Dataset Information

Heavy metals in Yinma River sediment in a major Phaeozems zone, Northeast China: Distribution, chemical fraction, contamination assessment and source apportionment.

ABSTRACT: Yinma River is a typical river in the major Phaeozems zone of Northeast China. It has been suffering an increasing environmental pressure from heavy metal contamination due to the rapid development of population, social-economy and urbanization as well as long term over cultivation. This study investigated the spatial distribution, chemical fraction of heavy metals (Cu, Pb, Zn, Cr, Cd, Ni, As, and Hg) in sediments of Yinma River based on BCR procedure, assessed the contamination level, and identified their sources via multivariate statistical analysis. The chemical fraction results indicated that Cd, Pb, Ni, and Zn exhibited higher mobility susceptibility and bioavailability with a significant and late anthropogenic origin. Hg and Cd might exert a potential hazardous influence on aquatic biota according to the geo-accumulation index (Igeo). The pollution load index (PLI) assessment suggested that all of the sediment samples have been contaminated. Multivariate statistical analysis revealed that Zn, Cu, Hg, Cd, and Pb reflected the anthropogenic sources with a close correlation with TOC and socio-economic development; Ni, As and Cr tended to represent the geochemical background. Furthermore, Changchun City and Shitoukoumen Reservoir as the major drinking water source may be hotspots of the heavy metal contamination in the watershed.

SUBMITTER: Guan J

PROVIDER: S-EPMC6093934 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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

Heavy metals in Yinma River sediment in a major Phaeozems zone, Northeast China: Distribution, chemical fraction, contamination assessment and source apportionment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets