Spatial-Temporal Variations, Ecological Risk Assessment, and Source Identification of Heavy Metals in the Sediments of a Shallow Eutrophic Lake, China.

Xiaomei Su, Hong Ling, Dan Wu, Qingju Xue, Liqiang Xie

Abstract read

In one paragraph

Article in Toxics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Uncertainties in Pollution and Risk Assessments of Heavy Metals in Lake Sediments Using Regional Background Soils in China.Toxics · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaomei SuJiangsu Provincial Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Sciences, Nanjing 210036, China.

Hong LingJiangsu Provincial Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Sciences, Nanjing 210036, China.

Dan WuJiangsu Provincial Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Sciences, Nanjing 210036, China.

Qingju XueState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.

Liqiang XieState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.

Funding

China Postdoctoral Science Foundation 2019M651754Open Research Fund of Jiangsu Province Key Laboratory of Environmental Engineering GYYS2021206Open Research Fund of Jiangsu Province Key Laboratory of Environmental Engineering ZX2018007Provincial Natural Science Foundation of Jiangsu of China BK20210953

6 · The paper itself

Abstract

The contamination of heavy metals (Pb, Cr, Hg, Cd, Ni, Cu, Zn, As, and Sb) in the sediments were investigated in Lake Yangcheng, a eutrophic lake in China. Results showed that the average concentrations of each metal in the surface sediments generally exceeded their corresponding background values. Higher values were observed in deeper zones, supporting the retention and accumulation of heavy metals in the core sediments. The spatial distributions of metal averages, pollution load index (PLI), and combined ecological risk index (RI) revealed that ecological risks were highest in the west lake, followed by middle lake, and were lowest in the east section. For the temporal variations of metal contents, the highest concentration was usually observed in the winter. However, the seasonal dynamics of Hg showed a different pattern with higher values in the autumn and lower values in the winter. According to contamination factor (CF), the Hg and Sb contaminations were considerable, while the other metals were moderate contamination. In terms of geoaccumulation index (I

Indexed as

heavy metallake sedimentrisk assessmentsource identificationspatial distribution

Identifiers

PMID35051058

DOI10.3390/toxics10010016

PMCPMC8778156

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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.