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.

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.

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.

Widespread dieback of natural Mongolian pine (Pinus sylvestris var. mongolica) forests in Hulunbuir sandy land since 2018 has raised concerns about their sustainability in afforestation programs. We hypothesized that this dieback is driven by two interrelated mechanisms: (1) anthropogenic fire suppression disrupting natural fire regime, and (2) climate change-induced winter warming reducing snow cover duration and depth. To test these, we quantified dieback using Green Normalized Difference Vegetation Index (GNDVI) across stands with varying fire histories via UAV-based multispectral imagery, alongside long-term climatic observations (1960-2024) of temperature, precipitation, and snow dynamics from meteorological stations combined with remote sensing datasets. Results showed that an abrupt change point in 2018 for both annual precipitation and mean temperature was identified, coinciding with dieback. Significant negative linear relationship between GNDVI (forest health) and last fire interval indicated prolonged fire exclusion exacerbating dieback, possibly via pathogen/pest accumulation. Winter temperature rose significantly during 1960-2023, with notable acceleration following abrupt change point in 1987. Concurrently, winters during 2018-2023 exhibited pronounced warming, with snow cover duration reduced by 23 days and snow depth diminished by 7.6 cm. These conditions reduced snowmelt -derived soil moisture (critical water source) recharge in early spring, exacerbating drought stress during critical growth periods and predisposing trees to pest and disease infestations. Our results support both hypotheses, demonstrating that dieback is synergistically driven by fire regime alteration and climate-mediated snowpack reductions. Converting pure pine forests into mixed pine-broadleaf forests via differentiated water sources was proposed to restore ecological resilience in sandy ecosystems.

Conventional materials necessitate a layer-by-layer rolling or tamping process for subgrade backfill projects, which hampers their utility in confined spaces and environments where compaction is challenging. To address this issue, a self-compacting poured solidified mucky soil was prepared. To assess the suitability of this innovative material for subgrade, a suite of performance including flowability, bleeding rate, setting time, unconfined compressive strength (UCS), and deformation modulus were employed as evaluation criteria. The workability and mechanical properties of poured solidified mucky soil were compared. The durability and solidification mechanism were investigated. The results demonstrate that the 28-day UCS of poured solidified mucky soil with 20% curing agent content reaches 2.54 MPa. The increase of organic matter content is not conducive to the solidification process. When the curing temperature is 20 degrees C, the 28-day UCS of the poured solidified mucky soil with curing agent content not less than 12% is greater than 0.8 MPa. The three-dimensional network structure formed with calcium silicate hydrate, calcium aluminate hydrate, and ettringite is the main source of strength formation. The recommended mud moisture content is not exceed 85%, the curing agent content is 16%, and the curing temperature should not be lower than 20 degrees C.

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.

To address the engineering problems of road subsidence and subgrade instability in aeolian soil under traffic loads, the aeolian soil was improved with rubber particles and cement. Uniaxial compression tests and Digital speckle correlation method (DSCM) were conducted on rubber particles-cement improved soil (RP-CIS) with different mixing ratios using the WDW-100 universal testing machine. The microcrack and force chain evolution in samples were analysed using PFC2D. The results showed that: (1) The incorporation of rubber particles and cement enhanced the strength of the samples. When the rubber particles content was 1% and the cement content was 5%, the uniaxial compressive strength of the RP-CIS reached its maximum. Based on the experimental results, a power function model was established to predict the uniaxial compressive strength of RP-CIS; (2) The deformation of the samples remains stable during the compaction stage, with cracks gradually developing and penetrating, eventually entering the shear failure stage; (3) The crack and failure modes simulated by PFC2D are consistent with the DSCM test. The development of microcracks and the contact force between particles during the loading are described from a microscopic perspective. The research findings provide scientific support for subgrade soil improvement and disaster prevention in subgrade engineering.

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.

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.