Exposing Halogenated Airborne Pollutants by Non-Targeted Screening of Passive Samplers Using Ion Mobility-Mass Spectrometry
Bibliographic record
Abstract
Air pollution poses significant risks to human health and the environment, necessitating comprehensive monitoring and analysis to identify and mitigate the presence of harmful pollutants. This study focuses on Hamilton and Sarnia, Ontario, Canada, known for their industrial activities and associated air pollution challenges. Employing gas chromatography coupled with ion mobility spectrometry and high-resolution mass spectrometry (GC-IMS-MS) on samples collected by polyurethane foam passive samplers, we aimed to uncover the presence of halogenated airborne organic pollutants, including those not typically monitored in standard air quality assessments. Our research successfully identified 19 groups of halogenated pollutants in the air samples. These include a range of chlorinated and brominated anisoles, as well as a previously undocumented polyfluoroalkyl substance (PFAS) that was confirmed with a synthesized standard. Polychlorinated biphenyls (PCBs), chlorinated organophosphate esters (OPEs) and various agricultural contaminants were also tentatively identified based on mass spectral interpretation. The study revealed significant differences in the pollutant profiles between the two cities, reflecting their distinct industrial influences. The application of non-target screening techniques also highlighted the complex nature of air pollution and the necessity for broader monitoring strategies to protect public health and the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".