Retrieval of particle size distribution based on a multi-objective genetic algorithm for multi-wavelength lidar

• Published in: Frontiers in environmental science 2023-05, Vol.11
• Main Authors: Bao, Jun, Qi, Liangliang, Mao, Jiandong, Gong, Xin
• Format: Article
• Language: English
• Subjects: aerosol; Aerosols; Algorithms; Atmospheric aerosols; Atmospheric conditions; Backscattering; Coefficients; Environmental science; Errors; Genetic algorithms; Growth factors; Lidar; multi-objective genetic algorithm; multi-wavelength lidar; NSGA-II; Optical communication; Optical properties; Outdoor air quality; Parameters; Particle size; Particle size distribution; Population genetics; Radiation; Radiative forcing; Regularization; Regularization methods; Remote sensing systems; Retrieval; Size distribution; Sorting algorithms; Wavelength
• ISSN: 2296-665X; 2296-665X
• DOI: 10.3389/fenvs.2023.1136411

Introduction: Aerosols affect the radiation budget of the Earth’s atmospheric system. The aerosol particle size distribution (PSD) is one of the main parameters for characterizing the effect of aerosol on radiative forcing. Methods: The extinction coefficient and backscattering coefficient at 355 and 532 nm and backscattering coefficient at 1064 nm of aerosol particles over Yinchuan area, China, which measured by a multi-wavelength lidar developed by North Minzu University, were used to retrieve the aerosol PSD. In view of the disadvantages of traditional regularization methods, the elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is selected to retrieve PSD. Results and Discussion: To verify the feasibility for retrieval of aerosol PSD, the NSGA-II with different errors in the input optical signal was simulated, in which the errors of the inverted PSD are still in the acceptable range when 35% error added into the optical parameters. Moreover, some experiments were carried out under different atmospheric conditions, including background sunny, cloudy and dusty days, and comparisons were performed with Multiple Population Genetic Algorithm (MPGA) and Simple Genetic Alogrithm (SGA) method. The results show that the retrieval effect of NSGA-II was better than that of MPGA and SGA, and the NSGA-II is very suitable for retrieve PSD by using the multi-wavelength lidar data.