Locating and quantifying gas emission sources using remotely obtained concentration data

TL;DR

This paper presents a novel method for detecting, locating, and quantifying atmospheric gas emission sources using remote concentration data, combining statistical modeling and atmospheric dispersion models, demonstrated on methane data from aircraft.

Contribution

The paper introduces a new integrated approach combining Gaussian mixture models, Markov random fields, and Bayesian inference for source detection and quantification from remote sensing data.

Findings

Effective detection and quantification of gas sources demonstrated on real aircraft data.

Accurate estimation of source locations and emission rates with quantified uncertainties.

Method successfully applied to complex real-world scenarios involving landfills and gas flares.

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 are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack.