Analysis of the input parameters for the infection risk model from CO₂ measurements

¹ Tampere University of Applied Sciences, School of Built Environment and Bioeconomy, Finland
² Tampere University of Applied Sciences, School of Pedagogical Innovations and Culture, Finland

^* Corresponding author: antti.j.makinen@tuni.fi

Abstract

The COVID-19 pandemic has highlighted the importance of indoor air hygiene and understanding infection risk of pathogens. Ventilation rate, room dimensions and occupancy have remarkable effects on the infection risk at indoor spaces. Multiple sensor-based CO₂ measurement enables continuous monitoring of the ventilation quality and the analysis of infection risk. This paper presents a practical case study of infection risk analysis in a classroom at different ventilation settings during lecture utilizing carbon dioxide sensors. The measurements consisted of 11 carbon dioxide sensors placed in different parts of the classroom. Three of the sensors were placed on the exhaust air valves of the room. The measurement situations were roughly two-hour lessons. A teacher and about 20 students were present during the lessons. In this case study, three different constant airflow settings were studied: 130 l/s, 200 l/s and 285 l/s. The infection risk model is based on the well-known Wells-Riley’s model. The input parameters such as ventilation flow rate and number of people in the classroom are analysed based on the CO₂ measurements in the room. The ventilation rate has been estimated based on the CO₂ decay rate after the finished lectures. CO₂ decay rate is proportional to the ventilation rate and can be quantified based on the exponential decay model. The number of people in the classroom has been calculated based on the measured saturation CO₂ concentration and the estimated CO₂ emission rate per person. The results showed that the ventilation rate and occupancy in the classroom can be estimated based on the CO₂ measurements and consequently this kind of real time monitoring can provide the needed parameters for automated during operation infection risk calculations.