Dataset Information

Risk assessment and source apportionment of trace elements in multiple compartments in the lower reach of the Jinsha River, China.

ABSTRACT: Studies on trace element (TE) pollution in abiotic matrices have typically focused on water, sediment, and soil, either separately or in pairs. The importance of multi-media connectivity has been ignored. This study analyzed the concentrations of 6 TEs in three connected environmental compartments of a 28-km section of the lower reach of the Jinsha River. The ecological risk posed by TEs was higher in soil than in sediment. The contribution of exposure pathways to human health risk were ranked as ingestion > dermal contact > inhalation. An improved regional environmental risk index (RERI) method was then developed to evaluate the comprehensive risk on both ecology and human health caused by TEs. The average RERI value was generally higher in the wet season (0.42) than in the dry (0.41) and dry-to-wet transition seasons (0.08) because of the combined effects of the high TE concentrations in riparian soil and the long exposure time. Source apportionment indicated that industrial activities, weathering of parent rock, and agricultural activities were possible sources of TEs in this region. The methods and results of this study could inform local environmental management and provide references for similar cases wherein multiple compartments of river systems should be considered.

SUBMITTER: He W

PROVIDER: S-EPMC8501140 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Risk assessment and source apportionment of trace elements in multiple compartments in the lower reach of the Jinsha River, China.

He Wenyan W Li Fei F Yu Jiang J Chen Min M Deng Yun Y Li Jia J Tang Xiliang X Chen Zhuoyu Z Yan Zhongluan Z

Scientific reports 20211008 1

Studies on trace element (TE) pollution in abiotic matrices have typically focused on water, sediment, and soil, either separately or in pairs. The importance of multi-media connectivity has been ignored. This study analyzed the concentrations of 6 TEs in three connected environmental compartments of a 28-km section of the lower reach of the Jinsha River. The ecological risk posed by TEs was higher in soil than in sediment. The contribution of exposure pathways to human health risk were ranked a ...[more]

PMID: 34625650

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Project description:Increasing complexity in human-environment interactions at multiple watershed scales presents major challenges to sediment source apportionment data acquisition and analysis. Herein, we present a step-change in the application of Bayesian mixing models: Deconvolutional-MixSIAR (D-MIXSIAR) to underpin sustainable management of soil and sediment. This new mixing model approach allows users to directly account for the 'structural hierarchy' of a river basin in terms of sub-watershed distribution. It works by deconvoluting apportionment data derived for multiple nodes along the stream-river network where sources are stratified by sub-watershed. Source and mixture samples were collected from two watersheds that represented (i) a longitudinal mixed agricultural watershed in the south west of England which had a distinct upper and lower zone related to topography and (ii) a distributed mixed agricultural and forested watershed in the mid-hills of Nepal with two distinct sub-watersheds. In the former, geochemical fingerprints were based upon weathering profiles and anthropogenic soil amendments. In the latter compound-specific stable isotope markers based on soil vegetation cover were applied. Mixing model posterior distributions of proportional sediment source contributions differed when sources were pooled across the watersheds (pooled-MixSIAR) compared to those where source terms were stratified by sub-watershed and the outputs deconvoluted (D-MixSIAR). In the first example, the stratified source data and the deconvolutional approach provided greater distinction between pasture and cultivated topsoil source signatures resulting in a different posterior distribution to non-deconvolutional model (conventional approaches over-estimated the contribution of cultivated land to downstream sediment by 2 to 5 times). In the second example, the deconvolutional model elucidated a large input of sediment delivered from a small tributary resulting in differences in the reported contribution of a discrete mixed forest source. Overall D-MixSIAR model posterior distributions had lower (by ca 25-50%) uncertainty and quicker model run times. In both cases, the structured, deconvoluted output cohered more closely with field observations and local knowledge underpinning the need for closer attention to hierarchy in source and mixture terms in river basin source apportionment. Soil erosion and siltation challenge the energy-food-water-environment nexus. This new tool for source apportionment offers wider application across complex environmental systems affected by natural and human-induced change and the lessons learned are relevant to source apportionment applications in other disciplines.

Dataset Information

Risk assessment and source apportionment of trace elements in multiple compartments in the lower reach of the Jinsha River, China.

Publications

Risk assessment and source apportionment of trace elements in multiple compartments in the lower reach of the Jinsha River, China.

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