; >300 W m(-2) for BC, similar to 270 W m(-2) for dust) and upward longwave radiation (ULR; >130 W m(-2)), inducing surface energy imbalances. This radiative forcing caused lower-tropospheric warming (up to +3 degrees C at 925 hPa for BC and 850 hPa for dust) and humidity deficits (-0.009 kg/kg), which stabilised the atmosphere, suppressed convection, and delayed monsoon onset. These findings highlight aerosol-radiation interactions as critical drivers of heatwave onset and persistence, emphasizing the need for their integration into regional climate models and early warning systems.

Infrastructure in northern regions is increasingly threatened by climate change, mainly due to permafrost thaw. Prediction of permafrost stability is essential for assessing the long-term stability of such infrastructure. A key aspect of geotechnical problems subject to climate change is addressing the surface energy balance (SEB). In this study, we evaluated three methodologies for applying surface boundary conditions in longterm thermal geotechnical analyses, including SEB heat flux, n-factors, and machine learning (ML) models by using ERA5-Land climate reanalysis data until 2100. We aimed to determine the most effective approach for accurately predicting ground surface temperatures for climate-resilient design of northern infrastructure. The evaluation results indicated that the ML-based approach outperformed both the SEB heat flux and n-factors methods, demonstrating significantly lower prediction errors. The feasibility of long-term thermal analysis of geotechnical problems using ML-predicted ground surface temperatures was then demonstrated through a permafrost case study in the community of Salluit in northern Canada, for which the thickness of the active layer and talik were calculated under moderate and extreme climate scenarios by the end of the 21st century. Finally, we discussed the application and limitations of surface boundary condition methodologies, such as the limited applicability of the n-factors in long-term analysis and the sensitivity of the SEB heat flux to inputs and thermal imbalance. The findings highlight the importance of selecting suitable boundary condition methodologies in enhancing the reliability of thermal geotechnical analyses in cold regions.

Amid global climate change, freeze-thaw cycles in cold regions have intensified, reducing the stability of infrastructures and significantly increasing the demand for grouting reinforcement. However, the deterioration in the durability of existing grouting materials under the combined effects of freeze-thaw cycles and low temperatures has become a major technical bottleneck restricting their application in cold regions. This paper focuses on polyurethane (PU) grouting materials with foaming and lifting characteristics, systematically reviewing the research progress and technical challenges associated with their engineering applications in cold regions. First, in terms of material composition and preparation, the core components and modified additives are detailed to establish a theoretical foundation for performance regulation. Second, addressing the application requirements in cold regions, standardized testing methods and comprehensive evaluation systems are summarized based on key indicators such as heat release temperature, impermeability, diffusion properties, mechanical strength, and expansion properties. Combined with microstructural characteristics, the deformation behavior and failure mechanisms of PU grouting materials under freeze-thaw cycles and salt-freezing environments are revealed. At the engineering application level, the challenges faced by PU grouting materials in cold regions-such as inhibited low-temperature reactivity and insufficient long-term durability-are highlighted. Finally, considering current research gaps, including the unclear mechanisms of microscopic dynamic evolution and the lack of studies on the combined effects of complex environments, future research directions are proposed. This paper aims to provide theoretical support for the development and application of PU grouting materials in cold-region geotechnical engineering.

Here, we present the result of different models for active layer thickness (ALT) in an area of the Italian Central Alps where a few information about the ALT is present. Looking at a particular warm year (2018), we improved PERMACLIM, a model used to calculate the Ground Surface Temperature (GST) and applied two different versions of Stefan's equation to model the ALT. PERMACLIM was updated refining the temporal basis (daily respect the monthly means) of the air temperature and the snow cover. PERMACLIM was updated also to minimize the bias of the snow cover in summer months using the PlanetScope images. Moreover, the contribution of the solar radiation was added to the air temperature to improve the summer GST. The modelled GST showed a good calibration and, among the two versions of Stefan's equation, the first (ALT1) indicates a maximum active layer thickness of 7.5 m and showed a better accuracy with R2 of 0.93 and RMSE of 0.32 m. The model underlined also the importance of better definition of the thermal conductivity of the ground that can strongly influence the ALT.