The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

With the development of the economy and society, environmental pollution and resource waste problems are emerging, especially in agricultural production, and the adoption of ecological agricultural technologies is a prerequisite to alleviate ecological pressure. Based on the Technology Acceptance Model—Theory of Planned Behavior (TAM-TPB) and using research data from Hubei, Hunan, and Anhui provinces, this paper empirically analyzes the factors influencing farmers’ intention to adopt rice and shrimp crop technologies using the PLS-SEM method. The configuration path of high technology intention was further investigated by the fsQCA method. The results showed that: 1) farmers’ intention to adopt rice-shrimp crop technology was mainly positively influenced by behavioral attitude, subjective norm, perceived behavioral control, behavioral attitude; 2) Perceived usefulness and perceived ease of use had a direct effect on farmers’ intention to adopt and an indirect effect with behavioral attitude as a mediating variable, while perceived ease of use had a positive effect and perceived usefulness did not. In doing so, four configuration paths of high technology acceptance intention were obtained. Given this, this paper makes relevant suggestions, suggesting that the relevant departments focus on the comprehensive benefits of rice-shrimp crop technology; agricultural technology departments provide technical assistance to farmers, and village committees organize regular inter-farmer exchanges.

Kaolinite clay mineral with a layered silicate structure is an abundant resource in China. Due to its advantages of excellent stability, high specific surface area and environmental friendliness, kaolinite is widely used in environment decontamination. By using kaolinite as a carrier, the photocatalytic technology in pure photocatalysts of poor activities, narrow spectral responses, and limited electron transport can be overcome, and the nano-Ag disinfectant’s limitation of the growth and aggregation of nanoparticles is released. Moreover, pure kaolinite used as an adsorbent shows poor surface hydroxyl activity and low cation exchange, leading to the poor adsorption selectivity and easy desorption of heavy metals. Current modification methods including heat treatment, acid modification, metal modification, inorganic salt modification, and organic modification are carried out to obtain better adsorption performance. This review systematically summarizes the application of kaolinite-based nanomaterials in environmental decontamination, such as photocatalytic pollutant degradation and disinfection, nano silver (Ag) disinfection, and heavy metal adsorption. In addition, applications on gas phase pollutant, such as carbon dioxide (CO₂), capture and the removal of volatile organic compounds (VOCs) are also discussed. This study is the first comprehensive summary of the application of kaolinite in the environmental field. The review also illustrates the efficiency and mechanisms of coupling naturally/modified kaolinite with nanomaterials, and the limitation of the current use of kaolinite.

Xunyang is rich in various metal minerals and is one of the four major metal mining areas in Shaanxi province, China. To explore the effects of soil heavy metals and metalloid pollution on the environment and human health around the mining areas, four places—Donghecun (D), Gongguan (G), Qingtonggou (Q) and Nanshagou (N)—were selected as the sampling sites. Potential ecological risk (PER) and health risk assessment (HRA) models were used to analyze the environmental and health risks around the mining areas. The concentration of heavy metals (Cd, Cr, Pb, Zn, Ni, Cu, Hg) and metalloid (As) in cultivated land in the vicinity of Xunyang mining areas indicated that, except for Cu, the remaining elements detected exceeded the threshold values at some sites. The geo-accumulation index (I_Geo) revealed that soils in G and Q could be identified as being extremely contaminated. PER indicated that there was significantly high risk at G and Q for Hg. In N, Pb recorded the highest $E_r^i$ , which also demonstrates a considerable pre-existing risk. HRA indicated that the hazard index (HI) for both carcinogenic and non-carcinogenic risks was much higher among children than among adults, and the ingestion pathway contributed the greatest risk to human health, followed by the dermal pathway and inhalation. Because the HI values of the metals and metalloid in the study areas were all lower than 1, there was no significant non-carcinogenic risk. However, the carcinogenic risk for Cr is relatively higher, surpassing the tolerable values in G, Q, and N. This study analyzed the ecological risks and human health risks of heavy metals and metalloid in farmland soils near the sampling mining areas, and demonstrated the importance of environmental changes caused by land development in the mining industry.

The main aim of this study was to compare and evaluate the performance of fractal dimension as input data in the landslide susceptibility mapping of the Baota District, Yan’an City, China. First, a total of 632 points, including 316 landslide points and 316 non-landslide points, were located in the landslide inventory map. All points were divided into two parts according to the ratio of 70%:30%, with 70% (442) of the points used as the training dataset to train the models, and the remaining, namely the validation dataset, applied for validation. Second, 13 predisposing factors, including slope aspect, slope angle, altitude, lithology, mean annual precipitation (MAP), distance to rivers, distance to faults, distance to roads, normalized differential vegetation index (NDVI), topographic wetness index (TWI), plan curvature, profile curvature, and terrain roughness index (TRI), were selected. Then, the original numerical data, box-counting dimension, and correlation dimension corresponding to each predisposing factor were calculated to generate the input data and build three classification models, namely the kernel logistic regression model (KLR), kernel logistic regression based on box-counting dimension model (KLR_box-counting), and the kernel logistic regression based on correlation dimension model (KLR_correlation). Next, the statistical indexes and the receiver operating characteristic (ROC) curve were employed to evaluate the models’ performance. Finally, the KLR_correlation model had the highest area under the curve (AUC) values of 0.8984 and 0.9224, obtained by the training and validation datasets, respectively, indicating that the fractal dimension can be used as the input data for landslide susceptibility mapping with a better effect.