Assessment of mobility trends and transportation-related emissions in Canadian cities during the post-COVID-19 pandemic period
Bibliographic record
Abstract
There were some new characteristics of urban transportation in the later stage of the COVID-19 pandemic. This study investigated the transportation-related emissions in major cities of Canada during the post-COVID-19 pandemic period, with a focus on evolving transportation behaviors and environmental effects. The analysis was based on data collected from various provinces in Canada, encompassing greenhouse gases (GHGs), traffic volume, fuel consumption by vehicles and airlines, and air quality. The aviation sector nearly reverted to pre-pandemic levels by 2022, with significant rebounding of kerosene-type jet fuel consumption. Emission analysis from September 2020 to December 2022 showed the changes in NO 2 , CO, SO 2 , PM 2.5 , and O 3 levels. Key observations include a gradual return to pre-pandemic emission levels. For instance, the average NO 2 levels in Vancouver showed variations from 14.2 ppb in 2020 to 15.4 ppb in 2022, while average CO levels fluctuated between 0.18 ppm in 2020 and 0.22 ppm in 2022. These changes are attributed to multiple factors, including the pandemic, fuel price hikes, increased electric vehicle usage, and altered commuting patterns. The results can help further explore the mobility and emissions patterns impacted by human activities, which have implications with respect to improving air quality and reducing GHG emissions in urban areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".