Improving 3-day deterministic air pollution forecasts using machine learning algorithms

Abstract 10, NO x 3 at different sites in Greater Stockholm, Sweden. 10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NO x 3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O3 but is compensated for using the ML models by taking lagged measurements into account. NO x R 2 10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O3 also modestly increased, with differences between metrics. NO x 10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Improving 3-day deterministic air pollution forecasts using machine learning algorithms ; volume:24 ; number:2 ; year:2024 ; pages:807-851 ; extent:45 ; Atmospheric chemistry and physics ; 24, Heft 2 (2024), 807-851 (gesamt 45)

Classification and Topics

• Classification: Soziale Probleme, Sozialdienste, Versicherungen

Contributors, Places and Time

• Creator: Zhang, Zhiguo ; Johansson, Christer ; Engardt, Magnuz ; Stafoggia, Massimo ; Ma, Xiaoliang

Further information

• DOI: 10.5194/acp-24-807-2024
• URN: urn:nbn:de:101:1-2024012503275245156570
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:21 AM CEST