Sudden releases of a toxic agent indoors can cause immediate and long-term harm to occupants. In order to protect building occupants from such threats, it is necessary to have a robust air monitoring system that can detect, locate, and characterize accidental or deliberate toxic gas releases. However, developing such a system is complicated by several requirements, in particular the need to operate in real-time. This task is further complicated when monitoring sensors are prone to false positive and false negative readings. We report on work towards developing an indoor monitoring system that is robust even in the presence of poor quality sensor data. The algorithm, named BASSET, combines deterministic modeling and Bayesian statistics to join prior knowledge of the contaminant transport in the building with real-time sensor information. We evaluate BASSET across several data sets, which varyin sensor characteristics such as accuracy, response time, and trigger level. Our results suggest that optimal designs are not always intuitive. For example, a network comprised of slower but more accurate sensors may locate the contaminant source more quickly than a network with faster but less accurate sensors.