HUMAN LUNG MORPHOLOGY MODELS FOR PARTICLE DEPOSITION STUDIES

Knowledge of human lung morphology is of paramount importance in calculating deposition patterns of inhaled particulate matter (PM) to be used in the definition of ambient air quality standards. Due to the inherently complex nature of the branching structure of the airway network, practical assumpti...

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Veröffentlicht in:Inhalation toxicology 2000, Vol.12 (S4), p.109-121
Hauptverfasser: B. Martonen, Ted, D. Schroeter, Jeffry, Hwang, Dongming, S. Fleming, John, Joy H. Conway
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Sprache:eng
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Zusammenfassung:Knowledge of human lung morphology is of paramount importance in calculating deposition patterns of inhaled particulate matter (PM) to be used in the definition of ambient air quality standards. Due to the inherently complex nature of the branching structure of the airway network, practical assumptions must be made for modeling purposes. The most commonly used mathematical models reported in the literature that describe PM deposition use Weibel's model A morphology. This assumes the airways of the lung to be a symmetric, dichotomously branching system. However, computer simulations of this model, when compared to scintigraphy images, have shown it to lack physiological realism (Martonen et al., 1994a). Therefore, a more physiologically realistic model of the lung is needed to improve the current PM dosimetry models. Herein, a morphological model is presented that is based on laboratory data from planar gamma camera and single-photon emission computed tomography (SPECT) images. Key elements of this model include: The parenchymal wall of the lung is defined in mathematical terms, the whole lung is divided into distinct left and right components, a set of branching angles is derived from experimental measurements, and the branching network is confined within the discrete left and right components (i.e., there is no overlapping of airways). In future work, this new, more physiologically realistic morphological model can be used to calculate PM deposition patterns for risk assessment protocols.
ISSN:0895-8378
1091-7691
DOI:10.1080/08958370050164941