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 lhctor analysis （PARAFAC...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 lhctor 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 lhctor framework-clustering analysis （PFFCA）,＂has been cleveloped in our earlier work to resolve this drawback of PARAFAC. In the present work, both PARAFAC and PFFCA were 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-of- fit 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 9 both methods. PFFCA score and actual concentration exhibited a higher correlation coefficient （R- = 0.870） compared with PARAFAC （R2〈 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.展开更多
基金We thank the National Natural Science Foundation of China （Grant No. 51538011）, the Collaborative Innovation Center of Suzhou Nano Science and Technology of the Ministry of Education of China for the support of this study.
文摘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 lhctor 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 lhctor framework-clustering analysis （PFFCA）,＂has been cleveloped in our earlier work to resolve this drawback of PARAFAC. In the present work, both PARAFAC and PFFCA were 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-of- fit 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 9 both methods. PFFCA score and actual concentration exhibited a higher correlation coefficient （R- = 0.870） compared with PARAFAC （R2〈 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.
