Articles | Volume 25, issue 19
https://doi.org/10.5194/acp-25-12549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-12549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Oct 2025
Research article |
| 09 Oct 2025
| 09 Oct 2025
Aerosol type classification with machine learning techniques applied to multiwavelength lidar data from EARLINET
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain
Department of Applied Physics, University of Granada, 18071 Granada, Spain
Pablo Ortiz-Amezcua
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain
Department of Applied Physics, University of Granada, 18071 Granada, Spain
Siham Tabik
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
Andalusian Research Institute in Data Science and Computational Intelligence (DASCI), University of Granada, 18071 Granada, Spain
Juan Antonio Bravo-Aranda
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain
Department of Applied Physics, University of Granada, 18071 Granada, Spain
Sol Fernández-Carvelo
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain
Department of Applied Physics, University of Granada, 18071 Granada, Spain
Lucas Alados-Arboledas
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain
Department of Applied Physics, University of Granada, 18071 Granada, Spain
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Mariana Adam, Iwona S. Stachlewska, Lucia Mona, Nikolaos Papagiannopoulos, Juan Antonio Bravo-Aranda, Michaël Sicard, Doina N. Nicolae, Livio Belegante, Lucja Janicka, Dominika Szczepanik, Maria Mylonaki, Christina-Anna Papanikolaou, Nikolaos Siomos, Kalliopi Artemis Voudouri, Luca Alados-Arboledas, Arnoud Apituley, Ina Mattis, Anatoli Chaikovsky, Constantino Muñoz-Porcar, Aleksander Pietruczuk, Daniele Bortoli, Holger Baars, Ivan Grigorov, and Zahary Peshev
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-759, https://doi.org/10.5194/acp-2021-759, 2021
Revised manuscript not accepted
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Gloria Titos, María A. Burgos, Paul Zieger, Lucas Alados-Arboledas, Urs Baltensperger, Anne Jefferson, James Sherman, Ernest Weingartner, Bas Henzing, Krista Luoma, Colin O'Dowd, Alfred Wiedensohler, and Elisabeth Andrews
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Jose Antonio Benavent-Oltra, Juan Andrés Casquero-Vera, Roberto Román, Hassan Lyamani, Daniel Pérez-Ramírez, María José Granados-Muñoz, Milagros Herrera, Alberto Cazorla, Gloria Titos, Pablo Ortiz-Amezcua, Andrés Esteban Bedoya-Velásquez, Gregori de Arruda Moreira, Noemí Pérez, Andrés Alastuey, Oleg Dubovik, Juan Luis Guerrero-Rascado, Francisco José Olmo-Reyes, and Lucas Alados-Arboledas
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Nikolaos Evangeliou, Stephen M. Platt, Sabine Eckhardt, Cathrine Lund Myhre, Paolo Laj, Lucas Alados-Arboledas, John Backman, Benjamin T. Brem, Markus Fiebig, Harald Flentje, Angela Marinoni, Marco Pandolfi, Jesus Yus-Dìez, Natalia Prats, Jean P. Putaud, Karine Sellegri, Mar Sorribas, Konstantinos Eleftheriadis, Stergios Vratolis, Alfred Wiedensohler, and Andreas Stohl
Atmos. Chem. Phys., 21, 2675–2692, https://doi.org/10.5194/acp-21-2675-2021, https://doi.org/10.5194/acp-21-2675-2021, 2021
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Following the transmission of SARS-CoV-2 to Europe, social distancing rules were introduced to prevent further spread. We investigate the impacts of the European lockdowns on black carbon (BC) emissions by means of in situ observations and inverse modelling. BC emissions declined by 23 kt in Europe during the lockdowns as compared with previous years and by 11 % as compared to the period prior to lockdowns. Residential combustion prevailed in Eastern Europe, as confirmed by remote sensing data.
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Short summary
This study applies machine learning (ML) techniques to classify aerosols using high-resolution multiwavelength lidar data from EARLINET network. We developed a reference dataset and evaluated six ML models, with LightGBM achieving over 90 % accuracy. Depolarization data proved critical for improving dust classification. Validated against independent datasets, our approach improves aerosol classification and may help refine lidar-based processing strategies.
This study applies machine learning (ML) techniques to classify aerosols using high-resolution...
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