A bibliometric analysis of microplastic pollution in aquatic environments from 2013 to 2023
Bibliographic record
Abstract
Abstract Plastics are extensively utilized in a variety of industries, including fisheries, agriculture, and commerce, due to their lightweight, corrosion resistance, versatility, and cost-effectiveness. From 2013 to 2023, the volume of research literature concerning microplastics (MPs) in aquatic environments has surged, underscoring the growing concern over MP pollution. This study aims to identify research hotspots and trends regarding MPs in water environments through a bibliometric analysis of publications from the Web of Science Core Collection (WoSCC). During the study period, we screened a total of 1,141 articles related to MPs in aquatic environments. The number of articles rose dramatically from 2 in 2013 to 289 in 2023, indicating significant growth in this research area. Notably, 887 studies focused on marine waters, followed by rivers (397), wastewater (350), and lakes (176), suggesting that oceans are a primary hotspot for MPs research. In the past five years alone, 1,025 studies on MP pollution in water have been published, accounting for 89.8% of the total literature, highlighting widespread concern. The journals publishing the most articles on MPs include Marine Pollution Bulletin (253 articles) and Science of the Total Environment (190 articles). Analysis of total citations and publication counts reveals that China, the UK, Canada, and the USA are leading countries in this field. Institutions such as China’s Chinese Academy of Sciences and East China Normal University are particularly influential. Furthermore, the collaborative research between China and the USA, as well as between China and Australia, stands out. This paper quantitatively assesses global research trends and hotspots related to MP pollution, emphasizing key areas such as risk assessment, pollution surveys, and mitigation technologies. We also address critical scientific issues that need attention in the context of global water pollution, aiming to provide insights for monitoring mechanisms and future standards for MP control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.134 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".