Continuous daily maps of fine particulate matter (PM2.5) air quality in East Asia by application of a random forest algorithm to GOCI geostationary satellite data

We use 2011-2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6km x 6km resolution over South Korea, eastern China, and Japan. We use PM2.5 observations from national networks to train and cross-validate a random forest (RF) algorithm that predicts PM2.5 from the gap-filled GOCI AOD, meteorological variables, and other predictor variables. The predicted 24-h PM2.5 for sites entirely withheld from training in a ten-fold crossvalidation procedure correlates highly with observed concentrations (R2 = 0.89) with single-value precision of 26-32% depending on country. Prediction of annual mean values has R2 = 0.96 and single-value precision of 12%. More information is available in the associated publication. Here we supply a NetCDF containing the inferred daily PM2.5 fields from 2011-19 for use in further research. If you use this data, please cite the associated publication, and feel free to reach out via email to discuss this work.