Abstract
New particle formation has been estimated to produce more than half of the global cloud condensation nuclei and profoundly impacts clouds, climate, and air quality. The initial growth from the cluster size ( ~ 1 nm) to a few nanometers, for which the underlying mechanisms can be very different from the subsequent growth, is the most critical stage for new particles to become climate-relevant. However, initial growth mechanisms evidenced by controlled laboratory experiments can rarely explain observations from the real atmosphere. Here we show that a large nanoparticle concentration gradient in the size space can drive unexpected rapid initial growth based on measurements across the globe. It accelerates the condensation of globally abundant oxygenated organic molecules onto a population of new particles compared to a single particle, and substantially increases the fraction of new particles that survive to climate- and air-quality-relevant sizes. Our findings provide insights into explaining the puzzle of the frequent new particle formation events in polluted urban environments and indicate an even more important role of new particle formation in climate predictions.
Similar content being viewed by others
Data availability
The full dataset shown in the figures in the main text and supplementary materials is publicly available at https://doi.org/10.5281/zenodo.13761850.81
Code availability
The monodisperse aerosol growth model for growth rate from a volatility basis set of oxygenated organic molecules is publicly available at https://doi.org/10.5281/zenodo.13761850.81
References
Carslaw, K. S., Lee, L. A., Reddington, C. L., Mann, G. W. & Pringle, K. J. The magnitude and sources of uncertainty in global aerosol. Faraday Discuss 165, 495–512 (2013).
Gordon, H. et al. Reduced anthropogenic aerosol radiative forcing caused by biogenic new particle formation. Proc. Natl. Acad. Sci. USA. 113, 12053–12058 (2016).
Blichner, S. M., Sporre, M. K. & Berntsen, T. K. Reduced effective radiative forcing from cloud–aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth system model. Atmos. Chem. Phys. 21, 17243–17265 (2021).
Kirkby, J. et al. Atmospheric new particle formation from the CERN CLOUD experiment. Nat. Geosci. 16, 948–957 (2023).
Zhao, B. et al. Global variability in atmospheric new particle formation mechanisms. Nature 631, 98–105 (2024).
Westervelt, D. M., Pierce, J. R. & Adams, P. J. Analysis of feedbacks between nucleation rate, survival probability and cloud condensation nuclei formation. Atmos. Chem. Phys. 14, 5577–5597 (2014).
Kulmala, M. et al. Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities? Faraday Discuss 226, 334–347 (2021).
Cai, R. et al. Survival probability of new atmospheric particles: closure between theory and measurements from 1.4 to 100 nm. Atmos. Chem. Phys. 22, 14571–14587 (2022).
Stolzenburg, D. et al. Atmospheric nanoparticle growth. Rev. Mod. Phys. 95, 045002 (2023).
Stolzenburg, D. et al. Enhanced growth rate of atmospheric particles from sulfuric acid. Atmos. Chem. Phys. 20, 7359–7372 (2020).
Tröstl, J. et al. The role of low-volatility organic compounds in initial particle growth in the atmosphere. Nature 533, 527–531 (2016).
Kulmala, M. et al. The contribution of new particle formation and subsequent growth to haze formation. Environ. Sci.: Atmos. 2, 352–361 (2022).
Dada, L. et al. Sources and sinks driving sulfuric acid concentrations in contrasting environments: implications on proxy calculations. Atmos. Chem. Phys. 20, 11747–11766 (2020).
Ehn, M. et al. A large source of low-volatility secondary organic aerosol. Nature 506, 476–479 (2014).
Stolzenburg, D. et al. Rapid growth of organic aerosol nanoparticles over a wide tropospheric temperature range. Proc. Natl. Acad. Sci. Usa. 115, 9122–9127 (2018).
Li, X. et al. Insufficient condensable organic vapors lead to slow growth of new particles in an urban environment. Environ. Sci. Technol. 56, 9936–9946 (2022).
Kulmala, M. et al. Direct observations of atmospheric aerosol nucleation. Science 339, 943–946 (2013).
Marten, R. et al. Survival of newly formed particles in haze conditions. Environ. Sci.: Atmos. 2, 491–499 (2022).
Cai, R. et al. The effectiveness of the coagulation sink of 3–10 nm atmospheric particles. Atmos. Chem. Phys. 22, 11529–11541 (2022).
Kulmala, M., Kerminen, V. M., Petäjä, T., Ding, A. J. & Wang, L. Atmospheric gas-to-particle conversion: why NPF events are observed in megacities? Faraday Discuss 200, 271–288 (2017).
Liu, Y. et al. Continuous and comprehensive atmospheric observations in Beijing: a station to understand the complex urban atmospheric environment. Big Earth Data 4, 295–321 (2020).
Yao, L. et al. Atmospheric new particle formation from sulfuric acid and amines in a Chinese megacity. Science 361, 278–281 (2018).
Deng, C. et al. Seasonal characteristics of new particle formation and growth in urban Beijing. Environ. Sci. Technol. 54, 8547–8557 (2020).
Qiao, X. et al. Contribution of atmospheric oxygenated organic compounds to particle growth in an urban environment. Environ. Sci. Technol. 55, 13646–13656 (2021).
Yli-Juuti, T. et al. Model for acid-base chemistry in nanoparticle growth (MABNAG). Atmos. Chem. Phys. 13, 12507–12524 (2013).
Stolzenburg, D., Wang, M., Schervish, M. & Donahue, N. M. Tutorial: Dynamic organic growth modeling with a volatility basis set. J. Aerosol Sci. 166, 106063 (2022).
Wang, J., McGraw, R. L. & Kuang, C. Growth of atmospheric nano-particles by heterogeneous nucleation of organic vapor. Atmos. Chem. Phys. 13, 6523–6531 (2013).
Olenius, T. et al. Robust metric for quantifying the importance of stochastic effects on nanoparticle growth. Sci. Rep. 8, 14160 (2018).
Kontkanen, J. et al. What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles? Environ. Sci.: Atmos. 2, 449–468 (2022).
Clement, C. F., Lehtinen, K. E. J. & Kulmala, M. Size diffusion for the growth of newly nucleated aerosol. J. Aerosol Sci. 35, 1439–1451 (2004).
Holten, V. & van Dongen, M. E. Comparison between solutions of the general dynamic equation and the kinetic equation for nucleation and droplet growth. J. Chem. Phys. 130, 014102 (2009).
McGraw, R. & Liu, Y. Kinetic potential and barrier crossing: a model for warm cloud drizzle formation. Phys. Rev. Lett. 90, 018501 (2003).
Malila, J., McGraw, R., Laaksonen, A. & Lehtinen, K. E. Communication: kinetics of scavenging of small, nucleating clusters: first nucleation theorem and sum rules. J. Chem. Phys. 142, 011102 (2015).
Stolzenburg, D. et al. Incomplete mass closure in atmospheric nanoparticle growth. npj Clim. Atmos. Sci. 8, 75 (2025).
Tang, L. et al. Ongoing uncoordinated anthropogenic emission abatement promotes atmospheric new particle growth in a Chinese megacity. Nat. Commun. 16, 6720 (2025).
Elm, J. et al. Modeling the formation and growth of atmospheric molecular clusters: A review. J. Aerosol Sci. 149, 105621 (2020).
Cai, R. et al. Sulfuric acid–amine nucleation in urban Beijing. Atmos. Chem. Phys. 21, 2457–2468 (2021).
Zhang, J. et al. Stratospheric air intrusions promote global-scale new particle formation. Science 385, 210–216 (2024).
Xiao, Q. et al. New particle formation in the tropical free troposphere during CAMP2Ex: statistics and impact of emission sources, convective activity, and synoptic conditions. Atmos. Chem. Phys. 23, 9853–9871 (2023).
Kürten, A. et al. Neutral molecular cluster formation of sulfuric acid-dimethylamine observed in real time under atmospheric conditions. Proc. Natl. Acad. Sci. USA. 111, 15019–15024 (2014).
Wang, M. et al. Rapid growth of new atmospheric particles by nitric acid and ammonia condensation. Nature 581, 184–189 (2020).
Li, Y. et al. The significant role of new particle composition and morphology on the hno3-driven growth of particles down to sub-10 nm. Environ. Sci. Technol. 58, 5442–5452 (2024).
Zaveri, R. A. et al. Rapid growth of anthropogenic organic nanoparticles greatly alters cloud life cycle in the Amazon rainforest. Sci. Adv. 8, eabj0329 (2022).
Aas, W. et al. Global and regional trends of atmospheric sulfur. Sci. Rep. 9, 953 (2019).
Gordon, H. et al. Causes and importance of new particle formation in the present-day and preindustrial atmospheres. J. Geophys. Res. Atmos. 122, 8739–8760 (2017).
Yan, C. et al. The synergistic role of sulfuric acid, bases, and oxidized organics governing new-particle formation in beijing. Geophys. Res. Lett. 48, e2020GL091944 (2021).
Nie, W. et al. Secondary organic aerosol formed by condensing anthropogenic vapours over China’s megacities. Nat. Geosci. 15, 255–261 (2022).
Cai, J. et al. Elucidating the mechanisms of atmospheric new particle formation in the highly polluted Po Valley, Italy. Atmos. Chem. Phys. 24, 2423–2441 (2024).
Zha, Q. et al. Oxidized organic molecules in the tropical free troposphere over Amazonia. Natl. Sci. Rev. 11, nwad138 (2023).
Jiang, J., Chen, M., Kuang, C., Attoui, M. & McMurry, P. H. Electrical mobility spectrometer using a diethylene glycol condensation particle counter for measurement of aerosol size distributions down to 1 nm. Aerosol Sci. Technol. 45, 510–521 (2011).
Cai, R., Chen, D.-R., Hao, J. & Jiang, J. A miniature cylindrical differential mobility analyzer for sub-3 nm particle sizing. J. Aerosol Sci. 106, 111–119 (2017).
Liu, J., Jiang, J., Zhang, Q., Deng, J. & Hao, J. A spectrometer for measuring particle size distributions in the range of 3 nm to 10 μm. Front. Environ. Sci. Eng. 10, 63–72 (2016).
Heinritzi, M. et al. Characterization of the mass-dependent transmission efficiency of a CIMS. Atmos. Meas. Tech. 9, 1449–1460 (2016).
Zhang, Y. et al. Iodine oxoacids and their roles in sub-3 nm particle growth in polluted urban environments. Atmos. Chem. Phys. 24, 1873–1893 (2024).
Stolzenburg, D., Steiner, G. & Winkler, P. M. A DMA-train for precision measurement of sub-10nm aerosol dynamics. Atmos. Meas. Tech. 10, 1639–1651 (2017).
Larriba, C. et al. The mobility–volume relationship below 3.0 nm examined by tandem mobility–mass measurement. Aerosol Sci. Technol. 45, 453–467 (2011).
Kulmala, M. et al. Towards a concentration closure of sub-6 nm aerosol particles and sub-3 nm atmospheric clusters. J. Aerosol Sci. 159, 105878 (2022).
Aalto, P. et al. Physical characterization of aerosol particles during nucleation events. Tellus B 53, 344–358 (2001).
Jokinen, T. et al. Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF. Atmos. Chem. Phys. 12, 4117–4125 (2012).
Kürten, A., Rondo, L., Ehrhart, S. & Curtius, J. Calibration of a chemical ionization mass spectrometer for the measurement of gaseous sulfuric acid. J. Phys. Chem. A 116, 6375–6386 (2012).
Cai, R. et al. Estimating the influence of transport on aerosol size distributions during new particle formation events. Atmos. Chem. Phys. 18, 16587–16599 (2018).
Tuovinen, S., Lampilahti, J., Kerminen, V.-M. & Kulmala, M. Intermediate ions as indicator for local new particle formation. Aerosol Res 2, 93–105 (2024).
Cai, R. et al. Impacts of coagulation on the appearance time method for new particle growth rate evaluation and their corrections. Atmos. Chem. Phys. 21, 2287–2304 (2021).
Deng, C., Cai, R., Yan, C., Zheng, J. & Jiang, J. Formation and growth of sub-3 nm particles in megacities: impacts of background aerosols. Faraday Discuss. 348-363 https://doi.org/10.1039/D0FD00083C (2020).
Cai, R. & Jiang, J. A new balance formula to estimate new particle formation rate: reevaluating the effect of coagulation scavenging. Atmos. Chem. Phys. 17, 12659–12675 (2017).
Kerminen, V. M. & Kulmala, M. Analytical formulae connecting the “real” and the “apparent” nucleation rate and the nuclei number concentration for atmospheric nucleation events. J. Aerosol Sci. 33, 609–622 (2002).
Pierce, J. R. & Adams, P. J. Efficiency of cloud condensation nuclei formation from ultrafine particles. Atmos. Chem. Phys. 7, 1367–1379 (2007).
Tuovinen, S. et al. Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing. Atmos. Chem. Phys. 22, 15071–15091 (2022).
Lehtipalo, K. et al. The effect of acid-base clustering and ions on the growth of atmospheric nano-particles. Nat. Commun. 7, 11594 (2016).
Olenius, T. & Roldin, P. Role of gas-molecular cluster-aerosol dynamics in atmospheric new-particle formation. Sci. Rep. 12, 10135 (2022).
Cai, R. Dataset for “Rapid initial growth of new atmospheric particles by large nanoparticle concentration gradient” [Dataset]. https://doi.org/10.5281/zenodo.13761850 (2024).
Donahue, N. M., Epstein, S. A., Pandis, S. N. & Robinson, A. L. A two-dimensional volatility basis set: 1. organic-aerosol mixing thermodynamics. Atmos. Chem. Phys. 11, 3303–3318 (2011).
Li, Y., Pöschl, U. & Shiraiwa, M. Molecular corridors and parameterizations of volatility in the chemical evolution of organic aerosols. Atmos. Chem. Phys. 16, 3327–3344 (2016).
Wu, H., Knattrup, Y., Jensen, A. B. & Elm, J. Cluster-to-particle transition in atmospheric nanoclusters. Aerosol Res 2, 303–314 (2024).
Seland, Ø et al. Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations. Geosci. Model Dev. 13, 6165–6200 (2020).
Berrisford, P. et al. The ERA-Interim. Arch. Version 2 0, 23 (2011).
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, https://doi.org/10.1029/2002JD002670 (2003).
Blichner, S. M. et al. Process-evaluation of forest aerosol-cloud-climate feedback shows clear evidence from observations and large uncertainty in models. Nat. Commun. 15, 969 (2024).
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).
Guenther, A. B. et al. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012).
Kirkevåg, A. et al. A production-tagged aerosol module for Earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo. Geosci. Model Dev. 11, 3945–3982 (2018).
Acknowledgements
We thank the National Natural Science Foundation of China (grant no. 22406024, 22188102, 22127811), the ACCC Flagship funded by the Academy of Finland grant number 337549, the Academy professorship funded by the Academy of Finland (grant no. 302958), and the Academy of Finland projects. 332547, 325656, 311932, 334792, 316114, 325647, 325681, 333397, 328616, 357902, 345510, 347782, 346370, 364223, 325656, 356134, “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation, “Gigacity” project funded by Wihuri foundation, European Research Council (ERC) project ATM-GTP Contract No. 742206 and CHAPAs (grant No. 850614), European Union via Non-CO2 Forcers and their Climate, Weather, Air Quality and Health Impacts (FOCI), CRiceS (101003826), RI-URBANS (101036245), EMME-CARE (856612), FORCeS (821205), and NPF-PANDA (895875), European Commission grants INTEGRATE no. 867599, and Swedish Research Council grant no. 2023-03842. The Technology Industries of Finland Centennial Foundation, via the urbaani ilmanlaatu 2.0 project, is gratefully acknowledged. This work has been funded by the Vienna Science and Technology Fund (WWTF) through project VRG22-003. University of Helsinki support via ACTRIS-HY is acknowledged. Support of the technical and scientific staff at Hyytiälä and BUCT/AHL is acknowledged.
Author information
Authors and Affiliations
Contributions
R.C., D.W., J.J., M.K., and J.K. designed the research. R.C., X.L., Y.Li, D.S., Q.Z., J.Cai, W.N., C.Y., D.D.H., Z.W., J.W., R.Y., N.S., W.H., S.H., L.A., F.B., Y.Liu, and J.K. conducted experiments and collected data. R.C., X.L., Y.Li, and S.B. analyzed data. R.C., X.L., Y.Li, S.B., D.S., S.T., L.Y., A.D., F.B., P.W., T.P., J.Chen, V.-M. K., L.W., D.W., J.J., M.K., and J.K. contributed to the scientific discussion. R.C. wrote the manuscript with inputs from all co-authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Cai, R., Li, X., Li, Y. et al. The key role of nanoparticle concentration gradient in aerosol initial growth. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70082-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-70082-2


