Quantifying trace gas emissions with measurements at near-source to regional scales

Abstract

Methane is the second most important anthropogenic greenhouse gas, but traditional sampling methodologies and platforms are not well suited for quantifying leak rates from large numbers of highly localized emission sources including well pads and linear segments of pipelines. Expansion of gas and oil infrastructure therefore raises concerns because fugitive emissions of methane can potentially offset the benefit of natural gas which otherwise emits less carbon dioxide per unit of energy than coal or petroleum. Besides methane, localized sources generally suffer from a limited ability to be quantified quickly and accurately, making it difficult to validate emission inventories needed for use in air quality models and trend analysis. To this end, I am designing sampling methodologies and sensing technologies for efficiently quantifying emissions from these sources using observations with fast mobile laboratory platforms and small unmanned aerial systems (sUAS) at multiple spatial scales. The first part of the thesis is to develop a compact and precise methane sensor that is capable of flying on a long duration sUAS and will enable a direct, near-source method for quantifying emissions. Results are presented from a long-duration flight in New Jersey and night-time measurements in Germany. Second, techniques are presented that are both simple and accurate and locate and quantify leaks from well pads using a sUAS at the meter scale. Controlled release testing is performed both independently and as part of a blind-test at a facility in Colorado. Third, a mobile laboratory platform is leveraged to obtain ammonia and methane measurements within several kilometers downwind of 41 animal feedlots in Colorado. This large sample reveals significant site-to-site variability, which can be distinguished from temporal variability. Fourth, the 2018 Kīlauea Volcanic eruption is studied at the island scale using a combination of mobile monitoring, existing surface networks, a Lagrangian vog model, and satellite products from OMI and OMPS. Together, this demonstrates how synthesizing available scientific datasets can be used for the most accurate and complete available picture of an emission event.