Estimation of Link–Based Emissions for a Truck Route in the Downtown Halifax, Canada
Bibliographic record
Abstract
Air quality degradation due to vehicular emission in urban areas poses a growing threat to human health. The objective of this study is to estimate vehicular emissions of a major truck route in Halifax, Canada and examine alternative policy scenarios for emission reduction. The study proposes a comprehensive emission estimation framework that utilizes information from field surveys and uses a simulation platform, the Motor Vehicle Emission Simulator (MOVES) 2010b. The paper focuses on major air pollutants, including , carbon monoxide (CO), nitrogen oxide (NOx), and particulate matter (PM10 and PM 2.5) that are estimated for the time periods of AM peak, mid day, off peak, PM peak and overnight. In total thirty simulation runs were conducted for the 10.39 km long route to estimate link by link emissions in both directions. The results suggest that emission rates are significantly affected by the traffic volume and time of the day. It is found that total NOx emissions are nearly 8 times higher than PM10 and PM 2.5 emissions and 1.15 times higher than CO emission. Existing truck traffic significantly contributes to the total emission in this busy road segment in the downtown core. Two alternative strategies were tested in order to reduce emission from the route. A ban on all trucks along the route significantly reduces all types of emission, ranging from 70.8% to 95.9% in comparison to the business-as-usual scenario. Limiting trucks at certain time period will reduces CO, NOx, PM10 and PM 2.5 by 34.5%, 28.9%, 26.8% and 26.79% respectively, which can be a reasonable solution in addressing the public concern regarding this truck route running through the Halifax urban core.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".