Machine learning for air quality forecasting: Insights from five provinces of Rwanda

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.