Size-resolved identification and quantification of micro/nano-plastics in indoor air using pyrolysis gas chromatography-ion mobility mass spectrometry
Bibliographic record
Abstract
Humans are exposed to differing levels of micro-/nanoplastics (MNPs) through inhalation, but few studies have attempted to measure <1 µm MNPs in air, in part due to a paucity of analytical methods. We developed an approach to identify and quantify MNPs in indoor air using a novel pyrolysis gas chromatographic cyclic ion mobility mass spectrometer (pyr-GCxcIMS). Four common plastic types were targeted for identification, namely (polystyrene (PS), polyethylene (PE), polypropylene (PP), and polymethyl methacrylate (PMMA). The method was applied to size-resolved particulate (56 nm-18µm) collected from two different indoor environments using a Micro-Orifice Uniform Deposit Impactors (MOUDI) model 110 cascade impactor. Comprehensive two-dimensional separation by GCxcIMS also enabled the retrospective analysis of other polymers and plastic additives. The mean concentrations of MNP particles with diameters <10 µm and <2.5 µm in the laboratory were 47 ± 5 and 27 ± 4 µg/m3 respectively. In the private residence, the concentrations were 24 ± 3 and 16 ± 2 µg/m3. PS was the most abundant MNP type in both locations. Approximately 57-67% of MNPs were characterized by particle diameters <2.5 µm, and 50-60% of the total particulate matter in the private residence was plastic. Non-targeted screening revealed the presence of plastic additives, such as TDCPP (Tris(1,3-dichloro-2-propyl)phosphate) whose abundance correlated with that of polyurethane (PU). This is consistent with their use as flame retardants in PU-based upholstered furniture and building insulation. This study provides evidence of exposure to MNPs which constitute over half of PM2.5 indoors and underlines the need for further study of this route of exposure to MNPs and the plastics additives carried with them.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".