Contribution of airborne dust particles to HONO sources
Bibliographic record
Abstract
Abstract. HONO is a major precursor for OH radicals in early mornings. Its formation has been mainly attributed to the heterogeneous hydrolysis of NO2 on surfaces such as soot, glass, mineral oxides and aerosol surfaces. In particular, dust events which are loaded with mineral oxide aerosols have been associated with higher HONO concentrations in the gas phase. In order to understand the mechanism of reactions related to this process, samples during dusty and non-dusty days were collected between October 2009 and April 2011. Based on HYSPLIT backward trajectories, data were divided between wind trajectories originating from Arabian or African deserts. In this study an increase of HONO levels was observed during dusty days. The increase in the acidic gas concentrations was accompanied by an increase in the PM nitrate and sulfate ion concentrations. During high relative humidity (African dusty days), it is proposed that the mechanism of NO2 hydrolysis predominates whereas during Arabian dusty days, where the air is relatively dry, a synergistic mechanism of adsorption and reaction between NO2 and SO2 on dust particles to produce HONO and sulfate in the particle phase is suggested. This study implies that the NOx reactivity on mineral oxide surfaces leads to a higher mixing level of OH. An increase in the sulfate forming capacity could account for the underestimation of sulfates in aerosols when the reactive uptake of SO2 alone is considered.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".