Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

ABSTRACT: To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor's input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor's outputs are PM mass in three size bins, specified as 100-300 nm, 300-600 nm, and 600-1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping

SUBMITTER: Jiang H 

PROVIDER: S-EPMC11548654 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature