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Rivers as the largest source of mercury to coastal oceans worldwide

Nature Geoscience volume 14, pages 672–677 (2021)Cite this article

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An Author Correction to this article was published on 21 September 2021

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Abstract

Mercury is a potent neurotoxic substance and accounts for 250,000 intellectual disabilities annually. Worldwide, coastal fisheries contribute the majority of human exposure to mercury through fish consumption. Recent global mercury cycling and risk models attribute all the mercury loading to the ocean to atmospheric deposition. Nevertheless, new regional research has noted that the riverine mercury export to coastal oceans may also be significant to the oceanic burden of mercury. Here we construct an unprecedented high-spatial-resolution dataset estimating global river mercury and methylmercury exports. We find that rivers annually deliver 1,000 (minimum–maximum: 893–1,224) Mg mercury to coastal oceans, threefold greater than atmospheric deposition. Furthermore, high flow events, which are becoming more common with climate change, are responsible for a disproportionately large percentage of the export. Coastal oceans constitute 0.2% of the entire ocean volume but receive 27% of the external mercury input to the ocean. We estimate that the river mercury export could be responsible for a net annual export of 350 (interquartile range: 52–640) Mg mercury across the coastal–open-ocean boundary, although there is still high uncertainty around this estimate. Our results show that river export is the largest source of mercury to coastal oceans worldwide, and continued mercury risk modelling should incorporate the impact of rivers.

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Fig. 1: Mercury exports from global rivers.
Fig. 2: Mercury exports into different coastal oceans from external sources.
Fig. 3: Yields of riverine mercury exports in global river basins.
Fig. 4: Updated global oceanic mercury budget demonstrating the impact of the riverine mercury export on the mercury cycle.

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Data availability

All data generated, collected or analysed in this study are included in the main text, Supplementary Information, or Dryad repository: https://doi.org/10.5061/dryad.gmsbcc2mx.

Code availability

The computer code used in this paper is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank R. Mason for his valuable discussion on this work, Y. He for his assistance on the coastal ocean model and L. Chen for contributing data. X.W. and M.L. acknowledge supports from the National Natural Science Foundation of China (41630748, 41977311 and 41821005) and the High-Performance Computing Platform of Peking University.

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Authors and Affiliations

  1. Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Maodian Liu, Qianru Zhang & Xuejun Wang

  2. School of the Environment, Yale University, New Haven, CT, USA

    Maodian Liu, Taylor Maavara, Shaoda Liu & Peter A. Raymond

  3. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Qianru Zhang

  4. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China

    Shaoda Liu

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Contributions

P.A.R., X.W. and M.L. designed the study; X.W. acquired the funding needed to complete the study; M.L., Q.Z., S.L. and T.M. performed data collection and processing and modelling; M.L. wrote the original manuscript in close discussion with P.A.R., T.M. and X.W.; and all authors contributed to manuscript revision and completion.

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Correspondence to Xuejun Wang or Peter A. Raymond.

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Peer review information Nature Geoscience thanks Jeroen Sonke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang.

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Extended data

Extended Data Fig. 1 River mercury exports into different ocean basins.

Data are based on combinations of different hydrologic datasets including WBMplus (divided based on the COSCAT segmentation scheme), Global NEWS, GloFAS-ERA5, ISLSCP II, and WBMsed (see method). In the figure, the term inland represents riverine Hg flowing into Caspian and Black Seas, while island represents grid data of the hydrologic datasets beyond borders of continents. The definition of rivers discharging into different ocean basins is based on the study of Amos et al., (2014)34. Details of the combinations: 1–water discharge and sediment flux from WBMsed; 2–from WBMsed and ISSLCP II, respectively; 3–from WBMsed and WBMplus/COSCAT, respectively; 4–from WBMsed and Global NEWS, respectively; 5–from GloFAS-ERA5 and WBMsed, respectively; 6–from WBMplus/COSCAT and WBMsed, respectively; 7–from Global NEWS and WBMsed, respectively; 8–from GloFAS-ERA5 and ISSLCP II, respectively; 9–from GloFAS-ERA5 and WBMplus/COSCAT, respectively; 10–from GloFAS-ERA5 and Global NEWS, respectively; 11–from WBMplus/COSCAT and ISSLCP II, respectively; 12–from Global NEWS and ISSLCP II, respectively; 13–from WBMplus/COSCAT; 14–from WBMplus/COSCAT and Global NEWS, respectively; 15–from Global NEWS and WBMplus/COSCAT, respectively; 16–from Global NEWS.

Extended Data Fig. 2 River methylmercury exports into different ocean basins.

Data are based on combinations of different hydrologic datasets including WBMplus (divided based on the COSCAT segmentation scheme), Global NEWS, GloFAS-ERA5, ISLSCP II, and WBMsed. In the figure, the term inland represents riverine MeHg flowing into Caspian and Black Seas, while island represents grid data of the hydrologic datasets beyond borders of continents. The definition of rivers discharging into different ocean basins is based on the study of Amos et al., (2014)34. Details of the combinations: 1–water discharge and sediment flux from WBMsed; 2–from WBMsed and ISSLCP II, respectively; 3–from WBMsed and WBMplus/COSCAT, respectively; 4–from WBMsed and Global NEWS, respectively; 5–from GloFAS-ERA5 and WBMsed, respectively; 6–from WBMplus/COSCAT and WBMsed, respectively; 7–from Global NEWS and WBMsed, respectively; 8–from GloFAS-ERA5 and ISSLCP II, respectively; 9–from GloFAS-ERA5 and WBMplus/COSCAT, respectively; 10–from GloFAS-ERA5 and Global NEWS, respectively; 11–from WBMplus/COSCAT and ISSLCP II, respectively; 12–from Global NEWS and ISSLCP II, respectively; 13–from WBMplus/COSCAT; 14–from WBMplus/COSCAT and Global NEWS, respectively; 15–from Global NEWS and WBMplus/COSCAT, respectively; 16–from Global NEWS.

Extended Data Fig. 3 Spatial distributions of mercury and methylmercury exports from global rivers.

Panels a and b, exports based on hydrologic datasets of WBMplus (divided based on the COSCAT segmentation scheme,) and Global NEWS, respectively.

Extended Data Fig. 4 Comparisons between modeled and observed concentrations of mercury and methylmercury.

HgP–particulate Hg; HgD–dissolved Hg; MeHgP–particulate MeHg; MeHgD–dissolved MeHg.

Extended Data Fig. 5 Top twelve coastal oceans receiving riverine mercury and monthly variations.

Please add a caption for Extended Data Fig. 5 here.

Extended Data Fig. 6 Mercury exports from Arctic rivers.

Panel a, spatial distributions of Hg and MeHg exports from Arctic rivers in a resolution of 0.1°×0.1°. Panel b, monthly variations of Hg exports from major Arctic rivers. Panel c, monthly variations of MeHg exports from major Arctic rivers. In panel a, the definition of rivers discharging into the Arctic Ocean is based on the study of Zolkos et al., (2020)38.

Supplementary information

Supplementary Information

Supplementary Texts 1–6 and Figs. 1–3.

Supplementary Table 1

Observations of riverine mercury and methylmercury concentrations and ancillary parameters.

Supplementary Table 2

Global and regional riverine freshwater and suspended sediment discharges into oceans extracted from different datasets.

Supplementary Table 3

Fitting models of riverine mercury and methylmercury concentrations used in global and regional estimates.

Supplementary Table 4

Global and regional riverine mercury and methylmercury exports into oceans from 16 combinations of datasets.

Supplementary Table 5

Evaluations of different fitting strategies for mercury and methylmercury concentration modelling.

Supplementary Table 6

Supplementary Table 6. Linear mixed-effect regression models for evaluation of different grouping strategies.

Supplementary Table 7

Parameters used for the model of the coastal ocean.

Supplementary Table 8

Uncertainty analysis of the coastal ocean model.

Supplementary Table 9

Global riverine mercury and methylmercury exports into oceans based on Global Water Balance and Transport Model and Coastline Segments with their Corresponding River Catchments.

Supplementary Table 10

Global riverine mercury and methylmercury exports into oceans based on Global NEWS.

Supplementary Table 11

Global mercury and methylmercury inputs from rivers and air into different coastal oceans.

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Liu, M., Zhang, Q., Maavara, T. et al. Rivers as the largest source of mercury to coastal oceans worldwide. Nat. Geosci. 14, 672–677 (2021). https://doi.org/10.1038/s41561-021-00793-2

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