Microplastics and nanoplastics science: collecting and characterizing airborne microplastics in fine particulate matter
Bibliographic record
Abstract
Microplastic (MP) pollution in the environment is increasing, leading to growing concerns about human exposures and the subsequent impact on health. Although marine MP research has received significant attention in recent years, only a few studies have attempted characterization of MP in air and examined the MP uptake and influence via inhalation on human health. Moreover, the methods used for MP characterization in the marine environment require further optimization to be applicable to MP in the air. This paper details method for collecting and characterizing MP < 2.5 μm in air samples for the purposes of toxicological assessment. The first phase of the study evaluated (a) the suitability of various filter types to collect respirable airborne MP <2.5 μm, and; (b) the ability of Raman and enhanced darkfield-hyperspectral spectroscopy methods to identify MP reference standards collected from spiked filters and in cells after exposure to reference MP. In the second phase, these methods were employed to characterize MP <2.5 μm in personal, indoor and outdoor filter air samples and in cells following exposure to filter extracted material. The results showed the presence of a variety of MP in the respirable size fraction (0.1–1 µm aerodynamic diameter). Silver membrane filters were found not suitable for collecting and analyzing MP <2.5 μm. While it was easy to detect reference MP in cells post-exposure, the identity of only two types of air-borne MP was confirmed in cells. The study highlighted possible sources of artifacts and inconsistencies in analyzing airborne MP.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".