Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth

Abstract Particulates smaller than 1.0 μm (PM 1.0 ) have strong associations with public health and environment, and considerable exposure data should be obtained to understand the actual environmental burden. This study presented a PM 1.0 estimation strategy based on the generalised regression neural network model. The proposed strategy combined ground-based observations of PM 2.5 and satellite-derived aerosol optical depth (AOD) to estimate PM 1.0 concentrations in China from July 2015 to June 2017. Results indicated that the PM 1.0 estimates agreed well with the ground-based measurements with an R 2 of 0.74, root mean square error of 19.0 μg/m 3 and mean absolute error of 11.4 μg/m 3 as calculated with the tenfold cross-validation method. The diurnal estimation performance displayed remarkable single-peak variation with the highest R 2 of 0.80 at noon, and the seasonal estimation performance showed that the proposed method could effectively capture high-pollution events of PM 1.0 in winter. Spatially, the most polluted areas were clustered in the North China Plain, where the average estimates presented a bimodal distribution during daytime. In addition, the quality of satellite-derived AOD, the robustness of the interpolation algorithm and the proportion of PM 1.0 in PM 2.5 were confirmed to affect the estimation accuracy of the proposed model.