Jung C.-RChen W.-TWEI-TING CHEN2022-04-252022-04-25202120724292https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115080002&doi=10.3390%2frs13183657&partnerID=40&md5=5a9336860a51f3f2ca6cba1de9241fa5https://scholars.lib.ntu.edu.tw/handle/123456789/606337Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 ?m (PM2.5 ) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2 ) of 0.98 and a root mean square error (RMSE) of 1.22 ?g/m3 . For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 ?g/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 ?g/m3 ). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.Aerosol optical depthPM2.5Random forest modelSatellite-based estimation modelAtmospheric humidityBoundary layer flowBoundary layersDecision treesLand useMean square errorPopulation statisticsTopography10-fold cross-validationBoundary layer heightsCoefficient of determinationCorrelation coefficientEpidemiological studiesMeteorological variablesMulti-angle implementation of atmospheric correctionsRoot mean square errorsRandom forests[SDGs]SDG15journal article10.3390/rs131836572-s2.0-85115080002