  This paper proposes a Bayesian framework for localization of multiple sources
in the event of accidental hazardous contaminant release. The framework
assimilates sensor measurements of the contaminant concentration with an
integrated multizone computational fluid dynamics (multizone-CFD) based
contaminant fate and transport model. To ensure online tractability, the
framework uses deep Gaussian process (DGP) based emulator of the multizone-CFD
model. To effectively represent the transient response of the multizone-CFD
model, the DGP emulator is reformulated using a matrix-variate Gaussian process
prior. The resultant deep matrix-variate Gaussian process emulator (DMGPE) is
used to define the likelihood of the Bayesian framework, while Markov Chain
Monte Carlo approach is used to sample from the posterior distribution. The
proposed method is evaluated for single and multiple contaminant sources
localization tasks modeled by CONTAM simulator in a single-story building of 30
zones, demonstrating that proposed approach accurately perform inference on
locations of contaminant sources. Moreover, the DMGP emulator outperforms both
GP and DGP emulator with fewer number of hyperparameters.
