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Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods
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This review examines current methods for detecting and measuring microplastics in water, soil, and biological samples, including microscopy and spectroscopy techniques. The authors highlight how artificial intelligence could make detection faster and more accurate. Standardized testing methods and better health risk assessments are needed to understand and manage the dangers microplastics pose to human health.
Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological samples, assessing pre-treatment procedures like digestion and separation. It examines the application and limitations of identification techniques, including microscopic observation, spectroscopic analysis, and thermal analysis. The review highlights the potential of AI technology to enhance detection efficiency and precision. It underscores the necessity of standardized protocols for consistent sampling and detection, and the importance of systematic risk assessment methodologies for managing environmental and health risks associated with MPs. The paper concludes with recommendations for future research, emphasizing the standardization of methods, advancement of detection technologies, integration of AI, and comprehensive health risk assessments. This review will be helpful for researchers to comprehensively understand the current main detection technologies and risk assessment methods of the MP, and to accelerate the establishment of an artificial intelligence regulatory framework for MPs.
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Recent progress and technological advancements for detection of micro/nano-plastics in the environment
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This review surveys the latest analytical tools for detecting micro- and nanoplastics across environmental samples, covering imaging, spectroscopy, electrochemical sensors, and artificial intelligence. It highlights how the very small size and chemical complexity of nanoplastics makes detection especially challenging, and discusses how AI integration is improving accuracy and throughput. Advancing detection methods is foundational to understanding the true scale of microplastic contamination and its risks to ecosystems and human health.
A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Microplastic Detection and Analysis
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Scientists are increasingly using AI to detect microplastics faster than traditional lab methods like microscopes and chemical testing, according to a review of 113 studies. While AI shows promise, sometimes spotting these tiny plastic particles with over 90% accuracy, the field still lacks standardized testing methods, making it hard to compare results across studies or trust the technology is ready for widespread use. This matters because better, faster microplastic detection could eventually help us understand how much of this pollutant we're exposed to in our food, water, and air.
From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification
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This review surveys the state of micro- and nanoplastic detection methods, concluding that integrating AI-driven spectral analysis, hyperspectral imaging, and thermogravimetric approaches with conventional techniques is essential to close the accuracy and throughput gaps needed for reliable environmental monitoring and risk assessment.
Artificial intelligence in microplastic detection and pollution control
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This review examines how artificial intelligence is being combined with spectroscopy and imaging techniques to dramatically improve the speed and accuracy of microplastic detection in the environment. Better detection methods are essential for tracking the sources and spread of microplastic pollution that ultimately affects human health through contaminated food and water.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
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This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.