Review of recent literature on the light absorption properties of black carbon: Refractive index, mass absorption cross section, and absorption function
Bibliographic record
Abstract
Knowledge of the optical properties of soot black carbon (BC) is required for the prediction of the radiative effects of freshly-emitted and aged BC particles. Here we review BC mass absorption cross section (MAC) and absorption function E(m) measurements, focusing on freshly-emitted BC. First, we review recently reported MACs at 550 nm wavelength as obtained from direct measurements of particulate absorption and mass concentration; we find an average of 8.0 ± 0.7 m2/g from ten measurements, not significantly higher (p > 0.26) than the widely used MAC of 7.5 ± 1.2 m2/g recommended by Bond and Bergstrom [Bond, T. C., and R. W. Bergstrom. 2006. Light absorption by carbonaceous particles: An investigative review. Aerosol Sci. Technol. 40(1):27–67]. Second, we review recently reported E(m), whose retrieval is more complex due to the need to combine measurements with numerical models to estimate the contribution of scattering to extinction. Third, we review recent numerical studies that have aimed to predict the BC MAC using various complex refractive indices (m = n + ik). Most of these studies have used m = 1.95 + 0.79i recommended by Bond and Bergstrom (2006), yet failed to predict a MAC as high as 7.5 or 8.0 m2/g at 550 nm wavelength. Fourth, we summarize a selected range of alternative values of m that has been reported by recent studies and place them in the context of measurements using a contour plot of E(m) on the n–k plane. We show that the widely used m = 1.95 + 0.79i corresponds to an E(m) that is too low to be consistent with the measured MAC values. We conclude that the E(m) of BC in the visible and near infrared should be greater than 0.32, and that the commonly used BC models or the refractive index, or both, are still in need of improvement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".