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Plastic waste discharge to the global ocean constrained by seawater observations

Zhang, Yanxu, Wu, Peipei, Xu, Ruochong, Wang, Xuantong, Lei, Lili, Schartup, Amina T., Peng, Yiming, Pang, Qiaotong, Wang, Xinle, Mai, Lei, Wang, Ruwei, Liu, Huan, Wang, Xiaotong, Luijendijk, Arjen, Chassignet, Eric, Xu, Xiaobiao, Shen, Huizhong, Zheng, Shuxiu, Zeng, Eddy Y.·2023·Nature Communications
DOI 10.1038/s41467-023-37108-5PMC PMC10011382
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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.

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Fig. 1 Estimated global total plastic waste discharge (million metric tons year−1) from land to ocean. Note that the x-axis is logarithm-based. Jambeck et al.4 consider the emissions from the coastal zones, while Lebreton et al.5, Schmidt et al.83, Mai et al.7, Meijer et al.8, and Weiss et al.9 consider only riverine discharge, and Lebreton and Andrady84 include both riverine and coastal zone sources.
Fig. 1 Estimated global total plastic waste discharge (million metric tons year−1) from land to ocean. Note that the x-axis is logarithm-based. Jambeck et al.4 consider the emissions from the coastal zones, while Lebreton et al.5, Schmidt et al.83, Mai et al.7, Meijer et al.8, and Weiss et al.9 consider only riverine discharge, and Lebreton and Andrady84 include both riverine and coastal zone sources.CC BY · PubMed Central
Fig. 2 Comparison between the modeled and observed total surface ocean plastic mass concentrations. a, d High emission scenario. b, e Middle emission scenario. c, f Low emission scenario. In panels a–c, the background colors are the modeled annual mean for the year 2018, while the circles are observations. In panels d–f, the dashed lines are 100:1, 10:1, 1:1, 1:10, and 1:100. All the concentrations and the calculation of R2 and RMSE are in a base 10 logarithmic scale with a unit of g km−2. Sources of observation data are listed in Supplementary Table 8.
Fig. 2 Comparison between the modeled and observed total surface ocean plastic mass concentrations. a, d High emission scenario. b, e Middle emission scenario. c, f Low emission scenario. In panels a–c, the background colors are the modeled annual mean for the year 2018, while the circles are observations. In panels d–f, the dashed lines are 100:1, 10:1, 1:1, 1:10, and 1:100. All the concentrations and the calculation of R2 and RMSE are in a base 10 logarithmic scale with a unit of g km−2. Sources of observation data are listed in Supplementary Table 8.CC BY · PubMed Central
Fig. 3 Sensitivity of the modeled total plastic mass in the surface ocean (Mt, metric tons) to different parameters. a Sensitivity to an individual parameter. b Sensitivity to multiple randomly-sampled parameters. Note the y-axis is on a logarithmic scale. The model scenarios include varying beaching, fragmentation, biofouling, and sedimentation rates. Additional members consider uniform beaching rates for all beaches, fragmentation rates depending on particle size and chemical composition, and low and high marine sources (Table 1).
Fig. 3 Sensitivity of the modeled total plastic mass in the surface ocean (Mt, metric tons) to different parameters. a Sensitivity to an individual parameter. b Sensitivity to multiple randomly-sampled parameters. Note the y-axis is on a logarithmic scale. The model scenarios include varying beaching, fragmentation, biofouling, and sedimentation rates. Additional members consider uniform beaching rates for all beaches, fragmentation rates depending on particle size and chemical composition, and low and high marine sources (Table 1).CC BY · PubMed Central
Fig. 4 Optimized global plastic discharge (Mt, metric tons). The estimation is derived from a three-dimensional variational method powered by a super ensemble of model parameters and emission inventories. The thicker black line represents the ensemble mean with thinner orange ones for each member. The curve is the probability distribution function (pdf) of the optimized global plastic emissions, which is assumed to be a log-normal distribution based on the simulated surface plastic mass (Supplementary Fig. 2). The short gray vertical lines mark the 95% confidence interval. The minimum and maximum discharges derived from models #52 and #51, which are not shown in the figure, are 102.9 and 107.0 Mt, respectively.
Fig. 4 Optimized global plastic discharge (Mt, metric tons). The estimation is derived from a three-dimensional variational method powered by a super ensemble of model parameters and emission inventories. The thicker black line represents the ensemble mean with thinner orange ones for each member. The curve is the probability distribution function (pdf) of the optimized global plastic emissions, which is assumed to be a log-normal distribution based on the simulated surface plastic mass (Supplementary Fig. 2). The short gray vertical lines mark the 95% confidence interval. The minimum and maximum discharges derived from models #52 and #51, which are not shown in the figure, are 102.9 and 107.0 Mt, respectively.CC BY · PubMed Central

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Metadata

Authors
Zhang, Y., Wu, P., Xu, R., Wang, X., Lei, L., Schartup, A. T., Peng, Y., Pang, Q., Wang, X., Mai, L., Wang, R., Liu, H., Wang, X., Luijendijk, A., Chassignet, E., Xu, X., Shen, H., Zheng, S., & Zeng, E. Y.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-03-13
Volume
14
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-023-37108-5
PMC
PMC10011382
Cited by
129
References
11
Language
en
Update notices
correction (2023-12-18)
Licence
Funders
National Natural Science Foundation of China (42177349)

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