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Zhu H, He J, Wu Y, Tong L, Zhang W, Zhuang L. Assessment of Global Antibiotic Exposure Risk for Crops: Incorporating Soil Adsorption via Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39031084 DOI: 10.1021/acs.est.4c03695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
The overuse and misuse of antibiotics could significantly increase their accumulation in soils. Consequently, antibiotics possibly enter food chain through crop uptake, posing a threat to global food security. Assessing the exposure risks of antibiotics for crops is crucial for addressing this global issue. In this study, we assessed global antibiotic exposure risk for crops, incorporating a machine learning adsorption model based on 4893 data sets from nine antibiotics. The optimized machine learning adsorption model, using the eXtreme Gradient Boosting algorithm and the class-specific modeling strategy, demonstrated relatively good performance. Notably, we introduced unsaturated soil conditions and considered spatiotemporal variations in soil moisture and temperature for the first time in such a risk assessment. Global distributions of antibiotic exposure risk for crops were predicted for March, June, September, and December. The results indicate that soil moisture significantly influences the exposure risk assessment. Relatively high exposure risk for crops was observed during months with colder local temperatures: generally June for the Southern Hemisphere and December for the Northern Hemisphere. The resulting map highlights high-risk agricultural regions, including southern Canada, western Russia, and southern Australia.
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Affiliation(s)
- Han Zhu
- Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Jianliang He
- Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Yanmei Wu
- Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Lizhi Tong
- National Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment (MEE), Guangzhou 510655, P. R. China
| | - Weihua Zhang
- Guangdong Provincial Key Laboratory of Environmental Pollution and Remediation Technology, Guangdong Provincial Engineering Research Center for Heavy Metal Contaminated Soil Remediation, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Luwen Zhuang
- Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
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Ding F, Li Y, He T, Ou D, Huang Y, Yin G, Yang J, Wu S, He E, Liu M. Urban agglomerations as an environmental dimension of antibiotics transmission through the "One Health" lens. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133283. [PMID: 38134700 DOI: 10.1016/j.jhazmat.2023.133283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/22/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
The spatiotemporal distributions of antibiotics in different media have been widely reported; however, their occurrence in the environmental dimension of the Chinese urban agglomerations has received less attention, especially in bioaccumulation and health risks of antibiotics through the "One Health" lens. The review presents the current knowledge on the environmental occurrence, bioaccumulation, as well as health exposure risks in urban agglomerations through the "One Health" lens, and identifies current information gaps. The reviewed studies suggested antibiotic concentrations in water and soil were more sensitive to social indicators of urban agglomerations than those in sediment. The ecological risk and resistance risk of antibiotics in water were much higher than those of sediments, and the high-risk phenomenon occurred at a higher frequency in urban agglomerations. Erythromycin-H2O (ETM-H2O), amoxicillin (AMOX) and norfloxacin (NFC) were priority-controlled antibiotics in urban waters. Tetracyclines (TCs) posed medium to high risks to soil organisms in the soil of urban agglomerations. Health risk evaluation based on dietary intake showed that children had the highest dietary intake of antibiotics in urban agglomerations. The health risk of antibiotics was higher in children than in other age groups. Our results also demonstrated that dietary structure might impact health risks associated with target antibiotics in urban agglomerations to some extent.
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Affiliation(s)
- Fangfang Ding
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Ye Li
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China.
| | - Tianhao He
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Dongni Ou
- Environment, Health and Safety Services, SGS-CSTC Standards Technical Services (Shanghai) Co., Ltd., 889 Yishan Road, Xuhui District, Shanghai 200233, China
| | - Ye Huang
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Guoyu Yin
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Jing Yang
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Shixue Wu
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Erkai He
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China
| | - Min Liu
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai 200241, China.
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