Energy Analysis and Environmental Impacts Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mail Stop 90R3058, Berkeley, CA 94720, USA.
Risk Anal. 2012 Dec;32(12):2032-42. doi: 10.1111/j.1539-6924.2012.01820.x. Epub 2012 May 2.
We present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. In an earlier article, Sohn and Lorenzetti developed a proof of concept algorithm that assumed samplers could return measurements only slowly (on the order of hours). This led to optimal "detect to treat" architectures that maximize the probability of detecting a release. This article develops a more general approach and applies it to samplers that can return measurements relatively quickly (in minutes). This leads to optimal "detect to warn" architectures that minimize the expected time to detection. Using a model of a real, large, commercial building, we demonstrate the approach by optimizing networks against uncertain release locations, source terms, and sampler characteristics. Finally, we speculate on rules of thumb for general sampler placement.
我们提出了一种概率方法来设计室内采样器网络，以检测意外或故意的化学或生物释放，并针对实际建筑物进行了演示。在早期的一篇文章中，Sohn 和 Lorenzetti 开发了一种概念验证算法，该算法假设采样器只能缓慢地（大约几个小时）返回测量值。这导致了最优的“检测以治疗”架构，最大限度地提高了检测到释放的概率。本文开发了一种更通用的方法，并将其应用于可以相对快速（在几分钟内）返回测量值的采样器。这导致了最优的“检测以警告”架构，该架构最小化了检测到的预期时间。我们使用真实大型商业建筑的模型，通过针对不确定的释放位置、源项和采样器特性来优化网络，演示了该方法。最后，我们对一般采样器放置的经验法则进行了推测。
