In the Duliujian River, 12 water environmental parameters corresponding to 45 sampling sites were analyzed over four seasons. With a statistics test (Spearman correlation coefficient) and multivariate statistical methods, including cluster analysis (CA) and principal components analysis (PCA), the river water quality temporal and spatial patterns were analyzed to evaluate the pollution status and identify the potential pollution sources along the river. CA and PCA results on spatial scale revealed that the upstream was slightly polluted by domestic sewage, while the upper-middle reach was highly polluted due to the sewage from feed mills, furniture and pharmaceutical factories. The middle-lower reach, moderately polluted by sewage from textile, pharmaceutical, petroleum and oil refinery factories as well as fisheries and livestock activities, demonstrated the water purification role of wetland reserves. Seawater intrusion caused serious water pollution in the estuary. Through temporal CA, the four seasons were grouped into three clusters consistent with the hydrological mean, high and low flow periods. The temporal PCA results suggested that nutrient control was the primary task in mean flow period and the monitoring of effluents from feed mills, petrochemical and pharmaceutical factories is more important in the high flow period, while the wastewater from domestic and livestock should be monitored carefully in low flow periods. The results may provide some guidance or inspiration for environmental management.

A river-estuary ecosystem usually features a distinct salinity gradient and a complex water environment, so it is enormously valuable to study the response mechanism of living organisms to multiple abiotic factors under salinity stress. Phytoplankton, as an important part of aquatic microorganisms, has always been of concern for its crucial place in the aquatic ecosystem. In this study, phytoplankton data and 18 abiotic factors collected from 15 stations in Duliujian River, a seagoing river, were investigated in different seasons. The results showed that the river studied was of a Cyanophyta-dominant type. Salinity (SAL) was the key control factor for phytoplankton species richness, while water temperature (WT) was critical not only for species richness, but also community diversity, and the abundance and biomass of dominant species. Apart from WT, the abundance and biomass of dominant species were also driven by total nitrogen (TN), nitrate (NO₃⁻), pH, and water transparency (SD). Moreover, total dissolved phosphorus (TDP), pH, and chemical oxygen demand (COD) were crucial for community diversity and evenness. The bloom of dominant species positively associated with TDP led to lower diversity and evenness in autumn. In addition, when available nitrogen was limited, Pseudoanabaena sp. could obtain a competitive advantage through the N₂ fixation function. Increased available nitrogen concentration could favor the abundance of Chlorella vulgaris to resist the negative effect of WT. The results show that Oscillatoria limosa could serve as an indicator of organic contamination, and nutrient-concentration control must be effective to inhibit Microcystis bloom. This could help managers to formulate conservation measures.

Microbial communities in sediment play an important role in the circulation of nutrients in aquatic ecosystems. In this study, the main environmental factors and sediment microbial communities were investigated bimonthly from August 2018 to June 2020 at River Taizicheng, a shallow temperate mountain river at the core area of the 2022 Winter Olympics. Microbial community structure was analyzed using 16S rRNA genes (bacteria 16S V3 + V4 and archaea 16S V4 + V5) and high-throughput sequencing technologies. Structure equation model (SEM) and canonical correspondence analysis (CCA) were used to explore the driving environmental factors of the microbial community. Our results showed that the diversity indices of the microbial community were positively influenced by sediment nutrients but negatively affected by water nutrients. Bacteroidetes and Proteobacteria were the most dominant phyla. The best-fitted SEM model indicated that environmental variables not only affected community abundance directly, but also indirectly through influencing their diversity. Flavobacterium, Arenimonas and Terrimonas were the dominant genera as a result of enriched nutrients. The microbial community had high spatial–temporal autocorrelation. CCA showed that DO, WT and various forms of phosphorus were the main variables affecting the temporal and spatial patterns of the microbial community in the river. The results will be helpful in understanding the driving factors of microbial communities in temperate monsoon areas.