Persoons Renaud, Maitre Anne, Bicout Dominique J
Environment and Health Prediction in Population Unit, Techniques de l'Ingénierie Médicale et de la Complexité (TIMC) Laboratory Unité Mixte de Recherche (UMR) Centre National de la Recherche Scientifique (CNRS) 5525 Joseph Fourier University, Grenoble, France.
Ann Occup Hyg. 2012 Oct;56(8):934-47. doi: 10.1093/annhyg/mes021. Epub 2012 May 4.
The aims of this study were to estimate inhalation exposure to chemicals and the resulting acute health risks for working scenarios characterized by successive peaks of pollutant concentrations.
A stochastic two-zone model combining a time-varying emission function and field-derived probabilistic distributed input parameter was used to predict both instantaneous and 15-min averaged pollutant concentrations during the decanting operations performed in a pathology laboratory. The location of the workers was taken into account in the model for computing probability distributions of inhalation exposures and for subsequently characterizing hazard quotients (HQ) for health risk purposes. The model was assessed by comparison with repeated individual monitoring performed on the workers during the same tasks.
Modelled inhalation exposure profiles revealed 15-min average concentrations of 1.7 and 208 mg m(-) (3) for formaldehyde (FA) and toluene (TOL), respectively. The individual monitoring performed showed similar average concentrations, with 1.2 and 175 mg m(-) (3) for FA and TOL. No more than three to five successive FA concentration peaks were generally sufficient in the modelling exercise to provide 15-min estimated exposures exceeding short-term exposure limits (STEL). Modelled HQ higher than unity and STEL exceedance probabilities higher than 0.5 were found for FA, whereas estimated TOL health risks were notably lower according to high exposure limits. Estimated inhalation exposure distributions frequently ranged over one order of magnitude for the two pollutants, reflecting both the natural exposure variability and the uncertainty of some of the two-zone model input parameters.
These findings indicate that the developed approach may be useful for modelling occupational exposures and acute health risks related to chemicals in situations involving time-varying emission sources. Modelled exposure distributions may also be used within Bayesian decision analysis frameworks for making exposure judgements and refining risk management measures.
本研究旨在估算污染物浓度呈连续峰值的工作场景中化学物质的吸入暴露量及其所致的急性健康风险。
采用一种随机双区模型，该模型结合了随时间变化的排放函数和现场得出的概率分布输入参数，以预测病理实验室倾倒操作过程中的瞬时污染物浓度和15分钟平均污染物浓度。在计算吸入暴露概率分布以及随后确定健康风险的危害商（HQ）时，模型考虑了工人的位置。通过与工人在相同任务期间进行的重复个体监测结果进行比较，对该模型进行了评估。
模拟的吸入暴露情况显示，甲醛（FA）和甲苯（TOL）的15分钟平均浓度分别为1.7和208毫克/立方米。所进行的个体监测显示出类似的平均浓度，FA和TOL分别为1.2和175毫克/立方米。在建模过程中，通常不超过三到五个连续的FA浓度峰值就足以使15分钟的估计暴露量超过短期暴露限值（STEL）。对于FA，发现模拟的HQ高于1且STEL超标概率高于0.5，而根据高暴露限值，估计的TOL健康风险明显较低。两种污染物的估计吸入暴露分布经常相差一个数量级，这既反映了自然暴露的变异性，也反映了双区模型一些输入参数的不确定性。
这些研究结果表明，所开发的方法可能有助于对涉及随时间变化排放源的情况下与化学物质相关的职业暴露和急性健康风险进行建模。模拟的暴露分布也可用于贝叶斯决策分析框架内，以做出暴露判断并完善风险管理措施。
