Inferring European fossil fuel CO ₂ emissions using TROPOMI NO ₂ data and sector-based NO x : CO ₂ emission ratios
- 1University of Edinburgh (Edinburgh, United Kingdom)
- 2National Centre for Earth Observation (Leicester, United Kingdom)
Abstract
Accurate monitoring of fossil fuel CO ₂ (ffCO ₂ ) emissions is essential for tracking climate mitigation, yet natural carbon-cycle fluxes often obscure human-induced signals in atmospheric observations. This study presents a proof-of-concept satellite-driven data assimilation framework that uses nitrogen oxides (NO x = NO + NO ₂ ) – short-lived trace gases co-emitted with CO ₂ –, to estimate ffCO ₂ emissions. We estimate European NO x emissions for 2021 by assimilating TROPOMI NO ₂ observations into an Ensemble Kalman Filter (EnKF) framework, optimised within the GEOS-Chem atmospheric transport model. We use a computationally efficient offline treatment of NO x chemistry, enabling large-ensemble inversions while retaining sensitivity to changes in photochemistry. Assimilating these data leads to a systematic reduction in the state vector uncertainty, with the mean uncertainty in total ffCO ₂ emissions over Europe decreasing from 5.6 % in the prior to 3.3 % in the posterior. As well as overall improvement in model agreement with observations that corresponds to an annual correlation increase, Δ r =0.12, derived from the correlation of all daily model–observation pairs across all grid cells in the European domain. By leveraging sector-specific NO x : CO ₂ emission ratios, we translate our posterior NO x flux estimates into corresponding ffCO ₂ estimates that capture enhanced seasonal variability. Our inferred ffCO ₂ emissions exhibit elevated values in autumn and winter, spatially concentrated over major source regions and consistent with surface temperature variability. The inferred posterior adjustments include substantial increases in national emissions in several regions (20 %–91 % in national annual combustion CO ₂ ), highlighting both the sensitivity of the method and the need for further validation and multi-species observational constraints. Independent evaluation against in situ measurements confirms significant improvements in mean error statistics in some regions. And updated national posterior ffCO ₂ emissions show improved agreement with EDGAR across five high emitting European countries. This study demonstrates the potential of ensemble data assimilation and reduced-complexity chemistry to provide a first-order constraint on European ffCO ₂ estimates, establishing a vital foundation for future joint NO ₂ –CO ₂ inversion systems.
Versions
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BibTeX
@article{schooling2026inferring,
title = {{Inferring European fossil fuel CO ₂ emissions using TROPOMI NO ₂ data and sector-based NO x : CO ₂ emission ratios}},
author = {Chlöe N. Schooling and Liang Feng and Ingrid Super and Paul I. Palmer},
journal = {Atmospheric Chemistry and Physics},
year = {2026},
month = oct,
doi = {10.5194/acp-26-13743-2026},
url = {https://acp.copernicus.org/articles/26/13743/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Inferring European fossil fuel CO ₂ emissions using TROPOMI NO ₂ data and sector-based NO x : CO ₂ emission ratios
AU - Chlöe N. Schooling
AU - Liang Feng
AU - Ingrid Super
AU - Paul I. Palmer
PY - 2026
DA - 2026/10/01
JO - Atmospheric Chemistry and Physics
DO - 10.5194/acp-26-13743-2026
UR - https://acp.copernicus.org/articles/26/13743/2026/
AB - Accurate monitoring of fossil fuel CO ₂ (ffCO ₂ ) emissions is essential for tracking climate mitigation, yet natural carbon-cycle fluxes often obscure human-induced signals in atmospheric observations. This study presents a proof-of-concept satellite-driven data assimilation framework that uses nitrogen oxides (NO x = NO + NO ₂ ) – short-lived trace gases co-emitted with CO ₂ –, to estimate ffCO ₂ emissions. We estimate European NO x emissions for 2021 by assimilating TROPOMI NO ₂ observations into an Ensemble Kalman Filter (EnKF) framework, optimised within the GEOS-Chem atmospheric transport model. We use a computationally efficient offline treatment of NO x chemistry, enabling large-ensemble inversions while retaining sensitivity to changes in photochemistry. Assimilating these data leads to a systematic reduction in the state vector uncertainty, with the mean uncertainty in total ffCO ₂ emissions over Europe decreasing from 5.6 % in the prior to 3.3 % in the posterior. As well as overall improvement in model agreement with observations that corresponds to an annual correlation increase, Δ r =0.12, derived from the correlation of all daily model–observation pairs across all grid cells in the European domain. By leveraging sector-specific NO x : CO ₂ emission ratios, we translate our posterior NO x flux estimates into corresponding ffCO ₂ estimates that capture enhanced seasonal variability. Our inferred ffCO ₂ emissions exhibit elevated values in autumn and winter, spatially concentrated over major source regions and consistent with surface temperature variability. The inferred posterior adjustments include substantial increases in national emissions in several regions (20 %–91 % in national annual combustion CO ₂ ), highlighting both the sensitivity of the method and the need for further validation and multi-species observational constraints. Independent evaluation against in situ measurements confirms significant improvements in mean error statistics in some regions. And updated national posterior ffCO ₂ emissions show improved agreement with EDGAR across five high emitting European countries. This study demonstrates the potential of ensemble data assimilation and reduced-complexity chemistry to provide a first-order constraint on European ffCO ₂ estimates, establishing a vital foundation for future joint NO ₂ –CO ₂ inversion systems.
SN - 1680-7324
ER -