Light Absorption by Ambient Black and Brown Carbon and its Dependence on Black Carbon Coating State for Two California, USA, Cities in Winter and Summer
Bibliographic record
Abstract
Abstract Observations from a wintertime and summertime field campaign are used to assess the relationship between black and brown carbon (BC and BrC, respectively) optical properties and particle composition and coating state. The wintertime campaign, in Fresno, CA, was impacted by primary emissions from residential wood burning, secondary organic and inorganic particle formation, and BC from motor vehicles. Two major types of BrC were observed in wintertime. One occurred primarily at night—the result of primary biomass burning emissions. The second was enhanced in daytime and strongly associated with particulate nitrate and the occurrence of fog. The biomass‐burning‐derived BrC absorbed more strongly than the nitrate‐associated BrC but had a weaker wavelength dependence. The wintertime BC‐specific mass absorption coefficient (MAC BC ) exhibited limited dependence on the ensemble‐average coating‐to‐BC mass ratio ( R coat‐rBC ) at all wavelengths, even up to R coat‐rBC of ~5. For the summertime campaign, in Fontana, CA, BC dominated the light absorption, with negligible BrC contribution even after substantial photochemical processing. The summertime MAC BC exhibited limited dependence on R coat‐rBC , even up to ratios of >10. Based on the four classes of BC‐containing particles identified by Lee et al. (2017, https://doi.org/10.5194/acp‐17‐15055‐2017 ) for the summertime measurements, the general lack of an absorption enhancement can be partly—although not entirely—attributed to an unequal distribution of coating materials between the BC‐containing particle types. These observations demonstrate that in relatively near‐source environments, even those impacted by strong secondary aerosol production, the ensemble‐average, mixing‐induced absorption enhancement for BC due to coatings can be quite small.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".