使用YNet进行城市地表水中的微塑料的智能分类和污染特征分析
1School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510641, China.
Journal of hazardous materials
|February 8, 2024
概括
一个名为YNet的新工具准确地识别了城市水域中的微塑料 (MPs) 污染. YNet有助于了解MP的分布和特征,揭示湿地和水库保留这些危险污染物.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 生态毒理学 生态毒理学
背景情况:
- 微塑料 (MPs,≤5mm) 是普遍存在的环境污染物,具有显著的生态和人类健康风险.
- 了解城市地表水中的MPs的数量,形状和分布对于有效的污染管理至关重要.
研究的目的:
- 开发和验证YNet,这是一个人工智能驱动的工具,用于准确识别和分类微塑料污染.
- 分析各种城市地表水体中的微塑料特性,包括数量和形状.
主要方法:
- 开发YNet,这是一个用于微塑料分析的深度学习模型.
- 在河水,湿地废水和水库水中对微塑料的数量和形状进行定量分析.
- 使用诸如子相似系数 (DSC),精度和回忆等指标对YNet的性能评估.
主要成果:
- 在微塑料的识别和分类方面,YNet表现出高精度 (DSC: 90.78%,精度: 94.17%,回忆: 89.14%).
- 湿地和水库显示出显著的微塑料保留能力,与流入水平相比,废水度较低 (27.0和26.3项/L).
- 微塑料度在河流下游 (45.6件/L) 高于河流上游 (22.0件/L).
- 在所有采样环境中,碎片构成了大多数的微塑料,而颗粒是最不丰富的形式.
结论:
- 训练有素的YNet工具准确地识别和分类微塑料,为城市水污染提供了有价值的见解.
- 湿地和水库在减轻城市水生系统中微塑料污染方面发挥着至关重要的作用.
- 这些发现强调了像YNet这样的先进分析工具对于监测和管理地表水中的微塑料污染的重要性.
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