Potential Influence of Microplastics on Cloud Formation through Heterogeneous Ice Nucleation
Bibliographic record
Abstract
Recent studies highlight the environmental threat of microplastic pollution, both in waterways and as airborne particles.1,2 Airborne microplastics may affect climate by influencing cloud formation and precipitation through heterogeneous ice nucleation.3,4 In addition, if microplastics are effective at nucleating ice, their lifetime may be influenced by ice nucleation followed by precipitation. Yet, the role of microplastics as ice-nucleating particles and their impact on cloud ice formation remains largely unknown.Here, we present evidence of ice nucleation in the immersion freezing mode induced by various microplastics. Specifically, two polypropylene samples and one polyethylene terephthalate sample exhibited heterogeneous freezing with median temperatures of -20.9°C, -23.2°C, and -21.9°C, respectively, while the water background froze at -25.8°C. The number of ice nucleation sites per surface area, ns(T), ranged from 10-1 to 104 cm-2 within a temperature range of -15 to -25°C, similar to volcanic ash and fungal spores. Following exposure to ozone or a combination of UV light and ozone, mimicking atmospheric aging, the ice nucleation activity either decreased or remained unchanged.5In addition, we investigated the ice nucleation ability of tire wear particles, which are classified as microplastics and are considered a dominant type of urban airborne microplastics.6 Tire wear aerosols were generated by running a truck on a dynamometer to simulate real-world driving conditions. Aerosols were collected on Nuclepore filters and tested for freezing activity, revealing freezing temperatures above the water background.Our freezing data suggest that microplastics including tire wear samples may promote ice formation in cloud droplets. In addition, based on a comparison of our freezing results and previous simulations using a global transport model, ice nucleation by microplastics will impact their long-range transport to faraway locations and global distribution. References:1. Dris, R.; Gasperi, J.; Rocher, V.; Saad, M.; Renault, N.; Tassin, B. Microplastic Contamination in an Urban Area: A Case Study in Greater Paris. Environ. Chem. 2015, 12 (5), 592–599.2. Allen, S.; Allen, D.; Baladima, F.; Phoenix, V. R.; Thomas, J. L.; Le Roux, G.; Sonke, J. E. Evidence of Free Tropospheric and Long-Range Transport of Microplastic at Pic Du Midi Observatory. Nat. Commun. 2021, 12 (1), 7242.3. Ganguly, M.; Ariya, P. A. Ice Nucleation of Model Nanoplastics and Microplastics: A Novel Synthetic Protocol and the Influence of Particle Capping at Diverse Atmospheric Environments. ACS Earth Space Chem. 2019, 3 (9), 1729–1739.4. Aeschlimann, M., Li, G., Kanji, Z.A. and Mitrano, D.M. Potential impacts of atmospheric microplastics and nanoplastics on cloud formation processes. Nat. Geosci. 2022, 15(12), 967-975.5. Seifried, T.M., Nikkho, S., Morales Murillo, A., Andrew, L.J., Grant, E.R. and Bertram, A.K. Microplastic particles contain ice nucleation sites that can be inhibited by atmospheric aging. Environ. Sci. Technol. 2024, 58(35), 15711-15721.6. Evangeliou, N., Grythe, H., Klimont, Z., Heyes, C., Eckhardt, S., Lopez-Aparicio, S. and Stohl, A. Atmospheric transport is a major pathway of microplastics to remote regions. Nat. Commun., 2020, 11(1), 3381.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".