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A chemometric analysis on the fluorescent dissolved organic matter in a full-scale sequencing batch reactor for municipal wastewater treatment
Abstract Rapid monitoring of water quality is crucial to the operation of municipal wastewater treatment plants (WWTPs). Fluorescence excitation-emission matrix (EEM) in combination with parallel factor analysis (PARAFAC) has been used as a powerful tool for the characterization of dissolved organic matter (DOM) in WWTPs. However, a recent work has revealed the drawback of PARAFAC analysis, i.e., overestimating the component number. A novel method, parallel factor framework-clustering analysis (PFFCA), has been developed in our earlier work to resolve this drawback of PARAFAC. In the present work, both PARAFAC and PFFCAwere used to analyze the EEMs of water samples from a full-scale WWTP from a practical application point of view. The component number and goodness-offit from these two methods were compared and the relationship between the relative score change of component and the actual concentration was investigated to evaluate the estimation error introduced by both methods. PFFCA score and actual concentration exhibited a higher correlation coefficient (R 2 = 0.870) compared with PARAFAC (R 2<0.771), indicating that PFFCA provided a more accurate relative change estimation than PARAFAC. The results suggest that use of PARAFAC may cause confusion in selecting the component number, while EEM-PFFCA is a more reliable alternative approach for monitoring water quality in WWTPs.
A chemometric analysis on the fluorescent dissolved organic matter in a full-scale sequencing batch reactor for municipal wastewater treatment
Abstract Rapid monitoring of water quality is crucial to the operation of municipal wastewater treatment plants (WWTPs). Fluorescence excitation-emission matrix (EEM) in combination with parallel factor analysis (PARAFAC) has been used as a powerful tool for the characterization of dissolved organic matter (DOM) in WWTPs. However, a recent work has revealed the drawback of PARAFAC analysis, i.e., overestimating the component number. A novel method, parallel factor framework-clustering analysis (PFFCA), has been developed in our earlier work to resolve this drawback of PARAFAC. In the present work, both PARAFAC and PFFCAwere used to analyze the EEMs of water samples from a full-scale WWTP from a practical application point of view. The component number and goodness-offit from these two methods were compared and the relationship between the relative score change of component and the actual concentration was investigated to evaluate the estimation error introduced by both methods. PFFCA score and actual concentration exhibited a higher correlation coefficient (R 2 = 0.870) compared with PARAFAC (R 2<0.771), indicating that PFFCA provided a more accurate relative change estimation than PARAFAC. The results suggest that use of PARAFAC may cause confusion in selecting the component number, while EEM-PFFCA is a more reliable alternative approach for monitoring water quality in WWTPs.
A chemometric analysis on the fluorescent dissolved organic matter in a full-scale sequencing batch reactor for municipal wastewater treatment
Qian, Chen (author) / Chen, Wei (author) / Li, Wei-Hua (author) / Yu, Han-Qing (author)
2017-07-01
8 pages
Article (Journal)
Electronic Resource
English
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