Locating and quantifying gas emission sources using remotely obtained concentration data Bill Hirst a , Philip Jonathan b, * , Fernando González del Cueto c , David Randell b , Oliver Kosut d a Shell Projects and Technology, Rijswijk, The Netherlands b Shell Projects and Technology, Thornton, UK c Shell Projects and Technology, Houston, USA d Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, USA highlights  Airborne method to characterise ground sources of emissions to the atmosphere.  Concentration modelled as sum of smooth background plus source contributions.  Gaussian plume eddy dispersion model.  Bayesian inference using reversible jump MCMC.  Markov random field background, Gaussian mixture model for sources. article info Article history: Received 9 November 2012 Received in revised form 15 March 2013 Accepted 21 March 2013 Keywords: Remote sensing Gaseous emissions Atmospheric background gas Bayesian inversion Gaussian mixture model Random field modelling Reversible jump MCMC 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 emis- sion 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 [ 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 first using a synthetic problem, then apply the method to real airborne data from a 1600 km 2 area containing two landfills, then a 225 km 2 area containing a gas flare stack. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction There is growing interest in developing methods for detecting and locating sources of gas emissions into the atmosphere. Greenhouse gases are of intense interest (e.g. Chen and Prinn, 2006; Shakhova et al., 2010). Other applications include moni- toring toxic gas emissions, locating explosives from their volatile emissions (e.g. Bhattacharjee, 2008), mapping naturally occurring gas seeps for oil and gas exploration (e.g. Hirst et al., 2004), iden- tifying sources of nuisance odours, and even understanding how moths are able to find mates by detecting pheromones at concen- trations corresponding to individual molecules (e.g. Vergassola et al., 2007). For greenhouse gases and oil and gas exploration the goal is to locate sources and quantify emission rates. For * Corresponding author. Tel.: þ44 151 272 5421; fax: þ44 151 44 5384. E-mail addresses: ygraigarw@gmail.com, philip.jonathan@shell.com (P. Jonathan). Contents lists available at SciVerse ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.03.044 Atmospheric Environment 74 (2013) 141e158