Bayesian Calibration of Using CO2Sensors 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 CO 2 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 CO 2 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 CO 2 level 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 CO 2 threshold is around 500 ppm of a school day (< 8 hr) for the three schools. Practical Implications The actual outdoor ventilation rate in a room cannot be easily measured, but it can be calculated by measuring the transient indoor CO 2 level. Uncertainty in input parameters can result in uncertainty in the calculated ventilation rate. Our three classrooms study shows that the estimated ventilation rate considering various input parameters’ uncertainties is between ± 8-20 %. As a result, the uncertainty of the ventilation rate contributes to the estimated COVID-19 airborne aerosol infection risk’s uncertainty up to ± 10 %. Other studies can apply the proposed Bayesian and MCMC method to estimating building ventilation rates and airborne aerosol infection risks based on actual measurement data such as CO 2 levels with uncertainties and sensitivity of input parameters identified. The outdoor ventilation rate and CO 2 threshold values as functions of exposure times could be used as the baseline models to develop correlations to be implemented by cheap/portable sensors to be applied in similar situations to monitor ventilation conditions and airborne risk levels.