Mapping the Rise in Machine Learning in Environmental Chemical Research: A Bibliometric Analysis.

ABSTRACT: Machine learning (ML) is reshaping how environmental chemicals are monitored and how their hazards are evaluated for human health. Here, we mapped this landscape by analyzing 3150 peer-reviewed articles (1985-2025) from the Web of Science Core Collection. Co-citation, co-occurrence, and temporal trend analyses in VOSviewer and R reveal an exponential publication surge from 2015, dominated by environmental science journals, with China and the United States leading in output. Eight thematic clusters emerged, centered on ML model development, water quality prediction, quantitative structure-activity applications, and per-/polyfluoroalkyl substances, with XGBoost and random forests as the most cited algorithms. A distinct risk assessment cluster indicates migration of these tools toward dose-r

SUBMITTER: Stanic B 

PROVIDER: S-EPMC12567794 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature