Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Locating and quantifying gas emission sources using remotely obtained concentration data
AU - Hirst, B.
AU - Jonathan, P.
AU - González del Cueto, F.
AU - Randell, D.
AU - Kosut, O.
PY - 2013
Y1 - 2013
N2 - 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. © 2013 Elsevier Ltd.
AB - 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. © 2013 Elsevier Ltd.
KW - Atmospheric background gas
KW - Bayesian inversion
KW - Gaseous emissions
KW - Gaussian mixture model
KW - Random field modelling
KW - Remote sensing
KW - Reversible jump MCMC
KW - Background gas
KW - Gaussian Mixture Model
KW - Random fields
KW - Atmospheric movements
KW - Image segmentation
KW - Markov processes
KW - Methane
KW - Uncertainty analysis
KW - Gas emissions
KW - methane
KW - aircraft emission
KW - atmospheric gas
KW - atmospheric plume
KW - Gaussian method
KW - Lagrangian analysis
KW - landfill
KW - Markov chain
KW - Monte Carlo analysis
KW - numerical model
KW - point source pollution
KW - remote sensing
KW - timescale
KW - aircraft
KW - article
KW - atmosphere
KW - Bayes theorem
KW - gas
KW - Monte Carlo method
KW - plume
KW - priority journal
KW - temperature
KW - velocity
KW - wind
U2 - 10.1016/j.atmosenv.2013.03.044
DO - 10.1016/j.atmosenv.2013.03.044
M3 - Journal article
VL - 74
SP - 141
EP - 158
JO - Atmospheric Environment
JF - Atmospheric Environment
SN - 1352-2310
ER -