Methane Emissions: Remote Mapping and Source Quantification Using an Open‐Path Laser Dispersion Spectrometer

Abstract

Reducing man-made greenhouse gas emissions depends on the effective detection and location of sources. We present a new method that remotely detects, locates, and quantifies gas emission rates by sequentially steering an optical beam between multiple retro-reflectors. The novel open-path laser gas sensor uses Laser Dispersion Spectroscopy (LDS), with seven beams up to 98 meters long deployed across open, flat terrain. LDS offers high precision (10-20 ppb), high dynamic range and linearity, enhanced immunity to atmospheric perturbations, with fast response to probe an area in 3 s. Simultaneous wind and concentration data were collected for four calibrated methane gas release schemes with emission rates of 1.3 kg/hr. The resulting data were processed using a Bayesian, Markov chain Monte-Carlo inverse solver to locate the sources and quantify their mass emission rates and uncertainty bounds. All the sources were located to within a few meters and mass emission rates established within the associated confidence bounds.Plain Language Summary The Earth's atmosphere contains 600 times as much CO 2 as methane (by mass), but the warming effect due to the small amount of methane is 58% of that due to all the CO 2 . Furthermore, methane's atmospheric lifetime is~10 yr whereas CO 2 's is~100 yr. So, reducing methane emissions not only provides much greater impact per unit mass but that reduction in atmospheric warming is realized in years not centuries. Many industrial activities produce methane emissions, but difficulties in remotely attributing and quantifying emission rates have severely impeded effective remedial action. We present a novel method to continuously detect, locate, and quantify methane emission sources distributed across extensive areas. We demonstrate its performance in simple controlled tests using a novel optical beam gas sensor to measure path-averaged gas concentrations. The data are analyzed using advanced statistical methods to locate and quantify the emission rates of the sources.

Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach

“…This work followed earlier “proof-of-concept” tests that demonstrated the effectiveness of the technique. 19 The new experiments incorporated improved sensor precision, refinements to the MCMC analysis, and inclusion of blind releases as a more objective measure of performance. The experimental arrangement is shown in Figure 1 .…”

“…Seven eye-safe beam paths radiate from the sensor to 5 cm aperture corner cube retroreflectors. From the demonstration study, 19 seven beams were found to be a good compromise to target a few-meter spatial resolution on locating sources. Path lengths ranged from 99.43 to 50.26 m. A 3D ultrasonic anemometer was installed at the center of the beam array, and it measured wind velocity in the horizontal and vertical planes at 20 Hz.…”

“…Bayesian methods are particularly suited to inverse problems as they allow for the explicit inclusion of prior information about the structure of the solution: for example, we expect the source map to be sparse, the background concentration to evolve smoothly in time and to be correlated between beams, and so forth. The data analysis method and its theoretical basis have been thoroughly described in ref ( 19 ). The posterior distribution is derived from the data likelihood and the parameter prior distributions, 24 and the posterior parameter space is explored through a MCMC algorithm.…”

“…More detail on the inversion algorithm and its mathematical background can be found in ref ( 19 ). For this first blind-trial evaluation, some refinements to the model and MCMC solver were made and compared with the initial demonstration 19 —these are described below: A more realistic prior distribution for the source emission rates was used, as compared with the initial demonstration.…”

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

“…Other works in the literature where emissions are detected, quantified, mapped, or localized include: A mid-wave infrared (MWIR) camera was used to compare eight supervised multivariate methods for detecting oil spills along the coastline in [41]. Using an array of stationary laser fetches, a controlled release emission is estimated using a Bayesian Markov chain Monte Carlo (MCMC) approach in [42]. There have been several works devoted to gas distribution mapping (GDM) using the Kernel DM/V methodology [43], including simultaneous localization and mapping (SLAM) [44].…”