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Keywords:

  • CONTAM;
  • indoor airflow;
  • optimization;
  • sampler networks

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.