Evaluation of optical surrogates for the characterization of DOM removal by coagulation

Optical surrogates (i.e., absorbance and fluorescence) are of interest to monitor drinking water treatment processes due to their potential for implementation as online sensors. This study compares the use of different optical surrogates to model dissolved organic carbon (DOC) removal by coagulation with aluminum sulfate in the dose range of 5 to 120 mg L-1 in 22 source waters with a wide range of water qualities (specific UV absorbance (SUVA(254)) -1.0 to 4.0 L mg(C)(-1) m(-1)). Linear regressions were developed relating percent DOC removal to percent fluorescence decrease at discrete wavelength pairs in an excitation-emission matrix (2029 wavelength pairings). DOC removal was modeled with an average 95% prediction interval between 10.5-15% at all emission wavelengths greater than 375 nm, suggesting that wavelength selection for use in fluorescence monitoring is relatively unimportant. Fluorescence data without inner filter corrections led to a decrease in model performance, but prediction intervals were still between 12-15% at emission wavelengths greater than 400 nm. By comparison, the average prediction interval for an analogous model with UV absorbance at 254 nm was 10.5% (DOCr% = 0.7 UVr%, R-2 = 0.91), performing the same as the best possible fluorescence wavelength combinations. Additional modeling found that tracking multiple optical surrogates in tandem does not improve model performance due to correlated independent variables. Raw water optical properties (SUVA(254) and fluorescence ratios) modeled DOC removal at a single coagulant dose (similar to 40 mg L-1), but fluorescence indicators did not significantly outperform SUVA(254). A PARA-FAC model built with 112 fluorescence samples with six validated components demonstrated the range of fluorescence behaviors in the dataset and guided the selection of fluorescence ratios to predict removal based on raw water characteristics. These results demonstrated that fluorescence wavelength is relatively unimportant for online monitoring approaches that measure both raw and clarified samples, but emission wavelength is a driving factor for predicting removal based solely on raw water characteristics.