Analysis of seasonal and spatial patterns of pm2.5 in Sri Lanka using satellite-derived data
Bibliographic record
Abstract
Sparse ground monitoring and limited reliable evidence for policy hinder air quality management in Sri Lanka. This study employs satellite-derived PM2.5 data from the Global High Air Pollutants (GHAP) product for 2017–2022 using Google Earth Engine and Python, aggregating the data to analyse the temporal dynamics of the pollutant distribution at national and provincial levels. The results show a significant monsoon-driven seasonal cycle—highest concentration in January–March and lowest concentration during the mid-year Southwest monsoon, with a late-year rebound—and persistent spatial disparities, with the Western Province (especially the Colombo urban area) consistently elevated relative to the central highlands. An exploratory weekday analysis for 1 January–31 December 2022 indicates workweek increases that peak mid-to-late week and relax on weekends, underscoring anthropogenic influences. These findings provide a baseline climatology of PM2.5 for Sri Lanka, highlight periods and regions of greatest concern, and motivate targeted weekday traffic/industrial controls in urban hubs.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".