Plastic waste discharge to the global ocean constrained by seawater observations

Yanxu Zhang (), Peipei Wu, Ruochong Xu, Xuantong Wang, Lili Lei (), Amina T. Schartup, Yiming Peng, Qiaotong Pang, Xinle Wang, Lei Mai, Ruwei Wang, Huan Liu, Xiaotong Wang, Arjen Luijendijk, Eric Chassignet, Xiaobiao Xu, Huizhong Shen, Shuxiu Zheng and Eddy Y. Zeng ()
Additional contact information
Yanxu Zhang: Nanjing University
Peipei Wu: Nanjing University
Ruochong Xu: Nanjing University
Xuantong Wang: Nanjing University
Lili Lei: Nanjing University
Amina T. Schartup: University of California, San Diego
Yiming Peng: Nanjing University
Qiaotong Pang: Nanjing University
Xinle Wang: Nanjing University
Lei Mai: Jinan University
Ruwei Wang: Jinan University
Huan Liu: Tsinghua University
Xiaotong Wang: Tsinghua University
Arjen Luijendijk: Delft University of Technology
Eric Chassignet: Florida State University
Xiaobiao Xu: Florida State University
Huizhong Shen: Southern University of Science and Technology
Shuxiu Zheng: Peking University
Eddy Y. Zeng: Jinan University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Marine plastic pollution poses a potential threat to the ecosystem, but the sources and their magnitudes remain largely unclear. Existing bottom-up emission inventories vary among studies for two to three orders of magnitudes (OMs). Here, we adopt a top-down approach that uses observed dataset of sea surface plastic concentrations and an ensemble of ocean transport models to reduce the uncertainty of global plastic discharge. The optimal estimation of plastic emissions in this study varies about 1.5 OMs: 0.70 (0.13–3.8 as a 95% confidence interval) million metric tons yr−1 at the present day. We find that the variability of surface plastic abundance caused by different emission inventories is higher than that caused by model parameters. We suggest that more accurate emission inventories, more data for the abundance in the seawater and other compartments, and more accurate model parameters are required to further reduce the uncertainty of our estimate.

Date: 2023
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DOI: 10.1038/s41467-023-37108-5

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