Assessment of airborne infection risk in naturally ventilated environments

During the COVID-19 pandemic, natural ventilation emerged as a widely recommended strategy to improve indoor air quality and reduce airborne infection risks. However, due to the inert uncertainty, accurately determining natural ventilation rates to assess the true impact of this practice proved challenging for building managers. Traditionally, the Wells-Riley approach has been used for airborne infection probability risk assessment when steady-state conditions are assumed. However, when this method is applied to naturally ventilated facilities, it may yield inaccuracies because of irregular ventilation rates caused by the occupants’ window opening patterns. The paper introduces a novel methodology for evaluating airborne infection probability in naturally ventilated environments. Firstly, natural ventilation rates are estimated using a grey box model of indoor CO 2 concentration. This approach was validated using in-situ data from a case study in different periods (spring, summer and winter). Then, the infection probability risk was calculated by discretizing the accumulative virus portion inhaled by the occupants at each time. The results prove the grey box model's effectiveness in estimating natural ventilation rates in educational facilities. Concerning the evaluation of infection probability risk, the proposed approach aligns with observations in previous research that link lower ventilation rates with higher infection risk. However, the methodology provides a better representation of real-world variability than the Wells-Riley approach and enables the identification of vulnerable periods. The integration of this methodology into natural ventilation system management could optimize window-opening strategies to mitigate airborne transmission diseases in educational facilities, considering diverse infective incidence rates and pathogens. • Airborne infection risk is assessed considering irregular ventilation and occupancy. • Methodology accurately estimates airborne infection risk in real-world scenarios. • Grey box modelling is used to determine natural ventilation rates. • The methodology could be used for predictive control of airborne infection risk.