Soil heavy metal contamination is crucial due to menacing food safety and mortal health. At present, with the fast advancement of urbanization and industrialization, heavy metals are increasingly released into the soil by anthropogenic activities, and the soil ecosystem contamination around the Danjiangkou Reservoir is directly associated with water quality security of the reservoir. In this paper, using 639 soil samples from the Danjiangkou Reservoir, Henan Province, China, we studied a variety of space distribution characteristics of heavy metals in soil. Geographic information system analysis (GIS), geo-accumulation index (I_geo), contamination factor (CF), principal component analysis (PCA) model, and positive matrix factorization (PMF) model were used together to recognize and quantify the distribution, contamination, and origin of heavy metals. We uncovered an exceptional variety of heavy metal concentrations among the tested soils: the mean arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), manganese (Mn), nickel (Ni), zinc (Zn), lead (Pb) and mercury (Hg) concentrations (14.54, 0.21, 18.69, 81.69, 898.42, 39.37, 79.50, 28.11, 0.04 mg/kg, respectively, in the topsoil (0–20 cm depth)), all exceed their background values. The mean I_geo value and CF values of these trace elements are both in descending order: Cd > Co > Mn > Ni > Pb > Zn > Cr > As > Hg. Cd was the highest contributor to the assessment of heavy metal pollution, with an average I_geo value over three, indicating that the study area is modestly contaminated by Cd. The PCA analysis and PMF model revealed three potential sources, including natural sources (PC1) for Cr, Co, Mn and Ni; agricultural sources (PC2) for Cd, Zn and Hg; and industrial emissions and transportation sources (PC3) for Pb. This study displays a map of heavy metal contamination in the eastern area topsoil of the Danjiangkou Reservoir, showing the most severe pollutant is Cd, which poses a threat to the water quality security of Danjiangkou Reservoir and provides a significant source identification for future contamination control.

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience driving or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities. And 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. There are five machine learning methods (Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)) were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.

As the largest artesian irrigation area in northern China, the Hetao Plain is also one of the major grain-producing areas in China. Meanwhile, there is a large amount of highly mineralized groundwater resulting in the soil salinization and desertification in this region. In addition, this study also uses the traditional hydro-geochemical methods to investigate the spatial evolution characteristics and formation mechanisms of highly mineralized groundwater. The results indicate that there is a large amount of highly mineralized groundwater (salinity > 3 g/L) in the shallow aquifer over the Hetao Plain. As far as the spatial patterns are concerned, there are significant spatial differences. In accordance with the structural, paleogeographic, landform, and hydrogeological conditions, the highly mineralized groundwater in the Hetao Plain can be divided into five zones, namely, the front fan depression, the north bank of the Yellow River, Xishanzui, Hasuhai in the Hubao Plain, and Dalad banner on the south bank of the Yellow River. Among them, the highly mineralized groundwater of Xishanzui exhibits the largest value of the salinity > 10 g/L. The main cations are Mg²⁺ and Na⁺, while the main anions are Cl⁻ and SO₄²⁻. Moreover, the groundwater in the highly mineralized area contains a large amount of I⁻. According to the analysis of Piper, Gibbs diagrams of groundwater, the proportion coefficients of various components and the indication of isotope, it can be seen that most of the chemical ions in groundwater in the highly mineralized zone come from evaporation-concentration, which are mainly affected by climate, sedimentary environment, hydrogeological conditions and hydrology. The source of high mineralization in Xishanzui are different from other high mineralization regions, and the highly mineralized groundwater in Xishanzui is formed by the infestation of deep underground salt brine. These results can provide scientific basis for the rational allocation of regional water resources and the promotion of water resources development and utilization.

Based on 447 samples collected from a shallow aquifer (depths from 0 to 150 m) in the Hetao Basin, Northern China, an integrated hydrogeochemical approach was used in this study to conceptualize the enrichment of high arsenic groundwater in the Hetao Basin. An unconventional method of distinguishing hydrogeochemical and physical processes from a dataset was tested by investigating the cumulative frequency distribution of ionic ratios expressed on a probability scale. By applying cumulative frequency distribution curves to characterize the distribution of ionic ratios throughout the Hetao Basin, hydrogeochemical indicators were obtained that distinguish the series of hydrogeochemical processes that govern groundwater composition. All hydrogeochemical processes can basically be classified as recharge intensity of groundwater, evaporation concentration intensity, and reductive degree controlling the spatial distribution of arsenic. By considering the three processes, we found that the concentration of arsenic was more than 10 μg/L when the (HCO₃⁻+CO₃²⁻)/SO₄²⁻ ratio was over 4.1 (strong reductive area). As the evaporation concentration intensity increased, the median value of arsenic increased from 10.74 to 382.7 μg/L in the median reductive area and rapidly increased from 89.11 to 461.45 μg/L in the strong reductive area. As the river recharge intensity increased (with the intensity index increasing from 0 to 5), the median value of arsenic dropped from 40.2 to 6.8 μg/L in the median reductive area and decreased more markedly from 219.85 to 23.73 μg/L in the strong reductive area. The results provide a new insight into the mechanism of As enrichment in groundwater.

Paleoclimate studies of loess in China have focused mostly on the time series of a single borehole or profile. However, research on loess strata and regional paleoenvironmental patterns could facilitate a deeper understanding of loess as a paleoenvironmental indicator and provide new insights into interpreting loess in sedimentary records of the paleoclimate. In this study, we determined the spatial pattern and regional characteristics of the paleoclimate during the Last Interglacial period in the Luohe River Basin, Shaanxi Province, China. We selected four representative boreholes in the study area (ZK04, ZK18, ZK13, and ZK19) from different landforms and zones, distributed from the northwest to the southeast, as well as three classic profiles (JB, JD, and LC). From north to south, comparative analyses were conducted of the loess strata, magnetic susceptibility, and grain size, and we analyzed the distribution characteristics of loess and paleosols in different geomorphological regions. The results showed that both the thickness and the sedimentation rate of loess in this river basin decreased from north to south. There were few paleosol horizons in the northern Liangmao area, but numerous such horizons in the southern plateau, and the degree of paleosol development increased from north to south. The magnetic susceptibility increased, whereas the particle composition tended to become thinner from north to south. The climate fluctuations of the Last Glacial recorded by the loess and paleosols in different regions were inconsistent.

The enrichment and migration patterns of different chemical elements record paleoclimatic information in loess formations. The chemical elemental measurements of 245 samples from the Shiyang profile in the Weinan Region were compared with the geochemical characteristics of typical wind-formed profiles, and the paleoclimatic evolution was discussed. The results showed the following: (1) the standardized curves of the cumulative concentrations of SiO₂, Al₂O_3, and CaO along with the Upper Continental Crust (UCC) in the Shiyang profile exhibited significant similarities with typical wind-formed profiles. This strongly suggests that the Shiyang profile has a wind-formed origin. (2) The mean value of the chemical index of alteration (CIA) of the Shiyang profile is 62.06, indicating that the Shiyang profile has been in the stage of primary chemical weathering. (3) The ratios of K₂O/Al₂O₃, TiO₂/Al₂O₃, and Fe₂O₃/Al₂O₃ in the Shiyang profile are comparable to those found in typical wind-formed profiles, suggesting a common source area and supporting the premise that the Shiyang profile is of wind-induced origin.(4) The regional climate has undergone a series of transitions: from a dry and cool phase in the early Holocene to warm and humid yet unstable conditions in the middle Holocene, and returning to dry and cool during the late Holocene.