Isocyanic acid in a global chemistry transport model: Tropospheric distribution, budget, and identification of regions with potential health impacts
Bibliographic record
Abstract
This study uses a global chemical transport model to estimate the distribution of isocyanic acid (HNCO). HNCO is toxic, and concentrations exceeding 1 ppbv have been suggested to have negative health effects. Based on fire studies, HNCO emissions were scaled to those of hydrogen cyanide (30%), resulting in yearly total emissions of 1.5 Tg for 2008, from both anthropogenic and biomass burning sources. Loss processes included heterogeneous uptake (pH dependent), dry deposition (like formic acid), and reaction with the OH radical (k = 1 × 10−15 molecule−1 cm3 s−1). Annual mean surface HNCO concentrations were highest over parts of China (maximum of 470 pptv), but episodic fire emissions gave much higher levels, exceeding 4 ppbv in tropical Africa and the Amazon, and exceeding 10 ppbv in Southeast Asia and Siberia. This suggests that large biomass burning events could result in deleterious health effects for populations in these regions. For the tropospheric budget, using the model‐calculated pH the HNCO lifetime was 37 days, with the split between dry deposition and heterogeneous loss being 95%:5%. Fixing the heterogeneous loss rate at pH = 7 meant that this process dominated, accounting for ∼70% of the total loss, giving a lifetime of 6 days, and resulting in upper tropospheric concentrations that were essentially zero. However, changing the pH does not notably impact the high concentrations found in biomass burning regions. More observational data is needed to evaluate the model, as well as a better representation of the likely underestimated biofuel emissions, which could mean more populations exposed to elevated HNCO concentrations.
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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) | 0.001 | 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 | 0.000 |
| 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".