Measuring the concentration of CO2 in indoor workplaces makes for an easier, more accurate IAQ management

IAQ (Indoor Air Quality) is one of the leading factors impacting the overall wellbeing of people in indoor environments. Management of IAQ can only be effective if there is a detailed knowledge of the environment, meaning that emission rates of contaminants and ventilation rates must both be known with limited uncertainty. Contaminants can be of anthropic or non-anthropic nature. The former can be monitored using CO2, that closely tracks bio-effluents, the main source of IAQ degradation. The latter are usually monitored using formaldehyde (CHOH) and other volatile organic compounds (VOC) released by furniture and materials. In this paper we present and discuss some CO2 time evolution tracks in workplaces and socially relevant environments. We underline how a simple analysis of these tracks leads to considerable insight into some important aspects of IAQ. First, compliance with limits of acceptability can be easily and accurately checked. Additionally, by fitting the CO2 time evolution tracks with a very simple perfectly diffused model, accurate determinations of the product [N × M] of the number of occupants (N) and the average metabolic rate (M) are possible. This allows the development of simulations that are a valuable tool whenever adjustments are needed to improve the existing indoor air quality. Individual estimates of N and M are accurate in environments where N is limited to no more than three, since in such cases only one combination of N and M is usually meaningful. Such estimates are however more uncertain in high-occupancy environments, where several different combinations of N and M provide fits of nearly identical quality. The global ventilation rate (Q), resulting from the contributions from natural and mechanical ventilation, can also be reliably determined. The effects on Q due to the temperature-induced outdoor-indoor differential pressure, as well as those due to window frame materials can be easily quantified. Significant variations of Q in time can also be identified, provided that they occur as step changes and they are mutually well separated in time.