Dynamic quantification of methane emissions at facility scale using laser tomography: demonstration of a farm deployment

Abstract

Detecting and quantifying greenhouse gas (GHG) emissions is essential for understanding global GHG budgets, updating emission inventories, and evaluating climate change mitigation efforts. Most anthropogenic emissions occur at the scale of facilities, and emission distribution in time and space relates to facility operations. This paper presents a novel GHG monitoring technique for facility-scale, dynamic emission quantification under complex wind conditions, referred to as laser dispersion tomography (LDT), which integrates laser dispersion spectroscopy (LDS) with Bayesian inversion methods. It uses sequential multi-beam open-path LDS measurements and wind data to infer dynamic GHG concentration and source maps at facility scale. In this work, the use of LDT for monitoring methane emissions in agriculture is demonstrated by deploying it on an operational farm. For this aim, computational methods used in data analysis of LDT are also further developed. Particularly, we introduce spatial constraints to the tomographic reconstruction based on prior knowledge on potential source locations -- information often available in facility-scale GHG monitoring applications. We investigate numerically whether such constraints could improve the tolerance of LDT to misrepresentations induced by complex wind fields caused by building effects, and/or presence of interfering external emission sources, both highly likely to characterise a real-world farm environment. The results of numerical studies indicate that including spatial constraints reduces the uncertainty and improves the reliability of source quantification in such conditions, with one simulation case showing an average reduction in posterior uncertainty of 36.2%. In the experimental study, dynamic emission patterns caused by various operations in the farm, such as slurry and dry manure management, are well captured, both temporally and spatially. The results support the feasibility of LDT as a tool for robust quantification of GHG mass emission rates at farms, especially when the spatial constraining of sources is possible. Owing to the fine spatial and temporal resolution of LDT, we foresee its use in improving GHG emission inventories through fine parametrisation, and also its extension to other GHGs and other sectors contributing to global emissions.