Single particle characterization of biomass burning organic aerosol (BBOA): evidence for non-uniform mixing of high molecular weight organics and potassium
Bibliographic record
Abstract
Abstract. Biomass burning is a major source of black carbon (BC) and primary organic aerosol globally. In particular, biomass burning organic aerosol (BBOA) is strongly associated with atmospheric brown carbon (BrC) that absorbs near ultraviolet and visible light, resulting in significant impacts on regional visibility degradation and radiative forcing. The mixing state of BBOA can play a critical role in the prediction of aerosol optical properties. In this work, single particle measurements from a soot-particle aerosol mass spectrometer coupled with a light scattering module (LS-SP-AMS) were performed to examine the mixing state of BBOA, refractory black carbon (rBC) and potassium (K+, a tracer for biomass burning aerosol) in an air mass influenced by aged biomass burning. Cluster analysis of single particle measurements identified five BBOA-related particle types. rBC accounted for 3–14 w.t. % of these particle types on average. Only one particle type exhibited a strong ion signal for K+, with mass spectra characterized by low molecular weight organic species. The remaining four particle types were classified based on the apparent molecular weight of the BBOA constituents. Two particle types were associated with low potassium content and significant amounts of high molecular weight (HMW) organic compounds. Our observations indicate non-uniform mixing of particles within a biomass burning plume in terms of molecular weight and illustrate that HMW BBOA can be a key contributor to low-volatility BrC observed in BBOA particles.
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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".