Evidence map›Paper›PMID 39815431›Full record

SynthesisWater science and technology : a journal of the International Association on Water Pollution Research2025

Systematic bibliographic analysis of heavy metal remediation.

Shan Chen, Yuanzhao Ding

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Water science and technology : a journal of the International Association on Water Pollution Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Impact Assessment and Sustainable Remediation of Heavy Metal and Radioactive Contamination in Aquatic Systems: A Review.Water environment research : a research publication of the Water Environment Federation · 2026
    Review
  2. 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

2 authors.

Shan ChenScience of Learning in Education Centre, National Institute of Education, Nanyang Technological University, Singapore 637616.ORCID https://orcid.org/0000-0002-5434-4883
Yuanzhao DingSchool of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK E-mail: armstrongding@163.com.ORCID https://orcid.org/0000-0003-0116-3648

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heavy metals pose a significant threat to human health, with contaminated water sources linked to severe conditions, including gastric cancer. Consequently, the effective remediation of heavy metals is crucial. This study employs a bibliographic analysis to examine key methodologies, leading organizations, and prominent countries involved in heavy metal remediation. By systematically reviewing around 1,000 records, the paper identifies the most critical remediation techniques and provides a comprehensive overview of current practices in the field. Additionally, the study explores prospects, emphasizing the potential of emerging technologies such as big data and machine learning to enhance remediation efforts. It highlights recent advancements, identifies significant trends, such as the growing use of bioremediation and nanotechnology, and addresses critical challenges in the remediation landscape, including regulatory hurdles and technological limitations. By making stronger connections between the identified trends and their implications for future research, this comprehensive analysis aims to provide valuable insights and guide the development of improved strategies for mitigating the impact of heavy metal contamination, ultimately safeguarding public health.

Indexed as

Environmental Restoration and RemediationMetals, HeavyWater Pollutants, ChemicalBiodegradation, EnvironmentalMetals, HeavyWater Pollutants, Chemicalbibliographic studyheavy metalsmachine learningremediationwater pollution

Identifiers

PMID39815431

What OpenQuestion holds

Textmetadata
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.