Development of a Procedure for Estimating the Parameters of Mechanistic Emission Source Models from Chamber Testing Data

In order to evaluate the impacts of volatile organic compounds (VOCs) emissions from building materials on the indoor pollution load and indoor air quality beyond the standard chamber test conditions and test period, mechanistic emission source models have been developed in the past. However, very limited data are available for the required model parameters including the initial concentration (Cm0), in-material diffusion coefficient (Dm), partition coefficient (Kma), and convective mass transfer coefficient (km). In this study, a procedure is developed for estimating the model parameters by using VOC emission data from standard small chamber tests. Multivariate regression analysis on the experimental data are used to determine the parameters. The Least Square and Global search algorithm with multi-starting points are used to achieve a good agreement in the normalized VOC concentrations between the model prediction and experimental data. To verify the procedure and estimate its uncertainty, simulated chamber test data are first generated by superposition of different levels of “experimental uncertainties” on the theoretical curve of the analytical solution to a mechanistic model, and then the procedure is used to estimate the model parameters from these data and determine how well the estimates converged to the original parameter values used for the data generation. Results indicated that the mean value of the estimated model parameters Cm0 was within -0.04%+/-2.47% of the true values if the “experimental uncertainty” were within +/-10% (a typical uncertainty present in small-scale chamber testing). The procedure was further demonstrated by applying it to estimate the model parameters from real chamber test data. Wide applications of the procedure will result in a database of mechanistic source model parameters for assessing the impact of VOC emissions on indoor pollution load, and for evaluating the effectiveness of various IAQ design and control strategies.