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# Global health effects of future atmospheric mercury emissions

Mercury is a potent neurotoxin that poses health risks to the global population. Anthropogenic mercury emissions to the atmosphere are projected to decrease in the future due to enhanced policy efforts such as the Minamata Convention, a legally-binding international treaty entered into force in 2017. Here, we report the development of a comprehensive climate-atmosphere-land-ocean-ecosystem and exposure-risk model framework for mercury and its application to project the health effects of future atmospheric emissions. Our results show that the accumulated health effects associated with mercury exposure during 2010–2050 are $19 (95% confidence interval: 4.7–54) trillion (2020 USD) realized to 2050 (3% discount rate) for the current policy scenario. Our results suggest a substantial increase in global human health cost if emission reduction actions are delayed. This comprehensive modeling approach provides a much-needed tool to help parties to evaluate the effectiveness of Hg emission controls as required by the Minamata Convention. ## Introduction Mercury (Hg) is a global pollutant, and its organic form, methylmercury (MeHg) is associated with neurocognitive deficits in human fetuses and cardiovascular effects in adults1,2. Human exposure to MeHg is predominantly via the consumption of food (e.g., seafood and rice)3,4. The annual death from the fatal heart attack that is attributable to MeHg exposure is estimated to be over 10,000 in China5. Economic losses from intelligence quotient (IQ) decrease of developing brains associated with MeHg exposure has been estimated at$16 billion in the U.S. and European Union3,5,6. To protect human health and the environment, the Minamata Convention on Mercury, a legally-binding international treaty, took effect in August 2017 to reduce anthropogenic emissions of Hg (https://www.mercuryconvention.org).

Future projections of global primary anthropogenic Hg emissions vary drastically driven by underlying social-economic and technological change7,8. The re-emissions from soils and oceans that receive past atmospheric depositions of Hg (legacy emissions) are also important sources, the magnitude of which is 2-3 times larger than the primary anthropogenic emissions9,10. The MeHg exposure is influenced by a chain of processes including atmospheric emission, atmospheric transport and deposition, air-sea exchange, air-land exchange, chemical transformation (especially Hg methylation), food web transfers, and human food intake11. These processes are modulated by the fluctuation and change in climate, land-use, ocean circulation, and ecosystem functions12,13. Earlier studies do not link emissions to exposure changes3,14,15,16,17. Later efforts in global Hg exposure modeling have considered only a subset of these processes. For instance, using atmospheric transport models, atmospheric deposition is considered as an indicator for the level of MeHg in seafood5,11,13. Zhang et al.13 included the impact of changing climate, land-use, and land-cover on atmospheric transport and deposition, and Amos et al.18 and Angot et al.19 considered the response of land/ocean re-emissions to anthropogenic emission change with a box model.

In this study, we develop a more comprehensive approach to project the change in human MeHg exposure responding to Hg emission changes. We integrate changes in anthropogenic emissions, climate, and biogeochemical cycles. We use a coupled three-dimensional atmosphere/ocean and two-dimensional land model. The Hg/MeHg levels in the environment are used to scale an intake inventory of MeHg for different countries, which are further used to calculate the health impact based on epidemiology-based dose-response relationships (see “Methods” for details). We present a map of MeHg-related health risks for all the countries. Based on this, we translate future Hg emission projections into health risks, and to help parties and stakeholders to evaluate impacts from changes in Hg emissions.

## Results and discussion

### Uncertainty

We assess the uncertainty and variability of the health effects projected by our integrated model by identifying key driving factors, including food consumption data, food MeHg concentrations, dose-response parameters linking MeHg exposure and health effects, and economic valuation (Fig. 5). We rely on the database of the United Nations’ Food and Agriculture Organization (FAO, http://www.fao.org) for food consumption. Compared with national data, the two data sources generally agree within a factor of 2 (Figure S6). This reflects both the different survey methods and variability among the population33,34. This results in a variability of cumulative economic loss for the CP scenario as $10 to$27 trillion (95% confidence interval in 2020 value and realized in 2050, same thereafter). This variability also propagates to the estimated benefits (or extra costs) for other scenarios (Fig. 5). By considering the log-normal distributions of food MeHg data, the cumulative effects for the CP scenario would range from $12 to$31 trillion. This indicates that the food intake and MeHg data contribute roughly equally to the uncertainty of exposure calculation. We find that the dose-response functions between MeHg intake and health effects have the largest contribution to the uncertainty, ranging from nearly $7.8 to$47 trillion. This reflects the large variability in the coefficients for IQ decrement and heart attack risk per unit hair Hg increase11,35, despite convincing evidence for the association between MeHg exposure and human health impact36. The pharmacokinetics parameters to link food exposure to blood and hair Hg levels play a much smaller role with a variability of 10–20%. Another source of uncertainty comes from the parameters for economic valuation, especially the VSL of heart attack deaths (a factor of 10)11. Using high and low assumptions for the economic valuation leads to a range of $5.8–24 trillion for the health effect of the CP scenario. By taking a Monte Carlo approach (see Methods), we also calculate the overall uncertainty range as$4.7–54 trillion.

Our ability to model the MeHg exposure and risk is limited by existing scientific knowledge and data, such as the food web dynamics of MeHg in higher trophic levels and the dose-response relationships between MeHg exposure and its health effects (Fig. 5). The future Hg emissions to water and soil are subjected to change37. We only consider the general populations, but not the so called high exposure population groups24. The fishery harvest and human food consumption patterns will also change in the future38. Our results do not show strong interannual variability for environmental Hg levels on a global scale, but the change in dietary structure and food web dynamics in high trophic levels that are not covered in this model may amplify these variabilities, especially at regional scales39. The permafrost stores a large amount of Hg and may serve as a potential Hg source as a consequence of thawing31. There are likely other health endpoints not considered in this study due to the limited epidemiological data36. Our assessment is thus considered illustrative and not a comprehensive projection of impacts. However, much uncertainty of the model framework could be reduced using a similar methodology as science and data evolve.

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

All data generated or analyzed during this study are available in the Supplementary Information and the research group website: https://www.ebmg.online/mercury. FAO/WHO global individual food consumption database: http://www.fao.org/nutrition/assessment/food-consumption-database/en/. World population prospects: https://population.un.org. Shared socioeconomic pathways database: https://tntcat.iiasa.ac.at/SspDb. Global hunger index: https://www.globalhungerindex.org. Fishbase database: https://www.fishbase.org. Marine trophic index: http://www.seaaroundus.org/mti-fib-rmti/. Global health estimates: http://www.who.int/healthinfo/global_burden_disease.

## Code availability

All model code is available at the research group website: https://www.ebmg.online/mercury.

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## Acknowledgements

We gratefully acknowledge financial support from the National Natural Science Foundation of China (NNSFC) 41875148, the Chinese Academy of Science Interdisciplinary Innovation Team (JCTD-2020-20), Jiangsu Innovative and Entrepreneurial Talents Plan, the Collaborative Innovation Center of Climate Change, Jiangsu Province. We thank Aijun Ding, Bin Wang, Guoxing Li, Qingru Wu, Lars-Eric Heimbürger, Noelle Selin, and Elsie Sunderland for helpful discussions. We are grateful to the High Performance Computing Center (HPCC) of Nanjing University for doing the numerical calculations in this paper on its blade cluster system.

## Author information

Authors

### Contributions

Y.Z. designed and conducted this study. Y.Z., Z.S., S.H., P.Z., Y.P., P.W., and J.G. compiled literature data. S.D, H.Z., S.W., L.C., and P.L. provided model tools or datasets. Y.Z. led the manuscript writing with supports from S.D., H.Z., S.W., F.W., L.C., S.W., and P.L.

### Corresponding author

Correspondence to Yanxu Zhang.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature Communications thanks Shaojie Song and other, anonymous, reviewers for their contributions to the peer review of this work. Peer review reports are available.

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Zhang, Y., Song, Z., Huang, S. et al. Global health effects of future atmospheric mercury emissions. Nat Commun 12, 3035 (2021). https://doi.org/10.1038/s41467-021-23391-7

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