Repeatability and reproducibility in the breathability measurement of surgical masks

Taborri, J.; Stocchi, B.; Calabro, G.; Rossi, S.

The pandemic COVID-19 is still requiring several countermeasures to adopted in order to decrease the virus spread. Among others, face masks have shown their potentiality to reduce the person-to-person transmission. For this reason, several research groups and/or industries focused their supply chain in the production of innovative materials for the face mask design. Considering the surgical mask, several parameters have to be checked before their commercialization, as for example the breathability. This study aims at evaluating the repeatability and reproducibility of the breathability measurement in surgical masks, and at assessing the effects induced by the selected measurement points on the mask. Three samples for each type I, II and IIR masks were tested within the experimental protocol. Breathability was measured by following the UNI EN 14683:2019. Fifteen measurement points were identified for each mask and the measure was repeated five times per each point. Standard deviation across the repeated measures on the single point and across the different tested areas were used to evaluate the repeatability and reproducibility of the procedure, respectively. The measurement uncertainty was also computed. In addition, in order to verify the effects induced by the selection of five points out the available fifteen, as requested by the UNI EN 14683:2019, all combinations of points were tested. Results showed a high value of reproducibility error, leading to the consideration that different areas of the same mask are characterized by different values of breathability. Thus, the selection of the five measurement points to perform the breathability measurement according to the standard is a crucial aspect that can influence the result of mask compliance. The results of the present paper could provide useful information for the standardization of the breathability measurement. © 2021 IEEE.