Capturing the vertical distribution of near-highway nitrogen dioxide using UAV-based measurements
Bibliographic record
Abstract
This pilot study evaluates the use of Uncrewed Aerial Vehicles (UAVs) to capture variations in the transport and dispersion of nitrogen dioxide (NO 2 ) under different atmospheric dispersion conditions. The vertical distribution of NO 2 concentration was observed using an unmodified handheld electrochemical gas sensor mounted onboard a UAV, beside a major Canadian highway. Observations were taken in both poor and good dispersion conditions, as determined by the Atmospheric Dispersion Index and Ventilation Index. Results reveal atmospheric stability has a significant impact on the vertical profiles of NO 2 . The low wind speeds and limited vertical mixing experienced during poor dispersion conditions resulted in concentrations increasing with altitude, contrasting with the stable NO 2 concentrations observed during good dispersion conditions. Statistical analysis reveals significant differences between ground-level measurements and simultaneous UAV-based readings at higher altitudes during poor dispersion, suggesting the influence of regional emissions accumulating due to poor mixing. These preliminary results demonstrate the potential for UAV-based measurements in air quality management. The ability to capture nuanced dynamics such as the accumulation of regional emissions is critical when studying pollutions events such as wildfires. • Vertical profiles of NO 2 were captured near a major Canadian highway under varying atmospheric dispersion conditions. • During poor dispersion conditions, NO 2 concentrations increased with altitude, while they remained relatively stable during good dispersion. • Significant differences were observed between ground-level measurements and UAV-based observations at higher altitudes during poor dispersion, suggesting the accumulation of regional emissions.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".