Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach
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 larg...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024007402 |
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| author | Jian Chen Li Zhao Bin Wang Xinyi He Lei Duan Gang Yu |
| author_facet | Jian Chen Li Zhao Bin Wang Xinyi He Lei Duan Gang Yu |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4415fd20e9fd48dca10c556c943b66df |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-4415fd20e9fd48dca10c556c943b66df2024-12-19T10:52:06ZengElsevierEnvironment International0160-41202024-12-01194109154Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approachJian Chen0Li Zhao1Bin Wang2Xinyi He3Lei Duan4Gang Yu5State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, ChinaGuangdong Institute for Drug Control, Guangdong, Guangzhou 510180, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, ChinaSchool of Biomedical Sciences, The University of Texas Health Science Center at Houston, TX 77030, USAState Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China; Advanced Interdisciplinary Institute of Environment and Ecology, Guangdong Provincial Key Laboratory of Wastewater Information Analysis and Early Warning, Beijing Normal University, Zhuhai 519087, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S0160412024007402Pesticide pollutionRisk quotientHuman exposureEcosystem securityGlobal prediction |
| spellingShingle | Jian Chen Li Zhao Bin Wang Xinyi He Lei Duan Gang Yu Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach Environment International Pesticide pollution Risk quotient Human exposure Ecosystem security Global prediction |
| title | Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| title_full | Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| title_fullStr | Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| title_full_unstemmed | Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| title_short | Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| title_sort | uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach |
| topic | Pesticide pollution Risk quotient Human exposure Ecosystem security Global prediction |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024007402 |
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