Development of a Bayesian inference model for assessing ventilation condition based on CO₂ meters in primary schools

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO₂ meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO₂ generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO₂ readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO₂ generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO₂ levels. The occupancy schedule becomes critical when the CO₂ data are limited, whereas a 15-min measurement interval could capture dynamic CO₂ profiles well even without the occupancy information. Hourly CO₂ recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m³ and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m³ and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m³ and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m³ and 8-9 occupancies) for three consecutive days.