Exposure assessment models are deterministic models derived from physical–chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters such as generation and ventilation rates are usually of interest since they are difficult to obtain. In this article, we outline a flexible Bayesian framework for parameter inference and exposure prediction. In particular, we devise Bayesian state space models by discretizing the differential equation models and incorporating information from observed measurements and expert prior knowledge. At each time point, a new measurement is available that contains some noise, so using the physical model and the available measurements, we try to obtain a more accurate state estimate, which can be called filtering. We consider Monte Carlo sampling methods for parameter estimation and inference under nonlinear and non-Gaussian assumptions. The performance of the different methods is studied on computer-simulated and controlled laboratory-generated data. We consider some commonly used exposure models representing different physical hypotheses. Supplementary materials for this article are available online.
Bibliographical noteFunding Information:
The work of the second author was supported (in part) by federal grants NSF/DMS 1513654, NSF/IIS 1562303, NIH/NIEHS 1R01ES027027, and NIH/NIEHS R01ES030210. The authors thank the editor, associate editor, and two anonymous referees for several constructive suggestions.
© 2019, © 2019 American Statistical Association and the American Society for Quality.
- Bayesian modeling
- Exposure assessment
- Industrial hygiene
- Kalman filters
- Physical models
- State-space modeling