Impact of COVID-19 lockdown on air quality of Sri Lankan cities
Bibliographic record
Abstract
The COVID-19 pandemic has led to the complete lockdown of many cities worldwide.Lockdowns have significantly changed human behaviour, such as fewer or no industrial activities and limited road and air transport, affecting the environment.In this study, we aimed to assess the variability and trends of PM2.5 (mass concentration of airborne particulate matter < 2.5µm) and carbon monoxide (CO) before and during the COVID-19 lockdown period in Sri Lanka.Data were collected in "Urban Background", "Public & Mixed Residential", and "Primary Residential" areas using small sensor technology, "KOALA" air quality sensor units, from five locations, three in Kandy, and two in Colombo city.Daily averages (24h) and daytime averages (08:00 AM to 8:00 PM) were calculated for the period before (before March 20 th ) and during (March 20 th to May 10 th ) the lockdown.Air pollutions level before & during COVID-19 lockdown were compared, then Mann-Kendall and Sen's slope tests were performed to determine trends of PM2.5 and CO, and the magnitude of the trend.Meteorological parameters were fairly similar before and during both cities' lockdown periods, while both PM2.5 and CO concentrations declined.The highest average reductions of PM2.5 and CO were observed in Colombo's "Urban Background" area (52.4% and 46.7%, respectively).In Kandy, "Urban Background" site had a higher reduction of PM2.5 and CO (30.2% and 41.2%, respectively), compared to "Primary Residential" (10% and 9%, respectively).The daily averages of the pollutants' concentrations were higher before the lockdown period compared to during.Overall, a significant downward trend was observed of air pollutants over the entire study period.In Sri Lanka, the COVID-19 lockdown improved air quality significantly in urban areas.
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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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".