With the development of economy in the past thirty years, many large cities in the eastern and southwestern China are experiencing increased haze events and atmospheric pollution, causing significant impacts on the regional environment and even climate. However, knowledge on the aerosol physical and chemical properties in heavy haze conditions is still insufficient. In this study, two winter heavy haze events in Beijing occurred in 2011 and 2012 were selected and investigated by using the ground-based remote sensing measurements. We used CIMEL CE318 sun-sky radiometer to derive haze aerosol optical, physical and chemical properties, including aerosol optical depth (AOD), size distribution, complex refractive indices and fractions of chemical components like black carbon (BC), brown carbon (BrC), mineral dust (DU), ammonium sulfate-like (AS) components and aerosol water content (AW). The retrieval results from a total of five haze days showed that the aerosol loading and properties during the two winter haze events were relatively stable. Therefore, a parameterized heavy haze characterization was drawn to present a research case for future studies. The averaged AOD is 3.2 at 440 nm and Ångström exponent is 1.3 from 440–870 nm. The coarse particles occupied a considerable fraction of the bimodal size distribution in winter haze events, with the mean particle radius of 0.21 and 2.9 μm for the fine and coarse mode respectively. The real part of the refractive indices exhibited a relatively flat spectral behavior with an average value of 1.48 from 440 to 1020 nm. The imaginary part showed obviously spectral variation with the value at 440 nm (about 0.013) higher than other three wavelengths (e.g. about 0.008 at 675 nm). The chemical composition retrieval results showed that BC, BrC, DU, AS and AW occupied 1%, 2%, 49%, 15% and 33% respectively on average for the investigated haze events. The comparison of these remote sensing results with in situ BC and PM_2.5 measurements were also presented in the paper

The signal recorded by the sensor at the satellite level contains information relative both to the atmosphere and the surface. Atmospheric correction is therefore necessary to extract the surface reflectance required within biophysical algorithms used to estimate canopy attributes. Aerosol characteristics are difficult to evaluate because they vary rapidly with time and space. The objective of this study is to develop an autonomous aerosol correction method exploiting the information content in MERIS images.A dedicated neural network was trained to retrieve aerosol optical thickness (AOT) from the top of atmosphere signal recorded in 13 MERIS bands. The training database was made of radiative transfer model simulations, SMAC code coupled to SAIL and PROSPECT models. In this preliminary study, aerosol were characterized only by their optical thickness at 550nm, considering only the continental aerosol type.Theoretical results derived from model simulations demonstrate the pertinence of the method, with a 0.058 Root Mean Square Error associated to the estimation of the AOT, inducing a RMSE on the estimated top of canopy reflectance better than 0.005. The method was then validated using 61 actual MERIS images acquired over AERONET sites. Results show RMSE values of 0.116 associated to the AOT estimation. Comparison with MODIS achieved over 31 among the 61 scenes show a RMSE value of 0.11 similar to the 0.10 value observed for MERIS with our algorithm. Comparison between atmospheric correction achieved with our algorithm and with those based on actual AERONET measurements show RMSE values on top of canopy reflectance close to 0.005.