Quantifying the impact of the COVID-19 lockdown on air quality 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, especially traffic emissions in urban areas, have declined significantly. Long-term measurements of trace gas concentrations in urban areas can be used to quantify the impact of emission reductions on local air quality. 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 surface CO, CO2 , and CH4 mole fractions during the lockdown in downtown Toronto, Canada, which is the fourth largest city in North America. The mean daily CO mole fraction anomaly (ΔCO) for the period from March 14 to May 18, 2020 declined by 46 ± 16% compared to the period before lockdown from January 13 to March 13, 2020. The mean daily ΔCO during the lockdown also declined relative to the same period in previous years: by 50 ± 20% relative to 2019 and by 44 ± 25% relative to 2018. Changes in the diurnal variations of CO, CO2 and CH4 during the lockdown are also investigated and compared to 2019 and 2018. Both CO and CO2 show early morning maxima on weekdays corresponding to rush hour. The change of the amplitude of the diurnal variation in CO during the lockdown is significant, compared to the period before lockdown. The differences in the diurnal variation in CO during the same two periods in 2019 and 2018 are not significant. Ratios of CO/CO2 anomalies show seasonal variations, which are also likely due to seasonal changes of emissions from local sources. These results show that the COVID-19 lockdown in Toronto modified surface mole fractions, diurnal variations, and ratios of air pollutants monitored by OP-FTIR. In addition, measured CO mole fractions are compared with simulated CO mole fractions by WRF-STILT to assess the relationship between atmospheric measurements and urban emissions from Toronto.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".