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

Exposure to fine particulate matter (PM) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM in South Africa due to the lack of high-resolution PM exposure estimates. We developed a random forest model to estimate daily PM concentrations at 1 km 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 PM 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 important roles in PM modeling. The cross-validation R and Root Mean Square Error of our model was 0.80 and 9.40 μg/m, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM standards, PM concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.