Relative impact of windblown dust versus anthropogenic fugitive dust in PM<sub>2.5</sub> on air quality in North America
Bibliographic record
Abstract
A new windblown dust emissions module was recently implemented into A Unified Regional Air Quality Modeling System (AURAMS), a Canadian regional air quality model, to investigate the relative impact of windblown dust versus anthropogenic fugitive dust on air quality in North America. In order to apply the windblown dust emissions module to the entire North American continent, a soil grain size distribution map was developed using the outputs of 4 monthly runs of AURAMS for 2002 and available PM2.5 dust content observations. The simulation results using the new soil grain size distribution map showed that inclusion of windblown dust emissions is essential to predict the impact of dust aerosols on air quality in North America, especially in the western United States. The windblown dust emissions varied widely by season, whereas the anthropogenic fugitive dust emissions did not change significantly. In the spring (April), the continental monthly average emissions rate of windblown dust (4.1 × 107 kg/d) was much higher than that of anthropogenic fugitive dust (1.5 × 107 kg/d). The total amount of windblown dust emissions in North America predicted by the model for 2002 was comparable to that of anthropogenic fugitive dust emissions. Even with the inclusion of windblown dust emissions, however, the model still had difficulty simulating dust concentrations. Further improvements are needed, in terms of both limitations of the windblown dust emission module and uncertainties in the anthropogenic fugitive dust emissions inventories, for improved dust modeling.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".