Tracing the Surface Water Pollution in a Chemical Park Based on the Fusion of Spectral and Chromatographic Characteristic Data

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Identification of the pollution source of surface water in a chemical park was difficult because many industrial enterprises with complex wastewater components and similar characteristics are located there. Therefore, a national-level chemical park in Jiaxing City was studied, and wastewater samples from ten batches of seven key enterprises in the park were collected and analyzed using 3D excitation emission matrix spectrometry （EEMS） and gas chromatography-mass spectrometry （GC-MS）. Parallel factor analysis was used to extract common components of EEMS spectra from different batches of drainage in the same enterprise to construct an EEMS characteristic data matrix. Furthermore, specific substances with high detection rates or that could effectively distinguish other enterprise drainage were screened out from the GC-MS data to construct a GC-MS characteristic data matrix. Pollution source identification was attempted with different models based on different data matrices. The results showed that regardless of whether being based on the EEMS original data matrix, the EEMS characteristic data matrix, or the GC-MS characteristic data matrix, the identification accuracy of the BP neural network model was not high, only 71.43%, 76.19%, and 71.43%, respectively, which was slightly higher than that of the support vector machine model （76.19%, 76.19%, and 57.14%）. However, when the EEMS and GC-MS characteristic data fusion matrix were used, the pollution source identification performance was significantly improved. The identification accuracy, macro precision, macro recall, and macro harmonic mean of the support vector machine model for the wastewater of the seven enterprises were 95.24%, 96.43%, 95.24%, and 95.10%, respectively, while the performance of the BP neural network model was better, with all four indicators close to 100%. The study provides an effective method for identifying surface water pollution sources in chemical parks.