Artificial Intelligence approach for Aerosol Indoor propagation using micro-lidar measurements

Abstract: 

Many viruses, including SARS-CoV-2, are known to spread via micron-scale aerosol droplets in indoor environments. The goal of this research project is to use novel micro-lidar measurements of aerosols indoors to develop a predictive data driven model for aerosol indoor propagation. Two main tasks will be accomplished through this project: (i) Firstly, experiments will be conducted to collect data about indoor aerosol propagation at the Cal COVID Cube[1] and UC Davis Hospital. (ii)Secondly, the data will be used to develop data-driven models of the aerosol propagation that can be used for aerosol indoor propagation modeling for different indoor configurations. This collaboration will leverage: a state-of the art short-range elastic backscatter micro-lidar named Colibri dedicated to high range resolution aerosol profiling developed by ONERA’s Optronics Department, the CAL COVID Cube at UC Berkeley’s Civil and Environmental Engineering Department and expertise and hospital experimental facilities at UC Davis Health. The team brings interdisciplinary strength to understand the various aspects of indoor aerosol transmission. The project, if successful, has significant relevance to developing a scientific understanding of both pathogen and pollutant dispersion from expelled aerosol plumes indoors. Specifically in the context of infectious diseases it is important to understand how exhaled particles move through air to an exposed person to better predict the airborne transmission impacts of diseases

Evan Variano
UC PI:
Evan Variano
Civil and Environmental Engineering, UC Berkeley

Romain Ceolato
France PI:
Romain Ceolato
DOTA - Optronics Department, ONERA, The French Aerospace Lab

Author: 
Evan Variano
Romain Ceolato
Publication date: 
July 1, 2022
Publication type: 
Funded Project