Development of a Bayesian inference model for assessing ventilation condition based on CO_(2)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 CO_(2)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 CO_(2)generation rates,and variable indoor and ambient conditions.This study proposed a Bayesian inference approach with sensitivity analysis to understand CO_(2)readings in four primary schools by identifying uncertainties and calibrating key parameters.The outdoor ventilation rate,CO_(2)generation rate,and occupancy level were identified as the top sensitive parameters for indoor CO_(2)levels.The occupancy schedule becomes critical when the CO_(2)data are limited,whereas a 15-min measurement interval could capture dynamic CO_(2)profiles well even without the occupancy information.Hourly CO_(2)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 m^(3)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 m^(3)and 21 occupancies),0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room#3(236 m^(3)and 19 occupancies),0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room#4(231 m^(3)and 8–9 occupancies)for three consecutive days.