The impact of Covid-19 on air quality in Bucharest, Romania
Bibliographic record
Abstract
In this study, the authors aimed to assess the air quality within Bucharest City, Romania’s capital, during the Covid-19 pandemic. It was well established that for quite a long period, Bucharest was among the worst Romanian cities in terms of air quality. Thus, in this study, an investigation of the effects of imposed quarantine and lockdown in terms of air pollution was carried out. The levels of the main air pollutants, particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2) and benzene (C6H6), were recorded within the period of January 2020–April 2022 by using six stationary monitoring stations (B-1, B-2, B-3, B-4, B-5 and B-6) belonging to the Romanian National Network for Monitoring Air Quality. During the lockdown period (16 March–14 May 2020), the measurements indicated significant reductions only for PM2.5, nitrogen dioxide and benzene, while for PM10, due to the fact that a sandstorm appeared, the results were unreliable. The results focused on the B-3 and B-6 traffic monitoring stations because road traffic was one of the main sources of pollution in cities. Compared with the 2018–2019 period, during the lockdown, all the air pollutants from all the measuring stations dramatically dropped, highlighting thus the important role of traffic and its significant contribution to air quality depreciation in Bucharest, particularly in terms of nitrogen dioxide pollution. Therefore, the urgent need for decisions to be made in terms of improving the air quality of the city, particularly from a road traffic perspective, arose.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".