Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.</p

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

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.</p

Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58–1.0 Wm−2 a−1 over the 2000–2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3–5.0  ×  10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000–2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear-sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm−2 a−1 at Goodwin Creek, MS, and +0.93 Wm−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990–2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk

Measuring Carbon Dioxide Emissions From Liquefied Natural Gas (LNG) Terminals With Imaging Spectroscopy

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. We present a method for measuring LNG terminal CO2 emissions using remote sensing imaging spectroscopy. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron. The power plant data set shows a robust correlation between our estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, eight point sources are identified with emission rates from 219.69&nbsp;±&nbsp;54.95 to 1,083.22&nbsp;±&nbsp;308.06&nbsp;t/hr. At Cameron, three point sources are identified with emission rates from 91.64&nbsp;±&nbsp;25.81 to 265.61&nbsp;±&nbsp;67.80&nbsp;t/hr. The liquefaction carbon intensity estimates also align with past study ranges. This illustrates that remote sensing can validate environmental reporting and CO2 inventories for industrial facilities

Empirical quantification of methane emission intensity from oil and gas producers in the Permian basin

Methane (CH _4 ) emissions from the oil and natural gas (O&G) supply chain have been demonstrated to be one of the largest anthropogenic greenhouse gas emission sources ripe for mitigation to limit near-term climate warming. In recent years, exploration and production (E&P) operators have made public commitments to reducing their greenhouse gas emission intensity, yet little empirical information has been made available in the public domain to allow an accurate comparison of their emissions performance. In this study, we utilize a series of aircraft surveys of large CH _4 point source emissions (∼10 ^1 –10 ^4 kg CH _4 hr ^−1 ) related to O&G production in the Permian Basin to enable comparison of company-level production-sector emission intensities. We calculate gas and total energy production normalized emission intensities for several of the largest E&P operators in the Permian Basin accounting for ∼85% of production within the flight region. We find differences of more than an order of magnitude in emission intensity across operators, with nearly half demonstrating a ⩾50% improvement in performance from 2019 to 2021. With the availability of such publicly attributed emissions data anticipated to increase in the future, we provide methodological insights and cautions to developing operator metrics from future empirical datasets