ABSTRACT
It is generally agreed that the earlier a release of a chemical or biological agent is detected, the higher the probability of saving lives. Placement of sensors for detection of chemical and/or biological agent release has been studied recently by others (Sohn et al., 2002; Sohn and Lorenzetti, 2007; Chen and Wen, 2008). The focus of many has been on the optimal placement of the sensor based on airow parameters. Chen and Wen (2008) noted that achieving maximum protection based on sensor placement requires sensors to not be placed in areas where the contaminant concentration would be low. Furthermore, Sohn and Lorenzetti (2007) contended that the sensor attributes are the key to determining a probabilistic solution to sensor placement. The common thread between each of the previous studies is that placement is typically based on a complicated algorithm requiring extensive knowledge of heating, ventilation, and air-conditioning (HVAC) parameters; a thorough understanding and application of uid dynamics; and signicant computational effort. The expertise required to implement these approaches puts optimum sensor placement beyond the resource means of those whose buildings are not considered to be high-value targets.
