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In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM_2.5 concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (<i>R</i>²) between the measured and predicted PM_2.5 concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 &#181;g/m³. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient <i>R</i>² of the measured and predicted results was 0.784, and the RMSE was 20.104 &#181;g/m³, which is only 8 &#181;g/m³ higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM_2.5 concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.