Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 2
Articles | Volume 24, issue 2
https://doi.org/10.5194/acp-24-807-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-807-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 2
Research article
 | 
19 Jan 2024
Research article |  | 19 Jan 2024

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

Zhiguo Zhang, Christer Johansson, Magnuz Engardt, Massimo Stafoggia, and Xiaoliang Ma

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Latest update: 08 May 2024
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Short summary
Up-to-date information on present and near-future air quality help people avoid exposure to high levels of air pollution. We apply different machine learning models to significantly improve traditional forecasts of PM10, NOx, and O3 in Stockholm, Sweden. It is shown that forecasts of all air pollutants are improved by the input of lagged measurements and taking calendar information into account. The final modelled errors are substantially smaller than uncertainties in the measurements.
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