A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa.

ABSTRACT: Exposure to fine particulate matter (PM2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM2.5 in South Africa due to the lack of high-resolution PM2.5 exposure estimates. We developed a random forest model to estimate daily PM2.5 concentrations at 1 km2 resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played i

SUBMITTER: Zhang D 

PROVIDER: S-EPMC8589277 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature