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China’s municipal wastewater policies enhanced seafood safety and offset health risks from atmospheric mercury emissions in the past four decades

Nature Food volume 6, pages 182–195 (2025) Cite this article

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Abstract

The neurotoxin methylmercury in seafood threatens food safety worldwide. China has implemented stringent wastewater policies, established numerous treatment facilities and enforced rigorous water quality standards to address pollution in its waterways. However, the impact of these policies on seafood safety and methylmercury exposure remains unknown. Here we developed a process-based model showing that, although mercury reductions from municipal wastewater policies accounted for only 9% of atmospheric mercury emissions during 1980–2022, these measures unexpectedly prevented \({\mathrm{102,000}}_{-\mathrm{6,600}}^{+\mathrm{11,000}}\) mercury-related deaths and counteracted nearly two thirds of potential deaths from those emissions. Furthermore, these policies ensured that \({146}_{-9}^{+8}\) megatonnes of freshwater seafood met the World Health Organization and China’s mercury-safety standards, preventing \({\mathrm{US}}\${498}_{-29}^{+32}\) billion in economic losses. Finally, we explore how China, as the primary global seafood producer and exporter, could develop municipal wastewater policies at the regional level to reduce aquatic pollutants and unlock the health benefits of seafood consumption.

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Fig. 1: Reductions in Hg levels in China’s freshwater seafood contributed through major influential factors during 1980–2022.
The alternative text for this image may have been generated using AI.
Fig. 2: Reduction in Hg release contributed by MWT policies and associated Hg-safe freshwater seafood production increases in China during 1980–2022.
The alternative text for this image may have been generated using AI.
Fig. 3: Changes in MeHg exposure through freshwater seafood consumption contributed by MWT policies and associated health benefits in China during 1980–2022.
The alternative text for this image may have been generated using AI.
Fig. 4: Study methodology.
The alternative text for this image may have been generated using AI.

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Data availability

The underlying data used in this study are provided in the Supplementary Information or are available from the sources listed in the main text and the Supplementary Information. Source data are provided with this paper.

Code availability

Data collection and analyses were conducted in Microsoft Excel 2020, Python v.3.9.10, ArcGIS v.10.8, ANUSPLIN v.4.4 and WebPlotDigitizer v.4.7. The code is available from the corresponding authors upon reasonable request.

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Acknowledgements

This work was partially funded by the National Natural Science Foundation of China (grant nos 41821005 and 42476127). M.L. is supported by the Fundamental Research Funds for the Central Universities (grant no. 7100604309) and the Laboratory for Earth Surface Processes, Ministry of Education, Peking University. Q.Z. acknowledges support from the China Postdoctoral Science Foundation (grant no. 2022M720005) and the Beijing Natural Science Foundation (grant no. 8244068). X.C. acknowledges support from Peking University-BHP Carbon and Climate Wei-Ming PhD Scholars (grant no. WM202209) and the High-Performance Computing Platform of Peking University.

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Authors and Affiliations

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

    Xingrui Cai, Maodian Liu, Qianru Zhang & Xuejun Wang

  2. The Bartlett School of Sustainable Construction, University College London, London, UK

    Xingrui Cai, Shijun Ma & Jing Meng

  3. School of Environment, Tsinghua University, Beijing, China

    Mengqi Yang

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

    Maodian Liu

  5. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yuang Chen

  6. Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, China

    Chenghao Yu

  7. Centre for Environmental Policy, Imperial College London, London, UK

    Haoran Zhang

  8. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Qianru Zhang

  9. Department of Earth System Science, Tsinghua University, Beijing, China

    Xinyu Dou

  10. Department of Earth System Science, Stanford University, Stanford, USA

    Xinyu Dou

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  1. Xingrui Cai
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Contributions

X.C., M.Y., J.M. and X.W. designed the research. X.W. and M.L. acquired the funding needed to complete the study. M.Y. and X.C. performed the data collection. X.C. conducted the data processing and modelling in close discussion with H.Z. and S.M. X.C., M.Y. and M.L. wrote the original paper in close discussion with C.Y., J.M. and X.W. Y.C., Q.Z. and X.D. provided important data to help complete the work. All authors contributed to paper revision and completion.

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Correspondence to Maodian Liu, Jing Meng or Xuejun Wang.

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Extended data

Extended Data Fig. 1 Hg-safe seafood production increases rates in different provinces in 2022.

The shades of the color blocks represent the rates of production increases contributed by MWT for each species across different provinces.

Extended Data Fig. 2 Provincial Hg-safe freshwater seafood production increases, disaggregated by production activity.

The pie plot shows provincial seafood production in 2022. The dark green line and brown line show the rates of production increases in Hg-safe freshwater seafood and economic output increases in China’s freshwater fishery industry, respectively. The confidence of uncertainty intervals is 80%, as shown by the shaded areas. Base map from https://www.tianditu.gov.cn/.

Extended Data Fig. 3 Changes in Hg-safe seafood production and contribution shares to PDI of MeHg.

National increase shares of Hg-safe seafood production and contribution shares to MeHg PDI decreases by major category of freshwater seafood species.

Extended Data Fig. 4 Changes in MeHg PDI through freshwater seafood consumption and associated health benefits in China in 2022.

a, Nationwide decreases of MeHg PDI (ng kg−1 d−1), disaggregated by gender, type of residence, and age group. b, Provincial decreases of MeHg PDI (ng kg−1 d−1). The yellow and green columns denote provincial IQ increase per fetus (point) and avoided deaths due to fatal heart attack in 2022, respectively. Base map in b from https://www.tianditu.gov.cn/.

Extended Data Fig. 5 Aquatic Hg concentration of sub-watersheds in China in the absence of MWT plants in 2022.

According to China’s Environmental quality standards for surface water110, the Hg concentration limit for Class-I and Class-II waterbodies is 0.05 μg L–1. The Hg concentration limit for Class-III waterbodies is 0.1 μg L–1, which is the Hg concentration limit for aquatic breeding areas. The Hg concentration limit for Class-IV and Class-V waterbodies is 1 μg L–1. Base map from https://www.tianditu.gov.cn/.

Extended Data Fig. 6 Interprovincial transport of common freshwater seafood species in 2002.

Transportation routes with volumes less than 1 ton were not depicted in the figure.

Extended Data Fig. 7 Interprovincial transport of common freshwater seafood species in 2022.

Transportation routes with volumes less than 1 ton were not depicted in the figure.

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Supplementary Texts 1–17, Tables 1–5, Figs. 1–15 and refs. 1–396, and references for Methods.

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Cai, X., Yang, M., Liu, M. et al. China’s municipal wastewater policies enhanced seafood safety and offset health risks from atmospheric mercury emissions in the past four decades. Nat Food 6, 182–195 (2025). https://doi.org/10.1038/s43016-024-01093-9

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