Contrasting the elemental composition of fine particulate matter in urban and remote samples using single particle inductively coupled plasma time-of-flight mass spectrometry (SP ICP-ToF-MS)
Bibliographic record
Abstract
Elevated concentrations of particulate matter (PM) are associated with poor air quality, and the health effects of PM exposure depend, in part, on its elemental composition. However, techniques to measure the elemental composition of fine and ultrafine PM at the single particle level are limited in terms of their ability to quantitatively detect a wide range of elements in a single particle. In this work, PM2.5 was collected and extracted from polycarbonate filtration membranes using four different methods to optimize recoveries. Based upon gravimetry, the best method tested gave an extraction efficiency of 73 ± 18%. The elemental compositions of the extracted particles were then determined using single particle inductively coupled plasma time-of-flight mass spectrometry (SP ICP-ToF-MS), which was applied to compare the composition of urban PM (Montreal, Canada) and PM collected from a remote high latitude region impacted by local mineral dust (Dhal Tʼàʼ, Canada). With respect to particle number, greater quantities of trace elements associated with anthropogenic sources were observed at the urban site. Specifically, Cr, Pb, Ni, Zn, Ag or Cu were found in 3.8% of the urban particles, often at high mole percentages, but only in 0.7% of the remote particles. Furthermore, the distribution of single particle Fe:Ni and Fe:Cu ratios observed at the urban site was shifted to lower values relative to the remote site. The results demonstrate the value of SP ICP-ToF-MS for analyzing ambient PM, although improved recoveries and sampling methodologies are needed to unlock the full potential of this technique. Copyright © 2025 American Association for Aerosol Research
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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 | 0.001 |
| 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".