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Duan Q, Zhai B, Zhao C, Liu K, Yang X, Zhang H, Yan P, Huang L, Lee J, Wu W, Zhou C, Quan X, Kang W. Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments. JOURNAL OF HAZARDOUS MATERIALS 2024; 477:135331. [PMID: 39067288 DOI: 10.1016/j.jhazmat.2024.135331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/13/2024] [Accepted: 07/24/2024] [Indexed: 07/30/2024]
Abstract
Microplastic (MP) accumulation has recently become a pressing global environmental challenge. As a major producer and consumer of plastic products, China's MP pollution has garnered significant attention from researchers. However, accurate and comprehensive investigations of national-level MP pollution are still lacking. In this study, we systematically collated a national MP pollution dataset consisting of 7766 water, soil, and sediment sampling sites from 544 publicly published studies, revealing the spatiotemporal distribution and potential risks of MP pollution in China. The results indicate that MP distribution is influenced by various regional factors, including economic development level, population distribution, and geographical environment, exhibiting considerable range and complexity. MP concentrations are generally higher in economically prosperous areas, but the degree of pollution varies significantly across different environmental media. Given the uncertainty and lack of standardized data in traditional microplastic risk assessment methods, this article highlights the urgency of developing a comprehensive big data and artificial intelligence (AI)-based regulatory framework. This work provides a substantial amount of accurate MP pollution data and offers a fresh perspective on leveraging AI for microplastic pollution regulation.
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Affiliation(s)
- Qiannan Duan
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Baoxin Zhai
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Chen Zhao
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Kangping Liu
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Xiangyi Yang
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Hailong Zhang
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Pengwei Yan
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China
| | - Lei Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China.
| | - Jianchao Lee
- Department of Environment Science, Shaanxi Normal University, Xi'an 710119, PR China.
| | - Weidong Wu
- Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an 710005, PR China
| | - Chi Zhou
- Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an 710005, PR China
| | - Xudong Quan
- Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an 710005, PR China
| | - Wei Kang
- Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an 710005, PR China
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2
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Jin H, Kong F, Li X, Shen J. Artificial intelligence in microplastic detection and pollution control. ENVIRONMENTAL RESEARCH 2024; 262:119812. [PMID: 39155042 DOI: 10.1016/j.envres.2024.119812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/04/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection. Focusing on image processing, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and hyperspectral imaging (HSI), the review highlights how AI enhances the efficiency and accuracy of these techniques. AI-driven image processing automates the identification and quantification of MPs, significantly reducing the need for manual analysis. FTIR and Raman spectroscopy accurately distinguish MP types by analyzing their unique spectral features, while HSI captures extensive spatial and spectral data, facilitating detection in complex environmental matrices. Furthermore, AI algorithms integrate data from these methods, enabling real-time monitoring, traceability prediction, and pollution hotspot identification. The synergy between AI and spectral imaging technologies represents a transformative approach to environmental monitoring and emphasizes the need to adopt innovative tools for protecting ecosystem health.
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Affiliation(s)
- Hui Jin
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Fanhao Kong
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xiangyu Li
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jie Shen
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China.
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3
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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. JOURNAL OF HAZARDOUS MATERIALS 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] [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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4
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Guselnikova O, Trelin A, Kang Y, Postnikov P, Kobashi M, Suzuki A, Shrestha LK, Henzie J, Yamauchi Y. Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams. Nat Commun 2024; 15:4351. [PMID: 38806498 PMCID: PMC11133413 DOI: 10.1038/s41467-024-48148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 04/21/2024] [Indexed: 05/30/2024] Open
Abstract
Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.
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Affiliation(s)
- Olga Guselnikova
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation.
| | - Andrii Trelin
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Yunqing Kang
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Pavel Postnikov
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Makoto Kobashi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Asuka Suzuki
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Lok Kumar Shrestha
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Science, Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Joel Henzie
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
| | - Yusuke Yamauchi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia.
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5
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Gicquel C, Bruzaud S, Kedzierski M. Generation of synthetic FTIR spectra to facilitate chemical identification of microplastics. MARINE POLLUTION BULLETIN 2024; 202:116295. [PMID: 38537498 DOI: 10.1016/j.marpolbul.2024.116295] [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: 11/24/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 05/08/2024]
Abstract
In a context where learning databases of microplastic FTIR spectra are often incomplete, the objective of our work was to test whether a synthetic data generation method could be relevant to fill the gaps. To this end, synthetic spectra were generated to create new databases. The effectiveness of machine learning from these databases was then tested and compared with previous results. The results showed that the creation of synthetic learning databases could avoid, to a certain extent, the need for learning databases of environmental microplastics FTIR spectra. However, some limitations were encountered, for example, when two different chemical classes had very similar reference spectra or when the intensities of the bands associated with fouling became too intense. The FTIR study of the ageing and fouling of microplastics in the natural environment is one of the identified ways that could further improve this approach.
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Affiliation(s)
- Chloé Gicquel
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France
| | - Stéphane Bruzaud
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France
| | - Mikaël Kedzierski
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France.
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6
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Peng C, Yang F, Yu J, Peng L, Zhang C, Chen C, Lin Z, Li Y, He J, Jin Z. Machine Learning Prediction Algorithm for In-Hospital Mortality following Body Contouring. Plast Reconstr Surg 2023; 152:1103e-1113e. [PMID: 36940163 DOI: 10.1097/prs.0000000000010436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
BACKGROUND Body contouring is a common procedure, but it is worth attention because of concern for a variety of complications, and even the potential for death. As a result, the purpose of this study was to determine the key predictors following body contouring and create models for the risk of mortality using diverse machine learning (ML) models. METHODS The National Inpatient Sample database from 2015 to 2017 was queried to identify patients undergoing body contouring. Candidate predictors, such as demographics, comorbidities, personal history, postoperative complications, and operative features, were included. The outcome was in-hospital mortality. Models were compared by area under the curve, accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis. RESULTS Overall, 8214 patients undergoing body contouring were identified, among whom 141 (1.72%) died in the hospital. Variable importance plot demonstrated that sepsis was the variable with greatest importance across all ML algorithms, followed by Elixhauser Comorbidity Index, cardiac arrest, and so forth. The naive Bayes model had a higher predictive performance (area under the curve, 0.898; 95% CI, 0.884 to 0.911) among these eight ML models. Similarly, in the decision curve analysis, the naive Bayes model also demonstrated a higher net benefit (ie, the correct classification of in-hospital deaths considering a tradeoff between false-negatives and false-positives) compared with the other seven models across a range of threshold probability values. CONCLUSION The ML models, as indicated by this study, can be used to predict in-hospital death for patients at risk who undergo body contouring.
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Affiliation(s)
- Chi Peng
- From the Department of Health Statistics, Second Military Medical University
| | - Fan Yang
- Departments of Plastic Surgery and Burns
| | - Jian Yu
- From the Department of Health Statistics, Second Military Medical University
| | - Liwei Peng
- Neurosurgery, Tangdu Hospital, Fourth Military Medical University
| | - Chenxu Zhang
- From the Department of Health Statistics, Second Military Medical University
| | - Chenxin Chen
- From the Department of Health Statistics, Second Military Medical University
| | - Zhen Lin
- From the Department of Health Statistics, Second Military Medical University
| | - Yuejun Li
- Departments of Plastic Surgery and Burns
| | - Jia He
- From the Department of Health Statistics, Second Military Medical University
| | - Zhichao Jin
- From the Department of Health Statistics, Second Military Medical University
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7
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Chen Q, Wang J, Yao F, Zhang W, Qi X, Gao X, Liu Y, Wang J, Zou M, Liang P. A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives. Mikrochim Acta 2023; 190:465. [PMID: 37953347 DOI: 10.1007/s00604-023-06044-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023]
Abstract
The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.
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Affiliation(s)
- Qiang Chen
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jiamiao Wang
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Fuqi Yao
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Wei Zhang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Xiaohua Qi
- Chinese Academy of Inspection and Quarantine (CAIQ), Beijing, 100123, China
| | - Xia Gao
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Yan Liu
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Jiamin Wang
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Mingqiang Zou
- Chinese Academy of Inspection and Quarantine (CAIQ), Beijing, 100123, China.
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China.
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8
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Yan X, Cao Z, Murphy A, Ye Y, Wang X, Qiao Y. FRDA: Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165340. [PMID: 37414174 DOI: 10.1016/j.scitotenv.2023.165340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.
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Affiliation(s)
- Xinyu Yan
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland; Luoyang Institute of Science and Technology, China.
| | - Zhi Cao
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Alan Murphy
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Yuhang Ye
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Xinwu Wang
- International Union Laboratory of New Civil Engineering Structure of Henan Province, China.
| | - Yuansong Qiao
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
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9
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Su J, Zhang F, Yu C, Zhang Y, Wang J, Wang C, Wang H, Jiang H. Machine learning: Next promising trend for microplastics study. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118756. [PMID: 37573697 DOI: 10.1016/j.jenvman.2023.118756] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/24/2023] [Accepted: 08/09/2023] [Indexed: 08/15/2023]
Abstract
Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.
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Affiliation(s)
- Jiming Su
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China
| | - Fupeng Zhang
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, PR China
| | - Chuanxiu Yu
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China
| | - Yingshuang Zhang
- School of Chemical Engineering and Technology, Xinjiang University, 830017, Urumqi, Xinjiang, PR China
| | - Jianchao Wang
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, PR China
| | - Chongqing Wang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, PR China
| | - Hui Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China.
| | - Hongru Jiang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China.
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10
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Luo Y, Su W, Xu D, Wang Z, Wu H, Chen B, Wu J. Component identification for the SERS spectra of microplastics mixture with convolutional neural network. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:165138. [PMID: 37379925 DOI: 10.1016/j.scitotenv.2023.165138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/16/2023] [Accepted: 06/24/2023] [Indexed: 06/30/2023]
Abstract
With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.
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Affiliation(s)
- Yinlong Luo
- College of Science, Hohai University, Changzhou 213022, China
| | - Wei Su
- College of Science, Hohai University, Changzhou 213022, China.
| | - Dewen Xu
- College of Science, Hohai University, Changzhou 213022, China
| | - Zhenfeng Wang
- College of Science, Hohai University, Changzhou 213022, China
| | - Hong Wu
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Bingyan Chen
- College of Science, Hohai University, Changzhou 213022, China
| | - Jian Wu
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410003, China
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11
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Liu Y, Yao W, Qin F, Zhou L, Zheng Y. Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:6656-6663. [PMID: 37052503 DOI: 10.1021/acs.est.2c08952] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Microplastics (MPs) are currently recognized as emerging pollutants; their identification and classification are therefore essential during their monitoring and management. In contrast to most studies based on small datasets and library searches, this study developed and compared four machine learning-based classifiers and two large-scale blended plastic datasets, where a 1D convolutional neural network (CNN), decision tree, and random forest (RF) were fed with raw spectral data from Fourier transform infrared spectroscopy, while a 2D CNN used the corresponding spectral images as the input. With an overall accuracy of 96.43% on a small dataset and 97.44% on a large dataset, the 1D CNN outperformed other models. The 1D CNN was the best at predicting environment samples, while the RF was the most robust with less spectral data. Overall, RF and 2D CNNs might be evaluated for plastic identification with fewer spectral data; however, 1D CNNs were thought to be the most effective with sufficient spectral data. Accordingly, an open-source MP spectroscopic analysis tool was developed to facilitate a quick and accurate analysis of existing MP samples.
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Affiliation(s)
- Yanlong Liu
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Wenli Yao
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Fenghui Qin
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Lei Zhou
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Yian Zheng
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
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12
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Höppener EM, Shahmohammadi M(S, Parker LA, Henke S, Urbanus JH. Classification of (micro)plastics using cathodoluminescence and machine learning. Talanta 2023. [DOI: 10.1016/j.talanta.2022.123985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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13
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Lin JY, Liu HT, Zhang J. Recent advances in the application of machine learning methods to improve identification of the microplastics in environment. CHEMOSPHERE 2022; 307:136092. [PMID: 35995191 DOI: 10.1016/j.chemosphere.2022.136092] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/06/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identification methods have demonstrated significant limitations such as low resolution, long imaging time, and limited particle size analysis. New and improved methods for MPs identification are topical research areas, with machine learning (ML) algorithms proven highly useful in recent years. Critical literature reviews on the latest developments in this area are limited. This study closes this gap and summarizes the progress made over the last 10 years in using ML algorithms for identifying and quantifying MPs. We identified diverse combinations of ML methods and fundamental techniques for MPs identification, such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and near-infrared spectroscopy. The most widely used ML model is the support vector machine, which effectively improves the conventional analysis method for spectral quality defects and improves detection accuracy. Artificial neural network models exhibit improved recognition effects, with shorter detection times and better MPs recognition efficiency. Our review demonstrates the potential of ML in improving the identification and quantification of MPs.
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Affiliation(s)
- Jia-Yu Lin
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hong-Tao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Jun Zhang
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China.
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14
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Choobbari ML, Ciaccheri L, Chalyan T, Adinolfi B, Thienpont H, Meulebroeck W, Ottevaere H. Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3840-3849. [PMID: 36169110 DOI: 10.1039/d2ay01215d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Size and concentration are two important parameters for the analysis of microplastics (MPs) in water. The analytical tools reported so far extract this information in a single-particle analysis mode, dramatically increasing the analysis time. Here, we present a combination of multi-angle static light scattering technique, called "Goniophotometry", with chemometric multivariate data processing for the batch analysis of size and concentration of MPs in water. Nine different sizes of polystyrene (PS) MPs with diameters between 500 nm and 20 μm are investigated in two different scenarios with uniform (monodisperse) and non-uniform (polydisperse) size distribution of MPs, respectively. It is shown that Principal Component Analysis (PCA) can reveal the existing relationship between the scattering data of mono- and polydisperse samples according to the size distribution of MPs in mixtures. Therefore, a Linear Discriminant Analysis (LDA) model is constructed based on the PCA of scattering data of PS monodisperse samples and is subsequently employed to classify the size of MPs not only in unknown mono- and polydisperse PS samples, but also for other types of MPs such as Polyethylene (PE) and Polymethylmethacrylate (PMMA). When the size of MPs is classified, their concentration is measured using a simple linear fit. Finally, a Linear Least Square (LLS) model is used to evaluate the reproducibility of the measurements.
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Affiliation(s)
- Mehrdad Lotfi Choobbari
- Vrije Universiteit Brussel, Department of Applied Physics and Photonics, Brussels Photonics, Pleinlaan 2, 1050 Brussels, Belgium
| | - Leonardo Ciaccheri
- CNR-Istituto di Fisica Applicata "Nello Carrara", Via Madonna del Piano 10 - 50019, Sesto Fiorentino (FI), Italy
| | - Tatevik Chalyan
- Vrije Universiteit Brussel and Flanders Make, Department of Applied Physics and Photonics, Brussels Photonics, Pleinlaan 2, 1050 Brussels, Belgium.
| | - Barbara Adinolfi
- CNR-Istituto di Fisica Applicata "Nello Carrara", Via Madonna del Piano 10 - 50019, Sesto Fiorentino (FI), Italy
| | - Hugo Thienpont
- Vrije Universiteit Brussel and Flanders Make, Department of Applied Physics and Photonics, Brussels Photonics, Pleinlaan 2, 1050 Brussels, Belgium.
| | - Wendy Meulebroeck
- Vrije Universiteit Brussel and Flanders Make, Department of Applied Physics and Photonics, Brussels Photonics, Pleinlaan 2, 1050 Brussels, Belgium.
| | - Heidi Ottevaere
- Vrije Universiteit Brussel and Flanders Make, Department of Applied Physics and Photonics, Brussels Photonics, Pleinlaan 2, 1050 Brussels, Belgium.
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15
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Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling. Foods 2022; 11:foods11162431. [PMID: 36010431 PMCID: PMC9407322 DOI: 10.3390/foods11162431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400–1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F1 and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.
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Orona-Návar C, García-Morales R, Loge FJ, Mahlknecht J, Aguilar-Hernández I, Ornelas-Soto N. Microplastics in Latin America and the Caribbean: A review on current status and perspectives. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 309:114698. [PMID: 35183939 DOI: 10.1016/j.jenvman.2022.114698] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/21/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
A literature review was carried out to analyze the current status of microplastic research in Latin America and the Caribbean (LAC). Specifically, this work focused on publications pertaining to (1) occurrence and distribution of microplastics in the environment, including water, sediments, and soil and (2) the environmental impact of MPs, particularly their presence and effects on aquatic and terrestrial organisms. The review included peer-reviewed articles from Scopus, Science Direct, Web of Science, Google Scholar and two iberoamerican open access databases (Redalyc and SciELO). It was found that LAC has only contributed to 5% of the global scientific output on microplastics, and overall the highest contributor within the region was Brazil (52%), followed by Chile (16%) and Mexico (13%). An additional section analyzing the barriers to conducting microplastic research in LAC and their exacerbation by the current COVID-19 pandemic was included to provide additional context behind the relatively low scientific production and improve recommendations encouraging research in this region.
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Affiliation(s)
- Carolina Orona-Návar
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico
| | - Raul García-Morales
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico; Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México, Carretera Tijuana-Ensenada Km. 107, C.P. 22860, Ensenada, B.C., Mexico
| | - Frank J Loge
- Department of Civil and Environmental Engineering, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Jürgen Mahlknecht
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico
| | - Iris Aguilar-Hernández
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico.
| | - Nancy Ornelas-Soto
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico.
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17
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The identification of microplastics based on vibrational spectroscopy data – a critical review of data analysis routines. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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