Uncertainty evaluation for the quantification of low masses of benzo[a]pyrene: Comparison between the Law of Propagation of Uncertainty and the Monte Carlo method

• 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.

A proper evaluation of the uncertainty associated to the quantification of micropollutants in the environment, like Polycyclic Aromatic Hydrocarbons (PAHs), is crucial for the reliability of the measurement results. The present work describes a comparison between the uncertainty evaluation carried out according to the GUM uncertainty framework and the Monte Carlo (MC) method. This comparison was carried out starting from real data sets obtained from the quantification of benzo[a]pyrene (BaP), spiked on filters commonly used for airborne particulate matter sampling. BaP was chosen as target analyte as it is listed in the current European legislation as marker of the carcinogenic risk for the whole class of PAHs.MC method, being useful for nonlinear models and when the resulting output distribution for the measurand is non-symmetric, can particularly fit the cases in which the results of intrinsically positive quantities are very small and the lower limit of a desired coverage interval, obtained according to the GUM uncertainty framework, can be dramatically close to zero, if not even negative.In the case under study, it was observed that 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 addition, in cases of analyte masses close to zero, the GUM uncertainty framework would give even negative lower limit of uncertainty coverage interval for the measurand, an unphysical result which is avoided when using MC method. MC simulations, indeed, can be configured in a way that only positive values are generated thus obtaining a coverage interval for the measurand that is always positive.

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