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Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

1
Department of BioNano Technology, Gachon University, 1342, Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea
2
Department of Beauty Design Management, Hansung University, 116 Samseongyoro-16gil, Seoul 02876, Korea
3
Biocell Korea Co., Ltd., 1-2F Janghan B/D,54 Bongeunsa-ro 30-gil, Gangnam-gu, Seoul 04631, Korea
4
Korea Railroad Research Institute (KRRI), 176 Cheoldobakmulkwan-ro, Uiwang-si Gyeonggi-do 16105, Korea
*
Authors to whom correspondence should be addressed.
Atmosphere 2020, 11(2), 137; https://doi.org/10.3390/atmos11020137
Received: 19 December 2019 / Revised: 22 January 2020 / Accepted: 24 January 2020 / Published: 26 January 2020
(This article belongs to the Section Aerosols)
The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches’ many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.
Keywords: computational models; in silico; human lung deposition; aerosol particles computational models; in silico; human lung deposition; aerosol particles
MDPI and ACS Style

Bui, V.K.H.; Moon, J.-Y.; Chae, M.; Park, D.; Lee, Y.-C. Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates. Atmosphere 2020, 11, 137.

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