Evidence map›Paper›PMID 42797667›Full record

ArticleToxics2026

Source Apportionment-Based Assessment of Ecological and Health Risk for Potentially Toxic Elements in the Soil Around a Uranium Mine.

Min Fan, Haiyan Liu, Zeqiang Chen, Zhao Chen, Zhen Wang, Binyu Lu, Narsimha Adimalla

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Min FanNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.
Haiyan LiuNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.ORCID 0000-0001-5487-107X
Zeqiang ChenNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.
Zhao ChenNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.
Zhen WangNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.
Binyu LuNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.
Narsimha AdimallaNational Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, 418 Guanglan Road, Jingkai District, Nanchang 330013, China.ORCID 0000-0002-6182-8317

Funding

iangxi Provincial Key Laboratory of Genesis and Remediation of Groundwater Pollution 2023SSY02041National Natural Science Foundation of China 42262029Natural Science Foundation of Jiangxi Province 20232BAB203066 and 20232BAB203047
6 · The paper itself

Abstract

Uranium is a critical strategic resource. However, uranium mining can cause severe contamination of surrounding soils by potentially toxic elements (PTEs). Conventional approaches that separately perform source apportionment and risk assessment fail to establish a direct linkage between pollution sources and their associated environmental and health risks. Herein, we develop an integrated framework that combines the absolute principal component score-multiple linear regression (APCS-MLR) model, the potential ecological risk index, and Monte Carlo simulation. The established framework was used to try quantifying the potential sources of soil PTEs and their corresponding ecological and human risks in a uranium mining area. The results showed that the concentrations of U, Th, Cd, and Cr significantly exceeded local background levels, with Cd and U exhibiting higher spatial variability than the other PTEs. The APCS-MLR model identified three potential sources of soil PTE contamination: mining activities, mixed anthropogenic-natural, and natural sources. Mining and mixed sources were the dominant contributors to ecological risk, jointly accounting for 88.7% of the total ecological risk, with Cd identified as the primary ecological risk pollutant. The mixed sources also contributed the largest proportions of non-carcinogenic and carcinogenic health risks, accounting for 52.56% and 67.5%, respectively. Furthermore, children were found to face greater health risks than adults due to higher exposure levels. Priority factor analysis indicated that pollution management should continuously monitor Cd derived from mining activities based on statistical inference. Overall, the proposed integrated framework successfully established a quantitative linkage between pollution sources and associated risks, providing a scientific basis for source-specific and zone-specific soil pollution management in uranium mining areas.

Indexed as

APCS-MLR receptor modelmine water and soil pollutionpotentially toxic elementsrisk assessmentsource apportionment

Identifiers

PMID42797667
PMCPMC13611104

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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.