Mapping the evolution of artificial intelligence in atmospheric aerosol research: a bibliometric and text-mining analysis

Artificial intelligence (AI) has become increasingly important in atmospheric aerosol and particulate-matter (PM) research because it can model nonlinear relationships, integrate heterogeneous observations, and generate high-resolution spatiotemporal information. This study maps the evolution and knowledge structure of AI-based atmospheric aerosol and PM research using 4,872 Web of Science (WoS) publications published through December 31, 2025. Bibliometric and text-mining analyses were used to characterize publication trends, leading countries and institutions, major journals, citation patterns, keyword co-occurrence networks, and abstract-based thematic clusters. In addition, 99 publications indexed in the Korea Citation Index (KCI) were examined separately to provide supplementary context on temporal publication trends in the Korean domestic literature. WoS publication activity increased sharply after 2019, with China contributing the largest publication output and total citation count, while several lower-output countries showed higher mean citations per publication. Keyword-network analysis identified machine learning as a central theme linking PM prediction, deep learning, aerosol optical depth, exposure assessment, and satellite remote sensing. Abstract-based clustering revealed five major thematic domains spanning prediction modeling, PM concentration and exposure, satellite-based aerosol retrieval, emission-related analysis, and aerosol monitoring and environmental applications. The results indicate a shift from model-specific prediction toward multisource integration, spatiotemporal analysis, remote sensing, and exposure-oriented applications. These findings provide a structured overview of the field and highlight the need for more interpretable, uncertainty-aware, and transferable AI approaches.