Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects

Area of Science:

• Environmental Science
• Analytical Chemistry
• Computer Science

Background:

• Microplastics (MPs) are persistent global contaminants, but their environmental behavior and ecological impacts are poorly understood due to analytical limitations.
• Current methods for characterizing MPs face significant challenges, hindering mechanistic insights.

Purpose of the Study:

• To critically review the challenges in microplastic analysis and the emerging applications of Artificial Intelligence (AI) in this field.
• To examine the reliability, generalizability, and ethical considerations of AI-driven approaches for microplastic research.

Main Methods:

• Critical review of existing literature on microplastic analysis and AI applications.
• Examination of methodological limitations in MP characterization techniques.
• Analysis of data accessibility, computational resources, environmental costs, and ethical dilemmas associated with AI in MPs research.

Main Results:

• AI offers powerful computational and data processing capabilities to advance microplastic analysis.
• Challenges remain in data accessibility, computational resources, and the environmental/ethical implications of AI.
• AI-driven approaches show promise but require scrutiny regarding reliability and generalizability.

Conclusions:

• A paradigm shift towards 'eco-conscious AI' is necessary, balancing ecological accountability with computational efficiency.
• Interdisciplinary collaboration is crucial for integrating AI into microplastic research sustainably.
• Future research must consider environmental, economic, and social factors for sustainable microplastic management.