Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5

Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. In contrast, satellite-based monitoring can provide aerosol optical depth (AOD) products with higher spatial resolution and continuous spatial coverage, but it cannot directly measure ground-level PM2.5 concentration. Observation-based and simulation-based approaches have been actively developed to retrieve ground-level PM2.5 concentrations from satellite AOD and sparse ground-level observations. However, the effect of aerosol characteristics (e.g., aerosol composition and size distribution) on the AOD–PM2.5 relationship is seldom considered in observation-based methods. Although these characteristics are considered in simulation-based methods, the results still suffer from model uncertainties. In this study, we propose an observation-based algorithm that considers the effect of the main aerosol characteristics. Their related effects on hygroscopic growth, particle mass extinction efficiency, and size distribution are estimated and incorporated into the AOD–PM2.5 relationship. The method is applied to quantify the PM2.5 distribution in China. Good agreements between satellite-retrieved and ground-observed PM2.5 annual and monthly averages are identified, with significant spatial correlations of 0.90 and 0.76, respectively, at 565 stations in China. The results suggest that this approach can measure large scale PM distributions with verified results that are at least as good as those from simulation-based estimations. The results also show the method's capacity to identify PM2.5 spatial distribution with high-resolution at national, regional, and urban scales and to provide useful information for air pollution control strategies, health risk assessments, etc.