Metal ratio mixing models clarify metal contamination sources to lake sediments in Yunnan, China.
Sci Total Environ
; 820: 153247, 2022 May 10.
Article
em En
| MEDLINE
| ID: mdl-35063530
ABSTRACT
Contaminated legacy sediments contribute to modern pollution loadings, particularly trace metals. These contributions are challenging to quantify as metal histories reconstructed from sediment records cannot be easily divided into legacy and concurrent contamination. In particular, the contribution from re-mobilization and delivery of legacy metals stored in catchment soil, colluvial, and fluvial environments are rarely considered or quantified when interpreting sediment records. Here, extended records of metals accumulation for a set of three lakes in Yunnan, China are compared with endmember chemistries using Monte Carlo-Markov Chain mixing models to help identify source contributions to the sediments. This approach allows attribution of metals transported by atmospheric and fluvial mechanisms in a region with a history of mining and metallurgy spanning millennia. These analyses reveal distinct source mixtures and demonstrate the sensitivity of lake records to basin sediment dynamics. In particular, substantial proportions of elevated metal concentrations in these lake systems seem to arise from soil contributions more than from atmospheric deposition of smelting emissions. The largest soil contributions seem to be in Erhai, a lake with erosion prone soils closely "connected" to the lake. Moreover, these invesigations illustrate the potential for mixing approaches to accommodate and clarify uncertainties in metal source and extraction as differences in extraction efficiency can be incorporated into source uncertainty estimates. Ultimately, these approaches emphasize the need to account for fluvial metal transport in interpretation of sediment histories.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Poluentes Químicos da Água
/
Metais Pesados
Tipo de estudo:
Prognostic_studies
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article