Nitrous acid (HONO) emissions under real-world driving conditions from vehicles in a UK road tunnel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Measurements of atmospheric boundary layer nitrous acid (HONO) and nitrogen oxides (NOx) were performed in summer 2016 inside a city centre road tunnel in Birmingham, United Kingdom. HONO and NOx mixing ratios were strongly correlated with traffic density, with peak levels observed during the early evening rush hour as a result of traffic congestion in the tunnel. A day-time ΔHONO∕ΔNOx ratio of 0.85 % (0.72 % to 1.01 %, 95 % confidence interval) was calculated using reduced major axis regression for the overall fleet average (comprising 59 % diesel-fuelled vehicles). A comparison with previous tunnel studies and analysis on the composition of the fleet suggest that goods vehicles have a large impact on the overall HONO vehicle emissions; however, new technologies aimed at reducing exhaust emissions, particularly for diesel vehicles, may have reduced the overall direct HONO emission in the UK. This result suggests that in order to accurately represent urban atmospheric emissions and the OH radical budget, fleet-weighted HONO∕NOx ratios may better quantify HONO vehicle emissions in models, compared with the use of a single emissions ratio for all vehicles. The contribution of the direct vehicular source of HONO to total ambient HONO concentrations is also investigated and results show that, in areas with high traffic density, vehicle exhaust emissions are likely to be the dominant HONO source to the boundary layer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it