We propose to develop innovative statistical tools for melding exposure models and field data arising from observations measured in a workplace. As a first step, we will construct a rich dataset of exposure scenarios in laboratory exposure chambers and real workplace settings, containing data on exposure determinants such as contaminant generation and ventilation rates and exposure measurements. We will develop a comprehen- sive and computationally feasible Bayesian statistical framework for melding the physical exposure models of occupational hygiene and experimental data from the workplace to effectively account for the sources of uncer- tainty and produce reliable statistical inference (estimation and predictions) for the system output (i.e., exposure) and inputs (i.e., exposure determinants). We will employ a Bayesian framework to validate physical models from monitoring data. Our framework will also include formal statistical measures for validating models with observed field data. We do so by assessing how adequately the models capture features and patterns in the monitoring data, applying sensitivity analysis to the choice of priors, and choosing or selecting a model among a set of com- peting models. We will also develop and disseminate a user-friendly statistical software package that will enable occupational hygienists to implement the proposed methods for a wide variety of physical models to analyze their data in a seamless and convenient manner. Upon successful completion of the project, our developments will allow hygienists to systematically evaluate retrospective exposure, to predict current and future exposure in the absence of the working process or operation, and to estimate exposure with only a small number of air samples with possibly high variability. With only a few monitoring data points, our Bayesian melding framework will provide more precise estimates of exposure than monitoring. With advances in computational methods and inexpensive software implementation, we purport to exalt formal modeling to an indispensable position in the industrial hygienists'armory.
This proposal attempts to advance scientific understanding of the underlying physical processes in occupational hygiene by developing innovative statistical tools for estimating and validating such models using field data. Exposure models can significantly improve the efficiency and effectiveness of risk assessment and management programs by helping to predict exposures for operations that have not yet been installed or by reconstructing exposures for processes that have long disappeared. The proposal outlines statistical methods that can be combined with the exposure model equations to produce a unifying framework for parameter estimation and model validation.
|Arnold, Susan F; Stenzel, Mark; Drolet, Daniel et al. (2016) Using checklists and algorithms to improve qualitative exposure judgment accuracy. J Occup Environ Hyg 13:159-68|