Micro(nano)plastics in the total environment – A holistic review
Bibliographic record
Abstract
Microplastics (MPs) and nanoplastics (NPs) in the environment pose a significant challenge. They can accumulate at high levels in soil, air, water, sediments, and living organisms. Although long-term exposure to MPs can harm all living organisms, including humans, the transport, fate, and impacts of global micro(nano)plastic (MNPs) pollution remain poorly understood. This review provides a holistic perspective on the production, degradation, accumulation, and fate of plastic particles in the total environment and sheds light on their complex interactions within various environmental matrices. Filling a critical gap in the literature, it integrates insights from environmental chemistry, toxicology, and regulatory science to deliver an interdisciplinary understanding of MNPs pollution. The review provides an overview of global plastic production and recycling rates. It goes on to address the degradation and classification of plastic debris and presents methodologies for detecting plastic particles in environmental samples. It also examines the impact of plastic particles on living organisms, including humans. Finally, the review highlights existing regulatory frameworks relating to industrial production, as well as the analysis and detection of plastics in consumer products and environmental samples. By emphasising the key factors that influence the transfer and transformation of plastic residues, this paper provides insights for future research and policy development and presents innovative solutions, underscoring the urgent need for a coordinated global response to manage plastic pollution.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".