Volume 11, No. 3, June 2011, Pages 309-314 PDF(1.37 MB)
User Influence on Indoor Aerosol Model Calibration
Tareq Hussein1, Bjarke Mølgaard2, Kaarle Hämeri2,3
1 The University of Jordan, Department of Physics, Amman 11942, Jordan
2 University of Helsinki, Department of Physics, P. O. Box 48, FI-00014 UHEL, Helsinki, Finland
3 Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A, FI-00250, Helsinki, Finland
Calibration, or in other words “validation”, of complex indoor aerosol models that simulate the dynamic behavior of particle number size distributions are often assumed to solely depend on the characteristics of the model and its setup. The user influence is rarely mentioned in that regard. This paper shows, with a simple exercise, the user influence on the calibration of an indoor aerosol model. It is also shown that a reasonable model simulation was achieved with a different understanding of the system modeled and several input parameter values. We utilized a single compartment and size-resolved indoor aerosol model approach to be calibrated against a model room. This kind of simple indoor aerosol models is very common in the literature and widely used in the analysis of indoor-to-outdoor relationship of aerosol particles. The input parameters for such indoor aerosol models are: outdoor particle number size distributions, indoor domain geometries, ventilation rate, penetration factor, and friction velocity (or deposition rate onto indoor surfaces). For simplicity, we considered the penetration factor and friction velocity are the only unknown input parameters to be chosen freely by the user so that the model is calibrated. We made it clear by this study that a user can influence the input parameter values significantly. Even though this suggests different sets of input parameter values can be valid for a model calibration, the model simulation differences between different calibration results remained within 1%. This implies that it is more challenging to calibrate a complex indoor aerosol model that requires many input parameters.
Indoor-to-outdoor relationship; Size distribution; Iteration; Least-squares.