Abstract. In order to study the environment or climate of an area, it is necessary to understand the composition of atmospheric aerosol particles, as well as microphysical properties, such as extinction cross section, scattering cross section, polarization degree, etc. For a long time, when calculating the microphysical properties of atmospheric aerosol particles, the aerosol particles are always be considered as spheres. Mie theory has been used to calculate the scattering properties of spherical particles with high accuracy. However, in reality, aerosol particles are not only spherical, they have complex composition and different shapes. The influence of non-spherical aerosol particles on atmospheric radiation, scattering and absorption cannot be ignored. Therefore, it is necessary to fully understand the micro-physical characteristics of non-spherical aerosol particles for fully understand the real atmospheric environment. Until now, T-Matrix method is one of the most effective and extensive methods to study the light scattering characteristics of non-sphericalrotationally symmetric aerosol particles. In this paper, the non-spherical aerosol particles extinction section, scattering cross section,the absorption cross section are calculated using T-Matrix method. The extinction, scattering, and the absorption properties arecalculated with variety of different types aerosol particles, and compared with the properties calculated by Mie scattering theory. Itlays a foundation for more accurate simulation of the microphysical properties of aerosol particles in real atmosphere.

Abstract. Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

Abstract. For a long time, the research of the optical properties of atmospheric aerosols has aroused a wide concern in the field of atmospheric and environmental. Many scholars commonly use the Klett method to invert the lidar return signal of Mie scattering. However, there are always some negative values in the detection data of lidar, which have no actual meaning，and which are jump points. The jump points are also called wild value points and abnormal points. The jump points are refered to the detecting points that are significantly different from the surrounding detection points, and which are not consistent with the actual situation. As a result, when the far end point is selected as the boundary value, the inversion error is too large to successfully invert the extinction coefficient profile. These negative points are jump points, which must be removed in the inversion process. In order to solve the problem, a method of processing jump points of detection data of lidar and the inversion method of aerosol extinction coefficient is proposed in this paper. In this method, when there are few jump points, the linear interpolation method is used to process the jump points. When the number of continuous jump points is large, the function fitting method is used to process the jump points. The feasibility and reliability of this method are verified by using actual lidar data. The results show that the extinction coefficient profile can be successfully inverted when different remote boundary values are chosen. The extinction coefficient profile inverted by this method is more continuous and smoother. The effective detection range of lidar is greatly increased using this method. The extinction coefficient profile is more realistic. The extinction coefficient profile inverted by this method is more favorable to further analysis of the properties of atmospheric aerosol. Therefore, this method has great practical application and popularization value.

Abstract. The geographical information, temporal and spatial distribution of pollution sources cannot be accurately described by the traditional aerosol lidar inversion data. In order to accurately locate the pollution sources and monitor the spatial and temporal distribution of pollutants, an aerosol lidar data visualization method based on ArcGIS is proposed in this paper. A natural neighborhood method is used to perform group interpolation on ArcGIS software, and the range range-extinction coefficient relationship diagram is plotted using the aerosol optical data obtained from Klett inversion algorithm. The geographic information is added to the lidar inversion data in this diagram. The aerosol spatial and temporal distribution range and the direction of aerosol diffusion are accurately displayed directly. The change of aerosol concentration is plotted in different colors, the visually displaying the concentration of pollutants in the detection area is realized. The aerosol detection data in the sunny and haze days in Yinchuan are visualized in this paper. The result shows that this method can quickly determine the geographical location of pollution sources and visualize the spatial and temporal distribution of aerosols. This method can be used to display aerosol data with geographic information for meteorological and environmental protection departments. This method can provide data support for meteorological and environmental protection departments to quickly determine the location and concentration of pollution sources.