Abstract
Current merging algorithms for particle size spectral data collected with electrical mobility and aerodynamic time-of-flight instruments either require a priori knowledge of densities and shape factors, or use alignment of the number spectra alone to determine an optimal fit and effective density. In this work, an enhanced algorithm is described in which the best fit between the two instrument datasets is achieved for the number, surface area, and volume spectra, also yielding estimated values of transition-regime effective density. When applied to data collected at a kerbside site, integrated aerosol mass calculated from the merged data correlates highly with independently measured PM10 mass data. Typical merged data from the site are shown and used to examine the diurnal and wind direction dependence of the estimated values of transition-regime effective density derived from the merging procedure.