Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH₃-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH₃-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH₃-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

It is challenging to investigate the transport and dispersion of contaminants in river confluence areas due to the complex flow dynamics. In recent studies on the flow dynamics in river confluence areas, it has been revealed that changes in inflow conditions (discharge ratio, width-depth ratio and concentration difference) can greatly influence pollutant diffusion. In this study, an asymmetric confluence-type river is modeled by a three-dimensional hydrodynamic water quality model, and three hydrodynamic scenarios are numerically simulated. The results show that a higher discharge ratio and width-depth ratio led to an increase in the lateral diffusion area of pollutants, deviation in the trajectory line of the mixing interface towards the opposite bank of the interchange, and an increase in the mixing rate of pollutants. For R = 0.267 and b/h = 3.75, the pollutants at the bottom are completely mixed in the exit end section. However, the difference in the pollutant concentration slightly affects the area, length and shape of the pollutant dispersion zone and notably affects only the concentration in each section.

The upper Xin’an River basin serves as a critical ecological barrier and water-conservation area for the Yangtze River Delta. However, with rapid economic development, nitrogen pollution in the surface waters of this region has become increasingly pronounced. This study analyzed river water samples collected on four occasions from the upper Xin’an River basin for ammonium (NH₄⁺–N), nitrate-nitrogen (NO₃⁻–N), total nitrogen (TN), and nitrate isotopic (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻). The sources of nitrate (NO₃⁻) were apportioned using the MixSIAR stable-isotope mixing model, and the spatial distribution of these sources across the basin was characterized. Across the four sampling rounds, the mean TN concentration exceeded 1.3 mg/L, with NO₃⁻–N accounting for over 45% of TN, indicating that nitrate was the dominant inorganic nitrogen species. The δ¹⁵N–NO₃⁻ values ranged from 2.17‰ to 13.0‰, with mean values following the order summer > winter > autumn > spring. The δ¹⁸O–NO₃⁻ values varied from −5.20‰ to −3.48‰, and the average value showed a completely opposite seasonal variation pattern to that of δ¹⁵N–NO₃⁻. Process-based analysis of nitrogen transformations revealed that nitrification predominates during nitrate transport and transformation, whereas denitrification is comparatively weak. MixSIAR-based estimates indicate marked seasonal differences in the source composition of nitrate pollution in the upper Xin’an River basin; NO₃⁻ derives primarily from soil nitrogen (SN) and livestock/sewage manure nitrogen (LSN). LSN was the dominant contributor in spring and summer (49.2% and 59.9%, respectively). SN dominated in autumn (49.2%) and winter (54.1%). Fertilizer nitrogen (FN) contributed more during summer and autumn, when fertilization is concentrated and rainfall is higher. Atmospheric deposition (AN) contributed approximately 1% across all seasons and was thus considered negligible. These findings provide a scientific basis for source control of nitrogen pollution and water-quality management in the upper Xin’an River.