Trends in atmospheric sulfur at Saturna Island, British Columbia, Canada
Bibliographic record
Abstract
The presence of sulfur-containing gas in the atmosphere and its potential ability to form particles plays an important role in the climate. These particles can absorb or scatter the solar radiation, contribute to CCN, modify the microphysical properties of clouds, and affect precipitation processes. Gas, aerosol, and precipitation samples collected as a part of CAPMoN network at Saturna Island were assessed for sulfur isotopic compositions from 1998-2012. This study analyzed the trends in atmospheric sulfur and evaluated the potential sources contributing to the atmospheric sulfur budget. We found higher (3-times) sulfur concentrations in SO2 than aerosol sulfate. Secondary aerosol formation was dominant (>75 %) compared to directly emitted primary aerosols in both aerosol and precipitation samples. More than 60 % of the gas and aerosol sulfur was related to anthropogenic fluxes, while precipitation had approximately equal concentrations from anthropogenic and biogenic emissions. Sulfur dioxide displayed a declining trend from 1998-2010, mostly related to anthropogenic sources. Summertime increases in aerosol d34S values and sulfate concentrations indicated the importance of phytoplankton DMS formation during warm seasons. This study also found that not only biogenic DMS, industrial, and sea-salt sulfate contribute to the atmospheric sulfur at Saturna Island, but O2 TMI oxidation and H2S formation from sulfate-reducing microorganisms are also important. Sulfur isotope fractionation during SO2 oxidation by O2 TMI, was revealed by lighter d34S values below the expected biogenic and anthropogenic isotopic compositions of aerosol sulfate. Although this study was not able to quantify the contribution of H2S, this species was found to influence up to 30 % of our samples and is considered as a viable potential fourth source, affecting the atmospheric sulfur at Saturna Island.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".