Yonghong Xiao

Shallow landslides are one of the main geological hazards that occur during heavy rainfall in Yuexi County every year, posing potential risks to the personal and property safety of local residents. A rainfall-induced shallow landslide named Baishizu No. 15 landslide in Yuexi Country was taken as a case study. Based on the field geological investigation, combined with physical and mechanical experiments in laboratory as well as numerical simulation, the failure mechanism induced by rainfall infiltration was studied, and the movement process after landslide failure was inverted. The results show that the pore-water pressure within 2 m of the landslide body increases significantly and the factory of safety (F_s) has a good corresponding relationship with rainfall, which decreased to 0.978 after the heavy rainstorm on July 5 and July 6 in 2020. The maximum shear strain and displacement are concentrated at the foot and front edge of the landslide, which indicates a “traction type” failure mode of the Baishizu No. 15 landslide. In addition, the maximum displacement during landslide instability is about 0.5 m. The residual strength of soils collected from the soil–rock interface shows significant rate-strengthening, which ensures that the Baishizu No. 15 landslide will not exhibit high-speed and long runout movement. The rate-dependent friction coefficient of sliding surface was considered to simulate the movement process of the Baishizu No. 15 landslide by using PFC^2D. The simulation results show that the movement velocity exhibited obvious oscillatory characteristics. After the movement stopped, the landslide formed a slip cliff at the rear edge and deposited as far as the platform at the front of the slope foot but did not block the road ahead. The final deposition state is basically consistent with the on-site investigation. The research results of this paper can provide valuable references for the disaster prevention, mitigation, and risk assessment of shallow landslides on residual soil slopes in the Dabie mountainous region.

The quality of shallow groundwater in agricultural areas is being increasingly threatened by nitrogen pollution. However, the complex interactions between natural and anthropogenic sources remain insufficiently studied. In this study, the water chemical characteristics and nitrogen pollution sources in key agricultural areas and counties of the Yangtze River Basin were systematically investigated. Forty-three groundwater samples were analyzed for major ions and nitrides (NH₄⁺, NO₂⁻, NO₃⁻) using hydrogeochemical analysis, spatial interpolation, and positive matrix factorization (PMF) models. The shallow groundwater in the study area is weakly alkaline (pH 7.36) and is dominated by calcium ions (mean 112.67 mg/L) and bicarbonate (mean 361.95 mg/L), which reveals that the hydrogeochemical characteristics are dominated by carbonate. The total hardness has increased, and the nitrogen concentration exhibits significant spatial variability. Nitrates (NO₃⁻) exceed safety thresholds across the entire region and are strongly correlated with Cl⁻. The PMF analysis identified the following four major pollution factors: Factor 1 represents a combination of anthropogenic pollution and natural processes; Factor 2 is attributed to agricultural fertilizer application and septic tank leakage; Factor 3 is sourced from the weathering of carbonates and the decomposition of organic matter in a reducing environment; and Factor 4 is due to the leakage of domestic sewage or livestock-derived wastewater. Spatial analysis revealed pollution hotspots in the vicinity of urban, agricultural, and livestock areas. This study emphasizes that human activities, such as over-fertilization and inadequate wastewater management, are the main contributors to groundwater nitrogen pollution in the study area. In addition, we compare the groundwater quality of the entire Yangtze River Basin and find that there are distinct regional variations.

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.