Automated detection and monitoring of methane super-emitters using satellite data

Abstract

Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit providing daily global coverage of methane mixing ratios at a resolution of up to 7 × 5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a Convolutional Neural Network to detect plume-like structures in the methane data and subsequently apply a Support Vector Classifier to distinguish emission plumes from retrieval artefacts. The models are trained on pre-2021 data, and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021 with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas/landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %) and coal mines (20 %). For twelve (clusters of) TROPOMI detections, we "tip-and-cue" targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, for the latter we find up to ten facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria

“…TROPOMI is an atmospheric monitoring instrument that provides daily global coverage of column-averaged, dry air mixing ratios of methane (XCH 4 ; in ppb) at a resolution of 7.5 km × 5.5 km in nadir, based on measurements in the shortwave infrared (SWIR) spectrum at 2.3 μm. , TROPOMI can detect large (>8 t/h) concentrated methane sources in a single overpass, , and it frequently detects plumes in both of our study areas. We use version 2.04 of the TROPOMI-CH 4 product .…”

“…We use Sentinel-2 to detect emissions from individual superemitters and provide a first quantification of their emission rates using a mass-balance approach. For the detection of Sentinel-2 sources, both the Hassi R'Mel and Hassi Messaoud areas were explored, partly guided by plume detections in TROPOMI data as in Schuit et al 13 We note that detection of individual sources is limited by the Sentinel-2 spatial resolution (20 m) and that we cannot separate sources located closer than that. Once a superemitter source is detected with Sentinel-2, we construct a 2020 time series for that source by manually labeling all scenes as "with plume", "no plume", or "bad data" (e.g., cloudy days).…”

“…8,13 The availability of global data from both TROPOMI and Sentinel-2 makes such an approach feasible in many areas. Especially regions with low cloud cover and uniform surface albedo are well-suited to satellite-based analyses (e.g., Libya, Iran), although satellite detection of emissions is also possible in, e.g., more northern regions (e.g., Schuit et al 13 ).…”

“…However, it is challenging to capture emission plumes in these relatively coarse region transport model simulations due to limited model resolution, transport errors, and the large volume of satellite data that makes it time-intensive to verify transport model performance on individual emission events. On the other hand, studies that focus on emission events [e.g., refs ,− ] or satellite-observed plumes in general , do not integrate these estimates in satellite-constrained area estimates. Therefore, the long-term area emission estimates do not fully exploit the increased data density of new satellites, while event-focused work only informs on emissions that result in large, concentrated plumes.…”

“…In recent years, the number of satellites that measure atmospheric methane has quickly expanded . In particular, the launch of the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P has been a major step forward, as it is the first instrument with daily, global coverage that can detect individual superemissions above 8 t/h [e.g., ref ] due to its high precision and spatial resolution of up to 7.5 km × 5.5 km at nadir. , However, in most cases it is not possible to link methane plumes detected by TROPOMI to individual facilities due to its spatial resolution.…”