Articles | Volume 18, issue 9
https://doi.org/10.5194/acp-18-6223-2018
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, David C. Carslaw, Alastair C. Lewis, Eirini Boleti, and Christoph Hueglin

Related authors

The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich
Stuart K. Grange, Pascal Rubli, Andrea Fischer, Dominik Brunner, Christoph Hueglin, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2024-2925,https://doi.org/10.5194/egusphere-2024-2925, 2024
Short summary
Why is ozone in South Korea and the Seoul metropolitan area so high and increasing?
Nadia K. Colombi, Daniel J. Jacob, Laura Hyesung Yang, Shixian Zhai, Viral Shah, Stuart K. Grange, Robert M. Yantosca, Soontae Kim, and Hong Liao
Atmos. Chem. Phys., 23, 4031–4044, https://doi.org/10.5194/acp-23-4031-2023,https://doi.org/10.5194/acp-23-4031-2023, 2023
Short summary
Linking Switzerland's PM10 and PM2.5 oxidative potential (OP) with emission sources
Stuart K. Grange, Gaëlle Uzu, Samuël Weber, Jean-Luc Jaffrezo, and Christoph Hueglin
Atmos. Chem. Phys., 22, 7029–7050, https://doi.org/10.5194/acp-22-7029-2022,https://doi.org/10.5194/acp-22-7029-2022, 2022
Short summary
Cellulose in atmospheric particulate matter at rural and urban sites across France and Switzerland
Adam Brighty, Véronique Jacob, Gaëlle Uzu, Lucille Borlaza, Sébastien Conil, Christoph Hueglin, Stuart K. Grange, Olivier Favez, Cécile Trébuchon, and Jean-Luc Jaffrezo
Atmos. Chem. Phys., 22, 6021–6043, https://doi.org/10.5194/acp-22-6021-2022,https://doi.org/10.5194/acp-22-6021-2022, 2022
Short summary
COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas
Stuart K. Grange, James D. Lee, Will S. Drysdale, Alastair C. Lewis, Christoph Hueglin, Lukas Emmenegger, and David C. Carslaw
Atmos. Chem. Phys., 21, 4169–4185, https://doi.org/10.5194/acp-21-4169-2021,https://doi.org/10.5194/acp-21-4169-2021, 2021
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model
Ross J. Herbert, Alberto Sanchez-Marroquin, Daniel P. Grosvenor, Kirsty J. Pringle, Stephen R. Arnold, Benjamin J. Murray, and Kenneth S. Carslaw
Atmos. Chem. Phys., 25, 291–325, https://doi.org/10.5194/acp-25-291-2025,https://doi.org/10.5194/acp-25-291-2025, 2025
Short summary
The role of interfacial tension in the size-dependent phase separation of atmospheric aerosol particles
Ryan Schmedding and Andreas Zuend
Atmos. Chem. Phys., 25, 327–346, https://doi.org/10.5194/acp-25-327-2025,https://doi.org/10.5194/acp-25-327-2025, 2025
Short summary
Warming effects of reduced sulfur emissions from shipping
Masaru Yoshioka, Daniel P. Grosvenor, Ben B. B. Booth, Colin P. Morice, and Ken S. Carslaw
Atmos. Chem. Phys., 24, 13681–13692, https://doi.org/10.5194/acp-24-13681-2024,https://doi.org/10.5194/acp-24-13681-2024, 2024
Short summary
The key role of atmospheric absorption in the Asian summer monsoon response to dust emissions in CMIP6 models
Alcide Zhao, Laura J. Wilcox, and Claire L. Ryder
Atmos. Chem. Phys., 24, 13385–13402, https://doi.org/10.5194/acp-24-13385-2024,https://doi.org/10.5194/acp-24-13385-2024, 2024
Short summary
Multi-model effective radiative forcing of the 2020 sulfur cap for shipping
Ragnhild Bieltvedt Skeie, Rachael Byrom, Øivind Hodnebrog, Caroline Jouan, and Gunnar Myhre
Atmos. Chem. Phys., 24, 13361–13370, https://doi.org/10.5194/acp-24-13361-2024,https://doi.org/10.5194/acp-24-13361-2024, 2024
Short summary

Cited articles

Anh, V., Duc, H., and Azzi, M.: Modeling anthropogenic trends in air quality data, J. Air Waste Manage., 47, 66–71, https://doi.org/10.1080/10473289.1997.10464406, 1997.
Barmpadimos, I., Hueglin, C., Keller, J., Henne, S., and Prévôt, A. S. H.: Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008, Atmos. Chem. Phys., 11, 1813–1835, https://doi.org/10.5194/acp-11-1813-2011, 2011.
Beevers, S., Carslaw, D., Westmoreland, E., and Mittal, H.: Air pollution and emissions trends in London, Tech. rep., King's College London, Environmental Research Group Leeds University, Institute for Transport studies, available at: http://naei.defra.gov.uk/reports/reports?report_id=589 (last access: 30 April 2018), 2009.
Biau, G., Devroye, L., and Lugosi, G.: Consistency of Random Forests and Other Averaging Classifiers, J. Mach. Learn. Res., 9, 2015–2033, 2008.
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1007/BF00058655, 1996.
Download
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.
Altmetrics
Final-revised paper
Preprint