A case study of integrated modelling of traffic, vehicular emissions, and air pollutant concentrations for Huron Church Road, Windsor
Bibliographic record
Abstract
The objectives of this research are to examine spatial and temporal variations in traffic-related NO 2 and benzene concentrations and to investigate the sensitivity of estimated vehicular emissions and ambient concentrations on input parameters. The case study was conducted for Huron Church Road (9.5 km) in Windsor, Ontario. Observed vehicle counts and emission factors from Mobile6.2 were used to estimate vehicular emissions. Ambient concentrations were estimated using the AERMOD dispersion model. Results showed that traffic on Huron Church Road significantly contributes to near-road air quality. The simulated annual mean NO 2 concentration of 2008 was 27 μg/m3 at 40 m from the road, which was higher than the background concentration of 21 μg/m3. Concentrations sharply decreased with distance from the road. At 600 m from the road, the simulated annual concentration was 9% of the concentrations at a distance of 40 m from the road (=2.4 μg/m3, less than background concentration). Similar patterns were observed for benzene. Ambient concentrations were higher during the nighttime than the daytime due to poor mixing. Traffic counts and wind speed explained 40% of variations in the both observed and simulated NO 2 concentrations. The relationship between the truck/car counts and NO 2 /benzene concentration ratios was linear. The model-measurement comparison showed that Mobile6.2 and AERMOD reasonably reproduced the hour-of-day variations and spatial fall-off pattern of NO 2 concentrations. However, AERMOD underestimated concentrations during the daytime potentially due to over-mixing. Sensitivity analysis of the Mobile6.2 showed that the emission factors were most sensitive to the choice of Vehicle Mile Traveled compositions (Ontario versus US), followed by the choice of vehicle age distribution (Ontario versus US), and the average speed of vehicles. In AERMOD simulations, the hour-of-day variation in emission should be considered. Stop-and-go movements increased the total NOx emission over the 9.5 km road by 24% compared to the case of cruise speed of 50km/h during the morning peak hour. Two correction (multiplication) factors were devised to adjust uniform emissions by Mobile6.2 near signalized intersections: an upstream correction factor of 3.2 to account for idling and acceleration emissions, and a downstream correction factor of 1.6 to account for acceleration 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".