Journal
JOURNAL OF SOILS AND SEDIMENTS
Volume 20, Issue 5, Pages 2460-2470Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11368-020-02604-4
Keywords
Ecological risk; Positive matrix factorization; Risk source apportionment; Sediment; Trace metal
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Funding
- National Natural Science Foundation of China [41807344]
- Beijing Advanced Innovation Program for Land Surface Science
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Purpose From perspective of risk management and control, the ecological risk contributed from various sources can give direct risk information in selecting priority pollutants to control in river ecosystem restoration. However, studies combined receptor model with risk assessment to obtain ecological risk in every source category that is still limited. It is essential to quantitatively and spatially analyse the contributions of various sources to both the content and ecological risk in river system. Materials and methods Ten trace metals in 47 sediment samples were collected in Le'an River, which is the main tributary of the largest freshwater lake in China, Poyang Lake and analysed by laboratory analysis. The potential ecological risk index (PERI) model was used to evaluate the ecological risk posed by sediment trace metals. The positive matrix factorization (PMF) was utilized to identify the source of sediment trace metal and apportion their contribution. The ecosystem risks of the sources were quantitatively analysed by combining PERI and PMF, termed PMF-PERI. In addition, we conducted a spatial dissimilarity analysis of the source contribution distributions. Results and discussion The ecological risk assessment results showed that the metals Cd, Cu, and Hg may cause the main ecological hazards. Source apportionment of the trace metal concentrations indicated that the dominant source of trace metals in the sediment was human activities (55.83%), such as Pb-Zn mining and agricultural activities (30.33%), gold smelting (13.62%) and copper mining (11.89%). Source apportionment of the ecological risks posed by the trace metals indicated that natural sources were the main ecosystem risk sources (64.75%), followed by Pb-Zn mining and agricultural activities (14.65%), copper mining (10.67%) and gold smelting (9.93%). The results of spatial dissimilarity analysis indicated that the significant spatial variation in the contribution compositions was related to the locations of the sources and the river. Conclusions The results of this study indicated that the emission reduction of agricultural activities should be given priority attention for protecting the watershed environment of the Le'an River. In addition, this study extends the use of risk assessment in receptor model source apportionment.
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