Evidence map›Paper›PMID 33671883›Full record

ArticleInternational journal of environmental research and public health2021

Pollution Distribution of Potentially Toxic Elements in a Karstic River Affected by Manganese Mining in Changyang, Western Hubei, Central China.

Zhao Liu, Ye Kuang, Shengtao Lan, Wenjia Cao, Ziqi Yan, Li Chen, Qianlong Chen, Qi Feng, Hong Zhou

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Zhao LiuInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Ye KuangInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Shengtao LanInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Wenjia CaoInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Ziqi YanInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Li ChenInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Qianlong ChenInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Qi FengInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
Hong ZhouInstitute of Geological Survey, China University of Geosciences, Wuhan 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigated the distribution, pollution level and potential ecological risk of potentially toxic elements (PTEs) from manganese mining in a karstic Danshui River, in Changyang, Western Hubei, Central China. River water and sediments were collected for seven PTEs measurement (As, Cd, Cr, Cu, Mn, Pb and Zn), as well as pH and Eh of the river water were measured. Results showed that the major pollutant was Mn, the river water environment was mainly acidic and oxidizing (288 < Eh, pH < 6.3), and the pollution distribution of Mn in the study area was dominated by the combination of natural processes and anthropogenic activities. In the river water, according to the contamination factor (CF) and pollution load index (I

Indexed as

Metals, HeavyWater Pollutants, ChemicalChinaEnvironmental MonitoringGeologic SedimentsManganeseMiningRisk AssessmentManganeseMetals, HeavyWater Pollutants, Chemicalmanganese mining activitypollution distributionpotential ecological riskpotentially toxic element

Identifiers

PMID33671883
PMCPMC7918950

What OpenQuestion holds

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Registered trials

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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.