Improving Urban Air Quality: Evaluation of Electric Vehicles and Nature-Based Solutions as Source and Sink Abatement Strategies for Ozone Pollution in Toronto, ON, Canada
Bibliographic record
Abstract
In this study, two air pollution abatement strategies are examined, focusing on sources and sinks. These include the reduction in ozone precursors (source) and impact of nature-based solutions (sink). For the first abatement strategy (source), two waves of COVID-19 lockdown periods are leveraged as proxies for the potential abatement of air quality pollutants in Toronto, Ontario, Canada, that could occur through electric vehicle deployment. Ground level ozone (O3) and its precursors (NO, NO2), were examined from April to December 2020, during the first two pandemic lockdown periods in Toronto. An ozone weekend effect framework was used to evaluate changes. Results showed that ozone precursors were the lowest of any of the preceding 10 years for both weekdays and weekends; however, ozone concentrations did not have a corresponding decrease but rather had a marked increase for both weekdays and weekends. These findings reflect reduced vehicular traffic and the ozone chemistry in an NOx-saturated (VOC-limited) environment. For the second abatement strategy (sink), a comparison of surface NO2 observations and NO2 satellite data showed the benefits of nature-based solutions as a sink abatement strategy, with the 2020 reduction amplified at the surface. Given the lack of ozone abatement realized through source reduction, deployment of nature-based solutions as a pollutant sink may present a more effective strategy for ground-level ozone abatement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".