Ventilation and disease monitoring of indoor spaces and public transportation using an NDIR sensor network

Abstract

With the COVID-19 pandemic still ongoing, there is a need to ensure that people are not subjected to the risk of getting infected with the disease. Since COVID-19 is airborne, engineering controls must be provided to monitor and mitigate the spread of the disease in the air. One of the measures is to ensure proper ventilation within indoor spaces where superspreader events were previously documented in poorly ventilated spaces. CO2 levels reflect the rate at which the used air within is replaced and, therefore, can be used as a proxy for COVID-19 risk. This study developed economical CO2 monitors which are deployed across indoor spaces and public transportation, such as air-conditioned jeepneys, to communicate the risk. Using the least squares method on the first-order ODE of mass balance, a multivariate method is devised for deriving the occupancy and the ventilation rate from the recorded CO2 levels. Using the multivariate method, the ventilation rates of different indoor spaces are determined and used for the subsequent computations using the Wells–Riley model to derive the respective infection risk, particularly of COVID-19.