Articles | Volume 19, issue 8
https://doi.org/10.5194/amt-19-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flow cytometry and machine learning enable identification of allergenic urban tree pollen
Département des sciences biologiques, Université du Québec à Montréal, Montréal, QC, Canada
Centre for Forest Research, Université du Québec à Montréal, Montréal, QC, Canada
Maria Raquel Kanieski
Universidade do Estado de Santa Catarina, Depto. Engenharia Florestal, Lages, SC, Brasil
Gauthier Lapa
Département des sciences biologiques, Université du Québec à Montréal, Montréal, QC, Canada
Centre for Forest Research, Université du Québec à Montréal, Montréal, QC, Canada
Grégoire Bonnamour
Département des sciences biologiques, Université du Québec à Montréal, Montréal, QC, Canada
Centre d'excellence en recherche sur les maladies orphelines-Fondation Courtois (CERMO-FC), Université du Québec à Montréal, Montréal, QC, Canada
Rita Sousa-Silva
Institute of Environmental Sciences, Department of Environmental Biology, Leiden University, Leiden, the Netherlands
Isabelle Laforest-Lapointe
Centre for Forest Research, Université du Québec à Montréal, Montréal, QC, Canada
Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada
Alain Paquette
Département des sciences biologiques, Université du Québec à Montréal, Montréal, QC, Canada
Centre for Forest Research, Université du Québec à Montréal, Montréal, QC, Canada
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-226, https://doi.org/10.5194/essd-2026-226, 2026
Preprint under review for ESSD
Short summary
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This study introduces the first global, spatially explicit dataset of tree diameter structure, capturing key aspects of forest organization, including average tree size, large-tree dominance, and within-stand variability. Built from over one million ground-based field plots combined with more than 50 satellite and environmental layers using machine learning, it provides a consistent representation of forest structure and supports ecosystem research, climate modeling, and forest management.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, John R. Evans, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Robert T. Furbank, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Viridiana Silva-Perez, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data, 18, 245–265, https://doi.org/10.5194/essd-18-245-2026, https://doi.org/10.5194/essd-18-245-2026, 2026
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We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
Kaisa Rissanen, Juho Aalto, Jaana Bäck, Heidi Hellén, Toni Tykkä, and Alain Paquette
Atmos. Chem. Phys., 25, 15415–15435, https://doi.org/10.5194/acp-25-15415-2025, https://doi.org/10.5194/acp-25-15415-2025, 2025
Short summary
Short summary
Urban trees emit biogenic volatile organic compounds (BVOC) that affect air quality through the formation of ozone and particulate matter. Trees in Montreal and Helsinki did not emit more BVOCs than expected based on measurements from forest trees, but the emissions varied between individual trees and growth environments. Avoiding high-BVOC emitting tree species and management strategies that protect trees from BVOC-inducing stress factors would help minimise their negative air quality impacts.
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Short summary
We developed a high-throughput method combining flow cytometry and machine learning to identify urban pollen. Using a reference database of 97 species across 34 genera, with values of particle size, granularity, and multi-channel fluorescence for each pollen grains, our method enables rapid species- and genus-level pollen identification. It provides an efficient alternative to microscopy, with potential for large-scale urban pollen monitoring and allergy management.
We developed a high-throughput method combining flow cytometry and machine learning to identify...