Particle Deposition in a Multiple-Path Model of the Human Lung

Predicting the amount of particle deposition in the human lung following exposure to airborne particulate matter is the rst step toward evaluating risks associated with exposure to airborne pollutants.Realistic deposition models are needed for accurate predictions of deposition in the lung, but a major limitation is the degree to which the lung geometry can be accurately reconstructed.Morphometric data for the entire airway tree of the human lung are not available.So far, idealistic lung structures have been used for deposition calculations.In this study, 10 statistical lung structures based on morphometric measurements of Raabe et al. (1976) were generated for the conducting airways of the human lung.A symmetric, dichotomous branching alveolar airway structure was attached to the end of the conducting airway tree of each lung structure.The total volume of the alveolar region was the same among the lung geometries.Using a mathematical scheme developed previously (Anjilvel and Asgharian 1995), regional, lobar, and per-generation depositions of particles were calculated in these geometries.The results were compared to deposition predictions using typical-path and ve-lobe symmetric lung geometry models.All three lung models showed very similar regional and generation-by-generation deposition results.Lobar deposition was found to strongly depend on the detailed morphometry of the lung structure that was used.Various deterministic models of particle deposition in the respiratory tract have been formulated to calculate the deposition of inhaled particles in humans and animals.The models range from empirical models that do not incorporate lung geometry (Rudolf et al. 1986(Rudolf et al. , 1990) ) to mathematically complex, many-path (multiple-path) models that are based on a lung structure constructed from actual airway measurements (Anjilvel and Asgharian 1995;Asgharian and Anjilvel 1998).In addition, a stochastic lung deposition model using statistical relationships