Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations

Abstract

As an important part of the anthropogenic aerosol, Black Carbon (BC) aerosols in the atmospheric environment have strong impacts on climate change. Recently, most remote sensing studies on aerosol components detection are limited to the inversion of aerosol optical properties, integration of chemistry models or in situ observations. In this paper, an algorithm based on Effective Medium Approximations (EMA) and statistically optimized aerosol inversion algorithm was integrated for retrieving the surface mass concentration of BC aerosols from satellite signals. The sensitivity analyses for the developed forward model proved that the volume fraction of vertical BC is sensitive to the satellite observations and significantly improved especially over bright surface targets or under polluted atmospheric conditions. By updating the forward model and retrieved parameters of the statistically optimized inversion algorithm, three cases of high aerosol loading days were retrieved from Polarization and Anisotropy of Reflectance for Atmospheric Sciences Coupled with Observations from a LiDAR (PARASOL) measurements, which shows a significant ability of BC aerosol detection. Additionally, the validation and closure studies of BC concentration retrievals also indicates an encouraging consistency with correlation (R) of 0.71, mean bias of 3.55, and root-mean-square error (RMSE) of 3.75 when compared against the in-situ observations over South Asia. The accuracy of the retrievals also demonstrates different trends under different levels of aerosol loadings, which shows a higher accuracy in biomass burning seasons (R = 0.75, RMSE = 4.04, Bias = 3.27) while exaggerates the results in the case of clear conditions (R = 0.47, RMSE = 4.83, Bias = 4.00). Finally, the uncertainties of three assumptions, including proposing uniform vertical profile for BC, neglecting light-absorbing aerosols and using spherical EMA models were discussed in our manuscript. The maximum standard deviations caused by these uncertainties on low BC aerosol volume fractions (fBC < 1%) are 0.8%, 0.35% and 0.2% while these deviations will change to 0.25%, 0.05% and 1.5% respectively under higher BC fractions (fBC > 5%). This conclusion confirmed that the proposed algorithm for BC surface concentration retrieval extends the application of satellite remote sensing in monitoring the extreme biomass burning pollution.