Kuang Litao, Liu Rui, Jin Meng, Lan Yaqiong, Su Yingying, Zhao Yuan, Chen Lujun
School 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.
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.
J Environ Sci (China). 2025 Nov;157:206-219. doi: 10.1016/j.jes.2024.04.026. Epub 2024 May 13.
In this study, we analyzed the characteristics of three-dimensional excitation-emission matrix spectra (EEMs) of 150 samples from five industrial wastewater types and domestic sewage to track water pollution sources effectively. We then developed a recognition model for wastewater EEMs by establishing a feature dataset containing fluorescence peak values and parameters derived from EEMs, integrated with machine learning techniques. This model enables the rapid and precise identification of pollution sources. Our findings suggest that although the EEMs of the six wastewater categories are distinct, visual differentiation is challenging. This was confirmed by cosine similarity assessments, showing some samples with low within-group (< 0.8) and high between-group (> 0.95) similarities. Despite significant variations in EEMs features across wastewater categories, identifying specific pollutants remains difficult, especially for pulp mills and leather effluents. Among the tested classification algorithms, Support Vector Machine (SVM) achieved the highest performance with 91.7 % accuracy, 94 % precision, 91 % recall, and 92 % F-score, outperforming K-Nearest Neighbors and Partial Least Squares Discriminant Analysis. The SVM significantly improved identification accuracy for pulp mill and leather processing wastewaters compared to other models. To enhance identification accuracy, further exploration of EEMs features and expanding the training dataset are recommended. Combining EEMs features with machine learning presents a promising method for improving water pollution supervision and source tracing in environmental management practices.
在本研究中，我们分析了来自五种工业废水类型和生活污水的150个样本的三维激发-发射矩阵光谱（EEMs）特征，以有效追踪水污染来源。然后，我们通过建立一个包含荧光峰值和从EEMs导出的参数的特征数据集，并结合机器学习技术，开发了一种废水EEMs识别模型。该模型能够快速、准确地识别污染源。我们的研究结果表明，虽然这六种废水类别的EEMs各不相同，但视觉区分具有挑战性。余弦相似性评估证实了这一点，结果显示一些样本组内相似度低（<0.8），组间相似度高（>0.95）。尽管不同废水类别之间的EEMs特征存在显著差异，但识别特定污染物仍然困难，尤其是对于造纸厂和皮革废水。在测试的分类算法中，支持向量机（SVM）的性能最高，准确率为91.7%，精确率为94%，召回率为91%，F值为92%，优于K近邻算法和偏最小二乘判别分析。与其他模型相比，SVM显著提高了造纸厂和皮革加工废水的识别准确率。为提高识别准确率，建议进一步探索EEMs特征并扩大训练数据集。将EEMs特征与机器学习相结合，为改善环境管理实践中的水污染监测和溯源提供了一种很有前景的方法。
