Aerosols play an important role in assessing radiation, climate, cloud formation, and environmental pollution. Additionally, their optical and physical properties exert a significant influence on the formation and transportation of air pollutants. Therefore, spatio-temporal distribution characteristics of tropospheric aerosols are vital for studying the uncertainties of aerosol environments and climate changes. It is of great significance to study the optical properties and vertical distribution changes of aerosols by effective observation methods. As a widely employed aerosol active detection instrument, lidar plays an irreplaceable role in detecting vertical aerosol distribution. Relevant scholars classify the aerosol types by classifying the distribution characteristics of optical parameters such as aerosol depolarization ratio, color ratio, and lidar ratio, which promotes the development of lidar detection research methods. High spectral resolution lidar (HSRL) can accurately detect optical parameters such as aerosol extinction coefficient and backscatter coefficient, and improve the inversion accuracy of aerosol optical parameters. This airborne high spectral lidar flight test is the first aerosol observation test with Air-ACDL, and the analysis results fully reflect the advantages of HSRL in detecting aerosol types and lay a foundation for spaceborne high spectral lidars to invert aerosol types.

Aerosol classification is based on the difference in optical parameters of different aerosol types to reflect their various characteristics. For example, aerosol depolarization ratio δ_a reflects the shape characteristics of particles, aerosol lidar ratio S_a characterizes the absorption characteristics of particles, and dual-wavelength color ratio C_r (532 nm/1064 nm) corresponds to particle size. These characteristics are the theoretical basis for aerosol classification. Generally, S_a varies with the size, shape, and composition of aerosol particles, and the value is higher for particles with strong absorption. δ_a is an important parameter for identifying dust aerosols, which is related to the shape regularity degree of particles. Meanwhile, the δ_a value of spherical particles is the smallest, and the more irregular shape leads to the greater value. The color ratio corresponds to the particle size, and generally the larger color ratio brings smaller particles. Based on these characteristics, the aerosol particle classification can be well achieved. According to the summary of the existing studies, the threshold ranges of S_a, δ_a, and C_r for different aerosol types are sorted out, and an aerosol classification lookup table is established based on the classification threshold standard of aerosols. Additionally, aerosols in the Shanhaiguan area are classified by combining the aerosol optical parameters detected by airborne high spectral lidar.

According to the comparison results of aerosol optical depth (AOD), the correlation between the airborne observation data, the ground-based sunphotometer, and the passive detector data carried by the satellite is greater than 0.90 (Fig. 2), Aerosol types on March 11, 2019 are classified by the established aerosol classification lookup table and detection data from airborne high spectral lidar [Fig. 6(a)]. The classification results are compared with those of CALIPSO [Fig. 6(c)], and then confirmed by combining meteorological data and backward trajectories (Figs. 4 and 7). The results show that the polluted air flow mainly comes from Mongolia, and it is prone to bring sand and dust aerosols over Shanhaiguan. In addition, since the experimental site is close to the Bohai Sea, there is marine aerosol over Shanhaiguan, and the flight path of CALIPSO passes over the Bohai Sea without marine aerosols. Thus, the classification results of the aerosol classification lookup table based on HSRL are more accurate. Then, by analyzing the aerosol classification results on March 14 and March 19, 2019, the feasibility of the proposed aerosol classification method is verified again.

We analyze the distribution characteristics of lidar ratio, depolarization ratio, and color ratio of different aerosol types, and establish the optical parameter lookup table of different aerosol types on the basis of summarizing the previous classification methods. Meanwhile, the aerosol types are divided into eight types, including ice particles, sand, mixed sand, ocean, polluted ocean, city, smoke, and fresh smoke. Based on the lookup table, the airborne observation data on March 11, 2019 are employed to achieve aerosol classification and identification in Qinhuangdao. The results show that there are aerosol types such as mixed sand and dust aerosols, marine aerosols and smoke over Tianshan Customs, and the feasibility of the aerosol classification method is verified by adopting HYSPLIT trajectory mode and meteorological data. The method applicability is verified by the correct identification of aerosol types on March 14 and March 19, 2019 during the observation period. March 14 and March 19, 2019 are polluted days, and there are dust aerosols from Mongolia over Shanhaiguan. Additionally, as Shanhaiguan is close to the Bohai Sea and the experiment is in the winter heating period, there are marine aerosols and smoke aerosol types over Shanhaiguan, and there will be ice particles in the air under large air humidity. This airborne hyperspectral lidar flight test is the first aerosol observation test with Air-ACDL. The analysis results fully reflect the advantages of HSRL in detecting aerosol types and lay a foundation for spaceborne high spectral lidars to invert aerosol types. In the future, as the ACDL spaceborne lidar data accumulate, they can be utilized to establish a more accurate and rich aerosol classification database and realize global aerosol classification.