Evidence map›Paper›PMID 39936144›Full record

ReviewRSC advances2025

Metal contamination - a global environmental issue: sources, implications & advances in mitigation.

Gabrijel Ondrasek, Jonti Shepherd, Santosha Rathod, Ramesh Dharavath, Muhammad Imtiaz Rashid, Martin Brtnicky, Muhammad Shafiq Shahid, Jelena Horvatinec, Zed Rengel

Abstract readReview
In one paragraph

Review in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
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  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Multi-Element Composition of WildFoods (Basel, Switzerland) · 2026
    Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. 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

9 authors.

Gabrijel OndrasekFaculty of Agriculture, The University of Zagreb 10000 Zagreb Croatia gondrasek@agr.hr.ORCID https://orcid.org/0000-0001-8398-0099
Jonti ShepherdFaculty of Agriculture, The University of Zagreb 10000 Zagreb Croatia gondrasek@agr.hr.
Santosha RathodICAR-Indian Institute of Rice Research Hyderabad 500030 India.
Ramesh DharavathDepartment of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad 826004 Jharkhand India.
Muhammad Imtiaz RashidCenter of Excellence in Environmental Studies, King Abdulaziz University 22252 Jeddah Saudi Arabia.
Martin BrtnickyDepartment of Agrochemistry, Soil Science, Microbiology and Plant Nutrition, Faculty of AgriSciences, Mendel University in Brno 61300 Brno Czech Republic.
Muhammad Shafiq ShahidDepartment of Plant Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University Al-Khoud 123 Muscat Oman.ORCID https://orcid.org/0000-0002-3550-0000
Jelena HorvatinecFaculty of Agriculture, The University of Zagreb 10000 Zagreb Croatia gondrasek@agr.hr.
Zed RengelUWA School of Agriculture and Environment, The University of Western Australia Perth WA 6009 Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metal contamination (MC) is a growing environmental issue, with metals altering biotic and metabolic pathways and entering the human body through contaminated food, water and inhalation. With continued population growth and industrialisation, MC poses an exacerbating risk to human health and ecosystems. Metal contamination in the environment is expected to continue to increase, requiring effective remediation approaches and harmonised monitoring programmes to significantly reduce the impact on health and the environment. Bio-based methods, such as enhanced phytoextraction and chemical stabilisation, are being used worldwide to remediate contaminated sites. A systematic plant screening of potential metallophytes can identify the most effective candidates for phytoremediation. However, the detection and prediction of MC is complex, non-linear and chaotic, and it frequently overlaps with various other constraints. Rapidly evolving artificial intelligence (AI) algorithms offer promising tools for the detection, growth and activity modelling and management of metallophytes, helping to fill knowledge gaps related to complex metal-environment interactions in different scenarios. By integrating AI with advanced sensor technologies and field-based trials, future research could revolutionize remediation strategies. This interdisciplinary approach holds immense potential in mitigating the detrimental impacts of metal contamination efficiently and sustainably.

Identifiers

PMID39936144
PMCPMC11811701

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.