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Methodology for modeling the microbial contamination of air filters.

Joe YH, Yoon KY, Hwang J - PLoS ONE (2014)

Bottom Line: The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively.It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases.The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.

ABSTRACT
In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

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The calculation algorithm used to solve the equations simultaneously.
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pone-0088514-g002: The calculation algorithm used to solve the equations simultaneously.

Mentions: The calculation algorithm to solve the proposed equations is expressed in Fig. 2. In a certain operating time step (), the amount of deposited dust (), penetrated bioaerosols (), deposited bioaerosols (), and surviving bioaersols () were calculated with filtration and antimicrobial efficiencies at the time step of . After the growth rate () and entrainment rate (), the maximum population of microorganisms on the filter () were updated by the calculated result. The existing () and entrained () bioaerosols were calculated according to Eq. 9–10 and Eq. 7–8, respectively. Finally, , which is the sum of the penetrated and entrained bioaerosols, was calculated. In order to compute the next time step, , the filtration efficiency and antimicrobial efficiency were updated by the value of determined at . This full set of steps was then repeated. In order to convert the data per hour to data per day, a summation of data per hour over one day was carried out sequentially.


Methodology for modeling the microbial contamination of air filters.

Joe YH, Yoon KY, Hwang J - PLoS ONE (2014)

The calculation algorithm used to solve the equations simultaneously.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921200&req=5

pone-0088514-g002: The calculation algorithm used to solve the equations simultaneously.
Mentions: The calculation algorithm to solve the proposed equations is expressed in Fig. 2. In a certain operating time step (), the amount of deposited dust (), penetrated bioaerosols (), deposited bioaerosols (), and surviving bioaersols () were calculated with filtration and antimicrobial efficiencies at the time step of . After the growth rate () and entrainment rate (), the maximum population of microorganisms on the filter () were updated by the calculated result. The existing () and entrained () bioaerosols were calculated according to Eq. 9–10 and Eq. 7–8, respectively. Finally, , which is the sum of the penetrated and entrained bioaerosols, was calculated. In order to compute the next time step, , the filtration efficiency and antimicrobial efficiency were updated by the value of determined at . This full set of steps was then repeated. In order to convert the data per hour to data per day, a summation of data per hour over one day was carried out sequentially.

Bottom Line: The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively.It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases.The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.

ABSTRACT
In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

Show MeSH