A Large Underestimate of Formic Acid from Tropical Fires: Constraints from Space-Borne Measurements
Bibliographic record
Abstract
Formic acid (HCOOH) is one of the most abundant carboxylic acids and a dominant source of atmospheric acidity. Recent work indicates a major gap in the HCOOH budget, with atmospheric concentrations much larger than expected from known sources. Here, we employ recent space-based observations from the Tropospheric Emission Spectrometer with the GEOS-Chem atmospheric model to better quantify the HCOOH source from biomass burning, and assess whether fire emissions can help close the large budget gap for this species. The space-based data reveal a severe model HCOOH underestimate most prominent over tropical burning regions, suggesting a major missing source of organic acids from fires. We develop an approach for inferring the fractional fire contribution to ambient HCOOH and find, based on measurements over Africa, that pyrogenic HCOOH:CO enhancement ratios are much higher than expected from direct emissions alone, revealing substantial secondary organic acid production in fire plumes. Current models strongly underestimate (by 10 ± 5 times) the total primary and secondary HCOOH source from African fires. If a 10-fold bias were to extend to fires in other regions, biomass burning could produce 14 Tg/a of HCOOH in the tropics or 16 Tg/a worldwide. However, even such an increase would only represent 15-20% of the total required HCOOH source, implying the existence of other larger missing sources.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".