Comparative Analysis of Pollutant Levels during Lockdowns Across Different Land-Use over the Emirate of Abu Dhabi, United Arab Emirates
Bibliographic record
Abstract
The outbreak of the COVID-19 pandemic has had a significant effect on people all over the world, posing health, economic, and social threats to the entire human population. As a part of preventive measures, at the end of March 2020 the UAE promulgated various lockdown measures to reduce the risk of the pandemic, which have a major impact on its local air quality levels. This research investigates the effect of the lockdown measures on the levels of the air pollutants like NO2 and PM2.5in Abu Dhabi Emirate using air quality stations data for the months of March and April 2020. Overall, NO2 levels have fallen dramatically by a range of 19% to 60% across all land use areas within the Emirate. Conversely, PM2.5 levels varied during the lockdown in April 2020, with increases ranging from 31% to 65% in rural and suburban industrial areas and decreases ranging from 2% to 33% in urban and suburban population areas. It can be observed that the lockdown measures had a huge impact on the NO2 levels due to reduced transportation and human activities while PM2.5 levels displayed great variability. The statistical analysis shows a significant moderate positive relationship (rs 0.476) at 0.05 level between NO2 and traffic volume crossing Musaffah Bridge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".