Evidence map›Paper›PMID 36355950›Full record

ArticleToxics2022

A New Method for Environmental Risk Assessment of Pollutants Based on Multi-Dimensional Risk Factors.

Le Li, Yuying Dong, Yuting Chen, Jian Jiao, Xuejun Zou

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 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

5 authors.

Le LiCollege of Environment and Resource, Dalian Minzu University, Dalian 116600, China.
Yuying DongCollege of Environment and Resource, Dalian Minzu University, Dalian 116600, China.ORCID 0000-0003-0683-3735
Yuting ChenCollege of Environment and Resource, Dalian Minzu University, Dalian 116600, China.
Jian JiaoCollege of Environment and Resource, Dalian Minzu University, Dalian 116600, China.
Xuejun ZouCollege of Environment and Resource, Dalian Minzu University, Dalian 116600, China.

Funding

Education Department of Liaoning Province for Basic Scientific Research Key Project LJKZ0019National Natural Science Foundation of China 22176028
6 · The paper itself

Abstract

Pollutant discharge causing the deterioration of the watershed environment has seriously threatened human health and ecosystem function. The importance of improving the risk warning system is becoming more and more prominent. Traditional chemical risk assessment methods focused on toxicity and the exposure of pollutants without considering the impact of persistent pollutants in different environmental media. In this study, a new approach was proposed to reflect multi-dimensional evaluation with a synthetic risk factor (SRF) of pollutants. The integrating parameters of SRF include toxicity endpoint values, environmental exposure level, persistent properties, and compartment features. Selected pesticides, perfluorinated compounds, organophosphate esters and endocrine disruptors were analyzed by the proposed and traditional methods. The results showed a higher risk outcome using SRF analysis for PFOS, imazalil, testosterone, androstenedione and bisphenol A, which were different from those obtained by the traditional method, which were consistent with existing risk management. The study demonstrated that the SRF method improved the risk assessment of various pollutants in different environmental media in a more robust fashion, and also provided a more accurate decision basis for ecological environment protection.

Indexed as

emerging pollutantsenvironmental risklife cyclemultidimensional evaluationsynthetic risk factorsystematic assessment

Identifiers

PMID36355950
PMCPMC9697580

What OpenQuestion holds

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Registered trials

None linked

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.