2 research outputs found

    Multiwavelength fluorescence lidar observations of smoke plumes

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    A five-channel fluorescence lidar was developed for the study of atmospheric aerosol. The fluorescence spectrum induced by 355 nm laser emission is analyzed in five spectral intervals using interference filters. Central wavelengths and the widths of these five interference filters are, respectively, as follows: 438 and 29, 472 and 32, 513 and 29, 560 and 40, and 614 and 54 nm. The relative calibration of these channels has been performed using a tungsten–halogen lamp with a color temperature of 2800 K. This new lidar system was operated during summer–autumn 2022, when strong forest fires occurred in the Moscow region and generated a series of smoke plumes analyzed in this study. Our results demonstrate that, for urban aerosol, the maximal fluorescence backscattering is observed in a 472 nm channel. For the smoke, the maximum is shifted toward longer wavelengths, and the fluorescence backscattering coefficients in 472, 513 and 560 nm channels have comparable values. Thus, from the analysis of the ratios of fluorescence backscattering in available channels, we show that it is possible to identify smoke layers. The particle classification based on single-channel fluorescence capacity (ratio of the fluorescence backscattering to the elastic one) has limitations at high relative humidity (RH). The fluorescence capacity indeed decreases when water uptake of particles enhances the elastic scattering. However, the spectral variation of fluorescence backscattering does not exhibit any dependence on RH and can be therefore applied to aerosol identification.</p

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

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    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 (β), particle depolarization ratio (δ), 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 (β, δ 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 (δ 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 δ 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 Atmosphérique, University of Lille, France.</p
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