Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods

To maintain a healthful and secure indoor environment, it is crucial to design an effective indoor air quality (IAQ) sensor network and interpret sensor outputs for prompt IAQ controls. This paper introduces how a probability concept based inverse modeling method – the adjoint probability method – can be used to assist in designing a high-performance IAQ sensor network and identifying potential contaminant source locations for an entire building based on limited sensor outputs. The study proposes a new IAQ sensor network design and optimization method for buildings with one or more compartments on the basis of the probability calculation. With responses from optimized sensors, a two-stage integrated inverse prediction algorithm is developed that is able to identify a potential IAQ source zone (or room) in a building as well as an exact location within the room. The paper demonstrates the design of sensor networks and the application of the source identification algorithms for a residential dwelling. The case study verifies the feasibility, effectiveness and accuracy of the proposed sensor design method and the two-stage algorithm for indoor contaminant control.