Seasonality in the Δ <sup>33</sup> S measured in urban aerosols highlights an additional oxidation pathway for atmospheric SO <sub>2</sub>
Bibliographic record
Abstract
Sulfates present in urban aerosols collected worldwide usually exhibit significant non-zero Δ 33 S signatures (from −0.6 ‰ to 0.5 ‰) whose origin still remains unclear. To better address this issue, we recorded the seasonal variations of the multiple sulfur isotope compositions of PM 10 aerosols collected over the year 2013 at five stations within the Montreal Island (Canada), each characterized by distinct types and levels of pollution. The δ 34 S-values ( n = 155) vary from 2.0 ‰ to 11.3 ‰ (±0.2 ‰, 2 σ ), the Δ 33 S-values from −0.080 ‰ to 0.341 ‰ (±0.01 ‰, 2 σ ) and the Δ 36 S-values from −1.082 ‰ to 1.751 ‰ (±0.2 ‰, 2 σ ). Our study evidences a seasonality for both the δ 34 S and Δ 33 S, which can be observed either when considering all monitoring stations or, to a lesser degree, when considering them individually. Among them, the monitoring station located at the most western end of the island, upstream of local emissions, yields the lowest mean δ 34 S coupled to the highest mean Δ 33 S-values. The Δ 33 S-values are higher during both summer and winter, and are < 0.1 ‰ during both spring and autumn. As these higher Δ 33 S-values are measured in “upstream” aerosols, we conclude that the mechanism responsible for these highly positive S-MIF also occurs outside and not within the city, at odds with common assumptions. While the origin of such variability in the Δ 33 S-values of urban aerosols (i.e. −0.6 ‰ to 0.5 ‰) is still subject to debate, we suggest that oxidation by Criegee radicals and/or photooxidation of atmospheric SO 2 in the presence of mineral dust may play a role in generating such large ranges of S-MIF.
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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 | 0.000 | 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".