Dissolved organic matter (DOM) is involved in many biogeochemical processes and plays an important role in aquatic ecosystems. This study integrated three-dimensional fluorescence excitation–emission matrix (EEM), fluorescence regional integration (FRI), and parallel factor analysis (PARAFAC) to better understand the distribution and component characteristics of DOM in the Yongding River and explore the response of DOM to natural and anthropogenic activities. The results showed that the relative abundance of fulvic-like materials of DOM in the river was the highest, with an average of 68.64%. PARAFAC identified three fluorescent components, namely, C1 (microbial humic-like components), C2 (terrestrial humic-like components), and C3 (protein-like components), and their changes with flow confirmed that the riverine DOM was generally influenced by microbial sources and terrestrial inputs. The upper reaches showed strong autochthonous characteristics and a high humification degree of DOM due to a fast flow rate, while the middle reaches showed biological or aquatic bacterial origin due to a moderate flow rate. The lower reaches of the river showed characteristics of biological and bacterial origin, most strongly influenced by human activities. The findings can help provide a basis for identifying DOM characteristics in the Yongding River basin and understanding the geochemical cycle of DOM at a regional scale.

Black bloom is a very serious water pollution phenomenon in eutrophic lakes, with Fe(II) and S(−II) being the key limiting factors for this problem. In this paper, three different machine learning methods, namely, Random Forest (RF), Gaussian Mixture Model (GMM), and Bayesian Network (BN), were used to explore the complex interactions among Fe(II), S(−II), and other aquatic factors in the estuary of Chaohu Lake to better characterize and monitor water degradation by black bloom. The results of RF showed that total nitrogen (TN), ammonia, total phosphorous (TP), suspended sediment concentration (SSC), and oxidation–reduction potential (ORP), which were chosen from 11 factors, had the most important relationships with Fe(II) and S(−II). The 69 sampling sites were divided in three groups identified as worst, worse, and bad according to the observed values of seven factors using the GMM. Then, the BN model was applied to three observation groups. The results showed that the structures of the interaction networks were different between the groups. S(−II) controlled only SSC production in the bad and worse group sites, while SSC was determined by both S(−II) and Fe(II) in the worst group. Ammonia and TN exhibited the most direct importance for S(−II) and Fe(II) production in all observation groups. According to the indications from the BNs, potential management strategies for different water pollution conditions were developed. Finally, the threshold values of Fe(II), S(−II), TP, ammonia, TN, SSC, and ORP, which were 0.80 mg/L, 0.04 mg/L, 0.45 mg/L, 3.44 mg/L, 4.15 mg/L, 55 mg/L, and 135 mv, respectively, were determined on the basis of the BN models. These values will be helpful to develop accurate strategies of oxygenation to quickly eliminate black bloom in the lake.

Viruses are the most abundant and active biological organisms in soil ecosystems. However, their spatial distribution and ecological functions remain inadequately addressed owing to a lack of systematic analysis and the complexity of the soil matrix. Therefore, this review systematically summarizes current knowledge of soil viruses through an examination of research methods, spatiotemporal distribution, and ecological functions. Our review indicates that current progress of soil viruses remains constrained by technological inconsistencies and difficulties in viral isolation techniques within complex soil matrices. Therefore, viral distribution analyses are largely limited to small-scale sections of agricultural land, forest, pastureland, and grassland. There is insufficient exploration of viral distribution across large-scale geographic areas and desert soil environments, as well as RNA virus diversity. The characteristics of soil viral communities are primarily influenced by environmental factors such as temperature, moisture, and pH. Overall, we conclude that soil viruses are pivotal drivers of microbial community dynamics and biogeochemical cycling, although our comprehensive understanding is severely bottlenecked by complex soil matrices and inconsistent viral isolation methodologies. Future research should focus on methodological innovations such as long-read sequencing and single-cell approaches to decipher virus–host interaction networks and their ecological roles across large-scale and extreme environments.

Aquatic ecosystems are fundamental to sustaining global biodiversity and providing essential ecosystem services, yet they face unprecedented threats from anthropogenic pollution