Using the power law size distribution to extrapolate and compare microplastic number and mass concentrations in environmental media

Studies reporting environmental MP concentrations typically do so for variable MP size ranges, depending on sampling, processing and analytical detection methods. However, microplastic (MP) number concentrations in the environment increase exponentially with decreasing particle size. This leads to difficulties in intercomparison and extrapolation of studies, which is critical for data reviews, plastic dispersion modelling, and environmental and human health risk assessment. To address these challenges, we collected 90 MP particle size distributions (PSDs) from 55 published studies that observed environmental MP in the atmosphere, ocean surface, and deep ocean. The data are compiled in the online MPsizeBase open access database (https://zenodo.org/records/17380284). Improving from published methods (Kooi et al., 2021; Kooi and Koelmans, 2019), a new MP size-alignment framework based on the power law distribution is proposed and validated (Segur et al., 2026). This framework is then applied to the MPsizeBase data to extrapolate observed MP number and mass concentrations to the full MP size range (1 to 5000 µm, noted MP_1-5000µm), or any other sub-size range. Our findings reveal distinct fragmentation patterns: power law slopes for fragments (−2.76 ± 0.62) are significantly steeper than for fibers (−1.84 ± 0.38), underscoring differences in their environmental behavior. Strikingly, reported airborne MP concentrations (0.8–37 MP m⁻³) fall 35–130 times below extrapolated values (up to 4800 MP m⁻³ for fragments), with mass concentrations reaching 0.06–22 µg m⁻³. Similarly, atmospheric deposition fluxes (90–190 MP m⁻² d⁻¹) are 80–140 times lower than extrapolated estimates (up to 16,000 MP m⁻² d⁻¹), with mass deposition of 10–190 µg m⁻² d⁻¹. These disparities underscore a pressing need: standardized size extrapolation is essential to harmonize datasets, refine risk assessments, and disentangle true environmental trends from methodological biases.

References

Kooi, M. and Koelmans, A. A.: Simplifying Microplastic via Continuous Probability Distributions for Size, Shape, and Density, Environ. Sci. Technol. Lett., 6, 551–557, https://doi.org/10.1021/acs.estlett.9b00379, 2019.

Kooi, M., Primpke, S., Mintenig, S. M., Lorenz, C., Gerdts, G., and Koelmans, A. A.: Characterizing the multidimensionality of microplastics across environmental compartments, Water Research, 202, 117429, https://doi.org/10.1016/j.watres.2021.117429, 2021.

Segur, T., Hough, I., Dobiasova, N., Voisin, D., Richon, C., Angot, H., Thomas, J. L., and Sonke, J. E.: Using the power law size distribution to extrapolate and compare microplastic number and mass concentrations in environmental media, https://doi.org/10.21203/rs.3.rs-8524083/v1, 8 January 2026.