Elastic Scattering Time–Gated Multi–Static Lidar Scheme for Mapping and Identifying Contaminated Atmospheric Droplets - Mui LV; Hung TN; Shinohara K; Yamanoi K; Shimizu T; Sarukura N; Shimadera H; Kondo A; Sumimura Y; Hai BV; Nguyen DV; Minh PH; et al. (MDPI (Basel, Switzerland), 2023)
Numerical simulations are performed to determine the angular dependence of the MIe scattering cross-section intensities of pure water droplets and pollutants such as contaminated water droplets and black carbon as a function of the wavelength of the incident laser light, complex refractive index, and size of the scatterer. Our results show distinct scattering features when varying the various scattering parameters, thereby allowing the identification of the scattering particle with specific application to the identification of atmospheric pollutants including black carbon. Regardless of the type of scatterer, the scattering intensity is nearly uniform with a slight preference for forward scattering when the size of the particle is within 20% of the incident laser’s wavelength. The scattering patterns start to exhibit distinguishable features when the size parameter equals 1.77, corresponding to an incident laser wavelength of 0.355 μm and a particle radius of 0.1 μm. The patterns then become increasingly unique as the size parameter increases. Based on these calculations, we propose a time-gated lidar scheme consisting of multiple detectors that can rotate through a telescopic angle and be placed equidistantly around the scattering particles to collect the backscattered light and a commercially available Q-switched laser system emitting at tunable laser wavelengths. By using a pulsed laser with 10-ns pulse duration, our scheme could distinguish scattering centers that are at least 3 m apart. Our scheme called MIe Scattering Time-gated multi-Static LIDAR (MISTS–LIDAR) would be capable of identifying the type of atmospheric pollutant and mapping its location with a spatial resolution of a few meters.
Keywords: MIe scattering; LIDAR; particulate matter; black carbon; atmospheric monitoring of pollutants