[1] Ghosh P, Mukherji S. Fate, detection technologies and toxicity of heterocyclic PAHs in the aquatic and soil environments[J]. Science of the Total Environment, 892, 164499(2023).

[2] Zhang Q Q, Xing J B, Wang H W et al. Analysis on contamination characteristics, pollution source identification and ecological risk assessment of polycyclic aromatic hydrocarbons of groundwater in a large coking plant site of province[J]. Environmental Science, 44, 807-815(2023).

[3] Zoboli O, Weber N, Braun K et al. Systematic underestimation of polycyclic aromatic hydrocarbon aqueous concentrations in rivers[J]. Environmental Science and Pollution Research International, 31, 38117-38127(2024).

[4] Zhang Y, Yuan L M, He S L et al. Contemporary research progress on the detection of polycyclic aromatic hydrocarbons[J]. International Journal of Environmental Research and Public Health, 19, 2790(2022).

[5] Xue Z H, Zheng X, Yu W C et al. Review: research progress in detection technology of polycyclic aromatic hydrocarbons[J]. Journal of the Electrochemical Society, 168, 057528(2021).

[6] Patel A B, Shaikh S, Jain K R et al. Polycyclic aromatic hydrocarbons: sources, toxicity, and remediation approaches[J]. Frontiers in Microbiology, 11, 562813(2020).

[7] Li X Q, Liu Y, He N J et al. Fluorescence excitation-emission matrix characteristics and implication of dissolved organic matter in groundwater at a typical refinery-polluted site[J]. Earth Science, 49, 2199-2212(2024).

[8] Zhao M, Jiang Y H, Jia Y F et al. Characterizing the composition of dissolved organic matter in groundwater at a polluted site and its indicating significance for natural attenuation[J]. Journal of Environmental Engineering Technology, 13, 1586-1594(2023).

[9] Zhang Y Z, Liu Y D, Zhou A G et al. Identification of groundwater pollution from livestock farming using fluorescence spectroscopy coupled with multivariate statistical methods[J]. Water Research, 206, 117754(2021).

[10] Wang C, Guo W D, Guo Z R et al. Characterization of dissolved organic matter in groundwater from the coastal Dagu River watershed, China using fluorescence excitation-emission matrix spectroscopy[J]. Spectroscopy and Spectral Analysis, 33, 2460-2465(2013).

[11] He X S, Xi B D, Zhang P et al. The seasonal distribution characteristics and its reasons of dissolved organic matter in groundwater[J]. China Environmental Science, 35, 862-870(2015).

[12] Zhang W, Li T, Dong B. Characterizing dissolved organic matter in Taihu Lake with PARAFAC and SOM method[J]. Water Science and Technology, 85, 706-718(2022).

[13] Yang R F, Zhao N J, Xiao X et al. Quantifying PAHs in water by three-way fluorescence spectra and second-order calibration methods[J]. Optics Express, 24, A1148-A1157(2016).

[14] Bian H Y, Yao H, Lin G H et al. Multiple kinds of pesticides detection based on back-propagation neural network analysis of fluorescence spectra[J]. IEEE Photonics Journal, 12, 6801009(2020).

[15] Yu Y S, Zhang H Y. Research on the water quality detection method based on fluorescence spectrometry and PSO-RBF network[J]. Measurement, 218, 113197(2023).

[16] Wang Y T, Zhang Y, Shang F K et al. Measurement of polycyclic aromatic hydrocarbons in water by BP neural network combined with ATLD and three-dimensional fluorescence spectrometry[J]. Spectroscopy and Spectral Analysis, 39, 3420-3425(2019).

[17] Xu R Z, Cao J S, Feng G Y et al. Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning[J]. Chemical Engineering Journal, 430, 132893(2022).

[18] Chen Q, Tang B, Miao J F et al. Water sample classification and fluorescence component identification based on fluorescence spectrum[J]. Acta Optica Sinica, 43, 0630001(2023).

[19] Wang Z D, Zhang Y G, Zhang L J et al. Research on three-dimensional fluorescence spectrum identification technology of petroleum pollutants based on deep learning[J]. Laser & Optoelectronics Progress, 60, 1530001(2023).

[20] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).