The impact of a future H <sub>2</sub> -based road transportation sector on the composition and chemistry of the atmosphere – Part 2: Stratospheric ozone
Bibliographic record
Abstract
Abstract. The prospective future adoption of hydrogen to power the road transportation sector could greatly improve tropospheric air quality but also raises the question whether the adoption would have adverse effects on stratospheric ozone. The possibility of these undesirable impacts must be fully evaluated to guide future policy decisions. Here we evaluate the possible impact of a future (2050) H2-based road transportation sector on stratospheric composition and chemistry, especially on stratospheric ozone, with the MOZART chemical transport model. Since future growth is highly uncertain we evaluate the impact for two world evolution scenarios, one based on a high emitting scenario (IPCC A1FI) and the other on a low emitting scenario (IPCC B1), as well as two technological options: H2 fuel cells and H2 internal combustion engines. We assume a H2 leakage rate of 2.5% and a complete market penetration of H2 vehicles in 2050. The model simulations show that a H2-based road transportation sector would reduce stratospheric ozone concentrations as a result of perturbed catalytic ozone destruction cycles. The magnitude of the impact depends on which growth scenario the world evolves and which H2 technology option is applied. For the same world evolution scenario, stratospheric ozone decreases more in the H2 fuel cell scenarios than in the H2 internal combustion engine scenarios because of the NOx emissions in the latter case. If the same technological option is applied, the impact is larger in the A1FI emission scenario. The largest impact, a 0.54% decrease in annual average global mean stratospheric column ozone, is found with a H2 fuel cell type road transportation sector in the A1FI scenario; whereas the smallest impact, a 0.04% increase in stratospheric ozone, is found with applications of H2 internal combustion engine vehicles in the B1 scenario. The impacts of the other two scenarios fall between the above two bounding scenarios. However, the magnitude of these changes is much smaller than the increases in 2050 stratospheric ozone expected as stratospheric ozone recovers due to the limits in ozone depleting substance emissions imposed in the Montreal Protocol.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".