Work

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

Year: 2025

Type: article

Abstract: The analysis of 104,471 scientific abstracts on microplastics and nanoplastics using bibliometric tools and machine learning models produced a comprehensive mapping of thematic trends and material-specific risk associations. A composite Harmfulness Score was constructed by integrating sentiment analysis, impact descriptors, and network centrality metrics. This score ranked polystyrene (PS) and polyethylene (PE) highest in association with terms such as oxidative stress, cytotoxicity, and genotoxicity, reflecting their prominence in the literature. Reporting frequencies for key physicochemical descriptors were low-particle size (3.91%), density (0.01%), and surface area (<0.01%)-limiting their use in computational modeling and risk assessments. Thematic clustering revealed dominant topics such as environmental policy and biological impact, alongside emerging areas in microbial degradation, enzymatic transformation, and legal-policy intersections. The results highlight the need for standardized metadata practices and expanded use of analytical frameworks to enhance research reproducibility and policy relevance.

Source: Macromolecular Rapid Communications

DOI: 10.1002/marc.202500559

Authors: Fernando Gomes Souza, Shekhar Bhansali, Thomas Thundat

Institutions: Universidade Federal do Rio de Janeiro, Florida International University, Vanderbilt University, University at Buffalo, State University of New York

Language: English

Topic: Microplastics and Plastic Pollution

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,550

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro