Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
11 May 2023
Technical note |
| 11 May 2023
| 11 May 2023
Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques
Jean-Maxime Bertrand
Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Alata, BP2, 60550 Verneuil-en-Halatte, France
Frédérik Meleux
CORRESPONDING AUTHOR
Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Alata, BP2, 60550 Verneuil-en-Halatte, France
Anthony Ung
Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Alata, BP2, 60550 Verneuil-en-Halatte, France
Gaël Descombes
Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Alata, BP2, 60550 Verneuil-en-Halatte, France
Augustin Colette
Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Alata, BP2, 60550 Verneuil-en-Halatte, France
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Total article views: 3,249 (including HTML, PDF, and XML)
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Cited
18 citations as recorded by crossref.
- Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method A. Riccio & E. Chianese
- Technical note: An interactive dashboard to facilitate quality control of in-situ atmospheric composition measurements Y. Brugnara et al.
- Improving WRF-Chem aerosol optical depth prediction over India using artificial neural network A. Khan et al.
- Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast S. Shetty et al.
- Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast R. Sarafian et al.
- Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al.
- Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring S. Shetty et al.
- Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0 A. Colette et al.
- Interpretable long-horizon air pollution forecasting using a transformer-based framework Z. Zhang et al.
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al.
- Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study P. Fazzini et al.
- High-resolution modelling of particulate matter chemical composition over Europe: brake wear pollution A. Upadhyay et al.
- Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations J. Pernov et al.
- Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks M. Karmoude et al.
- A Machine Learning Approach for High-Resolution Modeling and Apportionment of Black Carbon Concentrations in an Impacted Community S. Hamilton & R. Harley
- Prisma-Based Review Of Mis Solutions For Enhanced Disaster Response And Resource Allocation E. Haque & Z. Hasan
- A causal analysis of ground-level ozone, meteorological factors, and other air pollutants: an in-depth AI-based study applied to the climate of Craiova City, Romania Y. El Mghouchi & M. Udristioiu
- Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment M. Ramadan et al.
18 citations as recorded by crossref.
- Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method A. Riccio & E. Chianese
- Technical note: An interactive dashboard to facilitate quality control of in-situ atmospheric composition measurements Y. Brugnara et al.
- Improving WRF-Chem aerosol optical depth prediction over India using artificial neural network A. Khan et al.
- Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast S. Shetty et al.
- Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast R. Sarafian et al.
- Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al.
- Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring S. Shetty et al.
- Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0 A. Colette et al.
- Interpretable long-horizon air pollution forecasting using a transformer-based framework Z. Zhang et al.
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al.
- Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study P. Fazzini et al.
- High-resolution modelling of particulate matter chemical composition over Europe: brake wear pollution A. Upadhyay et al.
- Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations J. Pernov et al.
- Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks M. Karmoude et al.
- A Machine Learning Approach for High-Resolution Modeling and Apportionment of Black Carbon Concentrations in an Impacted Community S. Hamilton & R. Harley
- Prisma-Based Review Of Mis Solutions For Enhanced Disaster Response And Resource Allocation E. Haque & Z. Hasan
- A causal analysis of ground-level ozone, meteorological factors, and other air pollutants: an in-depth AI-based study applied to the climate of Craiova City, Romania Y. El Mghouchi & M. Udristioiu
- Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment M. Ramadan et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
Post-processing methods based on machine learning algorithms were applied to refine the forecasts of four key pollutants at monitoring sites across Europe. Performances show significant improvements compared to those of the deterministic model raw outputs. Taking advantage of the large modelling domain extension, an innovative
globalapproach is proposed to drastically reduce the period necessary to train the models and thus facilitate the implementation in an operational context.
Post-processing methods based on machine learning algorithms were applied to refine the...
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