Brown and Black Carbon Emitted by a Marine Engine Operated on Heavy Fuel Oil and Distillate Fuels: Optical Properties, Size Distributions, and Emission Factors
Bibliographic record
Abstract
Abstract We characterized the chemical composition and optical properties of particulate matter (PM) emitted by a marine diesel engine operated on heavy fuel oil (HFO), marine gas oil (MGO), and diesel fuel (DF). For all three fuels, ∼80% of submicron PM was organic (and sulfate, for HFO at higher engine loads). Emission factors varied only slightly with engine load. Refractory black carbon (rBC) particles were not thickly coated for any fuel; rBC was therefore externally mixed from organic and sulfate PM. For MGO and DF PM, rBC particles were lognormally distributed in size (mode at d rBC ≈120 nm). For HFO, much larger rBC particles were present. Combining the rBC mass concentrations with in situ absorption measurements yielded an rBC mass absorption coefficient MAC BC,780nm of 7.8 ± 1.8 m 2 /g at 780 nm for all three fuels. Using positive deviations of the absorption Ångström exponent (AAE) from unity to define brown carbon (brC), we found that brC absorption was negligible for MGO or DF PM (AAE(370,880 nm)≈1.0 ± 0.1) but typically 50% of total 370‐nm absorption for HFO PM. Even at 590 nm, ∼20 of the total absorption was due to brC. Using absorption at 880 nm as a reference for BC absorption and normalizing to organic PM mass, we obtained a MAC OM,370nm of 0.4 m 2 /g at typical operating conditions. Furthermore, we calculated an imaginary refractive index of (0.045 ± 0.025)( λ /370nm) −3 for HFO PM at 370 nm> λ > 660 nm, more than twofold greater than previous recommendations. Climate models should account for this substantial brC absorption in HFO PM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".