Scanning Mobility Particle Sizer (SMPS) is a high resolution nanoparticle sizing system that has long been hailed as the researcher's choice for airborne nanoparticle size characterization for nano applications including nanotechnology research and development. SMPS is widely used as the standard method to measure airborne particle size distributions below 1 m. It is composed of two devices: a Di erential Mobility Analyzer (DMA) selects particle sizes thanks to their electrical mobility and a Condensation Particle Counter (CPC) enlarges particles to make them detectable by common optical counters. System raw data represent the number of particles counted over several classes of mobility diameters. Then, common inversion procedures lead to the estimation of the aerosol size distribution. In this paper, we develop a methodology to compute the uncertainties associated with the estimation of the size distribution when several experiences have been carried out. The requirement to repeat the measure ensures a realistic variability on the simulated data to be generated. The work we present consists in considering both the uncertainties coming from the experimental dispersion and the uncertainties induced by the lack of knowledge on physical phenomena. Experimental dispersion is quanti ed with the experimental data while the lack of knowledge is modelled via the existing physical theories and the judgements of experts in the eld of aerosol science. Thus, running Monte-Carlo simulations give an estimation of the size distribution and its corresponding con dence region.