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.

Carbonaceous aerosols (CA) strongly impact regional and global climate through their light-absorbing and scattering properties, yet their effects remain uncertain in dust-influenced regions. We investigated the optical properties, source contributions, and radiative impacts of CA at two climatically distinct regions in northwestern India: an arid region (AR, Jodhpur; post-monsoon) and a semi-arid region (SAR, Kota; winter). Mean absorption & Aring;ngstr & ouml;m exponent (AAE) values were comparable between the two regions (AR: 1.416 +/- 0.173; SAR: 1.395 +/- 0.069), but temporal cluster analysis revealed source-specific variability, with lower AAE during traffic-dominated periods (similar to 1.30) and elevated AAE during solid fuel and biomass combustion (1.68 in AR and 1.52 in SAR). While equivalent BC (eBC) levels were higher in AR with a relatively uniform liquid-fuel contribution (BClf = 80.06 +/- 1.98 %), the mass absorption cross- of BC (MAC(BC)) in SAR was similar to 4.5X greater, driven by local solid fuel combustion and transported biomass burning emissions (BCsf = 34.61 +/- 6.88 %). Mie modelling indicated higher SSA in AR due to higher contribution of mineral dust, in contrast to SAR, where carbonaceous aerosols caused stronger absorption, forward scattering, and higher imaginary refractive index (k(OBD)). Although absorption enhancement (E-lambda) was slightly higher in AR (similar to 1.11 vs. similar to 0.99), SAR aerosols nearly doubled the warming potential (Delta RFE), with RFE values of similar to 0.87 W/m(2) in SAR versus similar to 0.43 W/m(2) in AR. These findings highlight strong source-specific and site-specific variability in aerosol absorption and radiative, emphasizing the need to integrate region-specific parameters into climate models and air quality assessments for data-scarce arid and semi-arid South Asian environments.

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.

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.

The depth of the soil freezing front serves as an integrated indicator of land-atmosphere interactions during the freezing period and plays a critical role in regulating the hydrological cycle, ecological processes, and regional climate on the Qingzang Plateau (QP). While previous studies have primarily focused on interannual variations in the annual maximum freezing depth, limited attention has been paid to the spatiotemporal dynamics of the soil freezing front throughout the freezing season. In this study, we simulated the spatiotemporal variations of the soil freezing front on the QP during the freezing period using the optimal model selected from three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results demonstrated that RF outperforms MLP and SVM in accurately simulating the depth of the soil freezing front (R2 = 0.81, RMSE = 28.09 cm, MAE = 18.02 cm). Spatially, the soil freezing front during the freezing period was deeper in the west and north and shallower in the east and south. From 1983 to 2019, both permafrost and seasonally frozen ground regions across the QP exhibited statistically significant declines in soil freezing front depth. From October to November, freezing depth decreases faster in permafrost than in seasonally frozen ground, whereas from December to January it decreases faster in seasonally frozen ground than in permafrost. A comparison between the sub-periods 1983-2000 and 2001-2019 reveals a marked acceleration in the reduction of freezing depth. Additionally, the influence of air temperature on the freezing front is modulated by its depth. The elevation effect is weak in October, strengthens to a predominantly negative influence in November-December, and becomes nonlinear in January, with the strongest negative impact at mid-high elevations and a weaker effect at the highest elevations.

Thaw hazards in high-latitude and glaciated regions are becoming increasingly frequent because of global climate warming and human activities, posing significant threats to infrastructure stability and environmental sustainability. However, despite these risks, comprehensive investigations of thaw-hazard susceptibility in permafrost regions remain limited. Here, this gap is addressed by a systematic and long-term investigation of thaw hazards in China's Qinghai Province as a representative permafrost area. A detailed inventory of 534 thawhazard sites was developed based on remote sensing, field verification, and surveys by a UAV, providing critical data for susceptibility analysis. Eleven environmental factors influencing thaw hazards were identified and analyzed using information gain and Shapley additive explanation. By using the random forest model, a susceptibility map was generated, categorizing the study area into five susceptibility classes: very low, low, moderate, high, and very high. The key influencing factors include precipitation, permafrost type, temperature change rate, and human activity. The results reveal that 17.5 % of the permafrost region within the study area is classified as high to very high susceptibility, concentrated primarily near critical infrastructure such as the Qinghai-Tibet Railway, potentially posing significant risks to its structural stability. The random forest model shows robust predictive capability, achieving an accuracy of 0.906 and an area under the receiver operating characteristic curve of 0.965. These findings underscore the critical role of advanced modeling in understanding the spatial distribution and drivers of thaw hazards, offering actionable insights for hazard mitigation and infrastructure protection in permafrost regions under a changing climate.

Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

Rice bakanae disease is a soil-borne disease mainly caused by Fusarium fujikuroi, which seriously damages the yield and quality of rice. Phenamacril targets Myosin-5, thereby inhibiting its ATPase activity to exert an antifungal effect, demonstrating significant bioactivity against Fusarium species. However, the resistance of Fusarium fujikuroi field populations to phenamacril in Jiangsu Province in recent years remains unclear. In this study, a total of 223 Fusarium fujikuroi isolates were collected in Jiangsu Province from 2022 to 2023, with the resistance frequency increase from 25.88 % to 49.28 %. Additionally, a novel mutation type (S420I) in FfMyosin-5 was identified and confirmed by genetic transformation. The compound fitness index (CFI) revealed that the fitness of FfMyosin5(S420I) point mutants (1 x 10(5) < CFI <= 2 x 10(5)) was significantly lower than sensitive strain (CFI = 10.26 x 10(5)) in terms of mycelial growth rate, conidia production and conidia germination. In summary, the S420I mutation in FfMyosin-5 induces resistance to phenamacril while also decreased the fitness of Fusarium fujikuroi.

In view of the pollution of unpaved road dust in the current mines, this study demonstrated the excellent dust suppression performance of the dust suppressant by testing the dynamic viscosity, penetration depth and mechanical properties of the dust suppressant, and apply molecular dynamics simulations to reveal the interactions between substances. The results showed that the maximum dust suppression rate was 97.75 % with a dust suppressant formulation of 0.1 wt% SPI + 0.03 wt% Paas + NaOH. The addition of NaOH disrupts the hydrogen bonds between SPI molecules, which allows the SPN to better penetrate the soil particles and form effective bonding networks. The SPI molecules rapidly absorb onto the surface of soil particles through electrostatic interactions and hydrogen bonds. The crosslinking between SPI molecules connects multiple soil particles, forming larger agglomerates. The polar side chain groups in the SPN interact with soil particles through dipole-dipole interactions, further stabilizing the agglomerates and resulting in an enhanced dust suppression effect. Soil samples treated with SPN exhibited higher compressive strength values. This is primarily attributed to the stable network structure formed by the SPN dust suppressant within the soil. Additionally, the SPI molecules and sodium polyacrylate (Paas) molecules in SPN contain multiple active groups, which interact under the influence of NaOH, restricting the rotation and movement of molecular chains. From a microscopic perspective, the SPN dust suppressant further strengthens the interactions between soil particles through mechanisms such as liquid bridge forces, which contribute to the superior dust suppression effect at the macroscopic level.

Granite residual soils (GRS) are often encountered in geotechnical projects in the Guangdong-Hong Kong-Macao Greater Bay Area (briefly written as the Greater Bay Area, or abbreviated as GBA). The rea experiences frequent rainfall, leading to wetting-drying cycles that progressively diminish the shear strength of GRS. This weakening effect is not only significant but also accumulates, exhibiting a direct positive correlation with the number of cycles. Current studies on the soil strength attenuation due to wetting-drying cycles are typically limited to no more than 10 cycles, which is rather insufficient to uncover the long-term water-weakening behaviors and their accumulative impacts on GRS. To address this gap, typical GRS samples were first taken from the GBA and then prepared by making them go through a certain number of wetting-drying cycles (maximum of up to 100). Next, a total of 552 small- and large-scale direct shear tests were conducted to investigate the mechanisms of water-weakening effects on soil internal friction angle, cohesion, and shear strength. The degree of saturation and number of cycles were also examined to see their effects on the cumulation of water weakening. Based on results from the small-scale direct shear tests, a model was developed for assessing the weakening impact of water on soil strength. The accuracy of the model prediction was statistically evaluated. Last, the effectiveness and efficiency of the proposed model were demonstrated by validating against the results from the large-scale direct shear tests.