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Risk of pesticide pollution at the global scale

Nature Geoscience volume 14, pages 206–210 (2021)Cite this article

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Abstract

Pesticides are widely used to protect food production and meet global food demand but are also ubiquitous environmental pollutants, causing adverse effects on water quality, biodiversity and human health. Here we use a global database of pesticide applications and a spatially explicit environmental model to estimate the world geography of environmental pollution risk caused by 92 active ingredients in 168 countries. We considered a region to be at risk of pollution if pesticide residues in the environment exceeded the no-effect concentrations, and to be at high risk if residues exceeded this by three orders of magnitude. We find that 64% of global agricultural land (approximately 24.5 million km2) is at risk of pesticide pollution by more than one active ingredient, and 31% is at high risk. Among the high-risk areas, about 34% are in high-biodiversity regions, 5% in water-scarce areas and 19% in low- and lower-middle-income nations. We identify watersheds in South Africa, China, India, Australia and Argentina as high-concern regions because they have high pesticide pollution risk, bear high biodiversity and suffer from water scarcity. Our study expands earlier pesticide risk assessments as it accounts for multiple active ingredients and integrates risks in different environmental compartments at a global scale.

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Fig. 1: Global map of pesticide RS.
Fig. 2: Global map of the number of AIs posing risks to the environment.
Fig. 3: Global map of the regions of concern defined by pesticide pollution risk, water scarcity and biodiversity.

Data availability

The georeferenced data that support the findings of this study are available via Figshare at https://doi.org/10.6084/m9.figshare.10302218 (ref. 60). Country-based data are available in Supplementary Tables 4 and 5. Source data are provided with this paper.

Code availability

The code used to calculate pesticide risk scores is provided as a MATLAB file available via Figshare at https://doi.org/10.6084/m9.figshare.10302218 (ref. 60).

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Acknowledgements

This work was supported by the University of Sydney through the SREI2020 EnviroSphere research programme. F.M. was also supported by the SOAR Fellowship awarded by the University of Sydney. We thank G. Porta for the discussion and advice on the uncertainty analysis. We acknowledge the Sydney Informatics Hub and the University of Sydney’s high-performance computing cluster Artemis for providing the high-performance computing resources that contributed to the results reported within this work. We acknowledge the use of the National Computational Infrastructure (NCI) which is supported by the Australian Government, and accessed through the Sydney Informatics Hub HPC Allocation Scheme supported by the Deputy Vice-Chancellor (Research), the University of Sydney and the ARC LIEF, 2019: Smith, Muller, Thornber et al., Sustaining and strengthening merit-based access to National Computational Infrastructure (LE190100021). We thank R. Hough and M. Liess for constructive comments on this manuscript.

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Authors and Affiliations

  1. Environmental Engineering, School of Civil Engineering, The University of Sydney, Sydney, New South Wales, Australia

    Fiona H. M. Tang & Federico Maggi

  2. ISA, School of Physics, The University of Sydney, Sydney, New South Wales, Australia

    Manfred Lenzen

  3. Sydney Institute of Agriculture, The University of Sydney, Sydney, New South Wales, Australia

    Alexander McBratney

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  1. Fiona H. M. Tang
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  2. Manfred Lenzen
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Contributions

F.H.M.T. and F.M. conceptualized the main research subject. F.H.M.T., M.L. and F.M. contributed to data collection and analysis. F.H.M.T., M.L., A.M. and F.M. contributed to the interpretation of the results and the writing of the manuscript. F.H.M.T., M.L., A.M. and F.M. contributed to acquiring funding for this work.

Corresponding authors

Correspondence to Fiona H. M. Tang or Federico Maggi.

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The authors declare no competing interests.

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Peer review information Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Clare Davis, Rebecca Neely.

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Extended data

Extended Data Fig. 1 The top 30 countries susceptible to high pesticide pollution risk.

a, The land area subject to low quantity and high variability of water supply and high risk of pollution by pesticide mixtures (that is, RS > 3 and AI count > 1). b, The land area bearing high biodiversity and subject to high risk of pollution by pesticide mixtures (that is, RS > 3 and AI count > 1). c, The land area inhabited by at least one endangered or critically endangered amphibian species and subject to pollution risk by pesticide mixtures (RS > 0 and AI count > 1).

Source data

Extended Data Fig. 2 The extent of pesticide pollution risk in groundwater, surface water, soil, and atmosphere expressed as percent agricultural land.

For example, surface water within 74% of global agricultural land is at some risk of pesticide pollution. High water risk regions refer to places suffering from low quantity and high variability of water supply defined as in AQUEDUCT-v2.1 database.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Tables 1–5.

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Tang, F.H.M., Lenzen, M., McBratney, A. et al. Risk of pesticide pollution at the global scale. Nat. Geosci. 14, 206–210 (2021). https://doi.org/10.1038/s41561-021-00712-5

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