Infrared-absorbing carbonaceous tar can dominate light absorption by marine-engine exhaust
Bibliographic record
Abstract
Abstract Ship engines in the open ocean and Arctic typically combust heavy fuel oil (HFO), resulting in light-absorbing particulate matter (PM) emissions that have been attributed to black carbon (BC) and conventional, soluble brown carbon (brC). We show here that neither BC nor soluble brC is the major light-absorbing carbon (LAC) species in HFO-combustion PM. Instead, “tar brC” dominates. This tar brC, previously identified only in open-biomass-burning emissions, shares key defining properties with BC: it is insoluble, refractory, and substantially absorbs visible and near-infrared light. Relative to BC, tar brC has a higher Angstrom absorption exponent (AAE) (2.5–6, depending on the considered wavelengths), a moderately-high mass absorption efficiency (up to 50% of that of BC), and a lower ratio of sp 2 - to sp 3 -bonded carbon. Based on our results, we present a refined classification of atmospheric LAC into two sub-types of BC and two sub-types of brC. We apply this refined classification to demonstrate that common analytical techniques for BC must be interpreted with care when applied to tar-containing aerosols. The global significance of our results is indicated by field observations which suggest that tar brC already contributes to Arctic snow darkening, an effect which may be magnified over upcoming decades as Arctic shipping continues to intensify.
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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".