Retrieval and analysis of the composition of an aerosol mixture through Mie-Raman-fluorescence lidar observations

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient (beta), particle depolarization ratio (delta), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (beta, delta and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (delta and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and delta are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL (ATmospheric Observation at liLLe) observatory, Laboratoire d'Optique Atmosph & eacute;rique, University of Lille, France.