Airborne measurements of the atmospheric emissions from a fuel ethanol refinery
Bibliographic record
Abstract
Abstract Ethanol made from corn now constitutes approximately 10% of the fuel used in gasoline vehicles in the U.S. The ethanol is produced in over 200 fuel ethanol refineries across the nation. We report airborne measurements downwind from Decatur, Illinois, where the third largest fuel ethanol refinery in the U.S. is located. Estimated emissions are compared with the total point source emissions in Decatur according to the 2011 National Emissions Inventory (NEI‐2011), in which the fuel ethanol refinery represents 68.0% of sulfur dioxide (SO2), 50.5% of nitrogen oxides (NOx = NO + NO2), 67.2% of volatile organic compounds (VOCs), and 95.9% of ethanol emissions. Emissions of SO2 and NOx from Decatur agreed with NEI‐2011, but emissions of several VOCs were underestimated by factors of 5 (total VOCs) to 30 (ethanol). By combining the NEI‐2011 with fuel ethanol production numbers from the Renewable Fuels Association, we calculate emission intensities, defined as the emissions per ethanol mass produced. Emission intensities of SO2 and NOx are higher for plants that use coal as an energy source, including the refinery in Decatur. By comparing with fuel‐based emission factors, we find that fuel ethanol refineries have lower NOx, similar VOC, and higher SO2 emissions than from the use of this fuel in vehicles. The VOC emissions from refining could be higher than from vehicles, if the underestimated emissions in NEI‐2011 downwind from Decatur extend to other fuel ethanol refineries. Finally, chemical transformations of the emissions from Decatur were observed, including formation of new particles, nitric acid, peroxyacyl nitrates, aldehydes, ozone, and sulfate aerosol.
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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".