Exposure to air pollution is one of the largest environmental health risks in Uganda. Nitrogen Dioxide (NO2) and Methane (CH4) are among the key pollutants with levels generally above the world health organisation guidelines for major cities in Uganda. The high levels of NO2 and CH4 are mainly attributed to traffic, reconditioned motor vehicles and industrial emissions. CH4 is also generated through livestock and other agricultural activities, as well as the decomposition of organic waste in landfills for solid waste. The key to efficient air quality management is quantifying pollution levels and identifying major pollution sources to allow early detection of such sources and inform air pollution mitigation efforts.

‍

Ground monitoring of gaseous pollutants such as NO2 and CH4 is nearly non-existent in Uganda due to the high cost of setting up and maintaining a monitoring network and the unavailable skilled labour to manage the network.  

‍

NO2 and CH4 satellite observations are now possible from a number of instruments with a very high spatial resolution which allows mapping of pollutant emission plumes from individual emission sources.

‍

Machine learning methods allow us to efficiently exploit the growing volume of satellite data available to quantify pollution levels and identify pollution sources to inform air pollution mitigation efforts.

‍

In AI for Air Quality, we are partnering with AirQo to create a deep learning model that automatically detects NO2 and CH4 plumes in the Greater Kampala Metropolitan Area. 

‍

AirQo uses low-cost air quality monitoring sensors and machine learning algorithms to provide timely accurate, and localised air quality data to communities, governments, and organisations across various African cities, with the aim of improving public health, raising awareness of air pollution and its effects and informing policy.

‍