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Enhancing carbon emission reduction strategies using OCO and ICOS data

Åström, Oskar LU ; Geldhauser, Carina ; Grillitsch, Markus LU orcid ; Hall, Ola LU and Sopasakis, Alexandros LU orcid (2025) In Scientific Reports 15.
Abstract

We propose a methodology to enhance local CO2 monitoring by integrating satellite data from the Orbiting Carbon Observatories (OCO-2 and OCO-3) with ground-level observations from the Integrated Carbon Observation System (ICOS) and weather data from the ECMWF Reanalysis v5 (ERA5). Unlike traditional methods that downsample national data, our approach uses multimodal data fusion for high-resolution CO2 estimations. We employ weighted K-nearest neighbor (KNN) interpolation with machine learning models to predict ground-level CO2 from satellite measurements, achieving a Root Mean Squared Error of 3.58 ppm. Our results show the effectiveness of integrating diverse data sources in capturing local emission... (More)

We propose a methodology to enhance local CO2 monitoring by integrating satellite data from the Orbiting Carbon Observatories (OCO-2 and OCO-3) with ground-level observations from the Integrated Carbon Observation System (ICOS) and weather data from the ECMWF Reanalysis v5 (ERA5). Unlike traditional methods that downsample national data, our approach uses multimodal data fusion for high-resolution CO2 estimations. We employ weighted K-nearest neighbor (KNN) interpolation with machine learning models to predict ground-level CO2 from satellite measurements, achieving a Root Mean Squared Error of 3.58 ppm. Our results show the effectiveness of integrating diverse data sources in capturing local emission patterns, highlighting the value of high-resolution atmospheric transport models. The developed model improves the granularity of CO2 monitoring, providing precise insights for targeted carbon mitigation strategies, and represents a novel application of neural networks and KNN in environmental monitoring, adaptable to various regions and temporal scales.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
15
article number
36297
publisher
Nature Publishing Group
external identifiers
  • scopus:105019063933
  • pmid:41107401
  • pmid:41107401
ISSN
2045-2322
DOI
10.1038/s41598-025-22022-1
language
English
LU publication?
yes
id
004335c8-ee5c-4c33-a31f-03806e4554dd
date added to LUP
2025-10-23 06:32:04
date last changed
2026-01-24 03:16:14
@article{004335c8-ee5c-4c33-a31f-03806e4554dd,
  abstract     = {{<p>We propose a methodology to enhance local CO<sub>2</sub> monitoring by integrating satellite data from the Orbiting Carbon Observatories (OCO-2 and OCO-3) with ground-level observations from the Integrated Carbon Observation System (ICOS) and weather data from the ECMWF Reanalysis v5 (ERA5). Unlike traditional methods that downsample national data, our approach uses multimodal data fusion for high-resolution CO<sub>2</sub> estimations. We employ weighted K-nearest neighbor (KNN) interpolation with machine learning models to predict ground-level CO<sub>2</sub> from satellite measurements, achieving a Root Mean Squared Error of 3.58 ppm. Our results show the effectiveness of integrating diverse data sources in capturing local emission patterns, highlighting the value of high-resolution atmospheric transport models. The developed model improves the granularity of CO<sub>2</sub> monitoring, providing precise insights for targeted carbon mitigation strategies, and represents a novel application of neural networks and KNN in environmental monitoring, adaptable to various regions and temporal scales.</p>}},
  author       = {{Åström, Oskar and Geldhauser, Carina and Grillitsch, Markus and Hall, Ola and Sopasakis, Alexandros}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Enhancing carbon emission reduction strategies using OCO and ICOS data}},
  url          = {{http://dx.doi.org/10.1038/s41598-025-22022-1}},
  doi          = {{10.1038/s41598-025-22022-1}},
  volume       = {{15}},
  year         = {{2025}},
}