Environmental Profile of NO<sub>x</sub> Reduction by a Photocatalytic Surface Coating and a Vehicle Catalytic Converter
Bibliographic record
Abstract
Nitrogen oxides (NOx) in urban air close to ground have significant health implications. Restrictions in traffic, mandatory use of catalytic converters on vehicles, and novel photocatalytic coatings on surfaces contribute to reducing the level of NOx in cities. The aim of this study is to establish environmental profiles of NOx removal by a Three-Way Catalyst (TWC) car converter and by a photocatalytic surface coating (for asphalt and concrete pavements) for fostering technological development in reducing the levels of NOx in urban air. We assessed the environmental performance for the removal of 1 kg NOx by the two technologies with Life Cycle Assessment (LCA; EF.3 impact assessment method). In order to do so, we established Life-Cycle-Inventory (LCI) data representing production, operation and end-of-life of the two technologies based on data from literature and industry. The production of photocatalytic surface coatings, used on concrete and asphalt, has environmental loads two orders of magnitude lower than the environmental benefits of NOx reduction expressed as a reduction in Photochemical Ozone Formation (POF), Acidification (A), and Terrestrial Eutrophication (TE). The vehicle catalytic converter shows similar results except that the use of rare earth elements in the production constitutes a significant load to Freshwater Ecotoxicity (FET) and that additional use of fuel during operation induces a modest Climate Change (CC) impact. For both technologies, the environmental benefits of reducing NOx far exceed any adverse environmental aspects of the production of the technologies.
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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".