Evaluation of Optical Surrogates for the Characterization of DOM Removal by Coagulation

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1 Electronic Supplementary Material (ESI) for Environmental Science: Water Research & Technology. This journal is The Royal Society of Chemistry 215 Evaluation of Optical Surrogates for the Characterization of DOM Removal by Coagulation Julie A. Korak 1, *, Fernando L. Rosario-Ortiz 1 and R. Scott Summers 1 1 Department of Civil, Environmental and Architectural Engineering, 428 UCB, University of Colorado Boulder, Boulder, CO 839 *Corresponding author, Julie.Korak@colorado.edu Supplemental Information 1. PARAFAC Model Development and Validation 1.1. Data Preparation The initial model contained 113 EEMs. 1 st order Rayleigh scattering was masked with the insertion of zeros. Second order Rayleigh scattering was masked with a margin of 22 nm and interpolated using the smoothing procedure in the dreem toolbox 1. First order Raman scattering was minimized by blank subtraction, but second order Raman scattering was interpolated using a margin of 1 nm. The data set was normalized prior to modeling to minimize the correlation between the PARAFAC components. All models were constrained to only include non-negative components Outlier Identification A preliminary outlier test investigated models containing 2 to 9 components. The initial investigation identified one sample as an outlier that exerted a significant leverage. This sample (Ft. Collins raw water) was removed from the data set (112 EEMs remaining in the final model) Component Number Screening The number of components was investigated by examining the sum squared error (SSE) and residuals of different component models. Systematic decreases in SSE occurred with each added component up to 6 components. Addition of a 6 th component eliminated a systematic residual centered around excitation 34 nm and emission 45 nm (Figure S-1). Little decease in SSE was observed with the addition of a 7 th component (Figure S-2). All components in the 6 - omponent model were physically realistic, only exhibiting one emission maxima. Based on these observations, a 6-component model was retained for validation. 1

2 Figure S-1. Sum squared error (SSE) as a function of excitation and emission wavelength for 2 through 6 component models. Figure S-2. Sum squared error (SSE) as a function of excitation and emission wavelength for 5 through 9 component models. 2

3 1.4. Model Validation A random initialization procedure was conducted to build 1 different models with different starting parameters to identify the stability of the solution and the overall model with a minimized error. The least squares solution had a core consistency value of 8.6%, and five other models in converged with similar core consistency values. The least squares model was further validated using split half analysis. The samples were randomly assigned to 4 different splits and combined into 4 different halves with 56 EEMs each (S 4 C 4 T 2 testing approach). Two of the splits required a smaller tolerance (1-8 ) and more analysis runs (5 instead of the default 3) to validate, but both split half analysis tests were validated for all comparisons. The models developed from each split were in good agreement with the overall, least squares model (Figure S-3). Figure ## presents contour plots of the final validated model. Figure S-3. Component loading for the overall model overlaid with the loadings for each of the 4 splits. Excitation spectra are dashed lines, and emission spectra are solid lines. 3

4 1.5. Model post-processing The normalization process was reversed to calculate the component loadings for each original sample. The model was also projected to calculate the loadings for the outlier originally removed from the dataset. A residual analysis of this projected model on the outlier found that it had a poor fit. Therefore, no PARAFAC component results are reported. Contour plots for each model component is presented in Figure S-4. Figure S-4. Contour plots of the 6 components in the final validated model. 2. Comparison of Raw Water Optical Properties and Coagulation Behaviors Multiple optical compositional indicators for the raw water samples were compared to establish the variability in the dataset. Table S-1 summarizes the optical properties of the raw water samples and range between source waters. PARAFAC component abundance is calculated as the component loading (F max ) divided by the sum of all component loadings (ΣF max ). Source 18 (Fort Collins) was an outlier in the PARAFAC model. Peak C is defined as the maximum intensity within the range: Excitation = 3-34 nm and Emission = 4-48 nm. Peak C emission wavelength is the emission wavelength at which the maximum intensity is located. Peak C/UV is the Peak C intensity divided by the absorbance at the corresponding excitation wavelength. Table S-2 demonstrates that most of the optical indicators are strongly correlated with one another. Figure S-5 compares the measured DOC removals to those predicted by the Edwards coagulation model 2. 4

5 Table S-1. Summary of raw water characteristics based on optical properties. No. Source DOC (mg/l) SUVA (L mg c -1 m -1 ) PARAFAC Component Abundance (%) C1 C2 C3 C4 C5 C6 FI Peak C Em Wavelength (nm) Specific Peak C (RU L mg c -1 ) Peak C/UV (RU-cm) 1 Betasso WTP % 23% 25% 8% 6% 4% Betasso WTP % 22% 25% 9% 6% 4% Betasso WTP % 21% 22% 9% 7% 6% Betasso WTP % 19% 2% 1% 1% 9% Boulder Reservoir % 11% 14% 19% 17% 13% Boulder Reservoir % 1% 14% 19% 17% 12% Boulder Reservoir % 11% 15% 18% 16% 11% Barr Lake % 19% 8% 16% 23% 13% Jackson Reservoir % 13% 11% 12% 21% 15% Lake Mead, NV % 1% 14% 2% 16% 1% Danville, KY % 19% 17% 11% 12% 4% Red River, ND % 16% 18% 16% 11% 3% Red Lake, MN % 17% 19% 17% 9% 3% Lake Erie, OH % 1% 11% 17% 2% 15% Arvada % 12% 12% 17% 17% 12% Boulder % 12% 14% 18% 15% 1% Evergreen % 18% 21% 12% 11% 5% Fort Collins Grand Junction % 11% 15% 18% 17% 9% Greely-Loveland % 12% 13% 19% 17% 8% Greely-Seaman % 15% 16% 17% 12% 5% Pueblo % 12% 14% 17% 16% 9% Minimum % 1% 8% 8% 6% 3% Maximum % 23% 25% 2% 23% 15%

6 Table S-2. Spearman rank correlation coefficients between compositional optical indicators in raw waters. Only statistically significant (p<.5) relationships are shown. SUVA C1% C2% C3% C4% C5% C6% FI Peak C Em Wavelength Specific Peak C Peak C/UV SUVA NaN C1% NaN C2% NaN C3% NaN C4% NaN C5% NaN C6% NaN FI NaN Peak C Em NaN Specific Peak C NaN -- Peak C/ UV NaN 6

7 7 6 Predicted DOC Removal (%) SUVA > 3 2 < SUVA < 3 SUVA < Measured DOC Removal (%) Figure S-5. Measured DOC removal compared to predicted DOC removal based on Edwards Model. The solid line represents the 1:1 line and the dashed lines are ±15% intervals. 7

8 3. Linear Regression Model Development# Excitation Wavelength (nm) Emission Wavelength (nm) Figure S-6. Wavelength combinations for exported and visually inspected residual plots Figure S-7. Contour plot exhibiting the p-value for the Kolmogorov-Smirnov test for models fit to different wavelength combinations for fully corrected fluorescence data. Value greater than.5 indicates non-normally distributed residuals. 8

9 DOC Removal (%) y=.93x R 2 pred =.85 PIavg=15% Comp Comp 2 y=.76x+.54 R 2 pred =.76 PIavg=18% DOC Removal (%) Comp 3 y=.79x+-.82 R 2 pred =.83 PIavg=15% Comp 4 y=.78x+-.82 R 2 pred =.84 PIavg=15% DOC Removal (%) y=.35x+.29 R 2 pred =.92 Comp 5 PIavg=35% y=.25x+.27 R 2 pred =.36 Comp 6 PIavg=35% Component Removal (%) Component Removal (%) Figure S-8. Regression analyses modeling DOC removal as a function of PARAFAC component removal. The model equation, R 2 pred and PI ag values are reported for each best-fit model. 9

10 Figure S-9. Parameter screening for linear regression (Eqn 5) fit to each wavelength combination for fluorescence data without inner filter corrections as a) Slope parameter estimate, b) Slope parameter p value (significance), c) Intercept parameter estimate, and d) Intercept parameter p value (significance). Figure S-1. Contour plot exhibiting the p-value for the Kolmogorov-Smirnov test for models fit to different wavelength combinations for fluorescence data without inner filter corrections. Value greater than.5 indicates non-normally distributed residuals. 1

11 Figure S-11. Parameter screening for regressions relating the relative decrease in UV absorbance to relative DOC removal at different absorbance wavelengths. Figure S-12. Regression model and diagnostic criteria predicting the relative decrease in DOC from the relative increase in FI. Solid line indicates model prediction, and dashed line indicates the 95% prediction interval. 11

12 Figure S-13. Multiple linear regression model and diagnostic criteria predicting the relative decrease in DOC from the relative decrease in UV 254 and relative increase in FI. Solid line indicates model prediction. Figure S-14. Multiple linear regression model and diagnostic criteria predicting the relative decrease in DOC from the relative decrease in UV 254 and final ph Solid line indicates model prediction. 12

13 4. DOC Removal Prediction based on Raw Water 1 a) C1:C2 1 b) C1:C3 1 c) C1:C DOC Removal (%) at 4 mg/l Alum d) C1:C g) C2:C e) C1:C h) C2:C f) C2:C i) C2:C DOC Removal (%) at 4 mg/l Alum j) C3:C m) C4:C k) C3:C n) C4:C l) C3:C o) C5:C Raw Water Ratio of Component F max values Figure S-15. PARAFAC Component intensity ratios in the raw water related to DOC removal at an alum dose of ~4 mg/l. Two samples were outliers and not included in the relationships (square, hollow markers). 13

14 DOC Removal (%) at 4 mg/l Alum y=3x+-.56 R 2 pred =.34 PIavg=39% Comp Comp 2 y=2.3x PIavg=29% R 2 pred = DOC Removal (%) at 4 mg/l Alum y=3.2x+-.15 R 2 pred =.85 PIavg=16% Comp Comp 4 y=-3x+.76 R 2 pred =.45 PIavg=34% DOC Removal (%) at 4 mg/l Alum Comp y=-3x+.8 R 2 pred =.85 PIavg=16% Component Abundance in Raw Water (%) Comp 6 y=-3.4x+.68 R 2 pred =.4 PIavg=38% Component Abundance in Raw Water (%) Figure S-16. PARAFAC Component abundance in the raw water related to DOC removal at an alum dose of ~4 mg/l. Two samples were outliers and not included in the relationships (square, hollow markers). 14

15 5. References 1. K. R. Murphy, C. A. Stedmon, D. Graeber, and R. Bro, Analytical Methods, 213, 5, M. Edwards, Journal AWWA, 1997, 89,