Application of Cloud Dynamics to Dosimetry of Cigarette Smoke Particles in the Lungs

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Abstract
Clinical data suggest a relationship between in vivo deposition patterns of cigarette smoke particles and the occurrence of tumors in the lung. Traditional dosimetry models fail to predict the preferential proximal deposition of cigarette smoke in the human airways, which resembles deposition of aerosol with a larger mass median aerodynamic diameter (MMAD) than that representative of cigarette smoke. Previous work has shown that accounting for the so-called cloud effect leads to enhanced proximal deposition and to better agreement with clinical and experimental data. This work presents an improved model of transport and deposition of cigarette smoke in the airways of smokers, accounting for possible particle-particle interactions (cloud effect) and their effect on the mobility of individual particles and on the deposition profile. Brinkman's effective medium approach is used for modeling the flow through and around the cloud, with the cloud's permeability changing according to the cloud's solid volume fraction. Although the weakest of all interparticle hydrodynamic interactions is considered, it significantly alters the deposition pattern along the respiratory tract, both alone and simultaneously with other synergistic processes (coagulation, hygroscopic growth) that dynamically modify the particle size distribution. Model results compare favorably with clinical data available on CSP deposition in the lungs and indicate that a combination of cloud behavior, hygroscopic growth, and coagulation may explain the preferential proximal deposition of smoke particles in the tracheobronchial region.