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Air pollution is a major public health problem worldwide although the lack ofdata is a global issue for most low and middle income countries. Ambient airpollution in the form of fine particulate matter (PM2.5) exceeds the WorldHealth Organization guidelines in Rwanda with a daily average of around 42.6microgram per meter cube. Monitoring and mitigation strategies require anexpensive investment in equipment to collect pollution data. Low-cost sensortechnology and machine learning methods have appeared as an alternativesolution to get reliable information for decision making. This paper analyzesthe trend of air pollution in Rwanda and proposes forecasting models suitableto data collected by a network of low-cost sensors deployed in Rwanda.

Trend analysis and forecasting air pollution in Rwanda