Prediction of BOD, COD, and total nitrogen concentrations in a typical urban river using a fluorescence excitation-emission matrix with PARAFAC and UV absorption indices.
Prediction of BOD, COD, and total nitrogen concentrations in a typical urban river using a fluorescence excitation-emission matrix with PARAFAC and UV absorption indices.
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DOI：
10.3390/s120100972
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Cho J
中科院分区：
文献类型：
--
作者：
Hur J;Cho J
开发一种用于评估水质的实时监测工具对于有效管理城市地区的河流污染至关重要。韩国的差距河是一条典型的城市河流，受到污水处理厂（WWTP）的流出物和各种人类活动的影响。在这项研究中，荧光激发-发射矩阵（EEM）与平行因子分析（PARAFAC）和紫外吸收值在220 nm和254 nm的应用，以评估的生化需氧量（BOD），化学需氧量（COD）和总氮（TN）浓度的河流样品的估计能力。利用荧光EEM数据建立的PARAFAC模型成功地识别出三种组分，其中每个荧光基团代表微生物类腐殖酸（C1）、陆生类腐殖酸有机物（C2）和蛋白质类有机物（C3），紫外吸收指数（UV 220和UV 254），选取PARAFAC 3个分量的得分值作为河流样品氮和有机污染的估算参数。在所选指标中，UV 220、C3和C1分别与BOD、COD和TN浓度的相关系数最高。用UV 220和C3进行的多元回归分析表明，对TN的预测能力增强。
The development of a real-time monitoring tool for the estimation of water quality is essential for efficient management of river pollution in urban areas. The Gap River in Korea is a typical urban river, which is affected by the effluent of a wastewater treatment plant (WWTP) and various anthropogenic activities. In this study, fluorescence excitation-emission matrices (EEM) with parallel factor analysis (PARAFAC) and UV absorption values at 220 nm and 254 nm were applied to evaluate the estimation capabilities for biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN) concentrations of the river samples. Three components were successfully identified by the PARAFAC modeling from the fluorescence EEM data, in which each fluorophore group represents microbial humic-like (C1), terrestrial humic-like organic substances (C2), and protein-like organic substances (C3), and UV absorption indices (UV220 and UV254), and the score values of the three PARAFAC components were selected as the estimation parameters for the nitrogen and the organic pollution of the river samples. Among the selected indices, UV220, C3 and C1 exhibited the highest correlation coefficients with BOD, COD, and TN concentrations, respectively. Multiple regression analysis using UV220 and C3 demonstrated the enhancement of the prediction capability for TN.
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