Global evaluation of HCHO summertime diurnal variability using Pandonia Global Network (PGN)
Bibliographic record
Abstract
Formaldehyde (HCHO) serves as an important proxy for emissions of volatile organic compounds (VOCs) and their subsequent photochemistry affecting air quality and climate. Understanding HCHO diurnal variability is essential to accurately represent emissions, chemistry, and planetary boundary layer (PBL) mixing in chemical transport models (CTMs). Here we compare HCHO diurnal variations from Pandora Global Network (PGN), GEOS-CF (0.25°x0.25°) and GEOS-Chem (2°x2.5°) CTMs at 55 sites, to characterize the HCHO diurnal patterns in urban and rural sites over North America (NA), Europe (EU) and East Asia (AS) in 2021-2022 summers. We find that HCHO total column (HCHOcol) from GEOS-CF model shows a comparable stronger diurnal variability (quantified by relative amplitude) with that from PGN measurements, which is lower in GEOS-Chem (10-200% bias in late afternoon). While models and PGN show comparable HCHOcol at rural sites (e.g., ChapelHillNC and DearbornMI), PGN shows significantly higher (a factor of 2 - 3) local noon HCHOcol in some urban areas (e.g., Busan and Bangkok), suggesting missing Volatile Organic Compounds (VOCs) emissions in the models. We further examine the relationship between HCHOcol and HCHO near-surface concentration (HCHOsurf). While both model and PGN show a linear relationship (p
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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 | 0.001 |
| 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".