Investigation of fluorescence methods for rapid detection of municipal wastewater impact on drinking water sources

• Common fluorescence components to wastewater effluents identified.
• A specific humic component was highly correlated with wastewater impact.
• High detection accuracies of low level wastewater impacts using fluorescence
• Unprocessed spectra yield better results compared to derived components.
• Support vector machines shown to be a promising fluorescence analysis tool.

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.

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