Quantification of Face Seal Leakage Using Parallel Resistance Model
Buddhi Pushpawela, PETER CHEA, Ryan X. Ward, Richard Flagan, California Institute of Technology
Abstract Number: 261
Working Group: Aerosol Physics
Abstract
Masks and respirators afford varying degrees of protection to the person wearing them. The degree of protection depends upon the material (filter medium), the design and construction of the masks, manufacture, fit of the mask (how well it seals to the face), the nature of the particles that carry the virus, the respiration rate, as well as the percentage of particles penetrating through the face seal leakage, the total flow rate through the filter medium and other factors. Therefore, identifying the leaking places of masks and quantifying the leakage flow rate is important to estimate the protection.
In our study, we hypothesized a model to quantify the leakage flow rate through the face mask based on a parallel resistance model. We used Ohm’s law as an analogy for the pressure leakage rate. The tests were performed in two ways; (i) mask material test, in which all masks were sealed to a flange to measure transmission through a full mask and prevent leakage around the edges (ii) mannequin mask test, in which masks are fitted to a mannequin head tightly. In this study, we have tested four different classes of masks: NIOSH-certified N95 Face Filtering Respirators (FFRs), KN95 masks, 3-ply, pleated, disposable procedure masks, and cloth masks. For all the FFRs and masks, the pressure drop was measured at eight different flow rates between 5 and 85 LPM, and it was increased linearly with the total flow rate (r2 >0.98). The results of the study showed that the leakage flow rate was 10% of the total flow rate, even for the best-fitted N95 FFRs and KN95 masks. They showed higher resistance to the leaks. The procedure masks and cloth masks showed a leakage flow rate of 25% of the value of the total flow rate, quite a large proportion of the flow. They had lower resistance to leaks. This parallel resistance model can help improve mask design and obtain better mask sealing.