Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Methane (CH₄) is an important anthropogenic greenhouse gas and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources is responsible for a large share of global methane emissions. Methane sources often show large uncertainties regarding their emissions or locations, especially at local scales, making their detection and quantification inevitable to support mitigating climate change.

The TROPOspheric Monitoring Instrument (TROPOMI) onboard on the Sentinel-5 Precursor (S5P) satellite, launched in October 2017, provides measurements of the column-averaged dry-air mole fraction of atmospheric methane (XCH₄) with a daily global coverage and a high spatial resolution of up to  km², enabling the detection and quantification of localized methane sources.

We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly XCH₄ TROPOMI dataset from the years 2018-2021, generated with the WFM-DOAS retrieval algorithm, developed at the University of Bremen. The detection process comprises several steps, including an analysis of the monthly dataset, where we first characterize each region by several quantities, such as the number of months in which the region shows a methane enhancement, and then marking the regions that fulfill the defined persistence criteria. We detect more than 200 potential persistent source regions (PPSRs), which account for about 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases we attribute one of the following source types to each detected region: coal, oil and gas, other anthropogenic sources (such as landfills or agriculture), wetlands, or unknown. Many of the detected regions are well-known methane source regions, like large oil and gas fields (e.g., Permian Basin in the USA, Galkynish and Dauletabad in Turkmenistan), coal mining areas (e.g., Bowen Basin in Australia, Upper Silesia Coal Basin in Poland), regions including large urban cities (Dhaka in Bangladesh, Mumbai in India, Rio de Janeiro in Brazil) or wetland areas (e.g., Pantanal in Brazil, Sudd in South Sudan).

In this presentation, the algorithm and some results, including a global overview of the detected regions and a more detailed analysis for some of the regions, are presented.