title: Investigation of fluorescence methods for rapid detection of municipal wastewater impact on drinking water sources.
abstract: Fluorescence spectroscopy as a means to detect low levels of treated wastewater impact on two source waters was investigated using effluents from five wastewater facilities. To identify how best to interpret the fluorescence excitation-emission matrices (EEMs) for detecting the presence of wastewater, several feature selection and classification methods were compared. An expert supervised regional integration approach was used based on previously identified features which distinguish biologically processed organic matter including protein-like fluorescence and the ratio of protein to humic-like fluorescence. Use of nicotinamide adenine dinucleotide-like (NADH) fluorescence was found to result in higher linear correlations for low levels of wastewater presence. Parallel factors analysis (PARAFAC) was also applied to contrast an unsupervised multiway approach to identify underlying fluorescing components. A humic-like component attributed to reduced semiquinone-like structures was found to best correlate with wastewater presence. These fluorescent features were used to classify, by volume, low (0.1-0.5%), medium (1-2%), and high (5-15%) levels by applying support vector machines (SVMs) and logistic regression. The ability of SVMs to utilize high-dimensional input data without prior feature selection was demonstrated through their performance when considering full unprocessed EEMs (66.7% accuracy). The observed high classification accuracies are encouraging when considering implementation of fluorescence spectroscopy as a water quality monitoring tool. Furthermore, the use of SVMs for classification of fluorescence data presents itself as a promising novel approach by directly utilizing the high-dimensional EEMs.
authors: Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, Ontario M5S 1A4, Canada. Electronic address: nicolas.peleato@mail.utoronto.ca.
authors: Nicolas M
authors: NM
authors: Peleato
authors: Department of Chemical Engineering, University of Waterloo, 200 University Ave., Waterloo, Ontario N2L 3G1, Canada.
authors: Raymond L
authors: RL
authors: Legge
authors: Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, Ontario M5S 1A4, Canada.
authors: Robert C
authors: RC
authors: Andrews
mesh_terms: D002947:Cities; D060766:Drinking Water / Q000737:chemistry*; D004784:Environmental Monitoring / Q000379:methods*; D005163:Factor Analysis, Statistical; D008962:Models, Theoretical; D013050:Spectrometry, Fluorescence; D062065:Wastewater / Q000737:chemistry*; D014874:Water Pollutants, Chemical / Q000032:analysis
chemical_list: D060766:Drinking Water; D062065:Wastewater; D014874:Water Pollutants, Chemical
keywords: Drinking water; Fluorescence spectroscopy; Organic matter characterization; Support vector machines; Wastewater