Wildfire Smoke Contributions to Polycyclic Aromatic Hydrocarbon Loadings in Western Canadian Urban Surface Grime
Bibliographic record
Abstract
Wildfires emit large amounts of polycyclic aromatic hydrocarbons (PAHs) into the atmosphere. As PAHs emitted from anthropogenic sources are known to accumulate in urban surface grime present on building exteriors and windows, we hypothesized that PAH-containing wildfire smoke plumes could similarly increase PAH grime loadings. To explore this hypothesis, we coupled analysis of PAHs in grime samples collected from August to November 2021 in two historically smoke-affected Canadian cities, Calgary and Kamloops, with contemporaneous field- and model-based indicators of wildfire influence. In Calgary, a single wildfire smoke day contributed over 20% of total grime PAH loadings during this study's 3-month sampling period, which implies that wildfire inputs have the potential to dominate the grime composition during a typical wildfire season. In Kamloops, although the PAH congener profile displayed a sustained background wildfire influence, total PAH loadings were dominated by a hyper-local combustion event, which highlights that even small-scale urban combustion activities have the potential to control pollutant loadings on nearby surfaces. In both locations, temporal PAH congener profiles showed no evidence of reactive loss, implying that biomass burning contributes to the presence of a persistent PAH reservoir available for direct exposure or runoff-mediated contamination of downstream environmental compartments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".