Extension of a gaseous dry deposition algorithm to oxidized volatile organic compounds and hydrogen cyanide for application in chemistry transport models
Bibliographic record
Abstract
Abstract. With increasing complexity of air quality models, additional chemical species have been included in model simulations for which dry deposition processes need to be parameterized. For this purpose, the gaseous dry deposition scheme of Zhang et al. (2003) is extended to include 12 oxidized volatile organic compounds (oVOCs) and hydrogen cyanide (HCN) based on their physicochemical properties, namely the effective Henry's law constants and oxidizing capacities. Modeled dry deposition velocity (Vd) values are compared against field flux measurements over a mixed forest in the southeastern U.S. during June 2013. The model captures the basic features of the diel cycles of the observed Vd. Modeled Vd values are comparable to the measurements for most of the oVOCs at night. However, modeled Vd values are mostly around 1 cm s−1 during daytime, which is much smaller than the observed daytime maxima of 2–5 cm s−1. Analysis of the individual resistance terms/uptake pathways suggests that flux divergence due to fast atmospheric chemical reactions near the canopy was likely the main cause of the large model-measurement discrepancies during daytime. The extended dry deposition scheme likely provides conservative Vd values for many oVOCs. While higher Vd values and bi-directional fluxes can be simulated by coupling key atmospheric chemical processes into the dry deposition scheme, we suggest that more experimental evidence of high oVOC Vd values at additional sites is required to confirm the broader applicability of the high values studied here. The underlying processes leading to high measured oVOC Vd values require further investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".