Due to its environmental and resource impacts, the geochemistry of uranium in coal is of both academic and practical significance. In order to give a comprehensive summary about the geochemistry of uranium in coals, the abundance, distribution, and modes of occurrence of uranium in Chinese coals were reviewed in this paper. Although some coals from southwestern and northwestern China are significantly enriched in uranium, the common Chinese coals are of a comparable uranium concentration to the world coals. The roof and floor rocks, and parting of coalbeds, or coal benches that are close to the surrounding rock are favorable hosts for uranium in one coalbed. The uranium concentrations in coals of different ages decrease in this order, e.g., Paleogene and Neogene > Late Permian > Late Triassic > Late Carboniferous and Early Permian > Late Jurassic and Early Cretaceous > Early and Middle Jurassic. Uranium in Chinese coals is mainly associated with organic matter, and is correspondingly enriched in subbituminous coal and lignite.

The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

Different types of subsidence lakes formed by underground coal mining are severely polluted by mine-production wastewater, domestic sewage, and agricultural irrigation water. Microbial communities perform a crucial role in biogeochemical cycling processes and responses to natural and anthropogenic disturbances in lake waters. Therefore, it is important to investigate the characteristics of microbial community diversity in subsidence lakes for the utilization of water resources in mining areas. In this study, we collected water samples from open and closed subsidence lakes and explored the bacterial communities based on Illumina amplicon high-throughput sequencing. Results showed that bacterial diversity and community and functional composition in open and closed lakes differed greatly, and the bacterial diversity in open lakes was significantly higher than that in closed lakes (p < 0.001). Canonical correspondence analysis found that pH explained 31.73% and 39.98% of the variation in the bacterial community and functional composition in closed lakes, while NH₄⁺ and COD accounted for 29.73% and 26.02% of the changes in the community and functional composition in open lakes, respectively. Modified stochasticity ratios based on null-model analysis demonstrated that stochastic processes were the main factor affecting bacterial community assembly in both closed (MST = 0.58) and open lakes (MST = 0.63). In the closed-lake network, hgcI_clade was inferred to be a keystone species, while Lactococcus, Acinetobacter, Psychrobacter, and Chryseobacterium were detected as keystone species in the open-lake network. By way of discussion, we provide evidence regarding the bacterial community diversity, structure, co-occurrence patterns, and assembly processes in closed and open subsidence lakes. This study contributes to providing a reference for the utilization of subsidence water resources.