A database of modeled gridded dry deposition velocities for 45 gaseous species and three particle size ranges across North America
Bibliographic record
Abstract
The dry deposition process refers to the flux loss of an atmospheric pollutant due to uptake of the pollutant by the earth's surfaces. Dry deposition flux of a chemical species is typically calculated as the product of its surface-layer concentration and its dry deposition velocity (Vd). Field measurement based Vd data are very scarce or do not exist for many chemical species considered in chemistry transport models. In the present study, gaseous and particulate dry deposition schemes were applied to generate a database of hourly Vd for 45 gaseous species and three particle size ranges for two years (2016–2017) at a 15 km by 15 km horizontal resolution across North America. Hourly Vd of the 45 gaseous species ranged from < 0.001 to 4.6 cm s−1 across the whole domain, with chemical species-dependent median (mean) values being in the range of 0.018–1.37 cm s−1 (0.05–1.43 cm s−1). The spatial distributions of the two-year average Vd showed values higher than 1–3 cm s−1 for those soluble and reactive species over certain land types. Soluble species have the highest Vd over water surfaces, while insoluble but reactive species have the highest Vd over forests. Hourly Vd of PM2.5 across the whole domain ranged from 0.039 to 0.75 cm s−1 with median (mean) value of 0.18 (0.20) cm s−1, while the mean Vd for PM2.5–10 is twice that of PM2.5. Uncertainties in the modeled Vd are typically on the order of a factor of 2.0 or larger, which needs to be considered when applying the dataset in other studies.
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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 | 0.000 | 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 | 0.001 |
| 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".