Statistical Analyses for Assessing Space-Time Exposure Data

项目摘要 暴露评估方面的技术进步，这是干预、控制和 合规性，最近提高了便携式直接读取的准确性、可靠性和可负担性 监视器。这些监测仪可以快速评估工人暴露在职业危害中的情况。通过将 估计与已知地点的接触，工业卫生员有能力将接触联系到 具体来源。危险在空间上集中的等高线图，称为集中图，具有 最近被用来评估灾害的空间变异性。浓度图有可能成为 强大，因为它们易于经理、暴露的员工和职业健康人员理解 科学家们找到了令人担忧的区域。减少或消除这些领域的暴露将改善工人 健康。虽然我们认为直读仪器有很大的潜力来帮助识别和 为了减少工作场所的暴露危害，在不采取任何措施的情况下采用这种方法是危险的。 了解与这种新形式的暴露评估相关的不确定性。到目前为止，没有统计数据 框架已应用于这些地图。这个项目目标是评估几个统计数字 用直读仪收集的工作场所暴露数据的分析方法。 这个项目有三个具体目标。首先，我们将使用空间统计映射方法来构建 使用先前收集的暴露数据集，可靠地映射暴露估计和置信度区间。 第二，我们将收集作为时间和空间函数的危险集中度的全面数据集 噪声水平和气溶胶浓度暴露量。第三，我们将从以下位置生成参考浓度图 高时间和空间分辨率的数据集，我们可以用它来评估更简单的统计模型。

Project Summary Technological advancements in exposure assessment, a necessary component of intervention, control, and compliance, have recently increased the accuracy, reliability, and affordability of portable, direct-reading monitors. These monitors can rapidly assess worker exposures to occupational hazards. By coupling the estimated exposure with a known location, an industrial hygienist has the ability to connect exposures to specific sources. Contour plots of the hazard concentration over space, known as concentration maps, have recently been used to assess the spatial variability of hazards. Concentration maps have the potential to be powerful because they are easily comprehensible for managers, exposed employees, and occupational health scientists to locate areas of concern. Reducing or eliminating exposures in these areas will improve worker health. While we believe there is great potential for direct-reading instruments to aid in the identification and mitigation of workplace exposure hazards, it can be dangerous to apply such a methodology without understanding the uncertainties associated with this new form of exposure assessment. To date, no statistical framework has been applied to these maps. The goal of this project is to evaluate several statistical approaches for the analysis of workplace exposure data collected with direct-reading instruments. There are three specific aims for this project. First, we will employ a spatial statistical mapping method to build reliable mapped exposure estimates and confidence intervals using a previously collected exposure dataset. Second, we will collect a comprehensive dataset of hazard concentration as a function of time and space for noise level and aerosol concentration exposures. Third, we will generate a reference concentration map from the high temporal and spatial resolution dataset, against which we may evaluate simpler statistical models.