Articles | Volume 18, issue 9
Atmos. Chem. Phys., 18, 6223–6239, 2018
Atmos. Chem. Phys., 18, 6223–6239, 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.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Stuart Grange on behalf of the Authors (21 Apr 2018)  Author's response    Manuscript
ED: Publish as is (23 Apr 2018) by Ronald Cohen
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.
Final-revised paper