ANALYSIS OF GAC EFFLUENT

BLENDING DURING THE 

ICR TREATMENT STUDIES

United States

Environmental 

Protection Agency

Office of Water

(4601)

EPA 815-C-99-002

September 1999
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Foreword

Between 1997 and 1999, 98 public water systems conducted treatment studies to evaluate

disinfection byproduct (DBP) precursor removal performance of granular activated carbon

(GAC) and membranes. The treatment study requirement was a part of the Information

Collection Rule (ICR) for Public Water Systems, Subpart M of the National Primary Drinking

Water Regulations, § 141.141(e).

Sixty-two public water systems evaluated GAC. The ICR required that DBP precursor removal

be evaluated in the effluent of a single contactor as a function of run time, to assess the

breakthrough of DBP precursors as the GAC was exhausted. In practice, full-scale plants can

reduce carbon usage rates by blending the effluents of multiple parallel contactors prior to

disinfection. When the treatment objective is reached in the blended effluent, the contactor with

the "oldest" GAC is taken off-line and replaced by a contactor with fresh GAC, and this

replacement occurs at regular intervals. GAC effluent blending extends the service life of each

contactor because water from contactors that exceed the treatment objective is blended with

water from contactors with fresher GAC that have effluent concentrations below the treatment

objective. The treatment objective must only be maintained in the blended contactor effluent.

A primary goal during analysis of the treatment study results by the USEPA will be to estimate

blended contactor run times to meet target regulatory treatment objectives. This information can

be used to estimate GAC treatment costs that reflect full-scale effluent blending. This study

provides the background and foundation for the analytical tools used to analyze treatment study

data, assessing the applicability and limitations of these tools.

One objective of this study was to evaluate mathematical models for representing single

contactor breakthrough data. From a data management perspective, model parameters will be

easier to manage than the entire experimental data sets comprising 8,000 to 9,000 breakthrough

curves generated by the 62 GAC treatment studies. A best-fit curve also facilitates interpolation

and extrapolation of the experimental data. Furthermore, a function that describes the single

contactor experimental data set is a prerequisite for calculating the integral breakthrough curve, a

tool for predicting blended contactor run times.

A second objective of this study was to evaluate and compare two approaches for predicting the

integral breakthrough curve. The first approach, based on application of the average value

function to the single contactor breakthrough curve, has been presented by previous researchers.

The second approach evaluated was a new computationally-simpler method developed by the

treatment study technical work group (TS-TWG).

These two objectives were applied to experimental results from bench-scale GAC runs on eight

water sources. Analytes evaluated included DBP surrogates, DBP class sums, and DBP species,

yielding an extensive experimental matrix for a thorough evaluation of model results. In

addition, GAC effluent blending was assessed experimentally to test model predictions of the

integral breakthrough curve.
Disclaimer

Mention of trade names or commercial products does not constitute endorsement or

recommendation for use.
ANALYSIS OF GAC EFFLUENT BLENDING DURING THE ICR

TREATMENT STUDIES

Prepared for:

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF GROUND WATER AND DRINKING WATER

STANDARDS AND RISK MANAGEMENT DIVISION

Technical Support Center

26 W. Martin Luther King Drive

Cincinnati, Ohio 45268

Prepared by:

INTERNATIONAL CONSULTANTS, INC.

4134 Linden Avenue, Suite 200

Dayton, Ohio 45432

SUMMERS & HOOPER, INC.

6 Knollcrest Drive

Cincinnati, Ohio 45237

Under ICI Contract 68-C-98-051, Task Order 4
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-viiAcknowledgments

This document was prepared by the United States Environmental Protection Agency, Office of

Ground Water and Drinking Water, Standards and Risk Management Division, Technical

Support Center. The Task Order Project Officer was Steven Allgeier. The Contract Project

Officer was Phyllis Branson.

Technical consultants played a significant role in the research performed and the preparation of

this document. This task was conducted jointly by International Consultants, Inc. (ICI) and

Summers & Hooper, Inc. (S&H), under ICI Contract 68-C-98-051, Task Order 4. Timothy

Soward and Christopher Hill acted as Project Managers for ICI. The S&H project team was led

by Stuart Hooper, who acted as the Technical Project Leader. Mr. Hooper was responsible for

technical direction and led the data analysis effort. Vanessa Hatcher (S&H) led and conducted

the associated experimental work, and was assisted by Kristina Trenkamp and Carrie Wyrick.

Jeff Welge and Rick Song of ICI performed mathematical modeling and statistical analyses, and

provided statistical guidance when necessary. Technical review of the final report was provided

by James Westrick (USEPA-TSC), Thomas Speth (USEPA-RREL), and R. Scott Summers

(University of Colorado-Boulder).

The experimental portion of this project was performed in conjunction with treatment studies

required under the Information Collection Rule. The following public water systems were

involved: Charleston Commissioners of Public Works, City of Aurora, City of Escondido, City

of Greensboro, City of Topeka, Iowa-American Water Company, Miami-Dade Water and Sewer

Department, and Sweetwater Authority. This work could not have been completed without the

use of the data generated as part of the treatment studies funded by these public water systems.
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-ixExecutive Summary

When granular activated carbon (GAC) contactors are utilized for disinfection byproduct (DBP)

precursor control, multiple contactors can be used more efficiently when operated in parallel

with staggered GAC replacement cycles, with the effluents of all contactors blended prior to

disinfection. By doing so, individual contactors can be operated past a point at which the

effluent exceeds a given treatment objective, because the treatment objective must only be

maintained in the blended effluent of all contactors.

The design of this study incorporated two main goals. The primary objectives were to evaluate

the ability of the logistic function to model single contactor breakthrough curve data and to

evaluate the success and limitations of predictive models used to determine the integral

breakthrough curve, a relationship between single contactor run time and blended contactor

water quality. The secondary objective of this study was to evaluate the applicability of these

models and predictive methods in the context of the Information Collection Rule (ICR) GAC

treatment study data analysis.

Full-, pilot-, and bench-scale GAC treatment studies were performed by 62 utilities in fulfillment

of ICR requirements. Regardless of scale, the ICR required that the effluent of single GAC

contactors be analyzed for DBP surrogate and formed DBP breakthrough as a function of run

time, to assess the breakthrough of DBP precursors as the GAC was exhausted. Bench-scale

GAC studies typically examined two empty-bed contact times (EBCTs) of 10 and 20 minutes

during each of four quarterly studies to account for seasonal variability in source water quality.

Pilot-scale GAC studies were typically composed of one to two sessions including 10 and 20

minute EBCT contactors. Thus, a large amount of data was generated and will be analyzed: the

62 GAC treatment studies performed will yield a total of 8,000 to 9,000 individual breakthrough

curves.

The logistic function has previously been used to model GAC breakthrough curves and is a

suitable model due to the characteristic 'S' shape of most breakthrough curves. Three

modifications to the logistic function were developed to improve its performance for modeling

single contactor data. Curve fitting involved determining which model was applicable based on

characteristics of the breakthrough curve, and applying the appropriate model to the

breakthrough curve for each parameter. These enhanced forms of the logistic function model

were able to successfully fit single contactor breakthrough curve data for all parameters,

including DBP surrogates, DBP sum class parameters, and individual trihalomethane (THM) and

haloacetic acid (HAA) species. A method was also employed to detect outlier data points and to

limit the influence of these deviant observations on the parameter estimates.

Two predictive approaches were compared for developing determining the integral breakthrough

curve, a relationship between operation time of each individual contactor and water quality in the

blended contactor effluent: the direct integration (DI) method and the surrogate correlation

approach (SCA). The DI method is a time-normalized integration of the logistic function

calculated using the average value function that yields the integral breakthrough curve. The

SCA method first utilizes the DI method to establish an integral breakthrough curve for total

organic carbon (TOC). Then data points on both the single contactor and integral breakthrough

curves at a given TOC concentration are mapped, and all other water quality parameters
-xassociated with the single contactor effluent data set at that TOC concentration are applied to the

blended effluent curve. The SCA method is especially applicable to the ICR data analysis

requirements because it minimizes the computations necessary to estimate blended contactor run

times to treatment objectives. An assessment of the concentration of other DBPs at any given

treatment objective will be performed as part of the data analysis effort, and the SCA procedure

is also suited to this task. The SCA procedure requires that GAC breakthrough curves for all

measured parameters be represented by the logistic function model curve fit. By doing so, a

smaller amount of data are needed to represent the entire breakthrough curve experimental data

set. This procedure inherently relies on the assumption that the relationship between TOC

concentration and the other water quality parameters established in the single contactor effluent

is maintained in the blended contactor effluent. This study verified this assumption and found

that it was valid between TOC and other DBP surrogates, DBP class summation parameters, and

individual DBP species. In addition, the correlation between TOC and bromine incorporation

factors for THMs and HAAs was shown to be consistent between the single contactor effluent

and experimental blended effluent.

The results of the DI and SCA model predictions were compared to experimental data for eight

GAC runs performed on eight water sources, with varying pretreatments, influent TOC

concentrations, bromide concentrations, and simulated distribution system (SDS) chlorination

conditions to evaluate DBP formation. An analysis of the model results across all waters and

analytes showed that the prediction error for the two models was equivalent. Both models were

biased negative, indicating a tendency to underpredict the experimental data. The SCA model

had a slightly higher negative bias than did the DI model. Since the SCA method simplifies and

reduces the amount of computations necessary to estimate the integral breakthrough curve, its

use is recommended to estimate blended contactor water quality during the ICR treatment study

data analysis.

An analysis of model results for individual parameters showed that the SCA method was more

successful in predicting the integral breakthrough curve of brominated DBP species, while the DI

method was a better predictor of non-brominated DBP species breakthrough. Prediction of the

breakthrough of individual DBP species in the blended effluent is important since individual

DBPs of potential health concern will be considered during analysis of the ICR treatment study

data. For sum parameters such as total THM (TTHM) and the sum of five HAAs (HAA5), the

SCA method yielded results that were comparable to or superior to the DI method predictions.

Both predictive methods rely on the assumption that an infinite number of contactors are on-line

and operated in parallel-staggered mode. This study examined this assumption and found that

the error incurred when applying run time estimates based on the infinite contactor assumption to

run times for finite numbers of contactors is impacted by the number of contactors and the

magnitude of the treatment objective examined in relation to the asymptotic concentration

approached by the single contactor breakthrough curve. Based on the logistic function model of

the GAC effluent breakthrough profile, the infinite contactor assumption will yield estimated run

times within 10 percent of actual run times for 13 or more contactors operated in parallelstaggered mode. For 10 contactors on-line, the infinite contactor assumption will yield run time

estimates within 12 percent of the actual run times. In all cases, run time estimates based on the

infinite contactor assumption are longer than those for a finite number of contactors, thus

providing a best case scenario for GAC performance. The applicability of the infinite contactor

assumption in this model to finite numbers of contactors is especially important for small plants.
-xiExtrapolation of the integral breakthrough curve was also examined during this study. During

the ICR treatment study data analysis, extrapolation of some TOC integral breakthrough curves

may be necessary to estimate GAC run times when target regulatory values for DBPs or their

surrogates are exceeded upon application of the SCA method. Extrapolation of runs performed

on two waters were compared to the same runs without extrapolation, yielding a 3 percent error

in the predicted blended effluent TOC concentration for a 21 percent run time extrapolation and

an 8 percent error in the predicted blended effluent TOC concentration for a 61 percent

extrapolation. The impact of extrapolation on the SCA procedure estimates of the integral

breakthrough curves for other DBP surrogates and formed DBPs was small: the mean error at

the end of the extrapolated integral breakthrough curve was 5 percent for a 21 percent

extrapolation and 9 percent for a 61 percent extrapolation. Therefore, based on the waters

examined in this study, data sets that do not exceed a given treatment objective may be

extrapolated, and the error in predicted blended contactor water quality incurred by extrapolation

up to 50 percent of the original run time should average less than 10 percent.
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Table of Contents

List of Tables ................................................................................................................................xvii

List of Figures................................................................................................................................xix

List of Abbreviations.....................................................................................................................xxv

1 Background and Method Development................................................................................... 1

1.1 Optimization of GAC Operation......................................................................................... 2

1.2 Modeling the Operation of Multiple Contactors Operated in Parallel-Staggered Mode .... 2

1.3 Single Contactor Breakthrough Curve Models ................................................................... 6

1.4 Direct Integration Approach................................................................................................ 8

1.5 Surrogate Correlation Approach ......................................................................................... 9

1.6 Impact of Bromide Concentration on GAC Effluent Blending Models............................ 11

1.7 Effluent Blending Modeling of Fewer than 10 Contactors............................................... 12

1.8 GAC Breakthrough Curve Extrapolation.......................................................................... 12

1.9 ICR GAC Treatment Study Data Analysis Context.......................................................... 13

1.10 Appropriateness of Model Assumptions to Full-Scale GAC Effluent Blending .............. 14

2 Study Objectives and Approach............................................................................................ 15

3 Materials and Methods.......................................................................................................... 17

3.1 Experimental Approach..................................................................................................... 17

3.1.1 Rapid Small-Scale Column Test ............................................................................... 17

3.1.1.1 GAC Preparation Procedures ................................................................................ 17

3.1.1.2 RSSCT Column Setup........................................................................................... 19

3.1.1.3 Batch Influent Preparation .................................................................................... 20

3.1.1.4 RSSCT Monitoring ............................................................................................... 20

3.1.2 Bench-Scale Blended Water Quality Assessment Approach.................................... 20

3.1.3 DBP Formation Assessment...................................................................................... 21

3.1.4 Assessment of the Impact of Sampling on the Integral Breakthrough Curve........... 22

3.2 Data Analysis and Modeling Approach ............................................................................ 23

3.2.1 Logistic Function Models.......................................................................................... 23

-xiii-
-xiv3.2.2 Outlier Methods......................................................................................................... 25

3.2.3 Direct Integration Approach...................................................................................... 26

3.2.4 Surrogate Correlation Approach ............................................................................... 27

3.2.5 Comparison of Methods for Predicting the Performance of GAC Contactors

Operated in Parallel-Staggered Mode ................................................................................... 28

3.2.6 Comparison of Single Contactor and Blended Effluent DBP Bromine Incorporation .

................................................................................................................................... 28

3.2.7 Breakthrough Curve Extrapolation ........................................................................... 29

3.3 Waters Examined .............................................................................................................. 29

3.3.1 Pretreatment and Water Quality................................................................................ 29

3.3.2 Simulated Distribution System Chlorination Conditions.......................................... 31

3.4 Analytical Methods ........................................................................................................... 31

3.5 Experimental QA/QC Summary ....................................................................................... 32

4 Results and Discussion.......................................................................................................... 35

4.1 Overview ........................................................................................................................... 35

4.2 Correlation between Surrogates and DBPs in Single Contactor and Blended Contactor

Effluents .................................................................................................................................... 37

4.2.1 Correlation between Surrogate Concentration and DBP Formation......................... 37

4.2.2 Correlation between Surrogates and DBP Speciation............................................... 38

4.3 Assessment of Logistic Function Fit to Single Contactor Breakthrough Curve Data ...... 67

4.3.1 Surrogates and Class Sum Logistic Function Curve Fits.......................................... 67

4.3.2 DBP Species Logistic Function Curve Fits............................................................... 68

4.4 Comparison of SCA and DI Methods Used to Predict the Blended Contactor Integral

Breakthrough Curve .................................................................................................................. 89

4.4.1 Surrogates and Class Sums ....................................................................................... 95

4.4.2 DBP Species.............................................................................................................. 96

4.5 Analysis of Model Applicability to Finite Number of Contactors.................................. 113

4.6 Impact of Extrapolation on Integral Breakthrough Curve Prediction............................. 121

5 Summary and Conclusions.................................................................................................. 131

6 References ........................................................................................................................... 135
-xvAppendix A: Breakthrough Curves Corrected for Impact of Sampling..................................... 137

Appendix B: SAS Code ............................................................................................................. 143

Appendix C: Full- and Bench-Scale Pretreatment Schematics.................................................. 147

Appendix D: Single Contactor and Blended Effluent DBP Surrogate and Formed DBP

Correlations ................................................................................................................................. 157

Appendix E: Logistic Function Model Curve Fits..................................................................... 223

Appendix F: Comparison of SCA Method to DI Approach for Integral Breakthrough Curve

Prediction .................................................................................................................................... 305

Appendix G: Logistic Function Model Best-Fit Parameters...................................................... 387

Appendix H: Impact of Extrapolation on SCA Prediction of the Integral Breakthrough Curve393

Appendix I: Impact of Extrapolation on DI Prediction of the Integral Breakthrough Curve .... 415
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-xviiList of Tables

1 Summary of RSSCT design parameters for all runs ..................................................................... 18

2 Relationship between blended effluent sample number and RSSCT effluent sample number..... 21

3 Summary of pretreatment and water quality................................................................................. 30

4 SDS chlorination conditions ......................................................................................................... 31

5 Summary of analytical methods and MRLs.................................................................................. 32

6 Summary of laboratories conducting analyses.............................................................................. 32

7 Summary of field duplicate precision for single contactor and blended effluent data.................. 34

8 Frequency of logistic function model used and R2

 values for all parameters and all waters........ 69

9 Summary of DI and SCA integral breakthrough curve prediction RSS values for Waters 1

through 4 ....................................................................................................................................... 90

10 Summary of DI and SCA integral breakthrough curve prediction RSS values for Waters 5

through 8 ....................................................................................................................................... 91

11 Summary of model prediction bias for Waters 1 through 4.......................................................... 92

12 Summary of model prediction bias for Waters 5 through 8.......................................................... 92

13 Summary of mean RSS, mean bias, normalized mean RSS, and normalized mean bias for all

waters ............................................................................................................................................ 93

14 Summary of run times to a 0.35 treatment objective .................................................................. 115

15 Summary of run times to a 0.50 treatment objective .................................................................. 115

16 Summary of run times to a 0.65 treatment objective .................................................................. 116

17 Summary of run times to a 0.80 treatment objective .................................................................. 116
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1 Schematic of blending of multiple GAC contactors operated parallel-staggered mode .............. 3

2 Effluent water quality during quasi steady-state operation of multiple contactors in parallelstaggered mode............................................................................................................................. 3

3 Operation of multiple contactors in parallel-staggered mode to various single contactor run

times: derivation of the blended effluent integral breakthrough curve ........................................ 5

4 Graphical summary of SCA procedure used for base analysis of GAC treatment studies ........ 10

5 Logistic function model curves.................................................................................................. 24

6 Correlations based on GAC effluent TOC concentration for single contactor and blended

effluents for Water 1 .................................................................................................................. 40

7 Correlations based on GAC effluent TOC concentration for single contactor and blended

effluents for Water 2 .................................................................................................................. 41

8 Correlations based on GAC effluent TOC concentration for single contactor and blended

effluents for Water 8 .................................................................................................................. 42

9 THM correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 2..................................................................................................... 43

10 THM correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 3..................................................................................................... 44

11 THM correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 8..................................................................................................... 45

12 HAA correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 5..................................................................................................... 46

13 HAA correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 7..................................................................................................... 47

14 HAA correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 2..................................................................................................... 48

15 HAA correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 4..................................................................................................... 49

16 HAA correlations based on GAC effluent TOC concentration for single contactor and

blended effluents for Water 7..................................................................................................... 50

17 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 1............................................................................. 51

18 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 2............................................................................. 51

19 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 3............................................................................. 52

20 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 4............................................................................. 52

21 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 5............................................................................. 53

22 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 6............................................................................. 53

23 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 7............................................................................. 54

24 Correlation between single contactor and blended effluent TOC concentration and THM

bromine incorporation factor (n) for Water 8............................................................................. 54

-xixList of Figures
25 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 1 ........................................................................... 55

26 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 2 ........................................................................... 55

27 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 3 ........................................................................... 56

28 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 4 ........................................................................... 56

29 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 5 ........................................................................... 57

30 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 6 ........................................................................... 57

31 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 7 ........................................................................... 58

32 Correlation between single contactor and blended effluent TOC concentration and HAA

bromine incorporation factor (n’) for Water 8 ........................................................................... 58

33 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 1............................................................................. 59

34 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 2............................................................................. 59

35 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 3............................................................................. 60

36 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 4............................................................................. 60

37 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 5............................................................................. 61

38 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 6............................................................................. 61

39 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 7............................................................................. 62

40 Correlation between single contactor and blended effluent UV absorbance and THM

bromine incorporation factor (n) for Water 8............................................................................. 62

41 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 1 ........................................................................... 63

42 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 2 ........................................................................... 63

43 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 3 ........................................................................... 64

44 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 4 ........................................................................... 64

45 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 5 ........................................................................... 65

46 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 6 ........................................................................... 65

47 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 7 ........................................................................... 66

-xx-
48 Correlation between single contactor and blended effluent UV absorbance and HAA

bromine incorporation factor (n’) for Water 8 ........................................................................... 66

49 Single contactor and blended effluent TOC breakthrough curves for Water 1.......................... 71

50 Single contactor and blended effluent TOC breakthrough curves for Water 2.......................... 71

51 Single contactor and blended effluent TOC breakthrough curves for Water 3.......................... 72

52 Single contactor and blended effluent TOC breakthrough curves for Water 4.......................... 72

53 Single contactor and blended effluent TOC breakthrough curves for Water 5.......................... 73

54 Single contactor and blended effluent TOC breakthrough curves for Water 6.......................... 73

55 Single contactor and blended effluent TOC breakthrough curves for Water 7.......................... 74

56 Single contactor and blended effluent TOC breakthrough curves for Water 8.......................... 74

57 Single contactor and blended effluent UV254 breakthrough curves for Water 5...................... 75

58 Single contactor and blended effluent UV254 breakthrough curves for Water 7...................... 75

59 Single contactor and blended effluent SDS-TOX breakthrough curves for Water 4................. 76

60 Single contactor and blended effluent SDS-TOX breakthrough curves for Water 8................. 76

61 Single contactor and blended effluent SDS-TTHM breakthrough curves for Water 3.............. 77

62 Single contactor and blended effluent SDS-TTHM breakthrough curves for Water 6.............. 77

63 Single contactor and blended effluent SDS-HAA9 breakthrough curves for Water 1 .............. 78

64 Single contactor and blended effluent SDS-HAA9 breakthrough curves for Water 2 .............. 78

65 Single contactor and blended effluent SDS-CF breakthrough curves for Water 4 .................... 79

66 Single contactor and blended effluent SDS-BDCM breakthrough curves for Water 6 ............. 79

67 Single contactor and blended effluent SDS-BDCM breakthrough curves for Water 7 ............. 80

68 Single contactor and blended effluent SDS-BDCM breakthrough curves for Water 4 ............. 80

69 Single contactor and blended effluent SDS-BDCM breakthrough curves for Water 8 ............. 81

70 Single contactor and blended effluent SDS-BF breakthrough curves for Water 1 .................... 81

71 Single contactor and blended effluent SDS-BF breakthrough curves for Water 6 .................... 82

72 Single contactor and blended effluent SDS-DCAA breakthrough curves for Water 3.............. 82

73 Single contactor and blended effluent SDS-TCAA breakthrough curves for Water 5 .............. 83

74 Single contactor and blended effluent SDS-DBAA breakthrough curves for Water 6.............. 83

75 Single contactor and blended effluent SDS-BCAA breakthrough curves for Water 1.............. 84

76 Single contactor and blended effluent SDS-DCBAA breakthrough curves for Water 2........... 84

77 Single contactor and blended effluent SDS-CDBAA breakthrough curves for Water 6........... 85

78 Single contactor and blended effluent SDS-TBAA breakthrough curves for Water 7 .............. 85

79 Single contactor and blended effluent SDS-BF breakthrough curves for Water 2 .................... 86

80 Single contactor and blended effluent SDS-CDBAA breakthrough curves for Water 5........... 86

81 Single contactor and blended effluent SDS-BF breakthrough curves for Water 3 (original

step-lag logistic function model curve fit) ................................................................................. 87

82 Single contactor and blended effluent SDS-BF breakthrough curves for Water 3 (fit to

step-lag-peak logistic function model)....................................................................................... 87

83 Cumulative frequency distribution plot of normalized residual sum-of-squares (RSS) for DI

and SCA model predictions...................................................................................................... 100

84 Cumulative frequency distribution plot of normalized bias for DI and SCA model

predictions ................................................................................................................................ 100

85 Comparison of DI and SCA methods for predicting the UV254 integral breakthrough curve

for Water 1 ............................................................................................................................... 101

86 Comparison of DI and SCA methods for predicting the UV254 integral breakthrough curve

for Water 3 ............................................................................................................................... 101

-xxi-
87 Comparison of DI and SCA methods for predicting the SDS-TTHM integral breakthrough

curve for Water 1...................................................................................................................... 102

88 Comparison of DI and SCA methods for predicting the SDS-TTHM integral breakthrough

curve for Water 7...................................................................................................................... 102

89 Comparison of DI and SCA methods for predicting the SDS-HAA5 integral breakthrough

curve for Water 2...................................................................................................................... 103

90 Comparison of DI and SCA methods for predicting the SDS-HAA5 integral breakthrough

curve for Water 7...................................................................................................................... 103

91 Comparison of DI and SCA methods for predicting the SDS-HAA6 integral breakthrough

curve for Water 3...................................................................................................................... 104

92 Comparison of DI and SCA methods for predicting the SDS-HAA6 integral breakthrough

curve for Water 5...................................................................................................................... 104

93 Comparison of DI and SCA methods for predicting the SDS-HAA9 integral breakthrough

curve for Water 5...................................................................................................................... 105

94 Comparison of DI and SCA methods for predicting the SDS-HAA9 integral breakthrough

curve for Water 6...................................................................................................................... 105

95 Comparison of DI and SCA methods for predicting the SDS-TOX integral breakthrough

curve for Water 6...................................................................................................................... 106

96 Comparison of DI and SCA methods for predicting the SDS-TOX integral breakthrough

curve for Water 7...................................................................................................................... 106

97 Comparison of DI and SCA methods for predicting the SDS-CF integral breakthrough

curve for Water 2...................................................................................................................... 107

98 Comparison of DI and SCA methods for predicting the SDS-CF integral breakthrough

curve for Water 6...................................................................................................................... 107

99 Comparison of DI and SCA methods for predicting the SDS-BDCM integral breakthrough

curve for Water 1...................................................................................................................... 108

100 Comparison of DI and SCA methods for predicting the SDS-BDCM integral breakthrough

curve for Water 8...................................................................................................................... 108

101 Comparison of DI and SCA methods for predicting the SDS-DBCM integral breakthrough

curve for Water 3...................................................................................................................... 109

102 Comparison of DI and SCA methods for predicting the SDS-DBCM integral breakthrough

curve for Water 7...................................................................................................................... 109

103 Comparison of DI and SCA methods for predicting the SDS-BF integral breakthrough

curve for Water 1...................................................................................................................... 110

104 Comparison of DI and SCA methods for predicting the SDS-BF integral breakthrough

curve for Water 5...................................................................................................................... 110

105 Comparison of DI and SCA methods for predicting the SDS-DCAA integral breakthrough

curve for Water 1...................................................................................................................... 111

106 Comparison of DI and SCA methods for predicting the SDS-DCAA integral breakthrough

curve for Water 4...................................................................................................................... 111

107 Comparison of DI and SCA methods for predicting the SDS-DBAA integral breakthrough

curve for Water 2...................................................................................................................... 112

108 Comparison of DI and SCA methods for predicting the SDS-BCAA integral breakthrough

curve for Water 8...................................................................................................................... 112

109 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=30; D=0.1)............................................................................................... 117

-xxii-
110 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=30; D=0.05).............................................................................................. 117

111 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=10; D=0.1)................................................................................................ 118

112 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=10; D=0.05).............................................................................................. 118

113 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=10; D=0.2)................................................................................................ 119

114 Integral breakthrough curves for varying numbers of contactors operated in parallelstaggered mode (B=30; D=0.2)................................................................................................ 119

115 Run time as a function of number of contactors in parallel, expressed as percent of run time

for infinite n (B=30; D=0.1)..................................................................................................... 120

116 Run time as a function of number of contactors in parallel, expressed as percent of run time

for infinite n (B=30; D=0.2)..................................................................................................... 120

117 Impact of extrapolation on DI prediction of the TOC integral breakthrough for Water 5....... 124

118 Impact of extrapolation on DI prediction of the TOC integral breakthrough for Water 8....... 124

119 Impact of extrapolation on SCA prediction of the UV254 integral breakthrough for Water 5 125

120 Impact of extrapolation on SCA prediction of the SDS-TOX integral breakthrough for

Water 5 ..................................................................................................................................... 125

121 Impact of extrapolation on SCA prediction of the SDS-TTHM integral breakthrough for

Water 5 ..................................................................................................................................... 126

122 Impact of extrapolation on SCA prediction of the SDS-HAA5 integral breakthrough for

Water 5 ..................................................................................................................................... 126

123 Impact of extrapolation on SCA prediction of the SDS-HAA6 integral breakthrough for

Water 5 ..................................................................................................................................... 127

124 Impact of extrapolation on SCA prediction of the SDS-HAA9 integral breakthrough for

Water 5 ..................................................................................................................................... 127

125 Impact of extrapolation on SCA prediction of the UV254 integral breakthrough for Water 8 128

126 Impact of extrapolation on SCA prediction of the SDS-TOX integral breakthrough for

Water 8 ..................................................................................................................................... 128

127 Impact of extrapolation on SCA prediction of the SDS-TTHM integral breakthrough for

Water 8 ..................................................................................................................................... 129

128 Impact of extrapolation on SCA prediction of the SDS-HAA5 integral breakthrough for

Water 8 ..................................................................................................................................... 129

129 Impact of extrapolation on SCA prediction of the SDS-HAA6 integral breakthrough for

Water 8 ..................................................................................................................................... 130

130 Impact of extrapolation on SCA prediction of the SDS-HAA9 integral breakthrough for

Water 8 ..................................................................................................................................... 130

-xxiii-
-xxivThis page intentionally left blank.
-xxvList of Abbreviations

BCAA Bromochloroacetic acid

BDCAA Bromodichloroacetic acid

BDCM Bromodichloromethane

BF Bromoform

BMRL Below the minimum reporting level

Br:TOC Bromide to TOC ratio

C(t) Effluent concentration

tC )( blended effluent concentration at individual contactor run time, t

C(tf) Last observed data point

C(tp) Measured peak concentration

C0 Influent concentration

CDBAA Chlorodibromoacetic acid

CF Chloroform

Cp Logistic function model best-fit concentration at tp

DBAA Dibromoacetic acid

DBCM Dibromochloromethane

DBP Disinfection byproduct

DCAA Dichloroacetic acid

DCBAA Dichlorobromoacetic acid

DI Direct integration

EBCT Empty bed contact time
-xxviEPA United States Environmental Protection Agency

f Fraction of organic matter remaining in the combined effluent

fi C(t)/C0

GAC Granular activated carbon

HAA Haloacetic acid

HAA5 Sum of five haloacetic acids: MCAA, DCAA, TCAA, MBAA, DBAA

HAA6 Sum of six haloacetic acids: HAA5, BCAA

HAA9 Sum of nine haloacetic acids: HAA6, DCBAA, CDBAA, TBAA

ICR Information Collection Rule

K Adsorption rate coefficient

MBAA Monobromoacetic acid

MCAA Monochloroacetic acid

MCL Maximum contaminant level

MRL Minimum reporting level

n Freundlich isotherm parameter

N Number of contactors

NA Not applicable

nBr Bromine incorporation factor for THMs

NC Adsorption capacity coefficient

n’Br Bromine incorporation factor for HAAs

NOM Natural organic matter

PAS Polyaluminum sulfate
-xxviiqi Throughput of the individual contactor when the treatment objective is

exceeded in the single contactor

qN Throughput of the nth contactor at the time the treatment objective is

exceeded in the blended effluent

R2 Coefficient of determination

RSS Residual sum of squares

RSSCT Rapid small-scale column test

RTBC Blended contactor run time

RTSC Single contactor run time

SAS Statistical Analysis Software

SCA Surrogate correlation approach

SDS Simulated distribution system

SDS-BCAA Bromochloroacetic acid evaluated under SDS conditions

SDS-BDCAA Bromodichloroacetic acid evaluated under SDS conditions

SDS-BDCM Bromodichloromethane evaluated under SDS conditions

SDS-BF Bromoform evaluated under SDS conditions

SDS-CDBAA Chlorodibromoacetic acid evaluated under SDS conditions

SDS-CF Chloroform evaluated under SDS conditions

SDS-DBAA Dibromoacetic acid evaluated under SDS conditions

SDS-DBCM Dibromochloromethane evaluated under SDS conditions

SDS-DBPs Disinfection byproducts evaluated under SDS conditions

SDS-DCAA Dichloroacetic acid evaluated under SDS conditions

SDS-HAA5 The sum of five haloacetic acids evaluated under SDS conditions
-xxviiiSDS-HAA6 The sum of six haloacetic acids evaluated under SDS conditions

SDS-HAA9 The sum of nine haloacetic acids evaluated under SDS conditions

SDS-MBAA Monobromoacetic acid evaluated under SDS conditions

SDS-MCAA Monochloroacetic acid evaluated under SDS conditions

SDS-TBAA Tribromoacetic acid evaluated under SDS conditions

SDS-TCAA Trichloroacetic acid evaluated under SDS conditions

SDS-TOX Total organic halides evaluated under SDS conditions

SDS-TTHM Total trihalomethanes evaluated under SDS conditions

SM Standard Methods

t Service time

tb Run time at which initial breakthrough above detectable levels occurs

TBAA Tribromoacetic acid

TCAA Trichloroacetic acid

THM Trihalomethane

THMs Trihalomethanes

TOC Total organic carbon

TOX Total organic halide

tp Run time at which the peak concentration occurs

TSUVA Specific ultraviolet absorbance based on TOC

TTHM Total trihalomethane

UV254 Ultraviolet absorbance at 254 nm

v Linear velocity
-xxixx Bed depth
-xxxThis page intentionally left blank.
-1-

1 Background and Method Development

Sixty-two public water systems required by the Information Collection Rule (ICR) to perform

treatment studies for disinfection byproduct (DBP) precursor removal fulfilled this requirement

by evaluating granular activated carbon (GAC), while membranes were examined by 36 utilities.

GAC treatment studies included bench-, pilot-, and full-scale studies. Regardless of the scale,

the ICR required that GAC treatment studies evaluate DBP precursor removal in the effluent of a

single contactor as a function of run time, to assess the breakthrough of DBP precursors as the

GAC was exhausted. In practice, full-scale plants can optimize GAC performance and reduce

carbon usage rates by blending the effluents of multiple contactors operated in parallel prior to

disinfection. To optimize the blending process, replacement of the GAC in each contactor is

staggered at regular intervals. By doing so, the service life of each contactor is extended because

water from contactors that exceed the treatment objective is blended with water from contactors

with fresher GAC that have effluent concentrations below the treatment objective. A cost

analysis based on single contactor run times will overestimate the actual treatment costs of fullscale operation of multiple contactors operated in parallel-staggered mode.

Previous researchers have developed mathematical relationships to characterize the water quality

in the blended effluent based on single contactor breakthrough curves. These relationships yield

a blended contactor effluent or integral "breakthrough" curve that provides a measure of the

extended run time for which each contactor can be operated to meet a treatment objective in the

blended effluent. The integral breakthrough curve does not directly represent blended effluent

water quality, but is a tool for determining the GAC replacement frequency for each individual

contactor (of multiple contactors operated in parallel-staggered mode) to maintain the blended

effluent below the treatment objective. For example, the results of a GAC study show that based

on a single contactor breakthrough curve, GAC effluent formed DBP levels are maintained

below the treatment objectives for 80 days of operation. This is the single contactor run time,

RTSC. Since full-scale implementation of GAC will involve multiple contactors operated in

parallel-staggered mode, integral breakthrough curves for each parameter are developed based on

the single contactor breakthrough curves. The integral breakthrough curves might show that the

level of DBPs formed in the GAC blended effluent are maintained below the treatment

objectives for 160 days of operation, indicating that each individual contactor can be operated for

160 days while maintaining the plant blended effluent water quality below the treatment

objective. The blended contactor run time (RTBC) is 160 days.

The basic method utilized to determine the integral breakthrough curve has been a mathematical

or numerical integration of the single contactor breakthrough curve. A minimal amount of

experimental verification has been performed to verify this approach. Some bench-scale

experimental verification data has been presented (Chowdhury et al., 1996; Summers et al.,

1998), whereby mathematical integration was simulated experimentally by continuously

collecting the effluent from a bench-scale GAC contactor in a large reservoir, and sampling from

this reservoir over time. An extensive verification study is needed to thoroughly evaluate the

appropriateness of the integration method for predicting the integral breakthrough curve.
-2-

1.1 Optimization of GAC Operation

The GAC in a contactor has to be replaced when the mass transfer zone begins to exit the column

as shown in Figure 1, and the effluent concentration exceeds the treatment objective. However,

at this point only part of the GAC bed is saturated and replacement of the GAC will result in

high carbon use rates (Snoeyink, 1990). Two common methods of lowering carbon usage rates

are to operate contactors in series or to operate multiple contactors in parallel with staggered

GAC replacement cycles.

For adsorption of micropollutants, the amount of water treated per mass GAC can be increased

by operation of two contactors in series. In this mode of operation, two contactors are operated

in series until the treatment objective is exceeded in the second contactor. At this point, the

GAC in the first contactor is replaced with virgin or reactivated GAC, and valves are switched so

that the second contactor is now operated in-line ahead of the first contactor. This cycle is

repeated to maintain effluent levels below the treatment objective. For efficient operation, the

mass transfer zone should be contained within the bed length of one contactor. This can be

achieved using reasonable bed lengths for adsorption of micropollutants, but the mass transfer

zone for TOC removal (and therefore DBP precursor removal) is usually too long. For DBP

precursor control, operation of two contactors in series does not result in significantly longer run

times over single contactor operation (Sontheimer et al., 1988).

Multiple contactors operated in parallel and staggered in terms of GAC replacement times, as

shown in Figure 1, yield blended effluent concentrations as shown in Figure 2. When the lead

contactor, which has been in operation the longest, is taken off-line and replaced with a contactor

with fresh GAC, the blended effluent concentration decreases as shown in Figure 2. The level of

this decrease is dependent on the number of contactors in operation. For two contactors, the

concentration will decrease by 50 percent, while for an infinite number of contactors the

decrease approaches zero.

Due to the blending of waters from multiple contactors producing varying levels of effluent

water quality, individual contactors can be operated past the point at which the effluent water

they are producing exceeds the treatment objective, because the treatment objective must be

maintained in the blended effluent. Therefore, evaluation of single contactor breakthrough curve

data will result in overestimates of the carbon usage rate for a full-scale system operating

multiple staggered contactors in parallel-staggered mode. For DBP precursor control, contactor

effluents are blended prior to disinfection.

1.2 Modeling the Operation of Multiple Contactors Operated in ParallelStaggered Mode

In modeling the operation of multiple contactors operated in parallel-staggered mode, the goal is

not to simulate the actual blended effluent water quality during normal operation (as shown in

Figure 2), but to derive the integral breakthrough curve. The integral breakthrough curve is a

tool used to determine the GAC replacement frequency for each individual contactor to maintain

the blended effluent below the treatment objective: it is a curve that relates single contactor run

time to blended contactor effluent water quality. Multiple contactor throughput to a treatment

objective can be estimated from the integral breakthrough curve by determining the operation
-3-

Figure 1 Schematic of blending of multiple GAC contactors operated in parallel-staggered

mode

Figure 2 Blended effluent water quality during quasi steady-state operation of multiple

contactors in parallel-staggered mode (adapted from Summers et al., 1998)

Influent water

Blended effluent

Disinfectant

Finished

water

Operation time

Blended effluent

concentration

Blended effluent treatment objective

New contactor

placed on-line
-4-

time when the curve intersects the treatment objective, as is done with single contactor

breakthrough curves.

Figure 3 shows a series of graphs that describe how the integral breakthrough curve is developed.

Graphs A through E depict eight GAC contactors on-line in parallel-staggered mode. For

simplification, identical breakthrough curves are assumed for each contactor. The interval

between GAC replacement, or the single contactor run time (RTSC), is increased from 16 to 99

days over these five graphs. At each RTSC, the single contactor effluent concentration, C(t), is

given as well as the blended contactor effluent concentration, C(t), which is calculated by

averaging the effluent water quality of each of the eight contactors at RTSC (shown as a short

dotted line representing the intersection of effluent concentration at RTSC). The dashed line

breakthrough curve represents the contactor that replaces the first contactor when it has reached

the end of its service life. As RTSC is increased, moving from Graph A through E, the C(t) at

RTSC increases. Therefore, the GAC replacement interval affects the blended contactor effluent

water quality. Specifically, as shown graphically by the integral breakthrough curve in Graph E,

as RTSC increases, blended effluent water quality declines. Using the integral breakthrough

curve, the single contactor run time at which the treatment objective is exceeded in the blended

effluent can be determined.

Assuming a linear breakthrough curve, Westrick and Cohen (1976) modeled the impact of

parallel contactor operation on blended effluent water quality, and derived the following

equation:

2

+1 = N

q N

q i

N (1)

where N is the number of parallel contactors, qN is the specific throughput of the Nth contactor at

the time the treatment objective is exceeded in the blended effluent, and qi is the throughput of

the individual contactor when the treatment objective is exceeded in that single contactor. For

large N, Equation 1 shows that qN approaches twice qi: the run time of each contactor when the

blended effluent treatment objective is exceeded will approach twice that of a single contactor

when the treatment objective is exceeded in the single contactor effluent. For a finite N, such as

10 contactors, qN is a factor of 1.8 times greater than qi.

Equation 1 establishes a relationship between the specific throughput of the Nth contactor at the

time the treatment objective was exceeded in the blended effluent and the throughput of a single

contactor to that same treatment objective, assuming a linear breakthrough curve. For large N,

Equation 1 shows that the run throughput of each contactor approaches twice that for a single

contactor:

i q∞ = 2q (2)

where q∞ is the throughput of each contactor when an infinite number of contactors are operated

in parallel-staggered mode. The relationship between q∞ and qi expressed in Equation 2 can be

substituted into Equation 1:
-5-

Figure 3 Operation of multiple contactors in parallel staggered mode to various single contactor run times: 

the blended effluent integral breakthrough curve

0.0.20.40.60.81.01.2

0 25 50 75 

Operation time, days

Concentration, 

C

C(RTSC ) = 0.64

C(RTSC ) = 0.99

(D)

RTSC = 61 days

0.0.20.40.60.81.01.2

0 25 50 75 10

Operation time, days

Concentration, 

C

RTSC = 16 days

C(RTSC ) = 0.11

C(RTSC ) = 0.21

(A)

Identical breakthrough curves represent 8 single contactors operated in parallel-stagered mode

0.0.20.40.60.81.01.2

0 25 50 75 10

Operation time, days

Concentration, 

C

C(RTSC ) = 0.31

C(RTSC ) = 0.67

(B)

RTSC = 31 days

0.0.20.40.60.81.01.2

0 25 50 75 10

Operation time, days

Concentration, 

C

0.51 C(RTSC ) =

C(RTSC ) = 0.94

(C)

RTSC = 46 days

0.0.20.40.60.81.01.2

0 25 50 75 

Operation time, days

Concentration, 

C

C(t)

C(t)

A

B

C

D

Blended 

break

(n = 

Single contactor 

breakthrough curve

0.0.20.40.60.81.01.2

0 25 50 75 

Operation time, days

Concentration, 

C

C(RT ) = 0.80 SC

C(RTSC ) = 1.00

(E)
-6-

∞ + = q N

N

qN 1 (3)

Therefore, under the linear breakthrough curve assumption, when 9 contactors are operated in

parallel-staggered mode, the throughput of each contactor when the blended effluent exceeds the

treatment objective will be 90 percent of that for an infinite number of contactors operated in

parallel-staggered mode. The analysis assumes a linear breakthrough curve and shows that N90 is

independent of the magnitude of the treatment objective.

Roberts and Summers (1982) examined the impact of contactor operation in parallel-staggered

mode on the run times of individual contactors. The authors showed that the fraction of organic

matter remaining in the combined effluent, f , could be estimated from a single contactor

breakthrough curve, assuming regular GAC replacement intervals:

∑

=

=

n

i

if

n

f

1

1 (4)

where N is the number of contactors and fi is the fraction, C(t)/C0, of organic matter remaining in

the effluent of the ith contactor, determined from a breakthrough curve. A plot of the integral

breakthrough curve over operation time provides an estimate of the service time of each

contactor in a multiple contactor scenario for a treatment objective. This relationship is referred

to as the integral breakthrough curve, and the development of this curve is explained graphically

in Figure 3.

1.3 Single Contactor Breakthrough Curve Models

A model used to describe single contactor effluent experimental data is needed for several

reasons. From a data management perspective, best-fit curve parameters that adequately

describe experimental data are less memory intensive than storing the entire experimental data

set. A best-fit curve also facilitates interpolation and extrapolation necessary to estimate run

times for given treatment objectives. Use of a best-fit model curve also provides an estimate of

the scatter in the data through the coefficient of determination, and the model minimizes the

impact of this scatter on run time estimates. Finally, a function that describes the single

contactor breakthrough curve is a prerequisite for calculating the integral breakthrough curve, a

curve that relates single contactor run time to blended effluent water quality under the

assumption that an infinite number of contactors are operated in parallel-staggered mode. Run

time estimates generated by the integral breakthrough curve are more applicable to full-scale

GAC operation where multiple contactors are operated in parallel-staggered mode to increase the

service time of each individual contactor.

Many researchers have applied various forms of the logistic function to predict GAC

breakthrough curves or to fit existing breakthrough curve data. The logistic function is a

symmetric S-shaped curve with a midpoint inflection. Oulman (1980) describes the

development of the Bohart-Adams equation, published in 1920, which was used to model the

service life of activated carbon for the removal of airborne chlorine by gas masks. The BohartAdams equation is:
-7-

KC t

v

KN x

C t

C c 0

0

( ) 1

ln = − 















− (5)

where C(t) is the effluent concentration at time t, C0 is the influent concentration, K is an

adsorption rate coefficient, Nc is an adsorption capacity coefficient, x is the bed depth, v is the

linear velocity, and t is the service time. The Bohart-Adams equation is a bed depth service

model and was derived based on surface reaction theory (Clark, 1987). Equation 5 can be

rewritten in the form of the logistic function:

-(a bt) C t e

C

+ + =

1

1

( )

0 (6)

where the variables a and b are defined as:

v

KN x

a − c = (7)

and

b = KC0 (8)

If the adsorption coefficients K and Nc are known, as well as other operational parameters

(velocity and depth), the effluent concentration at time t can be predicted by Equation 6.

Because GAC breakthrough curves are generally not symmetrical, Clark (1987) proposed the use

of the generalized logistic function to model GAC breakthrough curves. This generalized model

incorporates the Freundlich isotherm parameter and is as follows:

1

1

1

0

1

( ) −

−

−

















+ = n

rt

n

Ae

C C t (9)

where 1/n is the Freundlich isotherm parameter, r is a constant, and A, a constant, is defined as:

rt

n

e

C t

C A −

−

















− 













 = 1

( )

1

0 (10)

The derivation of this generalized logistic function is described in Clark, Symons, and Ireland

(1986). Their approach, which builds on the work of Oulman, is an effort to predict GAC

breakthrough curve profiles based on adsorption characteristics and influent concentration.

A predictive approach to single contactor GAC breakthrough curves was not examined as a part

of this study. Instead, a model was needed to curve fit experimental breakthrough data. Due to

the inherent ability of the logistic function to match the typical S-shaped breakthrough curve, and

the previous body of work that has utilized the logistic function to model GAC breakthrough

data, the logistic function was chosen as the equation used to fit GAC breakthrough data in this

study.
-8-

Chowdhury et al. (1996) and Summers et al. (1998) applied the following form of the logistic

function to model experimental GAC breakthrough data:

Dt Be

A C t − + =

1

( (11) )

where the values for A, B, and D are determined experimentally by a best-fit to the breakthrough

data. The parameter A represents the level to which the function approaches asymptotically.

Parameters B and D affect the shape of the curve. Equation 11 was found to adequately fit GAC

breakthrough curves for three water sources and the parameters total organic carbon (TOC) and

formed total trihalomethane (TTHM). With a few modifications, as described in Section 3.2.1

below, Equation 11 served as a basis for the single contactor breakthrough curve modeling work

contained in this study.

1.4 Direct Integration Approach

The average value function, a mathematical integration, assumes an infinite number of parallelstaggered contactors and replaces the numerical integration required for solving Equation 4.

However, it is important to understand the impact of the infinite contactor assumption on model

results. Based on Equation 1, nine contactors in parallel will yield qN=9 within 10 percent of q∞ .

Therefore, for 10 contactors or greater, the difference will be less than 10 percent, based on the

linear breakthrough curve assumption. Additionally, Westrick and Cohen (1976) found that the

carbon usage rate for individual contactors operated in parallel-staggered mode will approach

half the carbon usage rate based on meeting the treatment objective using a single contactor.

Roberts and Summers (1982) examined integral TOC breakthrough curves applied to eight case

studies, and found that GAC run times increased by a factor of two to three over those based on

the single contactor breakthrough curve, for a 50 percent TOC breakthrough objective. They

concluded that staggered multiple parallel contactor operation will lower carbon usage rates even

more than predicted by Westrick and Cohen, whose conclusions were based on a linear

breakthrough curve.

Chowdhury et al. (1996) and Summers et al. (1998) applied the average value function to show

that the integral breakthrough curve can be represented by:

∫ =

t

C t dt

t

C t

0

( ) 1 ( (12) )

where C(t) is the blended effluent concentration at individual contactor run time, t, and C(t) is

an equation that describes the single contactor effluent concentration as a function of time. This

time-averaged mathematical integration of the function that describes the breakthrough curve

yields the integral breakthrough curve, as it describes the average value of the function at any

point in time. This procedure is referred to here as the direct integration (DI) approach. While

Equation 4 provides an expression of the average blended concentration of N contactors in

parallel, Equation 12 represents a time-averaged blended effluent concentration of an infinite

number of staggered parallel contactors. Therefore, a plot of Equation 12 over operation time

represents the run time to any given treatment objective in the blended contactor effluent for
-9-

each individual contactor of an infinite number of parallel-staggered contactors. This plot is not

a direct representation of blended effluent water quality, but a tool to determine GAC run times

of each contactor operated in parallel-staggered mode.

Chowdhury et al. (1996) and Summers et al. (1998) applied Equation 12 to Equation 11 to

predict the integral breakthrough curve. Application of the DI approach utilizing different forms

of the logistic function (Equation 11) is described below in Section 3.2.3.

1.5 Surrogate Correlation Approach

Theoretically, the direct integration approach is a reliable method for estimating blended effluent

water quality. However, during the analysis approach for the ICR GAC treatment study data, the

treatment study technical work group (TS-TWG) determined that it would be computationally

intensive to apply the DI approach to the large number of breakthrough curves (8,000 to 9,000)

comprising the ICR treatment study data set. Furthermore, the DI approach may be less reliable

for breakthrough curves that do not follow the typical "S" pattern, such as "peak" curves or sharp

"S" shaped curves followed by a plateau. For these reasons, another approach for estimating

blended effluent water quality from single contactor data was developed for analysis of the ICR

treatment study GAC data.

The surrogate correlation approach (SCA) was developed by the TS-TWG as an alternative to

the direct integration approach for calculating the integral breakthrough curve (USEPA, 1999).

The SCA is based on the assumption that the relationship between TOC and all other parameters

(UV254 and SDS-DBPs) in the single contactor effluent is maintained in the blended effluent of

multiple staggered contactors. In other words, the concentration and speciation of DBPs formed

after chlorination of the blended effluent of multiple contactors with a given TOC concentration

is the same as that formed after chlorination of the single contactor effluent with the same TOC

concentration. This method of estimating blended effluent water quality from single contactor

data requires that an integral breakthrough curve be determined based on the single contactor

TOC breakthrough only. Once this curve is known, along with a relationship between TOC and

all other parameters in the single contactor effluent, integral breakthrough curves are estimated

for all other parameters. This approach not only allows for the evaluation of blended contactor

run times to various breakthrough criteria, but also for the occurrence of other DBPs at that run

time. For example, it will be important to evaluate the levels of individual DBPs, such as

BDCM, at various regulatory targets under consideration. This will allow EPA to assess whether

or not regulating one DBP or DBP group can effectively control the occurrence of other DBPs or

DBP groups.

An example of the SCA procedure is described in the steps below and summarized graphically in

Figure 4.

1. Select a treatment objective (e.g., TTHM = 32 μg/L).

2. Use the single contactor breakthrough curve for the parameter of interest and determine the

single contactor run time (RTSC) at which the treatment objective is exceeded.
-10-

SDS-TTHM

Chosen treatment

objective (Step 1)

C(t)

Run time, t

RTSC

SDS-HAA6

C(t)

Run time, t

SDS-CF

C(t)

Run time, t

(All measured parameters)

TOC

C(t)

Run time, t

TOC

C(t)

Run time, t

RTBC

TOC integral breakthrough curve

calculated using direct integration

approach

Step 3 (repeat for all

measured parameters)

Step 4 Step 5

Figure 4 Graphical summary of SCA procedure used for base analysis of GAC

treatment studies (adapted from USEPA, 1999)

Determine single contactor run time,

RTSC, to treatment objective (Step 2)

Logistic function best-fit of single

contactor experimental data
-11-

3. Use the RTSC from Step 2 to determine the concentrations of all other parameters from the

single contactor breakthrough curves. (In this step, the single contactor effluent

concentrations of all parameters are "linked" through the single contactor run time, RTSC).

4. Use the RTSC from Step 2 to determine the single contactor effluent TOC concentration that

corresponds to the treatment objective.

5. From the integral TOC breakthrough curve, calculated by the DI approach, determine the

blended contactor run time (RTBC) to reach the TOC concentration calculated in step 4. This

is the only point in the analysis where it is necessary to apply the DI method to establish an

integral breakthrough curve, and only the TOC integral breakthrough curve is required. If

the integral breakthrough curve does not exceed the TOC concentration calculated in Step 4,

extrapolation of the TOC integral breakthrough curve may be required.

This analysis makes the following assumptions:

1. The logistic function model can accurately describe the breakthrough of DBP precursors and

DBP precursor surrogates,

2. The relationship between TOC and DBP precursors (concentration and speciation) observed

in the single contactor effluent is maintained in the blended effluent,

3. The TOC breakthrough curve can be extrapolated with reasonable results,

4. The TOC integral breakthrough curve accurately predicts the blended water quality of an

infinite number of multiple contactors operated in parallel-staggered mode. Furthermore, the

TOC integral breakthrough curve based on an infinite number of contactors is a reasonable

approximation to a finite numbers of contactors.

All four of these assumptions are verified as a part of this study.

1.6 Impact of Bromide Concentration on GAC Effluent Blending Models

The second assumption listed for the SCA procedure is that the relationship between TOC and

DBP precursors (concentration and speciation) observed in the single contactor effluent is

maintained in the blended effluent This is an important assumption because the SCA applies

DBP formation and speciation at a given single contactor run time and TOC concentration to the

blended contactor run time at which an equivalent TOC concentration is achieved. The relative

concentrations of TOC and bromide may influence DBP formation and speciation.

A number of researchers have discussed the impact of bromide concentration on DBP formation

and speciation. Under constant chlorination conditions, and at a constant TOC concentration, as

the bromide concentration increases the bromide to TOC ratio increases and DBP speciation

shifts to favor the formation of brominated species (Summers et al., 1993). GAC treatment does

not remove bromide, while TOC is adsorbed, resulting in higher GAC effluent bromide to TOC

ratios in the GAC effluent as compared to those in the GAC influent. Due to this increase, GAC

effluent formed DBPs may undergo shifts in speciation to higher fractions of the more

brominated DBP species. In some cases effluent formed DBP species concentrations are

measured higher than those formed in the influent. It is important to track the breakthrough
-12-

behavior of specific DBP species, because some may be of potential health concern and a MCL

could be set for a specific DBP species.

The shift in DBP speciation for THMs can be measured by calculating the bromine incorporation

factor for THMs, nBr (Gould et al., 1983):

TTHM

i CHCl Br

n i

i i

Br

∑

=

− ⋅

=

3

0

3

(13)

where all concentrations (of species and TTHM) are expressed as molar concentrations. The

value of nBr can range from 0 (only chloroform formed) to 3 (only bromoform formed). Based

on the bromine incorporation factor for HAA6 (Shukairy et al., 1994), the bromine incorporation

factor, n'Br, for HAA9 is defined as:

9

1 1 1 2 2 3 ' HAA

MBAA BCAA DCBAA DBAA CDBAA TBAA

n Br

⋅ + ⋅ + ⋅ + ⋅ + ⋅ + ⋅ = (14)

where all concentrations (of species and HAA9) are expressed as molar concentrations.

The value of n'Br can range from 0 (only MCAA, DCAA, or TCAA formed) to 3 (only TBAA

formed). Examining and comparing nBr and n'Br values between single contactor and blended

effluent will help determine whether the second SCA method assumption described in Section

1.5 is valid.

1.7 Effluent Blending Modeling of Fewer than 10 Contactors

Both the DI and SCA methods for determining integral breakthrough curves rely on the

assumption of an infinite number of contactors operated in parallel-staggered mode. For 10 or

more contactors in parallel-staggered operation, the integration presented in Equation 12

approximates blended effluent water quality within 10 percent (Roberts and Summers, 1982).

Equation 12 is utilized exclusively for the DI approach, while the SCA approach relies on

Equation 12 to establish the integral breakthrough curve for TOC as part of the analysis.

Therefore, it is important to examine the impact of the infinite number of contactors assumption

on the ability of these methods to predict blended contactor effluent water quality for a finite

number of contactors. Furthermore, for smaller plants that operate fewer than 10 contactors in

parallel-staggered mode, the approximation given by Equation 12 will be less accurate and actual

service lives will be shorter than those predicted by Equation 12.

1.8 GAC Breakthrough Curve Extrapolation

The SCA procedure is limited by the highest TOC concentration reached by the TOC integral

breakthrough curve, which is typically 40 to 70 percent of the highest single contactor TOC

concentration. Therefore, although higher single contactor TOC concentrations are associated

with formed DBP concentrations, these cannot be applied to the integral breakthrough curve

during application of the SCA procedure unless the TOC integral breakthrough curve is

extrapolated. It is therefore important to establish the impact of extrapolation of the TOC
-13-

integral breakthrough curve on estimated run times to treatment objectives and blended contactor

water quality. The sensitivity of the predicted integral breakthrough curve to extrapolation was

evaluated for two waters in this study.

1.9 ICR GAC Treatment Study Data Analysis Context

The design of this study incorporated two main goals. The primary objectives were to evaluate

the ability of the logistic function to model single contactor breakthrough curve data and to

evaluate the success and limitations of predictive models used to determine the integral

breakthrough curve, a relationship between single contactor run time and blended contactor

water quality. The secondary objective of this study was to evaluate the applicability of these

models and predictive methods in the context of the ICR GAC treatment study data analysis.

A large amount of ICR treatment study data will be analyzed: the 62 GAC treatment studies

performed will generate a total of 8,000 to 9,000 individual breakthrough curves. The SCA

method is especially applicable to this data analysis procedure because it minimizes the

computations necessary to estimate blended contactor run times for treatment objectives. An

assessment of the concentration of other DBPs at any given treatment objective will be required

as part of the data analysis effort, and the SCA procedure is also suited to this task. The SCA

procedure requires that GAC breakthrough curves for all measured parameters be represented by

the logistic function model curve fit. By doing so, a smaller amount of data are needed to

represent breakthrough curve experimental data. The following steps outline the data analysis

procedure to be utilized during the ICR treatment study data analysis effort:

1. The logistic function model will be used to fit all water quality parameters in the GAC

effluent.

2. The TOC integral breakthrough curve will be determined using the DI approach. In some

cases, this curve may be extrapolated.

3. All logistic function model fit coefficients will be entered into a database to allow different

breakthrough criteria to be evaluated and queried across all studies.

4. The SCA procedure will be used to estimate run times for multiple contactors operated in

parallel-staggered mode by determining the effluent concentrations of various water quality

parameters linked to a common single contactor run time, and calculating the blended

effluent run time that corresponds to the TOC concentration. In this manner, simultaneous

treatment objectives, such as THM or HAA regulatory target concentrations, can be

evaluated to determine which parameter controls the design and operation of the process.

5. Results will be used to evaluate blended contactor run times that meet regulatory target

treatment objectives, and this information can be used to estimate costs. Simultaneous

treatment objectives will also be evaluated (e.g., HAAs and THMs, TOC and THMs, THMs

and BDCM).
-14-

1.10 Appropriateness of Model Assumptions to Full-Scale GAC Effluent Blending

Both the DI and SCA methods rely on the assumption that the GAC in each contactor of an array

of contactors is replaced at regular intervals, so that the service times of all contactors are equal.

They also assume that the breakthrough curve profiles of all single contactors are identical. In a

full-scale plant, these idealized conditions will rarely occur. Variability in source water quality

may impact the run time of the contactors, depending on when they are placed in service and

DBP precursor concentrations in the GAC influent during their service life. Variability in

distribution system conditions, especially temperature, may impact the contactor service life, as

DBP levels may increase with higher temperatures.

For a plant that operates a fixed number of contactors, water demand changes during the year

may directly impact the EBCT of each contactor, or the number of contactors in operation.

Under operation of a constant number of contactors, a contactor that is placed on-line at the

beginning of the summer high water demand months may be operated under a shorter EBCT as

compared to a contactor placed on-line during the winter. Furthermore, it is less desirable to

remove contactors from service to replace GAC during high demand periods. Another approach

is to increase the number of contactors on-line as water demand increases.
-15-

2 Study Objectives and Approach

This study was performed in conjunction with bench-scale GAC treatment studies that were

performed at one laboratory in fulfillment of ICR requirements for eight utilities. It was

designed to examine the following experimental objectives:

1. Assess the ability of the logistic function model to fit single contactor breakthrough data for

eight GAC runs using eight water sources and all measured parameters, including DBP

surrogates, DBP class sum parameters, and DBP species. The water sources represent a

range in TOC concentrations, DBP precursor levels, bromide concentrations, and SDS

chlorination conditions.

2. Verify through bench-scale experiments the accuracy of the direct integration (DI) method

for establishing the integral breakthrough curve, a relationship between single contactor

operation time and blended effluent water quality.

3. Examine the accuracy of the computationally-simpler surrogate correlation approach (SCA)

to predict the integral breakthrough curves of all measured parameters. Verify a basic

assumption of the SCA procedure, that the relationship between DBP formation and TOC

concentration in the single contactor effluent and in the blended effluent is maintained.

4. Examine the impact of GAC effluent blending on DBP speciation. Assess the accuracy of

models used to predict the integral breakthrough curve for individual THMs and HAAs, and

compare bromine incorporation into DBP formation in the single contactor and blended

contactor effluents.

5. Evaluate the impact of extrapolation of the integral breakthrough curve on blended effluent

water quality.

6. Verify the accuracy of the integral breakthrough curve predictive models, which assume an

infinite number of contactors, for the prediction of the service life of finite numbers of

contactors.
-16-

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-17-

3 Materials and Methods

3.1 Experimental Approach

3.1.1 Rapid Small-Scale Column Test

The performance of full-scale GAC contactors was simulated using the rapid small-scale column

test (RSSCT). Previous studies have shown that diffusion of natural organic matter (NOM) to

adsorption sites on GAC is proportional the particle size (Crittenden et al., 1989; Sontheimer et

al., 1988). Therefore, by grinding the GAC to a smaller size, rates of adsorption are increased in

proportion to the ratio of full-scale to RSSCT GAC particle sizes, or scaling factor. The scaling

factor also relates the RSSCT EBCT and superficial velocity to the full-scale contactor. If the

RSSCT utilized the full-scale contactor bed length, extremely long columns requiring very high

inlet pressures would be required. However, studies have shown that adjustments to the RSSCT

Reynold's number utilized (between 0.1 and 1.0) have a negligible impact on results. Therefore,

by decreasing the RSSCT Reynold's number, a much shorter column can be designed (with

consequently shorter superficial velocities to maintain a constant EBCT). Complete details on

the RSSCT design for precursor removal studies can be found in the literature (USEPA, 1996;

Summers et al., 1995; Summers et al., 1992; Crittenden et al., 1991).

The RSSCTs designed in this study followed the guidelines outlined in the GAC Precursor

Removal Studies section of the ICR Manual for Bench- and Pilot-Scale Treatment Studies

(USEPA, 1996). A summary of the RSSCT design used for each run is given in Table 1. For

most waters, a 20 minute full-scale EBCT contactor was simulated. However, 15 minute and 7.2

minute EBCT contactors were simulated for Waters 1 and 8, respectively. The designs were

based on the estimated or known GAC influent TOC concentration (which can directly impact

the rate of breakthrough), the full-scale water temperature at the time of sampling, the full-scale

GAC particle size, and the full-scale EBCT simulated. The full-scale bed porosity was assumed

to be 0.45 for all runs. The minimum Reynold's number used ranged from 0.48 to 0.60.

3.1.1.1 GAC Preparation Procedures

Representative batches of Filtrasorb 300 (F-300), and Filtrasorb 400 (F-400), bituminous coalbased GAC, were obtained from the manufacturer, Calgon Carbon Corporation. The

representative batch of the reactivated GAC/virgin GAC blend used for Water 4 was obtained

directly from the utility. Using a riffle splitter, a small (30 to 50 g) representative sample of the

GAC was obtained. Using a jar mill, the GAC was ground to the needed mesh size. Care was

taken to frequently remove and sieve the GAC in the jar mill. The GAC was ground until the

entire sample passed through the upper mesh size sieve. Usually, a recovery of 25 to 30 percent

was obtained, as defined by the amount of GAC retained between the two mesh size sieves and

divided by the total amount of GAC prior to grinding.

The ground GAC was transferred to a beaker, and covered with reagent grade (adsorbeddeionized) water. The GAC was washed by repeated additions and decantations of reagent grade
-18-

Design parameter

123456

GAC manufacturer Calgon Carbon Co. Calgon Carbon Co. Calgon Carbon Co. Reac/virgin blend Calgon Carbon Co. Calgon Carbon Co. Calgon Carbon Co. Calgon CGAC brand name F-40 F-30 F-40 F-30/TC-30 F-40 F-30 F-40 F-40GAC type Bituminous Bituminous Bituminous Bituminous Bituminous Bituminous BituGAC mesh size 12x40 8x30 12x40 75% 8x25/% 8x30 12x40 8x30 12x40 12x40Particle diameter, d LC (mm) 1.063 1.480 1.063 1.52 1.063 1.480 1.063 1.063

General design parametersMinimum Reynold's number, Re SC, min () 0.5 0.5 0.5 0.59 0.48 0.5 0.6 0.5

Full-scale operating temperature (°C) 25 20 28 24 18 20 24 Kinematic viscosity, νLC (m 2/s) 8.93E-07 1.0E-06 8.36E-07 9.14E-07 1.05E-06 1.0E-06 9.14E-07 1.0E-06

Bed porosity, εLC () 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45

Measured dry bed density, ρSC (g/cm 3) 0.462 0.46 0.49 0.47 0.468 0.467 0.467 0.439

RSSCT design parametersRSCT mesh size 10x20 10x20 140x230 10x20 140x230 10x20 10x20 140x230Particle diameter, d SC (mm) 0.13 0.13 0.85 0.13 0.85 0.13 0.13 0.85

Scaling factor, SF 9.4 13.6 12.57 13.52 12.57 13.6 9.4 12.57Hydraulic loading rate, v SC (m/hr) 6.5 7.23 8.01 7.6 9.62 7.23 7.89 9.62

Column diameter, D SC (mm) 12.6 9.0 10. 10. 10. 10. 12.6 9.0

Flow rate, Q SC (mL/min) 13.6 7. 10.5 10.2 12.6 9.5 16.4 10.2

Full-scale empty-bed contact time, EBCT LC (min) 15 20 20 20 20 20 20 

Estimated full-scale run time, t LC T (days) 126 321 321 253 24 305 14 176

Estimated RSCT run time, t SC T (days) 13.4 24. 25. 18.7 19.3 23. 12.0 14.0

Volume water required, V SC (L) 263 269 385 274 350 315 284 206

Mass GAC required, m SC (g) 9. 5.43 7.50 7.06 9.38 6.72 16.2 2.5

RSCT empty-bed contact time, EBCT SC (min) 1.59 1.52 1.59 1.48 1.59 1.52 2.1 0.57

Bed length, l SC (cm) 17.4 18.3 21. 19. 25. 18.3 27.9 9.2

Table 1 Summary of RSSCT design parameters for all runs

Design value for each water
-19-

water. The reagent grade water was added at a high rate and turbulence, to stir up the GAC and

release fines. The supernatant water containing GAC fines was decanted after the GAC was

allowed to settle. Towards the end of the cleaning procedure, the sample was sonicated twice for

5 to 10 seconds. The sonication step helped loosen fines that were subsequently removed by the

addition and decantation of reagent grade water.

The GAC was dried in an oven at 80 to 90°C for 6 to 12 hours. The temperature was then raised

to between 100 and 110°C and the sample was dried until it reached a constant weight. The

sample was removed and cooled inside a dessicator. Once cooled, if not immediately used, it

was stored in a glass vial sealed with a lid with Teflon-lined septum until ready for use.

The dry bed density was measured using a sample of dried and cooled GAC. Stored GAC was

dried in an oven as described above prior to the dry bed density measurement. To measure the

dry bed density, a sample of the GAC was placed inside a 10-mL glass graduated cylinder to a

level of 5 to 9 mL. The cylinder was tapped to pack the GAC. A volume was measured and

recorded. This GAC was then weighed on a balance. The volume reading of the graduated

cylinder was checked and calibrated if necessary by adding a known volume of water to it using

a 10-mL class A graduated pipette. The GAC dry bed density was calculated by dividing the

weight by the calibrated volume.

The calculated mass of GAC for each RSSCT was weighed, placed inside a clean beaker, and

covered with reagent grade water. The wetted GAC was usually allowed to sit for 12 to 24

hours, followed by placement in a vacuum for at least 1 hour to displace the air within the pores.

3.1.1.2 RSSCT Column Setup

The ground GAC used for the RSSCT was packed in glass chromatography columns. Due to the

range of GAC influent TOC concentrations, which correlates to the rate of TOC breakthrough,

columns with inner diameters ranging from 8.0 to 12.6 mm were utilized. The 8.0 and 11.0 mm

inner diameter columns were standard. Other column diameters (9.0, 10.0, and 12.6 mm) were

custom-ordered and the GAC required additional support to ensure the GAC was within the

effective length of the column. The GAC support for these special order columns consisted of a

stainless steel screen (60 or 100 mesh size), Teflon beads, glass wool, and two stainless steel

screens. The support for 8.0 and 11.0 mm inner diameter columns consisted of two stainless

steel screens placed on top of the Teflon fitting. The mesh size of the screens utilized were

based on the ground GAC mesh size. For 100x200 GAC, a 100 mesh screen and a 200 mesh

screen were used. For 140x230 GAC, a 200 mesh screen and a 325 mesh screen were used. For

all column inner diameter sizes, a small amount of glass wool was placed inside the Teflon

fitting, supported by a 60 mesh size stainless steel screen.

The GAC was added to the columns as a slurry and packed by repeatedly tapping the column

sides. The 20 minute full-scale equivalent EBCT RSSCTs were packed into two columns of the

same inner diameter placed in series. Only reagent grade water was used during the packing

process.
-20-

3.1.1.3 Batch Influent Preparation

Prior to RSSCT testing, all water samples were filtered through a 1.0-μm nominal pore size glass

fiber cartridge filter. The cartridge filter was pre-rinsed with deionized water. Dilute solutions

of sulfuric acid and sodium hydroxide were used to maintain the influent pH within 0.1 pH units

of the target pH during operation of the RSSCTs.

3.1.1.4 RSSCT Monitoring

The effluent flow rates were monitored constantly to ensure that the flow rates were maintained

within 5 percent of the design flow rate. The calibration of the effluent flow rate control system

was checked at least three times daily and adjusted when a flow rate differed by more than 3

percent from the design flow rate. The system pressure was monitored daily. The effluent TOC

concentration was monitored frequently so that samples could be taken at the required 5 to 8

percent increments of the average influent TOC concentration.

3.1.2 Bench-Scale Blended Water Quality Assessment Approach

To simulate the integral breakthrough curve obtained by blending multiple full-scale contactors

operated in parallel-staggered mode, the entire effluent from a single GAC contactor was

collected in a reservoir and sampled over time. In this study, the entire effluent from the RSSCT

was collected in a clean 30 or 55 gallon drum. The only effluent water not collected in the drum

was that required for monitoring and sampling of the RSSCT effluent. This included TOC

monitoring and sampling, UV254 sampling, and SDS chlorination. Over time, the blended

effluent drum was sampled and analyzed for TOC and UV254. Samples were also taken and

chlorinated under the same target SDS conditions as those applied to the single contactor study.

During every run, TTHM and HAA9 were analyzed, and during two runs, TOX was also

analyzed, in addition to TTHM and HAA9. Ten percent of blended effluent samples taken were

sampled in duplicate and all analyses were conducted on the duplicate samples (field sample

duplicates).

The first discrete sample taken from the RSSCT effluent also constituted the first blended

sample. Subsequently, seven samples were taken from the blended effluent drum, evenly spaced

over the course of the run. The RSSCTs were operated until at least 70 percent TOC

breakthrough was reached, and the last blended effluent sample was taken at that time. Since the

ICR required that twelve samples be taken from the RSSCT effluent at even increments of TOC

breakthrough, the blended effluent sampling schedule followed the RSSCT sampling. The

second blended effluent sample was taken when the third RSSCT effluent sample was taken.

The remaining blended effluent sample points were based on the RSSCT effluent sample number

as described in Table 2. In practice, the blended effluent samples were taken from the drum just

prior to the addition of the RSSCT effluent sample listed in Table 2. This way, the use of the

RSSCT effluent sample water for SDS chlorination had a smaller impact on the blended effluent

sample. All blended samples were chlorinated under the same SDS conditions as the RSSCT

effluent samples. For two of the waters, (Waters 6 and 8) the run was extended for five

laboratory days after 70 percent breakthrough had occurred. In these instances, eight blended
-21-

effluent samples were taken with the sixth at 70 percent breakthrough, the seventh 2.5 days later,

and the eighth five days later.

Blended effluent

sample number

RSSCT effluent sample

number

1 1

2 3

3 6

4 8

5 9

6 10

7 11

8 12

Table 2 Relationship between blended effluent sample number and RSSCT effluent

sample number

3.1.3 DBP Formation Assessment

The blended effluent samples were chlorinated under the same target conditions as the RSSCT

effluent. To minimize the volume of water sampled from the blended effluent reservoir, the

chlorine dose used was based on the single column effluent chlorine demand data, by relating

chlorine demand to TOC concentration. The sampling volume required was minimized based on

the DBPs to be analyzed. For 6 of the 8 waters, the blended effluent sample volume was 800

mL, sufficient for TOC, UV254, and SDS chlorination for the analysis of HAA9 and TTHM.

When TOX was also analyzed after chlorination, a 1,300 mL sample was required.

For single contactor effluent samples, chlorine demand studies were performed on the first

effluent sample and the influent water. For each sample, three 125-mL chlorine demand-free

amber glass bottles were used. A combined phosphate/borate buffer solution was added to each

bottle (2.0 mL/L) to maintain a constant target pH during incubation. Dilute solutions of sulfuric

acid or sodium hydroxide were used to adjust the pH prior to chlorination if necessary. Three

chlorine doses were selected based on the TOC concentration of the water and the results of a 5-

minute chlorine demand study (providing a relative measure of inorganic chlorine demand).

Each bottle was filled to 80 to 90 percent of capacity. Using a pipette, a measured amount of a

standardized chlorine solution (using Standard Methods 4500-Cl B) was added to each bottle.

The bottle was then filled and capped head-space free. The bottles were placed in a constant

temperature bath in the dark for the duration of the target incubation period. A titrimetric

procedure (Standard Methods 4500-Cl F) was used to measure the free chlorine residual after the

holding time. The data generated by the chlorine demand study was used to estimate the

chlorine dose to achieve the target residual for all effluent samples by correlating TOC
-22-

concentration to chlorine demand. When TTHM and HAA9 were analyzed, a clean chlorine

demand-free 500 mL amber glass bottle was used. When TOX was also analyzed, a 1000 mL

bottle was used. Caps with Teflon-lined septa were used. The order of DBP sampling was

THMs, HAAs, and TOX. DBP samples were taken in duplicate in prepared bottles with

appropriate preservatives and quenching agents based on the analytical method.

3.1.4 Assessment of the Impact of Sampling on the Integral Breakthrough Curve

Discrete sampling from the RSSCT effluent and sampling from the blended effluent reservoir

was required to obtain water quality data to generate single contactor and integral breakthrough

curves. However, the impact of both types of sampling on the integral breakthrough curve were

unknown. A model was developed to simulate the experimental procedure in an effort to assess

the impact of sampling on the integral breakthrough curve. Based on the model results, RSSCT

design and sampling procedures were optimized to minimize the impact of sampling on the

integral breakthrough curve.

The model was based on sampling the RSSCT effluent continuously in 3-L aliquots. It assumed

that these aliquots were sampled for TOC and UV254 to monitor breakthrough as needed. Based

on the TOC concentration of samples taken, effluent samples and duplicates at ICR-required

TOC increments were identified and chlorinated under SDS conditions.

All samples were added to the reservoir in the order of sampling, and at even increments over the

course of the run, samples were taken from the blended effluent reservoir for TOC and UV254

analysis and SDS chlorination. The results of these analyses comprised the integral

breakthrough curve. The sample volume required was minimized, and any unchlorinated sample

remaining after analysis was complete was reintroduced to the reservoir.

Based on these experimental procedures, a model was developed to assess the impact of

sampling on the integral breakthrough curve, prior to laboratory experiments. The model inputs

were influent TOC concentration, empty-bed contact time (EBCT), column inner diameter, fullscale GAC mesh size, RSSCT GAC mesh size, minimum Reynold's number, and full-scale

operating temperature. Based on a correlation between influent TOC concentration and bed

volumes to 50 percent TOC breakthrough (Summers et al., 1994; Hooper et al., 1996), and

adjustments to the correlation to estimate run times to different levels of TOC breakthrough, a

TOC breakthrough curve was predicted.

From the estimated single contactor breakthrough curve, two blended effluent breakthrough

curves were generated. A theoretical integral breakthrough curve assumed no column effluent or

blended effluent sampling occurred. The experimental integral breakthrough curve assumed

typical discrete RSSCT effluent sampling, as described in the ICR Manual for Bench- and PilotScale Treatment Studies, and also included the sampling required from the blended effluent

reservoir to characterize the blended effluent water quality as described in Section 3.1.2.

Increasing the RSSCT column inner diameter (and thus increasing the ratio of volume water

passed to water required for analyses) decreased the impact of RSSCT effluent sampling on the

integral breakthrough curve. The model also demonstrated that sampling from the blended

effluent reservoir had the greatest impact on the difference between theoretical and experimental

integral breakthrough curves. Thus, by increasing the volume of water passed through the
-23-

RSSCT (by increasing the column inner diameter or minimum Reynold's number) this effect

could be minimized.

After each GAC run was complete, the experimental TOC integral breakthrough curve was

compared to an integral curve from which the impact of actual single contactor and blended

effluent sampling was subtracted (corrected integral breakthrough curve). For TOC

breakthrough of each water, the two curves are compared in Appendix A. Overall, the impact of

sampling on the integral breakthrough curve was very small, yielding a mean difference in TOC

concentration at the end of the integral breakthrough curve (where the difference between the

two curves was typically maximized) of 3 percent. The difference ranged from 1 to 6 percent,

with shorter runs yielding higher percent differences. In all cases, the TOC concentration at the

end of the experimental integral curve was higher than that in the corrected integral breakthrough

curve.

3.2 Data Analysis and Modeling Approach

3.2.1 Logistic Function Models

In this study, the logistic function was used to fit GAC breakthrough data from 8 water sources

and GAC runs, and up to 20 measured GAC effluent parameters (including DBP surrogates,

formed DBP class sum parameters, and formed DBP species). In some cases, the logistic

function presented in Equation 11 did not satisfactorily fit the breakthrough curve data. Three

modifications were made to the logistic function model to enhance the ability of the model to fit

breakthrough curve data. The models are described in Figure 5. The first modification was to

include an additional parameter, improving the curve fit for breakthrough curves with moderate

to high immediate breakthrough levels. The step logistic function is as follows:

0 1

( ) A

Be

A C t Dt +

+ = − (15)

where C(t), B, D, and t are as defined previously in Section 1.3, Equation 11. The term A0

represents the step applied to the logistic function to match moderate to high immediate

breakthrough levels. The term A represents the asymptote to which the logistic function is

approaching. In this study, the step logistic function model was used to curve fit all single

contactor TOC breakthrough curves. The step logistic function model is also adequate for

modeling the GAC effluent chlorine demand breakthrough curve, which typically has relatively

high levels of immediate breakthrough. The addition of the step term also allows the curve to

have a negative y-intercept, which is necessary for incorporation into the step-lag logistic

function model described below.

Because many breakthrough curves (especially for SDS-DBPs) result in a relatively long initial

interval where effluent concentrations are below reportable limits prior to breakthrough, a lag

was incorporated into the function to shift the logistic function outward, allowing for a better fit

of experimental breakthrough data. The run time at which initial breakthrough above detectable

levels occurs is termed tb, and the step-lag logistic function is as follows:

C(t) = 0 t ≤ tb (16)
Figure 5 Logistic function model curves

Run time, t

Concentration, C

a. Logistic function

Dt Be

A tC

−

+

=

1

)(

Run time, t

Concentration, C

A b. Step logistic function 0

0 1 )( A BeA tC Dt + + = −

Run time, t

Concentration, C

c. Step-la

A 0 t b

0 1 )( A BeA tC Dt + + = −

tC = 0)(

Run time, t

Concentration, C

d. Step-lag-pea

A 0 t b

(t p , C p )

0 1 )( A BeA tC Dt + + = −

tC = 0)(

-24-
-25-

0 1

( ) A

Be

A C t Dt +

+ = − t > tb (17)

In many cases, a best-fit of the data will yield a negative value for A0 (a negative y-intercept). In

essence, this shifts the logistic function downward, so that the beginning of the curve is negative

(as shown by the dotted line on curve C in Figure 5). However, this occurs when t ≤ tb, so by

Equation 16, the result is set to zero. For ICR treatment study data analysis, the result will be set

to 50 percent of the MRL for DBP surrogates and species, and will be set to zero for DBP sum

parameters. This modification improves the ability of the logistic function to fit breakthrough

data that are not symmetrical.

Under certain conditions, some brominated DBP species exhibit increasing and decreasing

breakthrough curves. These "peak" curves can be modeled using the logistic function to the

maximum concentration. After this point, effluent concentrations decrease, and this decrease is

modeled using a simple linear function. Prior to the point of peak concentration, C(t) is

described by the step-lag logistic function model. The step-lag-peak logistic function model is as

follows:

C(t) = 0 t ≤ tb (18)

0 1

( ) A

Be

A C t Dt +

+ = − tb < t ≤ tp (19)

( ) ( ) p p C t = C + S t − t t > tp (20)

where Cp is the logistic function model best-fit concentration at tp, the run time at which the peak

occurs, and S is the slope of the linear best-fit curve. The following algorithm was used to detect

"peak" breakthrough curves:

1. The measured peak concentration, C(tp), is at least 20 percent greater than the concentration

at the last observed data point, C(tf).

2. The run time tp is less than 80 percent of the run time of the last observed data point, tf.

3. The data point corresponding to tp is located prior to the penultimate observed data point.

In all cases, a best-fit of the logistic function model to the data was generated by least squares

minimization approach. The coefficient of determination, R2

, was computed for all best-fits.

The logistic function models were fit by non-linear least-squares using PROC NLIN in version

6.12 of the SAS system (Littell et al., 1996). Additional details and SAS code are included in

Appendix B.

3.2.2 Outlier Methods

The large amount of data that will be processed during the ICR treatment studies data analysis

necessitates the use of automated curve fitting procedures. To ensure that the fitted models are

robust to extreme values, an outlier adjustment methodology that uses all experimental data
-26-

points but limits the influence of deviant observations on the parameter estimates was developed

and used for this analysis.

Indiscriminate deletion of deviant observations can cause goodness of fit measures (i.e., R2

) to be

unrealistically high. Rather than deleting potential outliers, suspect observations were replaced

by less extreme values by the following procedure:

1. Fit the logistic function model to the observed data and determine approximate 95 percent

prediction limits on the observations.

2. Observations that exceed the threshold Predicted + (U95 - L95)*K are adjusted to Predicted

+ (U95 - L95)*K, where U95 is the upper 95 percent confidence limit and L95 is the lower

95 percent confidence limit. The constant K determines the magnitude of the adjustment,

with larger values of K corresponding to fewer declared outliers and smaller adjustments to

those that are detected. The value for the constant K was set to 1/3 based on simulation

results that indicated a good balance between false alarms and power to identify substantial

outliers (>3 standard deviations from the best-fit prediction). Approximately 2.5 percent of

the data in the present study were identified as outliers and adjusted.

3. Refit the logistic function model using adjusted values.

3.2.3 Direct Integration Approach

The use of the integrated logistic function model for a given parameter to predict the integral

breakthrough curve for that parameter is termed the direct integration (DI) approach. The

average value function described by Equation 12 was applied to the expressions used to describe

the breakthrough curves presented in Section 3.2.1. An expression for the average value of the

step logistic function (Equation 15) is:

















+

+ = + +

−

B

Be

Dt

A C t A A

Dt

1

1 ( ) ln 0 (21)

Application of the average value function to the step-lag logistic function model (Equations 16

and 17) yields the following equations that represent the integral breakthrough curve:

C t (t) = 0 ≤ tb (22)

( ) 















+

+  +



 



 = + − −

−

Dtb

Dt

b

Be

Be

Dt

A

t

t C t A A

1

1 ( ) 1 ln 0 t > tb (23)

Application of the average value function to the step-lag-peak logistic function model at t > tb is

more complex due to the use of two functions to describe the experimental data. The application

of the average value function to the three functions used to describe the experimental data over

the range [0, t] is given by the following equation:
-27-





















= = + + ∫ ∫ ∫ ∫

t

t

t

t

t t

p

p

b

b

C t dt C t dt C t dt

t

C t dt

t

C t ( ) ( ) ( ) 1 ( ) 1 ( ) 2 3

0

1

0

(24)

where C1(t), C2(t), and C3(t) represent Equations 18, 19, and 20, respectively, the three equations

used to model the experimental data. For t ≤ tb, the integral breakthrough curve is represented

by:

C t (t) = 0 ≤ tb (25)

For tb < t ≤ tp the function used to predict the integral breakthrough curve reduces to the average

value of the step-lag logistic function:

( ) 















+

+  +



 



 = + − −

−

Dtb

Dt

b

Be

Be

Dt

A

t

t C t A A

1

1 ( ) 1 ln 0 tb < t ≤ tp (26)

At t > tp, Equation 19 is evaluated from tb to tp, and Equation 20 is evaluated from tp to t.

Combining the two equations yields:

( ) ( ) ( ) 















+ − + + − 















+

+ = + − + −

−

Dt p p p p

Dt

p b C t t S t t Stt

Be

Be

D

A A A t t

t

C t

b

p 2 2

0 2

1

1

1 ( ) ln 1 ( ) t > tp (27)

The logistic function integral approach to determine the integral breakthrough curve assumes an

infinite number of contactors operated in parallel-staggered mode. This assumption was verified

for finite N = 2, 3, 4, 6, 10, and 20 contactors. For finite numbers of contactors, numerical

integration of Equations 15 through 20 was performed as described by Equation 4.

3.2.4 Surrogate Correlation Approach

The surrogate correlation approach (SCA) is a procedure that simplifies and reduces the amount

of computations necessary to estimate DBP formation in the effluent of staggered multiple

contactors (blended effluent). The SCA procedure relies on a constant relationship between

TOC concentration and DBP formation in both the single contactor effluent and the blended

effluent. To apply the SCA method to a GAC run, the single contactor TOC, UV254, SDS-DBP

class sum parameters, and SDS-DBP species breakthrough curves are fit to the appropriate

logistic function model. The SCA procedure steps are summarized in Section 1.5 and

represented schematically in Figure 4.

Typically, a linear or second order polynomial relationship exists between GAC effluent TOC

concentration and any other parameter. The SCA method does not determine or use these

correlations directly but instead assumes they exist and are constant in both the single contactor

effluent and blended effluent. This relationship is exploited by linking the concentration of all

water quality parameters at a common single contactor run time. Using Equation 21, the integral

breakthrough curve is calculated for the TOC single contactor breakthrough curve only. Then

the single contactor TOC concentration, to which the concentrations of all other water quality
-28-

parameters are linked, is applied to the run time at which an equivalent TOC concentration is

reached in the TOC integral breakthrough curve.

The SCA procedure can be performed to determine blended contactor run times for a given GAC

run and blended effluent treatment objective. In this study, the procedure was verified by

comparing the SCA integral breakthrough curve against experimental data.

3.2.5 Comparison of Methods for Predicting the Performance of GAC Contactors

Operated in Parallel-Staggered Mode

During this study, the methods described in Sections 3.2.3 and 3.2.4 for predicting the integral

breakthrough curve were compared to the experimentally derived integral breakthrough curve for

eight GAC runs. Using the logistic function models, a best-fit of the experimental blended

effluent data was derived. The logistic function models were applied to the integral

breakthrough curve data because the blended effluent data curves were similar in shape to those

encountered in the single contactor effluent.

Predictions obtained by the SCA method, the DI method, and the best-fitting model were

compared using the calculated residual sums of squares (RSS) from the experimental blended

effluent data. For each parameter prediction, the prediction bias was determined by averaging

the calculated residuals for each model. An evaluation of the bias shows whether the model

tended to overpredict or underpredict the observed data. For some parameters, a limited amount

of data were measured above the minimum reporting level (MRL). In cases where fewer than

six points above the MRL were present, curve fitting was not performed.

3.2.6 Comparison of Single Contactor and Blended Effluent DBP Bromine

Incorporation

To verify the assumption that the impact of TOC on DBP bromine incorporation would be

similar in the single contactor and blended effluents, the bromine incorporation factors nBr and

n'Br were modeled as a polynomial function of TOC and UV254 concentrations for all waters

simultaneously with two regression models:

Y = Wi + β1X + β2X2 (28)

Y = Wi + β1X + β2X2 + γZ, (29)

where Y is nBr or n’Br and X is TOC or UV254. The intercept Wi is allowed to be different for

each water, to reflect natural differences in bromine incorporation across waters. In the second

model, different intercepts are fitted for single contactor (Z=0) and blended (Z=1) data. The

additional parameter γ represents the change in average bromine incorporation associated with

blending. Since the first model is a special case of the second with γ=0, the assumption that

blending will not impact average bromine incorporation would be supported if the goodness of

fit of the two models is similar. Models which allowed the linear and quadratic parameters β1

and β2 to differ for single contactor and blending were considered in order to demonstrate that

blended and single contactor scenarios are comparable in terms of the shape of the bromine

incorporation profile as well as mean bromine incorporation (i.e., the intercepts). Although these
-29-

models make efficient use of all of the data simultaneously in order to reach a general

conclusion, the presence of the water-specific random effects Wi makes ordinary least-squares

inappropriate. SAS PROC MIXED was therefore used to estimate the models via general

likelihood methods (Littell, et. al., 1996).

3.2.7 Breakthrough Curve Extrapolation

For two waters, the sensitivity of blended effluent water quality to a breakthrough curve

extrapolation procedure was verified. To verify the extrapolation approach, two GAC runs were

operated for five laboratory days beyond 70 percent TOC breakthrough. Based on the scaling

factor of each run, the five days were equivalent to 49 and 69 full-scale days for Waters 5 and 8,

respectively. The appropriate logistic function model was applied to the abbreviated data set,

which reached 70 percent TOC breakthrough. Next, the appropriate logistic function model was

applied to the entire available data set for each parameter. The model fit for the abbreviated data

set was extrapolated to the total column run time, and the blended effluent water quality

predicted based on extrapolation of the abbreviated data set was compared to that using the full

experimental data set.

During the ICR treatment studies, only the TOC integral breakthrough curve will be

extrapolated. The SCA method relies on projecting water quality data associated with single

contactor effluent TOC concentration to the integral TOC breakthrough curve. Since the integral

TOC breakthrough curve typically reaches only 40 to 70 percent of the single contactor

breakthrough curve, DBP data associated with higher TOC concentrations would not be

included. By extrapolating the integral TOC breakthrough curve, the benefit of the data set is

increased, because DBP formation associated with higher GAC effluent TOC concentrations will

be included in the analysis.

3.3 Waters Examined

3.3.1 Pretreatment and Water Quality

GAC runs on eight water sources were included in this study. Pretreatment schematics for each

water source are shown in Appendix C. The influent to GAC TOC concentration for these water

sources ranged from 2.0 to 5.6 mg/L. The specific UV absorbance for TOC (TSUVA, defined as

100*UV254/TOC) ranged from 1.6 to 2.3 L/mg-m. A wide range of bromide concentrations were

measured, ranging from 28 to 300 μg/L. The GAC influent bromide to TOC ratio (Br:TOC)

ranged from 10 to 68. Water 1, received from Miami, Florida, was a groundwater. Water 2,

received from Aurora, Illinois, was a mixture of groundwater and surface water. The other 6

waters were all surface waters. A summary of the source water, pretreatment, and treated water

quality is shown in Table 3.
TOC

(mg/L)

UV254

(1/cm)

TSUVA

(L/mg-m)

pH Alkalinity

(mg/L as

CaCO3)

Total

hardness

(mg/L as

CaCO3)

Br

(μ

1 MiamiDade

County,

Florida

Limesoftening 4.5 0.094 2.1 9.2 23 54 

2 Aurora,

Illinois

Limesoftening 2.6 0.055 2.1 9.4 58 131 

3 Topeka,

Kansas

Two-stage

softening

2.4 0.048 2.0 9.0 30 133 

4 Davenport,

Iowa

Conventional

(PAS*)

3.0 0.065 2.2 7.1 127 217 

5 Escondido,

California

Conventional

(alum)

3.1 0.051 1.7 7.4 109 225 

6 Charleston,

S. Carolina

Conventional

(alum)

2.6 0.060 2.3 6.3 9 29 

7 Sweetwater,

California

Conventional

(ferric)

5.6 0.109 2.0 7.6 138 221 

8 Greensboro,

N. Carolina

Conventional

(alum)

2.0 0.033 1.6 7.6 23 32 

* PAS: polyaluminum sulfateTable 3 Summary of pretreatment and water quality

Water Water

source

Pretreatment Treated water quality (GAC influent)

-30-
-31-

3.3.2 Simulated Distribution System Chlorination Conditions

Table 4 summarizes the SDS chlorination conditions for each water. The SDS conditions were

chosen to reflect site-specific distribution system conditions at the time the water was sampled.

SDS incubation times ranged from 6 to 48 hours; incubation pH ranged from 7.4 to 9.2; target

free chlorine residual ranged from 0.75 to 1.50 mg/L; incubation temperatures ranged from 18 to

26°C. All samples were buffered to maintain the target incubation pH constant during the

incubation period.

Water Date sampled Target SDS chlorination conditions

Incubation

time (hrs)

Incubation

temperature

(°C)

Free chlorine

residual

(mg/L)

pH

1 July 24, 1998 6 26 0.75 9.1

2 September 22, 1998 24 20 0.80 9.1

3 September 1, 1998 48 26 0.80 9.2

4 September 23, 1998 24 20 0.75 7.4

5 January 7, 1999 24 18 0.80 7.4

6 October 12, 1998 28 20 1.50 8.5

7 October 22, 1998 24 24 0.75 8.0

8 October 6, 1998 24 20 1.00 7.7

Table 4 SDS chlorination conditions

3.4 Analytical Methods

A list of all analytical methods and minimum reporting levels (MRLs) used during the study is

shown in Table 5. All analyses were conducted at Summers & Hooper, Inc. or Montgomery

Watson Laboratories as outlined in Table 6.
-32-

Analyte Waters Method Minimum reporting level (MRL)

Alkalinity All SM 2320 B 5 mg/L as CaCO3

Bromide All EPA 300.0 A 20 μg/L

Calcium hardness All EPA 200.7 5 mg/L as CaCO3

Chlorine dose (solution

standardization)

All SM 4500-Cl B NA

Chlorine residual All SM 4500-Cl F 0.2 mg/L as Cl2

HAA (DCAA, TCAA, MBAA,

DBAA, BCAA, BDCAA)

1-4, 6-8 EPA 552.2 1.0 μg/L (each analyte)

HAA (MCAA, CDBAA) 1-4, 6-8 EPA 552.2 2.0 μg/L (each analyte)

HAA (TBAA) 1-4, 6-8 EPA 552.2 4.0 μg/L

HAA (DCAA, TCAA, MBAA,

DBAA, BCAA, BDCAA)

5 SM 6251 B 1.0 μg/L (each analyte)

HAA (MCAA, CDBAA) 5 SM 6251 B 2.0 μg/L (each analyte)

HAA (TBAA) 5 SM 6251 B 4.0 μg/L

pH All 4500-H+

 B NA

Temperature All SM 2550 B NA

Total hardness All SM 2340 B 5 mg/L as CaCO3

Total organic carbon (TOC) All SM 5310 C 0.50 mg/L

Total organic halide (TOX) All SM 5320 B 25 μg/L as ClTHM (CF, BDCM, DBCM, BF) All EPA 551.1 1.0 μg/L (each analyte)

UV absorbance at 254 nm (UV254) All SM 5910 B 0.009 cm-1

SM: Standard Methods

NA: Not applicable

Table 5 Summary of analytical methods and MRLs

Analyses performed Waters Laboratory

Alkalinity, chlorine dose, chlorine

residual, pH, temperature, TTHM,

TOC, TOX, UV254

All Summers & Hooper, Inc.

HAA9 1-4, 6-8 Summers & Hooper, Inc.

Bromide, calcium hardness, total

hardness

All Montgomery Watson Laboratories

HAA9 5 Montgomery Watson Laboratories

Table 6 Summary of laboratories conducting analyses

3.5 Experimental QA/QC Summary

As a part of this study, field duplicates were performed on 10 percent of samples analyzed in the

blended effluent. The field duplicates for DBPs were generated by duplicate SDS chlorination of

split GAC effluent samples. The single contactor effluent samples were duplicated at a higher

rate, following ICR guidelines, which required that three field duplicates be collected from the
-33-

effluent of each RSSCT (25 percent duplication). The results of all field duplicate analyses are

summarized in Table 7.
-34-

Analyte Count

50th Mean Standard

percentile deviation

TOC 29 0.5 1.0 1.2

UV-254 29 0.7 1.2 1.7

SDS-TOX 26 2.3 3.8 3.7

SDS-THM 29 3.0 5.2 6.1

SDS-HAA5 28 13. 23.1 37.8

SDS-HAA6 28 12.7 16.5 16.5

SDS-HAA9 28 10.4 15. 17.6

THM Species

SDS-CF 25 3.5 5.8 7.0

SDS-BDCM 29 3.1 5.1 6.1

SDS-DBCM 29 2.8 4.8 6.4

SDS-BF 21 5.8 8.4 7.4

HAA Species

SDS-MCAA 1 20. 20. NA

SDS-DCAA 24 4.5 8. 1.0

SDS-TCAA 18 10. 34.2 62.5

SDS-MBAA 0 NA NA NA

SDS-DBAA 23 14.3 41.8 64.

SDS-BCAA 27 7.6 10.7 12.3

SDS-CDBAA 12 1.4 56.4 86.7

SDS-DCBAA 23 9.0 26.7 5.2

SDS-TBAA 4 18.6 62. 92.0

RPD: relative percent difference

NA: not applicable

Table 7 Summary of field duplicate precision for single contactor and blended effluent data

Relative percent difference (RPD)
-35-

4 Results and Discussion

4.1 Overview

A substantial portion of the results presented attempt to verify the underlying assumptions

behind the surrogate correlation approach (SCA) and examine the limitations of the application

of this method for determining the integral breakthrough curve, a relationship between single

contactor run time and blended contactor effluent water quality assuming contactor operation in

parallel-staggered mode. The SCA procedure requires that all GAC breakthrough data are fit to

a logistic function model. From the perspective of data analysis of the ICR GAC treatment study

data, there are several advantages to using a model to fit experimental data. Best-fit curve

parameters that adequately describe experimental data are less memory intensive than storing the

entire experimental data set. A best-fit curve also facilitates interpolation and extrapolation to

estimate run times for given treatment objectives. Use of a best-fit model curve provides an

estimate of the scatter in the data through the coefficient of determination, and the model

minimizes the impact of this scatter on run time estimates. Finally, a function that describes the

single contactor experimental data set is a prerequisite for determining the integral breakthrough

curve. Run time estimates generated by the integral breakthrough curve are more applicable to

full-scale GAC operation where multiple contactors are operated in parallel-staggered mode to

increase the service time of each individual contactor.

The SCA procedure (1) links single contactor TOC concentration to single contactor DBP

formation at a given run time (RTSC), (2) uses the direct integration (DI) approach to model the

integral TOC breakthrough curve, (3) determines the blended contactor effluent run time (RTBC)

at the TOC concentration used, and (4) applies the linked parameters to estimate DBP formation

at RTBC. This approach allows the blended contactor run time to be determined for all correlated

parameters based on the TOC integral breakthrough curve. The SCA procedure assumes that:

1. The correlation between TOC and DBP precursors (concentration and speciation) observed

in the single contactor effluent is maintained in the blended effluent. A discussion of

comparisons made to evaluate the consistency of these correlations is presented in Section

4.2.

2. The three forms of the logistic function model developed can accurately describe the range of

breakthrough of DBP precursors and DBP precursor surrogates evaluated. The results of

logistic function model fits applied to all parameters and all GAC runs are discussed in

Section 4.3.

3. The TOC integral breakthrough curve calculated by the DI approach is an accurate predictor

of blended effluent TOC concentration when multiple contactors are operated in parallelstaggered mode. TOC integral breakthrough curves are compared to experimental data in

Section 4.3, while an evaluation of the applicability of the infinite contactor assumption

inherent by application of the DI procedure is contained in Section 4.5.

4. The integral TOC breakthrough curve can be extrapolated with reasonable results. This

assumption was verified for two GAC runs, as discussed in Section 4.6.
-36-

Section 4.4 compares the integral breakthrough curves obtained by both the SCA and DI

methods against experimental data for the eight GAC runs and all measured parameters.
-37-

4.2 Correlation between Surrogates and DBPs in Single Contactor and Blended

Contactor Effluents

4.2.1 Correlation between Surrogate Concentration and DBP Formation

The correlation assumption is an important underlying foundation for the SCA procedure used to

model blended effluent water quality. Verifying the assumption would demonstrate that DBP

formation is constant at a given TOC concentration in both the single contactor and blended

contactor effluent. Since bromide is not adsorbed by GAC, the bromide to TOC ratio will also

be constant, at a given TOC concentration, so DBP speciation should not change between single

contactor and blended contactor effluents.

To verify the correlation assumption, plots of paired data were generated between TOC and other

parameters, such as SDS-TTHM, SDS-HAA6, SDS-CF, SDS-DCAA, etc. Both summed DBP

classes and individual compounds were examined. For each water, two experimental data sets

were plotted for comparison: single contactor effluent and blended effluent. The entire graph set

is included in Appendix D, which includes correlations based on both TOC and UV254 as

surrogates. Overall, both parameters served well as surrogates for DBP formation, and the

correlation between single contactor and blended effluent data was approximately equivalent. A

representative sample of the results obtained are discussed in this section.

Overall, the correlations observed between TOC and formed DBPs in the blended effluent were

very similar to those observed in the single contactors for each water. Figures 6 and 7 show the

correlation of UV254, SDS-TTHM, SDS-HAA6, and SDS-TOX to TOC for both the single

contactor and blended effluent of Waters 1 and 2 (with the exception of SDS-TOX, for which

blended effluent samples were not analyzed in these waters). The correlation between TOC and

UV254 in the blended effluent was very similar to that in the single contactor effluent for these

two waters, which was typical of that observed in most cases. The greatest difference in blended

effluent and single contactor correlations for TOC and SDS-TTHM occurred for Water 8 (Figure

8) which showed a similar disparity between the blended effluent and single contactor

correlations for TOC and UV254. Still, at a given TOC concentration, SDS-TTHM formation in

the blended effluent was within 5 μg/L of that in the single contactor effluent.

For the THM species, correlations between single contactor and blended effluent TOC and

formed concentrations are shown in Figures 9 and 10 for Waters 2 and 3, respectively. For all

four species, a very good agreement existed between single contactor and blended effluent TOC

correlations. This includes SDS-BF, which yielded a "peak" curve for these two runs. The peak

also occurred in the blended effluent, and the single contactor and blended effluent yielded very

similar curves when SDS-BF concentrations were plotted against TOC. The correlation of

formed THM compounds between single contactor and blended effluent was very good for all

waters. The largest difference between correlations seen in the single contactor and blended

effluent were observed for Water 8, shown in Figure 11, for which the formed THM species

concentrations were low.

Four of the predominant HAA species formed (DCAA, TCAA, DBAA, and BCAA) correlated to

TOC concentration for both single contactor and blended effluents are shown in Figures 12 and
-38-

13 for Waters 5 and 7, respectively. For Water 5 both single contactor and blended effluent

formed HAAs matched very well. For Water 7, the correlations for SDS-DBAA and SDSBCAA also matched very well. The single contactor and blended effluent correlations between

TOC and SDS-DCAA and SDS-TCAA showed slight differences, not exceeding 3 μg/L. The

formed levels of these two compounds were low for this water.

Correlations between single contactor and blended effluent for SDS-DCBAA, SDS-CDBAA,

SDS-HAA5, and SDS-HAA9 are shown for Waters 2 and 4 in Figures 14 and 15, respectively.

Water 2 is shown as it yielded the largest difference in the correlation between single contactor

and blended effluent for these parameters, although formed DCBAA and CDBAA levels were

low. Water 4 is more representative of what was typically observed, with the correlation

between TOC and these parameters in the blended effluent matching that observed in the single

contactor very well. For SDS-TBAA, only Water 7 yielded measured levels above the MRL (4

μg/L). The correlation between TOC and formed TBAA in the blended effluent agreed well

with that observed in the single contactor effluent, as shown in Figure 16.

The use of UV254 as a surrogate for these correlations instead of TOC was investigated. In a few

cases, UV254 as a surrogate yielded better correlations between single contactor and blended

effluent formed DBPs than did TOC. However, in other instances, and approximately the same

number of cases, the correlations using TOC were superior. Because the results for the

correlations using UV254 did not improve over those using TOC, the use of TOC as the surrogate

in the SCA procedure was continued. Appendix D also summarizes the correlations observed

based on UV254.

In summary, this analysis shows that the correlation between DBP formation and TOC

concentration in the single contactor and blended contactor effluents is constant. This find is

significant because it verifies one of the underlying assumptions behind the SCA procedure, that

TOC concentration can be correlated to DBP formation in the single contactor effluent, and that

this correlation can then be applied to blended contactor effluent. Section 4.3 will address how

single contactor effluent data can be modeled as a function of run time so that this correlation

can be utilized to predict blended contactor effluent DBP formation.

4.2.2 Correlation between Surrogates and DBP Speciation

For all four models relating bromine incorporation for THMs and HAA9 (nBr and n'Br,

respectively) to TOC and UV254, the polynomial models described in Section 3.2.6 were found to

be appropriate. The model variance over all four combinations of nBr and n'Br as a function of

TOC and UV254 yielded R2 values greater than 0.92 in each case. Although the effect (γ) of

blending on mean bromine incorporation was found to be statistically significant, including this

effect improved R2

 values trivially (less than 1 percent for all four cases) by. There was no

evidence that different linear or quadratic terms were needed to describe the single contactor and

blended effluent data. For THMs, Figures 17 through 24 show the results of fitting second order

polynomials to the relationship between nBr and TOC, combining single and blended contactor

data for each water. For HAA9, similar results for the relationship between n'Br and TOC are

shown in Figures 25 through 32. The second order polynomial curve fits for the correlation

between nBr and UV254 and n'Br and UV254 for both single and blended contactor data are shown

in Figures 33 through 48.
-39-

This analysis shows that there is no significant difference between DBP bromine incorporation

between single contactor and blended contactor effluents at equivalent TOC or UV254 values.

This conclusion supports the use of the SCA procedure, especially for individual DBP species,

because the SCA assumes that DBP formation and speciation in the single contactor effluent at a

given TOC concentration is equivalent to that in the blended contactor effluent at the same TOC

concentration.
-

Figure 6 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 1

0.000.010.020.030.040.050.060.07

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

UV-254 (1/cm)

Single contactorBlended effluent

0

255075

0.0 0.5 1.0 1.5 2.0 2.5 3.0 

TOC (mg/L)

SDS-TTHM (μg/L)

0

102030

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100150

0.0 0.5 1.0 1.5 2.0 2.5 3.0 

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-40-
-41-

Figure 7 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0.000.010.020.030.04

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

UV-254 (1/cm)

Single contactorBlended effluent

0

255075

100

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-TTHM (μg/L)

0

102030

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100150200

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-TOX (μg/L Cl-)
-42-

Figure 8 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

0.000.010.020.03

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

UV-254 (1/cm)

Single contactorBlended effluent

0

2550

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-TTHM (μg/L)

0

1020

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100150

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-TOX (μg/L Cl-)
-43-

Figure 9 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

05

10152025

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluentBlended effluent

05

1015202530

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BDCM (μg/L)

05

1015202530

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

05

1015

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BF (μg/L)
-44-

Figure 10 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 3

05

101520

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluentBlended effluent

05

10152025303540

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BDCM (μg/L)

05

1015202530

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

05

101520253035

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BF (μg/L)
-45-

Figure 11 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

05

10152025

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluentBlended effluent

012345

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BDCM (μg/L)

05

1015

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

012

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-BF (μg/L)
-46-

Figure 12 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 5

02468

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluentBlended effluent

01234567

0.0 0.5 1.0 1.5 2.0 

TOC (mg/L)

SDS-TCAA (μg/L)

01234

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DBAA (μg/L)

02468

0.0 0.5 1.0 1.5 2.0 

TOC (mg/L)

SDS-BCAA (μg/L)
-47-

Figure 13 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

02468

10

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluentBlended effluent

012345678

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

TOC (mg/L)

SDS-TCAA (μg/L)

05

101520

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DBAA (μg/L)

05

101520

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

TOC (mg/L)

SDS-BCAA (μg/L)
-48-

Figure 14 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0123

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluentBlended effluent

0123

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-CDBAA (μg/L)

05

101520

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA5 (μg/L)

05

1015202530

0.0 0.5 1.0 1.5 

TOC (mg/L)

SDS-HAA9 (μg/L)
-49-

Figure 15 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 4

0246

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluentBlended effluent

05

0.0 0.5 1.0 1.5 2.

TOC (mg/L)

SDS-CDBAA (μg/L)

0

10203040

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA5 (μg/L)

0

1020304050

0.0 0.5 1.0 1.5 2.

TOC (mg/L)

SDS-HAA9 (μg/L)
-50-

Figure 16 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

02468

1012

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluentBlended effluent

01234567

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

TOC (mg/L)

SDS-CDBAA (μg/L)

02468

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-TBAA (μg/L)

0

1020304050607080

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

TOC (mg/L)

SDS-HAA9 (μg/L)
0.0

0.5

1.0

1.5

2.0

2.5

01234

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.948)

c0 

TOC: 4.5 mg/L

Water 1

Figure 17 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 1

Bromide: 115 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.772)

c0 

TOC: 2.6 mg/L

Water 2

Figure 18 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 2

Bromide: 105 μg/L

-51-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.978)

c0 

TOC: 2.4 mg/L

Water 3

Figure 19 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 3

Bromide: 160 μg/L

0.00

0.25

0.50

0.75

1.00

0.0 0.5 1.0 1.5 2.0 2.5

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.874)

c0 

TOC: 3 mg/L

Water 4

Figure 20 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 4

Bromide: 29 μg/L

-52-
0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.406)

c0 

TOC: 3.1 mg/L

Water 5

Figure 21 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 5

Bromide: 70 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.781)

c0 

TOC: 2.6 mg/L

Water 6

Figure 22 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 6

Bromide: 140 μg/L

-53-
0.0

0.5

1.0

1.5

2.0

2.5

012345

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.981)

c0 

TOC: 5.6 mg/L

Water 7

Figure 23 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 7

Bromide: 300 μg/L

0.00

0.25

0.50

0.75

1.00

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.896)

c0 

TOC: 2 mg/L

Water 8

Figure 24 Correlation between single contactor and blended effluent TOC 

concentration and THM bromine incorporation factor (n) for Water 8

Bromide: 28 μg/L

-54-
0.0

0.5

1.0

1.5

2.0

2.5

01234

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.751)

c0 

TOC: 4.5 mg/L

Water 1

Figure 25 Correlation between single contactor and blended effluent TOC 

concentration and HAA9 bromine incorporation factor (n') for Water 1

Bromide: 115 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.423)

c0 

TOC: 2.6 mg/L

Water 2

Figure 26 Correlation between single contactor and blended effluent TOC 

concentration and HAA9 bromine incorporation factor (n') for Water 2

Bromide: 105 μg/L

-55-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.900)

c0 

TOC: 2.4 mg/L

Water 3

Figure 27 Correlation between single contactor and blended effluent TOC 

concentration and HAA9 bromine incorporation factor (n') for Water 3

Bromide: 160 μg/L

0.00

0.25

0.50

0.75

1.00

1.25

0.0 0.5 1.0 1.5 2.0 2.5

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.504)

c0 

TOC: 3 mg/L

Water 4

Figure 28 Correlation between single contactor and blended effluent TOC 

concentration and HAA9 bromine incorporation factor (n') for Water 4

Bromide: 29 μg/L

-56-
0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.283)

c0 

TOC: 3.1 mg/L

Water 5

Figure 29 Correlation between single contactor and blended effluent TOC concentration

bromine incorporation factor (n') for Water 5

Bromide: 70 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.926)

c0 

TOC: 2.6 mg/L

Water 6

Figure 30 Correlation between single contactor and blended effluent TOC concentration

bromine incorporation factor (n') for Water 6

Bromide: 140 μg/L

-57-
0.0

0.5

1.0

1.5

2.0

2.5

012345

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.422)

c0 

TOC: 5.6 mg/L

Water 7

Figure 31 Correlation between single contactor and blended effluent TOC concentration

bromine incorporation factor (n') for Water 7

Bromide: 300 μg/L

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.5 1.0 1.5 2.0

TOC concentration (mg/L)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.891)

c0 

TOC: 2 mg/L

Water 8

Figure 32 Correlation between single contactor and blended effluent TOC concentration

bromine incorporation factor (n') for Water 8

Bromide: 28 μg/L

-58-
0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.962)

c0 

UV-254: 0.094 1/cm

Water 1

Figure 33 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 1

Bromide: 115 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.781)

c0 

UV-254: 0.055 1/cm

Water 2

Figure 34 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 2

Bromide: 105 μg/L

-59-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.00 0.01 0.02 0.03

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.974)

c0 

UV-254: 0.048 1/cm

Water 3

Figure 35 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 3

Bromide: 160 μg/L

0.00

0.25

0.50

0.75

1.00

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.900)

c0 

UV-254: 0.065 1/cm

Water 4

Figure 36 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 4

Bromide: 29 μg/L

-60-
0.0

0.5

1.0

1.5

2.0

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.410)

c0 

UV-254: 0.051 1/cm

Water 5

Figure 37 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 5

Bromide: 70 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.783)

c0 

UV-254: 0.06 1/cm

Water 6

Figure 38 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 6

Bromide: 140 μg/L

-61-
0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.982)

c0 

UV-254: 0.109 1/cm

Water 7

Figure 39 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 7

Bromide: 300 μg/L

0.00

0.25

0.50

0.75

1.00

0.00 0.01 0.02 0.03

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for THMs, n

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.880)

c0 

UV-254: 0.033 1/cm

Water 8

Figure 40 Correlation between single contactor and blended effluent UV 

absorbance and THM bromine incorporation factor (n) for Water 8

Bromide: 28 μg/L

-62-
0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.746)

c0 

UV-254: 0.094 1/cm

Water 1

Figure 41 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 1

Bromide: 115 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.435)

c0 

UV-254: 0.055 1/cm

Water 2

Figure 42 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 2

Bromide: 105 μg/L

-63-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.00 0.01 0.02 0.03

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.906)

c0 

UV-254: 0.048 1/cm

Water 3

Figure 43 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 3

Bromide: 160 μg/L

0.00

0.25

0.50

0.75

1.00

1.25

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.484)

c0 

UV-254: 0.065 1/cm

Water 4

Figure 44 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 4

Bromide: 29 μg/L

-64-
0.0

0.5

1.0

1.5

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.269)

c0 

UV-254: 0.051 1/cm

Water 5

Figure 45 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 5

Bromide: 70 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.935)

c0 

UV-254: 0.06 1/cm

Water 6

Figure 46 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 6

Bromide: 140 μg/L

-65-
0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.410)

c0 

UV-254: 0.109 1/cm

Water 7

Figure 47 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 7

Bromide: 300 μg/L

0.0

0.1

0.2

0.3

0.4

0.5

0.00 0.01 0.02 0.03

UV absorbance at 254 nm, UV-254 (1/cm)

Bromine incorporation factor for HAA9, n'

Single contactor effluent

Blended effluent

Best fit (R^2 = 0.918)

c0 

UV-254: 0.033 1/cm

Water 8

Figure 48 Correlation between single contactor and blended effluent UV 

absorbance and HAA9 bromine incorporation factor (n') for Water 8

Bromide: 28 μg/L

-66-
-67-

4.3 Assessment of Logistic Function Fit to Single Contactor Breakthrough

Curve Data

A model used to describe single contactor effluent experimental data is needed for several

reasons. From a data management perspective, best-fit curve parameters that adequately

describe experimental data are less memory intensive than storing the entire experimental data

set. A best-fit curve also facilitates interpolation and extrapolation to estimate run times for

given treatment objectives. Use of a best-fit model curve provides an estimate of the scatter in

the data through the coefficient of determination, and the model minimizes the impact of this

scatter on run time estimates. Finally, a function that describes the single contactor experimental

data set is a prerequisite for determining the integral breakthrough curve, a curve that relates

single contactor run time to blended contactor water quality under the assumption that contactors

are operated in parallel-staggered mode. Run time estimates generated by the integral

breakthrough curve are more applicable to full-scale GAC operation.

The appropriate logistic curve function model (step, step-lag, or step-lag-peak, as described in

Section 3.2.1) was fit to the single contactor effluent data for eight GAC runs comprised of up to

20 parameters each. The GAC runs were performed using conventionally-treated water from

eight waters sources, including surface and ground waters. The parameters examined included

precursors or surrogates (TOC, UV254, and SDS-TOX), DBP class sums (TTHM, HAA5, HAA6,

and HAA9), and DBP species (CF, BDCM, DBCM, BF, MCAA, DCAA, TCAA, MBAA,

DBAA, BCAA, DCBAA, CDBAA, and TBAA). For some DBP species, samples taken during

the initial portion of the breakthrough curve were measured below the MRL. Curve fits were not

performed if fewer than six effluent data points were reported above the MRL.

Due to the large amount of graphs generated, all plots are summarized in Appendix E, and a

selection of plots are included together with this analysis as examples. The plots contain the

single contactor GAC effluent parameter concentration plotted against scaled operation time. A

line representing the logistic function model best-fit is included. In addition, the experimental

blended effluent data points are included in each plot, along with a dotted line representing the

DI method prediction of the integral breakthrough curve, to demonstrate the benefit obtained by

blending the effluents of multiple contactors operated in parallel staggered mode. An analysis of

the integral breakthrough curve predictive models is deferred until Section 4.4.

4.3.1 Surrogates and Class Sum Logistic Function Curve Fits

The step logistic function model was used to fit single contactor effluent TOC breakthrough

curves for all waters, as shown in Figures 49 through 56. GAC run times ranged from 76 to 287

days, and the GAC influent TOC concentration ranged from 2.0 to 5.6 mg/L. For these waters,

the step logistic function model provided excellent curve fit approximations: the R2

 values for

the curve fits ranged from 0.966 to 0.992.

Using the step-lag logistic function model, excellent curve fits were also obtained for single

contactor effluent UV254 breakthrough curves: the R2 values for the curve fits ranged from 0.982

to 0.998. The measured GAC influent UV254 ranged from 0.033 to 0.109 1/cm for the waters
-68-

examined. The results for Waters 5 and 7 are shown in Figures 57 and 58. The results for the

remaining waters can be found in Appendix E.

SDS-TOX breakthrough curves were also modeled using the step-lag logistic function with

excellent results: the R2

 values for the curve fits ranged from 0.990 to 0.999. The GAC influent

SDS-TOX ranged from 156 to 486 μg/L as Cl-. Figures 59 and 60 show examples of the data

obtained for Waters 4 and 8.

Both SDS-TTHM and the SDS-HAA sums (HAA5, HAA6, and HAA9) were modeled using the

step-lag logistic function model. Again, single contactor effluent data were well-represented by

the model used, as shown for SDS-TTHM in Figures 61 and 62 for Waters 3 and 6. The R2

values for SDS-TTHM curve fits ranged from 0.977 to 0.995. Overall, the step-lag logistic

function model was also successful when used to fit all three SDS-HAA species sums, with R2

values ranging from 0.952 to 0.994. Figures 63 and 64 show the step-lag logistic function model

curve fits applied to single contactor effluent SDS-HAA9 data for Waters 1 and 2.

Table 8 summarizes the R2

 values measured for all curve fits, including DBP surrogates, DBP

sum class parameters, and DBP species. For all waters and all parameters the mean R2 was

0.973 ± 0.046, indicating that all breakthrough curve data were successfully fit using the logistic

function models. For DBP surrogates and DBP sum class parameters only, the mean R2 value

was 0.982 ± 0.012.

4.3.2 DBP Species Logistic Function Curve Fits

THM Species. For all waters, the application of the step-lag logistic function resulted in good

curve fits for SDS-CF, as shown by R2 values ranging between 0.922 and 0.998. Typically, the

SDS-CF breakthrough curve shape was similar to that of SDS-TTHM, as shown in the example

given for Water 4 in Figure 65. For most SDS-BDCM and SDS-DBCM breakthrough curves,

the step-lag logistic function model was used and yielded good results: R2

 values ranged from

0.946 to 0.998. An example of the SDS-BDCM breakthrough curve is shown in Figure 66 for

Water 6 and an example of the SDS-DBCM breakthrough curve is shown in Figure 67 for Water

7. The step-lag logistic function model was able to adequately fit the sharp 'S' shape (steep

breakthrough followed by flat plateau) observed for the SDS-BDCM experimental data in Figure

66 for Water 6 (R2 = 0.979). Water 2 also exhibited this behavior, as did Waters 1 and 3 to a less

pronounced extent, but in all cases the data were successfully fit by the model. For Waters 4 and

8, a peak breakthrough curve for SDS-BDCM was detected by the curve fit algorithm. These

curves were fit using the step-lag-peak logistic function model, as shown in Figures 68 and 69.

The curve fit procedure successfully fit the single contactor peak curves; the R2

 values were

greater than 0.94 for both waters. For 3 out of 6 waters with SDS-BF levels measured above the

MRL, a peak curve was detected. The step-lag-peak logistic function model also successfully fit

these peak curves, as shown in Figures 70 and 71 for Waters 1 and 6. The R2

 values for SDS-BF

curve fits ranged from 0.910 to 0.984. Summary plots of the model fits for all THM species and

all waters are included in Appendix E.

HAA Species. Of the non-brominated HAA species, no curve fits were applied to SDS-MCAA

because only for a few samples was MCAA measured above the MRL (2.0 μg/L) in the GAC

effluent. The experimental breakthrough curves for SDS-DCAA and SDS-TCAA were well
-69-

Analyte

Step Step-lag Step-lag-peak No fit* Mean 

TOC 8 0 0 0 0.980 UV-254 0800 0.993 

SDS-TOX 0800 0.995 

SDS-TTHM 0800 0.985 

SDS-HAA5 0710 0.975 

SDS-HAA6 0800 0.974 

SDS-HAA9 0710 0.976 

SDS-CF 1700 0.991 

SDS-BDCM 0620 0.980 

SDS-DBCM 0800 0.992 

SDS-BF 0242 0.964 

SDS-MCAA 0 0 0 8 NA SDS-DCAA 0800 0.975 

SDS-TCAA 0503 0.973 

SDS-MBAA 0 0 0 8 NA SDS-DBAA 0602 0.957 

SDS-BCAA 0800 0.970 

SDS-CDBAA 0215 0.946 

SDS-DCBAA 0701 0.907 

SDS-TBAA 0017 0.976 

Total 9 105 10 36*Curve fits were not performed on breakthrough curves with fewer than 6 points measured above the MRLSD: Standard deviationNA: Not applicableTable 8 Frequency of logistic function model used and R2 values for all parameters and all waters

Number of each type of curve fit used for all waters Curve fit R2 v
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modeled by the step-lag logistic function. Examples of the curve fits are shown in Figure 72

(SDS-DCAA, Water 3) and Figure 73 (SDS-TCAA, Water 5). For all waters, R2

 values ranged

from 0.922 to 0.999. SDS-MBAA was not measured above the MRL (1.0 μg/L) in the GAC

effluent during any of the studies. Due to low or BMRL GAC effluent SDS-DBAA levels for

Waters 4 and 8 curve fitting procedures were not applied. For the remaining waters, the SDSDBAA breakthrough curve typically showed a sharp 'S' shape (steep breakthrough followed by

flat plateau) and was successfully fit by the step-lag logistic function model (R2 values ranging

from 0.859 to 0.992). An example of SDS-DBAA breakthrough and model fit is shown in

Figure 74 for Water 6. The breakthrough curves for SDS-BCAA were also well-fit by the steplag logistic function model (R2

 values ranging from 0.952 to 0.987), such as that for Water 1,

shown in Figure 75. Examples of curve fits for SDS-DCBAA, SDS-CDBAA, and SDS-TBAA

are shown in Figures 76, 77, and 78, respectively. For these three compounds, R2 values ranged

from 0.913 to 0.987 except for the curve fit for SDS-DCBAA for Water 3, which yielded an R2

value of 0.531. For this run, GAC effluent SDS-DCBAA concentrations were low, measured

between 1.0 and 2.0 μg/L.

A peak curve was detected in 10 cases out of 126 total curve fits. Every water examined yielded

at least one peak curve. In general, the step-lag-peak logistic function model was able to

adequately fit the peak curve data. Additional examples of peak curve fits are shown in Figures

79 and 80. Figure 81 (SDS-BF for Water 3) shows an example of single contactor data that

qualitatively shows a peak, but the peak curve algorithm did not detect it as such. The logistic

function under predicted experimental data at the peak by up to 4 μg/L, and over predicted it at

the end of the run by up to 3 μg/L. The R2 value for this curve fit was 0.856. When refit to the

step-lag-peak logistic function model, Figure 82, the R2 value improved to 0.984.

In general, the logistic function models successfully characterized the variety of breakthrough

curves observed in this study. Three logistic function models were utilized: step, step-lag, and

step-lag-peak. These models were able to match not only typical S-shaped breakthrough curves

but also peak curves observed for some parameters. Table 8 also summarizes the frequency of

use for each of the three types of logistic function models used. The step-lag logistic function

was the most commonly used, utilized in 66 percent of the curve fits performed. For the DBP

surrogates and class sums, the step-lag logistic function model was utilized for 82 percent of

curve fits. The step function was almost exclusively utilized for TOC breakthrough curves. For

23 percent of all data sets, no curve fit was performed because fewer than six data points were

measured above the MRL. Most of the parameters for which no curve fit was performed were

HAA species. For all data sets that were modeled, the mean R2

 was 0.974, indicating that

overall, the models were able to successfully fit the GAC breakthrough profiles.
0

1

2

3

4

0 20 40 60 80

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.966)

Blended effluent

DI prediction (RSS = 0.152)

c0 = 4.54 mg/L

EBCT = 20 min.

TOC

Figure 49 Single contactor and blended effluent TOC breakthrough curves 

for Water 1

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.972)

Blended effluent

DI prediction (RSS = 0.057)

c0 = 2.6 mg/L

EBCT = 20 min.

TOC

Figure 50 Single contactor and blended effluent TOC breakthrough curves 

for Water 2

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0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.986)

Blended effluent

DI prediction (RSS = 0.044)

c0 = 2.35 mg/L

EBCT = 20 min.

TOC

Figure 51 Single contactor and blended effluent TOC breakthrough curves 

for Water 3

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction (RSS = 0.05)

c0 = 2.98 mg/L

EBCT = 20 min.

TOC

Figure 52 Single contactor and blended effluent TOC breakthrough curves 

for Water 4

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0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction (RSS = 0.035)

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure 53 Single contactor and blended effluent TOC breakthrough curves 

for Water 5

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction (RSS = 0.015)

c0 = 2.64 mg/L

EBCT = 20 min.

TOC

Figure 54 Single contactor and blended effluent TOC breakthrough 

curves for Water 6

-73-
0

1

2

3

4

5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction (RSS = 0.065)

c0 = 5.58 mg/L

EBCT = 20 min.

TOC

Figure 55 Single contactor and blended effluent TOC breakthrough 

curves for Water 7

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.974)

Blended effluent

DI prediction (RSS = 0.033)

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure 56 Single contactor and blended effluent TOC breakthrough 

curves for Water 8

-74-
0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction (RSS = 0.002)

UV254

Figure 57 Single contactor and blended effluent UV254 breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 0.051 1/cm

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 25 50 75 100 125 150

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction (RSS = 0.0015)

UV254

Figure 58 Single contactor and blended effluent UV254 breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 0.109 1/cm

-75-
0

25

50

75

100

125

150

175

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.999)

Blended effluent

DI prediction (RSS = NA)

c0 = 288 μg/L ClEBCT = 20 min.

SDS-TOX

Figure 59 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 4

0

25

50

75

100

125

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction (RSS = NA)

c0 = 156 μg/L ClEBCT = 7.2 min.

SDS-TOX

Figure 60 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 8

-76-
0

25

50

75

100

125

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (RSS 0.23^2 = 0.986)

Blended effluent

DI prediction (RSS = 2.87)

SDS-TTHM

Figure 61 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 154 μg/L

0

25

50

75

100

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction (RSS = 6.43)

SDS-TTHM

Figure 62 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 128 μg/L

-77-
0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.988)

Blended effluent

DI prediction (RSS = 3.64)

SDS-HAA9

Figure 64 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 37 μg/L

0

5

10

15

20

25

30

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction (RSS = 0.78)

SDS-HAA9

Figure 63 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 29 μg/L

-78-
0

5

10

15

20

25

30

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.998)

Blended effluent

DI prediction (RSS = 0.41)

SDS-CF

Figure 65 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 50.7 μg/L

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction (RSS = 3.63)

c0 = 27.4 μg/L

EBCT = 20 min.

SDS-BDCM

Figure 66 Single contactor and blended effluent SDS-BDCM breakthrough

curves for Water 6

-79-
0

1

2

3

4

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction (RSS = 0.58)

c0 = 2.2 μg/L

EBCT = 20 min.

SDS-BDCM

Figure 68 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 4

0

10

20

30

40

50

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.996)

Blended effluent

DI prediction (RSS = 0.99)

SDS-DBCM

Figure 67 Single contactor and blended effluent SDS-DBCM breakthrough

curves for Water 7

EBCT = 20 min.

c0 = 66.2 μg/L

-80-
0

5

10

15

20

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.971)

Blended effluent

DI prediction (RSS = 2.76)

c0 = 3.7 μg/L

EBCT = 20 min.

SDS-BF

Figure 70 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 1

0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.946)

Blended effluent

DI prediction (RSS = 1.2)

c0 = 2.6 μg/L

EBCT = 7.2 min.

SDS-BDCM

Figure 69 Single contactor and blended effluent SDS-BDCM breakthrough

curves for Water 8

-81-
0

2

4

6

8

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction (RSS = 0.4)

SDS-DCAA

Figure 72 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 15.7 μg/L

0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.978)

Blended effluent

DI prediction (RSS = 3.47)

c0 = 3.3 μg/L

EBCT = 20 min.

SDS-BF

Figure 71 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 6

-82-
0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.989)

Blended effluent

DI prediction (RSS = 0.28)

c0 = 12.7 μg/L

EBCT = 20 min.

SDS-TCAA

Figure 73 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 5

0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction (RSS = 1.31)

c0 = 5.7 μg/L

EBCT = 20 min.

SDS-DBAA

Figure 74 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 6

-83-
0

2

4

6

8

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.981)

Blended effluent

DI prediction (RSS = 0.38)

c0 = 7 μg/L

EBCT = 20 min.

SDS-BCAA

Figure 75 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction (RSS = 0.48)

c0 = 3 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure 76 Single contactor and blended effluent SDS-DCBAA breakthrough

curves for Water 2

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0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.913)

Blended effluent

DI prediction (RSS = 0.72)

SDS-CDBAA

Figure 77 Single contactor and blended effluent SDS-CDBAA breakthrough

curves for Water 6

EBCT = 20 min.

c0 = 3 μg/L

0

2

4

6

8

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction (RSS = 1.92)

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure 78 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 7

-85-
0

2

4

6

8

10

12

14

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.91)

Blended effluent

DI prediction (RSS = 3.73)

c0 = 3.7 μg/L

SDS-BF EBCT = 20 min.

Figure 79 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 2

0

1

2

3

4

5

6

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.95)

Blended effluent

DI prediction (RSS = 0.88)

SDS-CDBAA

Figure 80 Single contactor and blended effluent SDS-CDBAA breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 3.7 μg/L

-86-
0

10

20

30

40

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction (RSS = 5.67)

c0 = 11.8 μg/L

EBCT = 20 min.

SDS-BF

Figure 82 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 3 (fit to step-lag-peak logistic function model)

0

10

20

30

40

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.856)

Blended effluent

DI prediction (RSS = 6.3)

c0 = 11.8 μg/L

EBCT = 20 min.

SDS-BF

Figure 81 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 3 (original step-lag logistic function model curve fit)

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-89-

4.4 Comparison of SCA and DI Methods Used to Predict the Blended Contactor

Integral Breakthrough Curve

Two methods were used to predict the integral breakthrough curve, which is used to estimate

blended contactor run times: direct integration (DI) and surrogate correlation approach (SCA).

The DI procedure was explained in Sections 1.4 and 3.2.3, while the steps followed by the SCA

procedure were described in Sections 1.5 and 3.2.4. The DI method has been presented in the

literature and is the traditional method for predicting the integral breakthrough curve, the curve

that relates blended contactor effluent water quality to single contactor run time. However,

previous verification of the DI method was limited to DBP surrogates and class sums.

Verification of the DI method here is expanded to include more GAC runs and water sources,

and a larger experimental matrix, including application to DBP species. The SCA method

developed in this study is verified against experimental data and compared to results obtained for

the DI procedure. The SCA method is computationally simpler than the DI procedure, an

important consideration when selecting a predictive method for application to the ICR GAC

treatment study data set, which may require 8,000 to 9,000 curve fits.

The results of these predictive models were compared to the experimentally-obtained blended

effluent data set. A best-fit curve was used to describe the observed data. The best-fit was

derived using the same logistic function models used for the single contactor data, as the shapes

observed for many of the blended effluent curves were similar to those encountered during single

contactor breakthrough curve analysis. The models were evaluated by comparing the predictions

to the experimental data (not the best-fit curve) and calculating the residual sum of squares

(RSS) and model bias. The model bias is defined as the mean of the residuals, calculated

between the model prediction and experimental results. A summary of the calculated model RSS

values is shown in Tables 9 and 10, while a summary of the calculated bias values is shown in

Tables 11 and 12. Note that the RSS and bias values have units equivalent to the units of the

parameter from which they are calculated; therefore, the magnitudes of these values for different

parameters may vary widely and are not directly comparable. A summary of average RSS and

bias values is given in Table 13. Based on direct comparisons between the RSS values for each

predictive approach, and across all parameters (110 comparisons) the two methods were equally

successful in predicting the observed data: for 52 percent of the predictions, the SCA method

RSS value was lower than that for the DI method.

To examine the performance of each model for predicting the integral breakthrough curve across

all water sources and water quality parameters, cumulative frequency distribution plots were

developed for the RSS and bias data. To provide a consistent basis of comparison of RSS and

bias data across different parameters, the data was normalized. This was accomplished by

dividing the RSS and bias values measured for each parameter and water by the average

concentration of that parameter in the blended effluent during each run. This procedure did

result in some extremely high normalized values, and overall, the average normalized values

were relatively high. However, this is due to the relatively low average effluent concentrations

of many parameters, by which the RSS and bias values were normalized. The normalized RSS

and bias values are tools that allow comparison of the two predictive models across all available

data in this study, and are not intended to provide an indication of model performance outside of
-90-

Parameter

DI SCA Lower DI SCA Lower DI SCA Lower DI SCA 

TOC (mg/L) 0.152 NA NA 0.057 NA NA 0.044 NA NA 0.050 NUV-254 (1/cm) 0.0011 0.0017 DI 0.0012 0.0015 DI 0.0010 0.0010 DI 0.0015 0.002TOX (μg/L Cl -) NA NA NA NA NA NA NA NA NA NA N

TTHM (μg/L) 3.93 2.55 SCA 8.49 4.38 SCA 2.87 2.64 SCA 1.39 2.HAA5 (μg/L) 0.38 1.58 DI 1.69 1.48 SCA 2.48 1.13 SCA 1.24 0.8HAA6 (μg/L) 0.63 1.26 DI 2.50 2.25 SCA 2.29 2.30 DI 1.34 1.0HAA9 (μg/L) 0.78 1.04 DI 3.64 3.43 SCA 4.36 2.20 SCA 1.70 1.12 CF (μg/L) 0.39 0.65 DI 0.57 1.59 DI 0.85 0.40 SCA 0.41 1.3BDCM (μg/L) 1.52 1.03 SCA 4.35 1.98 SCA 1.05 2.57 DI 0.58 0.5DBCM (μg/L) 0.40 0.73 DI 2.18 1.72 SCA 1.04 0.68 SCA 0.59 0.5BF (μg/L) 2.76 1.31 SCA 3.73 2.18 SCA 5.67 3.14 SCA NA NA MCAA (μg/L) NA NA NA NA NA NA NA NA NA NA NDCAA (μg/L) 0.35 1.46 DI 0.49 0.68 DI 0.40 0.37 SCA 0.32 0.4TCAA (μg/L) NA NA NA 0.27 0.34 DI NA NA NA 0.96 0.6MBAA (μg/L) NA NA NA NA NA NA NA NA NA NA NDBAA (μg/L) 0.46 0.39 SCA 1.10 0.60 SCA 1.02 1.56 DI NA NA BCAA (μg/L) 0.38 0.29 SCA 0.82 0.78 SCA 0.63 0.63 DI 0.31 0.2DCBAA (μg/L) NA NA NA 0.48 0.35 SCA 0.68 0.72 DI 0.49 0.5CDBAA (μg/L) NA NA NA NA NA NA NA NA NA NA NTBAA (μg/L) NA NA NA NA NA NA NA NA NA NA N

DI: 7/SCA: 5 DI: 4/SCA: 10 DI: 6/SCA: 7 

NA: not applicableTable 9 Summary of DI and SCA integral breakthrough curve prediction RSS values for Waters 1 through 4

Residual sum of squares (RSS)

Water 1 Water 2 Water 3 Water 4
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Parameter

DI SCA Lower DI SCA Lower DI SCA Lower DI SCA Lower 

TOC (mg/L) 0.035 NA NA 0.015 NA NA 0.065 NA NA 0.033 NA NA UV-254 (1/cm) 0.0020 0.0022 DI 0.0017 0.0026 DI 0.0015 0.0021 DI 0.0017 0.0017 DI TOX (μg/L Cl -) NA NA NA 2.39 6.26 DI 6.92 9.30 DI NA NA NA 

TTHM (μg/L) 1.89 1.30 SCA 6.43 5.91 SCA 15.22 9.72 SCA 2.59 3.87 DI HAA5 (μg/L) 0.56 0.52 SCA 1.02 1.54 DI 2.02 1.52 SCA 1.35 1.84 DI HAA6 (μg/L) 0.82 0.55 SCA 1.30 1.92 DI 2.09 2.25 DI 1.83 1.86 DI HAA9 (μg/L) 1.19 1.13 SCA 1.88 2.41 DI 4.52 4.70 DI 2.20 1.85 SCA CF (μg/L) 0.31 0.65 DI 1.37 0.74 SCA 0.85 0.46 SCA 0.58 1.62 DI BDCM (μg/L) 0.85 0.34 SCA 3.63 2.31 SCA 5.57 3.38 SCA 1.20 0.81 SCA DBCM (μg/L) 0.54 0.72 DI 1.02 1.84 DI 0.99 1.10 DI 1.15 1.19 DI BF (μg/L) 0.55 0.26 SCA 3.47 1.86 SCA 11.53 5.39 SCA NA NA NA MCAA (μg/L) NA NA NA NA NA NA NA NA NA NA NA NA DCAA (μg/L) 0.49 0.59 DI 0.47 0.31 SCA 0.59 0.63 DI 0.68 0.75 DI TCAA (μg/L) 0.28 0.39 DI NA NA NA 0.40 1.34 DI 0.73 1.00 DI MBAA (μg/L) NA NA NA NA NA NA NA NA NA NA NA NA DBAA (μg/L) 0.47 0.20 SCA 1.31 1.11 SCA 2.21 0.97 SCA NA NA NA BCAA (μg/L) 0.40 0.21 SCA 0.43 0.44 DI 0.35 1.10 DI 0.59 0.31 SCA DCBAA (μg/L) 0.40 0.34 SCA 0.34 0.34 DI 1.13 0.85 SCA 0.46 0.29 SCA CDBAA (μg/L) 0.88 1.10 DI 0.72 0.67 SCA 0.25 1.02 DI NA NA NA TBAA (μg/L) NA NA NA NA NA NA 1.92 1.84 SCA NA NA NA 

DI: 6/SCA: 9 DI: 8/SCA: 7 DI: 9/SCA: 8 DI: 8/SCA: 4 

NA: not applicableTable 10 Summary of DI and SCA integral breakthrough curve prediction RSS values for Waters 5 through 8

Water 5

Residual sum of squares (RSS)

Water 7Water 6 Water 8 
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Parameter

DI SCA DI SCA DI SCA DI SCA

TOC (mg/L) +0.074 NA +0.027 NA +0.038 NA +0.037 NA

UV-254 (1/cm) -0.0007 -0.0012 -0.0004 -0.0005 -0.0008 -0.0007 -0.0009 -0.0016

TOX (μg/L Cl -) NA NA NA NA NA NA NA NA

TTHM (μg/L) -2.54 -1.65 -7.69 -4.09 -2.55 +0.89 -0.81 -1.40

HAA5 (μg/L) -0.15 +0.29 -1.45 -1.20 +1.41 +0.22 +0.56 -0.57

HAA6 (μg/L) -0.16 +0.15 -2.16 -1.85 +1.48 +0.65 +0.44 -0.65

HAA9 (μg/L) -0.18 -0.03 -3.07 -2.87 +2.82 +1.23 +0.64 -0.36

CF (μg/L) +0.18 -0.39 -0.15 -0.92 +0.57 -0.22 -0.05 -0.89

BDCM (μg/L) -1.15 -0.74 -3.83 -1.61 -0.35 +1.53 -0.47 -0.09

DBCM (μg/L) +0.07 -0.48 -1.63 -1.15 +0.59 -0.08 -0.36 -0.38

BF (μg/L) -2.17 -0.33 -2.91 -1.50 -5.23 -1.17 NA NA

MCAA (μg/L) NA NA NA NA NA NA NA NA

DCAA (μg/L) +0.11 +0.28 -0.38 -0.57 +0.29 -0.22 +0.04 -0.33

TCAA (μg/L) NA NA -0.04 +0.00 NA NA +0.36 -0.44

MBAA (μg/L) NA NA NA NA NA NA NA NA

DBAA (μg/L) -0.21 +0.22 -0.90 -0.35 -0.03 +0.52 NA NA

BCAA (μg/L) +0.04 -0.06 -0.72 -0.66 +0.36 +0.17 -0.13 -0.09

DCBAA (μg/L) NA NA -0.35 -0.24 +0.58 +0.62 +0.34 +0.38

CDBAA (μg/L) NA NA NA NA NA NA NA NA

TBAA (μg/L) NA NA NA NA NA NA NA NA

NA: not applicable

Table 1 Summary of model prediction bias for Waters 1 through 4

Parameter

DI SCA DI SCA DI SCA DI SCA

TOC (mg/L) +0.023 NA -0.001 NA +0.044 NA -0.019 NA

UV-254 (1/cm) -0.0013 -0.0015 -0.0012 -0.0019 -0.0007 -0.0008 -0.0001 -0.0008

TOX (μg/L Cl -

) NA NA -1.53 -4.89 -6.24 -5.48 NA NA

TTHM (μg/L) -1.29 -0.89 -4.83 -4.11 -11.39 -7.42 -2.01 -3.15

HAA5 (μg/L) -0.22 -0.03 -0.55 -1.14 -1.35 -0.23 -0.20 -0.78

HAA6 (μg/L) -0.31 +0.03 -0.48 -1.44 -1.20 +0.33 -0.43 -0.87

HAA9 (μg/L) -0.86 -0.44 -0.10 -1.50 -0.54 +1.91 -0.43 -0.79

CF (μg/L) -0.04 -0.44 +0.55 -0.33 +0.59 -0.30 -0.25 -1.29

BDCM (μg/L) -0.62 +0.04 -2.30 -1.47 -3.28 -1.98 -0.85 -0.58

DBCM (μg/L) -0.25 -0.40 -0.21 -1.27 +0.71 -0.59 -0.90 -1.03

BF (μg/L) -0.45 -0.12 -2.73 -0.76 -9.15 -4.18 NA NA

MCAA (μg/L) NA NA NA NA NA NA NA NA

DCAA (μg/L) -0.35 -0.44 +0.34 -0.04 +0.48 +0.53 -0.13 -0.35

TCAA (μg/L) +0.00 -0.25 NA NA +0.03 -0.81 -0.09 -0.39

MBAA (μg/L) NA NA NA NA NA NA NA NA

DBAA (μg/L) -0.27 +0.10 -0.92 -0.81 -1.65 -0.07 NA NA

BCAA (μg/L) -0.10 +0.06 +0.07 -0.25 +0.17 +0.58 -0.25 -0.10

DCBAA (μg/L) -0.18 -0.21 +0.18 -0.10 +0.50 +0.05 +0.00 +0.14

CDBAA (μg/L) -0.28 -0.02 +0.34 +0.32 +0.05 +0.62 NA NA

TBAA (μg/L) NA NA NA NA +0.08 +1.02 NA NA

NA: not applicable

Table 12 Summary of model prediction bias for Waters 5 through 8

Water 5 Water 6 Water 7 Water 8

Model Bias

Model Bias

Water 1 Water 2 Water 3 Water 4
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Parameter Count

DI SCA DI SCA DI SCA DI SCA

TOC (mg/L) 0.06 NA 7.2 NA +0.028 NA 3.7 NA 8

UV-254 (1/cm) 0.0015 0.0019 18 23 -0.0008 -0.0011 -8.2 -12 8

TOX (μg/L Cl -

) 4.7 7.8 8.5 16 -3.88 -5.19 -7.4 -9.8 2

TTHM (μg/L) 5.4 4.1 19 18 -4.14 -2.73 -15 -10 8

HAA5 (μg/L) 1.3 1.3 26 28 -0.24 -0.43 -4.4 -7.9 8

HAA6 (μg/L) 1.6 1.7 23 25 -0.35 -0.46 -4.7 -6.1 8

HAA9 (μg/L) 2.5 2.2 29 26 -0.22 -0.36 -2.3 -3.9 8

CF (μg/L) 0.7 0.9 36 36 +0.17 -0.60 6.5 -22 8

BDCM (μg/L) 2.3 1.6 29 20 -1.61 -0.61 -16 -6.1 8

DBCM (μg/L) 1.0 1.1 19 21 -0.25 -0.67 -4.6 -13 8

BF (μg/L) 4.6 2.4 38 20 -3.77 -1.34 -30 -11 6

MCAA (μg/L) NA NA NA NA NA NA NA NA 0

DCAA (μg/L) 0.5 0.7 40 44 +0.05 -0.14 3.2 -8.9 8

TCAA (μg/L) 0.5 0.7 39 61 +0.05 -0.38 4.1 -29 5

MBAA (μg/L) NA NA NA NA NA NA NA NA 0

DBAA (μg/L) 1.1 0.8 28 21 -0.66 -0.07 -17 -1.6 6

BCAA (μg/L) 0.5 0.5 28 24 -0.07 -0.04 -3.5 -2.2 8

DCBAA (μg/L) 0.6 0.5 119 115 +0.15 +0.09 15 8.4 7

CDBAA (μg/L) 0.6 0.9 98 111 +0.04 +0.31 2.8 24 3

TBAA (μg/L) 1.9 1.8 87 84 +0.08 +1.02 3.8 46 1

NA: not applicable

Table 13 Summary of mean RSS, mean bias, normalized mean RSS, and normlized mean bias for all

waters

Mean bias Mean normalized bias

(%)

Mean RSS Mean normalized RSS

(%)
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the context of the actual magnitude of the RSS and bias values. For each parameter, the average

normalized RSS and bias across all waters are summarized in Table 13.

The cumulative frequency distribution plot of normalized RSS values is shown in Figure 83.

The distribution of normalized RSS values for both predictive approaches is similar, indicating

that there was little difference in the relative success of the two methods to predict experimental

data over the entire data set. The 25th to 75th percentile range of normalized RSS values for DI

predictions was 16 to 39 percent, while that for SCA predictions was similar, 16 to 38 percent.

The 10th to 90th percentile range of normalized RSS values for both methods was also similar, at

11 to 57 percent for the SCA method and 12 to 57 percent for the DI method.

Figure 84 shows the cumulative frequency distribution of normalized bias values. Across all

waters and water quality parameters, both predictive methods tended to underestimate the

experimental data. The median of the distribution for the DI method was -6 percent, while that

for the SCA method was -10 percent. The 25th to 75th percentile range of the distribution was -

16 to +3 percent for the DI method. The SCA method more often underpredicted the

experimental data as indicated by a 25th to 75th percentile range of the distribution of -20 to -1

percent. The 10th to 90th percentile range of the distribution for the DI method was -25 to +30

percent, while that for the SCA method was -28 to +10 percent.

Across all waters and analytes, the distribution of prediction error was shown to be similar based

on the cumulative frequency distribution of RSS. However, an evaluation of the results for each

parameter is still needed and is addressed below in Sections 4.4.1 and 4.4.2. By comparing the

error, as measured by RSS and bias, for each parameter across all waters, the relative

performance of each model for each individual analyte can be assessed. A summary of all model

predictions for all parameters and all waters is included in Appendix F. For this discussion,

examples of DI and SCA model results were selected from the eight runs.

4.4.1 Surrogates and Class Sums

As shown in Figures 49 through 56 (Section 4.3), the DI method was able to successfully predict

the integral breakthrough curve for TOC for the eight runs examined. The average DI model

RSS for all eight runs was 0.055 mg/L, which compares to a value of 0.025 mg/L for the average

of all eight best-fit curves applied to the data. The DI method prediction average RSS was

slightly more than twice that for a best curve fit, but both values were very low. Expressed as a

fraction of the average TOC concentration of the experimental blended effluent data for each

water, the normalized RSS for the DI prediction was low, 7 percent. The average bias for the DI

method prediction was low and positive, +0.028 mg/L. The mean normalized bias (bias

expressed as a fraction of the average blended effluent experimental data) was also low, 4

percent.

The success of the DI model for predicting the TOC integral breakthrough curve is an important

verification prior to the application of the SCA method, as this method relies on the TOC integral

breakthrough curve as a basis for predicting the integral breakthrough curves of all other

parameters. Although an excellent predictor of the TOC integral breakthrough curve, the DI

method was not as successful in predicting the integral breakthrough curve for DBPs, especially

brominated DBP species, as shown by the examples given in Section 4.3 (Figures 61 through
-95-

82). The results of integral breakthrough curves predicted by the computationally-simpler SCA

procedure will be compared to those predicted by the DI method.

Figures 85 and 86 compare the DI and SCA UV254 integral breakthrough predictions against

experimental data for Waters 1 and 3. During both these runs, the RSS of the predictions were

very low (all less than 0.002 1/cm). For Water 1, the DI method was able to match experimental

results early in the run very well, while the SCA method underpredicted the observed data. Later

in the run, both methods slightly underpredicted the experimental integral breakthrough curve.

The DI method yielded a closer prediction based on RSS values for Water 1. The bias for both

predictions was negative (DI: -0.0007 1/cm; SCA: -0.0012 1/cm), indicating quantitatively that

the models slightly underpredicted the experimental data. For Water 3, both methods

underpredicted blended effluent UV254 early in the run. Beyond the midpoint of the run, both

methods matched the experimental results more closely. The RSS values for each predictive

approach were equivalent, and the bias values were both slightly negative (DI: -0.0008 1/cm;

SCA: -0.0007 1/cm). Overall, the UV254 DI prediction results yielded lower RSS values for

seven of the eight runs.

The integral breakthrough curve predictions for SDS-TTHM are given in Figures 87 and 88 for

Waters 1 and 7. Based on the calculated RSS values, the SCA method yielded better predictions

of the observed data than the DI method (Water 1 DI RSS: 3.9 μg/L; Water 1 SCA RSS: 2.6

μg/L; Water 7 DI RSS: 15 μg/L; Water 7 SCA RSS: 9.7 μg/L), as was the case with six of the

eight runs. Early in the run, at the point of initial breakthrough, the SCA method underpredicted

experimental results, while the DI method matched the experimental data more closely.

However, later in the run, at higher breakthrough levels, the SCA method was able to better

predict the experimental data. This pattern was repeated during most runs. The more

pronounced deviation early in the run is preferable over later in the run, because the target

treatment objective for SDS-TTHM is usually exceeded later in the run, where the SCA

prediction is closest to the experimental data. The prediction bias for Water 1 for both methods

was negative (DI: -2.5 μg/L; SCA: -1.6 μg/L). Both predictive methods also yielded a negative

bias for Water 7 (DI: -11 μg/L; SCA: -7.4 μg/L), indicating that the experimental results were

underpredicted by both predictive models. The average bias for all runs was negative for both

methods.

Neither predictive method was consistently more successful for the prediction of the integral

breakthrough curve for SDS-HAA5, SDS-HAA6 and SDS-HAA9. For SDS-HAA5 and SDSHAA9, the DI method was slightly more successful, with closer matches to experimental data

based on RSS values in five out of eight cases for each parameter. For SDS-HAA6, the SCA

method was a better predictor in five out of eight cases. The average bias for both model

predictions for SDS-HAA5, SDS-HAA6, and SDS-HAA9 for all runs was negative.

Figures 89 and 90 show the model prediction results for SDS-HAA5 for Waters 2 and 7,

respectively. For these two waters, the SCA method yielded a better prediction of the observed

blended effluent data by comparison of calculated RSS values (Water 2 DI RSS: 1.7 μg/L; Water

2 SCA RSS: 1.5 μg/L; Water 7 DI RSS: 2.0 μg/L; Water 7 SCA RSS: 1.5 μg/L). For Water 2,

both methods underpredicted experimental results throughout the run, while for Water 7, the DI

method underpredicted the observed data throughout the run. The SCA method initially

underpredicted experimental data, but between the midpoint and end of the run was a close

match to the data. The calculated bias for both waters and both methods was negative (Water 2
-96-

DI bias: -1.4 μg/L; Water 2 SCA bias: -1.2 μg/L; Water 7 DI bias: -1.3 μg/L; Water 7 SCA

bias: -0.2 μg/L).

For Waters 3 and 5, Figures 91 and 92, respectively, compare the model prediction results for

SDS-HAA6. For Water 3, both models overpredicted blended effluent concentrations, especially

towards the end of the run (DI bias: +1.5 μg/L; SCA bias: +0.7 μg/L), and the RSS values were

similar (DI RSS: 2.3 μg/L; SCA RSS: 2.3 μg/L). For Water 5, both models matched the

observed results well, with the SCA method resulting in the closest match based on RSS values

(DI RSS: 0.8; SCA RSS: 0.6). The bias calculated for the DI prediction was negative (-0.3

μg/L), while that for the SCA prediction was slightly positive (+0.03 μg/L).

For Waters 5 and 6, the model predictions for blended contactor effluent SDS-HAA9 are shown

in Figures 93 and 94, respectively. The bias for both model predictions for both waters was

negative (Water 5 DI bias: -0.9 μg/L; Water 5 SCA bias: -0.4 μg/L; Water 6 DI bias: -0.1 μg/L;

Water 6 SCA bias: -1.5 μg/L). Although the SCA underpredicted blended effluent

concentrations early in the run, it yielded a very close match to the experimental data towards the

end of the run. For Water 5, the RSS value for the SCA method prediction (1.1 μg/L) was

slightly lower than that for the DI method prediction (1.2 μg/L). For Water 6, the DI method

yielded a better prediction of the integral breakthrough curve than did the SCA method, based on

RSS values (DI RSS: 1.9 μg/L; SCA RSS: 2.4 μg/L).

For two waters (Waters 6 and 7), SDS-TOX was also analyzed in the blended effluent. For these

two cases, the DI method was a better predictor of the integral breakthrough curve than the SCA

method. For Water 6 (Figure 95), the SCA method underpredicted experimental data throughout

most of the run, while for Water 7 (Figure 96), the SCA method underpredicted the experimental

results during the initial portion of the run. Later in the run the SCA method resulted in a very

good prediction of the integral breakthrough curve. For both waters, the RSS values for the DI

method were lower than those for the SCA method (Water 6 DI RSS: 2.4 μg/L Cl-

; Water 6 SCA

RSS: 6.3 μg/L Cl-; Water 7 DI RSS: 6.9 μg/L Cl-; Water 7 SCA RSS: 9.3 μg/L Cl-). The bias

values for both runs and both model predictions were negative (Water 6 DI bias: -1.5 μg/L Cl-

;

Water 6 SCA bias: -4.9 μg/L Cl-; Water 7 DI bias: -6.2 μg/L Cl-; Water 7 SCA bias: -5.5 μg/L

Cl-

).

4.4.2 DBP Species

With a few exceptions, the DI method was able to better predict non-brominated DBP species,

while the SCA method was a better predictor of the brominated species. This is due in part to the

marginal ability of the DI method to predict blended effluent concentrations that result when the

single contactor curve shows a peak, or a steep breakthrough followed by a flat plateau. The

influence of the bromide to TOC ratio on the single contactor breakthrough curve is not captured

by the DI method, while the SCA method, which inherently correlates DBP formation to TOC

concentration, was better able to predict the integral breakthrough curve for brominated DBP

species. The relationship between TOC concentration and DBP formation and speciation in both

the single and blended contactor effluents is discussed in Section 4.2.

For SDS-CF, the DI method yielded lower RSS values for experimental predictions in five out of

eight cases. Figures 97 and 98 show examples of model predictions for Waters 2 and 6,
-97-

respectively. For these two examples, the SCA method underpredicted the integral breakthrough

curve at the point of initial breakthrough and towards the end of the run. The SCA prediction

bias was negative for both runs (Water 2: -0.9 μg/L; Water 6: -0.3 μg/L). The DI method

yielded a better match to the observed for Water 2 (DI RSS: 0.6 μg/L; SCA RSS: 1.6 μg/L), but

the SCA method was a better predictor of the SDS-CF integral breakthrough curve for Water 6

(DI RSS: 1.4 μg/L; SCA RSS: 0.7 μg/L). The DI model bias was negative for Water 2 (-0.1

μg/L) and positive for Water 6 (+0.5 μg/L). Overall, the average bias for all runs was slightly

positive for the DI prediction, while that for the SCA prediction was slightly negative.

Figures 99 and 100 show the DI and SCA method predictions of SDS-BDCM experimental

results for Waters 1 and 8, respectively. Overall, the SCA method was a more successful model

for predicting observed data, with RSS values lower than those for the DI method for six of the

eight runs. In most cases, and as shown in the two examples, the DI method underpredicted the

experimental blended effluent concentration throughout the entire run. This was also reflected

by the negative bias values for the prediction results. The SDS-BDCM single contactor

breakthrough curves typically exhibited sharp breakthrough curves followed by a flat plateau or

peak curves. For the two examples given, the SCA method prediction provided a closer match to

the experimental data (Water 1 DI RSS: 1.5 μg/L; Water 1 SCA RSS: 1.0 μg/L; Water 8 DI

RSS: 1.2 μg/L; Water 8 SCA RSS: 0.8 μg/L). The SCA prediction bias was negative (Water 1:

-0.7 μg/L; Water 8: -0.6 μg/L), as was that for the DI predictions (Water 1: -1.1 μg/L; Water 8:

-0.8 μg/L). For all runs, the average model bias for both models was negative, although the SCA

prediction bias was lower in magnitude as compared to that for the DI method.

For SDS-DBCM, the DI method yielded a better fit to the observed data in five of eight cases.

Although a brominated DBP species, the breakthrough of SDS-DBCM was typically shaped

similar to that of SDS-CF: a gradual increase in concentration over time, without the "sharp

increase and plateau" or "peak" curve shapes observed for other brominated species. Figures 101

and 102 show examples of the results obtained for Waters 3 and 7, respectively. For Water 3

(Figure 101), both methods slightly underpredicted effluent concentrations at the beginning of

the run. The DI method overpredicted effluent concentrations towards the end of the run, while

the SCA method closely matched experimental data after the first third of the run. The SCA

prediction bias was positive (+0.6 μg/L), while that for the DI prediction was slightly negative (-

0.1 μg/L). The RSS value for the SCA prediction (0.7 μg/L) was lower than that for the DI

prediction (1.0 μg/L). For Water 7, shown in Figure 102, the SCA method underpredicted

blended effluent SDS-DBCM levels at the point of initial breakthrough and towards the end of

the run, while the DI method either overpredicted or matched effluent levels. In this case, the DI

prediction RSS value (1.0 μg/L) was slightly lower than those for the SCA prediction (1.1 μg/L).

For these two examples, a positive model bias was measured for the DI prediction (+0.7 μg/L),

while a negative model bias occurred for the SCA prediction (-0.6 μg/L). For all waters, the

mean prediction bias for both methods was negative. The magnitude of the mean bias was lower

for the DI procedure.

The SCA method resulted in closer matches to the observed data than did the DI method, based

on calculated RSS values, for all SDS-BF integral breakthrough curve predictions (six of the

eight runs yielded data above the MRL). Several single contactor effluent SDS-BF data formed

peak curves, and some of the integral breakthrough curves were also peak curves. The DI

method underpredicted blended effluent SDS-BF concentrations. The SCA method was able to

better match the peak-shaped integral breakthrough curves that were sometimes observed.
-98-

Examples of the model prediction results are given in Figures 103 and 104 for Waters 1 and 5,

respectively. The DI method underpredicted the experimental results throughout the entire run

for both waters. Although the SCA method initially underpredicted blended effluent

concentrations, at higher concentrations the accuracy of the predicted results increased,

especially relative to the DI method results. The measured bias values for the SCA prediction

(Water 1: -0.3 μg/L; Water 5: -0.1 μg/L) were lower in magnitude than those for the DI method

(Water 1: -2.2 μg/L; Water 5: -0.5 μg/L), and bias values were negative for both models. The

average model bias for the six runs analyzed (DI: -3.8 μ g/L; SCA: -1.3 μg/L) was negative for

both methods, and the magnitude of the DI method bias was greater than that for the SCA

method by a factor of 2.8.

The DI method continued to perform well for non-brominated HAA species, while the SCA

method was superior when predicting the integral breakthrough curve for brominated species.

Exceptions to this trend occurred for SDS-CDBAA and SDS-TBAA, for which the DI method

more often yielded better predictions. However, only four total cases were available for

evaluation of both species, due to concentrations not exceeding the MRLs during many runs.

Furthermore, analysis was complicated because formed levels were typically only slightly higher

than the MRL. Values below the MRL were assigned a value of zero, which was not the case for

the prediction methods. For the one case available to compare SDS-TBAA blended effluent

concentrations, the SCA method more closely matched effluent levels (three points) that were

measured above the MRL (4.0 μg/L). However, the overall calculated RSS for the SCA method

was slightly higher than that for the DI method. For SDS-DCBAA, comparisons were possible

during only three runs, and the SCA method was a more accurate predictor of the integral

breakthrough curve in all three cases. No comparisons were possible for SDS-MCAA or SDSMBAA.

For SDS-DCAA, the DI method resulted in lower RSS values in six of the eight runs. Examples

of the results are shown in Figures 105 and 106 for Waters 1 and 4, respectively. While the DI

method well-predicted the data throughout the entire run for Water 1 (RSS: 0.4 μg/L), the SCA

method initially underpredicted and then overpredicted the observed data (RSS: 1.5 μg/L). For

Water 4, the DI method again was a successful predictor of the SDS-DCAA integral

breakthrough curve (RSS: 0.3 μg/L), while the SCA method consistently underpredicted

experimental data (RSS: 0.5 μg/L). For Water 1, both predictions yielded a positive bias (DI:

+0.1 μg/L; SCA: +0.3 μg/L), while for Water 4, the bias for the SCA prediction was negative (-

0.3 μg/L), and that for the DI prediction was low and positive (+0.04 μg/L). Similar results were

obtained for SDS-TCAA predictions based on the two methods: for four of five possible

comparisons, the RSS value for the DI method was lower than that for the SCA method,

indicating a better prediction. The average bias for the DI method prediction (+0.1 μg/L) was

low and positive, while that for the SCA method was negative (-0.4 μg/L).

For SDS-DBAA and SDS-BCAA, whose single contactor breakthrough curve typically exhibit

sharp breakthrough curves followed by a flat plateau or peak curves, the SCA method

outperformed the DI method for integral breakthrough curve prediction. RSS values for the SCA

prediction were lower in four out six cases for SDS-DBAA, and in six out of eight cases for

SDS-BCAA. The average model bias was negative for both parameters. For SDS-DBAA, the

average bias magnitude was larger for DI predictions (DI: -0.7 μg/L; SCA: -0.1 μg/L), while for

SDS-BCAA, the average bias for both methods was low (DI: -0.07 μg/L; SCA: -0.04 μg/L).

Figure 107 shows the SDS-DBAA results obtained for Water 2 (DI RSS: 1.1 μg/L; SCA RSS:
-99-

0.6 μg/L), while Figure 108 shows the SDS-BCAA results obtained for Water 8 (DI RSS: 0.6

μg/L; SCA RSS: 0.3 μg/L). In both these examples, the SCA prediction was more accurate than

the DI prediction based on RSS values. The model bias for these two examples was negative for

both models, and lower in magnitude for SCA method predictions as compared to DI method

predictions .

This analysis showed that the DI prediction was successful in predicting the integral

breakthrough curve for TOC, which is an important step for use of the SCA method. The

cumulative frequency distribution comparisons of the SCA and DI model results showed that the

two methods were equivalent in their ability to predict the integral breakthrough curve, based on

a comparison across all waters and water quality parameters. Over the entire data set, both

methods were biased negative in their predictions of the experimental data. Both the DI and

SCA predictions agreed well with experimental data for surrogates and class sums. For

chlorinated DBP species, the DI method was a better predictor of the integral breakthrough curve

than the SCA method. However, SCA method predictions outperformed those determined by the

DI procedure for brominated DBP species integral breakthrough curves.

The main advantage of the SCA method over the DI procedure is that predictions using the SCA

minimize the number of calculations necessary to predict blended contactor water quality as a

function of single contactor run time. Based on the results of this study, use of the SCA

procedure for predicting the integral breakthrough curve is recommended during ICR GAC

treatment study data analysis.
Figure 83 Cumulateive frequency distribution plot of normalized residual sum-of-squares

(RSS) for DI and SCA model predictions

Figure 84 Cumulative frequency distribution plot of normalized bias for DI and SCA model

predictions

0

20

40

60

80

100

1 10 100 1000

Normalized residual sum-of-squares, RSS (%)

Percent at or below value

DI prediction

SCA prediction

0

20

40

60

80

100

-100 -50 0 50 100 150

Normalized Bias (%)

Percent at or below value

DI prediction

SCA prediction

-100-
0.000

0.005

0.010

0.015

0.020

0 50 100 150 200 250 300

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.001)

SCA prediction (RSS = 0.001)

c0 =

EBCT = 20 min.

UV254

Figure 86 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 3

0.048 1/cm

-101-

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 25 50 75

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0011)

SCA prediction (RSS = 0.0017)

c0 =

EBCT = 15 min.

UV254

Figure 85 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 1

0.094 1/cm
0

10

20

30

40

50

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.93)

SCA prediction (RSS = 2.55)

c0 =

EBCT = 15 min.

SDS-TTHM

Figure 87 Comparison of DI and SCA methods for predicting the SDS-TTHM 

integral breakthrough curve for Water 1

85 μg/L

0

25

50

75

100

125

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 15.22)

SCA prediction (RSS = 9.72)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure 88 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 7

200 μg/L

-102-
0

5

10

15

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.69)

SCA prediction (RSS = 1.48)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure 89 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 2

24 μg/L

0

5

10

15

20

25

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.02)

SCA prediction (RSS = 1.52)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure 90 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 7

65 μg/L

-103-
0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.29)

SCA prediction (RSS = 2.3)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure 91 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 3

43 μg/L

0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.82)

SCA prediction (RSS = 0.55)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure 92 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 5

34 μg/L

-104-
0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.19)

SCA prediction (RSS = 1.13)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure 93 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 5

48 μg/L

0

5

10

15

20

25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.88)

SCA prediction (RSS = 2.41)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure 94 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 6

61 μg/L

-105-
0

20

40

60

80

100

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = 2.39)

SCA prediction (RSS = 6.26)

c0 =

EBCT = 20 min.

SDS-TOX

Figure 95 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 6

305 μg/L Cl0

50

100

150

200

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = 6.92)

SCA prediction (RSS = 9.3)

c0 =

EBCT = 20 min.

SDS-TOX

Figure 96 Comparison of DI and SCA methods for predicting the SDSTOX integral breakthrough curve for Water 7

486 μg/L Cl-

-106-
0

5

10

15

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.57)

SCA prediction (RSS = 1.59)

c0 =

EBCT = 20 min.

SDS-CF

Figure 97 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 2

41.9 μg/L

0

2

4

6

8

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.37)

SCA prediction (RSS = 0.74) c0 =

EBCT = 20 min.

SDS-CF

Figure 98 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 6

55.3 μg/L

-107-
0

4

8

12

16

20

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.52)

SCA prediction (RSS = 1.03)

c0 =

EBCT = 15 min.

SDS-BDCM

Figure 99 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 1

19.3 μg/L

0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.2)

SCA prediction (RSS = 0.81)

c0 =

EBCT = 7.2 min.

SDS-BDCM

Figure 100 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 8

2.6 μg/L

-108-
0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.04)

SCA prediction (RSS = 0.68)

c0 =

EBCT = 20 min.

Figure 101 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 3

SDS-DBCM

44.5 μg/L

0

5

10

15

20

25

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.99)

SCA prediction (RSS = 1.1)

c0 =

EBCT = 20 min.

Figure 102 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 7

SDS-DBCM

66.2 μg/L

-109-
0

2

4

6

8

10

12

14

16

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.76)

SCA prediction (RSS = 1.31)

c0 =

EBCT = 15 min.

SDS-BF

Figure 103 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 1

3.7 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.55)

SCA prediction (RSS = 0.26)

c0 =

EBCT = 20 min.

SDS-BF

Figure 104 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 5

1.2 μg/L

-110-
0

2

4

6

8

10

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.35)

SCA prediction (RSS = 1.46)

c0 =

EBCT = 15 min.

Figure 105 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 1

SDS-DCAA

12.5 μg/L

0

1

2

3

4

5

6

7

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.32)

SCA prediction (RSS = 0.47)

c0 =

EBCT = 20 min.

Figure 106 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 4

SDS-DCAA

20.3 μg/L

-111-
0

2

4

6

8

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.1)

SCA prediction (RSS = 0.6)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure 107 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 2

5 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.59)

SCA prediction (RSS = 0.31)

c0 =

EBCT = 7.2 min.

SDS-BCAA

Figure 108 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 8

3 μg/L

-112-
-113-

4.5 Analysis of Model Applicability to Finite Number of Contactors

Both the DI and SCA models for predicting the integral breakthrough curve, a relationship

between single contactor run time and blended contactor effluent water quality, rely on the

assumption that an infinite number of contactors are operated on-line in parallel-staggered mode.

The application of the DI procedure to any parameter relies on the infinite contactor assumption;

the DI prediction of the TOC integral breakthrough curve is a preliminary step utilized by the

SCA method for predicting the integral breakthrough curves of all other water quality

parameters. The application of these methods to situations involving finite numbers of

contactors may be limited by this assumption. The following analysis was developed to

determine the acceptability of the error incurred by the infinite contactor assumption.

Using the step logistic function model, a series of integral breakthrough curves were developed

and plotted for the following numbers of contactors in parallel, N: 2, 3, 4, 6, 10, 20, and infinite.

For the case of an infinite number of contactors Equation 12 (Section 1.4) was used, while in all

other cases the numerical integration shown in Equation 4 (Section 1.2) was used. The single

contactor breakthrough curve from which the integral curves were developed was also plotted.

The results of this modeling are shown in Figures 109 through 114 for varying values of B and

D, logistic function parameters that affect the shape of the curve. Values used for B ranged from

10 to 30, while values for D ranged from 0.05 to 0.20. These value ranges for B and D were

chosen to reflect the range typically seen for fits of GAC breakthrough curve data. Set values

were used instead of experimental data to examine the impact of B and D while A and A0 were

held constant. For the curve fits performed in this study, the 25th and 75th percentiles for the

values of B were 5 and 25, respectively, while the 25th and 75th percentiles for the values of D

were 0.02 and 0.07, respectively. The best-fit parameters for all curve fits performed in this

study are included in Appendix G. The parameter values used for A0 and A were held constant

for all six series of breakthrough curves, at values of 0 and 1, respectively. (The logistic function

parameter A represents the asymptote to which the logistic function is approaching, while A0

represents a step value given to the function.) All the data were plotted over 100 full-scale

operation days.

The graphs in Figures 109 through 114 show that as the number of contactors operated in

parallel-staggered mode increases, the integral curve approaches the model results for an infinite

number of contactors. The largest incremental benefit afforded by operating contactors in

parallel-staggered mode over single contactor operation occurs when two contactors are

operated. The benefit realized by adding an additional contactor decreases as the number of

contactors on-line increases.

To quantitatively compare the results, the run time to a range of treatment objectives expressed

as a fraction of the parameter A, the value to which the breakthrough curve approaches

asymptotically, was estimated based on the curves developed for N contactors. These are not

percent breakthrough values; they represent instead a fraction of the concentration that the single

contactor curve approaches asymptotically (asymptotic concentration). Values of 0.35, 0.50, and

0.65 were utilized as treatment objectives. Note that each value corresponds to a lower percent

breakthrough value than that calculated based on the GAC influent concentration. An extreme

case of a 0.80 treatment objective was also examined. This analysis is summarized in Tables 14

through 17.
-114-

The goal of this analysis was to establish what the smallest value for N was for which the integral

curve (derived based on the infinite number of contactors assumption) would yield a maximum

10 percent error in the estimated blended contactor run times, assuming this level of error is

acceptable. The parameter N90 will represent this breakpoint and is defined as the minimum N at

which point the throughput of each contactor operated in parallel-staggered mode is 90 percent

of that for an infinite number of contactors on-line. This is equivalent to the point at which the

run time estimated using an assumption of an infinite number of contactors on-line is within 10

percent of the actual run time of each of N contactors.

Figures 115 and 116 establish a relationship between integral breakthrough curve run time

estimates based on an infinite number of contactors and that based on a finite number of

contactors. As shown in Figures 115 and 116, the minimum number of contactors operated in

parallel-staggered mode necessary so that the integral breakthrough curve for finite N90 is within

10 percent of the integral breakthrough curve for infinite contactors varies with treatment

objective. For B = 30 and D = 0.10 and treatment objectives ranging between 0.35 and 0.65, the

N90 ranged from 7 to 13 contactors. As the value used for the treatment objective was increased

(a less stringent treatment objective relative to GAC effluent concentration), N90 increased.

Similar results were obtained for B = 30 and D = 0.20. As the treatment objective was varied

between 0.35 and 0.65, the N90 ranged from 7 to 13 contactors. The N90 to meet the given

treatment objectives did not vary as B and D were varied: for treatment objectives between 0.35

and 0.65, the N90 varied from 7 to 13 contactors for both pairs of B and D values modeled.

Therefore, the error between run times estimated based on an infinite number of contactors and

that estimated for N contactors is independent of the shape of the curve, although it is dependent

on the extent of breakthrough achieved. For the extreme asymptotic concentration fraction value

of 0.80, N90 was 22 contactors.

The assumption of an infinite number of contactors operated in parallel-staggered mode

simplifies the analysis of the integral breakthrough curve, as does the assumption of a linear

breakthrough curve. However, the data presented here indicate that for breakthrough profiles

that follow the logistic function form, N90 varies between 7 and 13 contactors, as the treatment

objective is varied between 35 and 65 percent of the asymptotic concentration. Thus, when

using the infinite contactor assumption to model the integral breakthrough curve for less than 14

contactors in parallel, the magnitude of the treatment objective in relation to the single contactor

breakthrough curve is important in minimizing the error between qN, the specific throughput of a

finite number of contactors, N, and q∞ , the specific throughput of each contactor assuming an

infinite number of contactors on-line.

Based on the use of the logistic function model and for the range of treatment objectives

evaluated in this analysis, the integral breakthrough curve developed from the infinite contactor

assumption will yield estimated run times within 10 percent of actual run times for 13 or more

contactors operated in parallel-staggered mode. For 10 contactors on-line, the infinite contactor

assumption will yield run time estimates within 12 percent of the actual run times. In all cases,

run time estimates based on the infinite contactor assumption are longer than those for a finite

number of contactors, and thus yields a best case estimate of GAC performance. The

relationship developed between integral breakthrough curve run time estimates based on an

infinite number of contactors and that based on a finite number of contactors can be applied to
-115-

ICR treatment study data to obtain GAC run time estimates that are more applicable to GAC

applications with a small number of parallel contactors.

Number of

contactors,

N

Run time to treatment objective (0.35), expressed as percentage of run time for

infinite number of contactors

B = 30;

D = 0.10

B = 30;

D = 0.05

B = 10;

D = 0.10

B = 10;

D = 0.05

B = 10;

D = 0.2

B = 30;

D = 0.2

1 56 56 54 54 54 56

2 71 71 70 70 70 71

3 78 78 78 78 78 78

4 83 83 82 82 82 83

6 88 88 87 87 87 88

10 92 92 92 92 92 92

20 96 96 96 96 96 96

Table 14 Summary of run times to a 0.35 treatment objective

Number of

contactors,

N

Run time to treatment objective (0.50), expressed as percentage of run time for

infinite number of contactors

B = 30;

D = 0.10

B = 30;

D = 0.05

B = 10;

D = 0.10

B = 10;

D = 0.05

B = 10;

D = 0.2

B = 30;

D = 0.2

1 50 NA 50 50 50 50

2 67 NA 67 67 67 67

3 75 NA 75 75 75 75

4 80 NA 80 80 80 80

6 86 NA 86 86 86 86

10 91 NA 91 91 91 91

20 95 NA 95 95 95 95

NA: not applicable, treatment objective not exceeded

Table 15 Summary of run times to a 0.50 treatment objective
-116-

Number of

contactors,

N

Run time to treatment objective (0.65), expressed as percentage of run time for

infinite number of contactors

B = 30;

D = 0.10

B = 30;

D = 0.05

B = 10;

D = 0.10

B = 10;

D = 0.05

B = 10;

D = 0.2

B = 30;

D = 0.2

1 41 NA 43 NA 43 41

2 59 NA 60 NA 60 59

3 69 NA 70 NA 70 69

4 74 NA 75 NA 75 74

6 81 NA 82 NA 82 81

10 88 NA 88 NA 88 88

20 94 NA 94 NA 94 94

NA: not applicable, treatment objective not exceeded

Table 16 Summary of run times to a 0.65 treatment objective

Number of

contactors,

N

Run time to treatment objective (0.80), expressed as percentage of run time for

infinite number of contactors

B = 30;

D = 0.10

B = 30;

D = 0.05

B = 10;

D = 0.10

B = 10;

D = 0.05

B = 10;

D = 0.2

B = 30;

D = 0.2

1 NA NA NA NA 31 28

2 NA NA NA NA 48 45

3 NA NA NA NA 58 56

4 NA NA NA NA 64 63

6 NA NA NA NA 73 71

10 NA NA NA NA 82 81

20 NA NA NA NA 90 89

NA: not applicable, treatment objective not exceeded

Table 17 Summary of run times to a 0.80 treatment objective
  









   



































 



















 





 



  









  









  









0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 25 50 75 100 125

Run time (days)

Parameter concentration

1 2



3 4

6 10

20 Inf.

Figure 109 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 30; D = 0.1)

A0:

A:

B:

D: 

0 

1 

30 

0.1

Number of 

contactors

     













     









   









 









 









 





 









  













 





0.0

0.2

0.4

0.6

0.8

1.0

0 25 50 75 100 125

Run time (days)

Parameter concentration

1 2

 3 4

6 10

20 Inf.

Figure 110 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 30; D = 0.05)

A0:

A:

B:

D: 

0 

1 

30 

0.05

Number of 

contactors



-117-
  



 































































 









  









  









    

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 25 50 75 100 125

Run time (days)

Parameter concentration

1 2

 3 4

6 10

20 Inf.

Figure 111 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 10; D = 0.1)

A0:

A:

B:

D: 

0 

1 

10 

0.1

Number of 

contactors

  



















 









 









 











  









 









 





 









 





   

0.0

0.2

0.4

0.6

0.8

1.0

0 25 50 75 100 125

Run time (days)

Parameter concentration

1 2

 3 4

6 10

20 Inf.

Figure 112 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 10; D = 0.05)

A0:

A:

B:

D: 

0 

1 

10 

0.05

Number of 

contactors

-118-
 





   







     

















       

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 25 50 75 100 125

Run time (days)

Parameter concentration

1 2



3 4

6 10

20 Inf.

Figure 113 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 10; D = 0.2)

A0:

A:

B:

D: 

0 

1 

10 

0.2

Number of 

contactors























































































  





   









      













0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 25 50 75 100 125

Run time (days)

Parameter concentration



1 2

 3 4

6 10

20 Inf.

Figure 114 Integral breakthrough curves for varying numbers of contactors

operated in parallel-staggered mode (B = 30; D = 0.2)

A0:

A:

B:

D: 

0 

1 

30 

0.2

Number of 

contactors





-119-
40

50

60

70

80

90

100

0 5 10 15 20 25

Number of contactors, n

Run time, percent of integral curve for infinite 

n

0.35

0.50

0.65

Figure 115 Run time as a function of number of contactors in parallel, 

expressed as percent of run time for infinite n (B = 30; D = 0.1)

A0:

Af:

B:

D: 

0 

1 

30 

0.1

Treatment 

objective

90 percent

40

50

60

70

80

90

100

0 5 10 15 20 25

Number of contactors, n

Run time, percent of integral curve for infinite 

n

0.35

0.50

0.65

0.80

Figure 116 Run time as a function of number of contactors in parallel, 

expressed as percent of run time for infinite n (B = 30; D = 0.2)

A0:

Af:

B:

D: 

0 

1 

30 

0.2

Treatment 

objective

90 percent

-120-
-121-

4.6 Impact of Extrapolation on Integral Breakthrough Curve Prediction

Extrapolation of the integral TOC breakthrough curve may be necessary during ICR treatment

study data analysis because the SCA procedure is limited by the highest TOC concentration

estimated from the direct integration of the single contactor TOC breakthrough curve. The

highest blended effluent TOC concentration is typically 40 to 70 percent of the highest single

contactor TOC concentration. Although higher single contactor TOC concentrations are

associated with formed DBP concentrations, these cannot be applied to the integral breakthrough

curve during application of the SCA method unless the TOC integral breakthrough curve is

extrapolated. The impact of breakthrough curve extrapolation was analyzed for two GAC runs

to determine whether the error caused by extrapolation was acceptable for ICR data analysis.

For two water sources (Waters 5 and 8), the RSSCTs were operated longer than required by the

ICR. For Water 5, the run was extended 49 full-scale days after 70 percent TOC breakthrough

was reached, equivalent to a 21 percent extension of the required run time. For Water 8, the

extension beyond the required run time was 69 days, equivalent to a 61 percent increase. During

the extended run time, two additional single contactor GAC effluent samples were taken. Two

additional blended effluent samples were also taken. The first 11 GAC effluent data points that

comprised a normal ICR treatment study run reaching 70 percent TOC breakthrough were

modeled separately from the entire 13-point GAC effluent data set. Based on the logistic

function model best-fit of the first 11 data points, the water quality at the end of the run (21 or 61

percent extrapolation) was predicted by extending the model fit.

Using the DI method, the TOC integral breakthrough curve was calculated based on the

truncated logistic function model fit and then extrapolated to predict water quality at the end of

the entire run. The TOC integral breakthrough curve was also calculated based on the logistic

function best-fit of the entire single contactor effluent data set. For the other water quality

parameters, the SCA prediction of blended contactor effluent water quality based on the

extrapolated data set and was compared to that based on the entire data set. The impact of

extrapolation on the TOC integral breakthrough curve is of special interest, due to the application

of this extrapolation as a part of the SCA procedure during data analysis of the ICR treatment

studies.

The impact of extrapolation on integral breakthrough curve predictions is shown in Figures 117

through 130. In these figures, the single contactor and integral breakthrough curves are plotted

against scaled operation time. The data points used for the extrapolated single contactor data set

are plotted with filled symbols, to differentiate these from the open symbols used for the first 11

data points. For the single contactor data, the solid best-fit line represents the logistic function

best-fit using the entire data set, while the dashed line represents the best-fit of the first 11 points

and extrapolation to the end of the run. Similarly, a dashed line also represents the extrapolated

integral breakthrough curve. The R2

 value is given for both best-fits of the single contactor data.

Figures 117 and 118 show the impact of extrapolation on the TOC single contactor logistic

function curve fit and the TOC integral breakthrough curve predicted by the DI method for

Waters 5 and 8, respectively. At the end of both runs, the extrapolated logistic function curve fit

underpredicted single contactor effluent data. Based on the extrapolated logistic function, the

TOC concentration at the end of the run was 8 percent lower than that based on the logistic
-122-

function best-fit of all available single contactor data for Water 5. For Water 8, the extrapolated

predicted TOC concentration at the end of the run was 12 percent lower.

This underprediction of the single contactor effluent data by the extrapolated logistic function

best-fit had a smaller impact on the TOC integral breakthrough curve prediction. The error

associated with extrapolation of the TOC integral breakthrough curve at the end of the run was 3

percent (0.05 mg/L) for Water 5. For Water 8, the error was slightly larger, 8 percent (0.08

mg/L).

Application of the SCA procedure to the extrapolated single contactor breakthrough curves for

all parameters yielded an average error in the predicted blended contactor concentration at the

end of the run of 5 ± 3 percent for Water 5. Figures 119 through 124 show the impact of

extrapolation on SCA prediction of the blended contactor effluent for UV254, SDS-TOX, SDSTTHM, SDS-HAA5, SDS-HAA6, and SDS-HAA9. For these DBP surrogates and class sums,

the average error in the SCA prediction due to extrapolation was also 5 percent, (the run time

was extrapolated by 21 percent) and in all cases, the extrapolated prediction was lower than the

prediction using the entire data set. Comparisons of all water quality parameters, including DBP

species, are given in Appendix H.

For Water 8, the average error in the predicted blended contactor concentration at the end of the

run based on the SCA prediction of the extrapolated integral breakthrough curve was 9 ± 5

percent. This higher mean observed error for Water 8 as compared to Water 5 can be attributed

in part to the larger percent extrapolation, 61 percent, performed on Water 8 as compared to that

for Water 5, 21 percent. For UV254, SDS-TOX, SDS-TTHM, SDS-HAA5, SDS-HAA6, and

SDS-HAA9 the average error was 7 percent. Figures 125 through 130 show the impact of

extrapolation on the SCA prediction of the blended contactor concentration of these DBP

surrogates and class sums. The blended contactor effluent concentration at the end of the run

after extrapolation was lower than that based on the entire data set, except for SDS-HAA5.

An analysis of Water 8 was also made using an extrapolation of 29 percent, which is similar to

the extrapolation applied to Water 5. For this shorter level of extrapolation, the mean error in the

integral breakthrough curve at the end of the run for all parameters was 8 percent, only slightly

lower than that observed for an extrapolation of 61 percent. For UV254, SDS-TOX, SDS-TTHM,

SDS-HAA5, SDS-HAA6, and SDS-HAA9 the average error was 6 percent. Therefore, for

Water 8, extending the extrapolation from 29 to 61 percent of the run time did not proportionally

increase the mean observed error in the predicted integral breakthrough curve at the end of the

run.

Under the SCA procedure, the error in the extrapolated integral breakthrough curve for any

parameter (other than TOC) is dependent on the error in the extrapolated DI prediction of the

TOC integral breakthrough curve as well as on the difference between the parameter single

contactor curve fits using the entire data set and the truncated data set. This difference typically

increases over the course of the run, and can be especially large in the extrapolated portion of the

curve. However, the error at the end of the single contactor curve may not impact the prediction

of the integral breakthrough curve by the SCA method because the maximum TOC concentration

at the end of the extrapolated integral breakthrough curve is still less than the maximum TOC

concentration at the end of the single contactor curve. This upper bound on TOC limits the
-123-

concentrations of all other parameters to the initial and middle portions of the single contactor

curves when using the SCA procedure to predict integral breakthrough curves.

Conversely, the DI method prediction of the integral breakthrough curve relies on the entire

single contactor breakthrough curve for each parameter. Therefore, the error at the end of the

run due to extrapolation may have a larger impact on integral breakthrough curve predictions by

the DI method than predictions by the SCA procedure. Appendix I summarizes the integral

breakthrough curve predictions after extrapolation based on the DI method for all parameters.

The average difference at the end of the run between DI integral breakthrough curve predictions

with and without extrapolation was 3 percent for Water 5, which is slightly lower than the

average obtained using the SCA method. For Water 8, the average error was slightly higher, 10

percent.

Based on the two runs examined, extrapolation up to 61 percent beyond available experimental

data yielded a mean 9 percent error in the predicted water quality based on the extrapolated

integral breakthrough curve as compared to that based on the complete data set. For a shorter

extrapolation, 21 percent, the mean error was 5 percent. An understanding and acceptance of the

error due to breakthrough curve extrapolation is important because extrapolation may be used in

many cases during the ICR treatment study data analysis to gain additional information from

GAC breakthrough data sets. In particular, data sets that do not exceed a given treatment

objective may be extrapolated by up to 50 percent in an attempt to determine a GAC run time for

the treatment objective. Based on the two waters examined in this study, the error incurred by

extrapolation up to 50 percent of the original run time should average less than 10 percent.
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.984)

Extrapolated logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure 117 Impact of extrapolation on DI prediction of the TOC integral 

breakthrough curve for Water 5

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.974)

Extrapolated logistic function best fit (R^2 = 0.961)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure 118 Impact of extrapolation on DI prediction of the TOC integral 

breakthrough curve for Water 8

-124-
0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.992)

Extrapolated logistic function best fit (R^2 = 0.984)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 0.051 1/cm

EBCT = 20 min.

UV254

Figure 119 Impact of extrapolation on SCA prediction of the UV254 integral 

breakthrough curve for Water 5

0

25

50

75

100

125

150

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.995)

Extrapolated logistic function best fit (R^2 = 0.99)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 205 μg/L ClEBCT = 20 min.

SDS-TOX

Figure 120 Impact of extrapolation on SCA prediction of the SDS-TOX 

integral breakthrough curve for Water 5

-125-
0

10

20

30

40

50

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.979)

Extrapolated logistic function best fit (R^2 = 0.975)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 58 μg/L

EBCT = 20 min.

SDS-TTHM

Figure 121 Impact of extrapolation on SCA prediction of the SDS-TTHM 

integral breakthrough curve for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.965)

Extrapolated logistic function best fit (R^2 = 0.959)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 28 μg/L

EBCT = 20 min.

SDS-HAA5

Figure 122 Impact of extrapolation on SCA prediction of the SDS-HAA5 

integral breakthrough curve for Water 5

-126-
0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.976)

Extrapolated logistic function best fit (R^2 = 0.971)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 34 μg/L

EBCT = 20 min.

SDS-HAA6

Figure 123 Impact of extrapolation on SCA prediction of the SDS-HAA6 

integral breakthrough curve for Water 5

0

5

10

15

20

25

30

35

40

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.988)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 48 μg/L

EBCT = 20 min.

SDS-HAA9

Figure 124 Impact of extrapolation on SCA prediction of the SDS-HAA9 

integral breakthrough curve for Water 5

-127-
0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.994)

Extrapolated logistic function best fit (R^2 = 0.985)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 0.033 1/cm

EBCT = 7.2 min.

UV254

Figure 125 Impact of extrapolation on SCA prediction of the UV254 integral 

breakthrough curve for Water 8

0

25

50

75

100

125

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.98)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 156 μg/L ClEBCT = 7.2 min.

SDS-TOX

Figure 126 Impact of extrapolation on SCA prediction of the SDS-TOX 

integral breakthrough curve for Water 8

-128-
0

10

20

30

40

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.98)

Extrapolated logistic function best fit (R^2 = 0.966)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 42 μg/L

EBCT = 7.2 min.

SDS-TTHM

Figure 127 Impact of extrapolation on SCA prediction of the SDS-TTHM 

integral breakthrough curve for Water 8

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.955)

Extrapolated logistic function best fit (R^2 = 0.911)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 23 μg/L

EBCT = 7.2 min.

SDS-HAA5

Figure 128 Impact of extrapolation on SCA prediction of the SDS-HAA5 

integral breakthrough curve for Water 8

-129-
0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.956)

Extrapolated logistic function best fit (R^2 = 0.923)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 27 μg/L

EBCT = 7.2 min.

SDS-HAA6

Figure 129 Impact of extrapolation on SCA prediction of the SDS-HAA6 

integral breakthrough curve for Water 8

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.952)

Extrapolated logistic function best fit (R^2 = 0.924)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 30 μg/L

EBCT = 7.2 min.

SDS-HAA9

Figure 130 Impact of extrapolation on SCA prediction of the SDS-HAA9 

integral breakthrough curve for Water 8

-130-
-131-

5 Summary and Conclusions

The design of this study incorporated two main goals. The primary objectives were to evaluate

the ability of the logistic function to model single contactor breakthrough curve data and to

evaluate the success and limitations of predictive models used to determine the integral

breakthrough curve, a relationship between single contactor run time and blended contactor

water quality. The secondary objective of this study was to evaluate the applicability of these

models and predictive methods in the context of the ICR GAC treatment study data analysis. A

large amount of data will be analyzed: the 62 GAC treatment studies performed will generate

8,000 to 9,000 individual breakthrough curves. Experimental verification was performed on data

from eight bench-scale GAC runs with varying water sources, pretreatments, DBP precursor

concentrations, bromide concentrations, and SDS chlorination conditions. The GAC runs

utilized the rapid small-scale column test (RSSCT) and were performed according to ICR

requirements.

A primary requirement for the ICR GAC treatment study data analysis procedure is to model the

single contactor effluent breakthrough data, for all parameters analyzed, including DBP

surrogates, DBP class sums, and DBP species. A model used to describe single contactor

effluent experimental data is needed for several reasons. From a data management perspective,

best-fit curve parameters that adequately describe experimental data are less memory intensive

than storing the entire experimental data set. A best-fit curve also facilitates interpolation and

extrapolation to estimate run times for given treatment objectives. Use of a best-fit model curve

also provides an estimate of the scatter in the data through the coefficient of determination, and

the model minimizes the impact of this scatter on run time estimates. Finally, a function that

describes the single contactor experimental data set is a prerequisite for determining the integral

breakthrough curve, a curve that relates single contactor run time to blended effluent water

quality under the assumption that contactors are operated in parallel-staggered mode. Run time

estimates generated by the integral breakthrough curve are more applicable to full-scale GAC

operation.

Previous researchers have used various forms of the logistic function to predict and model GAC

breakthrough curve data, and the logistic function was found to be an adequate model in this

study, where it was applied to data from eight GAC runs (160 potential curve fits, but only 126

were performed because concentrations in the GAC effluent were below the MRL for some

parameters). However, poor curve fits occurred for some parameters, especially very sharp "S"

shaped breakthrough curves, and "peak" curves (breakthrough curves for brominated species that

increased and then decreased in concentration over the course of the run due to changes in the

bromide to TOC ratio). To improve the performance of the logistic function for modeling single

contactor data, three enhancements were made, yielding the step, step-lag, and step-lag-peak

logistic function models. The step logistic function model is applicable for GAC breakthrough

curves with relatively high levels of immediate breakthrough. The step-lag logistic function

model incorporates an initial phase where the model output is set to zero, to better fit DBP

breakthrough curves with relatively long initial intervals of effluent concentrations reported as

BMRL, prior to breakthrough. The step-lag-peak logistic function model incorporates a linear

decay term and is applied to "peak" breakthrough curves, which sometimes occur for brominated

DBP species.
-132-

Curve fitting involved determining which model was applicable, and applying it to the

breakthrough curve for each parameter. These enhanced forms of the logistic function model

were able to successfully fit single contactor breakthrough curve data for all parameters. A

method was also developed to detect outlier data points that limited the influence of deviant

observations on the parameter estimates. In general, the application of the three enhanced forms

of the logistic function curve to single contactor effluent breakthrough data was successful, with

a mean R2

 of 0.974.

Two predictive approaches for determining the integral breakthrough curve were examined: the

direct integration (DI) and the surrogate correlation approach (SCA). The results of these

procedures were compared to experimental results. Experimental data were obtained by

collecting the entire effluent from each of eight bench-scale GAC experiments in separate

reservoirs and sampling from these reservoirs over time. This experimental procedure simulates

the integral breakthrough curve for an infinite number of contactors operated in parallelstaggered mode.

The DI procedure applied the average value function to the logistic function model fit of the

experimental single contactor data, and is based on the assumption of an infinite number of

contactors operated in parallel-staggered mode with regular GAC replacement frequencies. For

DBP surrogates (TOC, UV254, and SDS-TOX), the DI procedure yielded excellent results in

comparison to experimental data. For class sums, such as SDS-TTHM and SDS-HAA5,

predictions were usually adequate. However, for individual DBP compounds, especially

brominated species, the DI approach did not always result in accurate predictions. The

inaccuracy of the DI approach for the prediction of some DBP species is problematic since

individual DBPs of potential health concern will be considered during analysis of the treatment

study data.

The SCA procedure was developed to reduce the computational requirements of estimating

blended contactor run times for any given regulatory treatment objective evaluated during the

ICR GAC treatment study data analysis. The SCA procedure relies on the DI method to

establish an integral breakthrough curve for TOC only. The DI method yielded excellent

predictions of the TOC integral breakthrough curve for the waters examined in this study, with a

mean error quantified by the residual sum of squares (RSS) of 0.055 mg/L, and a mean bias of

+0.028 mg/L. Once the TOC integral breakthrough curve is obtained, data points on both the

single contactor and integral breakthrough curves at a constant TOC concentration are mapped,

and all other water quality parameters associated with the single contactor effluent data set at that

TOC concentration are applied to the blended contactor integral breakthrough curve. The SCA

procedure requires that single contactor effluent data be adequately modeled using the logistic

function models. For eight GAC runs and eight water sources, this study showed that the

enhanced logistic function models successfully simulated a wide variety of GAC breakthrough

curve profiles. The SCA procedure inherently relies on the assumption that the relationship

between TOC and the other organic precursors, SDS DBP class sums, and SDS DBP species

established in the single contactor effluent is maintained in the blended contactor effluent. This

study found that this assumption is valid for the eight waters examined. In addition, the

correlation between TOC and bromine incorporation factors for THMs and HAAs was shown to

be consistent between the single contactor effluent and experimental blended effluent.
-133-

Application of the SCA procedure to the experimental GAC breakthrough curves for eight water

sources showed that the overall accuracy of the model was equivalent to the DI method for

predicting the integral breakthrough curve. This analysis was performed by calculating the RSS

and bias between model predictions and experimental data. The cumulative frequency

distribution of the normalized RSS showed that across all waters and all analytes, the prediction

error for the two models was equivalent. Both models were biased negative, indicating a

tendency to underpredict the experimental data. The SCA model had a slightly higher negative

bias than did the DI model. Based on these results, application of the computationally-simpler

SCA procedure to ICR treatment study data is recommended.

A comparative analysis of the two predictive models was also performed for each individual

analyte. The SCA method was more accurate than the DI method when applied to SDS-TTHM,

and was equally accurate when applied to SDS-HAA5, SDS-HAA6, and SDS-HAA9. For the

two waters that examined SDS-TOX, the DI method was a better predictor of experimental

results. For the predominant THM and HAA species (for which comparisons could be made for

six or more of the eight runs), the SCA method outperformed the DI method when applied to

brominated DBP species, with the exception of SDS-DBCM. The DI method generated more

accurate predictions of the non-brominated DBP species (SDS-CF, SDS-DCAA, and SDSTCAA). The impact of changing bromide to TOC ratio on the shape of the breakthrough curve

for brominated DBP species, yielding peak curves or very sharp curves followed by a plateau,

typically resulted in underpredictions by the DI method of the observed data. The SCA method

was able to better predict the integral breakthrough curves for the brominated species because it

relied on the relationship between TOC and DBP formation in the single contactor effluent,

which inherently accounts for changing bromide to TOC ratios.

An important limitation of the DI and SCA procedures is that they rely on the assumption of an

infinite number of contactors operated in parallel-staggered mode. Previous work has

maintained that for 10 or more contactors in operation, actual blended effluent run times are

within 10 percent of those estimated based on the infinite contactor assumption. This study

found that the error incurred when applying run time estimates based on the infinite contactor

assumption to run times for finite numbers of contactors is impacted by the number of contactors

and the magnitude of the treatment objective examined in relation to the asymptotic

concentration approached by the single contactor breakthrough curve. Based on the logistic

function model, the infinite contactor assumption will yield estimated run times within 10

percent of actual run times for 13 or more contactors operated in parallel-staggered mode. For

10 contactors on-line, the infinite contactor assumption will yield run time estimates within 12

percent of the actual run times. In all cases, run time estimates based on the infinite contactor

assumption are longer than those for a finite number of contactors, thus providing a best case

scenario for GAC performance.

The applicability of the infinite contactor assumption in this model to finite numbers of

contactors is especially important for small plants operating fewer than 10 contactors on-line.

Also evident was that the largest incremental benefit afforded by operating contactors in parallelstaggered mode occurs when two contactors are operated as compared to a single contactor. The

benefit realized by adding an additional contactor decreases as the number of contactors on-line

increases. The relationship developed between integral breakthrough curve run time estimates

based on an infinite number of contactors and that based on a finite number of contactors can be
-134-

applied to ICR treatment study data to estimate the performance of large and small GAC

systems.

During the total run time for any given single contactor TOC breakthrough curve, the blended

contactor integral breakthrough curve determined by the DI approach will typically reach

concentrations ranging from 40 to 70 percent of those measured at the end of the single contactor

GAC run. Therefore, when using the SCA procedure, the latter portion of the single contactor

breakthrough curve (consisting of higher surrogate and formed DBP levels) will not usually be

applied to the blended effluent data set. This may be an issue during ICR treatment study data

analysis if a significant number of integral breakthrough curves estimated by the SCA procedure

do not reach regulatory treatment objectives. For these runs, extrapolation of the TOC integral

breakthrough curve would increase the usefulness of the entire data set.

For two waters, experimental evaluation of the sensitivity predicted water quality to

extrapolation of the integral breakthrough curve prediction was performed. For one water,

extrapolation by 21 percent yielded a 3 percent error in the predicted integral TOC concentration

at the end of the run and a mean 5 percent error at the end of the run for all analytes predicted by

the SCA method. For a second water, which was extrapolated by 61 percent, the error in

predicted TOC concentration was 8 percent at the end of the run, and the mean error at the end of

the extrapolated run was 9 percent for all analytes.

An understanding and acceptance of the error due to breakthrough curve extrapolation is

important because extrapolation may be used in many cases during the ICR treatment study data

analysis to gain additional information from GAC breakthrough data sets. In particular, data sets

that do not exceed a given treatment objective may be extrapolated by a reasonable extent (up to

50 percent) in an attempt to determine a GAC run time for the treatment objective. Based on the

two waters examined in this study, the error incurred by extrapolation up to 50 percent of the

original run time should average less than 10 percent. This error may be acceptable for ICR

treatment study data analysis, given the benefit afforded by the extrapolation.

The models used to simulate GAC operation of multiple contactors in parallel-staggered mode

rely on the assumption that the GAC is replaced at regular intervals, so that the service times of

all contactors are equal. They also assume that the breakthrough curve profiles of all single

contactors are identical. In a full-scale plant, this idealized situation will rarely occur.

Variability in source water quality will impact the run time of the contactors, depending on when

they are placed in service and the concentration of DBP precursors in the GAC influent during

their service life. Variability in distribution system conditions, especially temperature, will

impact the contactor service life, as formed DBP levels increase with higher temperatures.

Furthermore, for a plant that operates a fixed number of contactors, water demand changes

during the year may directly impact the EBCT of each contactor. A contactor that is placed online at the beginning of the summer high demand months may be operated under a shorter EBCT

as compared to a contactor placed on-line during the winter. Furthermore, it is less desirable to

remove contactors from service to replace GAC during high demand periods. Another approach

is to increase the number of contactors on-line as water demand increases. These full-scale

issues should be considered when interpreting the results of the ICR treatment study data

analysis.
-135-

6 References

Clark, R.M. 1987. Modeling TOC Removal by GAC: The General Logistic Function. Jour.

AWWA. (79:1:33).

Chowdhury, Z.K., G. Solarik, D.M. Owen, S.M. Hooper, and R.S. Summers. 1996. NOM

Removal by GAC Adsorption: Implications of Blending. In Proc. of the AWWA Annual

Conference, Toronto, Ontario, Canada.

Clark, R.M., J.M. Symons, and J.C. Ireland. Evaluating Field Scale GAC Systems for Drinking

Water. Jour. Environ. Engr. (112:4:744).

Crittenden, J.C., P.S. Reddy, D.W Hand and H. Arora. 1989. Prediction of GAC Performance

Using Rapid Small-Scale Column Tests. AWWARF/AWWA.

Crittenden, J.C., P.S. Reddy, H. Arora, J. Trynoski, D.W. Hand, D.L. Perram, and R.S.

Summers. 1991. Predicting GAC Performance with Rapid Small-Scale Column Tests.

Jour. AWWA. (83:1:77).

Gould, J.P., L.E. Fitchorn, and E. Urheim. 1983. Formation of Brominated Trihalomethanes:

Extent and Kinetics. Water Chlorination: Environmental Impact and Health Effects.

Vol. 4 (R.L. Jolley et al., eds.), Ann Arbor Sci. Publ., Ann Arbor, Michigan.

Hooper, S.M., R.S. Summers, G. Solarik, and S. Hong. 1996. GAC Performance for DBP

Control: Effect of Influent Concentration, Seasonal Variation, and Pretreatment. In

Proc. of the AWWA Annual Conference, Toronto, Ontario, Canada.

Littell, R.C., G.A. Milliken, W.W. Stroup, and R.D. Wolfinger. 1996. SAS System for Mixed

Models. Cary, NC (SAS Institute Inc.)

Oulman, C.S. 1980. The Logistic Curve as a Model for Carbon Bed Design. Jour. AWWA

(72:1:50).

Roberts, P.V. and R.S. Summers. Performance of Granular Activated Carbon for Total Organic

Carbon Removal. Jour. AWWA. (74:2:113).

Shukairy, H.M., Miltner, R.J., and Summers, R.S. 1994. Bromide's Effect on DBP Formation,

Speciation and Control: Part 1, Ozonation. Jour. AWWA. (86:6:72).

Snoeyink, V.L. 1990. Adsorption of Organic Compounds. Water Quality and Treatment: A

Handbook for Community Supplies. 4th ed. (F.W. Pontius, ed.), AWWA and McGrawHill, Inc.

Sontheimer, H., J.C. Crittenden, and R.S. Summers. 1988. Activated Carbon for Water

Treatment. 2nd ed., DVGW-Forschungsstelle, Universitat Karlsruhe, Karlsruhe,

Germany.

Standard Methods for the Examination of Water and Wastewater. 1998. APHA, AWWA, and

WEF. Washington D.C. (20th ed.)
-136-

Summers, R.S., D.M. Owen, Z.K. Chowdhury, S.M. Hooper, G. Solarik, and K. Gray. 1998.

Removal of DBP Precursors by GAC Adsorption. AWWARF and AWWA, Denver,

Colorado.

Summers, R.S., S.M. Hooper, G. Solarik, D.M. Owen, and S. Hong. 1995. Bench-Scale

Evaluation of GAC for NOM Control. Jour. AWWA. (87:8:69).

Summers, R.S., S. Hong, S.M. Hooper, and G. Solarik. 1994. Adsorption of Natural Organic

Matter and Disinfection By-Product Precursors. In Proc. of the AWWA Annual

Conference, New York, NY.

Summers, R.S., M.A. Benz, H.M. Shukairy, and L. Cummings. 1993. Effect of Separation

Processes on the Formation of Brominated THMs. Jour. AWWA. (85:1:88).

Summers, R.S., L. Cummings, J. DeMarco, D.J. Hartman, D.H. Metz, E. Howe, B. MacLeod,

and M. Simpson. 1992. Standardized Protocol for the Evaluation of GAC. AWWARF

and AWWA, Denver, Colorado.

USEPA. 1999. GAC Base Analysis Approach for the ICR Treatment Study Database.

USEPA. 1996. ICR Manual for Bench- and Pilot-Scale Treatment Studies. EPA 814-B-96-003.

Technical Support Division, Office of Ground Water and Drinking Water, Cincinnati,

Ohio.

Westrick, J.J. and J.M. Cohen. 1976. Comparative Effects of Chemical Pretreatment on Carbon

Adsorption. 
-137-

Appendix A: Breakthrough Curves Corrected for Impact of Sampling
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 25 50 75 100

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 4.54 mg/L

Figure A-1 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 1

EBCT = 15 min.

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 2.6 mg/L

Figure A-2 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 2

EBCT = 20 min.

-138-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 2.35 mg/L

Figure A-3 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 3

EBCT = 20 min.

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 2.98 mg/L

Figure A-4 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 4

EBCT = 20 min.

-139-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 3.08 mg/L

Figure A-5 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 5

EBCT = 20 min.

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 2.64 mg/L

Figure A-6 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 6

EBCT = 20 min.

-140-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 25 50 75 100 125 150

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 5.58 mg/L

Figure A-7 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 7

EBCT = 20 min.

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

TOC concentration (mg/L)

Single contactor effluent

Blended effluent - experimental

Blended effluent - adjusted for sampling

c0 = 2.02 mg/L

Figure A-8 Comparison of blended effluent TOC adjusted for experimental 

sampling to blended effluent experimental results for Water 8

EBCT = 7.2 min.

-141-
-142-

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-143-

Appendix B: SAS Code

SAS code to fit the step-lag-peak logistic model and perform outlier

adjustment.

/*********************************************************************

 This program fits data with the following functions:

 1. Cs(t) = 0 for t <= t0

 2. Cs(t) = A0+A/(1+B*exp(-D*t)) for t0 < t <= Tmax

 3. Cs(t) = Cmax+S*(t-Tmax) for Tmax < t

 where t0 = (1/D)*LN(-A0*B/(A0+A)), Cmax = Cs(Tmax),

 Tmax is the runtime at which effluent reaches its maximum.

 Bounds: -Cmax/2 <= A0 <= Cmax/2, 0 < A <= 1.5*Cmax, 0<B, 0<D, S <= 0

 Input parameters:

 RawData - the input data set name, the data set contains

 the x variable time (ordered and no duplicates)

 and the y variable analyte (concentration).

 BasInfo - the input data set name, the data set contains

 Tmax and Cmax used to set bounds and the initial value

 Analyte - the y variable name of the NLIN regression

 PlotData- name of the data set containing data for curve fit plot

 FitInfo - name of the output data set containing curve fit infomation

;

**********************************************************************/

%macro FitF012(RawData=, BasInfo=, Analyte=, FitInfo=);

data _null_; set &BasInfo;

 if analyte="&Analyte";

 Cmax05 = Cmax/2;

 Cmax15 = Cmax*1.5;

 call symput('xCmax', Cmax);

 call symput('xCmax05', Cmax05);

 call symput('xCmax15', Cmax15);

 call symput('xTmax', Tmax);

run;

PROC NLIN DATA=&RawData NOPRINT;

 parms A0=0 A=&xCmax B=10 to 50 by 10 D=0.05 to 0.15 by 0.02 S=0;

 bounds -&xCmax05 <= A0 <= &xCmax05, 0< A <= &xCmax15, 0<B, 0<D, S<=0;

 if A0<-A/(1+B) then

 t0=log(-A0*B/(A0+A))/D;

 else t0=0;

 if time < t0 then do;

 dA0= A/(D*A0*(A0+A));

 dA =-1/D/(A0+A);

 dB = 1/(B*D);

 dD =-t0/D;

 BEXP =B*exp(-D*t0);

 BEXP2=(1+BEXP)**2;

 model &Analyte = A0+A/(1+BEXP);

 der.A0= 1+A*BEXP*D*dA0/BEXP2;
-144-

 der.A = (1+BEXP+A*BEXP*D*dA)/BEXP2;

 der.B =-A*BEXP*(1/B-D*dB)/BEXP2;

 der.D = A*BEXP*(t0+D*dD)/BEXP2;

 end;

 else if time < &xTmax then do;

 BEXP = B*exp(-D*time);

 model &Analyte = A0+A/(1+BEXP);

 der.A0= 1;

 der.A = 1/(1+BEXP);

 der.B =-A*BEXP/(B*(1+BEXP)**2);

 der.D = A*BEXP*time/(1+BEXP)**2;

 end;

 else do;

 BEXPmax = B*exp(-D*&xTmax);

 model &Analyte = A0+A/(1+BEXPmax)+S*(time-&xTmax);

 der.A0= 1;

 der.A = 1/(1+BEXPmax);

 der.B =-A*BEXPmax/(B*(1+BEXPmax)**2);

 der.D = A*BEXPmax*time/(1+BEXPmax)**2;

 der.S = time-&xTmax;

 end;

 output out=outp p=pred u95=u95 l95=l95;

*proc print;

data outp; set outp;

 winsor=&Analyte;

 delta=(u95-l95)/3;

 low=0;

 high=0;

 if &Analyte ne . and &Analyte<pred-delta then do;

 outlier=&Analyte; winsor=pred-delta; low=1; end;

 if &Analyte ne . and &Analyte>pred+delta then do;

 outlier=&Analyte; winsor=pred+delta; high=1; end;

 keep time &Analyte winsor outlier low high;

proc means data=outp noprint;

 var low high;

 output out=outout sum=outlow outhigh;

data outout; set outout;

 keep outlow outhigh;

*proc print;

PROC NLIN DATA=outp outest=outest NOPRINT;

 parms A0=0 A=&xCmax B=10 to 50 by 10 D=0.05 to 0.15 by 0.02 S=0;

 bounds -&xCmax05 <= A0 <= &xCmax05, 0< A <= &xCmax15, 0<B, 0<D, S<=0;

 if A0<-A/(1+B) then

 t0=log(-A0*B/(A0+A))/D;

 else t0=0;

 if time < t0 then do;

 dA0= A/(D*A0*(A0+A));

 dA =-1/D/(A0+A);

 dB = 1/(B*D);

 dD =-t0/D;

 BEXP =B*exp(-D*t0);

 BEXP2=(1+BEXP)**2;

 model &Analyte = A0+A/(1+BEXP);

 der.A0= 1+A*BEXP*D*dA0/BEXP2;

 der.A = (1+BEXP+A*BEXP*D*dA)/BEXP2;
-145-

 der.B =-A*BEXP*(1/B-D*dB)/BEXP2;

 der.D = A*BEXP*(t0+D*dD)/BEXP2;

 end;

 else if time < &xTmax then do;

 BEXP = B*exp(-D*time);

 model &Analyte = A0+A/(1+BEXP);

 der.A0= 1;

 der.A = 1/(1+BEXP);

 der.B =-A*BEXP/(B*(1+BEXP)**2);

 der.D = A*BEXP*time/(1+BEXP)**2;

 end;

 else do;

 BEXPmax = B*exp(-D*&xTmax);

 model &Analyte = A0+A/(1+BEXPmax)+S*(time-&xTmax);

 der.A0= 1;

 der.A = 1/(1+BEXPmax);

 der.B =-A*BEXPmax/(B*(1+BEXPmax)**2);

 der.D = A*BEXPmax*time/(1+BEXPmax)**2;

 der.S = time-&xTmax;

 end;

 output out=outq p=pred r=redis u95m=u95m l95m=l95m;

*proc print data=outest;

proc reg data=outq outest=r2 adjrsq noprint;

 model winsor = pred / noint;

data r2; set r2;

 keep _rsq_ _adjrsq_;

data outq; set outq;

 keep time pred redis l95m u95m;

data &Analyte; merge outp outq; by time;

 if &Analyte=. then missing=pred;

 if outlier=. then winsor=.;

 if l95m<0 then l95m=0;

RUN;

data std; set outest;

 if _type_='COVB';

 retain std_a0 std_a std_b std_d std_s;

 if _name_='A0' then std_a0=sqrt(a0);

 if _name_='A' then std_a=sqrt(a);

 if _name_='B' then std_b=sqrt(b);

 if _name_='D' then std_d=sqrt(d);

 if _name_='S' then std_s=sqrt(s);

 if _name_='S';

 keep std_a0 std_a std_b std_d std_s;

data estimate; set outest;

 if _type_='ITER' or _type_='FINAL';

proc sort data=estimate; by _type_;

data estimate; set estimate; by _type_;

 if last._type_;

proc means data=estimate noprint;

 var _iter_ _sse_ a0 a b d s;

 output out=outest mean=iter sse a0 a b d s;

data &FitInfo; merge outest std r2 outout;
-146-

 analyte="&Analyte";

 if _freq_=1 then converge='NO ';

 else converge='YES';

 if std_a0=0 or std_a=0 or std_b=0 or std_d=0 or std_s=0 then

singular='YES';

 else singular='NO ';

 if A0+A/(1+B) >= 0 then t0=0; else t0=(1/D)*log(-A0*B/(A0+A));

 keep analyte a0 a b d s t0 std_a0 std_a std_b std_d std_s

 _rsq_ converge singular outlow outhigh;

run;

%mend;

/*

%FitF012(RawData=Raw, BasInfo=BasInfo, Analyte=BDCM, FitInfo=FitInfo);

proc print data=FitInfo;

run;

*/
-147-

Appendix C: Full- and Bench-Scale Pretreatment Schematics
Figure C-1 Full- and bench-scale pretreatment schematic for Water 1

Influent Biscayne Aquifer

Full-Scale Softening Lime

Activated alumina

Bench-Scale

Cartridge Filtration

Bench-Scale Mixing

Bench-Scale GAC

Adsorption

Sulfuric acid

Full-Scale

Bench-Scale

-148-
Figure C-2 Full- and bench-scale pretreatment schematic for Water 2

Influent Fox River

Influent

Claricone

(softening/clarification)

Sulfuric acid or

sodium hydroxide

Full-Scale

Bench-Scale

PAC

Potassium permanganate

Alum

Cationic polymer

Deep/shallow wells

Bench-Scale

Cartridge Filtration

Bench-Scale Mixing

Bench-Scale GAC

Adsorption

-149-
Figure C-3 Full- and bench-scale pretreatment schematic for Water 3

Influent Kansas River

Full-Scale Rapid Mix Organic polymer

Full-Scale Primary

Presedimentation

Full-Scale Secondary

Presedimentation

Full-Scale Rapid Mix

Full-Scale Primary

Sedimentation

Alum

CaO

Organic polymer

SOC

Bench-Scale

Cartridge Filtration

Bench-Scale Mixing

Full-Scale

Bench-Scale

Full-Scale Secondary

Sedimentation

Bench-Scale GAC

Adsorption

Sulfuric acid

-150-
Figure C-4 Full- and bench-scale pretreatment schematic for Water 4

Influent Mississippi River

Full-Scale Rapid Mix Polyaluminum sulfate

Full-Scale Solids

Contact Clarification

Bench-Scale

Cartridge Filtration

Potassium permanganate

Cationic polymer

Bench-Scale GAC

Adsorption

Anionic Emulsion Polymer

Full-Scale

Bench-Scale

-151-
Figure C-5 Full- and bench-scale pretreatment schematic for Water 5

Influent Lake Dixon, Lake

Wohlford and/or

SDCWA

Full-Scale Rapid Mix Alum

Full-Scale

Flocculation

Full-Scale

Sedimentation

Bench-Scale

Cartridge Filtration

Bench-Scale GAC

Adsorption

Full-Scale

Bench-Scale

Organic polymer

Potassium permanganate

-152-
Figure C-6 Full- and bench-scale pretreatment schematic for Water 6

Influent Edisto River or Bushy

Park Reservoir

Full-Scale Rapid Mix Alum

Full-Scale

Flocculation

Full-Scale

Sedimentation

Bench-Scale

Cartridge Filtration

Bench-Scale GAC

Adsorption

Organic polymer

Full-Scale

Bench-Scale

-153-
Figure C-7 Full- and bench-scale pretreatment schematic for Water 7

Influent Sweetwater

Reservoir

Intake Tower Chlorine (shut off when

sampling filter effluent)

Flash Mix

Flocculation

Sedimentation

Filtration

Bench-Scale

Cartridge Filtration

PAC

Organic polymer

Ammonia

Ferric chloride

Potassium permanganate

Bench-Scale GAC

Adsorption

Full-Scale

Bench-Scale

-154-
Figure C-8 Full- and bench-scale pretreatment schematic for Water 8

Influent Lake Daniel Reservoir

and Lake Brandt

Reservoir

Full-Scale Rapid Mix Alum

Full-Scale

Flocculation

Full-Scale

Sedimentation

Bench-Scale

Cartridge Filtration

Bench-Scale GAC

Adsorption

Potassium permanganate

Full-Scale

Bench-Scale

-155-
-156-

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-157-

Appendix D: Single Contactor and Blended Effluent DBP Surrogate

and Formed DBP Correlations
Figure D-1 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 1

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

75

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-158-
Figure D-2 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0.00

0.01

0.02

0.03

0.04

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

75

100

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

200

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-15

9-
Figure D-3 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 3

0.00

0.01

0.02

0.03

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

75

100

125

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-160-
Figure D-4 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 4

0.00

0.01

0.02

0.03

0.04

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

40

50

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

200

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-161-
Figure D-5 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 5

0.00

0.01

0.02

0.03

0.04

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-162-
Figure D-6 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 6

0.00

0.01

0.02

0.03

0.04

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

75

100

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

40

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

200

250

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-163-
Figure D-7 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

75

100

125

150

175

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

30

40

50

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

200

250

300

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-164-
Figure D-8 Correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

0.00

0.01

0.02

0.03

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

UV-254 (1/cm)

Single contactor

Blended effluent

0

25

50

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TTHM (μg/L)

0

10

20

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA6 (μg/L)

0

50

100

150

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TOX (μg/L Cl-)

-165-
Figure D-9 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 1

0

5

10

15

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-DBCM (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-BF (μg/L)

-166-
Figure D-10 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

0

5

10

15

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BF (μg/L)

-167-
Figure D-11 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 3

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

40

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BF (μg/L)

-168-
Figure D-12 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 4

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DBCM (μg/L)

0

1

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BF (μg/L)

-169-
Figure D-13 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 5

0

5

10

15

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DBCM (μg/L)

0

1

2

3

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-BF (μg/L)

-170-
Figure D-14 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 6

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DBCM (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BF (μg/L)

-171-
Figure D-15 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

35

40

45

50

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DBCM (μg/L)

0

5

10

15

20

25

30

35

40

45

50

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-BF (μg/L)

-172-
Figure D-16 THM correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BDCM (μg/L)

0

5

10

15

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBCM (μg/L)

0

1

2

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BF (μg/L)

-173-
Figure D-17 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 1

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-TCAA (μg/L)

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-DBAA (μg/L)

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-BCAA (μg/L)

-174-
Figure D-18 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TCAA (μg/L)

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BCAA (μg/L)

-175-
Figure D-19 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 3

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TCAA (μg/L)

0

2

4

6

8

10

12

14

16

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BCAA (μg/L)

-176-
Figure D-20 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 4

0

2

4

6

8

10

12

14

16

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TCAA (μg/L)

0

1

2

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DBAA (μg/L)

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BCAA (μg/L)

-177-
Figure D-21 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 5

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-TCAA (μg/L)

0

1

2

3

4

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DBAA (μg/L)

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-BCAA (μg/L)

-1

78-
Figure D-22 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 6

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-TCAA (μg/L)

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-BCAA (μg/L)

-179-
Figure D-23 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-TCAA (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DBAA (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-BCAA (μg/L)

-180-
Figure D-24 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-TCAA (μg/L)

0

2

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DBAA (μg/L)

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-BCAA (μg/L)

-181-
Figure D-25 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 1

0

2

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

TOC (mg/L)

SDS-HAA9 (μg/L)

-182-
Figure D-26 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 2

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA9 (μg/L)

-183-
Figure D-27 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 3

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA9 (μg/L)

-184-
Figure D-28 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 4

0

2

4

6

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

5

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-CDBAA (μg/L)

0

10

20

30

40

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA5 (μg/L)

0

10

20

30

40

50

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA9 (μg/L)

-185-
Figure D-29 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 5

0

2

4

6

8

10

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

40

0.0 0.5 1.0 1.5 2.0 2.5 3.0

TOC (mg/L)

SDS-HAA9 (μg/L)

-186-
Figure D-30 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 6

0

2

4

6

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

40

45

0.0 0.5 1.0 1.5 2.0 2.5

TOC (mg/L)

SDS-HAA9 (μg/L)

-187-
Figure D-31 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 7

0

2

4

6

8

10

12

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-CDBAA (μg/L)

0

2

4

6

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-TBAA (μg/L)

0

10

20

30

40

50

60

70

80

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

TOC (mg/L)

SDS-HAA9 (μg/L)

-1

88-
Figure D-32 HAA correlations based on GAC effluent TOC concentration for single contactor and blended effluents for Water 8

0

1

2

3

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-CDBAA (μg/L)

0

5

10

15

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA5 (μg/L)

0

5

10

15

20

0.0 0.5 1.0 1.5 2.0

TOC (mg/L)

SDS-HAA9 (μg/L)

-189-
Figure D-33 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 1

0

25

50

75

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-190-
Figure D-34 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 2

0

25

50

75

100

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

200

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-191-
Figure D-35 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 3

0

25

50

75

100

125

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-192-
Figure D-36 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 4

0

25

50

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

40

50

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

200

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-193-
Figure D-37 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 5

0

25

50

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-194-
Figure D-38 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 6

0

25

50

75

100

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

40

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

200

250

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-195-
Figure D-39 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 7

0

25

50

75

100

125

150

175

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

30

40

50

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

200

250

300

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-196-
Figure D-40 Correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 8

0

25

50

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TTHM (μg/L)

Single contactor

Blended effluent

0

10

20

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA6 (μg/L)

0

50

100

150

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TOX (μg/L Cl-)

-197-
Figure D-41 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 1

0

5

10

15

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

25

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

5

10

15

20

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-BF (μg/L)

-1

98-
Figure D-42 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 2

0

5

10

15

20

25

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

5

10

15

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BF (μg/L)

-199-
Figure D-43 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 3

0

5

10

15

20

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

40

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

5

10

15

20

25

30

35

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BF (μg/L)

-200-
Figure D-44 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 4

0

5

10

15

20

25

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

1

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BF (μg/L)

-201-
Figure D-45 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 5

0

5

10

15

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

1

2

3

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BF (μg/L)

-202-
Figure D-46 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 6

0

5

10

15

20

25

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

35

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BF (μg/L)

-203-
Figure D-47 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 7

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

20

25

30

35

40

45

50

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

5

10

15

20

25

30

35

40

45

50

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-BF (μg/L)

-204-
Figure D-48 THM correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 8

0

5

10

15

20

25

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-CF (μg/L)

Single contactor effluent

Blended effluent

0

5

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BDCM (μg/L)

0

5

10

15

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DBCM (μg/L)

0

1

2

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BF (μg/L)

-205-
Figure D-49 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 1

0

2

4

6

8

10

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

2

4

6

8

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

2

4

6

8

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-BCAA (μg/L)

-206-
Figure D-50 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 2

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

2

4

6

8

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BCAA (μg/L)

-207-
Figure D-51 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 3

0

2

4

6

8

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

2

4

6

8

10

12

14

16

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BCAA (μg/L)

-208-
Figure D-52 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 4

0

2

4

6

8

10

12

14

16

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

5

10

15

20

25

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

1

2

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

1

2

3

4

5

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BCAA (μg/L)

-209-
Figure D-53 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 5

0

2

4

6

8

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

1

2

3

4

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

2

4

6

8

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BCAA (μg/L)

-210-
Figure D-54 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 6

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-BCAA (μg/L)

-211-
Figure D-55 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 7

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

8

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-BCAA (μg/L)

-212-
Figure D-56 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 8

0

2

4

6

8

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DCAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-TCAA (μg/L)

0

2

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DBAA (μg/L)

0

1

2

3

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-BCAA (μg/L)

-213-
Figure D-57 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 1

0

2

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

0.00 0.02 0.04 0.06 0.08

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-214-
Figure D-58 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 2

0

1

2

3

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-215-
Figure D-59 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 3

0

1

2

3

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

20

25

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-216-
Figure D-60 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 4

0

2

4

6

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

5

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

10

20

30

40

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

10

20

30

40

50

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-217-
Figure D-61 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 5

0

2

4

6

8

10

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

40

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-218-
Figure D-62 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 6

0

2

4

6

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

20

25

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

25

30

35

40

45

0.00 0.01 0.02 0.03 0.04

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-219-
Figure D-63 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 7

0

2

4

6

8

10

12

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

2

3

4

5

6

7

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

2

4

6

8

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-TBAA (μg/L)

0

10

20

30

40

50

60

70

80

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-220-
Figure D-64 HAA correlations based on GAC effluent UV-254 absorbance for single contactor and blended effluents for Water 8

0

1

2

3

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-DCBAA (μg/L)

Single contactor effluent

Blended effluent

0

1

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-CDBAA (μg/L)

0

5

10

15

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA5 (μg/L)

0

5

10

15

20

0.00 0.01 0.02 0.03

UV-254 (1/cm)

SDS-HAA9 (μg/L)

-221-
-222-

This page intentionally left blank.
-223-

Appendix E: Logistic Function Model Curve Fits
0

1

2

3

4

0 20 40 60 80

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.966)

Blended effluent

DI prediction

c0 = 4.54 mg/L

EBCT = 20 min.

TOC

Figure E-1 Single contactor and blended effluent TOC breakthrough curves 

for Water 1

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 20 40 60 80

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

UV254

Figure E-2 Single contactor and blended effluent UV254 breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 0.094 1/cm

-224-
0

50

100

150

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.997)

Blended effluent

DI prediction

c0 = 224 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-3 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 1

0

5

10

15

20

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.989)

Blended effluent

DI prediction

SDS-CF

Figure E-4 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 34.2 μg/L

-225-
0

5

10

15

20

25

30

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

c0 = 19.3 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-5 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 1

0

5

10

15

20

25

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction

SDS-DBCM

Figure E-6 Single contactor and blended effluent SDS-DBCM breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 28 μg/L

-226-
0

5

10

15

20

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.971)

Blended effluent

DI prediction

c0 = 3.7 μg/L

EBCT = 20 min.

SDS-BF

Figure E-7 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 1

0

20

40

60

80

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.977)

Blended effluent

DI prediction

SDS-TTHM

Figure E-8 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 85 μg/L

-227-
0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-MCAA

Figure E-9 Single contactor and blended effluent SDS-MCAA breakthrough 

curves for Water 1

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

12

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.986)

Blended effluent

DI prediction

SDS-DCAA

Figure E-10 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 12.5 μg/L

-228-
0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 3 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-11 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 1

Insufficient data measured above the MRL

to perform curve fit analysis

0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-12 Single contactor and blended effluent SDS-MBAA breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-229-
0

2

4

6

8

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

c0 = 4 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-13 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 1

0

5

10

15

20

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.977)

Blended effluent

DI prediction

SDS-HAA5

Figure E-14 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 20 μg/L

-230-
0

2

4

6

8

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.981)

Blended effluent

DI prediction

c0 = 7 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-15 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 1

0

5

10

15

20

25

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

SDS-HAA6

Figure E-16 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 27 μg/L

-231-
0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 2.3 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-17 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 1

Insufficient data measured above the MRL

to perform curve fit analysis

0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-18 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 1

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-232-
0

1

2

3

4

5

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-19 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 1

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

25

0 20 40 60 80

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

SDS-HAA9

Figure E-20 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 1

EBCT = 20 min.

c0 = 29 μg/L

-233-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.972)

Blended effluent

DI prediction

c0 =2.6 mg/L

EBCT = 20 min.

TOC

Figure E-21 Single contactor and blended effluent TOC breakthrough curves 

for Water 2

0.000

0.010

0.020

0.030

0.040

0 50 100 150 200 250

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

UV254

Figure E-22 Single contactor and blended effluent UV254 breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 0.055 1/cm

-234-
0

25

50

75

100

125

150

175

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.991)

Blended effluent

DI prediction

c0 =220 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-23 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 2

0

5

10

15

20

25

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.989)

Blended effluent

DI prediction

SDS-CF

Figure E-24 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 41.9 μg/L

-235-
0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

c0 =19.8 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-25 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 2

0

5

10

15

20

25

30

35

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

SDS-DBCM

Figure E-26 Single contactor and blended effluent SDS-DBCM breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 31.7 μg/L

-236-
0

2

4

6

8

10

12

14

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.91)

Blended effluent

DI prediction

c0 =3.7 μg/L

SDS-BF EBCT = 20 min.

Figure E-27 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 2

0

25

50

75

100

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

SDS-TTHM

Figure E-28 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 97 μg/L

-237-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 =BMRL

EBCT = 20 min.

SDS-MCAA

Figure E-29 Single contactor and blended effluent SDS-MCAA breakthrough 

curves for Water 2

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.975)

Blended effluent

DI prediction

SDS-DCAA

Figure E-30 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 14 μg/L

-238-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.968)

Blended effluent

DI prediction

c0 =5 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-31 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 2

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-32 Single contactor and blended effluent SDS-MBAA breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-239-
0

2

4

6

8

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.985)

Blended effluent

DI prediction

c0 =5 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-33 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 2

0

5

10

15

20

25

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.978)

Blended effluent

DI prediction

SDS-HAA5

Figure E-34 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 24 μg/L

-240-
0

2

4

6

8

10

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.977)

Blended effluent

DI prediction

c0 =9 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-35 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 2

0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-HAA6

Figure E-36 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 34 μg/L

-241-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction

c0 =3 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-37 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 2

0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-38 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 2

EBCT = 20 min.

c0 = BMRL

Insufficient data measured above the MRL

to perform curve fit analysis

-242-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 =BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-39 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 2

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.988)

Blended effluent

DI prediction

SDS-HAA9

Figure E-40 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 2

EBCT = 20 min.

c0 = 37 μg/L

-243-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.986)

Blended effluent

DI prediction

c0 = 2.35 mg/L

EBCT = 20 min.

TOC

Figure E-41 Single contactor and blended effluent TOC breakthrough curves 

for Water 3

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 50 100 150 200 250 300

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

UV254

Figure E-42 Single contactor and blended effluent UV254 breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 0.048 1/cm

-244-
0

25

50

75

100

125

150

175

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.997)

Blended effluent

DI prediction

c0 = 255 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-43 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 3

0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

SDS-CF

Figure E-44 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 60.3 μg/L

-245-
0

10

20

30

40

50

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

c0 = 36.2 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-45 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 3

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.996)

Blended effluent

DI prediction

SDS-DBCM

Figure E-46 Single contactor and blended effluent SDS-DBCM breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 44.5 μg/L

-246-
0

10

20

30

40

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

c0 = 11.8 μg/L

EBCT = 20 min.

SDS-BF

Figure E-47 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 3

0

25

50

75

100

125

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.986)

Blended effluent

DI prediction

SDS-TTHM

Figure E-48 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 154 μg/L

-247-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-MCAA

Figure E-49 Single contactor and blended effluent SDS-MCAA breakthrough 

curves for Water 3

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

SDS-DCAA

Figure E-50 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 15.7 μg/L

-248-
0

1

2

3

4

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 5 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-51 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 3

Insufficient data measured above the MRL

to perform curve fit analysis

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-52 Single contactor and blended effluent SDS-MBAA breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-249-
0

2

4

6

8

10

12

14

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.859)

Blended effluent

DI prediction

c0 = 9.7 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-53 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 3

0

5

10

15

20

25

30

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.977)

Blended effluent

DI prediction

SDS-HAA5

Figure E-54 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 30 μg/L

-250-
0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.97)

Blended effluent

DI prediction

c0 = 12.7 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-55 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 3

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.963)

Blended effluent

DI prediction

SDS-HAA6

Figure E-56 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 43 μg/L

-251-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.531)

Blended effluent

DI prediction

c0 = 4 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-57 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 3

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-58 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 3

EBCT = 20 min.

c0 = 2.5 μg/L

Insufficient data measured above the MRL

to perform curve fit analysis

-252-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-59 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 3

Effluent concentrations were not detected

above the MRL for this parameter

0

10

20

30

40

50

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.965)

Blended effluent

DI prediction

SDS-HAA9

Figure E-60 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 3

EBCT = 20 min.

c0 = 49 μg/L

-253-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

c0 = 2.98 mg/L

EBCT = 20 min.

TOC

Figure E-61 Single contactor and blended effluent TOC breakthrough curves 

for Water 4

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.998)

Blended effluent

DI prediction

UV254

Figure E-62 Single contactor and blended effluent UV254 breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 0.065 1/cm

-254-
0

25

50

75

100

125

150

175

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.999)

Blended effluent

DI prediction

c0 = 288 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-63 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 4

0

5

10

15

20

25

30

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.998)

Blended effluent

DI prediction

SDS-CF

Figure E-64 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 50.7 μg/L

-255
0

1

2

3

4

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

c0 = 2.2 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-65 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 4

0

2

4

6

8

10

12

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.993)

Blended effluent

DI prediction

SDS-DBCM

Figure E-66 Single contactor and blended effluent SDS-DBCM breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 15.1 μg/L

-256-
0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-BF

Figure E-67 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 4

Effluent concentrations were not detected

above the MRL for this parameter

0

10

20

30

40

50

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

SDS-TTHM

Figure E-68 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 68 μg/L

-257-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-MCAA

Figure E-69 Single contactor and blended effluent SDS-MCAA breakthrough 

curves for Water 4

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

SDS-DCAA

Figure E-70 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 20.3 μg/L

-258-
0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.981)

Blended effluent

DI prediction

c0 = 30.7 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-71 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 4

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-72 Single contactor and blended effluent SDS-MBAA breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-259-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 1 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-73 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 4

Insufficient data measured above the MRL

to perform curve fit analysis

0

10

20

30

40

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

SDS-HAA5

Figure E-74 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 52 μg/L

-260-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.954)

Blended effluent

DI prediction

c0 = 4.3 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-75 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 4

0

10

20

30

40

50

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-HAA6

Figure E-76 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 55 μg/L

-261-
0

2

4

6

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.983)

Blended effluent

DI prediction

c0 = 6.7 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-77 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 4

0.0

0.5

1.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-78 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 4

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-262-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-79 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 4

Effluent concentrations were not detected

above the MRL for this parameter

0

10

20

30

40

50

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.981)

Blended effluent

DI prediction

SDS-HAA9

Figure E-80 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 4

EBCT = 20 min.

c0 = 61 μg/L

-263-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure E-81 Single contactor and blended effluent TOC breakthrough curves 

for Water 5

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

UV254

Figure E-82 Single contactor and blended effluent UV254 breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 0.051 1/cm

-264-
0

25

50

75

100

125

150

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.995)

Blended effluent

DI prediction

c0 = 205 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-83 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 5

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

SDS-CF

Figure E-84 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 23.7 μg/L

-265-
0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.97)

Blended effluent

DI prediction

c0 = 10.8 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-85 Single contactor and blended effluent SDS-BDCM breakthrough 

curves for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.988)

Blended effluent

DI prediction

SDS-DBCM

Figure E-86 Single contactor and blended effluent SDS-DBCM breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 22.7 μg/L

-266-
0

1

2

3

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.969)

Blended effluent

DI prediction

c0 = 1.2 μg/L

EBCT = 20 min.

SDS-BF

Figure E-87 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 5

0

10

20

30

40

50

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

SDS-TTHM

Figure E-88 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 58 μg/L

-267-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 2 μg/L

EBCT = 20 min.

SDS-MCAA

Figure E-89 Single contactor and blended effluent SDS-MCAA breakthrough 

curves for Water 5

Insufficient data measured above the MRL

to perform curve fit analysis

0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.978)

Blended effluent

DI prediction

SDS-DCAA

Figure E-90 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 10.3 μg/L

-268-
0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.989)

Blended effluent

DI prediction

c0 = 12.7 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-91 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 5

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-92 Single contactor and blended effluent SDS-MBAA breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 1 μg/L

Effluent concentrations were not detected

above the MRL for this parameter

-269-
0

1

2

3

4

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

c0 = 2 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-93 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.965)

Blended effluent

DI prediction

SDS-HAA5

Figure E-94 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 28 μg/L

-270-
0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

c0 = 7.3 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-95 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 5

0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

SDS-HAA6

Figure E-96 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 34 μg/L

-271-
0

2

4

6

8

10

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

c0 = 10.7 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-97 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 5

0

1

2

3

4

5

6

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.95)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-98 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 5

EBCT = 20 min.

c0 = 3.7 μg/L

-272-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-99 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 5

Effluent concentrations were not detected

above the MRL for this parameter

0

10

20

30

40

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

SDS-HAA9

Figure E-100 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 5

EBCT = 20 min.

c0 = 48 μg/L

-273-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

c0 = 2.64 mg/L

EBCT = 20 min.

TOC

Figure E-101 Single contactor and blended effluent TOC breakthrough 

curves for Water 6

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0 50 100 150 200 250 300

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.998)

Blended effluent

DI prediction

UV254

Figure E-102 Single contactor and blended effluent UV254 breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 0.06 1/cm

-274-
0

25

50

75

100

125

150

175

200

225

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.997)

Blended effluent

DI prediction

c0 = 305 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-103 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 6

0

5

10

15

20

25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.991)

Blended effluent

DI prediction

SDS-CF

Figure E-104 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 55.3 μg/L

-275-
0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

c0 = 27.4 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-105 Single contactor and blended effluent SDS-BDCM 

breakthrough curves for Water 6

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.993)

Blended effluent

DI prediction

SDS-DBCM

Figure E-106 Single contactor and blended effluent SDS-DBCM 

breakthrough curves for Water 6

EBCT = 20 min.

c0 = 41.6 μg/L

-276-
0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.978)

Blended effluent

DI prediction

c0 = 3.3 μg/L

EBCT = 20 min.

SDS-BF

Figure E-107 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 6

0

25

50

75

100

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-TTHM

Figure E-108 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 128 μg/L

-277-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-MCAA

Figure E-109 Single contactor and blended effluent SDS-MCAA 

breakthrough curves for Water 6

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

SDS-DCAA

Figure E-110 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 17.3 μg/L

-278-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 13.3 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-111 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 6

Insufficient data measured above the MRL

to perform curve fit analysis

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-112 Single contactor and blended effluent SDS-MBAA 

breakthrough curves for Water 6

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-279-
0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction

c0 = 5.7 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-113 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 6

0

5

10

15

20

25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.969)

Blended effluent

DI prediction

SDS-HAA5

Figure E-114 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 36 μg/L

-280-
0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.967)

Blended effluent

DI prediction

c0 = 12.3 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-115 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 6

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.97)

Blended effluent

DI prediction

SDS-HAA6

Figure E-116 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 49 μg/L

-281-
0

1

2

3

4

5

6

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.975)

Blended effluent

DI prediction

c0 = 9 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-117 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 6

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.913)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-118 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 6

EBCT = 20 min.

c0 = 3 μg/L

-282-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-119 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 6

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

25

30

35

40

45

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.966)

Blended effluent

DI prediction

SDS-HAA9

Figure E-120 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 6

EBCT = 20 min.

c0 = 61 μg/L

-283-
0

1

2

3

4

5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

c0 = 5.58 mg/L

EBCT = 20 min.

TOC

Figure E-121 Single contactor and blended effluent TOC breakthrough 

curves for Water 7

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 25 50 75 100 125 150

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction

UV254

Figure E-122 Single contactor and blended effluent UV254 breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 0.109 1/cm

-284-
0

50

100

150

200

250

300

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction

c0 = 486 μg/L ClEBCT = 20 min.

SDS-TOX

Figure E-123 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 7

0

5

10

15

20

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.996)

Blended effluent

DI prediction

SDS-CF

Figure E-124 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 52.4 μg/L

-285-
0

10

20

30

40

50

60

70

80

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.991)

Blended effluent

DI prediction

c0 = 65.3 μg/L

EBCT = 20 min.

SDS-BDCM

Figure E-125 Single contactor and blended effluent SDS-BDCM 

breakthrough curves for Water 7

0

10

20

30

40

50

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.996)

Blended effluent

DI prediction

SDS-DBCM

Figure E-126 Single contactor and blended effluent SDS-DBCM 

breakthrough curves for Water 7

EBCT = 20 min.

c0 = 66.2 μg/L

-286-
0

10

20

30

40

50

60

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.971)

Blended effluent

DI prediction

c0 = 16 μg/L

EBCT = 20 min.

SDS-BF

Figure E-127 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 7

0

50

100

150

200

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

SDS-TTHM

Figure E-128 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 200 μg/L

-287-
0.0

0.5

1.0

1.5

2.0

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = 5 μg/L

EBCT = 20 min.

SDS-MCAA

Figure E-129 Single contactor and blended effluent SDS-MCAA 

breakthrough curves for Water 7

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

12

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.922)

Blended effluent

DI prediction

SDS-DCAA

Figure E-130 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 25.3 μg/L

-288-
0

2

4

6

8

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.999)

Blended effluent

DI prediction

c0 = 18 μg/L

EBCT = 20 min.

SDS-TCAA

Figure E-131 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 7

0.0

0.5

1.0

1.5

2.0

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-132 Single contactor and blended effluent SDS-MBAA 

breakthrough curves for Water 7

EBCT = 20 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-289-
0

5

10

15

20

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

c0 = 16.3 μg/L

EBCT = 20 min.

SDS-DBAA

Figure E-133 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 7

0

10

20

30

40

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction

SDS-HAA5

Figure E-134 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 65 μg/L

-290-
0

2

4

6

8

10

12

14

16

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction

c0 = 23.7 μg/L

EBCT = 20 min.

SDS-BCAA

Figure E-135 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 7

0

10

20

30

40

50

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.988)

Blended effluent

DI prediction

SDS-HAA6

Figure E-136 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 85 μg/L

-291-
0

2

4

6

8

10

12

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

c0 = 26.7 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure E-137 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 7

0

1

2

3

4

5

6

7

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-138 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 7

EBCT = 20 min.

c0 = 11.7 μg/L

-292-
0

2

4

6

8

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.976)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure E-139 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 7

0

20

40

60

80

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-HAA9

Figure E-140 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 7

EBCT = 20 min.

c0 = 124 μg/L

-293
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.974)

Blended effluent

DI prediction

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure E-141 Single contactor and blended effluent TOC breakthrough 

curves for Water 8

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Logistic function best fit (R^2 = 0.994)

Blended effluent

DI prediction

UV254

Figure E-142 Single contactor and blended effluent UV254 breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 0.033 1/cm

-294-
0

25

50

75

100

125

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

c0 = 156 μg/L ClEBCT = 7.2 min.

SDS-TOX

Figure E-143 Single contactor and blended effluent SDS-TOX breakthrough 

curves for Water 8

0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.985)

Blended effluent

DI prediction

SDS-CF

Figure E-144 Single contactor and blended effluent SDS-CF breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 29.1 μg/L

-295-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.946)

Blended effluent

DI prediction

c0 = 2.6 μg/L

EBCT = 7.2 min.

SDS-BDCM

Figure E-145 Single contactor and blended effluent SDS-BDCM 

breakthrough curves for Water 8

0

2

4

6

8

10

12

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-DBCM

Figure E-146 Single contactor and blended effluent SDS-DBCM 

breakthrough curves for Water 8

EBCT = 7.2 min.

c0 = 10.3 μg/L

-296-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 7.2 min.

SDS-BF

Figure E-147 Single contactor and blended effluent SDS-BF breakthrough 

curves for Water 8

Insufficient data measured above the MRL

to perform curve fit analysis

0

10

20

30

40

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

SDS-TTHM

Figure E-148 Single contactor and blended effluent SDS-TTHM breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 42 μg/L

-297-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 7.2 min.

SDS-MCAA

Figure E-149 Single contactor and blended effluent SDS-MCAA 

breakthrough curves for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.974)

Blended effluent

DI prediction

SDS-DCAA

Figure E-150 Single contactor and blended effluent SDS-DCAA breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 10.7 μg/L

-298-
0

1

2

3

4

5

6

7

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.93)

Blended effluent

DI prediction

c0 = 12.7 μg/L

EBCT = 7.2 min.

SDS-TCAA

Figure E-151 Single contactor and blended effluent SDS-TCAA breakthrough 

curves for Water 8

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-MBAA

Figure E-152 Single contactor and blended effluent SDS-MBAA 

breakthrough curves for Water 8

EBCT = 7.2 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-299-
0

2

4

6

8

10

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 7.2 min.

SDS-DBAA

Figure E-153 Single contactor and blended effluent SDS-DBAA breakthrough 

curves for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.955)

Blended effluent

DI prediction

SDS-HAA5

Figure E-154 Single contactor and blended effluent SDS-HAA5 breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 23 μg/L

-300-
0

1

2

3

4

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.971)

Blended effluent

DI prediction

c0 = 3 μg/L

EBCT = 7.2 min.

SDS-BCAA

Figure E-155 Single contactor and blended effluent SDS-BCAA breakthrough 

curves for Water 8

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.956)

Blended effluent

DI prediction

SDS-HAA6

Figure E-156 Single contactor and blended effluent SDS-HAA6 breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 27 μg/L

-301-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.939)

Blended effluent

DI prediction

c0 = 3 μg/L

EBCT = 7.2 min.

SDS-DCBAA

Figure E-157 Single contactor and blended effluent SDS-DCBAA 

breakthrough curves for Water 8

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

SDS-CDBAA

Figure E-158 Single contactor and blended effluent SDS-CDBAA 

breakthrough curves for Water 8

EBCT = 7.2 min.

c0 = BMRL

Effluent concentrations were not detected

above the MRL for this parameter

-302-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = NA)

Blended effluent

DI prediction

c0 = BMRL

EBCT = 7.2 min.

SDS-TBAA

Figure E-159 Single contactor and blended effluent SDS-TBAA breakthrough 

curves for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Logistic function best fit (R^2 = 0.952)

Blended effluent

DI prediction

SDS-HAA9

Figure E-160 Single contactor and blended effluent SDS-HAA9 breakthrough 

curves for Water 8

EBCT = 7.2 min.

c0 = 30 μg/L

-303-
-304-

This page intentionally left blank.
-305-

Appendix F: Comparison of SCA Method to DI Approach for Integral

Breakthrough Curve Prediction
0.0

0.5

1.0

1.5

2.0

2.5

0 25 50 75

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.152)

c0 = 4.54 mg/L

EBCT = 15 min.

TOC

Figure F-1 DI method prediction of the TOC integral breakthrough curve for 

Water 1

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 25 50 75

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0011)

SCA prediction (RSS = 0.0017)

c0 =

EBCT = 15 min.

UV254

Figure F-2 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 1

0.094 1/cm

-306-
0

1

2

3

4

5

6

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.39)

SCA prediction (RSS = 0.65)

c0 =

EBCT = 15 min.

SDS-CF

Figure F-4 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 1

34.2 μg/L

0

50

100

150

200

0 25 50 75

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-TOX

Figure F-3 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 1

224 μg/L ClSDS-TOX not analyzed in 

the blended effluent

-307-
0

2

4

6

8

10

12

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.4)

SCA prediction (RSS = 0.73)

c0 =

EBCT = 15 min.

Figure F-6 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 1

SDS-DBCM

28 μg/L

0

4

8

12

16

20

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.52)

SCA prediction (RSS = 1.03)

c0 =

EBCT = 15 min.

SDS-BDCM

Figure F-5 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 1

19.3 μg/L

-308-
0

2

4

6

8

10

12

14

16

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.76)

SCA prediction (RSS = 1.31)

c0 =

EBCT = 15 min.

SDS-BF

Figure F-7 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 1

3.7 μg/L

0

10

20

30

40

50

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.93)

SCA prediction (RSS = 2.55)

c0 =

EBCT = 15 min.

SDS-TTHM

Figure F-8 Comparison of DI and SCA methods for predicting the SDS-TTHM 

integral breakthrough curve for Water 1

85 μg/L

-309-
0

2

4

6

8

10

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.35)

SCA prediction (RSS = 1.46)

c0 =

EBCT = 15 min.

Figure F-10 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 1

SDS-DCAA

12.5 μg/L

0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-MCAA

Figure F-9 Comparison of DI and SCA methods for predicting the SDS-MCAA 

integral breakthrough curve for Water 1

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-310-
0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-TCAA

Figure F-11 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 1

3 μg/L

Blended effluent concentrations were not 

detected above the MRL for this parameter

0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-MBAA

Figure F-12 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 1

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-311-
0

5

10

15

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.38)

SCA prediction (RSS = 1.58)

c0 =

EBCT = 15 min.

SDS-HAA5

Figure F-14 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 1

20 μg/L

0

1

2

3

4

5

6

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.46)

SCA prediction (RSS = 0.39)

c0 =

EBCT = 15 min.

SDS-DBAA

Figure F-13 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 1

4 μg/L

-312-
0

1

2

3

4

5

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.38)

SCA prediction (RSS = 0.29)

c0 =

EBCT = 15 min.

SDS-BCAA

Figure F-15 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 1

7 μg/L

0

5

10

15

20

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.63)

SCA prediction (RSS = 1.26)

c0 =

EBCT = 15 min.

SDS-HAA6

Figure F-16 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 1

27 μg/L

-313-
0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-DCBAA

Figure F-17 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 1

2.3 μg/L

Insufficient data measured above the MRL to 

compare DI and SCA predictions

0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-CDBAA

Figure F-18 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 1

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-314-
0.0

0.5

1.0

1.5

2.0

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 15 min.

SDS-TBAA

Figure F-19 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 1

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

0

5

10

15

20

0 25 50 75

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.78)

SCA prediction (RSS = 1.04)

c0 =

EBCT = 15 min.

SDS-HAA9

Figure F-20 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 1

29 

-315-
0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 50 100 150 200 250

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.057)

c0 = 2.6 mg/L

EBCT = 20 min.

TOC

Figure F-21 DI method prediction of the TOC integral breakthrough curve for 

Water 2

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200 250

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0012)

SCA prediction (RSS = 0.0015)

c0 =

EBCT = 20 min.

UV254

Figure F-22 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 2

0.055 1/cm

-316-
0

5

10

15

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.57)

SCA prediction (RSS = 1.59)

c0 =

EBCT = 20 min.

SDS-CF

Figure F-24 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 2

41.9 μg/L

0

50

100

150

200

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-23 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 2

220 μg/L ClSDS-TOX not analyzed in 

the blended effluent

-317-
0

5

10

15

20

25

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.18)

SCA prediction (RSS = 1.72)

c0 =

EBCT = 20 min.

Figure F-26 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 2

SDS-DBCM

31.7 μg/L

0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 4.35)

SCA prediction (RSS = 1.98)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-25 Comparison of DI and SCA methods for predicting the 

SDS-BDCM integral breakthrough curve for Water 2

19.8 μg/L

-318-
0

2

4

6

8

10

12

14

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.73)

SCA prediction (RSS = 2.18)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-27 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 2

3.7 μg/L

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 8.49)

SCA prediction (RSS = 4.38)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-28 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 2

97 μg/L

-319-
0

1

2

3

4

5

6

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.49)

SCA prediction (RSS = 0.68)

c0 =

EBCT = 20 min.

Figure F-30 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 2

SDS-DCAA

14 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-29 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 2

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-320-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.27)

SCA prediction (RSS = 0.34)

c0 =

EBCT = 20 min.

SDS-TCAA

Figure F-31 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 2

5 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-32 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 2

BMRL

Blended effluent concentrations were not 

detected above the MRL for this parameter

-321-
0

5

10

15

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.69)

SCA prediction (RSS = 1.48)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-34 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 2

24 μg/L

0

2

4

6

8

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.1)

SCA prediction (RSS = 0.6)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-33 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 2

5 μg/L

-322-
0

2

4

6

8

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.82)

SCA prediction (RSS = 0.78)

c0 =

EBCT = 20 min.

SDS-BCAA

Figure F-35 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 2

9 μg/L

0

5

10

15

20

25

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.5)

SCA prediction (RSS = 2.25)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-36 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 2

34

-323-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.48)

SCA prediction (RSS = 0.35)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-37 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 2

3 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-38 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 2

BMRL

Insufficient data measured above the MRL 

to compare DI and SCA predictions

-324-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-39 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 2

BMRL

Blended effluent concentrations were not 

detected above the MRL for this parameter

0

5

10

15

20

25

30

0 50 100 150 200 250

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.64)

SCA prediction (RSS = 3.43)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-40 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 2

37 μg/L

-325-
0.00

0.25

0.50

0.75

1.00

1.25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.044)

c0 = 2.35 mg/L

EBCT = 20 min.

TOC

Figure F-41 DI method prediction of the TOC integral breakthrough curve for 

Water 3

0.000

0.005

0.010

0.015

0.020

0 50 100 150 200 250 300

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.001)

SCA prediction (RSS = 0.001)

c0 =

EBCT = 20 min.

UV254

Figure F-42 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 3

0.048 1/cm

-326-
0

1

2

3

4

5

6

7

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.85)

SCA prediction (RSS = 0.4)

c0 =

EBCT = 20 min.

SDS-CF

Figure F-44 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 3

60.3 μg/L

0

50

100

150

200

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-43 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 3

255 μg/L ClSDS-TOX not analyzed in 

the blended effluent

-327-
0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.04)

SCA prediction (RSS = 0.68)

c0 =

EBCT = 20 min.

Figure F-46 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 3

SDS-DBCM

44.5 μg/L

0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.05)

SCA prediction (RSS = 2.57)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-45 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 3

36.2 μg/L

-328-
0

5

10

15

20

25

30

35

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 5.67)

SCA prediction (RSS = 3.14)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-47 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 3

11.8 μg/L

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.87)

SCA prediction (RSS = 2.64)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-48 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 3

154 μg/L

-329-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.4)

SCA prediction (RSS = 0.37)

c0 =

EBCT = 20 min.

Figure F-50 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 3

SDS-DCAA

15.7 

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-49 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 3

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-330-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TCAA

Figure F-51 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 3

5 μg/L

Blended effluent concentrations were not detected 

above the MRL for this parameter

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-52 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 3

BMRL

Blended effluent concentrations were not 

detected above the MRL for this parameter

-331-
0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.48)

SCA prediction (RSS = 1.13)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-54 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 3

30 μg/L

0

2

4

6

8

10

12

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.02)

SCA prediction (RSS = 1.56)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-53 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 3

9.7 μg/L

-332-
0

1

2

3

4

5

6

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.63)

SCA prediction (RSS = 0.63)

c0 =

EBCT = 20 min.

SDS-BCAA

Figure F-55 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 3

12.7 μg/L

0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.29)

SCA prediction (RSS = 2.3)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-56 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 3

43 μg/L

-333-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.68)

SCA prediction (RSS = 0.72)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-57 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 3

4 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-58 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 3

2.5 μg/L

Blended effluent concentrations were not 

detected above the MRL for this parameter

-334-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-59 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 3

BMRL

Blended effluent concentrations were not detected above the 

MRL for this parameter

0

5

10

15

20

25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 4.36)

SCA prediction (RSS = 2.2)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-60 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 3

49 

-335-
0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.05)

c0 = 2.98 mg/L

EBCT = 20 min.

TOC

Figure F-61 DI method prediction of the TOC integral breakthrough curve for 

Water 4

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0015)

SCA prediction (RSS = 0.0023)

c0 =

EBCT = 20 min.

UV254

Figure F-62 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 4

0.065 1/cm

-336-
0

2

4

6

8

10

12

14

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.41)

SCA prediction (RSS = 1.33)

c0 =

EBCT = 20 min.

SDS-CF

Figure F-64 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 4

50.7 μg/L

0

50

100

150

200

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-63 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 4

288 μg/L ClSDS-TOX not analyzed in the 

blended effluent

-337-
0

2

4

6

8

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.59)

SCA prediction (RSS = 0.55)

c0 =

EBCT = 20 min.

Figure F-66 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 4

SDS-DBCM

15.1 μg/L

0

1

2

3

4

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.58)

SCA prediction (RSS = 0.55)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-65 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 4

2.2 μg/L

-338-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-67 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 4

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.39)

SCA prediction (RSS = 2.03)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-68 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 4

68 μg/L

-339-
0

1

2

3

4

5

6

7

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.32)

SCA prediction (RSS = 0.47)

c0 =

EBCT = 20 min.

Figure F-70 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 4

SDS-DCAA

20.3 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-69 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 4

BMRL

Blended effluent concentrations were not detected above the 

MRL for this parameter

-340-
0

2

4

6

8

10

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.96)

SCA prediction (RSS = 0.64)

c0 =

EBCT = 20 min.

SDS-TCAA

Figure F-71 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 4

30.7 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-72 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 4

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-341-
0

2

4

6

8

10

12

14

16

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.24)

SCA prediction (RSS = 0.87)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-74 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 4

52 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-73 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 4

1 μg/L

Blended effluent concentrations were not detected above the 

MRL for this parameter

-342-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.31)

SCA prediction (RSS = 0.22)

c0 =

EBCT = 20 min.

SDS-BCAA

Figure F-75 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 4

4.3 μg/L

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.34)

SCA prediction (RSS = 1.07)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-76 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 4

55 μg/L

-343-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.49)

SCA prediction (RSS = 0.5)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-77 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 4

6.7 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-78 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 4

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-344-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-79 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 4

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.7)

SCA prediction (RSS = 1.12)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-80 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 4

61 μg/L

-345-
0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.035)

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure F-81 DI method prediction of the TOC integral breakthrough curve for 

Water 5

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.002)

SCA prediction (RSS = 0.0022)

c0 =

EBCT = 20 min.

UV254

Figure F-82 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 5

0.051 1/cm

-346-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.31)

SCA prediction (RSS = 0.65)

c0 =

EBCT = 20 min.

SDS-CF

Figure F-84 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 5

23.7 μg/L

0

12

24

36

48

60

72

84

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-83 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 5

205 μg/L ClSDS-TOX not analyzed in 

the blended effluent

-347-
0

2

4

6

8

10

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.54)

SCA prediction (RSS = 0.72)

c0 =

EBCT = 20 min.

Figure F-86 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 5

SDS-DBCM

22.7 μg/L

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.85)

SCA prediction (RSS = 0.34)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-85 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 5

10.8 μg/L

-348-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.55)

SCA prediction (RSS = 0.26)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-87 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 5

1.2 μg/L

0

5

10

15

20

25

30

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.89)

SCA prediction (RSS = 1.3)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-88 Comparison of DI and SCA methods for predicting the SDS-TTHM 

integral breakthrough curve for Water 5

58 μg/L

-349-
0

1

2

3

4

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.49)

SCA prediction (RSS = 0.59)

c0 =

EBCT = 20 min.

Figure F-90 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 5

SDS-DCAA

10.3 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-89 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 5

2 μg/L

Blended effluent concentrations were not detected 

above the MRL for this parameter

-350-
0

1

2

3

4

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.28)

SCA prediction (RSS = 0.39)

c0 =

EBCT = 20 min.

SDS-TCAA

Figure F-91 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 5

12.7 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-92 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 5

1 μg/L

Blended effluent concentrations were not detected 

above the MRL for this parameter

-351-
0

2

4

6

8

10

12

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.56)

SCA prediction (RSS = 0.52)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-94 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 5

28 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.47)

SCA prediction (RSS = 0.2)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-93 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 5

2 μg/L

-352-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.4)

SCA prediction (RSS = 0.21)

c0 =

EBCT = 20 min.

SDS-BCAA

Figure F-95 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 5

7.3 μg/L

0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.82)

SCA prediction (RSS = 0.55)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-96 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 5

34 μg/L

-353-
0

1

2

3

4

5

6

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.4)

SCA prediction (RSS = 0.34)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-97 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 5

10.7 μg/L

0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.88)

SCA prediction (RSS = 1.1)

c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-98 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 5

3.7 μg/L

-354-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-99 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 5

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.19)

SCA prediction (RSS = 1.13)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-100 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 5

48 μg/L

-355-
0.00

0.25

0.50

0.75

1.00

1.25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.015)

c0 = 2.64 mg/L

EBCT = 20 min.

TOC

Figure F-101 DI method prediction of the TOC integral breakthrough curve 

for Water 6

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200 250 300

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0017)

SCA prediction (RSS = 0.0026)

c0 =

EBCT = 20 min.

UV254

Figure F-102 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 6

0.06 1/cm

-356-
0

2

4

6

8

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.37)

SCA prediction (RSS = 0.74) c0 =

EBCT = 20 min.

SDS-CF

Figure F-104 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 6

55.3 μg/L

0

20

40

60

80

100

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = 2.39)

SCA prediction (RSS = 6.26)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-103 Comparison of DI and SCA methods for predicting the SDS-TOX 

integral breakthrough curve for Water 6

305 μg/L Cl-

-357-
0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.02)

SCA prediction (RSS = 1.84)

c0 =

EBCT = 20 min.

Figure F-106 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 6

SDS-DBCM

41.6 μg/L

0

5

10

15

20

25

30

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.63)

SCA prediction (RSS = 2.31)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-105 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 6

27.4 μg/L

-358-
0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 3.47)

SCA prediction (RSS = 1.86)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-107 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 6

3.3 μg/L

0

10

20

30

40

50

60

70

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 6.43)

SCA prediction (RSS = 5.91)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-108 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 6

128 μg/L

-359-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.47)

SCA prediction (RSS = 0.31)

c0 =

EBCT = 20 min.

Figure F-110 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 6

SDS-DCAA

17.3 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-109 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 6

BMRL

Blended effluent concentrations were not detected above the 

MRL for this parameter

-360-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TCAA

Figure F-111 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 6

13.3 μg/L

Insufficient data measured above the MRL to compare 

DI and SCA predictions

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-112 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 6

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-361-
0

2

4

6

8

10

12

14

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.02)

SCA prediction (RSS = 1.54)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-114 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 6

36 μg/L

0

2

4

6

8

10

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.31)

SCA prediction (RSS = 1.11)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-113 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 6

5.7 μg/L

-362-
0

1

2

3

4

5

6

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.43)

SCA prediction (RSS = 0.44)

c0

EBCT = 20 min.

SDS-BCAA

Figure F-115 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 6

12.3 μg/L

0

5

10

15

20

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.3)

SCA prediction (RSS = 1.92)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-116 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 6

49 μg/L

-363-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.34)

SCA prediction (RSS = 0.34)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-117 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 6

9 μg/L

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.72)

SCA prediction (RSS = 0.67) c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-118 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 6

3 μg/L

-364-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-119 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 6

BMRL

Blended effluent concentrations were not detected above the 

MRL for this parameter

0

5

10

15

20

25

0 50 100 150 200 250 300

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.88)

SCA prediction (RSS = 2.41)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-120 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 6

61 μg/L

-365-
0.0

0.5

1.0

1.5

2.0

2.5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.065)

c0 = 5.58 mg/L

EBCT = 20 min.

TOC

Figure F-121 DI method prediction of the TOC integral breakthrough curve 

for Water 7

0.00

0.01

0.02

0.03

0.04

0 25 50 75 100 125 150

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0015)

SCA prediction (RSS = 0.0021)

c0 =

EBCT = 20 min.

UV254

Figure F-122 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 7

0.109 1/cm

-366-
0

1

2

3

4

5

6

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.85)

SCA prediction (RSS = 0.46) c0 =

EBCT = 20 min.

SDS-CF

Figure F-124 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 7

52.4 μg/L

0

50

100

150

200

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = 6.92)

SCA prediction (RSS = 9.3)

c0 =

EBCT = 20 min.

SDS-TOX

Figure F-123 Comparison of DI and SCA methods for predicting the SDSTOX integral breakthrough curve for Water 7

486 μg/L Cl-

-367-
0

5

10

15

20

25

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.99)

SCA prediction (RSS = 1.1)

c0 =

EBCT = 20 min.

Figure F-126 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 7

SDS-DBCM

66.2 μg/L

0

10

20

30

40

50

60

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 5.57)

SCA prediction (RSS = 3.38)

c0 =

EBCT = 20 min.

SDS-BDCM

Figure F-125 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 7

65.3 μg/L

-368-
0

10

20

30

40

50

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 11.53)

SCA prediction (RSS = 5.39)

c0 =

EBCT = 20 min.

SDS-BF

Figure F-127 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 7

16 μg/L

0

25

50

75

100

125

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 15.22)

SCA prediction (RSS = 9.72)

c0 =

EBCT = 20 min.

SDS-TTHM

Figure F-128 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 7

200 μg/L

-369-
0

1

2

3

4

5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.59)

SCA prediction (RSS = 0.63)

c0 =

EBCT = 20 min.

Figure F-130 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 7

SDS-DCAA

25.3 μg/L

0.0

0.5

1.0

1.5

2.0

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MCAA

Figure F-129 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 7

5 μg/L

Blended effluent concentrations were not detected above the 

MRL for this parameter

-370-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.4)

c0 = SCA prediction (RSS = 1.34)

EBCT = 20 min.

SDS-TCAA

Figure F-131 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 7

18 μg/L

0.0

0.5

1.0

1.5

2.0

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 20 min.

SDS-MBAA

Figure F-132 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 7

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-371-
0

5

10

15

20

25

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.02)

SCA prediction (RSS = 1.52)

c0 =

EBCT = 20 min.

SDS-HAA5

Figure F-134 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 7

65 μg/L

0

5

10

15

20

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.21)

SCA prediction (RSS = 0.97)

c0 =

EBCT = 20 min.

SDS-DBAA

Figure F-133 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 7

16.3 μg/L

-372-
0

2

4

6

8

10

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.35)

SCA prediction (RSS = 1.1)

c0 =

EBCT = 20 min.

SDS-BCAA

Figure F-135 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 7

23.7 μg/L

0

5

10

15

20

25

30

35

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.09)

SCA prediction (RSS = 2.25)

c0 =

EBCT = 20 min.

SDS-HAA6

Figure F-136 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 7

85 μg/L

-373-
0

1

2

3

4

5

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.13)

SCA prediction (RSS = 0.85)

c0 =

EBCT = 20 min.

SDS-DCBAA

Figure F-137 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 7

26.7 μg/L

0

1

2

3

4

5

6

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.25)

SCA prediction (RSS = 1.02)

c0 =

EBCT = 20 min.

SDS-CDBAA

Figure F-138 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 7

11.7 

-374-
0

1

2

3

4

5

6

7

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.92)

SCA prediction (RSS = 1.84)

c0 =

EBCT = 20 min.

SDS-TBAA

Figure F-139 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 7

BMRL

0

10

20

30

40

50

60

0 25 50 75 100 125 150

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 4.52)

SCA prediction (RSS = 4.7)

c0 =

EBCT = 20 min.

SDS-HAA9

Figure F-140 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 7

124 μg/L

-375-
0.00

0.25

0.50

0.75

1.00

1.25

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Observed data

Best fit

DI prediction (RSS = 0.033)

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure F-141 DI method prediction of the TOC integral breakthrough curve 

for Water 8

0.000

0.005

0.010

0.015

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Observed data

Best fit

DI prediction (RSS = 0.0017)

SCA prediction (RSS = 0.0017)

c0 =

EBCT = 7.2 min.

UV254

Figure F-142 Comparison of DI and SCA methods for predicting the UV254 

integral breakthrough curve for Water 8

0.033 1/cm

-376-
0

2

4

6

8

10

12

14

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.58)

SCA prediction (RSS = 1.62)

c0 =

EBCT = 7.2 min.

SDS-CF

Figure F-144 Comparison of DI and SCA methods for predicting the SDS-CF 

integral breakthrough curve for Water 8

29.1 μg/L

0

50

100

150

200

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-TOX

Figure F-143 Comparison of DI and SCA methods for predicting the SDSTOX integral breakthrough curve for Water 8

156 μg/L ClSDS-TOX not analyzed in 

blended effluent

-377-
0

2

4

6

8

10

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.15)

SCA prediction (RSS = 1.19)

c0 =

EBCT = 7.2 min.

Figure F-146 Comparison of DI and SCA methods for predicting the SDSDBCM integral breakthrough curve for Water 8

SDS-DBCM

10.3 μg/L

0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.2)

SCA prediction (RSS = 0.81)

c0 =

EBCT = 7.2 min.

SDS-BDCM

Figure F-145 Comparison of DI and SCA methods for predicting the SDSBDCM integral breakthrough curve for Water 8

2.6 μg/L

-378-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-BF

Figure F-147 Comparison of DI and SCA methods for predicting the SDS-BF 

integral breakthrough curve for Water 8

BMRL

Insufficient data measured above the MRL to 

compare DI and SCA predictions

0

5

10

15

20

25

30

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.59)

SCA prediction (RSS = 3.87)

c0 =

EBCT = 7.2 min.

SDS-TTHM

Figure F-148 Comparison of DI and SCA methods for predicting the SDSTTHM integral breakthrough curve for Water 8

42 μg/L

-379-
0

1

2

3

4

5

6

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.68)

SCA prediction (RSS = 0.75)

c0 =

EBCT = 7.2 min.

Figure F-150 Comparison of DI and SCA methods for predicting the SDSDCAA integral breakthrough curve for Water 8

SDS-DCAA

10.7 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-MCAA

Figure F-149 Comparison of DI and SCA methods for predicting the SDSMCAA integral breakthrough curve for Water 8

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-380-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.73)

SCA prediction (RSS = 1)

c0 =

EBCT = 7.2 min.

SDS-TCAA

Figure F-151 Comparison of DI and SCA methods for predicting the SDSTCAA integral breakthrough curve for Water 8

12.7 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-MBAA

Figure F-152 Comparison of DI and SCA methods for predicting the SDSMBAA integral breakthrough curve for Water 8

BMRL

Blended effluent concentrations were not detected above the 

MRL for this parameter

-381-
0

2

4

6

8

10

12

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.35)

SCA prediction (RSS = 1.84)

c0 =

EBCT = 7.2 min.

SDS-HAA5

Figure F-154 Comparison of DI and SCA methods for predicting the SDSHAA5 integral breakthrough curve for Water 8

23 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-DBAA

Figure F-153 Comparison of DI and SCA methods for predicting the SDSDBAA integral breakthrough curve for Water 8

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

-382-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.59)

SCA prediction (RSS = 0.31)

c0 =

EBCT = 7.2 min.

SDS-BCAA

Figure F-155 Comparison of DI and SCA methods for predicting the SDSBCAA integral breakthrough curve for Water 8

3 μg/L

0

2

4

6

8

10

12

14

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 1.83)

SCA prediction (RSS = 1.86)

c0 =

EBCT = 7.2 min.

SDS-HAA6

Figure F-156 Comparison of DI and SCA methods for predicting the SDSHAA6 integral breakthrough curve for Water 8

27 μg/L

-383-
0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 0.46)

SCA prediction (RSS = 0.29)

c0 =

EBCT = 7.2 min.

SDS-DCBAA

Figure F-157 Comparison of DI and SCA methods for predicting the SDSDCBAA integral breakthrough curve for Water 8

3 μg/L

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-CDBAA

Figure F-158 Comparison of DI and SCA methods for predicting the SDSCDBAA integral breakthrough curve for Water 8

BMRL

Blended effluent concentrations were not detected above 

the MRL for this parameter

-384-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = NA)

SCA prediction (RSS = NA)

c0 =

EBCT = 7.2 min.

SDS-TBAA

Figure F-159 Comparison of DI and SCA methods for predicting the SDSTBAA integral breakthrough curve for Water 8

BMRL

Blended effluent concentrations were not detected 

above the MRL for this parameter

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Observed data

Best fit

DI prediction (RSS = 2.2)

SCA prediction (RSS = 1.85)

c0 =

EBCT = 7.2 min.

SDS-HAA9

Figure F-160 Comparison of DI and SCA methods for predicting the SDSHAA9 integral breakthrough curve for Water 8

30 μg/L

-385-
-386-

This page intentionally left blank.
-387-

Appendix G: Logistic Function Model Best-Fit Parameters
-388-

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 3.28 11.11 0.104 NA NA 13

UV-254 Step-lag -0.03 0.09 3.21 0.058 NA NA 13

SDS-TOX Step-lag -55.82 186.75 4.02 0.070 NA NA 12

SDS-TTHM Step-lag -33.46 100.37 3.45 0.083 NA NA 11

SDS-HAA5 Step-lag -1.25 15.59 180.13 0.273 NA NA 11

SDS-HAA6 Step-lag -10.79 30.90 12.40 0.157 NA NA 11

SDS-HAA9 Step-lag -11.49 33.13 9.47 0.137 NA NA 11

SDS-CF Step-lag -2.12 20.37 19.81 0.068 NA NA 8

SDS-BDCM Step-lag -12.01 35.33 5.74 0.117 NA NA 11

SDS-DBCM Step-lag -7.70 31.82 5.91 0.066 NA NA 11

SDS-BF Step-lag-peak -0.40 13.59 200.86 0.388 -0.148 28 11

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -0.36 8.52 1.39E+04 0.470 NA NA 9

SDS-TCAA No fit NA NA NA NA NA NA 2

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag -0.05 5.35 124.71 0.302 NA NA 11

SDS-BCAA Step-lag -2.94 8.66 6.86 0.103 NA NA 9

SDS-CDBAA No fit NA NA NA NA NA NA 0

SDS-DCBAA No fit NA NA NA NA NA NA 3

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-1 Summary of single contactor best-fit logistic function model parameters for Water 1

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 1.78 7.48 0.037 NA NA 13

UV-254 Step-lag -0.02 0.05 2.03 0.022 NA NA 13

SDS-TOX Step-lag -75.03 218.15 2.32 0.025 NA NA 12

SDS-TTHM Step-lag -38.87 113.39 2.50 0.037 NA NA 12

SDS-HAA5 Step-lag -8.74 24.47 2.89 0.032 NA NA 11

SDS-HAA6 Step-lag -12.84 35.99 2.89 0.032 NA NA 11

SDS-HAA9 Step-lag -14.05 40.85 3.06 0.031 NA NA 11

SDS-CF Step-lag -9.99 35.27 3.20 0.016 NA NA 10

SDS-BDCM Step-lag -7.34 31.41 8.73 0.071 NA NA 11

SDS-DBCM Step-lag -12.70 37.33 3.51 0.036 NA NA 11

SDS-BF Step-lag-peak 0.06 11.27 11.03 0.127 -0.036 39 12

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -4.27 12.29 2.84 0.021 NA NA 9

SDS-TCAA Step-lag 0.00 2.10 3.51E+06 0.245 NA NA 6

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag -0.56 6.65 11.45 0.073 NA NA 11

SDS-BCAA Step-lag -4.10 11.54 2.86 0.030 NA NA 10

SDS-CDBAA No fit NA NA NA NA NA NA 2

SDS-DCBAA Step-lag -1.21 3.42 15.85 0.067 NA NA 8

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-2 Summary of single contactor best-fit logistic function model parameters for Water 2
-389-

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 1.50 9.36 0.027 NA NA 12

UV-254 Step-lag -0.01 0.04 3.15 0.019 NA NA 12

SDS-TOX Step-lag -24.55 161.23 4.95 0.019 NA NA 12

SDS-TTHM Step-lag -45.77 144.99 1.92 0.020 NA NA 12

SDS-HAA5 Step-lag-peak -0.33 30.45 24.34 0.026 0.000 140 11

SDS-HAA6 Step-lag 2.79 22.97 228.87 0.057 NA NA 11

SDS-HAA9 Step-lag-peak -1.91 48.87 17.53 0.024 0.000 140 11

SDS-CF Step-lag -0.91 18.88 36.59 0.022 NA NA 9

SDS-BDCM Step-lag -1.52 35.95 12.00 0.041 NA NA 12

SDS-DBCM Step-lag -7.80 36.72 5.97 0.018 NA NA 11

SDS-BF Step-lag-peak 3.92 27.13 32.31 0.080 -0.050 78 12

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -2.92 9.26 12.56 0.024 NA NA 6

SDS-TCAA No fit NA NA NA NA NA NA 5

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag 0.37 10.77 25.97 0.048 NA NA 11

SDS-BCAA Step-lag 0.44 6.91 85.06 0.047 NA NA 11

SDS-CDBAA No fit NA NA NA NA NA NA 2

SDS-DCBAA Step-lag -0.96 2.87 1.75 0.016 NA NA 9

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-3 Summary of single contactor best-fit logistic function model parameters for Water 3

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 2.10 27.87 0.044 NA NA 13

UV-254 Step-lag -0.01 0.05 6.52 0.025 NA NA 13

SDS-TOX Step-lag -75.14 246.00 7.02 0.028 NA NA 12

SDS-TTHM Step-lag -19.39 61.91 5.76 0.026 NA NA 11

SDS-HAA5 Step-lag -10.44 51.98 9.16 0.021 NA NA 10

SDS-HAA6 Step-lag -12.99 58.13 7.66 0.021 NA NA 10

SDS-HAA9 Step-lag -14.39 66.66 7.19 0.020 NA NA 11

SDS-CF Step-lag -10.41 41.38 7.49 0.023 NA NA 11

SDS-BDCM Step-lag-peak -1.62 4.69 3.64E+03 0.162 -0.006 91 10

SDS-DBCM Step-lag -5.61 16.64 6.38 0.032 NA NA 11

SDS-BF No fit NA NA NA NA NA NA 0

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -5.31 20.22 6.76 0.022 NA NA 10

SDS-TCAA Step-lag -4.35 31.76 15.48 0.022 NA NA 10

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA No fit NA NA NA NA NA NA 4

SDS-BCAA Step-lag -2.05 5.78 8.18 0.040 NA NA 10

SDS-CDBAA No fit NA NA NA NA NA NA 0

SDS-DCBAA Step-lag -1.52 8.53 4.59 0.017 NA NA 11

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-4 Summary of single contactor best-fit logistic function model parameters for Water 4
-390-

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 2.33 12.66 0.023 NA NA 13

UV-254 Step-lag -0.01 0.05 3.83 0.013 NA NA 13

SDS-TOX Step-lag -53.67 196.80 4.28 0.014 NA NA 13

SDS-TTHM Step-lag -22.75 68.25 3.01 0.015 NA NA 12

SDS-HAA5 Step-lag -7.50 22.06 4.85 0.021 NA NA 11

SDS-HAA6 Step-lag -10.90 32.01 4.84 0.021 NA NA 11

SDS-HAA9 Step-lag -17.05 50.40 6.20 0.023 NA NA 11

SDS-CF Step-lag -2.28 16.34 9.86 0.013 NA NA 10

SDS-BDCM Step-lag -7.19 20.65 3.14 0.021 NA NA 12

SDS-DBCM Step-lag -6.98 27.30 4.02 0.013 NA NA 12

SDS-BF Step-lag -1.20 3.34 116.01 0.090 NA NA 11

SDS-MCAA No fit NA NA NA NA NA NA 3

SDS-DCAA Step-lag -2.40 8.70 4.51 0.014 NA NA 10

SDS-TCAA Step-lag -3.00 9.30 7.63 0.019 NA NA 9

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag -1.60 4.65 62.79 0.064 NA NA 11

SDS-BCAA Step-lag -3.40 9.96 4.82 0.022 NA NA 11

SDS-CDBAA Step-lag-peak -2.60 7.06 148.78 0.060 -0.004 158 8

SDS-DCBAA Step-lag -4.25 12.75 4.99 0.019 NA NA 11

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-5 Summary of single contactor best-fit logistic function model parameters for Water 5

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 1.98 69.54 0.033 NA NA 13

UV-254 Step-lag -0.01 0.05 9.28 0.017 NA NA 13

SDS-TOX Step-lag -77.38 283.38 9.39 0.018 NA NA 11

SDS-TTHM Step-lag -44.21 131.09 12.29 0.027 NA NA 11

SDS-HAA5 Step-lag -9.22 30.95 10.99 0.020 NA NA 10

SDS-HAA6 Step-lag -14.16 44.81 12.39 0.022 NA NA 10

SDS-HAA9 Step-lag -18.87 57.94 14.38 0.023 NA NA 10

SDS-CF Step 0.00 24.13 372.30 0.031 NA NA 8

SDS-BDCM Step-lag -2.82 31.63 605.70 0.062 NA NA 11

SDS-DBCM Step-lag -13.11 44.11 14.75 0.022 NA NA 10

SDS-BF Step-lag-peak -0.13 15.07 8.23E+03 0.099 -0.057 131 11

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -2.97 12.66 17.57 0.018 NA NA 7

SDS-TCAA No fit NA NA NA NA NA NA 4

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag 0.18 7.36 1.73E+05 0.110 NA NA 10

SDS-BCAA Step-lag -4.62 13.86 18.59 0.026 NA NA 9

SDS-CDBAA Step-lag 0.04 2.64 7.20E+12 0.263 NA NA 7

SDS-DCBAA Step-lag -2.59 8.53 17.66 0.021 NA NA 7

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-6 Summary of single contactor best-fit logistic function model parameters for Water 6
-391-

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 4.01 15.49 0.049 NA NA 13

UV-254 Step-lag -0.03 0.09 4.57 0.032 NA NA 13

SDS-TOX Step-lag -134.27 420.30 4.63 0.036 NA NA 12

SDS-TTHM Step-lag -81.69 239.78 4.18 0.042 NA NA 12

SDS-HAA5 Step-lag -16.06 47.02 5.56 0.051 NA NA 11

SDS-HAA6 Step-lag -23.26 67.10 6.08 0.052 NA NA 11

SDS-HAA9 Step-lag -34.18 96.81 8.04 0.060 NA NA 11

SDS-CF Step-lag -2.35 25.39 33.37 0.034 NA NA 7

SDS-BDCM Step-lag -34.72 103.28 5.34 0.045 NA NA 11

SDS-DBCM Step-lag -15.55 64.36 7.25 0.031 NA NA 10

SDS-BF Step-lag 1.39 35.62 459.64 0.187 NA NA 12

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -4.10 13.83 3.28 0.026 NA NA 10

SDS-TCAA Step-lag -0.03 6.60 6.68E+05 0.193 NA NA 6

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA Step-lag -0.06 16.06 136.54 0.127 NA NA 11

SDS-BCAA Step-lag -7.20 20.05 7.76 0.058 NA NA 10

SDS-CDBAA Step-lag -1.78 7.56 262.52 0.138 NA NA 9

SDS-DCBAA Step-lag -2.77 16.70 9.30 0.027 NA NA 9

SDS-TBAA Step-lag-peak -3.67 10.33 471.50 0.174 -0.034 65 9

NA: not applicable

*Number of observations above the MRL

Table G-7 Summary of single contactor best-fit logistic function model parameters for Water 7

Analyte Type of curve A 0 ABD S t p n*

fit

TOC Step 0.00 1.53 27.83 0.064 NA NA 13

UV-254 Step-lag -0.01 0.03 5.40 0.037 NA NA 13

SDS-TOX Step-lag -56.70 162.37 6.31 0.042 NA NA 13

SDS-TTHM Step-lag -18.32 52.36 5.00 0.036 NA NA 11

SDS-HAA5 Step-lag -6.73 20.20 5.50 0.034 NA NA 10

SDS-HAA6 Step-lag -8.03 22.41 9.66 0.052 NA NA 10

SDS-HAA9 Step-lag -9.14 25.34 14.18 0.062 NA NA 10

SDS-CF Step-lag -11.78 35.35 4.06 0.024 NA NA 11

SDS-BDCM Step-lag-peak -1.91 4.97 1.26E+03 0.196 -0.004 113 11

SDS-DBCM Step-lag -5.12 14.78 9.93 0.055 NA NA 11

SDS-BF No fit NA NA NA NA NA NA 0

SDS-MCAA No fit NA NA NA NA NA NA 0

SDS-DCAA Step-lag -3.55 10.03 8.52 0.048 NA NA 10

SDS-TCAA Step-lag -3.19 9.56 5.04 0.030 NA NA 9

SDS-MBAA No fit NA NA NA NA NA NA 0

SDS-DBAA No fit NA NA NA NA NA NA 0

SDS-BCAA Step-lag -1.30 3.68 2.49E+03 0.199 NA NA 10

SDS-CDBAA No fit NA NA NA NA NA NA 0

SDS-DCBAA Step-lag -0.97 3.05 1.44E+04 0.236 NA NA 9

SDS-TBAA No fit NA NA NA NA NA NA 0

NA: not applicable

*Number of observations above the MRL

Table G-8 Summary of single contactor best-fit logistic function model parameters for Water 8
-392-

This page intentionally left blank.
-393-

Appendix H: Impact of Extrapolation on SCA Prediction of the

Integral Breakthrough Curve
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.984)

Extrapolated logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure H-1 Impact of extrapolation on DI prediction of the TOC integral 

breakthrough curve for Water 5

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.992)

Extrapolated logistic function best fit (R^2 = 0.984)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 0.051 1/cm

EBCT = 20 min.

UV254

Figure H-2 Impact of extrapolation on SCA prediction of the UV254 integral 

breakthrough curve for Water 5

-394-
0

25

50

75

100

125

150

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.995)

Extrapolated logistic function best fit (R^2 = 0.99)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 205 μg/L ClEBCT = 20 min.

SDS-TOX

Figure H-3 Impact of extrapolation on SCA prediction of the SDS-TOX 

integral breakthrough curve for Water 5

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.982)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 23.7 μg/L

EBCT = 20 min.

SDS-CF

Figure H-4 Impact of extrapolation on SCA prediction of the SDS-CF integral 

breakthrough curve for Water 5

-395-
0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.97)

Extrapolated logistic function best fit (R^2 = 0.967)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 10.8 μg/L

EBCT = 20 min.

SDS-BDCM

Figure H-5 Impact of extrapolation on SCA prediction of the SDS-BDCM 

integral breakthrough curve for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.988)

Extrapolated logistic function best fit (R^2 = 0.986)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 22.7 μg/L

EBCT = 20 min.

SDS-DBCM

Figure H-6 Impact of extrapolation on SCA prediction of the SDS-DBCM 

integral breakthrough curve for Water 5

-396-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.969)

Extrapolated logistic function best fit (R^2 = 0.969)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 1.2 μg/L

EBCT = 20 min. SDS-BF

Figure H-7 Impact of extrapolation on SCA prediction of the SDS-BF integral 

breakthrough curve for Water 5

0

10

20

30

40

50

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.979)

Extrapolated logistic function best fit (R^2 = 0.975)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 58 μg/L

EBCT = 20 min.

SDS-TTHM

Figure H-8 Impact of extrapolation on SCA prediction of the SDS-TTHM 

integral breakthrough curve for Water 5

-397-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 2 μg/L

EBCT = 20 min.

SDS-MCAA

Figure H-9 Impact of extrapolation on SCA prediction of the SDS-MCAA 

integral breakthrough curve for Water 5

Insufficient data measured above the MRL

to perform curve fit and extrapolation analysis

0

1

2

3

4

5

6

7

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.978)

Extrapolated logistic function best fit (R^2 = 0.963)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 10.3 μg/L

EBCT = 20 min.

SDS-DCAA

Figure H-10 Impact of extrapolation on SCA prediction of the SDS-DCAA 

integral breakthrough curve for Water 5

-398-
0

1

2

3

4

5

6

7

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.989)

Extrapolated logistic function best fit (R^2 = 0.987)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 12.7 μg/L

EBCT = 20 min.

SDS-TCAA

Figure H-11 Impact of extrapolation on SCA prediction of the SDS-TCAA 

integral breakthrough curve for Water 5

0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 1 μg/L

EBCT = 20 min.

SDS-MBAA

Figure H-12 Impact of extrapolation on SCA prediction of the SDS-MBAA 

integral breakthrough curve for Water 5

Effluent concentrations were not detected

above the MRL for this parameter

-399-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.992)

Extrapolated logistic function best fit (R^2 = 0.992)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 2 μg/L

EBCT = 20 min. SDS-DBAA

Figure H-13 Impact of extrapolation on SCA prediction of the SDS-DBAA 

integral breakthrough curve for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.965)

Extrapolated logistic function best fit (R^2 = 0.959)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 28 μg/L

EBCT = 20 min.

SDS-HAA5

Figure H-14 Impact of extrapolation on SCA prediction of the SDS-HAA5 

integral breakthrough curve for Water 5

-400-
0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.982)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 7.3 μg/L

EBCT = 20 min.

SDS-BCAA

Figure H-15 Impact of extrapolation on SCA prediction of the SDS-BCAA 

integral breakthrough curve for Water 5

0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.976)

Extrapolated logistic function best fit (R^2 = 0.971)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 34 μg/L

EBCT = 20 min.

SDS-HAA6

Figure H-16 Impact of extrapolation on SCA prediction of the SDS-HAA6 

integral breakthrough curve for Water 5

-401-
0

2

4

6

8

10

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.987)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 10.7 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure H-17 Impact of extrapolation on SCA prediction of the SDS-DCBAA 

integral breakthrough curve for Water 5

0

1

2

3

4

5

6

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.95)

Extrapolated logistic function best fit (R^2 = 0.947)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 3.7 μg/L

EBCT = 20 min.

SDS-CDBAA

Figure H-18 Impact of extrapolation on SCA prediction of the SDS-CDBAA 

integral breakthrough curve for Water 5

-402-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure H-19 Impact of extrapolation on SCA prediction of the SDS-TBAA 

integral breakthrough curve for Water 5

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

25

30

35

40

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.988)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 48 μg/L

EBCT = 20 min.

SDS-HAA9

Figure H-20 Impact of extrapolation on SCA prediction of the SDS-HAA9 

integral breakthrough curve for Water 5

-403-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.974)

Extrapolated logistic function best fit (R^2 = 0.961)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure H-21 Impact of extrapolation on DI prediction of the TOC integral 

breakthrough curve for Water 8

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.994)

Extrapolated logistic function best fit (R^2 = 0.985)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 0.033 1/cm

EBCT = 7.2 min.

UV254

Figure H-22 Impact of extrapolation on SCA prediction of the UV254 integral 

breakthrough curve for Water 8

-404-
0

25

50

75

100

125

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.98)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 156 μg/L ClEBCT = 7.2 min.

SDS-TOX

Figure H-23 Impact of extrapolation on SCA prediction of the SDS-TOX 

integral breakthrough curve for Water 8

0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.985)

Extrapolated logistic function best fit (R^2 = 0.917)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 29.1 μg/L

EBCT = 7.2 min.

SDS-CF

Figure H-24 Impact of extrapolation on SCA prediction of the SDS-CF 

integral breakthrough curve for Water 8

-405-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.946)

Extrapolated logistic function best fit (R^2 = 0.942)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 2.6 μg/L

EBCT = 7.2 min.

SDS-BDCM

Figure H-25 Impact of extrapolation on SCA prediction of the SDS-BDCM 

integral breakthrough curve for Water 8

0

2

4

6

8

10

12

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.98)

Extrapolated logistic function best fit (R^2 = 0.978)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 10.3 μg/L

EBCT = 7.2 min.

SDS-DBCM

Figure H-26 Impact of extrapolation on SCA prediction of the SDS-DBCM 

integral breakthrough curve for Water 8

-406-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-BF

Figure H-27 Impact of extrapolation on SCA prediction of the SDS-BF 

integral breakthrough curve for Water 8

Insufficient data measured above the MRL

to perform curve fit and extrapolation analysis

0

10

20

30

40

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.98)

Extrapolated logistic function best fit (R^2 = 0.966)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 42 μg/L

EBCT = 7.2 min.

SDS-TTHM

Figure H-28 Impact of extrapolation on SCA prediction of the SDS-TTHM 

integral breakthrough curve for Water 8

-407-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-MCAA

Figure H-29 Impact of extrapolation on SCA prediction of the SDS-MCAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.974)

Extrapolated logistic function best fit (R^2 = 0.953)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 10.7 μg/L

EBCT = 7.2 min.

SDS-DCAA

Figure H-30 Impact of extrapolation on SCA prediction of the SDS-DCAA 

integral breakthrough curve for Water 8

-408-
0

1

2

3

4

5

6

7

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.93)

Extrapolated logistic function best fit (R^2 = 0.835)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 12.7 μg/L

EBCT = 7.2 min.

SDS-TCAA

Figure H-31 Impact of extrapolation on SCA prediction of the SDS-TCAA 

integral breakthrough curve for Water 8

0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-MBAA

Figure H-32 Impact of extrapolation on SCA prediction of the SDS-MBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

-409-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-DBAA

Figure H-33 Impact of extrapolation on SCA prediction of the SDS-DBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.955)

Extrapolated logistic function best fit (R^2 = 0.911)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 23 μg/L

EBCT = 7.2 min.

SDS-HAA5

Figure H-34 Impact of extrapolation on SCA prediction of the SDS-HAA5 

integral breakthrough curve for Water 8

-410-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.971)

Extrapolated logistic function best fit (R^2 = 0.97)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 3 μg/L

EBCT = 7.2 min. SDS-BCAA

Figure H-35 Impact of extrapolation on SCA prediction of the SDS-BCAA 

integral breakthrough curve for Water 8

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.956)

Extrapolated logistic function best fit (R^2 = 0.923)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 27 μg/L

EBCT = 7.2 min.

SDS-HAA6

Figure H-36 Impact of extrapolation on SCA prediction of the SDS-HAA6 

integral breakthrough curve for Water 8

-411-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.939)

Extrapolated logistic function best fit (R^2 = 0.914)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 3 μg/L

EBCT = 7.2 min. SDS-DCBAA

Figure H-37 Impact of extrapolation on SCA prediction of the SDS-DCBAA 

integral breakthrough curve for Water 8

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-CDBAA

Figure H-38 Impact of extrapolation on SCA prediction of the SDS-CDBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

-412-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-TBAA

Figure H-39 Impact of extrapolation on SCA prediction of the SDS-TBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.952)

Extrapolated logistic function best fit (R^2 = 0.924)

Blended effluent

SCA prediction

SCA prediction - extrapolated

c0 = 30 μg/L

EBCT = 7.2 min.

SDS-HAA9

Figure H-40 Impact of extrapolation on SCA prediction of the SDS-HAA9 

integral breakthrough curve for Water 8

-413-
-414-

This page intentionally left blank.
-415-

Appendix I: Impact of Extrapolation on DI Prediction of the Integral

Breakthrough Curve
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.984)

Extrapolated logistic function best fit (R^2 = 0.979)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3.08 mg/L

EBCT = 20 min.

TOC

Figure I-1 Impact of extrapolation on the DI prediction of the TOC integral 

breakthrough curve for Water 5

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 50 100 150 200 250 300 350

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.992)

Extrapolated logistic function best fit (R^2 = 0.984)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 0.051 1/cm

EBCT = 20 min.

UV254

Figure I-2 Impact of extrapolation on the DI prediction of the UV254 integral 

breakthrough curve for Water 5

-416-
0

25

50

75

100

125

150

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.995)

Extrapolated logistic function best fit (R^2 = 0.99)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 205 μg/L ClEBCT = 20 min.

SDS-TOX

Figure I-3 Impact of extrapolation on the DI prediction of the SDS-TOX 

integral breakthrough curve for Water 5

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 23.7 μg/L

EBCT = 20 min.

SDS-CF

Figure I-4 Impact of extrapolation on the DI prediction of the SDS-CF integral 

breakthrough curve for Water 5

-417-
0

2

4

6

8

10

12

14

16

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.97)

Extrapolated logistic function best fit (R^2 = 0.967)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 10.8 μg/L

EBCT = 20 min.

SDS-BDCM

Figure I-5 Impact of extrapolation on the DI prediction of the SDS-BDCM 

integral breakthrough curve for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.988)

Extrapolated logistic function best fit (R^2 = 0.986)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 22.7 μg/L

EBCT = 20 min.

SDS-DBCM

Figure I-6 Impact of extrapolation on the DI prediction of the SDS-DBCM 

integral breakthrough curve for Water 5

-418-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.969)

Extrapolated logistic function best fit (R^2 = 0.969)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 1.2 μg/L

EBCT = 20 min. SDS-BF

Figure I-7 Impact of extrapolation on the DI prediction of the SDS-BF integral 

breakthrough curve for Water 5

0

10

20

30

40

50

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.979)

Extrapolated logistic function best fit (R^2 = 0.975)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 58 μg/L

EBCT = 20 min.

SDS-TTHM

Figure I-8 Impact of extrapolation on the DI prediction of the SDS-TTHM 

integral breakthrough curve for Water 5

-419-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 2 μg/L

EBCT = 20 min.

SDS-MCAA

Figure I-9 Impact of extrapolation on the DI prediction of the SDS-MCAA 

integral breakthrough curve for Water 5

Insufficient data measured above the MRL

to perform curve fit and extrapolation analysis

0

1

2

3

4

5

6

7

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.978)

Extrapolated logistic function best fit (R^2 = 0.963)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 10.3 μg/L

EBCT = 20 min.

SDS-DCAA

Figure I-10 Impact of extrapolation on the DI prediction of the SDS-DCAA 

integral breakthrough curve for Water 5

-420-
0

1

2

3

4

5

6

7

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.989)

Extrapolated logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 12.7 μg/L

EBCT = 20 min.

SDS-TCAA

Figure I-11 Impact of extrapolation on the DI prediction of the SDS-TCAA 

integral breakthrough curve for Water 5

0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 1 μg/L

EBCT = 20 min.

SDS-MBAA

Figure I-12 Impact of extrapolation on the DI prediction of the SDS-MBAA 

integral breakthrough curve for Water 5

Effluent concentrations were not detected

above the MRL for this parameter

-421-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.992)

Extrapolated logistic function best fit (R^2 = 0.992)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 2 μg/L

EBCT = 20 min. SDS-DBAA

Figure I-13 Impact of extrapolation on the DI prediction of the SDS-DBAA 

integral breakthrough curve for Water 5

0

5

10

15

20

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.965)

Extrapolated logistic function best fit (R^2 = 0.959)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 28 μg/L

EBCT = 20 min.

SDS-HAA5

Figure I-14 Impact of extrapolation on the DI prediction of the SDS-HAA5 

integral breakthrough curve for Water 5

-422-
0

2

4

6

8

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.982)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 7.3 μg/L

EBCT = 20 min.

SDS-BCAA

Figure I-15 Impact of extrapolation on the DI prediction of the SDS-BCAA 

integral breakthrough curve for Water 5

0

5

10

15

20

25

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.976)

Extrapolated logistic function best fit (R^2 = 0.971)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 34 μg/L

EBCT = 20 min.

SDS-HAA6

Figure I-16 Impact of extrapolation on the DI prediction of the SDS-HAA6 

integral breakthrough curve for Water 5

-423-
0

2

4

6

8

10

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.987)

Extrapolated logistic function best fit (R^2 = 0.987)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 10.7 μg/L

EBCT = 20 min.

SDS-DCBAA

Figure I-17 Impact of extrapolation on the DI prediction of the SDS-DCBAA 

integral breakthrough curve for Water 5

0

1

2

3

4

5

6

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.95)

Extrapolated logistic function best fit (R^2 = 0.947)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3.7 μg/L

EBCT = 20 min.

SDS-CDBAA

Figure I-18 Impact of extrapolation on the DI prediction of the SDS-CDBAA 

integral breakthrough curve for Water 5

-424-
0

1

2

3

4

5

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 20 min.

SDS-TBAA

Figure I-19 Impact of extrapolation on the DI prediction of the SDS-TBAA 

integral breakthrough curve for Water 5

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

25

30

35

40

0 50 100 150 200 250 300 350

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.988)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 48 μg/L

EBCT = 20 min.

SDS-HAA9

Figure I-20 Impact of extrapolation on the DI prediction of the SDS-HAA9 

integral breakthrough curve for Water 5

-425-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (mg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.974)

Extrapolated logistic function best fit (R^2 = 0.961)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 2.02 mg/L

EBCT = 7.2 min.

TOC

Figure I-21 Impact of extrapolation on the DI prediction of the TOC integral 

breakthrough curve for Water 8

0.000

0.005

0.010

0.015

0.020

0.025

0 50 100 150 200

Scaled operation time (days)

Absorbance (1/cm)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.994)

Extrapolated logistic function best fit (R^2 = 0.985)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 0.033 1/cm

EBCT = 7.2 min.

UV254

Figure I-22 Impact of extrapolation on the DI prediction of the UV254 integral 

breakthrough curve for Water 8

-426-
0

25

50

75

100

125

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L Cl-)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.99)

Extrapolated logistic function best fit (R^2 = 0.98)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 156 μg/L ClEBCT = 7.2 min.

SDS-TOX

Figure I-23 Impact of extrapolation on the DI prediction of the SDS-TOX 

integral breakthrough curve for Water 8

0

5

10

15

20

25

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.985)

Extrapolated logistic function best fit (R^2 = 0.917)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 29.1 μg/L

EBCT = 7.2 min.

SDS-CF

Figure I-24 Impact of extrapolation on the DI prediction of the SDS-CF 

integral breakthrough curve for Water 8

-427-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.946)

Extrapolated logistic function best fit (R^2 = 0.942)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 2.6 μg/L

EBCT = 7.2 min.

SDS-BDCM

Figure I-25 Impact of extrapolation on the DI prediction of the SDS-BDCM 

integral breakthrough curve for Water 8

0

2

4

6

8

10

12

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.98)

Extrapolated logistic function best fit (R^2 = 0.978)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 10.3 μg/L

EBCT = 7.2 min.

SDS-DBCM

Figure I-26 Impact of extrapolation on the DI prediction of the SDS-DBCM 

integral breakthrough curve for Water 8

-428-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-BF

Figure I-27 Impact of extrapolation on the DI prediction of the SDS-BF 

integral breakthrough curve for Water 8

Insufficient data measured above the MRL

to perform curve fit and extrapolation analysis

0

10

20

30

40

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.98)

Extrapolated logistic function best fit (R^2 = 0.966)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 42 μg/L

EBCT = 7.2 min.

SDS-TTHM

Figure I-28 Impact of extrapolation on the DI prediction of the SDS-TTHM 

integral breakthrough curve for Water 8

-429-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-MCAA

Figure I-29 Impact of extrapolation on the DI prediction of the SDS-MCAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.974)

Extrapolated logistic function best fit (R^2 = 0.953)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 10.7 μg/L

EBCT = 7.2 min.

SDS-DCAA

Figure I-30 Impact of extrapolation on the DI prediction of the SDS-DCAA 

integral breakthrough curve for Water 8

-430-
0

1

2

3

4

5

6

7

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.93)

Extrapolated logistic function best fit (R^2 = 0.835)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 12.7 μg/L

EBCT = 7.2 min.

SDS-TCAA

Figure I-31 Impact of extrapolation on the DI prediction of the SDS-TCAA 

integral breakthrough curve for Water 8

0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-MBAA

Figure I-32 Impact of extrapolation on the DI prediction of the SDS-MBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

-431-
0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-DBAA

Figure I-33 Impact of extrapolation on the DI prediction of the SDS-DBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.955)

Extrapolated logistic function best fit (R^2 = 0.911)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 23 μg/L

EBCT = 7.2 min.

SDS-HAA5

Figure I-34 Impact of extrapolation on the DI prediction of the SDS-HAA5 

integral breakthrough curve for Water 8

-432-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.971)

Extrapolated logistic function best fit (R^2 = 0.97)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3 μg/L

EBCT = 7.2 min.

SDS-BCAA

Figure I-35 Impact of extrapolation on the DI prediction of the SDS-BCAA 

integral breakthrough curve for Water 8

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.956)

Extrapolated logistic function best fit (R^2 = 0.923)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 27 μg/L

EBCT = 7.2 min.

SDS-HAA6

Figure I-36 Impact of extrapolation on the DI prediction of the SDS-HAA6 

integral breakthrough curve for Water 8

-433-
0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.939)

Extrapolated logistic function best fit (R^2 = 0.914)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 3 μg/L

EBCT = 7.2 min.

SDS-DCBAA

Figure I-37 Impact of extrapolation on the DI prediction of the SDS-DCBAA 

integral breakthrough curve for Water 8

0.0

0.5

1.0

1.5

2.0

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-CDBAA

Figure I-38 Impact of extrapolation on the DI prediction of the SDS-CDBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

-434-
0

1

2

3

4

5

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = NA)

Extrapolated logistic function best fit

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = BMRL

EBCT = 7.2 min.

SDS-TBAA

Figure I-39 Impact of extrapolation on the DI prediction of the SDS-TBAA 

integral breakthrough curve for Water 8

Effluent concentrations were not detected

above the MRL for this parameter

0

5

10

15

20

0 50 100 150 200

Scaled operation time (days)

Concentration (μg/L)

Single contactor effluent

Extrapolation experimental data points

Logistic function best fit - all data (R^2 = 0.952)

Extrapolated logistic function best fit (R^2 = 0.924)

Blended effluent

DI prediction

DI prediction - extrapolated

c0 = 30 μg/L

EBCT = 7.2 min.

SDS-HAA9

Figure I-40 Impact of extrapolation on the DI prediction of the SDS-HAA9 

integral breakthrough curve for Water 8

-435-