A Comprehensive Analysis of the per- and poly-fluoroalkyl substances (PFAS) research landscape through AI-assisted text mining

IF 6.6 Q1 ENGINEERING, ENVIRONMENTAL
Yoshiyuki Kobayashi , Takumi Uchida , Takahiro Inoue , Yusuke Iwasaki , Rie Ito , Hiroshi Akiyama
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Abstract

Per- and poly-fluoroalkyl substances (PFAS) have been widely used in various industrial applications due to their unique properties. This study aims to provide a comprehensive analysis of PFAS research trends using a novel approach combining text mining techniques and large-scale language models (LLMs). PFAS-related scientific literature published from 1980 to 2024 was gathered from Scopus, and KH Coder and Claude 3 were used to perform the analysis. The results showed a significant increase in research output and a clear shift in research topics over the past 40 years. Whereas in the past, the focus was on analytical methods, more recently, the emphasis has been on environmental fate, toxicity assessment, alternative compounds, and regulation. With Claude 3, research areas can now be identified without reviewing the results of expert text mining. Comparisons of AI-extracted trends with insights from traditional review articles showed strong agreement, confirming the effectiveness of this approach. These findings suggest the need for continued interdisciplinary research on PFAS such as the development of remediation strategies, elucidation of health effects, and evidence-based policymaking. This study showed the possibility of integrating text mining and LLM for a comprehensive analysis of research trends, which will accelerate future research and development strategies.

通过人工智能辅助文本挖掘全面分析全氟和多氟烷基物质 (PFAS) 研究现状
全氟烷基和多氟烷基物质(PFAS)因其独特的性质被广泛应用于各种工业领域。本研究旨在采用一种结合文本挖掘技术和大规模语言模型(LLM)的新方法,对 PFAS 的研究趋势进行全面分析。研究人员从 Scopus 收集了 1980 年至 2024 年发表的与 PFAS 相关的科学文献,并使用 KH Coder 和 Claude 3 进行分析。结果表明,在过去 40 年中,研究成果大幅增加,研究课题也发生了明显变化。过去的重点是分析方法,而最近的重点是环境归宿、毒性评估、替代化合物和监管。有了 Claude 3,现在无需查看专家文本挖掘的结果,就能确定研究领域。将人工智能提取的趋势与传统综述文章的见解进行比较,结果显示两者非常一致,证实了这种方法的有效性。这些发现表明,有必要继续开展有关全氟辛烷磺酸的跨学科研究,如制定补救策略、阐明对健康的影响以及循证决策。这项研究表明,可以将文本挖掘和 LLM 结合起来,对研究趋势进行全面分析,从而加快未来的研究和发展战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of hazardous materials letters
Journal of hazardous materials letters Pollution, Health, Toxicology and Mutagenesis, Environmental Chemistry, Waste Management and Disposal, Environmental Engineering
CiteScore
10.30
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0.00%
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0
审稿时长
20 days
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