An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events
An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events
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DOI：
10.1111/j.1539-6924.2006.00742.x
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发表时间：
2006-04-01
期刊：
影响因子：
3.8
通讯作者：
Fischbeck, PS
中科院分区：
文献类型：
--
作者：
Dombroski, MJ;Fischbeck, PS
放射性弥散装置(RDD)或“脏”炸弹是一种包裹在放射性材料中的常规炸药。恐怖分子可能使用RDD在人口稠密地区散布放射性物质，造成人员伤亡和/或经济损失。几乎所有的RDDS风险评估模型在其健康评估中都对公众行为做出了不切实际的假设，包括公众将无限期地站在户外的假设。在这篇文章中，我们描述了一种评估RDD事件风险的方法，其中包括物理分散和行为反应变量。以宾夕法尼亚州匹兹堡市为例，对一般方法进行了检验。大气模型模拟RDD攻击及其可能的尘埃，而辐射暴露模型则评估致命的癌症风险。我们根据一天中的不同时间对人口的不同地理分布进行建模。我们评估不同公共应对措施(即就地避难、疏散)的总体健康影响。我们发现，当前使用的RDD模型可以通过集成行为组件来改进。利用模型的结果，我们展示了风险如何在几个行为变量和物理变量之间变化。我们表明，向公众推荐的最佳政策取决于许多不同的变量，例如世贸中心遗址的创伤数量，急救人员将创伤受害者快速有效地送往当地医院的能力，城市疏散的速度，以及避难所可用的屏蔽量。使用参数分析，我们开发了行为现实的风险评估，我们确定了可以影响最佳风险降低策略的变量，我们发现，通过在各种RDD情景发生之前评估创伤和癌症死亡之间的权衡，可以改善决策。
A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.