Using satellite remote sensing to estimate the distribution of pm10: a case study of Kampala City, Uganda.

Abstract

Air quality has globally decreased and this has majorly been caused by air pollution, with PM10 as one of the pollutants with the greatest impact. Monitoring of PM10 is traditionally done by the use of ground stations. In Uganda, the permanent air monitoring stations are very few to an extent that Kampala city has only one reference grade monitor station installed, yet Kampala’s air pollution levels have frequently exceeded levels deemed safe by the World Health Organization (WHO) reaching up to six times the WHO limits. In this regard, remote sensing was used to estimate the distribution of PM10 so as to obtain spatio-temporal continuous data. The major objective of this study was to use Landsat derived spectral indices to estimate the distribution of PM10 in Kampala city. Different spectral indices were computed from Landsat images, multiple correlation carried out on them to find the best predictors of PM10 distribution. SAVI and EVI-2 were the best predicting indices and hence used to predict the PM10 distribution of Kampala city in 2015, 2019 and 2022. Air quality of Kampala was found to be unhealthy, and therefore policy makers need to implement the policies they make regarding air quality.