Detection, Localization, Quantification
Detection, Localization, & Quantification (DLQ)
Reducing methane emissions is a key component of short-term climate action. Methane is a potent greenhouse gas with 84 times more heat trapping potential than carbon dioxide over a 20-year period. Methane has a relatively short lifetime in the atmosphere, meaning that emission reduction efforts can be felt in our lifetime. The oil and gas sector accounts for 32% of anthropogenic methane emissions in the United States, making it a promising avenue for emission reduction. Empirical data and transparent models are key pillars of emission reduction efforts.
A range of methane measurement technologies exists, including satellites, aircraft, and ground-based continuous monitoring systems (CMS). Yet, while satellites and aircraft provide extensive coverage, they lack the sensitivity and measurement frequency needed to reliably detect what are often small, short-lived emissions at the site level. Further, these types of technologies are often unable to accurately account for unique site-level characteristics.
On the other hand, CMS offer real time, granular data, which help to ameliorate many of the issues with existing technologies. But even CMS are limited by the need for an advanced analytical framework to translate raw concentration and wind data into reliable emission estimates and insights about site-level emission behavior. These complexities demand a solution that balances detection sensitivity, localization accuracy, and quantification reliability
Our open-source DLQ algorithm is designed to precisely address these challenges and goals in an effort to strengthen near real time alerting and also inventory development. To do so, the DLQ algorithm is used to estimate methane emission start and end times (detection), source location (localization), and emission rate (quantification) using concentration observations from a network of point-in-space CMS.
To evaluate our DLQ framework, we applied it to a month-long series of controlled methane releases at Methane Emissions Technology Evaluation Center (METEC) testing site with an example release shown below.
Our results from the METEC tests were promising. The DLQ framework detected all emissions, correctly localized 82% of them, and provided quantification estimates with an average error of 3.9% for smaller emissions (<1 kg/h) and 4.3% for larger emissions (>1 kg/h). Importantly, 90% of estimates for larger emissions fell within a percent error range of -49.3% to 78.8%, demonstrating its reliability for higher emission rates, as shown in the following figure.
Comparing the DLQ approach to others, such as satellite-based or bottom-up inventory approaches, emphasizes its strengths. Unlike satellites which have high detection thresholds, the CMS-based DLQ framework captures intermittent and low-rate emissions. Moreover, the DLQ leverages real-time data, which allows for characterizing highly intermittent emissions and enables operators to quickly respond to super-emitter events.
As quantification capabilities improve, CMS can be used directly for site-level quantification and reporting. The current DLQ framework is intended for relatively simple sites where it is reasonable to assume that only one source is emitting at a time. Quantifying multi-source emissions is a considerably harder problem, but a statistical model for doing so is currently under development.
Ultimately, because our DLQ framework is open-source, it can serve as a transparent benchmarking tool for stakeholders in all corners of this field.