Comparison of two models to estimate deposition of fungi and bacteria in the human respiratory tract

Jessica A. Sagona, Lynn E. Secondo, Gediminas Mainelis

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Understanding the deposition of bioaerosols in the respiratory system may help determine the risk of disease; however, measuring deposition fraction in-situ is difficult. Computational models provide estimates of particle deposition fraction for given breathing and particle parameters; however, these models traditionally have not focused on bioaerosols. We calculated deposition fractions in an average-sized adult with a new bioaerosol-specific lung deposition model, BAIL, and with two multiple-path models for three different breathing scenarios: "default" (subject sitting upright and breathing nasally), "light exercise", and "mouth breathing". Within each scenario, breathing parameters and bioaerosol characteristics were kept the same across all three models. BAIL generally calculated a higher deposition fraction in the extrathoracic (ET) region and a lower deposition fraction in the alveolar region than the multiple-path models. Deposition fractions in the tracheobronchial region were similar among the three models; total deposition fraction patterns tended to be driven by the ET deposition fraction, with BAIL resulting in higher deposition in some scenarios. The difference between deposition fractions calculated by BAIL and other models depended on particle size, with BAIL generally indicating lower total deposition for bacteria-sized bioaerosols. We conclude that BAIL predicts somewhat lower deposition and, potentially, reduced risk of illness from smaller bioaerosols that cause illness due to deposition in the alveolar region. On the other hand, it suggests higher deposition in the ET region, especially for light exercise and mouth-breathing scenarios. Additional comparisons between the models for other breathing scenarios, people's age, and different bioaerosol particles will help improve our understanding of bioaerosol deposition.

Original languageEnglish (US)
Article number561
JournalAtmosphere
Volume11
Issue number6
DOIs
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Environmental Science (miscellaneous)

Keywords

  • Bioaerosols
  • Human lung
  • Modeling
  • Particle deposition

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