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Wang J, Huang R, Liang Y, Long X, Wu S, Han Z, Liu H, Huangfu X. Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk. JOURNAL OF HAZARDOUS MATERIALS 2024; 466:133563. [PMID: 38262323 DOI: 10.1016/j.jhazmat.2024.133563] [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: 11/10/2023] [Revised: 01/07/2024] [Accepted: 01/17/2024] [Indexed: 01/25/2024]
Abstract
Although the sorption of antibiotics in soil has been extensively studied, their spatial distribution patterns and sorption mechanisms still need to be clarified, which hinders the assessment of antibiotic resistance risk. In this study, machine learning was employed to develop the models for predicting the soil sorption behavior of three classes of antibiotics (sulfonamides, tetracyclines, and fluoroquinolones) in 255 soils with 2203 data points. The optimal independent models obtained an accurate predictive performance with R2 of 0.942 to 0.977 and RMSE of 0.051 to 0.210 on test sets compared to combined models. Besides, a global map of the antibiotic sorption capacity of soil predicted with the optimal models revealed that the sorption potential of fluoroquinolones was the highest, followed by tetracyclines and sulfonamides. Additionally, 14.3% of regions had higher antibiotic sorption potential, mainly in East and South Asia, Central Siberia, Western Europe, South America, and Central North America. Moreover, a risk index calculated with the antibiotic sorption capacity of soil and population density indicated that about 3.6% of soils worldwide have a high risk of resistance, especially in South and East Asia with high population densities. This work has significant implications for assessing the antibiotic contamination potential and resistance risk.
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Affiliation(s)
- Jingrui Wang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Ruixing Huang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Youheng Liang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Xinlong Long
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Sisi Wu
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Zhengpeng Han
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Hongxia Liu
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Xiaoliu Huangfu
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China.
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Jiang BN, Zhang YY, Zhang ZY, Yang YL, Song HL. Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N 2O mitigation effect in constructed wetlands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120335. [PMID: 38368804 DOI: 10.1016/j.jenvman.2024.120335] [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: 10/30/2023] [Revised: 01/29/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024]
Abstract
Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.
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Affiliation(s)
- Bi-Ni Jiang
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China; Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China
| | - Ying-Ying Zhang
- Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China
| | - Zhi-Yong Zhang
- Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China.
| | - Yu-Li Yang
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China
| | - Hai-Liang Song
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China.
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