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Integration of the Predictions of Two Models with Dose Measurements in a Case Study of Children Exposed to the Emissions from a Lead Smelter

机译:在铅冶炼厂排放儿童的案例研究中,将两种模型的预测与剂量测量相结合

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摘要

The predictions of two source-to-dose models are evaluated with observed data collected in a village polluted by an operating secondary lead smelter. Both models were built up from several sub-models linked together and run using Monte-Carlo simulation. The first model system provides the distribution of the media-specific lead concentrations (air, soil, fruit, vegetables, and blood) in the whole area investigated. The second model provides an estimate of the concentration of exposure of specific individuals living in the study area. The predictions of the first model system were improved by performing a sensitivity analysis and using field data to correct the default value provided for the leaf wet density. However, in this case study, the model system tends to overestimate the exposure due to exposed vegetables. The second model was tested for nine children with contrasting exposure conditions. It managed to capture the blood levels for eight of them. In the last case, the exposure of the child by pathways not considered in the model may explain the failure of the model. The interest of this integrated model is to provide outputs with lower variance than the first model system, but further tests are necessary to conclude about its accuracy.
机译:利用从一个正在运行的二次铅冶炼厂污染的村庄收集到的观测数据,评估了两种来源-剂量模型的预测。这两个模型都是由链接在一起的几个子模型构建的,并使用蒙特卡洛模拟运行。第一个模型系统提供了在整个调查区域中特定于介质的铅浓度(空气,土壤,水果,蔬菜和血液)的分布。第二个模型提供了对居住在研究区域内的特定个体的暴露浓度的估计。通过执行敏感性分析并使用田间数据校正为叶片湿密度提供的默认值,可以改善第一模型系统的预测。但是,在本案例研究中,模型系统往往会高估由于暴露的蔬菜引起的暴露。第二个模型针对暴露条件不同的九名儿童进行了测试。它设法捕获了其中八个的血液水平。在最后一种情况下,通过模型中未考虑的途径对儿童进行的暴露可以解释模型的失败。该集成模型的目的是为输出提供比第一个模型系统更低的方差,但是需要进一步的测试来得出其准确性的结论。

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