Evidence map›Paper›PMID 42198492›Full record

ArticleToxics2026

Heavy Metals Risk Assessment and Source Apportionment in Agricultural Soils of the Central Yunnan Dry-Hot Valley.

Lin Song, Tao Zhang, Hedian Yan, Jie Xu, Weizhi Chen, Yong Ba, Hu Wang, Kun Qian, Yuanlong Li, Wenlin Wu and 1 more

Abstract read
In one paragraph

Article in Toxics, 2026. 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
–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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Lin SongKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Tao ZhangCollege of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China.
Hedian YanCollege of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China.
Jie XuKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Weizhi ChenKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Yong BaKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Hu WangKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Kun QianKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Yuanlong LiKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Wenlin WuKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.
Ya ZhangKunming General Survey of Natural Resources Center, China Geological Survey, Kunming 650100, China.

Funding

China Geological Survey DD20208070National Natural Science Foundation of China 42307058
6 · The paper itself

Abstract

Heavy metal contamination in agricultural soils threatens ecosystem safety and sustainable land use, particularly in geologically sensitive areas. This study aimed to assess the pollution status, ecological risks and source contributions of eight heavy metals (Hg, Cd, Pb, As, Cr, Cu, Ni and Zn) in soils from a dry-hot agricultural region of central Yunnan, China. To improve source apportionment, this study applied and compared three models: APCS-MLR, PMF, and Random Forest. Analysis of 1790 soil samples showed mean concentrations (mg/kg) of 0.03 for Hg, 0.17 for Cd, 25.01 for Pb, 7.46 for As, 85.91 for Cr, 36.20 for Cu, 31.75 for Ni, and 69.24 for Zn. Pollution assessment indicated that Cu and Cd were the main pollutants, while ecological risk assessment identified Cd and Hg as the dominant ecological risk factors. Four major sources were identified: industrial hybrid sources, natural background, atmospheric deposition and agricultural activities, with industrial hybrid sources contributing the largest share. These results indicate that integrating APCS-MLR, PMF, and Random Forest provides a more reliable framework for source identification and supports targeted soil pollution control in regions affected by both natural and anthropogenic inputs.

Indexed as

APCS-MLRfarmlandheavy metalsPMFRandom Forestsource apportionment

Identifiers

PMID42198492
PMCPMC13211422

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.