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Estimating the Concentration and Biodegradability of Organic Matter in 22 Wastewater Treatment Plants Using Fluorescence Excitation Emission Matrices and Parallel Factor Analysis

机译:利用荧光激发发射矩阵和并行因子分析估算22个废水处理厂中有机物的浓度和生物降解性

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摘要

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).
机译:这项研究旨在监测韩国22家生物废水处理厂废水样品中荧光成分的变化,并探讨其对总有机碳(TOC),溶解有机碳(DOC),生化需氧量(BOD),化学需氧量的预测能力(COD),并使用基于荧光激发发射矩阵和并行因子分析(EEM-PARAFAC)的光学传感技术对废水进行生物降解。通过使用EEM-PARAFAC从样品中鉴定出三个荧光成分,包括蛋白样(C1),黄褐斑样(C2)和腐殖质样(C3)组分。在这里研究的所有处理类型中,C1的去除效率最高(69%±26%–81%±8%),其次是C2(37%±27%–65%±35%),而腐殖质样成分(即,C3)倾向于在生物处理过程中积累。 C1在总荧光中的百分比(%C1)从进水的54%±8%降低到出水的28%±8%,而C2和C3的百分比(%C2和%C3)从43%±6增加分别为%至62%±9%和3%±7%至10%±8%。与其他两个荧光成分相比,TOC,DOC,BOD和COD的浓度与C1的荧光强度(Fmax)最为相关(r = 0.790–0.817)。通过使用基于C1和悬浮固体(SS)的Fmax的多元回归(r = 0.856–0.865)进行多元回归,可以提高C1对TOC,BOD和COD的预测能力。 BOD / COD中有机物的可生物降解性与每个PARAFAC组分及其组合显着相关(r = -0.598–0.613,p <0.001),%C1的相关系数最高。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)。

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