Global continental and oceanic emissions of atmospheric microplastics inferred from pattern-restricted Bayesian inversion

Abstract. In this paper, we present global atmospheric microplastic (MP) emissions calculated using remote deposition measurements, Lagrangian transport modelling and a pattern-restricted Bayesian inversion. Emissions were constrained using four source patterns representing agricultural activity, bare-soil resuspension, road dust, and oceanic resuspension. A Gibbs-sampling framework combined with an orthogonal transformation of the inverseproblem was used to quantify posterior source budgets and their associated uncertainties. We estimate annual atmospheric MP emissions to be 1.46×1018 particles y−1, equivalent to 1001±781 Gg y−1 for particles between 5 and 250 μm. Oceanic emissions dominate the global budget, contributing 763 ± 611 Gg y−1, while agriculture, road dust, and bare-soil resuspension contribute 170 ± 125, 51 ± 31, and 17 ± 14 Gg y−1, respectively. Independent validation against global atmospheric MP observations gives a correlationcoefficient of 0.57, with 50% of modelled values within one order of magnitude and only 12% as outliers. Restricting this comparison to only observations over the ocean, together with a sensitivity experiment in which marine emissions were strongly reduced, reveals that oceanic emissions are likely non-negligible. The measured size distribution further indicates a strong decoupling between particle number and mass, with the 10–25 μm fraction dominating particle-number emissions, but not emitted mass. Consequently, assumptions regarding particle size, density, and geometry affect global mass estimates significantly and explain the large spread observed in previously reported inventories. Our results provide an observation-constrained estimate of the global atmospheric MP budget, in which the accuracy of the emissions is supported by an independent comparison.