Characterization and recognition of three-dimensional excitation-emission matrix spectra of wastewater from six typical categories.

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.