Micro(nano)plastics sources, fate, and effects: What we know after ten years of research
Bibliographic record
Abstract
The last decade has been transformative for micro(nano)plastic (MnP) research with recent discoveries revealing the extent and magnitude of MnP pollution, even in the world's most remote places. Historically, while researchers recognized that most plastic pollution was derived from land-based sources, it was generally believed that microplastic particles (i.e., plastic fragments <5 mm) was only a marine pollution issue with effects largely impacting marine biota. However, over the last decade MnP research has progressed rapidly with recent discoveries of MnPs in freshwater, snow, ice, soil, terrestrial biota, air and even found in ocean spray. MnPs have now been found in every environmental compartment on earth, within tissues and gastrointestinal tracts of thousands of species, including humans, resulting in harmful effects. The last 10 years has also seen the development of new techniques for MnP analysis, and re-purposing of old technologies allowing us to determine the extent and magnitude of plastic pollution down to the nano size range (<1 µm). This short review summarizes what key milestones and major advances have been made in microplastic and nanoplastic research in the environment, including their sources, fate, and effects over the last decade.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".