Machine learning for air quality forecasting: Insights from five provinces of Rwanda
Bibliographic record
Abstract
Accurately predicting air quality is a crucial challenge for public health and environmental management. This study compares and contrasts machine learning approaches to benchmark best practices for the Rwandan context and to evaluate the added value of advanced statistical methods for air quality monitoring in data-scarce settings. We forecast fine particulate matter (PM 2.5 ) concentrations across five provinces in Rwanda, using multi-year meteorological and air quality data to identify context-specific patterns. This work establishes a methodological foundation for context-optimized early warning systems and informs policy interventions to improve air quality management in Rwanda. By rigorously testing machine learning capabilities against regional constraints, we demonstrate how machine learning can reduce population exposure to pollution, quantify attribution gaps in under-monitored regions, and improve sustainable environmental governance in resource-limited settings. The results indicate significant seasonal variability, with higher PM 2.5 levels during dry seasons than wet seasons. Our evaluation demonstrates that machine learning models can capture complex, non-linear relationships between environmental variables and pollution trends, although performance varies between algorithms. Limitations remain, including the integration of real-time data streams and localized variables such as industrial emissions, road traffic, and agricultural practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".