The black-odor phenomenon has been widely reported worldwide and recognized as a global ecological risk for aquatic environments. However, driving factors for black-odor-related microorganisms and potential self-remediation strategies are still poorly understood. This study collected eight water samples (sites A–H) disturbed by different factors from the Jishan River located in Jinmen, Hubei Province, China. Black-odor-related environmental factors and functional bacterial structure were further measured based on the basic physicochemical parameters. The results indicated that different types of disturbed conditions shape the distribution of water quality and microbial community structures. Site B, which was disturbed by dams, had the worst water quality, the lowest abundance of functional microbes for Mn, Fe, and S biotransformation, and the highest abundance of functional microbes for fermentation. The natural wetlands surrounding the terminus of the river (site H) were keys to eliminating the black-odor phenomenon. Potential black-odor-forming microorganisms include Lactococcus, Veillonella, Clostridium sensu stricto, Trichococcus, Rhodoferax, Sulfurospirillum, Desulfobulbus, and Anaeromusa-Anaeroarcus. Potential black-odor-repairing microbes include Acinetobacter, Mycobacterium, and Acidovorax. pH and COD were paramount physiochemical factors contributing to blackening-odor-related microorganisms. This study deepens our understanding of driving factors for black-odor-related microorganisms and provides a theoretical basis for eradicating the black-odor phenomenon.

Natural biofilms, which are widely distributed in various aquatic environments, can not only serve as bioindicators of various anthropogenic contaminants but also participate in the purification and degradation of various pollutants. However, the inherent purification capacity of natural biofilms and their physiochemical and biological properties are still poorly understood. In this study, outdoor sampling and indoor experiments were used to explore the purification abilities of natural biofilms. The physiochemical and biological properties of natural biofilms were further investigated to reveal their purification mechanism. The results demonstrated that natural biofilms had an excellent purification effect on heavily polluted water. Indoor experiments showed that the purification capacity of natural biofilms was dominated by microbial biodegradation rather than physical biosorption, and after 14.0 days of incubation, the removal rates of COD, TP, NH₄⁺-N, and NO₃^--N could reach 93.6, 80.83, 85.93, and 81.03%, respectively. The SEM, FTIR spectra, and component analyses revealed that natural biofilms were mainly composed of polysaccharides and proteins. The dominant phyla in the bacterial community structure were Campilobacterota, Proteobacteria, Bacteroidota, Firmicutes, and Desulfobacterota, and the major phyla in the fungal community structure were Chytridiomycota and Ascomycota. These microorganisms might be the main degraders of riverine pollutants.

The enrichment of iron (Fe) and manganese (Mn) in arid groundwater is jointly influenced by multi-source environmental processes, which complicates the delineation of their spatial risks using traditional methods. Taking the Yinchuan Plain as the study area, 228 groundwater samples collected in 2021 were integrated with multi-source environmental variables were integrated to construct a multi-paradigm machine learning predictive framework, aimed at identifying potential environmental controls and high-risk distributions of Fe/Mn. Six machine learning algorithms, including Logistic Regression, Random Forest, LightGBM, XGBoost, TabNet, and Multi-Layer Perceptron, were compared, and a probability-based ensemble model was developed. The results demonstrate that tree-based ensemble algorithms (LightGBM, Random Forest, and XGBoost) outperformed other models, with multi-model probability ensembles providing moderate predictive performance for Fe (AUC = 0.748) and better predictive performance for Mn (AUC = 0.852). Interpretability analysis revealed distinct enrichment pathways for the two elements: Fe enrichment is strongly correlated with soil nitrogen and silt content, whereas Mn exhibits a higher dependency on climatic variables, specifically vapor pressure deficit. Because groundwater samples from multiple aquifer systems were integrated into a single modeling framework, the predictions represent integrated groundwater conditions across the study area, which may limit the model’s ability to capture aquifer-specific heterogeneity. The model generated risk probability maps help identify potential contamination hotspots and priority areas for monitoring and control, providing a scientific foundation for formulating differentiated groundwater safety strategies in arid regions.