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Bayesian Calibration of Using CO2 Sensors to Assess Ventilation Conditions and Associated COVID-19 Airborne Aerosol Transmission Risk in Schools

Ventilation rate plays a significant role in preventing the airborne transmission of diseases in indoor spaces. Classrooms are a considerable challenge during the COVID-19 pandemic because of large occupancy density and mainly poor ventilation conditions. The indoor CO2 level may be used as an index for estimating the ventilation rate and airborne infection risk. In this work, we analyzed a one-day measurement of CO2 levels in three schools to estimate the ventilation rate and airborne infection risk. Sensitivity analysis and Bayesian calibration methods were applied to identify uncertainties and calibrate key parameters. The outdoor ventilation rate with a 95% confidence was 1.96{+/-}0.31ACH for Room 1 with mechanical ventilation and fully open window, 0.40{+/-}0.08 ACH for Rooms 2, and 0.79{+/-}0.06 ACH for Room 3 with only windows open. A time-averaged CO2 level < 450 ppm is equivalent to a ventilation rate > 10 ACH in all three rooms. We also defined the probability of the COVID-19 airborne infection risk associated with ventilation uncertainties. The outdoor ventilation threshold to prevent classroom COVID-19 aerosol spreading is between 3-8 ACH, and the CO2 threshold is around 500 ppm of a school day (< 8 hr) for the three schools.