Bayesian modeling of exposure and airflow using two-zone models.
Bayesian modeling of exposure and airflow using two-zone models.
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使用两区模型对暴露和气流进行贝叶斯建模。
DOI：
10.1093/annhyg/mep017
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Ramachandran,Gurumurthy
中科院分区：
文献类型：
--
作者：
Zhang,Yufen;Banerjee,Sudipto;Yang,Rui;Lungu,Claudiu;Ramachandran,Gurumurthy
数学模型越来越多地被用作评估职业暴露的手段。然而，由于缺乏对接触决定因素的定量知识，在真实的环境中预测接触受到限制。因此，在职业环境中验证模型是一项挑战。不仅需要知道模型参数，模型还需要以一定的精度预测输出。在本文中，贝叶斯统计框架用于估计模型参数和暴露浓度的两个区域模型。该模型根据甲苯产生速率、通过室的空气通风速率以及近场和远场之间的气流来预测靠近源和远离源的区域中的浓度。该框架将物理模型上的先验或专家信息与观测数据沿着组合。该框架适用于模拟数据，以及从实验室中进行的数据。在不同的气流方向、人体模型的存在和人体模型的模拟体热条件下，从源产生甲苯蒸气。贝叶斯框架解释了测量中的不确定性以及近场和远场之间的未知气流速率。结果表明，该方法对层间气流的估计总是接近于平衡解，说明该方法是有效的。模拟和真实的数据的近场浓度预测结果与真实值吻合较好，表明双区模型的假设与实际情况吻合较好，适用于污染物浓度的预测。因此，比较的估计模型和它的误差幅度与实验数据，使验证的物理模型假设。该方法说明了如何暴露模型和模型参数的信息，以及这些数量的不确定性和可变性的知识，不仅可以用来提供更好的估计模型输出，而且模型参数。
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.