The relationship between CDOM (Chromophoric dissolved organic matter) and the bacterial community was investigated in ice-covered Baiyangdian Lake. The results showed that environmental parameters significantly differed in Baiyangdian Lake, whereas a-diversity was not significantly different. Moreover, the microbial and functional communities exhibited significant differences, and T (Temperature), pH, ORP (Oxidation-reduction potential), DO (Dissolved oxygen), NO3−-N, NH4+-N, and Mn (Manganese) were the main environmental factors of these differences, based on redundancy analysis and the Mantel test. Biomarkers of the microbial and functional communities were investigated through linear discriminant analysis effect size and STAMP analysis. The number of biomarkers in the natural area was highest among the typical zones, and most top functions were related to carbohydrate metabolism. Two protein-like components (C1 and C2) and one humic-like component (C3) were identified by parallel factor analysis, and C1 was positively related to C2 (R = 0.99, p < 0.001), indicating the same sources. Moreover, CDOM significantly differed among the typical zones (p < 0.001). The high biological index, fluorescence index, β:α, and low humification index indicated a strong autochthonous component and aquatic bacterial origin, which was consistent with the results of UV-vis absorption spectroscopy. Network analysis revealed non-random co-occurrence patterns. The bacterial and functional communities interacted closely with CDOM. The dominant genera were CL500-29_marine_group, Flavobacterium, Limnohabitans, and Candidatus_Aquirestis. Random forest analysis showed that C1, C2, and C3 are important predictors of α- and β-diversity in the water bacterial community and its functional composition. This study provides insight into the interaction between bacterial communities and DOM (Dissolved organic matter) in ice-covered Baiyangdian Lake.

The excessive input of nutrients into rivers can lead to contamination and eutrophication, which poses a threat to the health of aquatic ecosystems. It is crucial to identify the sources of contaminants to develop effective management plans for eutrophication. However, traditional methods for identifying pollution sources have been insufficient, making it difficult to manage river health effectively. High-throughput sequencing offers a novel method for microbial community source tracking, which can help identify dominant pollution sources in rivers. The Wanggang River was selected for study, as it has suffered accelerated eutrophication due to considerable nutrient input from riparian pollutants. The present study identified the dominant microbial communities in the Wanggang River basin, including Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Firmicutes. The Source Tracker machine-learning classification system was used to create source-specific microbial community fingerprints to determine the primary sources of contaminants in the basin, with agricultural fertilizer being identified as the main pollutant source. By identifying the microbial communities of potential pollution sources, the study determined the contributing pollutant sources in several major sections of the Wanggang River, including industry, urban land, pond culture, and livestock land. These findings can be used to improve the identification of pollution sources in specific environments and develop effective pollution management plans for polluted river water.