Seasonal effects of wind-blown dust emissions on size-resolved aerosol acidity over the U.S
Bibliographic record
Abstract
Wind-blown dust emitted by the Earth’s surface is one of the major sources of dust emissions especially in non-vegetated areas like deserts and can affect both climate and human health. Acidity is an important property of atmospheric aerosols impacting a series of related processes and can be affected by these emissions of alkaline dust. In this work, we use a wind-blown dust emissions model to quantify the wind-blown dust emissions over the continental United States during February and July 2017. The modeling domain covers a region of 4752 × 2952 km2 including northern Mexico and southern Canada with a horizontal grid resolution of 36 × 36 km. Then, the hybrid version of aerosol dynamics in PMCAMx (Particulate Matter Comprehensive Air-quality Model with Extensions) chemical transport model is used to simulate size-resolved aerosol acidity. In this version of PMCAMx for fine (PM1) particles, bulk equilibrium is assumed, while for larger particles a dynamic model is used to simulate the mass transfer to each size section. Two cases of simulations are performed. The first is the base case simulation and includes the wind-blown dust emissions for both months. The second one neglects these emissions in order to study their effects on aerosol acidity during a wintertime and a summertime period as a function of particle size and altitude.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".