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

Ji Hoon Seo, Hyun Woo Jeon, Joung Sook Choi and Jong-Ryeul Sohn
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Ji Hoon Seo: Department of Health & Environmental Science, Korea University, Seoul 02841, Korea
Hyun Woo Jeon: Department of Health and Safety Convergence Science, Korea University, Seoul 02841, Korea
Joung Sook Choi: Department of Health and Safety Convergence Science, Korea University, Seoul 02841, Korea
Jong-Ryeul Sohn: Department of Health & Environmental Science, Korea University, Seoul 02841, Korea

IJERPH, 2020, vol. 17, issue 19, 1-14

Abstract: 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 ( R 2 = 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 ( R 2 = 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.

Keywords: indoor air quality; bioaerosol; hospital environment; prediction model; particle number (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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