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Pastor-López EJ, Escolà M, Kisielius V, Arias CA, Carvalho PN, Gorito AM, Ramos S, Freitas V, Guimarães L, Almeida CMR, Müller JA, Küster E, Kilian RM, Diawara A, Ba S, Matamoros V. Potential of nature-based solutions to reduce antibiotics, antimicrobial resistance, and pathogens in aquatic ecosystems. a critical review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174273. [PMID: 38925380 DOI: 10.1016/j.scitotenv.2024.174273] [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: 12/22/2023] [Revised: 06/03/2024] [Accepted: 06/22/2024] [Indexed: 06/28/2024]
Abstract
This comprehensive scientific review evaluates the effectiveness of nature-based solutions (NBS) in reducing antibiotics (ABs), combating antimicrobial resistance (AMR), and controlling pathogens in various aquatic environments at different river catchment levels. It covers conventional and innovative treatment wetland configurations for wastewater treatment to reduce pollutant discharge into the aquatic ecosystems as well as exploring how river restoration and saltmarshes can enhance pollutant removal. Through the analysis of experimental studies and case examples, the review shows NBS's potential for providing sustainable and cost-effective solutions to improve the health of aquatic ecosystems. It also evaluates the use of diagnostic indicators to predict NBS effectiveness in removing specific pollutants such as ABs and AMR. The review concludes that NBS are feasible for addressing the new challenges stemming from human activities such as the presence of ABs, AMR and pathogens, contributing to a better understanding of NBS, highlighting success stories, addressing knowledge gaps, and providing recommendations for future research and implementation.
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Affiliation(s)
- Edward J Pastor-López
- Department of Environmental Chemistry, IDAEA-CSIC, c/Jordi Girona, 18-26, E-08034 Barcelona, Spain
| | - Mònica Escolà
- Department of Environmental Chemistry, IDAEA-CSIC, c/Jordi Girona, 18-26, E-08034 Barcelona, Spain
| | - Vaidotas Kisielius
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Carlos A Arias
- Department of Biology, Aarhus University, Aarhus, Denmark; WATEC - Centre for Water Technology, Aarhus University, Aarhus, Denmark
| | - Pedro N Carvalho
- Department of Environmental Science, Aarhus University, Roskilde, Denmark; WATEC - Centre for Water Technology, Aarhus University, Aarhus, Denmark
| | - Ana M Gorito
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Portugal
| | - Sandra Ramos
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Portugal; Faculty of Sciences, University of Porto, Porto, Portugal
| | - Vânia Freitas
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Portugal
| | - Laura Guimarães
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Portugal
| | - C Marisa R Almeida
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Portugal; Faculty of Sciences, University of Porto, Porto, Portugal
| | - Jochen A Müller
- Institute for Biological Interfaces (IBG-5), Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Eberhard Küster
- Helmholtz Centre for Environmental Research - UFZ, Dept. Bioanalytical Ecotoxicology, Leipzig, Germany
| | - R M Kilian
- Kilian Water Ltd., Torupvej 4, 8654 Bryrup, Denmark
| | - Abdoulaye Diawara
- Department of Geology and Mines, École Nationale d'Ingénieurs - Abderhamane Baba Touré (ENI-ABT), Bamako, Mali
| | - Sidy Ba
- Department of Geology and Mines, École Nationale d'Ingénieurs - Abderhamane Baba Touré (ENI-ABT), Bamako, Mali
| | - Víctor Matamoros
- Department of Environmental Chemistry, IDAEA-CSIC, c/Jordi Girona, 18-26, E-08034 Barcelona, Spain.
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2
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Jiang P, Sun S, Goh SG, Tong X, Chen Y, Yu K, He Y, Gin KYH. A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments. WATER RESEARCH 2024; 262:122079. [PMID: 39047454 DOI: 10.1016/j.watres.2024.122079] [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: 12/14/2023] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.
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Affiliation(s)
- Peng Jiang
- Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore.
| | - Shuyi Sun
- Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; Department of Industrial Systems Engineering & Management, National University of Singapore, Singapore 119260, Singapore
| | - Shin Giek Goh
- NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore
| | - Xuneng Tong
- NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore
| | - Yihan Chen
- School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Kaifeng Yu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yiliang He
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Karina Yew-Hoong Gin
- NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore; Department of Civil & Environmental Engineering, National University of Singapore, Singapore 117576, Singapore.
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Yang J, Xiang J, Goh SG, Xie Y, Nam OC, Gin KYH, He Y. Food waste compost and digestate as novel fertilizers: Impacts on antibiotic resistome and potential risks in a soil-vegetable system. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 923:171346. [PMID: 38438039 DOI: 10.1016/j.scitotenv.2024.171346] [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: 12/25/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/06/2024]
Abstract
As a novel agricultural practice, the reuse of food waste compost and digestate as fertilizers leads to a circular economy, but inevitably introduces bio-contaminants such as antibiotic resistance genes (ARGs) into the agroecosystem. Moreover, heavy metal and antibiotic contamination in farmland soil may exert selective pressures on the evolution of ARGs, posing threats to human health. This study investigated the fate, influencing mechanisms and potential risks of ARGs in a soil-vegetable system under different food waste fertilization and remediation treatments and soil contamination conditions. Application of food waste fertilizers significantly promoted the pakchoi growth, but resulted in the spread of ARGs from fertilizers to pakchoi. A total of 56, 80, 84, 41, and 73 ARGs, mobile genetic elements (MGEs) and metal resistance genes (MRGs) were detected in the rhizosphere soil (RS), bulk soil (BS), control soil (CS), root endophytes (RE), and leaf endophytes (LE), respectively. Notably, 7 genes were shared in the above five subgroups, indicating a specific soil-root-endophytes transmission pathway. 36 genes were uniquely detected in the LE, which may originate from airborne ARGs. The combined application of biochar and fertilizers reduced the occurrence of ARGs and MGEs to some extent, showing the remediation effect of biochar. The average abundance of ARGs in the RS, BS and CS was 3.15 × 10-2, 1.31 × 10-2 and 2.35 × 10-1, respectively. Rhizosphere effects may reduce the abundance of ARGs in soil. The distribution pattern of ARGs was influenced by the types of soil, endophyte and contaminant. MGEs is the key driver shaping ARGs dynamics. Soil properties and pakchoi growth status may affect the bacterial composition, and consequently regulate ARGs fate, while endophytic ARGs were more impacted by biotic factors. Moreover, the average daily doses of ARGs from pakchoi consumption is 107-109 copies/d/kg, and its potential health risks should be emphasized.
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Affiliation(s)
- Jun Yang
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore
| | - Jinyi Xiang
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Shin Giek Goh
- NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore
| | - Yu Xie
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ong Choon Nam
- NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore
| | - Karina Yew-Hoong Gin
- Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore
| | - Yiliang He
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 201306, China.
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Tong X, Goh SG, Mohapatra S, Tran NH, You L, Zhang J, He Y, Gin KYH. Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6781-6792. [PMID: 38560895 PMCID: PMC11025116 DOI: 10.1021/acs.est.3c10467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 04/04/2024]
Abstract
Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.
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Affiliation(s)
- Xuneng Tong
- Department
of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Singapore
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Shin Giek Goh
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Sanjeeb Mohapatra
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Ngoc Han Tran
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Luhua You
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Jingjie Zhang
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
- Northeast
Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Shenzhen
Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen518055,China
| | - Yiliang He
- School
of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Karina Yew-Hoong Gin
- Department
of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Singapore
- NUS
Environmental Research Institute, National
University of Singapore, 1 Create way, Create Tower, #15-02, Singapore 138602, Singapore
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5
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Liu F, Luo Y, Xu T, Lin H, Qiu Y, Li B. Current examining methods and mathematical models of horizontal transfer of antibiotic resistance genes in the environment. Front Microbiol 2024; 15:1371388. [PMID: 38638913 PMCID: PMC11025395 DOI: 10.3389/fmicb.2024.1371388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
The increasing prevalence of antibiotic resistance genes (ARGs) in the environment has garnered significant attention due to their health risk to human beings. Horizontal gene transfer (HGT) is considered as an important way for ARG dissemination. There are four general routes of HGT, including conjugation, transformation, transduction and vesiduction. Selection of appropriate examining methods is crucial for comprehensively understanding characteristics and mechanisms of different HGT ways. Moreover, combined with the results obtained from different experimental methods, mathematical models could be established and serve as a powerful tool for predicting ARG transfer dynamics and frequencies. However, current reviews of HGT for ARG spread mainly focus on its influencing factors and mechanisms, overlooking the important roles of examining methods and models. This review, therefore, delineated four pathways of HGT, summarized the strengths and limitations of current examining methods, and provided a comprehensive summing-up of mathematical models pertaining to three main HGT ways of conjugation, transformation and transduction. Finally, deficiencies in current studies were discussed, and proposed the future perspectives to better understand and assess the risks of ARG dissemination through HGT.
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Affiliation(s)
- Fan Liu
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yuqiu Luo
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Tiansi Xu
- School of Environment, Tsinghua University, Beijing, China
| | - Hai Lin
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yong Qiu
- School of Environment, Tsinghua University, Beijing, China
| | - Bing Li
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
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6
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Liu X, Tong X, Wu L, Mohapatra S, Xue H, Liu R. An integrated modelling framework for multiple pollution source identification in surface water. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 347:119126. [PMID: 37778063 DOI: 10.1016/j.jenvman.2023.119126] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 06/29/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023]
Abstract
Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.
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Affiliation(s)
- Xiaodong Liu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Yangtze Institute for Conservation and Development, Hohai University, Jiangsu 210098, China
| | - Xuneng Tong
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore.
| | - Lei Wu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Sanjeeb Mohapatra
- E2S2-CREATE, NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, Singapore 138602, Singapore
| | - Hongqin Xue
- School of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Ruochen Liu
- Jiangsu Suli Environmental Technology Co., Ltd., Nanjing, Jiangsu 210036, China
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Wang J, Xu S, Zhao K, Song G, Zhao S, Liu R. Risk control of antibiotics, antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) during sewage sludge treatment and disposal: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 877:162772. [PMID: 36933744 DOI: 10.1016/j.scitotenv.2023.162772] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/14/2023] [Accepted: 03/06/2023] [Indexed: 05/06/2023]
Abstract
Sewage sludge is an important reservoir of antibiotics, antibiotic resistance genes (ARGs), and antibiotic resistant bacteria (ARB) in wastewater treatment plants (WWTPs), and the reclamation of sewage sludge potentially threats human health and environmental safety. Sludge treatment and disposal are expected to control these risks, and this review summarizes the fate and controlling efficiency of antibiotics, ARGs, and ARB in sludge involved in different processes, i.e., disintegration, anaerobic digestion, aerobic composting, drying, pyrolysis, constructed wetland, and land application. Additionally, the analysis and characterization methods of antibiotics, ARGs, and ARB in complicate sludge are reviewed, and the quantitative risk assessment approaches involved in land application are comprehensively discussed. This review benefits process optimization of sludge treatment and disposal, with regard to environmental risks control of antibiotics, ARGs, and ARB in sludge. Furthermore, current research limitations and gaps, e.g., the antibiotic resistance risk assessment in sludge-amended soil, are proposed to advance the future studies.
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Affiliation(s)
- Jiaqi Wang
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Yangze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Beijing 100038, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siqi Xu
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kai Zhao
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ge Song
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shunan Zhao
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Ruiping Liu
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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