Plastic burning: An important global source of atmospheric nanoplastic particles
Bibliographic record
Abstract
Small nano-sized plastic particles can enter the atmosphere and be transported globally from source areas to remote regions. In contrast to secondary nanoplastic emissions, plastic materials exposed to high temperatures can emit large amounts of nanoplastics directly into the atmosphere. However, very little is known about emission rates and physical and chemical characteristics of these particles. In this work, we conducted laboratory smoldering experiments to simulate smoldering emissions of PVC, PP, LDPE, PET and PS. We measured the chemical composition using aerosol mass spectrometry show that both polymeric materials (characteristic of nanoplastics) and thermo-oxidation products are emitted in submicron particles. Based on the emission factors measured, we estimate that plastic waste burning and building fires can contribute roughly 0.5–5 megatons per year of nanoplastics, which exceeds emissions from oceans, and comparable to tire wear.The chemical fate of these particles was also examined by exposing the particles to atmospheric oxidants. We observe that these particles can age at appreciable rates under simulated oxidation conditions, on the order of days to weeks. These rates are similar to that of organic aerosol. This extent of oxidation in the atmosphere has strong implications on their hygroscopicity and their atmospheric fate, suggesting extensive oxidation prior to their deposition. Our laboratory studies provide mechanistic understanding for modeling atmospheric processes of nanoplastic particles and quantitative information for estimating atmospheric burden from plastic burning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".