Jiang Xinyue, Liu Defu, Jiang Guodong, Xie Yuqun
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China.
School of Material and Chemical Engineering, Hubei University of Technology, 28, Nanli Road, Hong-shan District, Wuhan 430068, China.
ACS Omega. 2024 Jun 25;9(27):29252-29262. doi: 10.1021/acsomega.4c00169. eCollection 2024 Jul 9.
A technique for monitoring chemical oxygen demand (COD), total nitrogen (TN), ammonia (N-NH), and phosphate (P-PO) in surface water with a targeted signal multielectrode system (Cu, Ir, Rh, Co(OH), and Zr(OH) electrodes) is proposed for the first time. Each water quality index is specifically detected by at least two electrodes with distinct selectivity sensing mechanisms. Cyclic voltammetry and electrochemical impedance measurements are employed for multidimensional signal acquisition, complemented by normalization and Least Absolute Shrinkage and Selection Operator (LASSO) for principal feature extraction and dimension reduction. Multiple linear regression (MLR), partial least-squares (PLS), and eXtreme Gradient Boosting (XGBoost) were employed to evaluate the established prediction model. The precisions of the multielectrode system are ±10%/±5 ppm of COD, ±10%/±0.2 ppm of TN, ±5%/±0.1 ppm of N-NH, and ±5%/±0.01 ppm of P-PO. The analysis time of the multielectrode system is reduced from hours to minutes compared with traditional analysis, without any sample pretreatment, facilitating continuous online monitoring in the field. The developed multielectrode system offers a feasible strategy for online monitoring of surface water quality.
首次提出了一种利用靶向信号多电极系统（铜、铱、铑、氢氧化钴和氢氧化锆电极）监测地表水中化学需氧量（COD）、总氮（TN）、氨（N-NH）和磷酸盐（P-PO）的技术。每个水质指标由至少两个具有不同选择性传感机制的电极进行特异性检测。采用循环伏安法和电化学阻抗测量进行多维信号采集，并辅以归一化和最小绝对收缩选择算子（LASSO）进行主要特征提取和降维。采用多元线性回归（MLR）、偏最小二乘法（PLS）和极端梯度提升（XGBoost）对所建立的预测模型进行评估。多电极系统的精度为COD±10%/±5 ppm、TN±10%/±0.2 ppm、N-NH±5%/±0.1 ppm和P-PO±5%/±0.01 ppm。与传统分析相比，多电极系统的分析时间从数小时缩短至数分钟，无需任何样品预处理，便于现场连续在线监测。所开发的多电极系统为地表水水质在线监测提供了一种可行的策略。
