Li Peng, Qu Jiangbei, He Yiliang, Bo Zhang, Pei Mengke
School of Environmental Science and Engineering, Shanghai Jiao Tong University Shanghai 200240 China
China-UK Low Carbon College, Shanghai Jiao Tong University Shanghai 200240 PR China.
RSC Adv. 2020 Jun 1;10(35):20691-20700. doi: 10.1039/c9ra10732k. eCollection 2020 May 27.
In recent years, rural sewage treatment facilities have grown rapidly in China, and yet the water quality of the effluent has not been well monitored. The detection of chemical oxygen demand (COD) ultraviolet-visible (UV-Vis) spectroscopy is an emerging technology with advantages of low cost and easy maintenance, which make it appropriate for the on-line monitoring of effluents from rural sewage treatment facilities. Because there are numerous sewage treatment devices in rural regions and as their locations are usually very scattered, it is difficult to calibrate the COD estimation model for each monitoring site. Hence, a COD estimation model with global calibration is a specific problem for application in rural regions. However, little research was performed on real rural sewage, yet much is desired in terms of the model accuracy and robustness. Consequently, a practical COD detection method with UV-Vis spectroscopy was established in this study. The COD estimation model was globally calibrated with effluents from rural sewage treatment devices. In order to avoid misleading data for evaluating the model performance caused by the differences in the COD concentration range of training sets, two new criteria, namely the Root Mean Square Relative Error (RMSRE) and Relative Error Variance (REV), were proposed to evaluate the model accuracy and robustness. Differences in the organic composition as characterized by excitation-emission matrix (EEM) fluorescence spectroscopy were shown to significantly affect the accuracy of the global calibration model. Through comparison among the methods of the partial least squares (PLS), support vector machine (SVM), and back-propagation neural network, PLS was verified to be able to attain sufficient accuracy and to be suitable for applying to the modeling with global calibration. A simplified modeling method was proposed to replace the absorption spectra at the full wavelength band with the absorbance at some specific wavelengths that were selected by interval partial least-squares regression (iPLSR) and synergy interval partial least-squares regression (siPLSR). In this study, the simplified model was proven to be reliable with three specific wavelengths: 251, 356, and 363 nm. An on-line COD monitor utilizing UV-Vis spectroscopy was thus developed for combining with the global calibration model.
近年来，中国农村污水处理设施发展迅速，但出水水质监测工作却未得到很好的开展。采用紫外可见（UV-Vis）光谱法检测化学需氧量（COD）是一项新兴技术，具有成本低、维护简便的优点，适用于农村污水处理设施出水的在线监测。由于农村地区污水处理设备众多且位置通常非常分散，因此难以针对每个监测点校准COD估算模型。所以，具有全局校准功能的COD估算模型在农村地区的应用是一个特殊问题。然而，针对实际农村污水开展的研究很少，而在模型准确性和稳健性方面却有很大需求。因此，本研究建立了一种实用的UV-Vis光谱法COD检测方法。利用农村污水处理设备的出水对COD估算模型进行全局校准。为避免因训练集COD浓度范围差异对评估模型性能产生误导性数据，提出了两个新的标准，即均方根相对误差（RMSRE）和相对误差方差（REV），用于评估模型的准确性和稳健性。结果表明，以激发发射矩阵（EEM）荧光光谱表征的有机组成差异对全局校准模型的准确性有显著影响。通过比较偏最小二乘法（PLS）、支持向量机（SVM）和反向传播神经网络等方法，验证了PLS能够达到足够的准确性，适用于全局校准建模。提出了一种简化建模方法，用区间偏最小二乘回归（iPLSR）和协同区间偏最小二乘回归（siPLSR）选择的某些特定波长处的吸光度替代全波段吸收光谱。在本研究中，证明了采用251、356和363 nm这三个特定波长的简化模型是可靠的。由此开发了一种结合全局校准模型的UV-Vis光谱法在线COD监测仪。
