RESUMO
During the last decade, computational fluid dynamics techniques proved to be a powerful tool in the modelling of biological processes and the design of biomedical devices. In this work, a computational fluid dynamics method was applied to model the transport of inhaled air and radioactive particles within the human respiratory tract. A finite volume numerical approach was used to compute the flow field characteristics and particle trajectories in the lumen of the first five airway generations of the human tracheobronchial tree, leading to the right upper lobe. The computations were performed for breathing and exposure conditions characteristic of uranium mines and homes. Primary radon daughter deposition patterns and energy distributions were computed, exhibiting highly inhomogeneous particle and energy deposition patterns. The results of the present modelling effort can serve as input data in lung cancer risk analysis.
Assuntos
Neoplasias Pulmonares/fisiopatologia , Pulmão/fisiopatologia , Pulmão/efeitos da radiação , Modelos Biológicos , Lesões por Radiação/fisiopatologia , Radiometria/métodos , Produtos de Decaimento de Radônio/efeitos adversos , Administração por Inalação , Aerossóis/efeitos adversos , Poluentes Radioativos do Ar/efeitos adversos , Sobrevivência Celular/efeitos da radiação , Simulação por Computador , Relação Dose-Resposta à Radiação , Humanos , Transferência Linear de Energia/efeitos da radiação , Neoplasias Pulmonares/etiologia , Doses de Radiação , Lesões por Radiação/etiologia , Produtos de Decaimento de Radônio/administração & dosagem , Reologia/métodosRESUMO
Although occupational epidemiological studies and animal experimentation provide strong evidence that radon-222 (222Rn) progeny exposure causes lung cancer, residential epidemiological studies have not confirmed this association. Past residential epidemiological studies have yielded contradictory findings. Exposure misclassification has seriously compromised the ability of these studies to detect whether an association exists between 222Rn exposure and lung cancer. Misclassification of 222Rn exposure has arisen primarily from: 1) detector measurement error; 2) failure to consider temporal and spatial 222Rn variations within a home; 3) missing data from previously occupied homes that currently are inaccessible; 4) failure to link 222Rn concentrations with subject mobility; and 5) measuring 222Rn gas concentration as a surrogate for 222Rn progeny exposure. This paper examines these methodological dosimetry problems and addresses how we are accounting for them in an ongoing, population-based, case-control study of 222Rn and lung cancer in Iowa.