来自六个典型类别的废水的三维激发发射矩阵光谱的表征和识别
Litao Kuang1, Rui Liu2, Meng Jin2
1School of Environmental Science and Engineering, Changzhou University, Changzhou 213164, China; Zhejiang Provincial Key Laboratory of Water Science and Technology, Department of Environment in Yangtze Delta Region Institute of Tsinghua University of Zhejiang, Jiaxing 314006, China.
Journal of environmental sciences (China)
|July 2, 2025
概括
本研究使用3D激发发射矩阵光谱 (EEM) 和机器学习来识别工业废水污染源. 支持矢量机 (SVM) 在区分废水类型方面实现了91.7%的准确性.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 水质监测 水质监测
背景情况:
- 有效追踪水污染源对于环境管理至关重要.
- 三维激发发射矩阵光谱 (EEMs) 提供了关于水样的详细化学信息.
- 使用传统方法,区分各种工业废水类型和家庭污水可能具有挑战性.
研究的目的:
- 开发和验证基于机器学习的模型,用于使用EMS准确识别水污染源.
- 评估不同机器学习算法的有效性,以分类废水EEMs.
- 在环境管理中提高废水源追踪的精度和速度.
主要方法:
- 使用3D EEM分析了来自五种工业类型和家庭污水的150个废水样本.
- 开发一个特征数据集,包括光峰值和来自EEMs的参数.
- 机器学习算法的应用和比较,包括支持矢量机 (SVM),K-最近邻居和部分最小平方差异分析.
主要成果:
- 对于不同的废水类别,EEM显示出不同的特征,尽管视觉区分很困难.
- 同位数相似性评估表明,在区分类似的废水类型方面存在挑战.
- 支持矢量机 (SVM) 模型实现了最高的准确性 (91.7%),精度 (94%),回忆 (91%) 和F1得分 (92%),显著改善了纸厂和皮革加工废水的识别.
结论:
- 将EMS功能与机器学习 (特别是SVM) 结合起来,提供了一种强大而准确的方法来识别废水污染源.
- 开发的模型增强了对水污染监督和环境管理中的来源追踪能力.
- 建议进行进一步的研究,包括扩展数据集和改进的EEM功能,以进一步提高识别准确性.
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