Rapidly Locating Sources and Predicting Contaminant Dispersion in Buildings
Contaminant releases in or near a building can lead to significant human exposures unless prompt response measures
are taken. However, selecting the proper response depends in part on knowing the source locations, the amounts released,
and the dispersion characteristics of the pollutants. We are developing a Bayesian statistical approach that estimates
this information in real time by synthesizing data streams from multiple local sensors. The approach will predict the
pollutant release conditions and the operating state of the building - including uncertainty estimates - and continuously
update the predictions as measurements stream in from the sensors. Figure 1 depicts our approach. We have applied the
approach to a hypothetical release in a five-room building
(Download, 875.5 KB, 12 pp). We also
have preliminary results from an application to a real building and dataset
(Download, 265.5 KB, 6 pp).
