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.

Second-hand Smoke Exposure Among Home Care Workers (HCWs) in Scotland

“…While HCWs are likely exposed to a clinically insignificant amount of PM 2.5 derived from SHS exposure for much of their time, a worker in a reasonable worst-case scenario ( Marquart et al , 2001 ) may be exposed to approximately 15 µg m ‐3 of additional PM 2.5 over the course of a day compared to a similar colleague not involved in visits to homes where smoking takes place. Increased exposure to PM 2.5 of more than 10 µg m ‐3 over the course of a year have been found to lead to significant increases in both cardiopulmonary (23%) and all-cause mortality (12%) ( Pope et al , 2017 ).…”

Monte Carlo Simulation to Reconstruct Formaldehyde Exposure Levels from Summary Parameters Reported in the Literature

“…In addition to information describing exposure generating processes and tasks, quantitative exposure measurements are reported in a number of studies. Several authors have underlined the limits of the use of published data in the scope of risk analysis (Caldwell et al, 2001;Marquart et al, 2001;Money and Margary, 2002;Tielemans et al, 2002). Hence, some studies lack details about determinants of exposure, about characteristics of the study population or about statistical parameters.…”