Studies of Emissions From Oil Fires
Bibliographic record
Abstract
ABSTRACT Over 45 mesoscale burns were conducted to study various aspects of diesel and crude oil burning in situ,. Extensive sampling and monitoring of these burns were conducted at downwind stations, upwind stations, and in the smoke plume. Particulate samples were taken in air and analyzed for polycyclic aromatic hydrocarbons (PAHs). PAHs were found to be lower in the soot than in the starting oil, although higher concentrations of the larger molecular PAHs were found in the soot and residue, especially for diesel burns. Overall, the amount of PAHs in the soot and residue were about 2 to 8% of that in the starting oil. This implies a destruction of PAHS by 92 to 98%. Particulates in the air were measured by several means and were found to be greater than recommended exposure levels up to 500 meters downwind at ground level, depending on the size and type of fire. Diesel fires emit much more particulate matter and have longer exposure zones. Combustion gases including carbon dioxide and carbon monoxide are below exposure level maximums. Volatile organic compound (VOC) emissions are extensive from fires, but the levels were less than from an evaporating crude oil spill. Over 140 compounds were identified and quantified. Carbonyls, including aldehydes and ketones, were found to below human health concern levels. Emission data from over 45 experimental burns have been used to develop prediction equations for over 150 specific compounds or emission categories. These are used to calculate safe distances and levels of concern for a standard burn size of 500 square meters, an amount that would typically be contained in a boom. The safe distance for a crude oil burn of this size is about 500 m and for a diesel burn, much further.
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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 | 0.000 | 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".