Chen Xiaowei, Zhao Nanjing, Zhu Wanjiang, Yin Gaofang, Jia Renqing, Yang Ruifang, Ma Mingjun
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine, Mechanics, Chinese Academy of Sciences, Hefei, China.
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine, Mechanics, Chinese Academy of Sciences, Hefei, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Jan 5;324:124968. doi: 10.1016/j.saa.2024.124968. Epub 2024 Aug 13.
Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.
紫外可见（UV-Vis）吸收光谱法因其高灵敏度和实时在线监测能力，是雨水管网中外部水快速识别最有前景的工具之一。然而，获取实际样品存在困难导致真实样品不足，且废水成分复杂会影响雨水管网中外部水的准确溯源分析。本研究提出了一种用少量样品识别雨水管网中外部水的新方法。该方法首先使用生成对抗网络（GAN）算法从水样的吸收光谱生成光谱数据；随后应用多元散射校正（MSC）算法处理不同类型水样的紫外可见吸收光谱；接着采用变分模态分解（VMD）算法对MSC后的光谱进行分解和重组；最后使用长短期记忆（LSTM）算法建立重组光谱与水源类型之间的识别模型，并确定分解光谱的最佳数量K。研究结果表明，当分解光谱数量K为5时，对不同来源的生活污水、地表水和工业废水的识别准确率最高，总体准确率为98.81%。此外，该方法的性能通过混合水样（雨水与生活污水、雨水与地表水、雨水与工业废水的组合）进行了验证。结果表明，该方法识别雨水中外部水来源的准确率达到98.99%，检测时间在10秒以内。因此，所提出的方法可以成为雨水管网中外部水快速识别和溯源分析的潜在方法。
