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Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
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This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
Microplastics (MPs) have emerged as pervasive and persistent global environmental contaminants. However, the current inability to robustly analyze these particles hinders a deeper mechanistic understanding of their environmental behavior and ecological consequences. Artificial Intelligence (AI), with its computational power and data processing capabilities, is revolutionizing the analysis of MPs and transforming methodological paradigms. This critical review summarizes key challenges in the field, including persistent methodological limitations in characterization techniques and the interpretation of environmental behavior. We also critically examine emerging applications of AI in MPs domain. We explore the reliability and generalizability of these AI-driven approaches, highlighting that while data accessibility and computational resources pose core operational challenges, the environmental costs and ethical dilemmas of AI also demand scrutiny. Finally, we envision a paradigm shift towards an eco-conscious AI that harmonizes ecological accountability with computational efficiency. Fostering this transition through interdisciplinary collaboration is crucial for guiding MPs research toward a future that is sustainable across environmental, economic, and social consideration.
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