Quantification of black carbon mixing state from traffic: implications for aerosol optical properties
Bibliographic record
Abstract
Abstract. The climatic impacts of black carbon (BC) aerosol, an important absorber of solar radiation in the atmosphere, remain poorly constrained and are intimately related to its particle-scale physical and chemical properties. Using particle-resolved modelling informed by quantitative measurements from a soot-particle aerosol mass spectrometer, we confirm that the mixing state (the distribution of co-emitted aerosol amongst fresh BC-containing particles) at the time of emission significantly affects BC-aerosol optical properties even after a day of atmospheric processing. Both single particle and ensemble aerosol mass spectrometry observations indicate that BC near the point of emission co-exists with hydrocarbon-like organic aerosol (HOA) in two distinct particle types: HOA-rich and BC-rich particles. The average mass fraction of black carbon in HOA-rich and BC-rich particle classes was < 0.1 and 0.8, respectively. Notably, approximately 90 % of BC mass resides in BC-rich particles. This new measurement capability provides quantitative insight into the physical and chemical nature of BC-containing particles and is used to drive a particle-resolved aerosol box model. Significant differences in calculated single scattering albedo (an increase of 0.1) arise from accurate treatment of initial particle mixing state as compared to the assumption of uniform aerosol composition at the point of BC injection into the atmosphere.
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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 | 0.001 |
| 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 | 0.001 | 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".