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

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
Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach
Eirini Boleti, Christoph Hueglin, Stuart K. Grange, André S. H. Prévôt, and Satoshi Takahama
Atmos. Chem. Phys., 20, 9051–9066, https://doi.org/10.5194/acp-20-9051-2020,https://doi.org/10.5194/acp-20-9051-2020, 2020
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Improved simulations of biomass burning aerosol optical properties and lifetimes in the NASA GEOS Model during the ORACLES-I campaign
Sampa Das, Peter R. Colarco, Huisheng Bian, and Santiago Gassó
Atmos. Chem. Phys., 24, 4421–4449, https://doi.org/10.5194/acp-24-4421-2024,https://doi.org/10.5194/acp-24-4421-2024, 2024
Short summary
Sharp increase in Saharan dust intrusions over the western Euro-Mediterranean in February–March 2020–2022 and associated atmospheric circulation
Emilio Cuevas-Agulló, David Barriopedro, Rosa Delia García, Silvia Alonso-Pérez, Juan Jesús González-Alemán, Ernest Werner, David Suárez, Juan José Bustos, Gerardo García-Castrillo, Omaira García, África Barreto, and Sara Basart
Atmos. Chem. Phys., 24, 4083–4104, https://doi.org/10.5194/acp-24-4083-2024,https://doi.org/10.5194/acp-24-4083-2024, 2024
Short summary
Temporal and spatial variations in dust activity in Australia based on remote sensing and reanalysis datasets
Yahui Che, Bofu Yu, and Katherine Bracco
Atmos. Chem. Phys., 24, 4105–4128, https://doi.org/10.5194/acp-24-4105-2024,https://doi.org/10.5194/acp-24-4105-2024, 2024
Short summary
Sensitivity of global direct aerosol shortwave radiative forcing to uncertainties in aerosol optical properties
Jonathan Elsey, Nicolas Bellouin, and Claire Ryder
Atmos. Chem. Phys., 24, 4065–4081, https://doi.org/10.5194/acp-24-4065-2024,https://doi.org/10.5194/acp-24-4065-2024, 2024
Short summary
Molecular-level study on the role of methanesulfonic acid in iodine oxoacid nucleation
Jing Li, Nan Wu, Biwu Chu, An Ning, and Xiuhui Zhang
Atmos. Chem. Phys., 24, 3989–4000, https://doi.org/10.5194/acp-24-3989-2024,https://doi.org/10.5194/acp-24-3989-2024, 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