Combining multiple methods for provenance discrimination based on rare earth element geochemistry in lake sediment

Geochemical properties of rare earth elements (REEs) have the potential to represent the provenance and depositional history of surface sediment in aquatic environments. In this study, both surface and core sediment samples were collected from Dongting Lake to investigate the distribution characteristics and source of REEs by combining the methodologies of geostatistics, positive matrix factorization (PMF) model, discriminant function (DF), and provenance index (PI) based on REEs geochemical parameters. The results indicated that the total REEs content in sediment samples ranged from 129.12 to 284.02 mu g g(-1), with the average REEs content calculated to be 197.95 mu g g(-1). Light REEs (LREEs) comprised >90% of the total REEs, indicating that there was an enrichment of LREEs in the sediment samples. The REEs of the surface sediment showed strong spatial variation, with relatively high values located in Eastern Dongting Lake and relatively lower levels in Western Dongting Lake. Moreover, the vertical distributions of Sigma REEs, Sigma LREEs and Sigma HREEs at most sampling sites behaved similarly with rapid increase until about 6-8 cm, followed by a downward trend with some irregularities. The strong association between most REEs confirmed that they often have a positive correlation and co-existence in sediment. The PMF model revealed that most of the REEs in the surface sediment were derived from natural sources with some anthropogenic inputs also serving as contributing sources. The DF and PI results indicated that the REEs distribution pattern in the surface sediment of Dongting Lake was similar to that of the Yangtze River, suggesting that Yangtze River had been more of an influence on sediment loads than the upstream tributaries. This study highlights the broader applicability of the REEs tracing method in sediment transport processes and can provide new knowledge regarding source apportionment analysis of sediment-related contaminants in aquatic environments. (C) 2019 Elsevier B.V. All rights reserved.