Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach

Abstract

Pesticides typically co-occur in agricultural surface waters and pose a potential threat to human and ecosystem health. As pesticide screening in global agricultural surface waters is an immense analytical challenge, a detailed risk picture of pesticides in global agricultural surface waters is largely missing. Here, we create the first global maps of human health and ecological risk from pesticides in agricultural surface waters using random forest models based on 27,411 measurements of 309 pesticides and 30 geospatial parameters. Our global risk maps identify the hotspots, mainly in Southern Asia and Africa, with extensive pesticide use and poor wastewater management infrastructure. We identify 4 and 5 priority pesticides for protecting the human and ecosystem health, respectively. Importantly, we estimate that 305 million people worldwide are at potential health risk associated with the surface-water pesticide mixture exposure, with the vast majority (86%) being in Asia. We further identify the hotspots in the Ganges River basin in India, where more than 170 million people are at potential health risk. As pesticides are increasingly used to ensure the food production due to future population growth and climate change, our findings have implications for raising awareness of pesticide pollution, identifying the hotspots and helping to prioritize testing.

Keywords

• Pesticide pollution
• Risk quotient
• Human exposure
• Ecosystem security
• Global prediction