Influence of alveolar mixing and multiple breaths of aerosol intake on particle deposition in the human lungs.

ABSTRACT

Predictive dosimetry models play an important role in assessing health effect of inhaled particulate matter and in optimizing delivery of inhaled pharmaceutical aerosols. In this study, the commonly used 1D Multiple-Path Particle Dosimetry model (MPPD) was improved by including a mechanistically based model component for alveolar mixing of particles and by extending the model capabilities to account for multiple breaths of aerosol intake. These modifications increased the retained fraction of particles and consequently particle deposition predictions in the deep lung during tidal breathing. Comparison with an existing dataset (J. Aerosol Sci., 9927-39, 2016) obtained under two breathing conditions referred to as slow and fast breathing showed significant differences in 1 µm particle deposition between predictions based on subject-specific breathing patterns and lung volume (slow 30 ± 1%, fast 21 ± 1%, (average ± standard deviation), N = 7) and measurements (slow 43 ± 9%, fast 30 ± 5%) when the prior version of MPPD (single breath and no mixing, J. Aerosol Sci., 151105647, 2021) was used. Adding a mixing model and multiple breaths moved the predictions (slow 34 ± 2%, fast25 ± 2%) closer to the range of deposition measurements. For 2.9 µm particles, predictions from both the original (slow 70 ± 2%, fast 57 ± 2%) and the revised MPPD model (slow 71 ± 2%, fast 59 ± 3%) compared well with experiments (slow 67 ± 8%, fast 58 ± 10%). This was expected as suspended fraction of 2.9 µm particles was small and thus the addition of alveolar mixing and multi breath capability only slightly increased the retained fraction for particles of this size and greater. The revised 1D model improves dose predictions in the deep lung and support human risk assessment from exposure to airborne particles.