Intelligent technology-enabled comprehensive research on microplastics: Detection, fate, and ecological effects
- 影响因子：7.2
- DOI码：10.1016/j.jece.2026.122269
- 所属单位：长春工业大学
- 发表刊物：Journal of Environmental Chemical Engineering
- 关键字：Microplastics
Artificial intelligence
Machine learning
Detection
Fate
Ecological effects
- 摘要：Microplastics (MPs) research faces critical challenges in detection, source identification, and risk assessment,
where traditional methods are limited by efficiency and analytical capacity for complex systems. This review
systematically evaluates the integration of artificial intelligence (AI) and machine learning (ML) across the entire
MPs research chain. Key technological advances are examined, including computer vision for automated
morphological analysis, deep learning for vibrational spectroscopy interpretation, and AI-assisted processing of
Py-GC/MS data. Furthermore, the role of AI in integrating multi-source data for pollution source tracking,
environmental fate prediction, and emerging risk dimensions is explored. Despite significant potential, widespread
adoption remains constrained by data standardization issues, limited model generalizability, interpretability
challenges, and insufficient integration with physical mechanisms. Future directions include developing
standardized databases, domain-specific foundational models, explainable AI, physics-informed ML approaches,
and intelligent decision support platforms. This review aims to provide a roadmap for advancing data-driven MPs
research and informing precise pollution management strategies.
- 论文类型：期刊论文
- 卷号：14
- 期号：3
- 页面范围：122269
- 是否译文：否
- 发表时间：2026-03-01
- 收录刊物：SCI
- 发布期刊链接：https://doi.org/10.1016/j.jece.2026.122269
附件：
2026【JECE】-2区-综述-工大建筑-[通讯]-微塑料AI(2).pdf
