On the estimation of characteristic indoor air quality parameters using analytical and numerical methods

Indoor exposure to air contaminants penetrating from the outdoor environment depends on a number of key processes and parameters such as the ventilation rate, the geometric characteristics of the indoor environment, the outdoor concentration and the indoor removal mechanisms. In this study two alternative methods are used, an analytical and a numerical one, in order to study the time lag and the reduction of the variances of the indoor concentrations, and to estimate the deposition rate of the air contaminants in the indoor environment employing both indoor and outdoor measurements of air contaminants.The analytical method is based on a solution of the mass balance equation involving an outdoor concentration pulse which varies sinusoidally with the time, while the numerical method involves the application of the MIAQ indoor air quality model assuming a triangular pulse. The ratio of the fluctuation of the indoor concentrations to the outdoor ones and the time lag were estimated for different values of the deposition velocity, the ventilation rate and the duration of the outdoor pulse.Results have showed that the time lag between the indoor and outdoor concentrations is inversely proportional to the deposition and ventilation rates, while is proportional to the duration of the outdoor pulse. The decrease of the ventilation and the deposition rate results in a rapid decrement of the variance ratio of indoor to outdoor concentrations and to an increment of the variance ratio, respectively.The methods presented here can be applied for gaseous species as well as for particulate matter. The nomograms and theoretical relationships that resulted from the simulation results and the analytical methods respectively were used in order to study indoor air phenomena. In particular they were used for the estimation of SO2 deposition rate.Implications of the studied parameters to exposure studies were estimated by calculating the ratio of the indoor exposure to the exposure outdoors. Limitations of the methods were explored by testing various scenarios which are usually met in the indoor environment. Strong indoor emissions, intense chemistry and varying ventilation rates (opening and closing of the windows) were found to radically influence the time lag and fluctuation ratios.