Evidence map›Paper›PMID 32112358›Full record

ArticleEnvironmental science and pollution research international2020

Identification of the heavy metal pollution sources in the rhizosphere soil of farmland irrigated by the Yellow River using PMF analysis combined with multiple analysis methods-using Zhongwei city, Ningxia, as an example.

Min Zhang, Xueping Wang, Chang Liu, Jiayu Lu, Yuhong Qin, Yunkan Mo, Pengjun Xiao, Ying Liu

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 46 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Min ZhangCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Xueping WangGuangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou, 535011, China.
Chang LiuCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Jiayu LuCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Yuhong QinCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Yunkan MoCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Pengjun XiaoCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
Ying LiuCollege of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China. liuying4300@163.com.ORCID http://orcid.org/0000-0002-2929-2524
Minzu University of China · CNBeibu Gulf University · CN

Funding

Funded by the independent research project of the master's degree of the Minzu University SSZZKY-2019106National Natural Science Foundation of China 21177163Special Guidance Fund of Building World First-class Universities (Disciplines) and Characteristic Development 2019, 10301-0190040129Undergraduate Scientific Research and Training Program of Minzu University of China BEIJ2018110005
6 · The paper itself

Abstract

In recent years, with the frequent occurrences of heavy metal (HM) pollution in agriculture, the problem of HM pollution in farmland soil, especially in the areas irrigation by the Yellow River, has been attracted increasing attention because of the complex sources of pollution. Qualitative identification of pollution sources and quantification of their contributions to HMs in soil are the key links in the prevention and control of HM pollution. The contents of 11 heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sn, V, and Zn) in the rhizosphere soil of the Ningxia irrigation area were determined by inductively coupled plasma mass spectrometry (ICP-MS). Multiple methods were used for source identification, including positive matrix factorization (PMF) analysis combined with multiple other analyses (single factor index method (Pi), coefficient of variation(CV), correlation analysis(CA), enrichment factor(EF), and principal component analysis(PCA)). The results showed that (1) the over-standard rates of As, Cd, Cr, Mn, Pb, Sn, and Zn in the study area were 100%, of which Cd was seriously polluted, while As, Zn, and Sn were moderately polluted. (2) The HM contributions from irrigation and silt soil formed by the Yellow River sediment were the highest (42.45%), followed by the smelting industry and traffic pollution (16.06%). (3) The contribution of agricultural pollution to HMs in the region was 15.54%, in which As was mainly from pesticides and Cd was mainly from fertilizers.

Indexed as

ChinaCitiesEnvironmental MonitoringFarmsMetals, HeavyRhizosphereRiversSoilSoil PollutantsMetals, HeavySoilSoil PollutantsHeavy metals (HMs)Positive matrix factorization (PMF)Rhizosphere soilSource analysis

Identifiers

PMID32112358
OpenAlexW3007959843

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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.