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Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment

1
Department of Health & Environmental Science, Korea University, Seoul 02841, Korea
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BK21PLUS Program in Embodiment: Health-Society Interaction, Department of Public Health Sciences, Graduate School, Korea University, Seoul 02841, Korea
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Department of Health and Safety Convergence Science, Korea University, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 7237; https://doi.org/10.3390/ijerph17197237
Received: 5 September 2020 / Revised: 28 September 2020 / Accepted: 30 September 2020 / Published: 3 October 2020
(This article belongs to the Section Environmental Science and Engineering)
Indoor microbiological air quality, including airborne bacteria and fungi, is associated with hospital-acquired infections (HAIs) and emerging as an environmental issue in hospital environment. Many studies have been carried out based on culture-based methods to evaluate bioaerosol level. However, conventional biomonitoring requires laborious process and specialists, and cannot provide data quickly. In order to assess the concentration of bioaerosol in real-time, particles were subdivided according to the aerodynamic diameter for surrogate measurement. Particle number concentration (PNC) and meteorological conditions selected by analyzing the correlation with bioaerosol were included in the prediction model, and the forecast accuracy of each model was evaluated by the mean absolute percentage error (MAPE). The prediction model for airborne bacteria demonstrated highly accurate prediction (R2 = 0.804, MAPE = 8.5%) from PNC1-3, PNC3-5, and PNC5-10 as independent variables. Meanwhile, the fungal prediction model showed reasonable, but weak, prediction results (R2 = 0.489, MAPE = 42.5%) with PNC3-5, PNC5-10, PNC > 10, and relative humidity. As a result of external verification, even when the model was applied in a similar hospital environment, the bioaerosol concentration could be sufficiently predicted. The prediction model constructed in this study can be used as a pre-assessment method for monitoring microbial contamination in indoor environments. View Full-Text
Keywords: indoor air quality; bioaerosol; hospital environment; prediction model; particle number indoor air quality; bioaerosol; hospital environment; prediction model; particle number
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MDPI and ACS Style

Seo, J.H.; Jeon, H.W.; Choi, J.S.; Sohn, J.-R. Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment. Int. J. Environ. Res. Public Health 2020, 17, 7237. https://doi.org/10.3390/ijerph17197237

AMA Style

Seo JH, Jeon HW, Choi JS, Sohn J-R. Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment. International Journal of Environmental Research and Public Health. 2020; 17(19):7237. https://doi.org/10.3390/ijerph17197237

Chicago/Turabian Style

Seo, Ji H., Hyun W. Jeon, Joung S. Choi, and Jong-Ryeul Sohn. 2020. "Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment" International Journal of Environmental Research and Public Health 17, no. 19: 7237. https://doi.org/10.3390/ijerph17197237

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