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Temporal Dynamics of Microplastic Pollution in the Meishe River: Decreasing Surface Water Abundance versus Increasing Sediment Accumulation

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Good news and bad news: a Chinese river had less microplastic floating in its water in 2025 than in 2020, thanks to better pollution controls, but the plastic that remains is breaking into smaller, harder to remove pieces and building up in the riverbed sediment. Since tinier particles can be easier for organisms (and eventually us, through food and water) to absorb, this shift matters even as overall risk stayed moderate.

Study Type Environmental

A comprehensive understanding of the changes in microplastic pollution in urban rivers is a key prerequisite for effective pollution control and management. The Meishe River, the longest urban river in Haikou (Hainan Island, China), transports microplastic pollution from terrestrial to marine ecosystems. This study investigated the spatiotemporal distribution, characteristics, and ecological risks of microplastics by analyzing surface water and sediment samples collected from nine sites in 2020 and 2025. Microplastics were extracted via density separation and digestion, identified by Fourier Transform Infrared Spectroscopy (FTIR), and assessed for risk with the Pollution Load Index (PLI). Results showed a significant decrease in surface water microplastic abundance (from 1638.5 ± 796.1 to 601.5 ± 241.9 pieces·m⁻3, P = 0.001) but a concurrent significant increase in sediments (from 711.5 ± 537.6 to 1139.6 ± 610.8 pieces·kg⁻1, P = 0.010). Fragments dominated both compartments. A notable temporal shift occurred from larger (> 1.0–5.0 mm) to smaller (0.1–0.5 mm) particles. Primary polymers were polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). Overall ecological risk was moderate, with a significant decrease in surface water PLI (P = 0.001). These findings indicate targeted regulations and enhanced wastewater management effectively reduce urban river microplastic contamination. Limitations include potential seasonal bias between sampling campaigns, requiring future long-term monitoring.

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