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 paper we outline a
flexible Bayesian framework for parameter inference and exposure prediction. In
particular, we propose using 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
