Estimation of atmospheric aerosol composition from ground-based remote sensing measurements of Sun-sky radiometer

Remote sensing provides aerosol loading information, but to address climate and air quality model validation, there are additional needs to acquire aerosol composition information. In this study, a comprehensive aerosol composition model is established to quantify black carbon (BC), brown carbon (BrC), mineral dust (DU), particulate organic matters, ammonium sulfate like (AS), sea salt, and aerosol water uptake. We develop forward modeling of aerosol components, including microphysical parameters (real and imaginary refractive indices, volume fraction ratio of fine to coarse mode, and sphericity) and hygroscopic growth models, and propose an optimization scheme to estimate the components. The uncertainties caused by input parameters are also assessed. Sun-sky radiometer measurements and meteorological data obtained during a campaign in Huairou, Beijing, are processed to estimate aerosol components, which are further compared with synchronous in situ chemical measurements. The results show generally good consistencies between remotely estimated and measured components (e.g., correlation coefficients for BC, BrC, AS, and PM2.5 lie in about 0.8-0.9). The comparisons between modeled and observed microphysical parameters also show good agreements, with the exception of sphericity, which is likely caused by high uncertainties of this parameter. Sensitivity studies show that BC and BrC are highly sensitive to imaginary refractive index, while DU is strongly correlated to both volume size and sphericity. The performance of composition retrieval is expected to be improved when the sphericity uncertainty is significantly reduced.

Duke Scholars

Author Michael Howard Bergin Civil and Environmental Engineering