Grenoble Alpes University, TIMC-IMAG (UMR 5525 CNRS - UJF), EPSP Team (Environment and Health Prediction of Populations), UFR de Médecine de Grenoble,F-38000 Grenoble, France.
Grenoble Alpes University, TIMC-IMAG (UMR 5525 CNRS - UJF), EPSP Team (Environment and Health Prediction of Populations), UFR de Médecine de Grenoble, F-38000 Grenoble, France.
Ann Work Expo Health. 2017 May 1;61(4):440-456. doi: 10.1093/annweh/wxx017.
Similar exposure groups (SEGs) are needed to reliably assess occupational exposures and health risks. However, the construction of SEGs can turn out to be rather challenging because of the multifactorial variability of exposures.
The objective of this study is to put forward a semi-empirical approach developed to construct and implement a SEG database for exposure assessments. An occupational database of airborne levels of polycyclic aromatic hydrocarbons (PAHs) was used as an illustrative and working example.
The approach that was developed consisted of four steps. The first three steps addressed the construction and implementation of the occupational Exporisq-HAP database (E-HAP). E-HAP was structured into three hierarchical levels of exposure groups, each of which was based on exposure determinants, along 16 dimensions that represented the sampled PAHs. A fourth step was implemented to identify and generate SEGs using the geometric standard deviation (GSD) of PAH concentrations.
E-HAP was restructured into 16 (for 16 sampled PAHs) 3 × 3 matrices: three hierarchical levels of description versus three degrees of dispersion, which included low (the SEG database: GSD ≤ 3), medium (3 < GSD ≤ 6), and high (GSD > 6). Benzo[a]pyrene (BaP) was the least dispersed particulate PAH with 41.5% of groups that could be considered as SEGs, 48.5% of groups of medium dispersion, and only 8% with high dispersion. These results were comparable for BaP, BaP equivalent toxic, or the sum of all carcinogenic PAHs but were different when individual gaseous PAHs or ∑PAHG were chosen.
Within the framework of risk assessment, such an approach, based on groundwork studies, allows for both the construction of an SEG database and the identification of exposure groups that require improvements in either the description level or the homogeneity degree toward SEG.
为了可靠地评估职业暴露和健康风险,需要类似的暴露组(SEG)。然而,由于暴露的多因素可变性,SEG 的构建可能会变得相当具有挑战性。
本研究的目的是提出一种半经验方法,用于构建和实施暴露评估的 SEG 数据库。使用多环芳烃(PAH)的空气水平职业数据库作为说明性和工作示例。
开发的方法包括四个步骤。前三个步骤解决了职业 Exporisq-HAP 数据库(E-HAP)的构建和实施问题。E-HAP 分为三个层次的暴露组,每个暴露组都基于暴露决定因素,以及代表采样 PAH 的 16 个维度。第四个步骤是使用 PAH 浓度的几何标准差(GSD)来识别和生成 SEG。
E-HAP 被重新构建为 16 个(用于 16 个采样 PAH)3×3 矩阵:三个层次的描述与三个离散度水平,包括低(SEG 数据库:GSD≤3)、中(3<GSD≤6)和高(GSD>6)。苯并[a]芘(BaP)是最分散的颗粒状 PAH,有 41.5%的组可以被认为是 SEG,48.5%的组为中度分散,只有 8%的组为高度分散。对于 BaP、BaP 等效毒性或所有致癌 PAH 的总和,这些结果是可比的,但当选择单个气态 PAH 或∑PAHG 时,结果则不同。
在风险评估框架内,这种基于基础研究的方法既允许构建 SEG 数据库,又允许识别需要在描述水平或 SEG 均匀度方面进行改进的暴露组。