Evidence map›Paper›PMID 41000167›Full record

ReviewJournal of environmental health science & engineering2025

Advancing microplastic pollution management in aquatic environments through artificial intelligence.

Mudita Nagpal, Krrishika Gupta, Tanisha Gupta, Ankit Mittal, Nidhi Sharma

Abstract readReview
In one paragraph

Review in Journal of environmental health science & engineering, 2025. 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

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

5 authors.

Mudita NagpalSchool of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, Delhi, 110034 India.ORCID 0009-0007-9471-7086
Krrishika GuptaSchool of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, Delhi, 110034 India.
Tanisha GuptaSchool of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, Delhi, 110034 India.
Ankit MittalDepartment of Chemistry, Shyam Lal College, University of Delhi, Delhi, 110032 India.
Nidhi SharmaSchool of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, Delhi, 110034 India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising infiltration of microplastics (MPs) into aquatic environments is a complex and alarming threat jeopardizing marine biodiversity, destabilizing entire ecosystems, and endangering human health. Traditional methods for identifying and characterizing microplastics are often manual, requiring significant time and effort due to the small size, diverse shapes, and varying sources of microplastics. By integrating artificial intelligence (AI) with traditional environmental approaches, we can make significant progress in mitigating the influence of microplastics on aquatic ecosystems and health of humans. This review emphasizes the goals, benefits, results, and key insights of emerging robotics and various AI models across three critical areas: collection and sorting of microplastic waste, characterization of microplastic waste to determine its abundance, size and chemical composition and predicting and monitoring microplastic degradation. Several countries and organizations are using AI technologies to address microplastic pollution through innovative projects and supportive policies. The review aims to highlight these successful initiatives focused on monitoring, prevention, and cleanup of microplastics in aquatic environments. Further, challenges and future research opportunities on integrating robotics and AI technologies in mitigating microplastic pollution have also been discussed.

Indexed as

Artificial intelligenceCharacterizationDegradationMicroplasticsSorting

Identifiers

PMID41000167
PMCPMC12457277

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.