Development of a parametrised atmospheric NO x chemistry scheme to help quantify fossil fuel CO 2 emission estimates Article Swipe
C. N. Schooling
,
P. I. Palmer
,
P. I. Palmer
,
A Visser
,
N. Bousserez
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/acp-25-15631-2025
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/acp-25-15631-2025
Success of the Paris Agreement relies on rapid reductions in fossil fuel CO2 (ffCO2) emissions. Atmospheric data can verify the ffCO2 reductions pledged by nations in their nationally determined contributions. However, estimating ffCO2 from atmospheric CO2 is challenging due to natural fluxes and varying backgrounds. One approach is to combine with nitrogen oxides (NOx = NO + NO2), which are co-emitted with CO2 during combustion. A key challenge in using NOx to estimate ffCO2 is the computational cost of modelling atmospheric photochemistry. Additionally, the NO2 : NO column ratio must be well understood to convert model NOx columns to NO2 columns for comparison with satellite data. We use random forest regression to parameterise NOx chemistry, relying only on meteorological parameters and NOx concentration. The regression is trained on outputs from a nested GEOS (Goddard Earth Observing System)-Chem model simulation for mainland Europe in 2019. We develop a monthly NOx chemistry parameterisation that performs well when tested on perturbed emission runs (R2 > 0.95) and on unseen meteorology for 2021 (R2 > 0.79). We also parameterise the NO2 : NO ratio (R2 > 0.99 on perturbed outputs, R2 > 0.92 on unseen meteorology). Additionally, we present an alternative method to predict NOx rates by scaling baseline NOx rates with changes in NOx concentration (R2 = 1.0 on perturbed outputs). Our models reproduce NO2 columns with minimal deviation from full-chemistry models, with reconstruction error smaller than the TROPOspheric Monitoring Instrument (TROPOMI) precision in over 99.9 % of cases, supporting robust ffCO2 inversion efforts. These results provide a robust framework for accurately estimating fossil fuel CO2 emissions from atmospheric data, enabling more reliable monitoring and verification of global emissions reductions.
Related Topics
Concepts
NOx
Troposphere
Atmospheric chemistry
Inversion (geology)
Fossil fuel
Environmental science
Meteorology
Atmospheric sciences
Emission inventory
Nitrogen oxides
Satellite
Scaling
Air pollution
Chemistry
Atmospheric emissions
Atmospheric models
Earth system science
Nitrogen oxide
Atmospheric model
Flux (metallurgy)
Absolute deviation
Random error
Atmospheric dispersion modeling
Regression
Chemical transport model
Regression analysis
Standard deviation
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/acp-25-15631-2025
- OA Status
- gold
- References
- 31
- OpenAlex ID
- https://openalex.org/W7105830406
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105830406Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/acp-25-15631-2025Digital Object Identifier
- Title
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Development of a parametrised atmospheric NO x chemistry scheme to help quantify fossil fuel CO 2 emission estimatesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-11-17Full publication date if available
- Authors
-
C. N. Schooling, P. I. Palmer, P. I. Palmer, A Visser, N. BousserezList of authors in order
- Landing page
-
https://doi.org/10.5194/acp-25-15631-2025Publisher landing page
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://doi.org/10.5194/acp-25-15631-2025Direct OA link when available
- Concepts
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NOx, Troposphere, Atmospheric chemistry, Inversion (geology), Fossil fuel, Environmental science, Meteorology, Atmospheric sciences, Emission inventory, Nitrogen oxides, Satellite, Scaling, Air pollution, Chemistry, Atmospheric emissions, Atmospheric models, Earth system science, Nitrogen oxide, Atmospheric model, Flux (metallurgy), Absolute deviation, Random error, Atmospheric dispersion modeling, Regression, Chemical transport model, Regression analysis, Standard deviationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
31Number of works referenced by this work
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.raw_source_name | Atmospheric Chemistry and Physics |
| primary_location.landing_page_url | https://doi.org/10.5194/acp-25-15631-2025 |
| publication_date | 2025-11-17 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3010561629, https://openalex.org/W2069293643, https://openalex.org/W2138017294, https://openalex.org/W2911964244, https://openalex.org/W4206210420, https://openalex.org/W4245567757, https://openalex.org/W2112983045, https://openalex.org/W4366988447, https://openalex.org/W2967981435, https://openalex.org/W2600399632, https://openalex.org/W2047852430, https://openalex.org/W2749447601, https://openalex.org/W1973208866, https://openalex.org/W788234842, https://openalex.org/W2141519646, https://openalex.org/W2780724679, https://openalex.org/W2141085693, https://openalex.org/W2121085229, https://openalex.org/W3165968561, https://openalex.org/W129650804, https://openalex.org/W4388934302, https://openalex.org/W4284988729, https://openalex.org/W4387802917, https://openalex.org/W2130686417, https://openalex.org/W2021142818, https://openalex.org/W3036222531, https://openalex.org/W4403309092, https://openalex.org/W4408142269, https://openalex.org/W2901580848, https://openalex.org/W4388183655, https://openalex.org/W2138756490 |
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