Atmospheric evolution of emissions from a boreal forest fire: the formation of highly functionalized oxygen-, nitrogen-, and sulfur-containing organic compounds
Bibliographic record
Abstract
Forest fires are major contributors of reactive gas- and particle-phase organic compounds to the atmosphere. We used offline high-resolution tandem mass spectrometry to perform a molecular-level speciation of gas- and particle-phase compounds sampled via aircraft from an evolving boreal forest fire smoke plume in Saskatchewan, Canada. We observed diverse multifunctional compounds containing oxygen, nitrogen, and sulfur (CHONS), whose structures, formation, and impacts are understudied. The dilution-corrected absolute ion abundance of particle-phase CHONS compounds increased with plume age by a factor of 6.4 over the first 4 h of downwind transport, and their relative contribution to the observed functionalized organic aerosol (OA) mixture increased from 19 % to 40 %. The dilution-corrected absolute ion abundance of particle-phase compounds with sulfide functional groups increased by a factor of 13 with plume age, and their relative contribution to observed OA increased from 4 % to 40 %. Sulfides were present in up to 75 % of CHONS compounds and the increases in sulfides were accompanied by increases in ring-bound nitrogen; both increased together with CHONS prevalence. A complex mixture of intermediate- and semi-volatile gas-phase organic sulfur species was observed in emissions from the fire and depleted downwind, representing potential precursors to particle-phase CHONS compounds. These results demonstrate CHONS formation from nitrogen- and oxygen-containing biomass burning emissions in the presence of reduced sulfur species. In addition, they highlight chemical pathways that may also be relevant in situations with elevated emissions of nitrogen- and sulfur-containing organic compounds from residential biomass burning and fossil fuel use (e.g., coal), respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".