Alkaline dust deposition to foliage surfaces likely enhances the dry deposition velocity of SO <sub>2</sub> : An investigation in the Alberta Oil-Sands Region using the GEM-MACH air-quality model
Bibliographic record
Abstract
Abstract. We examine the potential impact of alkaline particle deposition on foliage and its influence on sulphur dioxide dry deposition velocities, using a new theoretical development, a high resolution air-quality model, and comparisons to observations. Our study domain encompasses the Athabasca Oil Sands Region, an industrial area where base cation-bearing fugitive dust from open-pit mining coexists with elevated SO2 emissions from large stacks. Our pH-modulated dry deposition scheme links thin aqueous films on foliage to local chemical conditions, including alkaline dust accumulation. We present the mechanism's theoretical basis, along with a simplified algorithm predicting foliage water pH and linked to SO2 deposition velocities. We predict enhanced SO2 deposition, due to increased leaf surface pH from dust co-deposition near major dust sources, often by more than 1 cm s⁻¹. These result in dry deposition fluxes 2.5 to 10 times greater than in the absence of these effects – consistent with estimates from aircraft studies. The enhanced deposition reduces surface SO2 concentrations by up to 60% near sources, improves agreement with continuous monitoring data, and reduces normalized mean bias at several stations. Taylor diagram statistics show improved model temporal variability performance. Further from sources of base-cation-containing dust, aqueous films on foliage remain acidic, reducing SO2 deposition velocity and increasing concentrations. We make specific recommendations for new observation data which would reduce formulation uncertainties. The findings have broad implications for global SO2 budgets, given the significant role of wind-blown mineral dust in influencing atmospheric acidity and trace gas removal.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".