In situ chemical characterization of airborne nanoplastic particles by aerosol mass spectrometry
Bibliographic record
Abstract
Extensive use of plastic products has introduced a large amount of plastic pollutants in urban areas and even in remote environments. Nanoplastic particles, in particular, can remain airborne for weeks and transported across greater distances. Characterization of atmospheric nanoplastic particles has been limited. Sampling methods used for larger microplastic particles, such as deposition sampling, and characterization methods, such as spectroscopy, are not applicable to nanoplastic particles. Furthermore, offline sampling methods involve extensive sample preparation procedures which can alter physical and chemical properties of the particles.In this work, we investigate the use of aerosol mass spectrometry (AMS) as an in situ technique to characterize nanoplastic particle size and composition. We generate plastic particles via three different techniques: thermal decomposition of PET plastic bottles, 3D printing (using PET, ABS, and PLA filaments) and mechanical abrasion. Particle size was characterized using a Scanning Mobility Particle Sizer (SMPS). Particles were also sampled into a high resolution time of flight aerosol mass spectrometer (HR-ToF-AMS) and onto quartz filters for offline characterization using pyrolysis gas chromatography mass spectrometry (Py-GC/MS).We found that the AMS produced real-time particle mass spectra that were very similar to those measured by Py-GC/MS analysis of particles collected on filters. The consistency between the two techniques demonstrate that AMS can provide similar information about polymeric content as Py-GC/MS, which is a widely used technique for plastic materials, but at a substantially lower detection limit and higher time resolution. On the other hand, the use chromatographic separation in Py-GC/MS provides more comprehensive evaluation of polymeric composition. For example, we were able to detect changes in ratios between monomers and dimers of PET using Py-GC/MS. Py-GC/MS was also able to provide simultaneous measurement of rubber polymers and additives in tire wear particles, an important source of particles in the near road atmosphere.Since AMS is a commonly used technique for non-refractory components in atmospheric aerosol, optimizing the AMS for nanoplastic particle detection will help understand the sources, dynamics, mixing state and fate of nanoplastic particles in the atmosphere.
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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 | 0.001 | 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) | 0.001 | 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".