Nanoscale plastic pollution: sources, identification and potential mitigation
Bibliographic record
Abstract
The amount of global plastic waste has been increasing steadily since synthetic polymers were introduced over a century ago and plastic products have become ubiquitous in modern societies. A significant portion of this waste can accumulate and persist for many decades as plastic particulate pollution that can interact with natural ecosystems, and in urban and rural environments. The size of these plastic particles can vary widely, from millimetres to micrometre and nanometre scales, depending on several factors including material properties, production, application, age and environmental exposure. Importantly, the properties and potential impact of plastic pollution can depend strongly on particle size, particularly for nanoscale dimensions, or nanoplastics. Nanoplastics, and slightly larger microplastics, are more difficult to detect, can spread more easily, and potentially interact more directly with biological organisms and ecosystems. This review provides a detailed synopsis of nanoscale plastic pollution. After an overview of plastic particle pollution in general, the sources and impact of nanoplastics, both environmental and biological are discussed. Methods for identifying and characterising nanoplastics via microscopy, spectroscopy, spectrometry and related techniques are then covered along with practical challenges that can often hinder detection. Potential solutions for mitigating nanoplastics waste and pollution, both at the source and after production, and lastly, future directions and outlook round out the review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".