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Quantitative assessment on the distribution patterns of microplastics in global inland waters

Communications Earth & Environment 2025 14 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Jin Xia, Zimin Li, Josep Peñuelas, Jordi Sardans, Qiqian Wu, Peng Yan, Petr Hědenec, Zhijie Li, Chaoxiang Yuan, Yuan Ji, Zihao Chen, Zemin Zhao, Fuzhong Wu, Kai Yue

Summary

Researchers compiled data from over 5,000 observations across 301 published studies to map microplastic levels in freshwater bodies worldwide, finding concentrations ranging from near zero to 4.3 million particles per cubic meter. Human development, farming activity, and water runoff were the strongest predictors of contamination, with China and parts of Asia showing particularly high levels.

Study Type Review

Microplastics (MPs) contamination in global inland waters has raised concerns recently. However, quantitative research on the abundance of MP in these environments remains limited. Here we compiled a comprehensive dataset of MPs in global inland waters from 5365 observations across 301 publications, revealing a diverse distribution of MP abundance in inland waters, ranging from 0.00 to 4,275,800.70 items m−3 (mean: 25,255.47 ± 132,808.40 items m−3). Human development index, evapotranspiration, cropland, and land surface runoff emerged as the primary factors influencing MPs levels in water. The predicting map showed particularly high MP abundance in China, but also relatively high abundance in Asia, Europe, Africa, and the eastern United States. Urgent action is needed to monitor and manage MPs in inland waters, especially the small-sized MPs (<1 mm). Implementing effective strategies to regulate plastic production and waste management is imperative for protecting freshwater ecosystems, particularly in countries with high MP pollution. Microplastic concentrations in global inland waters vary from 0 to 4.3 million items per cubic meter with primary influences from human development index, evapotranspiration, cropland, and land surface runoff, according to a global meta-analysis coupled with predictive mapping.

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