Evidence map›Paper›PMID 27658408›Full record

ArticleEnvironmental science and pollution research international2016

Distribution and risk assessment of metals and arsenic contamination in man-made ditch sediments with different land use types.

Mathieu Nsenga Kumwimba, Bo Zhu, Tao Wang, Diana Kavidia Muyembe

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Article in Environmental science and pollution research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mathieu Nsenga KumwimbaKey Laboratory of Mountain Surface Processes and Ecological Regulation, Chinese Academy of Sciences, Chengdu, 610041, China.
Bo ZhuKey Laboratory of Mountain Surface Processes and Ecological Regulation, Chinese Academy of Sciences, Chengdu, 610041, China. bzhu@imde.ac.cn.
Tao WangKey Laboratory of Mountain Surface Processes and Ecological Regulation, Chinese Academy of Sciences, Chengdu, 610041, China. wangt@imde.ac.cn.
Diana Kavidia MuyembeSchool of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ditches are subjected to a large input of nutrients, trace metals, and arsenic and the enhancement of sedimentation due to human activities. However, the influence of different types of land uses on the distribution and associated environmental risk of metals and arsenic in the Red purple Sichuan Basin remains largely unclear, which is needed for water management. This study was carried out to characterize metal/metalloid status in ditch sediments from different land uses. A total of 68 surface sediment samples (0-5 cm) were collected from open ditches distributed in different land use types, i.e., cultivated ditches (CD), barren land ditches (BLD), roadside ditches (RSD), and residential ditches (RD), within the Sichuan Basin. Mean concentrations of Cr, Ni, Cu, Zn, Cd, Pb, and Mn in both RD and RSD were above the soil background values of Sichuan Basin, but Cd in ditch sediments of the basin posed considerable ecological risk to the environment. Overall, metals/metalloid (except Pb) decreased in the following order of RD > RSD > BLD > CD. Of the different land use types in the hilly region, residential and roadside land uses were likely to adverse effects on aquatic life. Multivariate statistical analysis showed that Mn, As, Cu, Ni, Zn, Fe, and Al were mainly influenced by natural weathering (erosion), while Pb might come from heavy vehicular traffic. The degree of contamination (Md), enrichment factor (EF), and the geo-accumulation index (Igeo) showed that Cd causes strong sediment pollution in the basin. Sediment quality guidelines SQG-Q values displayed that metals and arsenic created medium-low potential of adverse biological effects. These results provide baseline information on the metals and arsenic pollution in the Sichuan Basin. Awareness of land use type contributions to metals and arsenic requires that these man-made ditches be considered for their mitigation of pollutants in this region.

Indexed as

ArsenicEcologyEnvironmental MonitoringGeologic SedimentsHuman ActivitiesHumansMetals, HeavyMultivariate AnalysisRisk AssessmentWater Pollutants, ChemicalWater Pollution, ChemicalArsenicMetals, HeavyWater Pollutants, ChemicalLand useManagementMan-made ditch sedimentMetal/metalloidPotential ecological risk indicesPurple Sichuan Basin

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.