Evidence map›Paper›PMID 36287861›Full record

ArticleToxics2022

Pollution Characteristics, Source Apportionment, and Health Risk Assessment of Potentially Toxic Elements (PTEs) in Road Dust Samples in Jiayuguan, Hexi Corridor, China.

Kai Xiao, Xiaoqing Yao, Xi Zhang, Ning Fu, Qiuhong Shi, Xiaorui Meng, Xuechang Ren

Open access · goldAbstract read
PubMed Full text at PMC Publisher
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
0.5field-weighted citation impact, top 46% of its field
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

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 1 country.

Kai XiaoCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Xiaoqing YaoCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Xi ZhangCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Ning FuCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Qiuhong ShiCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Xiaorui MengCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Xuechang RenCollege of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Lanzhou Jiaotong University · CNGuangdong Province Environmental Monitoring Center · CN

Funding

Jiayuguan Ecological Environment Bureau JYGZCDL2019041G
6 · The paper itself

Abstract

The sources of potentially toxic elements (PTEs) in road dust are complex and potentially harmful to humans, especially in industrial cities. Jiayuguan is the largest steel-producing city in Northwest China, and this study was the first to conduct a related study on PTEs in road dust in this city, including the pollution characteristics, source apportionment, and health risk assessment of PTEs in road dust. The results showed that the highest concentration of PTEs in the local road dust samples were Mn, Ba, Zn, and Cr. The enrichment factor (EF) of Se was the highest, and it was "Very high enrichment" in areas other than the background area, indicating that the local Se was more affected by human activities. The geoaccumulation index (I

Indexed as

health risk assessmentJiayuguanpollution characteristicspotentially toxic elementsroad dustsource apportionment

Identifiers

PMID36287861
PMCPMC9607028
OpenAlexW4304187632

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.