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High-resolution U.S. methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills

انبعاثات الميثان الأمريكية عالية الدقة المستخلصة من عكس بيانات الأقمار الصناعية TROPOMI لعام 2019: مساهمات من الولايات الفردية والمناطق الحضرية ومدافن النفايات
Authors: H. Nesser; H. Nesser; D. J. Jacob; J. D. Maasakkers; A. Lorente; Z. Chen; X. Lu; +10 Authors

High-resolution U.S. methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills

Abstract

Abstract. We quantify 2019 methane emissions in the contiguous U.S. (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the U.S. Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a-1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find livestock emissions of 10.4 (10.0–10.7) Tg a-1, oil and gas of 10.4 (10.1–10.7) Tg a-1, coal of 1.5 (1.2–1.9) Tg a-1, landfills of 6.9 (6.4–7.5) Tg a-1, wastewater of 0.6 (0.5–0.7), and other anthropogenic sources of 1.1 (1.0–1.2) Tg a-1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 73 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI’s new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 34 % larger than the 2022 GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a-1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Related Organizations
Keywords

Atmospheric Science, Atmospheric sciences, Environmental Engineering, QC1-999, Astronomy, Organic chemistry, Stratospheric Chemistry and Climate Change Impacts, Oceanography, Greenhouse gas, Inversion (geology), Environmental science, Low-Cost Air Quality Monitoring Systems, Meteorology, Atmospheric Aerosols and their Impacts, High resolution, QD1-999, Atmospheric Composition, Global and Planetary Change, Methane emissions, Atmospheric methane, Geography, Physics, FOS: Environmental engineering, Air Quality Monitoring, Paleontology, Fossil fuel, Geology, Geomorphology, FOS: Earth and related environmental sciences, Remote sensing, Structural basin, Earth and Planetary Sciences, Chemistry, Emissions, Satellite, Global Methane Emissions and Impacts, Environmental Science, Physical Sciences, Methane

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Average
Top 10%
hybrid