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Global Atmospheric Microplastics Emissions Estimated Using Constrained Bayesian Inverse Modeling

2026 Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Nikolaos Evangeliou Ondřej Tichý, Nikolaos Evangeliou Ioanna Evangelou, Václav Šmídl, Ioanna Evangelou, Ioanna Evangelou, Ondřej Tichý, Nikolaos Evangeliou Ondřej Tichý, Ondřej Tichý, Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Nikolaos Evangeliou Ioanna Evangelou, Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Ioanna Evangelou, Ondřej Tichý, Nikolaos Evangeliou Ioanna Evangelou, Ioanna Evangelou, Nikolaos Evangeliou Michal Uliáš, Nikolaos Evangeliou Nikolaos Evangeliou Václav Šmídl, Václav Šmídl, Nikolaos Evangeliou Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Ioanna Evangelou, Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Ondřej Tichý, Václav Šmídl, Václav Šmídl, Nikolaos Evangeliou Nikolaos Evangeliou Václav Šmídl, Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Ondřej Tichý, Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou Nikolaos Evangeliou

Summary

Scientists used advanced computer modeling to estimate how much microplastic pollution is being released into the air around the world. These tiny plastic particles can travel through the atmosphere and end up in the air we breathe, on our food, and in water sources. This research helps us better understand how much microplastic pollution humans are exposed to, which is important for assessing potential health risks from breathing or consuming these particles.

We present an analysis of global atmospheric microplastics (MPs) concentration and deposition measurements using constrained Bayesian inverse modeling to estimate global MPs emissions. The proposed Bayesian framework explicitly accounts for unknown ratios between size fractions inherent to MPs measurements and incorporates prior emission information to stabilize the inversion. The coupling between observations and unknown emissions is established using the atmospheric transport model FLEXPART version 11 operated in backward mode for each measurement. Model parameters are inferred using a variational Bayes approach, resulting in an iterative estimation scheme that updates both model parameters and the effective spatial structure of the computational domain. This methodology reduces the need for manual intervention during the inversion process and limits potential bias in the results. The resulting global MPs emission estimates are evaluated against previously published ones. Acknowledgment:This research has been supported by the Czech Science Foundation (grant no. GA24-10400S). N.E. was funded by the Norwegian Research Council (NFR) project MAGIC (No.: 334086). FLEXPART model simulations are cross-atmospheric research infrastructure services provided by ATMO-ACCESS (EU grant agreement No 101008004). The computations were performed on resources provided by Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.

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