Nanoplastic Particle Emissions from Plastic Smoldering Combustion
Bibliographic record
Abstract
Atmospheric nanoplastic particles (NPPs) are an emerging environmental concern due to their potential adverse effects on human and ecosystem health. Many recently identified sources involve subjecting plastic materials to elevated temperatures; however, fundamental understanding of airborne emissions is limited. This study is the first systematic characterization of particle and volatile organic compound emissions from plastic smoldering combustion. Five common plastic types were studied, including low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). For all types of plastics, a dominant submicron mode of emitted particles is observed. The composition of NPPs and oxidation products is identified in aerosol mass spectra, where mass fractions of NPPs are estimated based on their distinctive fragment ions: hydrocarbon ions (LDPE and PP), aromatic ions (PS), phthalate ions (PET), and organochlorine and polycyclic aromatic hydrocarbons ions (PVC). Emission factors of submicron particles (0.5–769 g kg –1 ) and VOCs (46–393 g kg –1 ) are calculated. NPPs from open plastic combustion are estimated to exceed oceanic microplastic emissions and be comparable to microplastics from tire and brake wear. Open plastic combustion is suggested to be an important source of atmospheric NPPs and to play a central role in the global plastic cycle.
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".