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