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Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning
Lovrić, M., Pavlović, K., Vuković, M., Grange, S. K., Haberl, M., & Kern, R. (2021). Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environmental Pollution, 274, 115900 (9 pp.). https://doi.org/10.1016/j.envpol.2020.115900
Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach
Boleti, E., Hueglin, C., Grange, S. K., Prévôt, A. S. H., & Takahama, S. (2020). Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach. Atmospheric Chemistry and Physics, 20(14), 9051-9066. https://doi.org/10.5194/acp-20-9051-2020
Post-Dieselgate: evidence of NO<sub>x</sub> emission reductions using on-road remote sensing
Grange, S. K., Farren, N. J., Vaughan, A. R., Davison, J., & Carslaw, D. C. (2020). Post-Dieselgate: evidence of NOx emission reductions using on-road remote sensing. Environmental Science and Technology Letters, 7(6), 382-387. https://doi.org/10.1021/acs.estlett.0c00188
Random forest meteorological normalisation models for Swiss PM<sub>10</sub> trend analysis
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., & Hueglin, C. (2018). Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmospheric Chemistry and Physics, 18(9), 6223-6239. https://doi.org/10.5194/acp-18-6223-2018