TY - JOUR
T1 - Artificial intelligence-empowered collection and characterization of microplastics
T2 - A review
AU - Guo, Pengwei
AU - Wang, Yuhuan
AU - Moghaddamfard, Parastoo
AU - Meng, Weina
AU - Wu, Shenghua
AU - Bao, Yi
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/6/5
Y1 - 2024/6/5
N2 - Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.
AB - Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.
KW - Artificial intelligence (AI)
KW - Automatic collection and characterization
KW - Deep learning
KW - Machine learning
KW - Microplastics
KW - Robots
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U2 - 10.1016/j.jhazmat.2024.134405
DO - 10.1016/j.jhazmat.2024.134405
M3 - Review article
C2 - 38678715
AN - SCOPUS:85191579744
SN - 0304-3894
VL - 471
JO - Journal of Hazardous Materials
JF - Journal of Hazardous Materials
M1 - 134405
ER -