Quantifying the Impact of the COVID-19 Pandemic Restrictions on CO, CO2, and CH4 in Downtown Toronto Using Open-Path Fourier Transform Spectroscopy
Bibliographic record
Abstract
During the global COVID-19 pandemic, anthropogenic emissions of air pollutants and greenhouse gases (GHGs), especially traffic emissions in urban areas, have declined. Long-term measurements of trace gas concentrations in urban areas can be used to quantify the impact of emission reductions on GHG mole fractions. Open-path Fourier transform infrared (OP-FTIR) spectroscopy is a non-intrusive technique that can be used to simultaneously measure multiple atmospheric trace gases in the boundary layer. This study investigates the reduction of mole fractions and mole fraction enhancements above background for surface CO, CO2, and CH4 in downtown Toronto, Canada (the fourth largest city in North America) during the 2020 and 2021 COVID-19 stay-at-home periods. Mean values obtained from these periods were compared with mean values from a reference period prior to the 2020 restrictions. Mean CO mole fraction enhancement declined by 51 ± 23% and 42 ± 24% during the 2020 and 2021 stay-at-home periods, respectively. The mean afternoon CO2 mole fraction enhancement declined by 3.9 ± 2.6 ppm (36 ± 24%) and 3.5 ± 2.8 ppm (33 ± 26%) during the stay-at-home periods in 2020 and 2021. In contrast, CH4 mole fraction enhancement did not show any significant decrease. Diurnal variation in CO during the stay-at-home period in 2020 was also significantly reduced relative to the reference period in 2020. These reductions in trace gas mole fraction enhancements coincide with the decline of local traffic during the stay-at-home periods, with an estimated reduction in CO and CO2 enhancements of 0.74 ± 0.15 ppb and 0.18 ± 0.05 ppm per percentage decrease in traffic, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".