Air Pollution Measurements near Roadways: Verifying Measurements and Discerning Traffic-related Signal from Background
Bibliographic record
Abstract
The work in this thesis examined multiple years of air quality data collected in near-roadway and urban background environments as part of an air quality monitoring pilot study, the first of its kind in Canada. In addition to validation strategies involved in assuring accuracy of these data, near-road pollutant concentrations were compared with urban background concentrations (between 2-10 km away from each near-road receptor) in order to isolate excess pollution arising from vehicle emissions, understood to be approximately equal to the difference between the two locations. One major hypothesis in this work was that these excess pollutant quantities can be estimated using the near-road data alone, an important research question as oftentimes studies do not have concurrent background measurements, and two strategies for doing this were compared with the measured site differences. Namely, utilizing differences between measurements made downwind and upwind of the roadway, and applying a background-subtraction algorithm to the near-road time series data. It was found that, relative to the explicitly measured site differences, downwind/upwind differences were 40% larger on average and not universally applicable, whereas an optimized background-subtraction algorithm replicated the site differences well and was robust across all sites. After isolating these local vehicle-related concentrations, the role of meteorological variability on vehicular plume dispersion in the near-road environment was quantified. It was found that the relationship between normalized local concentrations and wind speed was well-described with a simple exponential equation. Wind direction had the effect of enhancing this by a factor of 1.5-2.0 when downwind of the roadway, and suppressing it by a factor of 0.25-0.50 when upwind at these sites. Lastly, 15 years of continuous ambient near-road ultrafine particulate matter data were analyzed utilizing this background-subtraction methodology. It was found that local concentrations contributed ~45% to total ambient concentrations, and while these total concentrations have been steadily declining, this local fraction has remained relatively consistent. Future utilization of the methodologies developed and validated in this thesis will be useful for continued monitoring of vehicular fleet emissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".