Assessing reasonable worst-case full-shift exposure levels from data of variable quality.

Abstract

Exposure assessors involved in regulatory risk assessments often need to estimate a reasonable worst-case full-shift exposure level from very limited exposure information. Full-shift exposure data of very high quality are rare. A full-shift value can also be calculated from (short term) task-based values, either derived from measured data or from models. The most simple option is to use the task based exposure levels as the full-shift value. A second option is to calculate a time-weighted average (TWA), using (reasonable worst case) estimates of the duration and the exposure level of the relevant tasks. The third option is to use a Monte Carlo analysis with estimated input distributions for exposure level and duration of exposure. If an estimated distribution of respiratory volume is also included, this leads to a distribution of inhaled amounts. The 90th percentile of such a distribution is generally substantially lower than the fixed point estimates calculated using high end values for each parameter. This technique can thus prevent unnecessary conservative estimates in risk assessment. The output distribution can also be used as valuable input to the risk management process, because it provides information on probabilities of exposure levels, that can influence the cost-benefit analysis of the risk management process. Finally, the sensitivity analysis of Monte Carlo simulation can give guidance for further studies to increase the accuracy of the exposure assessment.