Despite its seemingly low concentration in the atmosphere of less than 0.0002%, methane (CH4) is the second most important anthropogenic greenhouse gas, due to its high global warming potential. Over a period of a hundred years, methane is about thirty times more powerful than CO2. The 2021 IPCC report indicates a 0.28oC contribution to the total global warming of 1.1oC since pre-industrial times. Together with other Dutch partners, SRON has developed key technology for the TROPOMI instrument on the Copernicus Sentinel-5 Precursor mission. SRON is responsible for the CH4 data product and uses this data to estimate methane emissions worldwide. We specifically target super-emitters, for example some landfills or fossil fuel facilities. These have the potential to be the low-hanging fruit in efforts to mitigate global warming. SRON also develops the targeted TANGO mission to observe localized methane emission sources, together with our Dutch partners: ISIS Space, TNO, and KNMI.
The Dutch Tropospheric Monitoring Instrument (TROPOMI) is flying onboard the Copernicus Sentinel-5p mission, launched in 2017. Every day it scans the entire globe for e.g. methane and carbon monoxide concentrations at city-scale level.
The Twin Anthropogenic Greenhouse Gas Observers (TANGO) comprises two cubesats: TANGO-Carbon and TANGO-Nitro. It is a candidate mission of ESA’s SCOUT program to be launched in 2026.
We use machine learning techniques to detect methane plumes in satellite data. The first project focused on detection of methane plumes in TROPOMI data.
The United Nations International Methane Emissions Observatory (IMEO) is setting up a Methane Alert Response System (MARS) to support governments and businesses to reduce their methane emissions as promised under the Methane Pledge.
Emissions from landfills are responsible for about 11% of global human-made methane emissions. We have recently demonstrated we can detect and quantify large landfill methane emissions from space by using a combination of satellites.
CCAC Methane Studies is a project to estimate methane emissions from oil & gas fields using TROPOMI (and other) satellite data.
TROPOMI aboard Sentinel-5p can be used to detect large methane emission plumes everywhere around the world. These plumes are automatically detected using the machine-learning setup described in Schuit et al. (2023, ACPD preprint). The weekly world maps show approximate source locations based on single TROPOMI plumes as well as initial source rate estimates based on an automated mass balance method.
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 the emission plumes from retrieval artifacts. 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 and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the 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, and for the latter, we find up to 10 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.
The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually, and evaluated periodically through a “Global Stocktake.” Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (also called smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25-1.3 Tg CH4/yr/yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Southern Brazilian; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.
This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors.
We collaborate with climate researchers and modelers, and together contribute to the development of physical instruments and the promotion of scientific activities outside SRON.
Most important human-made greenhouse gas
Hard to monitor emissions because of long lifetime
Reactions with atmospheric gases contribute to global warming
Trace gas to calculate CO₂ emissions from forest fires
Small particles in the atmosphere
Largest unknown factor in climate change
Strong impact on air quality
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