Atmospheric black carbon mass absorption cross-section: a literature review
Bibliographic record
Abstract
Black carbon (BC) aerosol particles are emitted by the incomplete combustion of carbonaceous fuels. These particles absorb solar radiation and BC-dominated aerosol mixtures with low single scattering albedo have a positive radiative forcing, thus heating the atmosphere. Radiative transfer models make use of the BC mass absorption cross section (MACBC) to derive the radiative forcing of BC given a certain particle mass concentration. Freshly emitted BC has a MAC value of 8 ± 1 m2/g at 550 nm (Bond et al., 2013). However, MAC can increase as aerosols age in the atmosphere due to increase in particle coating. This is the so-called lensing effect, which leads to MACBC observations of up to 15 m2/g at 550 nm (Li et al., 2022; Savadkoohi et al., 2024). The effect of coatings and the evolution of MACBC with ageing have been and still are a matter of intense scientific discussions.The determination of MACBC is carried out in the lab and in the field using various methods for light absorption and BC mass measurement. The most common techniques for absorption measurement include filter-based attenuation measurements, whereas the most common technique for mass measurement is thermo-optical analysis, which quantifies elemental carbon mass (EC; EN 16909:2017). The development of more accurate techniques with operational and scientific advantages for both light absorption and BC mass quantification has led to more reliable MACBC field measurements, allowing researchers to have a clearer picture of how atmospheric ageing and regional conditions affect the optical properties of BC.In this study, we have reviewed 63 publications that provide atmospheric MACBC values and present the results in terms of aerosol type, measurement technique, regional variability, and how interpretation of results using these factors can help the community to use the appropriate MAC in models. We provide guidance and perspectives for future studies and how the literature on MACBC can be exploited and interpreted in order to improve radiative models that include BC.References Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., … Zender, C. S. (2013). Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Research: Atmospheres, 118(11), 5380–5552. https://doi.org/10.1002/jgrd.50171Hanyang Li & Andrew A. May (2022) Estimating mass-absorption cross-section of ambient black carbon aerosols: Theoretical, empirical, and machine learning models, Aerosol Science and Technology, 56:11, 980-997, https://doi.org/10.1080/02786826.2022.2114311Savadkoohi, Marjan, Marco Pandolfi, Cristina Reche, Jarkko V. Niemi, Dennis Mooibroek, Gloria Titos, David C. Green, et al. (2023) The Variability of Mass Concentrations and Source Apportionment Analysis of Equivalent Black Carbon across Urban Europe. Environment International 178: 108081. https://doi.org/10.1016/j.envint.2023.108081.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".