Multifrequency Lidar Probing of the Microstructure of Multicomponent Urban Aerosols

Abstract

We consider the inverse problem of recovering the microstructure of multicomponent urban aerosols from lidar signals measured at λ = 0.355, 0.532, 1.064, and 1.5 μm. To solve this problem, we use a regression method based on previously constructed regression relations between the optical and microstructural parameters of the aerosol, and a numerical method including parametrization of the particle size distribution and regularization with selection of the regularization parameter using the residual. With closed numerical modeling, we show that it is possible to recover the mass concentrations of particles of sizes ≤1 μm, ≤2.5 μm, and ≤10 μm (respirable particles). The regression method for solving the inverse problem is significantly more robust than its iterative analog relative to variations in the complex refractive indices of the aerosol components and uncertainties in the optical measurements. We have obtained equations for multiple regressions between the mass concentrations of respirable aerosol fractions and the spectral extinction coefficients of the aerosol, allowing us to interpret the data from multifrequency lidar probing with minimal use of a priori information. We have carried out a numerical experiment on lidar probing of the microstructure of aerosol in the background atmosphere and in a smoke plume using the regressions obtained, demonstrating the possibility of complete automation of the measurement process.