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Ground monitoring stations are widely used to monitor particulate matter (PM2.5). However, they are expensive to maintain and provide information localized to the stations, and hence are limited for large-scale use. Analysis of in situ PM2.5 shows that it varies spatially and temporally with distinct seasonal differences. This study, therefore, explored the use of satellite images (Sentinel-2 and Landsat-8) for determining the spatial and temporal variations in PM2.5 for Kampala District in Uganda. Firstly, satellite-derived aerosol optical depth (AOD) was computed using the Code for High Resolution Satellite mapping of optical Thickness and aNgstrom Exponent algorithm (CHRISTINE code). The derived AOD was then characterised with reference to meteorological factors and then correlated with in situ PM2.5 to determine satellite-derived PM2.5 using geographically weighted regression. In the results, correlating in situ PM2.5 and AOD revealed that the relationship is highly variable over time and thus needs to be modelled for each satellite’s overpass time, rather than having a generic model fitting, say, a season. The satellite-derived PM2.5 showed good model performance with coefficient of correlation (R2) values from 0.69 to 0.89. Furthermore, Sentinel-2 data produced better predictions, signifying that increasing the spatial resolution can improve satellite-derived PM2.5 estimations.
Atuhaire et al. (Thu,) studied this question.