Rochelle Schneider is a Research Fellow in Artificial Intelligence for Earth Observation at European Space Agency (ESA), and she expounded the development of a multi-stage satellite-based machine learning (ML) model to estimate daily PM2.5 levels across Great Britain during 2008-2018. Stage-1 estimated PM2.5 concentrations in monitors with only PM10 records. Stage-2 imputed missing satellite aerosol-optical-depth due to cloudiness and bad retrievals. Stage-3 applied the Random Forest algorithm to estimate PM2.5 concentrations using a combined dataset from Stage-1, Stage-2, and a list of spatiotemporally synchronised predictors. Stage-4 estimated daily PM2.5 using Stage-3 model. The relatively high precision allowed these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.