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Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

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Harmfulness Score: A Data-Driven Framework for Ranking Environmental Risks of Microplastics.

Fernando Gomes Souza^1,2,3, Shekhar Bhansali⁴, Thomas Thundat⁵

¹Instituto De Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro, Brazil.

Macromolecular Rapid Communications

|October 22, 2025

Summary

This summary is machine-generated.

Bibliometric analysis of microplastics and nanoplastics reveals polystyrene (PS) and polyethylene (PE) pose the highest risks. Limited reporting of particle properties hinders comprehensive risk assessment and policy development.

Keywords:

harmfulness Score machine learning microplastics polymer risk assessment regulatory science

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Published on: July 28, 2018

Area of Science:

Environmental Science
Toxicology
Data Science

Background:

Microplastics and nanoplastics represent a growing environmental concern.
Understanding their risks requires comprehensive analysis of scientific literature.

Purpose of the Study:

To map thematic trends and material-specific risk associations of microplastics and nanoplastics.
To develop a composite Harmfulness Score for ranking plastic types by risk.

Main Methods:

Bibliometric analysis of 104,471 scientific abstracts.
Application of machine learning models, sentiment analysis, and network centrality metrics.
Thematic clustering to identify dominant and emerging research areas.

Main Results:

Polystyrene (PS) and polyethylene (PE) were ranked highest for harmfulness, associated with oxidative stress, cytotoxicity, and genotoxicity.
Low reporting frequencies for particle size, density, and surface area were observed.
Dominant themes included environmental policy and biological impact; emerging themes involved microbial degradation and legal-policy intersections.

Conclusions:

Standardized metadata practices are crucial for microplastic and nanoplastic research.
Expanded use of analytical frameworks is needed to improve research reproducibility and policy relevance.