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Application of Mathematical Models in Combination with Monte Carlo Simulation for Prediction of Isoflurane Concentration in an Operation Room Theater
Applicability of two mathematical models in inhalation exposure prediction (well mixed room and near field-far field model) were validated against standard sampling method in one operation room for isoflurane. Ninety six air samples were collected from near and far field of the room and quantified b...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Institute of Occupational Safety and Health, Japan
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4202737/ https://www.ncbi.nlm.nih.gov/pubmed/23912206 http://dx.doi.org/10.2486/indhealth.2012-0130 |
Sumario: | Applicability of two mathematical models in inhalation exposure prediction (well mixed room and near field-far field model) were validated against standard sampling method in one operation room for isoflurane. Ninety six air samples were collected from near and far field of the room and quantified by gas chromatography-flame ionization detector. Isoflurane concentration was also predicted by the models. Monte Carlo simulation was used to incorporate the role of parameters variability. The models relatively gave more conservative results than the measurements. There was no significant difference between the models and direct measurements results. There was no difference between the concentration prediction of well mixed room model and near field far field model. It suggests that the dispersion regime in room was close to well mixed situation. Direct sampling showed that the exposure in the same room for same type of operation could be up to 17 times variable which can be incorporated by Monte Carlo simulation. Mathematical models are valuable option for prediction of exposure in operation rooms. Our results also suggest that incorporating the role of parameters variability by conducting Monte Carlo simulation can enhance the strength of prediction in occupational hygiene decision making. |
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