Estimate the high-resolution distribution of ground-level particulate matter based on space observations and a physical-based model

Atmospheric particulate matter estimated by using satellite data is gaining more attention due to their wide spatial coverage advantages. Here, instead of empirical statistical approach, we describe a physical-based approach that reduces the uncertainty of surface PM
10
estimation from satellite data. In our approach, particulate matter mass concentration retrievals require the inclusion of optical properties of aerosol particles and meteorological parameters. We use one year of MODIS aerosol optical depth data at 550 nm and meteorological data to estimate surface level PM
10
over China. As compared to regression coefficients obtained through simple correlation (R = 0.44) or multiple regression (R = 0.53) techniques, the physical-based approach derives hourly PM
10
data that compared with ground-based measurements with R = 0.74. Although the degree of improvement varies over different sites and seasons in China, this study demonstrates the potential for using physical-based approach for operational air quality monitoring.