Changes in Aerosols in an Urban Cold Climate During and Before the COVID-19 Outbreak
Bibliographic record
Abstract
Atmospheric aerosols are of significant importance in climate change and health research and are essential to consider in air quality and climate modeling. Quebec is the second-largest province in Canada by population and much of the population lives in urban areas. Limitation of public activities, public transportation as well as some suspended operations of educational institutions and many commercial establishments in Quebec while severe lockdown policy was implemented, had strong repercussions on the pollutant concentration level. By analyzing a combination of air pollutants observational data (e.g. CO, SO2, PM10, O3, and NO2), this study attempts to investigate the impact of lockdown due to the COVID-19 pandemic on the pollution level of the local urban environment. Since meteorology can play an important role in air quality, the variation in diverse meteorological factors (e.g. temperature, humidity, wind, pressure, and sunlight) is evaluated as well. By separating long-term trends, seasonal signals, and meteorological contributions concerning climatology, this study estimates the relative contributions of human activities to changes in particulate concentrations. We herein discuss the implications of these results on air quality and climate modeling.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".