High Resolution Dynamical Analysis of Volatile Organic Compounds (VOC) Measurements During the BIO‐MAÏDO Field Campaign (Réunion Island, Indian Ocean)
Bibliographic record
Abstract
Abstract This study presents a high‐resolution dynamical analysis of Volatile Organic Compound (VOC) concentrations measured from March 11 to April 4 2019 at Réunion Island during the BIO‐MAÏDO (Bio‐physicochemistry of tropical clouds at Maïdo: processes and impacts on secondary organic aerosols formation) campaign. We detail the dynamical and chemical processes that govern atmospheric VOC concentrations at two targeted sites of the western slope of Réunion Island: Petite France (PF), 950 m above sea level (a.s.l.) and Maïdo Observatory (MO), 2150 m a.s.l. A dynamical connection between PF and MO is found during four selected days: March 28 and 31, April 1 and 3. Trajectory calculations using the coupling of Meso‐Computing Advection‐interpolation of atmospheric parameters and Trajectory tool (CAT) (100 m horizontal resolution of Meso‐NH high‐resolution non‐hydrostatic model and the CAT trajectory model) Lagrangian transport model showed that air masses were dynamically linked between the two measurements sites for 44.8% of the time during the complete campaign. Trajectories from the Meso‐CAT model combined with the Corine Land Cover‐2018 register shows that backward‐trajectories are frequently located above biogenic area (mixed forest, 8%–67% of total number trajectory point) and cultures area (e.g., sugar cane plantation, 0%–21%). Regarding VOCs concentrations, air masses coming from downhill MO are associated with significant measured concentrations of isoprene, isoprene oxidation products and benzene. Averaged concentration daytime ratios of isoprene and isoprene oxidation products from PF to MO are 0.73 ± 1.01 and 0.26 ± 0.26 respectively illustrating a loss of these VOCs due to deposition, oxidation, or possibility dilution on clouds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".