NDLI: 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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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

Content Provider	MDPI
Author	Hur, Jin Cho, Jinwoo
Abstract	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.
File Size	1926144
Ending Page	986
Page Count	15
Starting Page	972
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s120100972
Journal	Sensors
Issue Number	1
Volume Number	12
Language	English
Publisher Date	2012-01-16
Access Restriction	Open
Subject Keyword	fluorescence spectroscopy parallel factor analysis water quality monitoring urban river multiple regression analysis
Content Type	Text
Resource Type	Article

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