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Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools
Danlin Hou1, Liangzhu Leon Wang1, Ali Katal1
1Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec H3G 1M8 Canada.
Building Simulation
|August 29, 2022
Summary
Bayesian inference clarifies CO2 meter data in schools, identifying ventilation and occupancy as key factors for indoor air quality. Shorter CO2 measurement intervals are recommended over hourly readings for accurate assessment.
Area of Science:
- Environmental Health Engineering
- Indoor Air Quality Monitoring
- Infectious Disease Transmission Control
Background:
- Outdoor fresh air ventilation is crucial for reducing airborne disease transmission in indoor environments like school classrooms.
- The COVID-19 pandemic highlighted challenges in maintaining adequate ventilation in schools due to high occupancy and uncertain conditions.
- Interpreting CO2 meter data for air quality assessment in schools is complicated by manual readings, variable occupancy, and environmental factors.
Purpose of the Study:
- To develop and apply a Bayesian inference approach to understand CO2 readings in primary school classrooms.
- To identify and calibrate key parameters influencing indoor CO2 levels, including ventilation, CO2 generation, and occupancy.
- To analyze the sensitivity of CO2 levels to different parameters and measurement intervals.
Main Methods:
- Utilized a Bayesian inference approach combined with sensitivity analysis to model CO2 dynamics in four primary school rooms.
- Identified and calibrated parameters such as outdoor ventilation rate, CO2 generation rate, and occupancy levels.
- Evaluated the impact of different CO2 measurement intervals (e.g., 15-min, hourly) and occupancy data availability.
Main Results:
- Outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the most sensitive parameters affecting indoor CO2.
- A 15-minute measurement interval effectively captured dynamic CO2 profiles, even without occupancy information, unlike hourly readings.
- Calibrated ventilation rates (ACH) with 95% confidence levels were determined for mechanical and naturally ventilated rooms under specific conditions.
Conclusions:
- Bayesian inference provides a robust method for interpreting CO2 data and understanding ventilation in school settings.
- Accurate CO2 monitoring requires appropriate measurement intervals; hourly readings are insufficient for capturing peak values and can overestimate ventilation.
- Occupancy schedules are critical for accurate CO2 data interpretation, especially when measurement data is limited.
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