CO2 sensor placement for the prompt identification of high virus transmission-risk conditions

Abstract

Poor Indoor Air Quality (IAQ) can contribute to airborne viruses remaining viable for longer time periods with the increased risk of infecting more people. Various methodologies, usually by considering CO2 concentrations and well-mixed zones, have been proposed for the indirect assessment of the virus transmission risk. However, IAQ conditions can vary significantly within a room and the position of sensors is crucial to ensure real-time estimation of the virus transmission risk. In this work, a novel sensor placement methodology is presented for the prompt identification of increased virus transmission risk conditions, through the early detection of increased CO2 concentrations in the exhaled air. Specifically, the 3D airflow vectors for multiple ventilation scenarios are used to compute the impact of each possible sensor placement, and the optimal sensor positions are computed using multi-objective optimization. Simulation results are provided for calculating the optimal positions for 1-3 CO2 sensors in a real office-room.