Yaobin Meng

Understanding the dynamic behavior of landslides is essential for effective risk assessment. This study examines the Yanguan landslide, which occurred on 29 October 2017, in the Three Gorges Reservoir (TGR) region of China. Due to its unique capability in modeling discontinuum behaviors during landslide fragmentation, the discrete element method was utilized to analyze the movement characteristics of this landslide. The investigation began with a field survey to assess the geological features and failure mechanism of the landslide, which indicates that the landslide was likely triggered by prolonged variations in reservoir water levels and heavy rainfall preceding the event. Following this, a three-dimensional numerical model of the landslide was constructed using pre- and post-event terrain data. The accuracy of the numerical model was validated by comparing its simulation results with field survey data. Finally, the landslide’s movement behavior and energy transformation were analyzed based on the validated model. This work can enhance landslide risk assessment by quantifying dynamic parameters critical for impact prediction, further provide a scientific basis for the study of the landslides in the TGR area, and contribute to disaster prevention.

Heavy metals (HMs) entering soils are redistributed by natural processes such as surface runoff and soil erosion. These natural processes are characterized by topographic factors (TFs, e.g., Topographic Wetness Index (TWI), Total Catchment Area (TCA), Slope, and Aspect), which are commonly quantified by a digital elevation model (DEM) of a certain spatial resolution. Nevertheless, few studies have examined how DEM resolutions affect the detection of influential TFs of soil HMs. In this study, we first applied the GeoDetector method to explore the coupling between the eight TFs and the concentrations of eight soil HMs under a gradient of DEM resolutions. We found that the important sorting of eight TFs on one HM at different resolutions is inconsistent. For example, for Hg, TWI emerged to be the dominant factor among the eight TFs at 90 m resolution, whereas TCA took the lead at 3000 m resolution. Moreover, the results strongly deny the existence of an optimal resolution (OR) among the HMs for any specific topographical factor. We further applied a source apportionment model (Positive Matrix Factorization—PMF) to explore the effects of five identified pollution sources and the underlying environmental processes on the inconsistent ORs. The main reason for such OR inconsistency is that each HM may be released from various sources and subsequently undergo environmental processes that are topographically modulated at different spatial scales. The main reason for such OR inconsistency is that each HM may have various sources and subsequent environmental processes that happen at different spatial scales. Moreover, each TF could simultaneously reflect different transport and transformation processes. Therefore, the apparent OR for one metal is jointly composed of the preferences of all the sources it contains; thus, it cannot be determined by the OR preferences of a single source alone. Based on the composition and intensity of pollution sources, we propose three possible strategies for a more robust GeoDetector analysis. The findings reported here provide new insights into the proper use of GeoDetector for selecting the appropriate DEM resolutions when identifying influential environmental factors.

Free estrogens (estrone, E1; 17β-estradiol, 17β-E2; estriol, E3; and 17α-ethinylestradiol, EE2), their corresponding sulfate and glucuronide conjugates, and bisphenol A (BPA) were investigated in water and sediments in the Fen River catchment, an arid and semiarid area in northwest China. E1 and BPA were frequently detected in the wet and dry sampling seasons. In addition to the sulfate conjugates, other conjugated estrogens were not detected in water and sediments. The concentrations of these compounds in water generally increased from upstream to downstream. The concentrations in water samples of most sites were higher in the wet season than those in the dry season, but concentrations in sediments of most sites were higher in the dry season than those in the wet season. The distributions of these compounds in sediments were positively correlated with the total organic carbon (TOC) contents of sediments (0.3 < R ² < 0.6, p < 0.01) and concentrations in water (0.25 < R ² < 0.50, p < 0.01). In this catchment, E1 was the main contributor to endocrine disrupting risk. The surface water in most of the tributaries and the sewage in the drainage channels were at risk. The pore waters of sediments were at risk at most sampling sites.