机器学习在微塑料和纳米塑料检测方面的进展和策略
Lifang Xie1,2,3, Minglu Ma1,2,3, Qiuyue Ge1,2,3
1Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, People's Republic of China.
Environmental science & technology
|April 28, 2025
概括
机器学习 (ML) 和光谱学增强了微塑料和纳米塑料的检测. 本综述探讨了改善塑料污染监测的ML策略,并确定了未来环境保护的挑战.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 微塑料 (MP) 和纳米塑料 (NP) 构成重大环境和健康风险.
- 目前针对国会议员和国民议员的检测技术在分辨率,数据处理和速度方面面临限制.
研究的目的:
- 系统地审查机器学习 (ML) 技术与光谱学相结合的应用,以改进MP和NP的检测和表征.
- 确定开发有效的ML模型的策略,并分析使用ML检测NP的当前挑战.
主要方法:
- 系统性文献综述,重点关注MP和NP检测的ML和光谱学.
- 基于ML模型开发策略和对现有挑战的分析对文献的分类.
主要成果:
- ML与光谱学相结合,为克服当前MP和NP检测技术的局限性提供了一个有希望的解决方案.
- 谱学 (IR和Raman) 提供了独特的分子指纹,这对于基于ML的塑料识别至关重要.
- 当前的ML方法面临着需要解决的挑战,以加强NP检测和监测.
结论:
- ML在促进塑料污染环境监测方面发挥着至关重要的作用.
- 为未来的环境管理和公共卫生保护,解决目前在ML中对NP检测的挑战至关重要.
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