Background: Heavy metal pollution of aquatic systems is a global issue that has received considerable attention. Canonical correlation analysis (CCA), principal component analysis (PCA), and potential ecological risk index (PERI) have been applied to heavy metal data to trace potential factors, identify regional differences, and evaluate ecological risks. Sediment cores of 200 cm in depth were taken using a drilling platform at 10 sampling sites along the Xihe River, an urban river located in western Shenyang City, China. Then they were divided into 10 layers (20 cm each layer). The concentrations of the As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were measured for each layer. Eight heavy metals, namely Pb, Zn, As, Cd, Cr, Cu, Ni, and Hg, were measured for each layer in this study. Results: The average concentrations of the As, Cd, Cu, Hg, and Zn were significantly higher than their background values in soils in the region, and mainly gathered at 0–120 cm in depth in the upstream, 0–60 cm in the midstream, and 0–20 cm downstream. This indicated that these heavy metals were derived from the upstream areas where a large quantity of effluents from the wastewater treatment plants enter the river. Ni, Pb, and Cr were close or slightly higher than their background values. The decreasing order of the average concentration of Cd was upstream > midstream > downstream, so were Cr, Cu, Ni and Zn. The highest concentration of As was midstream, followed by upstream and then downstream, which was different to Cd. The potential factors of heavy metal pollution were Cd, Cu, Hg, Zn, and As, especially Cd and Hg with the high ecological risks. The ecological risk levels of all heavy metals were much higher in the upstream than the midstream and downstream. Conclusions: Industrial discharge was the dominant source for eight heavy metals in the surveyed area, and rural domestic sewage has a stronger influence on the Hg pollution than industrial pollutants. These findings indicate that effective management strategies for sewage discharge should be developed to protect the environmental quality of urban rivers.

Rivers are well known as one of the most threatened aquatic environments, whose structure and water quality can be deeply impacted by intensive anthropogenic activities. Despite the fact that anthropogenic influences on river ecosystems could indeed be deduced from the composition and chemistry of fluvial dissolved organic matter (DOM), sources of anthropogenic loading to DOM are still poorly explored. Here, by uniting fluorescence excitation-emission matrices (EEM) and principal component absolute coefficient, four sources of DOM from seventeen rivers in major drainage basins of China could be identified, i.e., originating from municipal sewage, domestic wastewater, livestock wastewater, and natural origins, and thus being defined as MS-DOM, DW-DOM, LW-DOM, NO-DOM, respectively. Based on the random forest model, special nodes in EEM could be traced from four sources, respectively. According to parallel factor analysis, DOM mainly contained protein-like, microbial humic-like, and fulvic-like fluorescence substances, among which protein-like was dominant in MS-DOM and DW-DOM, microbial humic-like in LW-DOM, and fulvic-like in NO-DOM. Based on key peaks and essential nodes in EEM, the identifying source indices were first proposed, which could be introduced to simply distinguish the different anthropogenic-derived sources of fluvial DOM. It was associated with intensity ratios of the key peaks and the essential nodes of EEM spectra from four sources, i.e., municipal sewage (MS-SI: Ex/Em = 280/(335, 410) nm), domestic wastewater (DW-SI: Ex/Em = 280/(340, 410) nm), livestock wastewater (LW-SI: Ex/Em = 235/(345, 380) nm), and natural origins (NO-SI: Ex/Em = 260/(380, 430) nm). By statistical analysis, the high identifying source indices of municipal sewage (>0.5) and natural origins (>0.4) values could be related to MS-DOM and NO-DOM, respectively. The identifying source indices of domestic wastewater with 0.1–0.3 might be linked to DW-DOM and the identifying source indices of livestock wastewater with 0.3–0.4 to LW-DOM. Compared with conventional optical indices, the novel identifying source indices showed remarkable discrimination for the sources of fluvial DOM with different forms of anthropogenic disturbances. Hence the innovative approach could be relatively convenient and accurate to evaluate water quality or pollution risk in river ecosystems.