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Human activities shape important geographic differences in fish mercury concentration levels

Nature Food volume 5, pages 836–845 (2024)Cite this article

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Abstract

Fish consumption is a major route of human exposure to mercury (Hg), yet limited understanding of how anthropogenic activities drive geographic variations in fish Hg worldwide hinders effective Hg pollution management. Here we characterized global geographic variations in total Hg (THg) and methylmercury (MeHg), compared THg and MeHg levels between the United States and China, and used a structural equation model to link the geographic variability of MeHg in fish to human activities. Despite previously reported higher Hg emissions in China, Chinese fish have lower THg and MeHg levels than fish in the United States owing to a lower trophic magnification slope, shortened food chains and shorter fish lifespans. The structural equation model revealed strong impacts of human activities on MeHg levels in fish. In the future, China may face elevated MeHg levels in fish with the ongoing recovery of food web ecology, highlighting the importance of local policies.

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Fig. 1: Geographic patterns of fish Hg concentrations from different regions worldwide.
Fig. 2: Geographic patterns of fish Hg concentrations from different regions of China.
Fig. 3: Geographic patterns of fish Hg concentrations from different regions of the United States.
Fig. 4: Comparison of fish Hg concentrations between China and the United States.
Fig. 5: TMS, baseline (the intercept) and primary consumer (TL2) Hg concentration of China and the United States.
Fig. 6: Factors affecting the Hg bioaccumulation process in fish.

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Data availability

The authors declare that all data supporting the findings of this study are available within the article and Supplementary Information. Source data are provided with this paper.

Code availability

The LMM code is available from the corresponding author Yongguang Yin (ygyin@rcees.ac.cn) upon request.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (22193051, 42394091, 22425606, 22006151, 21527901), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0750200) and NSF program (ECS1905239). This is publication #1765 from the Institute of Environment at Florida International University. Y.Y. acknowledges the support from the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y202011). We extend our gratitude for the support from the Fundamental Research Funds for the Central Universities (SWU-XDJH202320) from Southwest University to the Climate Change and its Environmental Implications (CCEI) Thrust. We thank Y. Zhang and Z. Song of Nanjing University for their help in calculating the annual Hg deposition and atmospheric Hg concentration.

Author information

Authors and Affiliations

  1. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yuping Xiang, Yongguang Yin, Yong Cai & Guibin Jiang

  2. Laboratory of Environmental Nanotechnology and Health Effect, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yuping Xiang, Yongguang Yin & Yong Cai

  3. Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing, China

    Yuping Xiang & Dingyong Wang

  4. Institute of Environment and Health, Jianghan University, Wuhan, China

    Guangliang Liu & Yongguang Yin

  5. Department of Chemistry and Biochemistry and Institute of Environment, Florida International University, Miami, FL, USA

    Guangliang Liu & Yong Cai

  6. University of Chinese Academy of Sciences, Beijing, China

    Yongguang Yin & Guibin Jiang

  7. Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education and College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, China

    Yanbin Li

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Contributions

Conceptualization: G.L., Y.Y. and Y.C. Investigation: Y.X. and Y.Y. Funding acquisition: Y.X., Y.Y., Y.C. and G.J. Writing—original draft: Y.X. Writing—review and editing: G.L., Y.Y., Y.L., D.W., Y.C. and G.J.

Corresponding authors

Correspondence to Yongguang Yin or Yong Cai.

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The authors declare no competing interests.

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Peer review information

Nature Food thanks Maodian Liu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Map of the site locations of peer-reviewed studies.

a, Worldwide, b, the US and c, China. Administrative boundary data of worldwide and China are supported by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). Administrative boundary data of the US are supported by Natural Earth (https://www.naturalearthdata.com). The list of papers reviewed and the detailed sample points are present in the Source Data.

Source data

Extended Data Fig. 2 THg and MeHg concentrations (μg kg-1 ww), as well as MeHg% in fish of China with different dietary behaviors.

a–c, Freshwater fish, d–f, marine fish. The detailed sample sizes are provided in the Source Data, and “n” represents the number of data points per graph. Green: herbivores, yellow: planktivores, blue: ominivores, and orange: carnivores.

Source data

Extended Data Fig. 3 Hg concentrations of different fish species in China.

a, THg (µg kg-1 ww), b, MeHg (µg kg-1 ww), c, MeHg%. The difference analysis of THg, MeHg and MeHg% among fish species was conducted by Rank transformed one-way ANOVA, owing to the non-normal distribution (Kolmogorov-Smirnov test, K-S test) and non-homogeneity of the variances (Levene test). Categories that share common letter do not differ significantly (two-sided tests): lower case letters are for comparison of Hg among freshwater species and capital letters are for comparison of Hg among marine fish species. The detailed sample sizes are provided in the Source Data, and “n” represents the number of data points per graph.

Source data

Extended Data Fig. 4 Hg concentrations in wild and farmed fish across China.

a, THg (µg kg-1 ww), b, MeHg (µg kg-1 ww), c, MeHg%. Data are the comprehensive presentation of Fig. 2. FW and FF are short for freshwater wild and farmed fish, and MW and MF are short for marine wild and farmed fish, respectively. The detailed sample sizes are provided in the Source Data, and “n” represents the number of data points per graph. A Mann-Whitney U test was performed to compared fish Hg levels between groups. Based on Monte Carlo analysis (two-sided), a p-value less than 0.05 indicates statistically significance.

Source data

Extended Data Fig. 5 Trophic levels of fish observed in China and the US.

FW and MW are short for freshwater and marine wild fish, respectively. The detailed sample sizes are provided in the Source Data, and “n” represents the number of data points per graph. A Mann-Whitney U test was performed to compared fish TL between groups. Based on Monte Carlo analysis (two-sided), a p-value less than 0.05 indicates statistically significance.

Source data

Extended Data Fig. 6 The growth traits of wild fish across China and the US collected.

a, Fish age, b, growth rate (cm/year). FW and MW are short for freshwater and marine wild fish, respectively. The detailed sample sizes are provided in the Source Data, and “n” represents the number of data points per graph. A Mann-Whitney U test was performed to compared fish age and growth rate between groups. Based on Monte Carlo analysis (two-sided), a p-value less than 0.05 indicates statistically significance.

Source data

Extended Data Fig. 7 The hypothesized fish mercury accumulation model summarizing pathways that directly and/or indirectly mediated mercury levels in fish.

Solid arrows pointing straight to fish mercury without any intermediate factors are the predicted direct pathways, while others are the predicted indirect pathways. Dotted arrows represent the modulating effect.

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and 5, Notes 1–9 and References.

Reporting Summary

Supplementary Table 4

Detailed calculation process of UDF 1 for each sampling site in China (a) and the United States (b).

Source data

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Xiang, Y., Liu, G., Yin, Y. et al. Human activities shape important geographic differences in fish mercury concentration levels. Nat Food 5, 836–845 (2024). https://doi.org/10.1038/s43016-024-01049-z

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