Evaluating sensor characteristics for real-time monitoring of high-risk indoor contaminant releases

Rapid detection of toxic agents in the indoor environment is essential for protecting building occupants from accidental or intentional releases. While there is much research dedicated to designing sensors to detect airborne toxic contaminants, little research has addressed how to incorporate such sensors into a monitoring system designed to protect building occupants. To design sensor systems, one must quantify design tradeoffs, such as response time and accuracy, and select values to optimize the performance of an overall system. We illustrate the importance of a systems approach for properly evaluating such tradeoffs, using data from tracer gas experiments conducted in a three-floor building at the Dugway Proving Grounds, Utah. We explore how well a Bayesian interpretation approach can characterize an indoor release using threshold sensor data. We use this approach to assess the effects of various sensor characteristics, such as response time, threshold level, and accuracy, on overall system performance. The system performance is evaluated in terms of the time needed to characterize the release (location, amount released, and duration). We demonstrate that a systems perspective enables selecting sensor characteristics that optimize the system as a whole.