Estimating hourly ground-level aerosols using GEMS aerosol optical depth: A machine learning approach

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch in 2020, GEMS has provided hourly daytime air quality information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate the effectiveness of the first 1.5-year GEMS aerosol optical depth (AOD) data in estimating ground-level particulate matter (PM) concentrations at an hourly scale. To do so, we employ random forest models and use GEMS AOD data and meteorological variables as input features to estimate PM10 and PM2.5 concentrations, respectively, in South Korea. The model-estimated PM concentrations are strongly correlated with ground measurements, but they exhibit negative biases, particularly during high aerosol loading months. Our results indicate that GEMS AOD values represent underestimates compared to ground-measured AOD values, possibly leading to negative biases in the final PM estimates. Further, we demonstrate that more training data could significantly improve random forest model performance, thus indicating the potential of GEMS for high-resolution surface PM prediction when sufficient data are accumulated over the coming years. Our results will serve as a reference to aid the evaluation of future GEMS AOD retrieval algorithm improvements and also provide initial guidance for data users.