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Guo P, Wang Y, Moghaddamfard P, Meng W, Wu S, Bao Y. Artificial intelligence-empowered collection and characterization of microplastics: A review. J Hazard Mater 2024; 471:134405. [PMID: 38678715 DOI: 10.1016/j.jhazmat.2024.134405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.
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Affiliation(s)
- Pengwei Guo
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Yuhuan Wang
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Parastoo Moghaddamfard
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Weina Meng
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shenghua Wu
- Department of Civil, Coastal, and Environmental Engineering, University of South Alabama, Mobile, AL 36688, United States
| | - Yi Bao
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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2
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Przyborowski Ł, Cuban Z, Łoboda A, Robakiewicz M, Biegowski S, Kolerski T. The effect of groyne field on trapping macroplastic. Preliminary results from laboratory experiments. Sci Total Environ 2024; 921:171184. [PMID: 38401733 DOI: 10.1016/j.scitotenv.2024.171184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024]
Abstract
Macroplastic, a precursor of microplastic pollution, has become a new scope of research interest. However, the physical processes of macroplastic transport and deposition in rivers are poorly understood, which makes the decisions of where to locate macroplastic trapping infrastructure difficult. In this research, we conducted a series of experiments in a laboratory channel, exploring the impact of groynes and flexible artificial vegetation on the floating macroplastic litter. The goal was to investigate the litter paths with different obstruction arrangements, which was done by implementing a particle tracking technique on video recordings from each experimental run. We found that increasing discharge correlated with the number of plastic litter floating into the recirculation zone within the groyne fields, especially if the upstream groyne had an extended length. This produced a strong mixing interface between the main flow and the groyne field, while a vegetation patch added in the same groyne field changed the paths of plastic litter by deflecting the flow. We noticed that during a moderate discharge rate, the litter pieces flowing into the groyne field with the vegetation circulated there for the longest period, and some of them got entangled between floating stems when discharge was at its lowest. This phenomenon points to the conclusion that low flow velocity paired with the presence of vegetation can be a primer for plastic deposition and consequently, its degradation. The insights from the experiment allowed us to recommend a place downstream of an extended groyne as the desirable (efficient) area for installing a plastic trapping infrastructure or conducting plastic cleaning actions.
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Affiliation(s)
- Łukasz Przyborowski
- Institute of Geophysics Polish Academy of Sciences, Księcia Janusza 64, 01-452 Warszawa, Poland.
| | - Zuzanna Cuban
- Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
| | - Anna Łoboda
- Institute of Geophysics Polish Academy of Sciences, Księcia Janusza 64, 01-452 Warszawa, Poland; University of Twente, Water Engineering and Management Department, Drienerlolaan 5, 7522 NB Enschede, Netherlands
| | - Małgorzata Robakiewicz
- Institute of Hydro-Engineering Polish Academy of Sciences, Kościerska 7, 80-328 Gdańsk, Poland
| | - Stanisław Biegowski
- Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
| | - Tomasz Kolerski
- Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
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Basini G, Grolli S, Bertini S, Bussolati S, Berni M, Berni P, Ramoni R, Scaltriti E, Quintavalla F, Grasselli F. Nanoplastics induced oxidative stress and VEGF production in aortic endothelial cells. Environ Toxicol Pharmacol 2023; 104:104294. [PMID: 37838301 DOI: 10.1016/j.etap.2023.104294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 06/26/2023] [Accepted: 10/11/2023] [Indexed: 10/16/2023]
Abstract
Plastic is an important environmental issue and a more critical aspect concerns plastic fragments, mainly in term of nanoplastics (NPs). We demonstrated that NPs interfere with reproductive and adipose stromal cells. Since several research underlined an increased cardiovascular risk due to NPs, present study was undertaken to investigate their effect on aortic endothelial cells (AOC). We explored the specificity of their interaction with endothelial cells, quantifying their load in treated cells. Then, NPs effect was assessed on cell growth, generation of free radicals and antioxidant defence. Our data demonstrate that NPs colocalize with AOC. We found a significant (p < 0.01) increase both in metabolic activity and Vascular Endothelial Growth Factor (VEGF) production (p < 0.01). Redox status appeared to be disrupted (p < 0.05) by NPs. Taken together, the normal function of cultured AOC appeared negatively affected by AOC. Since NPs have been detected in blood, our present data appear of particular interest.
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Affiliation(s)
- Giuseppina Basini
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy.
| | - Stefano Grolli
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Simone Bertini
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Simona Bussolati
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Melissa Berni
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Strada dei Mercati 13a, 43126 Parma, Italy
| | - Priscilla Berni
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Roberto Ramoni
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Erika Scaltriti
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Strada dei Mercati 13a, 43126 Parma, Italy
| | - Fausto Quintavalla
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Francesca Grasselli
- Dipartimento di Scienze Medico-Veterinarie, Università degli Studi di Parma, Via del Taglio 10, 43126 Parma, Italy
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Wang YQ, Wang HC, Song YP, Zhou SQ, Li QN, Liang B, Liu WZ, Zhao YW, Wang AJ. Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer. Water Res 2023; 246:120676. [PMID: 37806124 DOI: 10.1016/j.watres.2023.120676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/08/2023] [Accepted: 09/27/2023] [Indexed: 10/10/2023]
Abstract
Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.
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Affiliation(s)
- Yu-Qi Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Hong-Cheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
| | - Yun-Peng Song
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Shi-Qing Zhou
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, China
| | - Qiu-Ning Li
- Department of Chemical and Biological Engineering, Monash University, Clayton, Australia
| | - Bin Liang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Wen-Zong Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Yi-Wei Zhao
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Ai-Jie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
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Yu Y, Zhang Y, Liu Y, Lv M, Wang Z, Wen LL, Li A. In situ reductive dehalogenation of groundwater driven by innovative organic carbon source materials: Insights into the organohalide-respiratory electron transport chain. J Hazard Mater 2023; 452:131243. [PMID: 36989787 DOI: 10.1016/j.jhazmat.2023.131243] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 02/24/2023] [Accepted: 03/17/2023] [Indexed: 05/03/2023]
Abstract
In situ bioremediation using organohalide-respiring bacteria (OHRB) is a prospective method for the removal of persistent halogenated organic pollutants from groundwater, as OHRB can utilize H2 or organic compounds produced by carbon source materials as electron donors for cell growth through organohalide respiration. However, few previous studies have determined the suitability of different carbon source materials to the metabolic mechanism of reductive dehalogenation from the perspective of electron transfer. The focus of this critical review was to reveal the interactions and relationships between carbon source materials and functional microbes, in terms of the electron transfer mechanism. Furthermore, this review illustrates some innovative strategies that have used the physiological characteristics of OHRB to guide the optimization of carbon source materials, improving the abundance of indigenous dehalogenated bacteria and enhancing electron transfer efficiency. Finally, it is proposed that future research should combine multi-omics analysis with machine learning (ML) to guide the design of effective carbon source materials and optimize current dehalogenation bioremediation strategies to reduce the cost and footprint of practical groundwater bioremediation applications.
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Affiliation(s)
- Yang Yu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Yueyan Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Yuqing Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Mengran Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Zeyi Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Li-Lian Wen
- College of Resource and Environmental Science, Hubei University, Wuhan 430062, China.
| | - Ang Li
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
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Mohsen A, Kiss T, Kovács F. Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery. Environ Sci Pollut Res Int 2023; 30:67742-67757. [PMID: 37118393 DOI: 10.1007/s11356-023-27068-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 04/12/2023] [Indexed: 05/25/2023]
Abstract
Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary.
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Affiliation(s)
- Ahmed Mohsen
- Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, Szeged, 6722, Hungary
- Department of Irrigation and Hydraulics Engineering, Tanta University, Tanta, 31512, Egypt
| | - Tímea Kiss
- Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, Szeged, 6722, Hungary
| | - Ferenc Kovács
- Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, Szeged, 6722, Hungary.
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