Abstract
A method is presented for ranking task-specific exposures using time-weighted average samples collected during the performance of multiple tasks. The task ranking can be used for purposes such as prioritizing further assessment or control. No a priori estimates of the individual task concentration distributions are required. Sample concentrations and task-specific concentrations are assumed to be log-normally distributed, and each known sample concentration is modeled as the geometric time-weighted average of the unknown task concentrations. Since the task durations are usually not known, the task time-weights are estimated as a crude fraction of sampling period. Log transformed sample concentrations are aggregated based on the non-occurrence of each task during some samples, resulting in a set of linear equations which are solved to yield estimates of the log-transformed task median concentrations. The performance of the method was tested under a variety of conditions using simulated sample data. The method was found to yield remarkably reliable ranking of task median concentrations, especially for the high exposure tasks, provided that the number of samples was adequate and the task concentration distributions were not highly overlapped. The performance of the method can be easily modeled using simulated data over the range of plausible task concentration distributions for any number of samples and any job scenario. Even under the conservative assumption that some task concentration distributions are highly overlapped, the assigned ranking can be usefully interpreted in the light of the modeling to determine whether a task is relatively high exposure or low exposure.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call