Retrieval of aerosol composition using ground-based remote sensing measurements

The chemical composition and mixing status of ambient aerosol are the main factors deciding aerosol microphysical and optical properties, and thus have significant impacts on regional or global climate change and air quality. Traditional approaches to detect atmospheric aerosol composition include sampling with laboratory analysis and in-situ measurement. They can accurately acquire aerosol components, however, the sampling or air exhausting could change the status of aerosol or have some mass loss. Additionally, aerosol is usually sampled at the surface level so that it is difficult to detect the columnar aerosol properties. Remote sensing technology, however, can overcome these problems because it investigate aerosol information by optical and microphysical properties without destructing the natural status of ambient aerosol. This paper introduce a method to acquire aerosol composition by the remote sensing measurements of CIMEL CE318 ground-based sun-sky radiometer. A six component aerosol model is used in this study, including one strong absorbing component Black Carbon (BC), two partly absorbing components Brown Carbon (BrC) and Mineral Dust (MD), two scattering components Ammonia Sulfate-like (AS) and Sea Salt (SS), and Aerosol Water uptake (AW). Sensitivity analysis are performed to find the most sensitive parameters to each component and retrieval method for each component is accordingly developed. The residual minimization method is used by comparing remote sensing measurements and simulation outputs to find the optimization of aerosol composition (including volume fraction and mass concentration of each component). This method is applied to real measurements obtained from Beijing site under different weather conditions, including polluted haze, dust storm and clean days, to investigate the impacts of mixing states of aerosol particles on aerosol composition retrieval.