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It explains how numerical models support systematic investigation of transport parameters and offer a holistic view of microplastic fate in ocean settings. Physical-parameter incorporation improves predictions, yet data on microplastic properties remains limited, motivating machine learning and computer-vision to capture environmental information and refine model accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/recent-trends-in-marine-microplastic-modeling-and-machine-learning-tools-potential-for-long-term-microplastic-monitoring/290568/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/recent-trends-in-marine-microplastic-modeling-and-machine-learning-tools-potential-for-long-term-microplastic-monitoring/290568.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-12","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is long-term microplastic monitoring important in this work?","Question",{"text":113,"@type":114},"The review links growing plastic use to microplastic pollution concerns and argues that efficient monitoring is necessary to understand long-term effects and how microplastic impacts progress in the environment.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do numerical models contribute to studying microplastic transport?",{"text":118,"@type":114},"Numerical models help examine the influence of different transport parameters systematically, improving understanding of fate and transport processes and enabling more accurate predictions such as sinking velocities and pathways in marine environments.",{"name":120,"@type":111,"acceptedAnswer":121},"What role do machine learning and computer-vision tools play?",{"text":122,"@type":114},"Machine learning and computer-vision tools help acquire environmental information when data on microplastic physical and chemical parameters is sparse, supporting the development of more accurate models and verification of their predictions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290568,1789642094,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Recent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring  \n\n| journal or\u003Cbr>publication title | Journal of Applied Physics |\n| --- | --- |\n| volume | 133 |\n| number | 2 |\n| page range | 020701 |\n| year | 2023-01-11 |\n| Publisher | American Institute of Physics |\n| Rights | (C) 2023 The Authors. |\n| Author's flag | publisher |\n| URL | [http://id.nii.ac.jp/1394/00002645/](http://id.nii.ac.jp/1394/00002645/) |\n\ndoi: info:doi/10 . 1063/5 .0126358  \nCreative Commons Attribution 4.0 International ([https://creativecom](https://creativecom)[mons.org/licenses/by/4.0/](mons.org/licenses/by/4.0/))  \nRecent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring   \n\n| Cite as: J. Appl. Phys. 133, 020701 (2023); [https://doi.org/10.1063/5.0126358](https://doi.org/10.1063/5.0126358)\u003Cbr>Submitted: 15 September 2022 • Accepted: 06 December 2022 • Published Online: 11 January 2023 |  |  |  |\n| --- | --- | --- | --- |\n|  Samantha Phan and  Christine K. Luscombe |  |  |  |\n| COLLECTIONS\u003Cbr>Paper published as part of the special topic on Special Collection Recognizing Women in Applied Physics\u003Cbr> This paper was selected as Featured\u003Cbr> This paper was selected as Scilight |  |  |  |\n| | | | |\n\nARTICLES YOU MAY BE INTERESTED IN  \nA review on recent machine learning applications for imaging mass spectrometry studies Journal of Applied Physics 133, 020702 (2023); [https://doi.org/10.1063/5.0100948](https://doi.org/10.1063/5.0100948)  \nSpin and spin current—From fundamentals to recent progress  \nJournal of Applied Physics 133, 020902 (2023); [https://doi.org/10.1063/5.0133335](https://doi.org/10.1063/5.0133335)  \n[Tutorial: Systematic development of polynomial machine learning potentials for elemental](Tutorial: Systematic development of polynomial machine learning potentials for elemental)[ ](Tutorial: Systematic development of polynomial machine learning potentials for elemental)and alloy systems  \nJournal of Applied Physics 133, 011101 (2023); [https://doi.org/10.1063/5.0129045](https://doi.org/10.1063/5.0129045)  \nJ. Appl. Phys. 133, 020701 (2023); [https://doi.org/10.1063/5.0126358](https://doi.org/10.1063/5.0126358) 133, 020701 © 2023 Author(s) .  \nRecent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring   \n\n| Cite as: J. Appl. Phys. 133, 020701 (2023); doi: 10. 1063/5.0126358 Submitted: 15 September 2022 · Accepted: 6 December 2022 ·\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>View Online Export Citation CrossMark\u003Cbr>Published Online: 11 January 2023 |\n| --- |\n| Samantha Phan  and Christine K. Luscombea)  |\n| AFFILIATIONS\u003Cbr>pi-Conjugated Polymers Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 904–0495, Japan\u003Cbr>Note: This paper is part of the Special Collection Recognizing Women in Applied Physics.\u003Cbr>a)Author to whom correspondence should be addressed: [christine.luscombe@oist.jp](christine.luscombe@oist.jp) |\n| ABSTRACT\u003Cbr>The increase in the global demand for plastics, and more recently during the pandemic, is a major concern for the future of plastic waste pollution and microplastics. Efficient microplastic monitoring is imperative to understanding the long-term effects and progression of microplastic effects in the environment. Numerical models are valuable in studying microplastic transport as they can be used to examine the effects of different parameters systematically to help elucidate the fate and transport processes of microplastics, thus providing a holistic view of microplastics in the ocean environment. By incorporating physical parameters (such as size, shape, density, and identity of microplastics), numerical models have gained better understanding of the physics of microplastic transport, predicted sinking velocities more accurately, and estimated microplastic pathways in marine environments. However, availability of large amounts of information about","cbCaitvPE69smLPh","https://ap.wps.com/l/cbCaitvPE69smLPh","pdf",1425393,"English","# Abstract\n# Introduction\n# Numerical Modeling for Microplastic Transport\n# Machine Learning and Computer Vision Tools\n# Bridging Modeling and Machine Learning\n# Conclusion","[{\"question\":\"Why is long-term microplastic monitoring important in this work?\",\"answer\":\"The review links growing plastic use to microplastic pollution concerns and argues that efficient monitoring is necessary to understand long-term effects and how microplastic impacts progress in the environment.\"},{\"question\":\"How do numerical models contribute to studying microplastic transport?\",\"answer\":\"Numerical models help examine the influence of different transport parameters systematically, improving understanding of fate and transport processes and enabling more accurate predictions such as sinking velocities and pathways in marine environments.\"},{\"question\":\"What role do machine learning and computer-vision tools play?\",\"answer\":\"Machine learning and computer-vision tools help acquire environmental information when data on microplastic physical and chemical parameters is sparse, supporting the development of more accurate models and verification of their predictions.\"}]","Recent trends in marine microplastic modeling and machine learning tools - Potential for long-term microplastic monitoring | PDF"]