Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing

Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performance of the AHI hourly AOT products with ground-based AOT observations from four Aerosol Robotic NETwork (AERONET) sites for 2018 over Beijing under different pollution levels and aerosol types. Near-surface PM concentrations are used to categorize air quality as clean, moderate, and heavy pollution, and the aerosol types (biomass burning and urban/industrial (BMA_UIA), maritime (MA), dust (DA), mixed-type (MIXA)) are classified based on the threshold limits of AERONET aerosol properties. Overall, the AHI hourly AOT achieves good consistency with AERONET measurements in Beijing (correlation coefficient (R) = 0.78 and a root-mean-square-error (RMSE) = 0.29). Under clean conditions, the AOT successful retrieval rate is more than 80%, while it is deficient for moderate and heavy pollution. AOT for clean and moderate pollution performs better than that for heavy pollution. Moreover, AHI AOT retrievals for fine mode aerosol (BMA_UIA) are in good agreement with AERONET observations, and the performance is better than that under coarse mode aerosol (MA and DA) conditions. AHI AOT may capture the spatiotemporal distribution of fine particles pollution over Beijing. These findings will provide useful information for the improvement of the aerosol model of the retrieval algorithm and the monitoring of urban fine particles pollution.;Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performance of the AHI hourly AOT products with ground-based AOT observations from four Aerosol Robotic NETwork (AERONET) sites for 2018 over Beijing under different pollution levels and aerosol types. Near-surface PM concentrations are used to categorize air quality as clean, moderate, and heavy pollution, and the aerosol types (biomass burning and urban/industrial (BMA_UIA), maritime (MA), dust (DA), mixed-type (MIXA)) are classified based on the threshold limits of AERONET aerosol properties. Overall, the AHI hourly AOT achieves good consistency with AERONET measurements in Beijing (correlation coefficient (R) = 0.78 and a root-mean-square-error (RMSE) = 0.29). Under clean conditions, the AOT successful retrieval rate is more than 80%, while it is deficient for moderate and heavy pollution. AOT for clean and moderate pollution performs better than that for heavy pollution. Moreover, AHI AOT retrievals for fine mode aerosol (BMA_UIA) are in good agreement with AERONET observations, and the performance is better than that under coarse mode aerosol (MA and DA) conditions. AHI AOT may capture the spatiotemporal distribution of fine particles pollution over Beijing. These findings will provide useful information for the improvement of the aerosol model of the retrieval algorithm and the monitoring of urban fine particles pollution.