Dataset Information

Occurrence, Ecological and Human Health Risks, and Seasonal Variations of Phenolic Compounds in Surface Water and Sediment of a Potential Polluted River Basin in China.

ABSTRACT: Five phenolic compounds in water and sediment of Yinma River Basin were investigated. The average concentration of phenol was the highest in water samples as well as in sediment samples during the wet season, 101.68 ng/L and 127.76 ng/g, respectively. 2,4,6-Trichlorophenol (2,4,6-TCP) and pentachlorophenol (PCP) was not detected in some sampling sites. Shitou Koumen Reservoir and the neighboring area were the severest areas of phenolic pollution. The lower reach was more polluted in three water seasons than the middle reach and upper reach. Phenol had ecological risks in sediment during three water seasons. 2-Nitrophenol (2-NP) and 2,4-dichlorophenol (2,4-DCP) had ecological risks in sediment in both the normal and wet season. The concentrations of five phenolic compounds from high to low were in the wet season, normal season, and dry season in water and sediment, respectively. There were middle risks in water of total concentrations for five phenolic compounds in several sampling sites. Total concentrations for five phenolic compounds in sediment had high ecological risks in all sampling sites. However, there was no human health risk in the Yinma River Basin.

SUBMITTER: Zhou M

PROVIDER: S-EPMC5664641 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Occurrence, Ecological and Human Health Risks, and Seasonal Variations of Phenolic Compounds in Surface Water and Sediment of a Potential Polluted River Basin in China.

Zhou Mo M Zhang Jiquan J Sun Caiyun C

International journal of environmental research and public health 20170927 10

Five phenolic compounds in water and sediment of Yinma River Basin were investigated. The average concentration of phenol was the highest in water samples as well as in sediment samples during the wet season, 101.68 ng/L and 127.76 ng/g, respectively. 2,4,6-Trichlorophenol (2,4,6-TCP) and pentachlorophenol (PCP) was not detected in some sampling sites. Shitou Koumen Reservoir and the neighboring area were the severest areas of phenolic pollution. The lower reach was more polluted in three water ...[more]

PMID: 28953252

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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

Occurrence, Ecological and Human Health Risks, and Seasonal Variations of Phenolic Compounds in Surface Water and Sediment of a Potential Polluted River Basin in China.

Publications

Occurrence, Ecological and Human Health Risks, and Seasonal Variations of Phenolic Compounds in Surface Water and Sediment of a Potential Polluted River Basin in China.

Similar Datasets

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