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Artificial intelligence-enabled identification of hazardous waste (medical waste): Advances and future directions based on multi-source data
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Hui-huang ZOU1, Pin-jing HE1, 2, Fan LYU1, 2, Wei PENG1, 2, Hua ZHANG1, 2, *
China Environmental Science | 2025, 45(12) : 6717 - 6725
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China Environmental Science | 2025, 45(12): 6717-6725
• Solid Waste •
Artificial intelligence-enabled identification of hazardous waste (medical waste): Advances and future directions based on multi-source data
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Hui-huang ZOU1, Pin-jing HE1, 2, Fan LYU1, 2, Wei PENG1, 2, Hua ZHANG1, 2, *
Affiliations
  • 1.Institute of Waste Treatment and Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
  • 2.Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
Published: 2025-12-20
Outline
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Data-driven intelligent identification technologies for hazardous waste (medical waste) were systematically reviewed. General principles for modeling based on artificial intelligence and spectroscopy were summarized. Key challenges, including data acquisition, class imbalance, model generalization, and interpretability, were analyzed. Future research directions were proposed, including AI-assisted spectroscopic modeling, transfer learning, multimodal data fusion, and machine-learning-based modeling of relationships among spectra, structures, and properties. A systematic reference for the intelligent identification and management of hazardous (medical) waste was provided, and guidance to accelerate practical deployment in treatment and disposal, resource recovery, and intelligent monitoring was offered.

hazardous waste  /  medical waste  /  intelligent identification  /  spectroscopy  /  data-driven
邹惠煌, 何品晶, 吕凡, 彭伟, 章骅. 基于人工智能的危险废物(医疗废物)智能识别:多源数据驱动的研究进展与未来方向. 中国环境科学, 2025 , 45 (12) : 6717 -6725 .
Hui-huang ZOU, Pin-jing HE, Fan LYU, Wei PENG, Hua ZHANG. Artificial intelligence-enabled identification of hazardous waste (medical waste): Advances and future directions based on multi-source data[J]. China Environmental Science, 2025 , 45 (12) : 6717 -6725 .
Year 2025 volume 45 Issue 12
45
10
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Article Info
  • Receive Date:2025-04-25
  • Online Date:2026-10-08
  • Published:2025-12-20
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  • Received:2025-04-25
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Affiliations
    1.Institute of Waste Treatment and Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
    2.Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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