AI applications and challenges in addressing microplastic pollution

PENG Zheng-dong; SHEN Dong-sheng; XU Yin-yin; LONG Yu-yang; SUN Xiao-hui; GU Fo-quan

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China Environmental Science ›› 2026, Vol. 46 ›› Issue (1) : 399-408.

Emerging Contaminants

AI applications and challenges in addressing microplastic pollution

PENG Zheng-dong¹, SHEN Dong-sheng¹, XU Yin-yin², LONG Yu-yang¹, SUN Xiao-hui², GU Fo-quan¹

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Abstract

Microplastics (MPs) are emerging contaminants of global concern, posing a potential and serious threat to ecosystems and human health due to their small size, ease of migration, and slow degradation. MPs pollution control is urgently needed. Artificial intelligence (AI) is becoming increasingly integral to MPs pollution management, leveraging its superior data processing, pattern recognition, and predictive power. This paper presents a comprehensive review of recent advances in AI applications across the MPs management lifecycle, including collection and detection, source apportionment, impact assessment, and strategic intervention. The AI models utilized in each phase are comparatively evaluated, with a focus on their accuracy and limitations. This paper critically examines the extant challenges and outlines prospective development pathways, ultimately aiming to facilitate more informed and effective strategies for the scientific and intelligent management of MPs pollution.

Key words

artificial intelligence / deep learning / MPs / model

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PENG Zheng-dong, SHEN Dong-sheng, XU Yin-yin, LONG Yu-yang, SUN Xiao-hui, GU Fo-quan. AI applications and challenges in addressing microplastic pollution[J]. China Environmental Science. 2026, 46(1): 399-408

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