Influence of catastrophic flood on microplastics organization in surface water of the Three Gorges Reservoir, China

The Three Gorges Dam (TGD) is the world's largest hydropower project. It could potentially influence the footprint and transport of microplastics (MPs) in Yangtze River, which is the largest riverine input of oceanic MPs worldwide. In addition to analyzing the MP particles of all size categories and polymer groups, we also evaluated the stability, pollution risk and source identification of MPs after the catastrophic flood of 2020 in the Three Gorges Reservoir (TGR) and downstream of the TGD. We found that the MP abundance (6214 ± 5394 particles/m3) in the TGR water increased by a 57.9% growth after this catastrophic flood. Interestingly, we observed the small-sized MPs (SMPs; < 300 ?m) were dominant in the TGR (accounting for ?65.4% of the total MP particles). After flooding, the main morphological types were fragment and fiber, while the major polymer was polyethylene (PE). Although the MP level was at a low pollution risk, 13.6% of the sampling sites in the TGR water faced potential ecological risks driven by SMPs. In particular, there was no significant difference in the abundances, morphological types, and polymer composition of MPs between upstream and downstream of the TGD (p > 0.05), indicating flooding control operation could weaken the barrier effect of the dam on MPs. Further, based on the conditional fragmentation model, the PE fragments in SMPs of the TGR remained at a stable state. MPs in the TGR mainly originated from anthropogenic activities (wastewater, containers, and agriculture films), with atmospheric deposition as a potential transport pathway for polymers. Our study demonstrates that dam operation during the flood period can influence the MP organization in TGR, providing new insights of the global land-sea transportation of MPs in the Yangtze River.

» Author: Dongyu Xu, Bo Gao, Xiaohong Wan, Wenqi Peng, Baohao Zhang

» Publication Date: 01/03/2022

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


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