Mapping the distribution of contaminants identified by non-targeted screening of passively sampled urban air
Bibliographic record
Abstract
Air pollution is closely associated with increased lung cancer incidence and mortality. Because many semi-volatile industrial chemicals, pesticides and combustion by-products are endocrine-disrupting or genotoxic, their mixtures in outdoor urban air, even at trace levels, could have cumulative effects. However, evidence for the impact of outdoor air pollution on human health remains limited, partly because its composition is poorly characterized. Non-targeted analysis (NTA) based on the combination of liquid (LC) and gas (GC) chromatographic separation with high resolution mass spectrometry (MS), allows for the comprehensive analysis of contaminants in environmental samples. Here, we present the first application of an NTA approach using both LC- and GC-MS to characterize the chemical mixture in outdoor urban air by analyzing forty passive samplers deployed for 82 days during summer 2021 across the Island of Montreal, Canada. The confirmed 25 molecular features included nitrophenols, pesticides/repellents, plasticizers, organophosphorus compounds, organohalogen compounds, other industrial chemicals, and natural products. Triethylene glycol monomethyl ether, triethylene glycol monobutyl ether, n-octyl-pyrrolidone, quinoline n-oxide, 4-hydroxy-acetophenone, citric acid, 2-phenyl acetamide, and hexachloroethane were detected for the very first time in the atmosphere. Previously reported in atmospheric particles, 4-nitrophenol, 2,4-dinitrophenol, tri- and tetraethylene glycol, nonanoic acid, diethylene glycol dibenzoate, caprolactam, phenylacrylic acid, pinonic acid and triphenylphosphine oxide were also detected in the outdoor atmospheric gas phase. The spatial grouping of data between residential and public areas emphasizes the impact of anthropogenic activities on atmospheric vapor composition. This study facilitates the assessment of airborne exposure to chemical mixtures and its effects on human health.
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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".