A continuous 2011–2022 record of fine particulate matter (PM2.5) in East Asia at daily 2-km resolution from geostationary satellite observations: population exposure and long-term trends

Abstract. We construct a continuous 24-h daily fine particulate matter (PM_2.5) record with 2×2 km² resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM_2.5 observations from the national surface networks as training data. PM_2.5 network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM₁₀. PM_2.5 network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM_2.5 dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM_2.5 peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM_2.5 in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM_2.5 measurements. Mapping of an extreme pollution event in Seoul with GOCI PM_2.5 shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km² product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM_2.5 information is essential.