  A new method is proposed for estimating the rate of fugitive emissions of
particulate matter from multiple time-dependent sources via measurements of
deposition and concentration. We cast this source inversion problem within the
Bayesian framework, and use a forward model based on a Gaussian plume solution.
We present three alternate models for constructing the prior distribution on
the emission rates as functions of time. Next, we present an industrial case
study in which our framework is applied to estimate the rate of fugitive
emissions of lead particulates from a smelter in Trail, British Columbia,
Canada. The Bayesian framework not only provides an approximate solution to the
inverse problem, but also quantifies the uncertainty in the solution. Using
this information we perform an uncertainty propagation study in order to assess
the impact of the estimated sources on the area surrounding the industrial
site.
