Effect of Varying the Combustion Parameters on the Emissions of Carbon Monoxide and Nitrogen Oxides in the Exhaust Gases from Propane-Fueled Vehicles
Bibliographic record
Abstract
Propane-fueled forklifts are one source of carbon monoxide (CO) contamination of workplace air. The previous study carried out by the Quebec Occupational Health and Safety Research Institute dealt with worker exposure to CO during forklift use in buildings. It recommends that exhaust gas emissions be kept below a 1 percent concentration. However, this control has not produced a significant reduction in worker exposure to CO, when factors (ventilation, type of work tasks, and management of vehicle fleet) specific to companies are taken into account. Consequently, a reduction in CO emissions below the threshold of 0.3 percent should be considered. The experience acquired with propane-fueled ice resurfacers can be used to determine the effect of combustion parameters on exhaust gas emissions. It is known that a reduction in CO emissions from ice resurfacers resulted in the appearance of nitrogen oxides (NOx) and eventually in nitrogen dioxide (NO2) poisoning. Few publications present NOx results in relation to the CO measured in the exhaust gases of propane-fueled vehicles. The objective of this study is to define the level to which CO emissions can be reduced without increasing NOx concentrations. This real-situation study quantified the CO, NO, and NOx in the exhaust gases of a fleet of propane-fueled forklifts in relation to the mixture ratio. The results show the impact of the motor speed and mixture ratio on the CO, NO, and NO2 concentrations. They confirm an increase in NOx concentrations when CO concentrations are reduced. They also show that proper maintenance of forklifts combined with optimal adjustments can reduce CO and NOx emissions. The study proposes a compromise between CO and NOx emissions by taking into account worker health and safety as well as vehicle performance. Monitoring must be done to control air quality in work areas and worker exposure to CO and NO2. A forklift preventive maintenance program and general building ventilation are the favored and recommended means of control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".