Novel Analysis to Quantify Plume Crosswind Heterogeneity Applied to Biomass Burning Smoke
Bibliographic record
Abstract
We present a novel method, the Gaussian observational model for edge to center heterogeneity (GOMECH), to quantify the horizontal chemical structure of plumes. GOMECH fits observations of short-lived emissions or products against a long-lived tracer (e.g., CO) to provide relative metrics for the plume width ( w i / w CO ) and center ( b i / w CO ). To validate GOMECH, we investigate OH and NO 3 oxidation processes in smoke plumes sampled during FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality, a 2019 wildfire smoke study). An analysis of 430 crosswind transects demonstrates that nitrous acid (HONO), a primary source of OH, is narrower than CO ( w HONO / w CO = 0.73–0.84 ± 0.01) and maleic anhydride (an OH oxidation product) is enhanced on plume edges ( w maleicanhydride / w CO = 1.06–1.12 ± 0.01). By contrast, NO 3 production [P(NO 3 )] occurs mainly at the plume center ( w P(NO 3 ) / w CO = 0.91–1.00 ± 0.01). Phenolic emissions, highly reactive to OH and NO 3, are narrower than CO ( w phenol / w CO = 0.96 ± 0.03, w catechol / w CO = 0.91 ± 0.01, and w methylcatechol / w CO = 0.84 ± 0.01), suggesting that plume edge phenolic losses are the greatest. Yet, nitrophenolic aerosol, their oxidation product, is the greatest at the plume center ( w nitrophenolicaerosol / w CO = 0.95 ± 0.02). In a large plume case study, GOMECH suggests that nitrocatechol aerosol is most associated with P(NO 3 ). Last, we corroborate GOMECH with a large eddy simulation model which suggests most (55%) of nitrocatechol is produced through NO 3 in our case study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".