Indirect Positive Effects of the COVID-19 Pandemic on Air Pollution in Canadian CMAs/CAs
Bibliographic record
Abstract
This paper investigated how the restrictions during the COVID-19 pandemic have affected air pollution within Canadian Census Metropolitan Areas/Census Agglomerations (CMAs/CAs) and provinces/territories in 2020 compared to 2019 (pre-pandemic). It explored the connection based on emission levels of two air pollutants (NO and PM ) obtained from the National Air Pollution 2 2.5 Surveillance Program (NAPS) website, pandemic restrictions in Canadian cities obtained from COVID19 Government Measures Dataset, and socio-demographic factors from the 2016 Census which are the pollutants causes. This secondary data compiled in Excel database was used within multiple analyses which included, descriptives, pairwise t-test, correlation, stepwise regression done in SPSS, and using GIS methods to map the final results. The results showed a reduction in NO emi2sions in many CMAs/CAs and provinces, but increased PM emiss2.5s in 31 CMAs/CAs and 8 provinces. Reductions in NO 2nd PM wer2.5ost prominent in March and April of 2020 when the most stringent COVID-19 restrictions were implemented. The overall results showed a small but significant connection between the air pollutants and the few socio-demographic variables chosen through the stepwise technique (i.e. commuting time, public transit, and work location) contributed to the connection. These results corroborate findings from previous studies on air pollution and mobility restrictions and indicate that strategies such as remote work, and conversion to environmentally friendly modes of transportation (hybrid vehicles) can be leveraged to reducing NO and PM emissions in the future. These strategies 2 2.5 could especially be beneficial to the provinces and CMAs/CAs with high PM emissi2.5. Lack of data limited how thorough the analysis of the topic could be conducted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".