Evidence map›Paper›PMID 41723254›Full record

ArticleScientific reports2026

Human and environmental controls on soil contamination in a dust-prone region revealed by random forest and Shapley additive explanations analysis.

Zohre Ebrahimi-Khusfi, Shamsollah Ayoubi, Seyed Arman Samadi-Todar, Narjes Okati

Abstract read
In one paragraph

Article in Scientific reports, 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
–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

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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

4 authors.

Zohre Ebrahimi-KhusfiDepartment of Environmental Sciences and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran. Zohreebrahimi2018@ujiroft.ac.ir.ORCID http://orcid.org/0000-0002-1036-6386
Shamsollah AyoubiDepartment of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, 8415683111, Iran.ORCID http://orcid.org/0000-0002-4455-3139
Seyed Arman Samadi-TodarDepartment of Remote Sensing, Faculty of Geographical Sciences, University of Tehran, Tehran, Iran.ORCID http://orcid.org/0009-0004-1177-8288
Narjes OkatiDepartment of Environment, Faculty of Natural Resources, University of Zabol, Zabol, Iran. Narjesokati@uoz.ac.ir.ORCID http://orcid.org/0000-0001-7646-7203

Funding

Iranian National Science Foundation 4034915
6 · The paper itself

Abstract

Accurate prediction of the spatial distribution of potentially toxic elements (PTEs) and identification of the most important environmental drivers are essential for reducing their adverse effects on human health and the environment. In this regard, the present study was conducted to predict the spatial distribution of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), and lead (Pb) in a dust-prone area of central Iran using the RF model under 11 scenarios constructed based on human activity-based factors (HAF), land-based factors (LSF), physicochemical soil properties) PSP), meteorological factors (MF), and remote sensing auxiliary data (RSAD). The overall contribution of the influencing factors in predicting soil PTEs was determined using the SHapley additive exPlanations (SHAP) analysis. Soil PTEs and some other properties of 107 surface soil samples were measured in the laboratory. The best performance of RF in predicting As, Co, and Cr was observed under scenario VI (HAF + PSP) with the R

Indexed as

DustEnvironmental MonitoringEnvironmental PollutionSoilSoil PollutantsArsenicCadmiumHumansIranLeadRandom ForestArsenicCadmiumDustLeadSoilSoil PollutantsArid regionsHuman activitiesLand degradationMachine learningRemote sensingSoil pollution

Identifiers

PMID41723254
PMCPMC13022290

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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.