Microplastics Bioaccumulation and Trophic Transfer Research (2010–2025): A Bibliometric Analysis Using LDA and HJ-Biplot

The proliferation of microplastics and nanoplastics is a global environmental challenge, particularly because of their biological uptake, bioaccumulation, biomagnification, and trophic transfer across ecosystems. This study presents a bibliometric and thematic analysis of 4040 documents on microplastics ecotoxicology related to bioaccumulation and trophic transfer, indexed in Scopus and Web of Science between 2010 and 2025 (the last full year covered), using Latent Dirichlet Allocation (LDA) and HJ-Biplot modeling. The field grew at a compound annual rate of 46.37% through three phases: foundational latency (2010–2014), methodological take-off (2015–2020), and exponential expansion (2021–2025). The LDA model identified 24 core topics, revealing a thematic transition from environmental monitoring and marine debris toward nanoplastic toxicity, physiological stress responses, human health risks, and pollutant interactions: seven topics show significant positive temporal trends after correction for multiple comparisons, with nanoplastic cellular toxicity growing fastest, while marine debris and pollutant-vector topics decline. Spatial and network analyses show a geographically concentrated landscape: China accounts for 32.1% of the corpus, more than four times the share of India, and countries in Africa and Central Asia are largely absent. East Asian nations are increasingly visible in nanotoxicology research and co-authorship networks. Editorial mapping indicates that leading environmental journals are closely associated with mechanistic studies on cellular toxicity, intestinal toxicity, and heavy metal interactions. These results show that computational text-mining approaches complement conventional synthesis methods by identifying thematic, temporal, geographical, and editorial patterns in large environmental corpora, and help to delineate research frontiers and persistent geographical gaps that can inform future research priorities.