We present a robust integral method to estimate the daily mean per-person ventilation rate 𝑄𝑝𝑝 based on carbon
dioxide (CO2
) concentration measurements in operational spaces, and limited other data. The method makes
no assumptions regarding the ventilation provision throughout the day, nor requires the room to be in a steady
state, nor the air within to be well-mixed. We demonstrate that several integral parameters remain reliably close
to a value of unity, despite large variations in room conditions. Evaluating the likely distributions of integral
parameters provides a method to quantify the uncertainty bounds and therefore assess the reliability of these
ventilation estimates. Taking school classrooms as a case study, estimates of 𝑄𝑝𝑝 based on measured CO2 are
shown to exhibit uncertainty bounds (of 95% confidence intervals) of approximately ±24% if no other data than
the classroom timetable is available. Deploying four CO2
sensors within a classroom is expected to halve the
uncertainty bounds to around ±12%. Moreover, the framework presented herein evidences that when the same
classroom experiences similar usage on two different days, the relative per-person ventilation rate achieved
during these two days can be simply determined by the ratio of their integral excess CO2
concentrations.
These significant findings offer great scope to facilitate more reliable ventilation estimates, particularly from
large-scale data sets of CO2 measured in operational spaces, to better inform assessments of indoor air quality.