Evidence map›Paper›PMID 24414987›Full record

ArticleEcotoxicology (London, England)2014

Health risk assessment of semi-volatile organic pollutants in Lhasa River China.

Feng Liu, Yan Liu, Dongsheng Jiang, Rongfei Zhang, Yibin Cui, Mei Li

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Ecotoxicology (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

6 authors.

Feng LiuState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University (Xianlin Campus), 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China.
Yan Liu
Dongsheng Jiang
Rongfei Zhang
Yibin Cui
Mei Li

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The semi-volatile organic compounds in Lhasa River were determined qualitatively and quantitatively by gas chromatography method with mass spectrometric detection. Total concentrations of 23 organic pollutants in samples from five sites ranged from 1.56 to 2.78 μg/L. The average concentrations for ΣPAEs, ΣPAHs and ΣBTEXs obtained in this study were 1.53, 0.33 and 0.51 μg/L, respectively. Moreover, the results of analysis of variable showed that there were significant differences (P < 0.05) among the sites for levels of ΣPAHs, ΣPAEs and ΣBTEXs. Cluster analysis was applied to detect spatial similarity for grouping of sites under the monitoring network. The results indicated that the five sites in this study could be divided into two significant groups, i.e. low and high pollutant groups. Health risk assessment was conducted by multimedia environmental goals (MEG), risk quotient (RQ) for each pollutant and hazard quotient (HQ) approach from USA ecological risk assessment (ERA) for screening stage. Calculated both total ambient severity and RQ were less than 1 and therefore minimal risk to human and ecological health. For analysis results of HQ, whether for the ingestion exposure or dermal adsorption pathway were all less than 1, the results also agreed with the RQ model and MEG model for evaluating the potential for adverse health effects due to exposure semi-volatile organic compounds from surface water. Therefore, SVOCs in Lhasa River posed little or no threat to the health of local consumers and ecological environment.

Indexed as

Environmental MonitoringCluster AnalysisHumansRisk AssessmentRiversTibetVolatile Organic CompoundsWater Pollutants, ChemicalWater QualityWater SupplyVolatile Organic CompoundsWater Pollutants, Chemical

Identifiers

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.