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Direct radiative effects of airborne microplastics

Nature volume 598, pages 462–467 (2021)Cite this article

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Abstract

Microplastics are now recognized as widespread contaminants in the atmosphere, where, due to their small size and low density, they can be transported with winds around the Earth1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25. Atmospheric aerosols, such as mineral dust and other types of airborne particulate matter, influence Earth’s climate by absorbing and scattering radiation (direct radiative effects) and their impacts are commonly quantified with the effective radiative forcing (ERF) metric26. However, the radiative effects of airborne microplastics and associated implications for global climate are unknown. Here we present calculations of the optical properties and direct radiative effects of airborne microplastics (excluding aerosol–cloud interactions). The ERF of airborne microplastics is computed to be 0.044 ± 0.399 milliwatts per square metre in the present-day atmosphere assuming a uniform surface concentration of 1 microplastic particle per cubic metre and a vertical distribution up to 10 kilometres altitude. However, there are large uncertainties in the geographical and vertical distribution of microplastics. Assuming that they are confined to the boundary layer, shortwave effects dominate and the microplastic ERF is approximately −0.746 ± 0.553 milliwatts per square metre. Compared with the total ERF due to aerosol–radiation interactions27 (−0.71 to −0.14 watts per square metre), the microplastic ERF is small. However, plastic production has increased rapidly over the past 70 years28; without serious attempts to overhaul plastic production and waste-management practices, the abundance and ERF of airborne microplastics will continue to increase.

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Fig. 1: Concentrations of airborne microplastics reported by previous studies.
Fig. 2: Optical properties of microplastic fragments and fibres.
Fig. 3: ERF of airborne microplastics.

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Data availability

GCM data that support the findings of this study are available at https://doi.org/10.5281/zenodo.5093843. Source data are provided with this paper.

Code availability

Custom code generated in this study is available at https://doi.org/10.5281/zenodo.5093843.

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Acknowledgements

This research was supported by the Royal Society of New Zealand Marsden Fund (contract number MFP-UOC1903). We acknowledge the UK Met Office for the use of the MetUM, SOCRATES and the Continual Intercomparison of Radiation Codes (CIRC). We acknowledge the contribution of New Zealand eScience Infrastructure (NeSI) high-performance computing facilities to the results of this research. New Zealand’s national facilities are provided by NeSI and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation and Employment’s Research Infrastructure programme (https://www.nesi.org.nz, last access: 20 April 2021). We also acknowledge the use of the Rāpoi computing facility at Victoria University of Wellington, along with the open source software used in the analysis: Devuan GNU+Linux, Python, numpy, scipy and matplotlib. L.E.R. thanks Jonny Williams for technical assistance. L.E.R. and P.K. acknowledge R. Martinez Gazoni for helpful discussions.

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Author notes

  1. Peter Kuma

    Present address: Department of Meteorology, Stockholm University, Stockholm, Sweden

Authors and Affiliations

  1. School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand

    Laura E. Revell, Peter Kuma & Sally Gaw

  2. The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington, New Zealand

    Eric C. Le Ru & Walter R. C. Somerville

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Contributions

L.E.R. Conceptualized the study, acquired funding, supervised the study, wrote the original draft and conducted the analysis together with P.K. and W.R.C.S. . P.K. contributed to the methodology, software, validation, and writing of the original draft. E.C.L.R. contributed to the methodology, validation, funding acquisition, supervision and review and editing of the manuscript. W.R.C.S. contributed to the methodology and validation, and writing of  the original draft. S.G. reviewed and edited the manuscript and contributed to funding acquisition.

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Correspondence to Laura E. Revell.

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Extended data figures and tables

Extended Data Fig. 1 Composition of airborne microplastics collected in previous studies compared with reported plastic production data.

The studies included disaggregated composition by morphotype and are presented for (a) fragments; (b) fibres. Polymer compositions include acrylic (ACR, including polyacrylonitrile and poly(N-methyl acrylamide)), polyamide (PA, including nylon), polyethylene and polypropylene (PE-PP), polyester (PES, including polyethylene terephthalate), polystyrene (PS), polyurethane (PUR), polyvinyl acetate (PVA), polyvinyl chloride (PVC), resins (RES, including epoxy, phenoxy and alkyd resins), and various other types (OTH)

Source data.

Extended Data Fig. 2 Size distributions of microplastic fragments reported by previous studies.

A gamma distribution was fitted to match the majority of the empirical distributions. The distributions are normalized to unity and approximated by a gamma distribution with the shape parameter of 2 and scale parameter 15 μm

Source data.

Extended Data Fig. 3 Size distributions of microplastic fibre lengths reported by previous studies.

A gamma distribution was fitted to match the majority of the empirical distributions. The distributions are normalized to unity and approximated by a gamma distribution with the shape parameter of 2.5 and scale parameter 250 μm

Source data.

Extended Data Fig. 4

Morphotypes of airborne microplastic collected in previous studies

Source data.

Extended Data Fig. 5 Refractive index of polymers based on a literature survey.

Polymer compositions include high-density polyethylene (HDPE), polyacrylic acid (PAA), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS) and polyvinyl chloride (PVC). The mean calculated over regular wavelength intervals on a log10 scale is shown by the dashed black lines. In (a) equation (2) was fitted to the mean. In (b) equation (3) was used to fit a 4th degree polynomial to the log10 of the mean using the least squares method. The solid black lines represent the fits given by equations (2) and 3, and these fits were used in the calculations of microplastic optical properties

Source data.

Extended Data Fig. 6 Colours of airborne microplastics collected in previous studies, where colour was reported.

Black includes grey; blue includes turquoise; green includes lime; red includes pink, purple, brown and orange; white includes transparent

Source data.

Extended Data Fig. 7 The empirical aspect ratio of fibres collected in European and Arctic snow (the only study to date to report fibre aspect ratio).

A least squares fit of the form \(D=A\,\log \left(1+\frac{L}{B}\right)\) is also shown, where D is the fibre diameter, L is the fibre length and A and B are fitted coefficients, rounded to the nearest integer

Source data.

Extended Data Table 1 Prescribed microplastic surface concentrations in GCM simulations 
Full size table
Extended Data Table 2 Optical properties of microplastic fragments and fibres supplied to the GCM in the shortwave and longwave bands
Full size table

Source data

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Revell, L.E., Kuma, P., Le Ru, E.C. et al. Direct radiative effects of airborne microplastics. Nature 598, 462–467 (2021). https://doi.org/10.1038/s41586-021-03864-x

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