This paper presents the application of a Bayesian framework for retrospective exposure assessment of workers in a nickel smelter. Using only sparsely available historical measurements will result in exposure estimates with large uncertainties. However, additional information, in the form of expert judgments informed by knowledge of historical plant conditions, can be brought to bear on this process. The experts are provided with an information packet that contains historical process information, process throughput levels for each year, the dimensions of the workplace, ventilation records, and task descriptions for each job category. Based on this information, the experts provide subjective prior probability distributions for input parameters to a general ventilation model that predicts building concentrations. These priors can be synthesized with the historical measurements using Bayes theorem. The prior distributions of exposures are updated using the average measured exposures (historical measurements) and their associated variances to obtain the posterior probability distributions for building concentrations as well as concentrations at specific locations in the building. Expert input was also obtained from a plant industrial hygienist, in the form of probability distributions, regarding the amounts of time spent by each job category in different locations in the building. Monte Carlo sampling, from the posterior probability distributions of concentrations in different micro-environments and the probability distributions of time spent by each job category in those micro-environments, was used to obtain worker exposures using a time-weighted averaging model.
- Bayesian, sparse data, expert judgment, modeling of exposures
- Monte Carlo
- retrospective, exposure assessment