Using Gaussian processes for spatial prediction of PM 2.5 concentration based on calibrated data from distributed low-cost sensor networks

Air pollution is a major environmental and public health risk globally leading to millions of premature deaths annually and negative economic effects. One of the key challenges in managing air quality is the availability of actionable spatial air quality data. The sparse networks or absence of air quality monitoring stations in many places means that there are limited data and information on air pollution in places without coverage. The spatial prediction of air quality can contribute to increasing data access for locations without air quality monitoring, ultimately improving awareness of the risk of air pollution exposure for vulnerable people. In this study, we investigated the air quality prediction task in two cities in Uganda (i.e., Jinja and Kampala), with unique geographic and economic contexts. Primarily, we used Gaussian processes to predict the PM 2.5 levels in the two cities, selected because of their relative importance in the country and their varying characteristics. We achieved promising results with an average root-mean-square error (RMSE) of 18.32 μg/m 3 and 16.88 μg/m 3 in Kampala and Jinja, respectively. These results provide valuable insights into the air quality profiles of two urban sub-Saharan cities with different demographics, which can in turn aid in decision-making for targeted actions at different levels.