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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.
Estimating the amount of plastics that enters the ocean is subject to significant uncertainty. This study uses ocean plastic abundance data to refine our estimate and reduce this uncertainty, enabling more effective control and mitigation polices.
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1 Nanjing University, School of Atmospheric Sciences, Nanjing, China (GRID:grid.41156.37) (ISNI:0000 0001 2314 964X); Nanjing University, Frontiers Science Center for Critical Earth Material Cycling, Nanjing, China (GRID:grid.41156.37) (ISNI:0000 0001 2314 964X)
2 Nanjing University, School of Atmospheric Sciences, Nanjing, China (GRID:grid.41156.37) (ISNI:0000 0001 2314 964X)
3 University of California, San Diego, Scripps Institution of Oceanography, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)
4 Jinan University, Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Guangzhou, China (GRID:grid.258164.c) (ISNI:0000 0004 1790 3548)
5 Tsinghua University, State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
6 Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740); Hydraulic Engineering, Deltares, Delft, Netherlands (GRID:grid.6385.8) (ISNI:0000 0000 9294 0542)
7 Florida State University, Center for Ocean–Atmospheric Prediction Studies (COAPS), Tallahassee, USA (GRID:grid.255986.5) (ISNI:0000 0004 0472 0419)
8 Southern University of Science and Technology, School of Environmental Science and Technology, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790)
9 Peking University, College of Urban and Environmental Sciences, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)