A 21-year global dataset of particle number concentrations for aerosol-cloud interaction studies

作者：Aino Ovaska, Daniel Holmberg, Elio Rauth, Mansour Alghamdi, Paulo Artaxo, Eija Asmi, John Backman, Benjamin Bergmans, Matthew Boyer, Liezl Bredenkamp, Maurizio Busetto, Delano Campos De Oliveira, Juan Andrés Casquero-Vera, Darius Čeburnis, Tak Chan, Tommy Chan, Sébastien Conil, Daniele Contini, Suzanne Crumeyrolle, Valentin Duflot, Konstantinos Eleftheriadis, Johan Esveld, Ekaterina Ezhova, Markus Fiebig, Shahzad Gani, Olga Garmash, Francisco J. Gómez‐Moreno, Roy M. Harrison, András Hoffer, Rakesh K. Hooda, A. Hyvärinen, Tareq Hussein, Jorma Joutsensaari, Nikos Kalivitis, Heinz Kaminski, Jutta Kesti, Radovan Krejčí, Adam Kristensson, Chongai Kuang, Markku Kulmala, Lauri Laakso, Ari Leskinen, Heikki Lihavainen, Andreas Maßling, Maik Merkel, Steffen M. Noe, Jakub Ondráček, Noemí Perez, Jean‐Eudes Petit, Tuukka Petäjä, Michael Pikridas, Christopher Pöhlker, Mira Pöhlker, Jean-Philippe Putaud, Ximeng Qi, Cristina Reche, Sergio Rodrı́guez, Petr Roztocil, Jean Sciare, Karine Sellegri, Dongjie Shang, Ashish Singh, Mikko Sipilä, Henrik Skov, M. Sorribas, Tamanna Subba, J F Sun, Peter Tunved, Ville Vakkari, Pieter G. van Zyl, Aki Virkkula, Jens Voigtländer, K Weinhold, Alfred Wiedensohler, Hee-Jung Yoo, Putian Zhou, Kai Puolamäki, Tuomo Nieminen, Veli‐Matti Kerminen, Victoria A. Sinclair, Pauli Paasonen · 年份：2026 · DOI：10.5194/essd-2026-415 · 研究领域：Atmospheric aerosols and clouds、Atmospheric chemistry and aerosols、Air Quality and Health Impacts

Abstract. Aerosol particles larger than roughly 50–100 nm in diameter are climatically important because they can act as cloud condensation nuclei (CCN), making their global number concentrations essential for understanding aerosol–cloud interactions. However, observationally constrained, long-term global datasets of particle number concentrations in this size range remain scarce. In this investigation, we present a global dataset of ground-level particle number concentrations for the period 2003–2024, produced by combining in situ observations with a machine-learning approach. The dataset includes two variables: the number concentrations for particles larger than 100 nm (N100) and larger than 50 nm (N50), provided at 0.75° × 0.75° spatial resolution and daily temporal resolution. To generate this dataset, we trained an eXtreme Gradient Boosting (XGB) model using measurements from 62 in situ stations as targets and reanalysis variables as predictors, enabling a data-driven representation of particle number concentrations at the global scale. We evaluated the dataset against independent observations from 12 additional stations. At 2/3 of these stations, the dataset shows good performance, capturing the median concentrations within a factor of 1.5 from the observations. Furthermore, we describe the main characteristics of the dataset in terms of global spatial patterns, temporal variability, and seasonal cycles, and demonstrate its ability to capture long-term trends in particl...