Characterization of natural organic matter and precursors to trihalomethanes using spectral flourescence signatures

Disinfection is an essential process to kill pathogens (i.e., disease causing organisms) in source water during the production of drinking water. Chlorine is most widely used disinfectant because it is effective, affordable, and also provides chlorine residual to ensure that the water is safe through the distribution system. Nonetheless, chlorine reacts with Natural Organic Matter (NOM) and forms potentially carcinogenic Disinfection By-products (DBPs). The major chlorination DBPs are dominantly Trihalomethanes (THMs). However, not all organic compounds are equally reactive to THMs formation. NOM in water samples collected from the Delaware & Raritan Canal and its tributaries (Central New Jersey) was isolated by resin adsorption into six fractions: Hydrophobic acid (HPOA), Hydrophobic neutral (HPON), Hydrophobic base (HPOB), Hydrophilic acid (HPIA), Hydrophilic neutral (HPIN), and Hydrophilic base (HPIB). HPIN, HPON, and HPOA were the major fractions in most of samples. Moreover, the fractions\u27 seven-day THMs Formation Potentials (THMFP) were determined HPOA was found to be the most reactive fraction to THMs formation in addition to being one of the most abundant fractions in the source water. Additionally, the six fractions were also characterized by fluorescence spectroscopy to obtain three-dimensional fluorescence spectra. The spectra shape and peak locations are unique characteristics of organic compounds and also called Spectral Fluorescence Signature (SFS). The SFS is the total sum of emission intensity of a sample at different excitation wavelengths, recorded as a matrix of fluorescent intensity in coordinates of excitation and emission wavelengths. Among the six fractions, HPOA spectra were large and the peak intensity was also high. Therefore, fluorescence spectroscopy could be a promising technique for characterization of HPOA fraction or THMs precursors in the source water.Although a large number of intensities are related to THMs precursors, many of them are highly correlated by nature. Principle component analysis was then used to transform the fluorescence intensities into independent parameters called Principle Components (PCs). Best Subset Algorithm was performed to select the most important PCs for the prediction of THMFP by multiple linear regression. The prediction of THMFP using SFS is a rapid, inexpensive, reagent-free technique and thus can be used for optimization of water treatment processes