Locating and quantifying gas emission sources using remotely obtained concentration data

By B. Hirst, Philip Jonathan, F. González del Cueto, David Randell, Oliver Kosut. 2012

51 citations recorded by the source corpus.

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random eld to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed‘2-‘1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters, including plume model spreading and Lagrangian turbulence time scale, are also estimated. We investigate the performance of the approach rst using a synthetic problem, then apply the method to real airborne data from a 1600km 2 area containing two landlls,

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f8c46bcc8b9e4a8e53d471bcbe4f433be08c9d07