Microplastic research trends in the Gulf region from a global perspective
Bibliographic record
Abstract
Introduction Microplastic (MP) pollution research has experienced significant global growth, with an exponential increase in publications since 2015. Despite this surge, research focused on the Gulf region remains limited. This gap is particularly concerning given the Gulf's dense industrial activities and substantial waste production. This study provides the first bibliometric analysis of MP research globally and within the Gulf region, examining the current state and emerging trends while identifying specific topics for future studies. Methods A bibliometric analysis of peer-reviewed articles published between 2000 and 2024 was conducted for global and Gulf region research. Bibliometric analysis employed several tools to identify trends, keyword networks, and research gaps, with manual refinement of keywords to enhance accuracy. The study also analyzed leading countries and institutions contributing to MP research. Results and Discussion The analysis highlights significant global contributions, with China emerging as a leader in MP research globally and Iran playing a prominent role within the Gulf region. While global publications have increased exponentially since 2015, the Gulf region's research output remains underrepresented, accounting for only about 1.1% of global publications. This underscores the need for enhanced regional research to better integrate the Gulf into the global discourse on MPs. Keyword analysis reveals that certain areas of MP research remain unexplored in the Gulf region. The trend analysis shows an evolving focus globally, shifting from basic interactions of MPs with marine environments to more complex issues such as environmental health and ecosystem impacts. This progression indicates a maturing field that increasingly addresses the broader implications of MP pollution.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.069 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".