Indoor air quality in schools, and classrooms, is of critical importance for the health and well-being of pupils and staff. The recent COVID-19 pandemic highlighted the essential role that indoor air quality and ventilation systems play in limiting the spread of airborne diseases. Within this context, the research in this thesis seeks to determine how exposure varies within naturally ventilated classrooms, which are prevalent in the UK.
First, CO2 data gathered from 45 classrooms in 11 different schools are analysed and estimates of the likelihood of airborne infection occurring within these classrooms is calculated. Results show that, assuming relatively quiet desk-based work, the number of secondary infections is likely to remain low; however, the risk can vary widely between classrooms of the same school even when the same ventilation system is present. Crucially, even without accounting for the variation in disease prevalence, the data highlight significant variation in infection risk between the seasons, with January being nearly twice as risky as July.
The second part of the thesis provides a detailed examination of the validity of using CO2 as a proxy for far-field exposure. For this, a generic naturally ventilated UK classroom in wintertime is simulated using CFD. The ventilation provision is through high and low-level openings and driven by buoyancy. CO2 is modelled as a passive scalar and shown not to be well-mixed within the environment. The ratio between actual exposure arising from a single infected individual and proxy exposure calculated from point measurements of CO2 is also analysed. In doing so, the proxy exposure estimated from the CO2 concentration is found to be within a factor of two of the actual far-field exposure. Whilst this factor of two might appear large, it is small relative to the typical uncertainties associated with factors required to model airborne disease transmission.
Although this thesis focuses on COVID-19, the tools presented herein are useful to study and improve indoor air quality more broadly.