Estimation of air change rate by CO2 sensor network in workplace with COVID-19 outbreak

Abstract

Objectives: This study aimed to measure the air change per hour (ACH) in a workplace that spanned 880 m2 and had a ceiling height of 3 m. This workplace experienced clusters of coronavirus disease (COVID-19) cases, and the study measured ACH before and after remediation. The objective was to provide a quantitative estimate of ACH in various compartments. Methods: A network of CO2 sensors was set up in the workplace. The data from the sensors were analyzed using a generalized linear mixed model and dynamic time distortion to estimate the ACH in each area. Results: During the cluster outbreak, the ACH was in the range of 0.408 to 1.178/hour (p<.001), which was relatively low and likely contributed to the outbreak. Additionally, the room’s ventilation was imbalanced due to partitioning. However, the ACH improved significantly from 1.835 to 2.551/hour (p<.001) by simply opening the windows and allowing natural ventilation. Conclusions: Based on the evidence that the transmission of COVID-19 was contained following the enhancement of ventilation, an ACH rate of below 2/hour was the primary factor in developing COVID-19 clusters within the facility under investigation.

Keywords

• aerosolized particles and droplets
• built environment
• sars-cov-2