Transition metal‐catalyzed oxidation of atmospheric sulfur: Global implications for the sulfur budget
Bibliographic record
Abstract
We use observations of the oxygen‐17 excess (Δ 17 O) of sulfate in the Arctic to quantify the sulfate source from aqueous SO 2 (S(IV)) oxidation by O 2 catalyzed by transition metals. Due to the lack of photochemically produced OH and H 2 O 2 in high latitudes during winter, combined with high anthropogenic SO 2 emissions in the Northern Hemisphere, oxidation by O 3 is predicted to dominate sulfate formation during winter in this region. However, Δ 17 O measurements of sulfate aerosol collected in Alert, Canada, are not consistent with O 3 as the dominant oxidant and indicate that a S(IV) oxidant with near‐zero Δ 17 O values (O 2 ) is important during winter. We use a global chemical transport model to interpret quantitatively the Alert observations and assess the global importance of sulfate production by Fe(III)‐ and Mn(II)‐catalyzed oxidation of S(IV) by O 2 . We scale anthropogenic and natural atmospheric metal concentrations to primary anthropogenic sulfate and dust concentrations, respectively. The solubility and oxidation state of these metals is determined by cloud liquid water content, source, and sunlight. By including metal‐catalyzed S(IV) oxidation, the model is consistent with the Δ 17 O magnitudes in the Alert data during winter. Globally, we find that this mechanism contributes 9–17% to sulfate production. The inclusion of metal‐catalyzed oxidation does not resolve model discrepancies with surface SO 2 and sulfate observations in Europe. Oxygen isotope measurements of sulfate aerosols collected near anthropogenic and dust sources of metals would help to verify the importance of this sulfur oxidation pathway.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".