Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new parameterization scheme of the aerosol model, which optimized the independent SSA value into mixed combination of three basic aerosol components, then constructed lookup table to obtain hourly SSA from the Advanced Himawari Image (AHI) sensor. The comparison is encouraging the retrieved SSA results agree well with AERONET, especially for high aerosol loading (AOD > 0.3 at 470nm), with correlation coefficient of R = 0.50, RMSE = 0.02, and approximately 93% of retrieval results falling within the 5% EE envelope. Under normal conditions, R = 0.46, RMSE = 0.04, and approximately 68% of retrieved SSA fall in within the uncertainty of$\Delta$SSA = ±0.05. The comparison demonstrates the algorithm has the excellent ability to estimate SSA in pollution over land.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. In 2013, China launched Gaofen-1 satellite, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. In this paper, we calculated AOD at 100m from Gaofen-1 and AQUA data based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm over Beijing, China. The experimental results are compared with the Aerosol Robotic Network (AERONET) for preliminary validation. The correlation coefficient is about 0.9 and a root-mean-square error (RMSE) of about 0.13. The experimental results show that the method have higher accuracy, and further validation work is continuing.

Aerosols playa significant role in earth-atmospheric radiant balance and global climate changes. FengYun-4A(FY-4A) is the first three-axis stabilized geostationary satellite in China, the Advanced Geosynchronous Radiation Imager (AGRI) is one of the four payloads onboard the satellite. In fact, FY-4A AOD products is not officially available so far, and there are few researches on AOD retrieval of FY-4A. Therefore, AGRI data was used to develop a new algorithm for retrieval of AOD over land in this paper. We used MCD43C2 datasets to obtain the band surface reflectance, established the surface reflectance ratio database. Next used 6SV model to build lookup table (LUT), calculated hourly AOD eventually. To validate our algorithm, the AGRI -derived AOD was quantitatively compared with AERONET ground-based measurements. It shows the better accuracy and coverage than JMA-AOD.

The global long-term aerosol optical depth (AOD) dataset has a great significance for the study of global climate change. Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) satellites can provide global observation from 1978 to present. In this paper, we have improved the algorithm for the retrieval of the AOD over land proposed by Xue et al in 2017 [1]. We obtain 0.64μm band surface reflectance by utilizing a linear relationship between the surface reflectance at the wavelength of3.75μm and 0.64μm, which has been verified in the Moderate Resolution Imaging Spectroradiometer (MODIS). Considering difference of spectral response between AVHRR's bands and MODIS's bands, we calibrate this empirical relationship by fitting the simulated surface reflectance of the relative AVHRR and MODIS bands. A radiative transfer model for Lambertian surface and the look-up table (LUT) method are applied to NOAA-7, 9,11,14, 18 over China mainland (15° - 60° N, 70° - 140° E) from 1982 to 2011. Comparison of retrieval AVHRR AOD against AErosol RObotic NETwork (AERONET) data shows good consistency with more than 60% points within uncertainty of$\pm$(0.05+0.25xAOD).