Exploring the atmospheric sulfur trends and the potential contribution of sulfate-reducing microorganism activities to the atmospheric sulfur budget at Saturna Island, B.C
Bibliographic record
Abstract
<p>Anthropogenic and biogenic activities, along with the fluxes of sea salt, volcanic, wildfire and oceanic sulfate-reducing microorganisms (SRM), contribute significantly to the atmospheric sulfur budget.<sup>(1,2)</sup></p><p>There is still uncertainty and debate between studies about the magnitude of the importance of oceanic hydrogen sulfide (H<sub>2</sub>S) produced by SRM, as well as its ability to diffuse to the upper water column and its contribution to the atmospheric sulfur budget. While some studies believe that the majority of H<sub>2</sub>S is re-oxidized and is less likely to reach the atmosphere <sup>(3,4)</sup>, there is evidence of the existence of H<sub>2</sub>S in the upper water columns and even in the atmosphere <sup>(2,5)</sup>. H<sub>2</sub>S produced by SRM, emitted to the atmosphere, along with the anthropogenic sulfur dioxide (SO<sub>2</sub>) and dimethyl sulfide (DMS), undergo atmospheric oxidation processes. Sulfate (SO<sub>4</sub><sup>2-</sup>), as one of the main oxidized products, may nucleate with water vapor, ammonia and organic oxides <sup>(6,7)</sup>, and subsequently grow to bigger particle sizes. These particles affect the climate directly and indirectly and change the radiation balance of the Earth-atmosphere system. <sup>(8,9,10)</sup></p><p>This study assessed the seasonal trends of major atmospheric sulfur species including SO<sub>2</sub>, sulfate, and biogenic and anthropogenic sulfate of gas, aerosol and precipitation samples, collected by Canadian Air and Precipitation Monitoring Network (CAPMoN), Environment of Canada, at Saturna Island, B.C, between 1998-2010. We then explored the oceanic phytoplankton activities and DMS production, based on sulfur isotope composition and found the importance of DMS contribution to the summertime atmospheric sulfur budget. A handful of samples (~10-30%) displayed negative sulfur isotope compositions, outside the range of anthropogenic and biogenic isotope values. Potential factors that could produce such negative sulfur isotope composition values include isotopic fractionation, fluxes from mineral dust events, volcanic eruptions, wildfires and microbial sulfate reduction (MSR). Our study found that MSR was the only feasible explanation for these very negative sulfur isotope compositions in non-sea salt sulfate samples. H<sub>2</sub>S in our study was a 4<sup>th</sup> potential contributor to the atmospheric sulfur budget, along with the 3 major sources of anthropogenic, biogenic DMS, and sea-salt sulfate, in this long-term atmospheric sulfur study.</p><p> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".