Articles | Volume 18, issue 9
Atmos. Chem. Phys., 18, 6223–6239, 2018
https://doi.org/10.5194/acp-18-6223-2018
Atmos. Chem. Phys., 18, 6223–6239, 2018
https://doi.org/10.5194/acp-18-6223-2018

Research article 03 May 2018

Research article | 03 May 2018

Random forest meteorological normalisation models for Swiss PM10 trend analysis

Stuart K. Grange et al.

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Latest update: 17 May 2021
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Short summary
Weather (meteorology) has a strong effect on air quality; if not accounted for, there is uncertainty surrounding what drives features in air quality time series. We present a machine learning approach to account for meteorology using PM10 data in Switzerland. With the exception of one site, all Swiss normalised PM10 trends were found to significantly decrease, which validates air quality management efforts. The machine learning models were interpreted to investigate interesting processes.
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