A Review of Atmospheric Micro/Nanoplastics: Insights into Source and Fate for Modelling Studies
Bibliographic record
Abstract
Abstract Purpose of Review Micro/nanoplastics (MNPs), as emerging pollutants, have attracted increasing attention due to their potential adverse effects on human health, ecosystems, and climate. The rapid, turbulent, and large-scale nature of atmospheric transport facilitates both horizontal and vertical movement of MNPs over long distances within a short time, largely independent of topographical constraints, thereby accelerating their global cycle and exacerbating their impacts. Despite growing interest, our understanding of the atmospheric lifecycle of MNPs remains limited. This review aims to synthesise recent advances in understanding the atmospheric lifecycle of MNPs, including their emission sources, long-range transport characteristics, and driving mechanisms. It also evaluates the key sources of uncertainty—particularly those related to emission flux estimates—and provides corresponding recommendations for future research. Recent Findings Current research on the atmospheric processes of MNPs primarily relies on a combination of observational data and numerical modelling. Two major uncertainties in atmospheric micro/nanoplastic (AMNP) emissions persist: the wide variability in marine emission estimates, which span four orders of magnitude, and the unresolved question of whether terrestrial or marine sources are the dominant contributors to AMNP emissions. Furthermore, this review highlights critical factors driving these uncertainties, including limited data availability, inconsistencies in observational methodologies, oversimplified simulations, and gaps in understanding atmospheric cycling mechanisms. Additionally, variations in the particle size ranges targeted by different observational and modelling studies hinder cross-comparisons and model evaluations, representing another important source of uncertainty. Summary AMNP research is constrained by multiple uncertainties that hinder a comprehensive understanding of their emissions, transport, and fate. To address these issues, we call for establishing a global network of standardised observations, improving sampling and simulation practices, and incorporating artificial intelligence. These strategies will enhance our understanding of the complete atmospheric cycle of MNPs, paving the way for more effective environmental management and better-informed policy decisions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".