Comparison between the uncertainty evaluation carried out according to the GUM uncertainty framework and the Monte Carlo (MC) method starting from real data sets obtained from the quantification of the mass of benzo[a]pyrene (BaP).

The two approaches for the uncertainty evaluation provide different results for BaP masses in samples containing different masses of the analyte, MC method giving larger coverage intervals.

In cases of analyte masses close to zero, the GUM uncertainty framework leads to a coverage interval stretching into a region of negative unfeasible values for the measurand.

Application of MC simulation to the propagation of probability distributions particularly fits the cases of measurement results of intrinsically positive quantities close to zero.

MC simulation can be configured in a way that only positive values are generated thus obtaining a coverage interval for the measurand that is always reliable.