[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1309":3,"related-1309":90},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":42,"slug":43,"view_count":44,"doi":45,"paper":46,"created_at":89},1309,"Artificial intelligence for microplastic pollution monitoring, predictive modeling, and risk assessment: advances, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-026-13075-0","Abstract Microplastic (MP) pollution constitutes a pervasive and complex environmental crisis, with particles infiltrating ecosystems globally and posing multifaceted risks to ecological integrity and human health. The inherent heterogeneity of MPs (in size, shape, polymer type, and degree of weathering) coupled with their presence in complex matrices, presents formidable analytical challenges that constrain traditional detection, quantification, and source-tracking methods. This review synthesizes the transformative potential of Artificial Intelligence (AI) and machine learning (ML) in overcoming these bottlenecks and advancing every facet of MP research. This review details how AI-driven computer vision automates the morphological analysis of MPs, while ML algorithms enhance the speed and accuracy of spectroscopic polymer identification. Beyond analytics, AI facilitates predictive modeling of MP fate and transport, enables intelligent source apportionment, and optimizes emerging remediation technologies. Using the United States as a critical case study, we examine the interplay between a fragmented regulatory landscape and AI innovation. However, significant challenges persist, including data scarcity, algorithmic limitations, and the gap between laboratory validation and field deployment. The reviewed findings argue that the integration of AI represents a necessary paradigm shift, moving from descriptive observation to predictive and prescriptive science. For this potential to be fully realized, concerted efforts in standardized data generation, interdisciplinary collaboration, and the development of transparent, robust AI frameworks are urgently required to inform effective mitigation policies and remediation strategies on a global scale.","微塑料（MP）污染构成了一场普遍而复杂的环境危机，颗粒物已渗透至全球生态系统，对生态完整性和人类健康构成多方面风险。微塑料固有的异质性（在尺寸、形状、聚合物类型及风化程度方面）加之其存在于复杂基质中，带来了严峻的分析挑战，制约了传统检测、定量及溯源方法的应用。本综述综合探讨了人工智能（AI）和机器学习（ML）在克服这些瓶颈、推动微塑料研究各领域发展方面的变革潜力。本文详细阐述了AI驱动的计算机视觉如何实现微塑料形态分析的自动化，同时ML算法如何提升光谱聚合物鉴定的速度和准确性。在分析手段之外，AI还促进了微塑料归趋与迁移的预测建模，实现了智能源解析，并优化了新兴的修复技术。以美国为典型案例，我们审视了碎片化的监管环境与AI创新之间的相互作用。然而，重大挑战依然存在，包括数据稀缺、算法局限性以及实验室验证与实地部署之间的差距。综述的研究结果表明，AI的整合代表了一种必要的范式转变，即从描述性观察转向预测性和规范性科学。要充分实现这一潜力，亟需在标准化数据生成、跨学科合作以及开发透明、稳健的AI框架方面采取协同努力，以期为全球范围内的有效减缓政策和修复策略提供信息支撑。",null,"Environmental Earth Sciences","2026-08-31T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为微塑料污染监测，与三农、农业信息化等无关，不进入精选。",[19],{"name":10,"url":6},[21],"环境保护",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":41},[29],{"code":30,"score":31,"supported":32,"title":33,"abstract":35,"tags":40},"I2",17,true,[34],"artificial intelligence",[36,37,38,34,39],"机器学习","计算机视觉","machine learning","computer vision",[],"农业相关性需要复核",[],"Artificialintelligenceformicroplasticpol-1309",2,"10.1007\u002Fs12665-026-13075-0",{"doi":45,"openalex_id":47,"authors":48,"venue":10,"cited_by_count":15,"oa_url":80,"card":81,"direction":87,"ingested_from":88},"W7204764130",[49,52,55,57,59,61,64,66,68,70,72,74,76,78],{"name":50,"orcid":51},"Obinna Chigoziem Akakuru","https:\u002F\u002Forcid.org\u002F0000-0002-2491-6481",{"name":53,"orcid":54},"Patrick Ray","https:\u002F\u002Forcid.org\u002F0000-0001-9495-2317",{"name":56,"orcid":9},"Uzochi Bright Onyeanwuna",{"name":58,"orcid":9},"Moses O. Eyankware",{"name":60,"orcid":9},"Godwin O. Aigbadon",{"name":62,"orcid":63},"Adedibu Sunny Akingboye","https:\u002F\u002Forcid.org\u002F0000-0003-2195-6098",{"name":65,"orcid":9},"Kenneth O. Iheme",{"name":67,"orcid":9},"Ayatu Usman",{"name":69,"orcid":9},"Chinyere C. Amadi",{"name":71,"orcid":9},"Ifeyinwa J. Ofoh",{"name":73,"orcid":9},"Samuel O. Onyekuru",{"name":75,"orcid":9},"Alexander I. Opara",{"name":77,"orcid":9},"Bernerd E. B. Akudinobi",{"name":79,"orcid":9},"Thomas J. Algeo","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12665-026-13075-0.pdf",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"综述人工智能在微塑料污染监测、预测建模和风险评估中的应用、挑战与未来方向。","综述AI\u002FML技术，包括计算机视觉、光谱识别、预测建模等，并以美国为案例。","AI集成是必要的范式转变，从描述性观察转向预测性和规范性科学，但面临数据稀缺等挑战。","农业绿色发展与碳","可探索AI在农业微塑料污染监测与风险评估中的应用，结合标准化数据生成和跨学科合作，开发透明鲁棒的AI框架。","农业遥感与作物表型","openalex","2026-09-01T23:30:19.954775Z",{"total":15,"page":91,"page_size":91,"items":92},1,[]]