Chemical Characterization of Exhaust Emissions from Selected Canadian Marine Vessels: The Case of Trace Metals and Lanthanoids
Bibliographic record
Abstract
This paper reports the chemical composition of exhaust emissions from the main engines of five ocean going cargo vessels, as they traveled in Canadian waters. The emission factors (EFs) of PM2.5 and SO2 for vessels tested on various intermediate fuel oils (IFO), ranged from 0.4 to 2.2 g kW(-1) hr(-1) and 4.7 to 10.3 g kW(-1) hr(-1), respectively, and were mainly dependent on the content of sulfur in the fuel. Average NOx, CO, and CO2 EFs for these tests were 12.7, 0.45, and 618 g kW(-1) hr(-1), respectively and were generally below benchmark values commonly used by regulatory agencies. The composition of PM2.5 was dominated by hydrated sulfates, organic carbon and trace metals which accounted for 80-97% of total PM2.5 mass. A substantial decrease of measured emission factors for PM2.5 and SO2 was observed when the fuel was changed from IFO to marine diesel oil (MDO), in one of the tested vessels. The main component of PM2.5 in this case was organic carbon accounting for 65% of PM2.5 mass. In addition to commonly reported pollutants, this study presents EFs of the lanthanoid elements and showed that their distribution patterns in ship-exhaust PM2.5 were very similar to the PM2.5 emitted by oil refining facilities. Hence, using La:Ce:V tertiary diagrams and La/V ratios is necessary to distinguish ship plumes from primary emissions related to accidental and/or routine operation of oil-refining industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".