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Atmospheric warming contributions from airborne microplastics and nanoplastics

Nature Climate Change volume 16, pages 598–605 (2026)Cite this article

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Abstract

Microplastic and nanoplastic particles (MNPs) are pervasive in the atmosphere, yet their direct radiative forcing (DRF) remains poorly constrained. Using a radiative transfer model combined with experimentally derived optical properties and simulated atmospheric distributions, we show that coloured MNPs exhibit strong light absorption, with a mean refractive index of 1.49–0.22i at 550 nm and absorption coefficients 74.8 times higher than those of pristine particles. Atmospheric ageing produces minimal net optical change, as yellowing-induced absorption in white particles is largely offset by bleaching of red ones. Modelled global surface concentrations reach 4.18 MP m−3 for microplastics and 3.67 ng m−3 for nanoplastics. Resulting simulations yield mean DRF of 0.039 ± 0.019 W m−2 for MNPs, equivalent to 16.2% of black carbon forcing. Regional DRF peaks over the North Pacific Subtropical Gyre (~1.34 W m−2), exceeded located black carbon by 4.7-fold, highlighting MNPs as previously unrecognized climate forcing agents.

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Fig. 1: Spectral optical properties of airborne MNPs.
The alternative text for this image may have been generated using AI.
Fig. 2: Annual mean distribution concentrations at near-surface (<100 m agl) and burden of MNP in the global atmosphere.
The alternative text for this image may have been generated using AI.
Fig. 3: Annual mean global distribution of absorptive DRF.
The alternative text for this image may have been generated using AI.

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Data availability

FLEXPART model output data used in this study are available from the official FLEXPART website (https://www.flexpart.eu). Meteorological data required for FLEXPART simulations were obtained from the NCAR GDEX dataset (https://gdex.ucar.edu/datasets/d083002/dataaccess/). The SBDART model data used for radiative transfer simulations can be accessed from the official SBDART website (http://www.sbdart.com). For SBDART simulations, ERA5 reanalysis data (temperature, wind and humidity fields) provided by ECMWF are available from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu). The maps were produced in MeteoInfoMap (http://www.meteothink.org) using latitude–longitude point data and kriging for visualization only, with basemap and shapefile sources obtained from Natural Earth (https://www.naturalearthdata.com/). The source data generated in this work are available via Zenodo at https://doi.org/10.5281/zenodo.19042838 (ref. 55). Other raw data are available from the authors on request. Source data are provided with this paper.

Code availability

Custom code generated in this study is available via Zenodo at https://doi.org/10.5281/zenodo.19042838 (ref. 55).

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Acknowledgements

H.F. discloses support for the research of this work from National Natural Science Foundation of China (grant nos. 22576035, 22176038, 22376029, 91744205 and 21777025), National Key R&D Program of China (grant no. 2022YFC3701102) and Natural Science Foundation of Shanghai City (grant no. 22ZR1404700). The computations in this research were performed using the CFFF platform of Fudan University. This research also benefited from high-performance computing (HPC) resources provided by the Beijing Super Cloud Center (BSCC).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Air Quality and Environmental Health, Department of Environmental Science & Engineering, Fudan University, Shanghai, China

    Yu Liu, Hongbo Fu, Yunhang Wang & Jianmin Chen

  2. Institute of Eco–Chongming (IEC), Shanghai, China

    Yu Liu, Hongbo Fu & Jianmin Chen

  3. IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China

    Hongbo Fu, Hongliang Zhang & Xu Tang

  4. Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

    Hongbo Fu

  5. School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai, China

    Hongliang Zhang

  6. Center for Aerosol Science and Engineering, Department of Energy, Environmental, and Chemical Engineering, Washington University in St Louis, St Louis, MO, USA

    Rajan K. Chakrabarty

  7. Jiangxi Key Laboratory of Environmental Pollution Control, Jiangxi Academy of Eco-Environmental Sciences & Planning, Nanchang, China

    Xiang Tu

  8. Department of Chemistry, Purdue University, West Lafayette, IN, USA

    Alexander Laskin

  9. Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA

    Gregory R. Carmichael

  10. Department of Earth and Environmental Sciences and Department of Chemistry, University of Pennsylvania, Philadelphia, PA, USA

    Joseph S. Francisco

  11. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Drew T. Shindell

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Contributions

Y.L. contributed to the methodology, software, validation, visualized the data in plots, conducted the analysis and wrote the original draft together. H.F. conceptualized the study, acquired funding, supervised the study and wrote the original draft. D.T.S. reviewed and edited the paper. Y.W. contributed to the software. R.K.C. contributed to the methodology. H.Z. validated the original draft together with X. Tu and X. Tang. A.L., G.R.C., J.C. and J.S.F. reviewed and edited the paper.

Corresponding authors

Correspondence to Hongbo Fu or Drew T. Shindell.

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The authors declare no competing interests.

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Nature Climate Change thanks Gilberto Binda, Joerg Meyer, Johannes Quaas and Andreas Stohl for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Three hypothetical size distributions of NPs.

NPs diameters are assumed to follow lognormal distributions with three parameter sets, namely geometric mean diameter 100 nm with geometric standard deviation 2, geometric mean diameter 250 nm with geometric standard deviation 3, and geometric mean diameter 400 nm with geometric standard deviation 4, and the vertical axis shows normalized probability density.

Source data

Extended Data Fig. 2 Annual seasonal variation of MNP concentrations in the global atmosphere.

a, Monthly mean particle concentration of MPs at different altitudes throughout the year. b, Monthly mean mass concentration of NPs at different altitudes throughout the year. Different colours represent various altitudes, as indicated in the legend. The error bars represent the standard deviation, calculated from daily output data.

Source data

Extended Data Fig. 3 Annual mean distribution of atmospheric MNPs’ DRF.

a, Global distribution of DRF of MPs. b, Global distribution of the annual mean NPs’ DRF.

Source data

Extended Data Fig. 4 Annual mean global distribution of clear-sky absorptive DRF.

a, Spatial distribution of absorptive DRF for MNPs. b, Spatial distribution of absorptive DRF by BC. The colour scale indicates DRF values (W m⁻2). c, The ratio of MNPs’ DRF versus that of BC.

Source data

Extended Data Fig. 5 Spectral n and k values of representative aged red MNPs exhibiting “bleaching”.

The two particle sizes are (a) 500 nm and (b) 50 nm. The insets show each particle, and the plotted n and k values are derived from the corresponding single particle.

Source data

Supplementary information

Supplementary Information (download PDF )

Supplementary Discussion, Tables 1–3 and Figs. 1–10.

Source data

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Liu, Y., Fu, H., Zhang, H. et al. Atmospheric warming contributions from airborne microplastics and nanoplastics. Nat. Clim. Chang. 16, 598–605 (2026). https://doi.org/10.1038/s41558-026-02620-1

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