How to Use Hydrogen in a New Strategy to Mitigate Urban Air Pollution and Preserve Human Health
Bibliographic record
Abstract
If transport is an essential means for the development of the economy, society and its mobility, it has the drawback of leading to significant atmospheric pollution. As traffic density is very high in large cities, air pollution is amplified by the various means of transport resulting from the combustion of fossil fuels. Urban air pollution is mainly caused by vehicles generating emissions harmful to human health. Our objective of this work is to analyze a strategy to eliminate or reduce the emission of these pollutants (NOx, CO, CO2) during combustion. This strategy aims to explore a clean energy source alternative to fossil fuels. This approach consists of completely replacing the internal combustion scalar with the engine powered by fuel cells using hydrogen. This motivates decision makers to choose hydrogen as an alternative fuel to protect the urban environment and the health human from air pollution. This study shows that it is possible to perfectly mitigate pollutants from urban transport systems by using a PEMFC as an alternative clean energy source. Analyze a strategy to eliminate or reduce the emissions of these pollutants (NOx, CO, CO2) during the combustion of full fossil fuel in vehicle engines. This strategy aims to exploit the energy vector represented by hydrogen in order to save human life in more populated areas and protect the environment. The pressure, temperature and concentration of each species (O2, H2 and H2O) are obtained from the resolution of the electrochemical model coupled to the dynamic model, which we do not present here.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".