Marine plastic debris has become a growing concern in recent years due to its potential threat to wildlife and humans. Existing bottom-up emission inventories vary among studies for two to three orders of magnitudes (OMs). Here, Prof. Yanxu Zhang and Prof. Lili Lei’s team adopts 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 to 1.5 OMs. On March 13, 2023, Nature Communications published a paper titled "Plastic waste discharge to the global ocean constrained by seawater observations".
Fig. 1: Estimated global total plastic waste discharge (million metric tonsyear−1) from land to ocean.
Ocean plastic waste mainly derives from terrestrial sources via either riverine discharge or the erosion of waste from coastal zones while a smaller contribution is from direct dumping by shipping and fishing activities (~25% of the total terrestrial discharge). Many studies have been conducted to estimate the flux of plastic waste to the global ocean, but these studies have some limitations. One limitation is the data availability as we have only ~102 rivers with data so far, compared to the large number (>106) of all the rivers worldwide. Regression models are often developed between the measured discharge and proxy data, such as population, plastic use, mismanaged plastic waste (MPW) generation, income level, land use, runoff, and precipitation. These models are then applied to other rivers without measurement data to achieve a global estimate. Using different subsets of rivers and choosing different proxy data, the estimated global flux can range by up to three orders of magnitudes (OMs) among these studies (Fig. 1). Also, rivers act as reservoirs for plastics, affecting the discharge to oceans. There is a similar or even worse paucity of data for the emissions from the erosion of waste from coastal zones.
Fig. 2: Optimized global plastic discharge (Mt, metric tons).
In contrast to directly measured emissions, surface ocean plastic abundances have been extensively measured in the recent decade. So this study uses a top-down approach to estimate the plastic waste discharge to the global ocean based on surface ocean plastic abundance data. This study develops a three-dimensional Euler-based global ocean plastic model (NJU-MP, Nanjing University Marine Plastic Model), which explicitly includes a comprehensive representation of the important processes for plastic particles in the ocean (e.g., sinking and rising, drifting, fragmentation/abrasion, beaching, and biofouling/defouling). The authors use the riverine plastic emission inventories from Lebreton et al., Mai et al., and Weiss et al., and are referred to as the High, Middle, and Low scenarios, respectively. A super model ensemble containing 156 (=52 × 3) members is constructed by a Monte Carlo approach by randomly generated model parameters based on their ranges reported in the literature (n = 52) and the three emission inventories (i.e., High, Middle, and Low). The global plastic emissions are optimized by minimizing a cost function that measures the deviation from the prior emission inventory and the observed surface ocean plastic abundance. 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. The authors also find that the variability of surface plastic abundance caused by different emission inventories is higher than that caused by model parameters. It is suggested 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 the estimate.
Professor Yanxu Zhang, Professor Lili Lei of our school and Professor Eddy Y. Zeng from School of Environment of Jinan University are corresponding authors. Professor Yanxu Zhang and Ph.D. student Peipei Wu are co-first authors of the paper. Researchers from Nanjing University, Scripps Institution of Oceanography, Jinan University, Tsinghua University, Delft University of Technology, Florida State University, Southern University of Science and Technology and Peking University are co-authors. This work is financially supported by the National Key R&D Program of China grant 2019YFA0606803, the National Natural Science Foundation of China grant 42177349, the Frontiers Science Center for Critical Earth Material Cycling, the Fundamental Research Funds for the Central Universities grant 14380168 and 14380188, and the Collaborative Innovation Center of Climate Change, Jiangsu Province.
Article link: https://doi.org/10.1038/s41467-023-37108-5