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

Abstract. 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. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, is important 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 TROPOMI XCH₄ dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands and 46.5 % by unknown sources. Many of the identified PPSRs are well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas, the Bowen Basin coal mining area in Australia, or the Pantanal wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil and gas dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties with results from other studies, but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil and gas production basins.