No Evidence for S Isotope Fractionation During SO2 Oxidation at a Continental Location
Bibliographic record
Abstract
Abstract Introduction Sulfur isotope fractionation during SO 2 oxidation has been shown to occur in laboratory experiments [1] but this has not been observed in whole air samples [2]. Here we replicate the laboratory experiments in a systematic manner using ambient air. Methods Particulate matter and SO 2 in ambient air were collected at Calgary in the fall of 2012 using high volume samplers and impingers. Atmospheric SO 2 and SO 4 concentrations and isotope characteristics were determined. Results Variations in concentrations did not reflect changes in δ 34 S values, suggesting the independence of δ 34 S from SO 2 and SO 4 concentrations in the atmosphere. d 34 S SO2 values for high volume samples was, on average, +13.2‰ ± 0.2‰. SO 2 from the impinger method over the same sampling period yielded a δ 34 S SO2 value of +14.0‰ ± 0.2‰. δ 34 S SO4 values ranged from +9.9‰ ± 0.5‰ to +15.3‰ ± 0.2‰. Discussion and Conclusions δ 34 S SO2 values from the high volume and impinger samples were similar (+13.2‰ versus +14.0‰, respectively) and shows d 34 S values from these collection methods are equivalent. Differences between the impinger and high volume sampler d 34 S values for SO 2 and submicron aerosol sulfate were used to gauge sulfur isotope fractionation. Standard deviations for differences were greater than averages (Δ d 34 S SO2 avg.= -0.80‰, s=1.76‰; fine Δ d 34 S SO4 avg.=+0.28‰, s=5.15‰), indicating little to no fractionation. Additionally, δ 34 S SO2 and δ 34 S SO4 values were compared to the maximum percent SO 2 that may have reacted to form SO 4 . No pattern was evident so the conclusion is that sulfur isotope fractionation in ambient air is negligible under the conditions sampled. Literature 1. E. Harris, B. Sinha, P. Hoppe, S. Foley, S. Borrmann, Atmos. Chem. Phys. 12 , 2012. 2. A.L. Norman, H.R. Krouse, J. MacLeod, Air Pollution Modeling and Its Application XVI , 2004.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".