Technical Note: An empirical algorithm estimating dry deposition velocity of fine, coarse and giant particles
Bibliographic record
Abstract
Abstract. An empirical algorithm is developed for calculating bulk dry deposition velocity (Vd) of fine (PM2.5) – particles having a diameter of ≤ 2.5 μm), coarse (PM2.5−10 – particles having a diameter of 2.5–10 μm), and giant (PM10+ – particles having a diameter of > 10 μm) atmospheric particles. The algorithm is developed from an empirical fit of Vd data calculated using the size-resolved Vd scheme of Zhang et al. (2001) with assumed lognormal size distributions of PM2.5, PM2.5−10 and PM10+. In the new algorithm, the surface deposition velocity (Vds) is parameterized as a simple linear function of friction velocity (u*) for PM2.5 and as a polynomial function of u* for both PM2.5−10 and PM10+ over all the 26 land use categories (LUCs). An adjustment factor as an exponential function of u* and leaf area index (LAI) is also applied to Vds of PM2.5−10 and PM10+ over 9 of the 26 LUCs that have variable LAI. Constant gravitational settling velocities are provided for PM2.5, PM2.5−10 and PM10+. Aerodynamic resistance between a reference height and the surface can be calculated using available analytical formulas from the literature. The bulk Vd of PM2.5, PM2.5−10 and PM10+ at the reference height can then be calculated by combining the gravitational settling velocity, aerodynamic resistance and the parameterized Vds. Vd values calculated using the new algorithm are within ±20% of those using the original size-resolved scheme for fine, coarse and giant particles. Uncertainties in Vd values from the new algorithm due to the pre-assumed size distributions are on the order of 20% for fine particles and on the order of a factor of 2.0 for coarse and giant particles. The new algorithm provides an alternative approach for calculating Vd of bulk aerosol particles. Vd of any particulate species can be simply estimated using this scheme as long as the mass fractions in fine, coarse and giant particles are known or can be assumed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".