采用荧光激发发射矩阵和并行因子分析估算 22 家污水处理厂有机物的浓度和可生物降解性。

Estimating the concentration and biodegradability of organic matter in 22 wastewater treatment plants using fluorescence excitation emission matrices and parallel factor analysis.

机构信息

Department of Environment & Energy, Sejong University, 98 Gunja-dong, Gwangjin-ku, Seoul 143-747, Korea.

出版信息

Sensors (Basel). 2014 Jan 20;14(1):1771-86. doi: 10.3390/s140101771.

This study aimed at monitoring the changes of fluorescent components in wastewater samples from 22 Korean biological wastewater treatment plants and exploring their prediction capabilities for total organic carbon (TOC), dissolved organic carbon (DOC), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and the biodegradability of the wastewater using an optical sensing technique based on fluorescence excitation emission matrices and parallel factor analysis (EEM-PARAFAC). Three fluorescent components were identified from the samples by using EEM-PARAFAC, including protein-like (C1), fulvic-like (C2) and humic-like (C3) components. C1 showed the highest removal efficiencies for all the treatment types investigated here (69% ± 26%-81% ± 8%), followed by C2 (37% ± 27%-65% ± 35%), while humic-like component (i.e., C3) tended to be accumulated during the biological treatment processes. The percentage of C1 in total fluorescence (%C1) decreased from 54% ± 8% in the influents to 28% ± 8% in the effluents, while those of C2 and C3 (%C2 and %C3) increased from 43% ± 6% to 62% ± 9% and from 3% ± 7% to 10% ± 8%, respectively. The concentrations of TOC, DOC, BOD, and COD were the most correlated with the fluorescence intensity (Fmax) of C1 (r = 0.790-0.817), as compared with the other two fluorescent components. The prediction capability of C1 for TOC, BOD, and COD were improved by using multiple regression based on Fmax of C1 and suspended solids (SS) (r = 0.856-0.865), both of which can be easily monitored in situ. The biodegradability of organic matter in BOD/COD were significantly correlated with each PARAFAC component and their combinations (r = -0.598-0.613, p < 0.001), with the highest correlation coefficient shown for %C1. The estimation capability was further enhanced by using multiple regressions based on %C1, %C2 and C3/C2 (r = -0.691).

本研究旨在监测来自 22 个韩国生物污水处理厂的废水样本中荧光成分的变化，并利用基于荧光激发发射矩阵和并行因子分析（EEM-PARAFAC）的光学传感技术，探索其对总有机碳（TOC）、溶解有机碳（DOC）、生化需氧量（BOD）、化学需氧量（COD）和废水可生物降解性的预测能力。通过 EEM-PARAFAC 从样品中鉴定出三种荧光成分，包括蛋白质样（C1）、腐殖酸样（C2）和腐殖酸样（C3）。C1 对所有研究的处理类型均表现出最高的去除效率（69%±26%-81%±8%），其次是 C2（37%±27%-65%±35%），而腐殖酸样成分（即 C3）在生物处理过程中倾向于积累。总荧光中的 C1 百分比（%C1）从进水的 54%±8%下降到出水的 28%±8%，而 C2 和 C3 的百分比（%C2 和 %C3）从 43%±6%增加到 62%±9%和从 3%±7%增加到 10%±8%。TOC、DOC、BOD 和 COD 浓度与 C1 的荧光强度（Fmax）最相关（r = 0.790-0.817），而与其他两种荧光成分相比。通过使用基于 C1 的 Fmax 和悬浮固体（SS）的多元回归来提高 C1 对 TOC、BOD 和 COD 的预测能力（r = 0.856-0.865），这两种物质都可以很容易地原位监测。BOD/COD 中有机物的可生物降解性与每个 PARAFAC 成分及其组合显著相关（r = -0.598-0.613，p<0.001），其中与%C1 的相关性最高。通过使用基于%C1、%C2 和 C3/C2 的多元回归进一步增强了估计能力（r = -0.691）。