IRSN-Institute for Radiological Protection and Nuclear Safety, Internal Dosimetry Department, IRSN/DRPH/SDI, B.P. 17, F-92262 Fontenay-aux-Roses Cedex, France.
Health Phys. 2010 Oct;99(4):517-22. doi: 10.1097/HP.0b013e3181cd3d47.
Potential internal contaminations of workers are monitored by periodic bioassays interpreted in terms of intake and committed effective dose through biokinetic and dosimetric models. After a prospective evaluation of exposure at a workplace, a suitable monitoring program can be defined by the choice of measurement techniques and frequency of measurements. However, the actual conditions of exposure are usually not well defined and the measurements are subject to errors. In this study we took into consideration the uncertainties associated with a routine monitoring program in order to evaluate the minimum intake and dose detectable for a given level of confidence. Major sources of uncertainty are the contamination time, the size distribution and absorption into blood of the incorporated particles, and the measurement errors. Different assumptions may be applied to model uncertain knowledge, which lead to different statistical approaches. The available information is modeled here by classical or Bayesian probability distributions. These techniques are implemented in the OPSCI software under development. This methodology was applied to the monitoring program of workers in charge of plutonium purification at the AREVA NC reprocessing facility (La Hague, France). A sensitivity analysis was carried out to determine the important parameters for the minimum detectable dose. The methods presented here may be used for assessment of any other routine monitoring program through the comparison of the minimum detectable dose for a given confidence level with dose constraints.
工人的潜在内污染通过定期生物监测来监测，通过生物动力学和剂量学模型来解释摄入和实际有效剂量。在对工作场所的暴露进行前瞻性评估后，可以通过选择测量技术和测量频率来定义合适的监测方案。然而，实际的暴露情况通常没有得到很好的定义，而且测量结果存在误差。在这项研究中，我们考虑了常规监测方案相关的不确定性，以便评估在给定置信水平下可检测到的最小摄入量和剂量。不确定性的主要来源包括污染时间、颗粒的尺寸分布和血液吸收率，以及测量误差。为了对不确定的知识进行建模，可以采用不同的假设，这会导致不同的统计方法。在这里，我们使用经典或贝叶斯概率分布来模拟这些信息。这些技术在正在开发的 OPSCI 软件中实现。该方法应用于 AREVA NC 后处理设施（法国拉阿格）负责钚纯化的工人的监测计划。进行了敏感性分析，以确定最小可检测剂量的重要参数。通过将给定置信水平下的最小可检测剂量与剂量限制进行比较，本研究中提出的方法可用于评估任何其他常规监测方案。
