Review of elastic light scattering from single aerosol particles and application in bioaerosol detection. (March 2022)

• Record Type: Journal Article
• Authors: Pan, Yong-Le; Aptowicz, Kevin; Arnold, Jessica; Cheng, Samuel; Kalume, Aimable; Piedra, Patricio; Wang, Chuji; Santarpia, Joshua; Videen, Gorden
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 279(2022)
• Publication Date: 2022-03
• Subjects: Elastic light scattering (ELS) -- Detection and characterization -- Aerosol particle -- Bioaerosol -- Pattern and mueller matrix -- Machine learning (ML); Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2022.108067
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Elastic light scattering (ELS) of aerosol particles. Detection and characterization of single aerosol particles. Bioaerosol detection using elastic light scattering. Discriminating bioaerosol from atmospheric aerosols. Abstract: Elastic light scattering (ELS) from single micron-sized particles has been used as a fast, non-destructive diagnostic tool in life science, physics, chemistry, climatology, and astrophysics. Due to the large scattering cross-section, ELS can be used to find trace amounts of suspect particles such as bioaerosols among complex, diverse atmospheric aerosols, based on single-particle interrogation. In this article, we briefly summarized the main computational models and instrumentation developed for ELS, then reviewed how properties like particle size, refractive index, degree of symmetry, and surface roughness, in addition to packing density, shape of primary particles in an aggregate, and special helix structures in compositions can be determined from ELS measurements. Meanwhile, we emphasize on how these parameters obtained from ELS measurements can be used for bioaerosol detection, characterization, and discrimination from atmospheric aerosol particles using different classification algorithms.