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Although a great deal of research effort has been focused on the forward prediction of the dispersion of contaminants
(e.g., chemical and biological warfare agents) released into the turbulent atmosphere, much less work
has been directed toward the inverse prediction of agent source location and strength from the measured concentration,
even though the importance of this problem for a number of practical applications is obvious. In
general, the inverse problem of source reconstruction is ill-posed and unsolvable without additional information.
It is demonstrated that a Bayesian probabilistic inferential framework provides a natural and logically consistent
method for source reconstruction from a limited number of noisy concentration data. In particular, the Bayesian
approach permits one to incorporate prior knowledge about the source as well as additional information regarding
both model and data errors. The latter enables a rigorous determination of the uncertainty in the inference of
the source parameters (e.g., spatial location, emission rate, release time, etc.), hence extending the potential of
the methodology as a tool for quantitative source reconstruction.
A model (or, source-receptor relationship) that relates the source distribution to the concentration data
measured by a number of sensors is formulated, and Bayesian probability theory is used to derive the posterior
probability density function of the source parameters. A computationally efficient methodology for determination
of the likelihood function for the problem, based on an adjoint representation of the source-receptor relationship,
is described. Furthermore, we describe the application of efficient stochastic algorithms based on Markov chain
Monte Carlo (MCMC) for sampling from the posterior distribution of the source parameters, the latter of
which is required to undertake the Bayesian computation. The Bayesian inferential methodology for source
reconstruction is validated against real dispersion data for two cases involving contaminant dispersion in highly
disturbed flows over urban and complex environments where the idealizations of horizontal homogeneity and/or
temporal stationarity in the flow cannot be applied to simplify the problem. Furthermore, the methodology is
applied to the case of reconstruction of multiple sources.
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