Abstract
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 field 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 ℓ2-ℓ1 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 first using a synthetic problem, then apply the method to real airborne data from a 1600km2 area containing two landfills, then a 225km2 area containing a gas flare stack.
Original language | English (US) |
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Pages (from-to) | 141-158 |
Number of pages | 18 |
Journal | Atmospheric Environment |
Volume | 74 |
DOIs | |
State | Published - Aug 2013 |
Externally published | Yes |
Keywords
- Atmospheric background gas
- Bayesian inversion
- Gaseous emissions
- Gaussian mixture model
- Random field modelling
- Remote sensing
- Reversible jump MCMC
ASJC Scopus subject areas
- General Environmental Science
- Atmospheric Science