Bayesian modeling of exposure and airflow using two-zone models

Yufen Zhang, Sudipto Banerjee, Rui Yang, Claudiu Lungu, Gurumurthy Ramachandran

Research output: Contribution to journalReview article

Abstract

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters.

Original languageEnglish (US)
Pages (from-to)409-424
Number of pages16
JournalAnnals of Occupational Hygiene
Volume53
Issue number4
DOIs
StatePublished - Jun 2009
Externally publishedYes

Fingerprint

Manikins
Toluene
Uncertainty
Occupational Exposure
Ventilation
Hot Temperature
Air
Direction compound

Keywords

  • Bayesian statistics
  • Exposure assessment
  • Indoor air modeling
  • Industrial hygiene
  • Markov chain Monte Carlo
  • Two-zones modeling
  • Worker's exposure

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Bayesian modeling of exposure and airflow using two-zone models. / Zhang, Yufen; Banerjee, Sudipto; Yang, Rui; Lungu, Claudiu; Ramachandran, Gurumurthy.

In: Annals of Occupational Hygiene, Vol. 53, No. 4, 06.2009, p. 409-424.

Research output: Contribution to journalReview article

Zhang, Yufen ; Banerjee, Sudipto ; Yang, Rui ; Lungu, Claudiu ; Ramachandran, Gurumurthy. / Bayesian modeling of exposure and airflow using two-zone models. In: Annals of Occupational Hygiene. 2009 ; Vol. 53, No. 4. pp. 409-424.
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