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Risk of pesticide pollution at the global scale

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).

References

  1. 1.

    Oerke, E. C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).

    Google Scholar 

  2. 2.

    FAOSTAT: Database Collection of the Food and Agriculture Organization of the United Nations (FAO, 2019); http://www.fao.org/faostat/en/#data

  3. 3.

    Beketov, M. A., Kefford, B. J., Schäfer, R. B. & Liess, M. Pesticides reduce regional biodiversity of stream invertebrates. Proc. Natl Acad. Sci. USA 110, 11039–11043 (2013).

    Google Scholar 

  4. 4.

    Nicolopoulou-Stamati, P., Maipas, S., Kotampasi, C., Stamatis, P. & Hens, L. Chemical pesticides and human health: the urgent need for a new concept in agriculture. Front. Public Health 4, 148 (2016).

    Google Scholar 

  5. 5.

    Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).

    Google Scholar 

  6. 6.

    Bouwman, A., Boumans, L. & Batjes, N. Modeling global annual N2O and NO emissions from fertilized fields. Glob. Biogeochem. Cycles 16, 1080 (2002).

    Google Scholar 

  7. 7.

    Cordell, D., Drangert, J. O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).

    Google Scholar 

  8. 8.

    Ippolito, A. et al. Modeling global distribution of agricultural insecticides in surface waters. Environ. Pollut. 198, 54–60 (2015).

    Google Scholar 

  9. 9.

    Silva, V. et al. Pesticide residues in European agricultural soils—a hidden reality unfolded. Sci. Total Environ. 653, 1532–1545 (2019).

    Google Scholar 

  10. 10.

    Stehle, S. & Schulz, R. Agricultural insecticides threaten surface waters at the global scale. Proc. Natl Acad. Sci. USA 112, 5750–5755 (2015).

    Google Scholar 

  11. 11.

    Maggi, F., la Cecilia, D., Tang, F. H. M. & McBratney, A. The global environmental hazard of glyphosate use. Sci. Total Environ. 717, 137167 (2020).

    Google Scholar 

  12. 12.

    Li, Y. F., Scholtz, M. T. & Van Heyst, B. J. Global gridded emission inventories of β-hexachlorocyclohexane. Environ. Sci. Technol. 37, 3493–3498 (2003).

    Google Scholar 

  13. 13.

    Shunthirasingham, C. et al. Spatial and temporal pattern of pesticides in the global atmosphere. J. Environ. Monit. 12, 1650–1657 (2010).

    Google Scholar 

  14. 14.

    Gassert, F., Luck, M., Landis, M., Reig, P. & Shiao, T. Aqueduct Global Maps 2.1: Constructing Decision-Relevant Global Water Risk Indicators (World Resources Institute, 2014).

    Google Scholar 

  15. 15.

    Bird Species Distribution Maps of the World v.2019.1 (BirdLife International and Handbook of the Birds of the World, 2019); http://datazone.birdlife.org/species/requestdis

  16. 16.

    IUCN & CIESIN Gridded Species Distribution: Global Mammal Richness Grids, 2015 Release (NASA SEDAC, 2015); https://doi.org/10.7927/H4N014G5

  17. 17.

    IUCN & CIESIN Gridded Species Distribution: Global Amphibian Richness Grids, 2015 Release (NASA SEDAC, 2015); https://doi.org/10.7927/H4RR1W66

  18. 18.

    Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677 (2017).

    Google Scholar 

  19. 19.

    Trevisan, M., Di Guardo, A. & Balderacchi, M. An environmental indicator to drive sustainable pest management practices. Environ. Model. Softw. 24, 994–1002 (2009).

    Google Scholar 

  20. 20.

    Maggi, F., Tang, F. H. M., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).

    Google Scholar 

  21. 21.

    Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

    Google Scholar 

  22. 22.

    Zhan, Y. & Zhang, M. PURE: A web-based decision support system to evaluate pesticide environmental risk for sustainable pest management practices in California. Ecotoxicol. Environ. Safe. 82, 104–113 (2012).

    Google Scholar 

  23. 23.

    Maloney, E., Morrissey, C., Headley, J., Peru, K. & Liber, K. Can chronic exposure to imidacloprid, clothianidin, and thiamethoxam mixtures exert greater than additive toxicity in Chironomus dilutus? Ecotoxicol. Environ. Safe. 156, 354–365 (2018).

    Google Scholar 

  24. 24.

    Pape‐Lindstrom, P. A. & Lydy, M. J. Synergistic toxicity of atrazine and organophosphate insecticides contravenes the response addition mixture model. Environ. Toxicol. Chem. 16, 2415–2420 (1997).

    Google Scholar 

  25. 25.

    Davidson, C., Shaffer, H. B. & Jennings, M. R. Spatial tests of the pesticide drift, habitat destruction, UV‐B, and climate‐change hypotheses for California amphibian declines. Conserv. Biol. 16, 1588–1601 (2002).

    Google Scholar 

  26. 26.

    Köhler, H. R. & Triebskorn, R. Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond? Science 341, 759–765 (2013).

    Google Scholar 

  27. 27.

    Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).

    Google Scholar 

  28. 28.

    Ansara-Ross, T. M., Wepener, V., Van den Brink, P. J. & Ross, M. J. Pesticides in South African fresh waters. Afr. J. Aquat. Sci. 37, 1–16 (2012).

    Google Scholar 

  29. 29.

    Li, J., Li, F. & Liu, Q. Sources, concentrations and risk factors of organochlorine pesticides in soil, water and sediment in the Yellow River estuary. Mar. Pollut. Bull. 100, 516–522 (2015).

    Google Scholar 

  30. 30.

    van Vliet, M. T., Flörke, M. & Wada, Y. Quality matters for water scarcity. Nat. Geosci. 10, 800–802 (2017).

    Google Scholar 

  31. 31.

    Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).

    Google Scholar 

  32. 32.

    McBratney, A., Field, D. J. & Koch, A. The dimensions of soil security. Geoderma 213, 203–213 (2014).

    Google Scholar 

  33. 33.

    Möhring, N. et al. Pathways for advancing pesticide policies. Nat. Food 1, 535–540 (2020).

    Google Scholar 

  34. 34.

    Kudsk, P., Jørgensen, L. N. & Ørum, J. E. Pesticide load—a new Danish pesticide risk indicator with multiple applications. Land Use Policy 70, 384–393 (2018).

    Google Scholar 

  35. 35.

    Wendling, Z. A. et al. 2020 Environmental Performance Index (Yale Center for Environmental Law & Policy, 2020).

    Google Scholar 

  36. 36.

    Baker, N. T. Estimated Annual Agricultural Pesticide Use by Major Crop or Crop Group for States of the Conterminous United States, 1992–2016 (USGS, 2018); https://doi.org/10.5066/F7NP22KM

  37. 37.

    Gutsche, V. & Rossberg, D. SYNOPS 1.1: a model to assess and to compare the environmental risk potential of active ingredients in plant protection products. Agric. Ecosyst. Environ. 64, 181–188 (1997).

    Google Scholar 

  38. 38.

    Röpke, B., Bach, M. & Frede, H. DRIPS – a decision support system estimating the quantity of diffuse pesticide pollution in German river basins. Water Sci. Technol. 49, 149–156 (2004).

    Google Scholar 

  39. 39.

    Waitz, M., Freijer, J., Kreule, P. & Swartjes, F. The VOLASOIL Risk Assessment Model Based on CSOIL for Soils Contaminated with Volatile Compounds RVIM Report No. 715810014, 1–189 (National Institute of Public Health and The Environment, 1996).

  40. 40.

    European Chemicals Bureau Technical Guidance Document on Risk Assessment in Support of Commission Directive 93/67/EEC on Risk Assessment for New Notified Substances, Commission Regulation (EC) No 1488/94 on Risk Assessment for Existing Substances, and Directive 98/8/EC of the European Parliament and of the Council Concerning the Placing of Biocidal Products on the Market (Institute for Health and Consumer Protection, 2003).

  41. 41.

    Lewis, K. A., Tzilivakis, J., Warner, D. J. & Green, A. An international database for pesticide risk assessments and management. Hum. Ecol. Risk. Assess. 22, 1050–1064 (2016).

    Google Scholar 

  42. 42.

    European Commission. Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration. Off. J. Eur. Union L 372, 19–31 (2006).

    Google Scholar 

  43. 43.

    Nagai, T. Ecological effect assessment by species sensitivity distribution for 68 pesticides used in Japanese paddy fields. J. Pestic. Sci. 41, 6–14 (2016).

    Google Scholar 

  44. 44.

    Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Google Scholar 

  45. 45.

    Global Soil Data Products CD-ROM Contents (IGBP-DIS) Data Set (Oak Ridge National Laboratory Distributed Active Archive Center, 2014); https://doi.org/10.3334/ORNLDAAC/565

  46. 46.

    Dai, Y. et al. A global high‐resolution dataset of soil hydraulic and thermal properties for land surface modeling. J. Adv. Model. Earth Syst. 11, 2996–3023 (2019).

    Google Scholar 

  47. 47.

    Brooks, R. H. & Corey, A. T. Properties of porous media affecting fluid flow. J. Irrig. Drain. Div. 92, 61–90 (1966).

    Google Scholar 

  48. 48.

    Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    Google Scholar 

  49. 49.

    Pelletier, J. et al. Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers (Oak Ridge National Laboratory Distributed Active Archive Center, 2016); https://doi.org/10.3334/ORNLDAAC/1304

  50. 50.

    NOAA/OAR/ESRL PSD CPC Global Unified Precipitation Dataset (Physical Sciences Laboratory, 2019); https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html

  51. 51.

    Zhang, Y. et al. Monthly global observation-driven Penman-Monteith-Leuning (PML) evapotranspiration and components v2. CSIRO Data Collection https://doi.org/10.4225/08/5719A5C48DB85 (2016).

  52. 52.

    Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).

    Google Scholar 

  53. 53.

    Fischer, G. et al. Harmonized World Soil Database v1.2 (GAEZ, IIASA & FAO, 2008); http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/

  54. 54.

    Pesticide Fact Sheet, Aminopyralid Report No. 7501C (United States Office of Prevention, Pesticides Environmental Protection and Toxic Substances Agency, 2005).

  55. 55.

    Herner, A. E. The USDA-ARS pesticide properties database: a consensus data set for modelers. Weed Technol. 6, 749–752 (1992).

    Google Scholar 

  56. 56.

    Mao, L., Zhang, L., Zhang, Y. & Jiang, H. Ecotoxicity of 1, 3-dichloropropene, metam sodium, and dazomet on the earthworm Eisenia fetida with modified artificial soil test and natural soil test. Environ. Sci. Pollut. Res. 24, 18692–18698 (2017).

    Google Scholar 

  57. 57.

    National Institutes of Health, Health & Human Services ChemIDplus (US National Library of Medicine, 2019); https://chem.nlm.nih.gov/chemidplus/

  58. 58.

    Public Release Summary on the Evaluation of the New Active Saflufenacil in the Product SHARPEN WG HERBICIDE (Previously Heat Herbicide) APVMA Product Number 62853 (APVMA, 2012).

  59. 59.

    Pesticide Fact Sheet, Saflufenacil Report No. 7505P (United States Office of Prevention, Pesticides Environmental Protection and Toxic Substances Agency, 2009).

  60. 60.

    Tang, F. H. M., Lenzen, M., McBratney, A. & Maggi, F. Global pesticide pollution risk data sets. Figshare https://doi.org/10.6084/m9.figshare.10302218 (2021).

  61. 61.

    Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22, GB1003 (2008).

    Google Scholar 

  62. 62.

    Dell’Oca, A., Riva, M. & Guadagnini, A. Moment-based metrics for global sensitivity analysis of hydrological systems. Hydrol. Earth Syst. Sci. 21, 6219–6234 (2017).

    Google Scholar 

  63. 63.

    Hvězdová, M. et al. Currently and recently used pesticides in Central European arable soils. Sci. Total Environ. 613, 361–370 (2018).

    Google Scholar 

  64. 64.

    Fenner, K., Canonica, S., Wackett, L. P. & Elsner, M. Evaluating pesticide degradation in the environment: blind spots and emerging opportunities. Science 341, 752–758 (2013).

    Google Scholar 

  65. 65.

    Deneer, J. W. Toxicity of mixtures of pesticides in aquatic systems. Pest Manag. Sci. 56, 516–520 (2000).

    Google Scholar 

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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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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.

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

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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