Li Qingbo, Liu Rui, Bi Zhiqi
School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Beijing, China.
School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Beijing, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Feb 15;307:123635. doi: 10.1016/j.saa.2023.123635. Epub 2023 Nov 8.
Water resources are one of the most important strategic resources for human survival and development. At present, surface water pollution incidents occur frequently, most of which are caused by enterprises' over-discharge, stolen discharge, and other activities to evade supervision. Automatic and rapid determination of pollution source types is conducive to further targeting pollution-causing enterprises and realizing scientific accountability in law enforcement. The existing method mainly adopts the pattern recognition method for pollution discrimination, which is only suitable for the situation of a single source of pollutant, and cannot identify the pollution for multiple pollution sources mixed surface water. To solve the problem of identification of mixed chemical pollutants in surface water pollution sources and identification of simultaneous emission of multiple pollution sources, a total pollution source analysis method based on spectral unmixing is proposed in this paper, which is a radial basis bilinear mixing model automatic encoder algorithm. The unsupervised autoencoder neural network method was used to solve the proportion of water pollution types by using the spectral database of water pollution sources to realize the identification function of water pollution types and determine the types of pollutant discharge enterprises. In this paper, surface water was collected as experimental samples, mixed with domestic sewage, industrial sewage, agricultural sewage, and other pollution sources, and simulated experiments were carried out to estimate the type and proportion of water pollution. Experimental results show that the detection accuracy of the proposed algorithm is significantly improved compared with the traditional algorithm. Among them, the accuracy of judging whether there is industrial sewage in the mixed experiment of three types of pollution is as high as 95.2%. This method provides an important basis for pollution source investigation and accountability.
水资源是人类生存与发展最重要的战略资源之一。当前，地表水环境污染事件频发，其中多数是企业超标排放、偷排等逃避监管行为所致。自动快速判定污染源类型有利于进一步锁定污染企业，实现执法的科学问责。现有方法主要采用模式识别法进行污染判别，仅适用于单一污染物源的情况，无法识别多种污染源混合地表水的污染情况。为解决地表水污染源中混合化学污染物的识别以及多种污染源同时排放的识别问题，本文提出一种基于光谱分解的全污染源分析方法，即径向基双线性混合模型自动编码器算法。利用污染源光谱数据库，采用无监督自动编码器神经网络方法求解水污染类型比例，实现水污染类型识别功能并确定污染物排放企业类型。本文采集地表水作为实验样本，与生活污水、工业污水、农业污水等污染源混合进行模拟实验，以估算水污染类型及比例。实验结果表明，与传统算法相比，所提算法的检测精度显著提高。其中，在三种污染混合实验中判断是否存在工业污水的准确率高达95.2%。该方法为污染源排查与问责提供了重要依据。
