Removal of DBP Precursors by GAC Adsorption - Water Research ...

American Water Works Association 

RESEARCH FOUNDATION 

Removal of 

DBP Precursors by 

GAG Adsorption 

Subject Area: 

Water Treatment


DBP Precursors by 

GAG Adsorption

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DBF Precursors by 

GAC Adsorption 

Prepared by 

Douglas M. Owen and Zaid K. Chowdhury 

Malcolm Pirnie, Inc. 

703 Palomar Airport Road, Suite 150 

Carlsbad, CA 92009 

R. Scott Summers, Stuart M. Hooper, and 

Gabriele Solarik 

Department of Civil and Environmental Engineering 

University of Cincinnati, Cincinnati, OH 45221 

Kimberly Gray 

Department of Civil Engineering 

Northwestern University, Evanston, IL 60208 

Sponsored by: 

AWWA Research Foundation 

6666 West Quincy Avenue 

Denver, CO 80235-3098 

Published by the 

AWWA Research Foundation and 

American Water Works Association

Disclaimer 

This study was funded by the AWWA Research Foundation (AWWARF). 

AWWARF assumes no responsibility for the content of the research study 

reported in this publication or for the opinions or statements of fact expressed in the 

report. The mention of trade names for commercial products does not represent or imply 

the approval or endorsement of AWWARF. This report is presented solely for informational purposes. 

Library of Congress Cataloging-in-Publication Data 

Removal of DBF precursors by gac adsorption / prepared by R. Scott 

Summers ... [et al.]; sponsored by AWWA Research Foundation, 

xxxiii, 284 p. 21.5x28cm. 

Includes bibliographical references. 

ISBN 0-89867-941-9 

1. Water-Purification-Organic compounds removal. 2. Water- 

Purification Adsorption. 3. Carbon, Activated. 4. Water- 

Purification-Disinfection-By-products. I. Summers, R. Scott. 

II. AWWA Research Foundation. 

TD427.07R46 1998 

628.1 '64 dc21 97-31908 

CIP 

Copy right 1998 

by 


and 


Printed in the U.S.A. 

ISBN 0-89867-941 -9 Printed on recycled paper.

Contents 

List of Tables ............................................... xi 

List of Figures ............................................... xv 

Foreword ................................................. xxv 

Acknowledgments ........................................... xxvii 

Executive Summary .......................................... xxix 

Chapter 1 Introduction ...................................... 1 

Regulatory Agenda, 2 

GAC Treatment Issues, 2 

Adsorbability of Natural Organic Matter, 2 

Blending of Contactor Effluents, 3 

DBF Speciation and Formation Conditions, 3 

Evaluation of Process Variables, 3 

Study Objectives, 4 

Chapter 2 Materials and Methods 

Experimental Approach, 7 

Testing Program, 7 

Sample Collection and Handling, 8 

Experimental Procedures, 8 

Analytical Methods, 12 

Standard Methods, 12 

Nonstandard Methods, 15 

Quality Assurance-Quality Control, 18 

Chapter 3 Using the RSSCT for Prediction of Field-Scale 

NOM Removal and DBF Control by GAC 

Adsorption ....................................... 21 

Objectives and Approach, 22 

Results and Discussion, 23 

Ohio River Water, 23 

Lake Gaillard Water, 23

vi Removal of DBF Precursors by GAC Adsorption 

Mississippi River Water, 24 

Passaic River Water, 24 

Summary, 24 

Chapter 4 Comparison of Breakthrough Patterns for 

Simulated Distribution System and Uniform 

Formation Condition DBFs ......................... 37 

Experimental Conditions, 37 


Ohio River Water, 38 

Lake Gaillard Water, 39 

Mississippi River Water, 39 

Summary, 40 

Chapter 5 NOM Characterization ............................ 47 

Objectives, 47 

Raw Water NOM Characterization, 48 

Approach, 48 


Field-Scale NOM Characterization, 51 

Approach, 51 


Bench-Scale NOM Characterization, 54 

Approach, 54 


Summary, 60 

Chapter 6 Design and Operational Considerations .............. 81 

Impact of EBCT on Adsorption Behavior, 82 



Impact of Blending, 85 



NOM Desorption and the Impact of Backwashing, 88 



Summary, 90 

Impact of EBCT on Adsorption Behavior, 90 

Impact of Blending, 91 

NOM Desorption and the Impact of Backwashing, 91

Contents vii 

Chapter 7 The Impact of Optimized Coagulation on NOM 

Removal and DBF Control by GAC Adsorption ....... 109 




Effect of Coagulation Pretreatment on 

Specific DBF Yield of GAC Effluent, 114 

Impact of Pretreatment on NOM Fraction Characteristics 

After GAC, 117 

Humic-Nonhumic Fractionation, 117 

Molecular Size Fractionation, 118 

PY-GC-MS Fractionation, 119 

Summary, 120 

Chapter 8 The Impact of Ozonation and Biological Filtration on 

NOM Removal and DBF Control by GAC ............ 135 



Impact of Pretreatment on GAC Breakthrough Behavior: 


Effect of Ozonation and Biological Filtration on Specific 

DBP Yield of GAC, 140 

Impact of Ozonation and Biological Filtration on NOM 

Fraction Characteristics After GAC, 142 



PY-GC-MS Fractionation, 144 

Summary, 146 

Chapter 9 Impact of Treatment on DBP Speciation .............. 167 


GAC Effluent DBP Speciation, 167 

Bromine Incorporation Factor, 168 

Optimized Coagulation, 169 

Ozonation and Biological Filtration, 170 

Summary, 172 

Chapter 10 The Impact of NOM Preloading on SOC Adsorption 

Behavior ........................................ 181 



Summary, 184

viii Removal of DBF Precursors by GAC Adsorption 

Chapter 11 Relationship Among NOM Characteristics, Removal 

of DBF Precursors by GAC Treatment, and 

DBF Formation .................................. 189 

Overview of Characterization Techniques, 189 



The Role of TOC and UV2J4 , 192 

Pyrolysis GC-MS, 192 

Raw Water Characteristics, 193 

Effect of Coagulation, 193 

Effect of Ozone and Biofiltration, 194 

Chapter 12 Evaluation of Design Criteria and Costs .............. 199 

GAC Design Parameters, 199 

Empty Bed Contact Time, 200 

Carbon Usage Rate, 200 

Effect of Pretreatment, 201 

Determination of Design Criteria, 201 

Water Quality Goals, 201 

Single-Contactor Breakthrough Curves, 202 

Effect of Blending From Multiple Contactors, 204 

Cost Curve Development, 204 

Process Components for GAC, 204 

Process Components for Pretreatment, 205 

Design Parameters, 205 

Cost Factors, 206 

Cost Curves, 208 

Verification of Costs, 208 

GAC Treatment Costs for Participating Utilities, 210 

Cincinnati Waterworks, 210 

City of Phoenix, 212 

Passaic Valley Water Commission, 212 

Florida Cities Water Company, 213 

Implications for Process Optimization, 213 

Empty Bed Contact Time, 213 

Pretreatment Optimization, 214 

Chapter 13 Conclusions and Recommendations .................. 223 

Verification of RSSCT Results, 223 

Impact of GAC Treatment on NOM Characteristics and 

DBP Yield, 224

Contents ix 

Impact of Operational Characteristics and Pretreatment 

on GAC Performance, 225 

Estimation of Design Criteria and Conceptual Costs 

for GAC Treatment, 226 

Appendix A: RSSCT Design .................................. 229 

Appendix B: A Statistical Method for the Comparison of 

RSSCT and Field-Scale GAC Breakthrough 

Curves ........................................ 231 

Appendix C: Development of a New Test for Disinfection 

By-Product Formation Assessment: Uniform 

Formation Conditions ............................ 247 

References ................................................. 277 

List of Abbreviations ......................................... 283

Tables 

ES.l Participating utilities and source water characteristics xxxi 

ES.2 Summary of experimental program xxxi 

ES.3 Parameters for the UFC test xxxiii 

2.1 Summary of water quality parameters for water sources 6 

2.2 Field-scale GAC systems 7 

2.3 Summary of experimental program 8 

2.4 Laboratories performing sample analyses for experimental 

program 9 

2.5 Field-scale and RSSCT verification operation parameters 10 

2.6 Summary of analytical methods 13 

2.7 UFC and SDS chlorination conditions 15 

2.8 Specifications for PY-GC-MS analysis 18 

2.9 Summary of detection limits and precisions for analytical 

methods 19 

3.1 RSSCT and field-scale GAC contactor design parameters 22 

4.1 UFC and SDS chlorination conditions 38 

4.2 DBF formation in GAC influent under UFC and SDS 

conditions 39 

5.1 Summary of raw water fractionation and BDOC results 49 

5.2 Specific TTHM yields of raw waters 49 

5.3 PY-GC-MS classification of raw waters 49 

5.4 Summary of seasonal water quality monitoring BDOC results 50 

5.5 PY-GC-MS classification of seasonal samples 51 

5.6 Summary of field-scale GAC influent humic-nonhumic 

fractionation results for ORW, LOW, MRW, and PRW 52 

5.7 Summary of ORW and PRW field-scale BDOC results 53 

5.8 Summary of bench-scale GAC influent fractionation results 55 

5.9 Specific TTHM yields of bench-scale GAC influents 56 

5.10 Summary of BDOC results for PRW at bench scale 5 8 

5.11 PY-GC-MS classification of Ohio River water 5 8 

5.12 PY-GC-MS classification of Lake Gaillard water 58 

5.13 PY-GC-MS classification of Mississippi River water 59 

5.14 PY-GC-MS classification of Passaic River water 59

xii Removal of DBF Precursors by GA C Adsorption 

5.15 PY-GC-MS classification of Salt River Project water 61 

5.16 PY-GC-MS classification of Florida groundwater 61 

5.17 Average DOC distribution of river waters with 

breakthrough: Nonhumic and humic fractions 62 

5.18 Average DOC distribution of river waters with 

breakthrough: Molecular size fractions 62 

6.1 Summary of bed lives for different effluent criteria 84 

6.2 Impact of blending on bed life 89 

7.1 Raw water characteristics for Chapter 7 experiments 111 

7.2 Treated water characteristics for Chapter 7 experiments 111 

7.3 Summary of DBP surrogate and DBF concentrations prior to 

GAC treatment 112 

7.4 Effect of coagulation pretreatment on GAC breakthrough 

characteristics 113 

7.5 Effect of coagulation pretreatment on relationship between 

GAC effluent TOC and DBP formation 116 

7.6 Impact of coagulation treatment on humic-nonhumic and 

molecular size fractionation 117 

7.7 PY-GC-MS classification of ORW: Enhanced coagulation 120 

7.8 PY-GC-MS classification of SRPW: Enhanced coagulation 121 

8.1 Summary of experimental conditions for ozonation and 

biological filtration 137 

8.2 Summary of GAC influent characteristics after ozonation 

and biological filtration 137 

8.3 Summary of impact of ozonation and biological filtration on 

GAC performance 138 

8.4 Summary of the effect of ozonation and biotreatment on the 

relationship between TOC and DBP formation 141 

8.5 Impact of ozonation and biotreatment on humic-nonhumic 

and MS fractionation results 143 

8.6 PY-GC-MS classification of ORW: Ozonation and 

biotreatment 145 

8.7 PY-GC-MS classification of PRW: Ozonation and 


8.8 PY-GC-MS classification of FGW: Ozonation and 


9.1 Effect of optimized coagulation on DBP speciation 169 

9.2 Effect of ozonation and biological filtration on DBP 

speciation 171 

10.1 Summary of operating conditions for preloaded GAC 182

Tables 

xiii 

10.2 Comparison of GAC residual capacity for DCE 183 

12.1 Estimates of CURs for the participating utilities 203 

12.2 Treatment plant flows considered for cost curves 206 

12.3 Indices used in developing cost curves 207 

12.4 Factors applied to construction cost estimates 207 

12.5 Unit costs for building and operating materials 207 

12.6 Comparison of costs for the GAC processes 209 

12.7 Comparison of costs for optimized coagulation 209 

12.8 Comparison of ozone facilities costs 210 

12.9 Estimates of GAC treatment costs for the participating 

utilities 211

Figures 

3.1 Full-scale and RSSCT TOC breakthrough for Ohio River 

water 26 

3.2 Full-scale and RSSCT UV254 breakthrough for Ohio River 

water 26 

3.3 Full-scale and RSSCT SDS-TOX breakthrough for Ohio 

River water 27 

3.4 Full-scale and RSSCT SDS-TTHM breakthrough for Ohio 


3.5 Full-scale and RSSCT SDS-HAA6 breakthrough for Ohio 


3.6 Full-scale and RSSCT SDS-CH breakthrough for Ohio River 

water 28 

3.7 Pilot-scale and RSSCT TOC breakthrough for Lake Gaillard 

water 29 

3.8 Pilot-scale and RSSCT SDS-TOX breakthrough for Lake 

Gaillard water 29 

3.9 Pilot-scale and RSSCT SDS-TTHM breakthrough for Lake 


3.10 Pilot-scale and RSSCT SDS-HAA5 breakthrough for Lake 


3.11 Pilot-scale and RSSCT TOC breakthrough for Mississippi 


3.12 Pilot-scale and RSSCT UV254 breakthrough for Mississippi 


3.13 Pilot-scale and RSSCT SDS-TOX breakthrough for 

Mississippi River water 32 

3.14 Pilot-scale and RSSCT SDS-TTHM breakthrough for 


3.15 Pilot-scale and RSSCT SDS-HAA6 breakthrough for 


3.16 Pilot-scale and RSSCT SDS-CH breakthrough for 


3.17 Pilot-scale and RSSCT TOC breakthrough for Passaic 


3.18 Pilot-scale and RSSCT UV 254 breakthrough for Passaic 


XV

xvi Removal of DBF Precursors by GAC Adsorption 

3.19 Pilot-scale and RSSCT SDS-TOX breakthrough for Passaic 


3.20 Pilot-scale and RSSCT SDS-TTHM breakthrough for Passaic 


3.21 Pilot-scale and RSSCT SDS-HAA6 breakthrough for Passaic 


3.22 Pilot-scale and RSSCT SDS-CH breakthrough for Passaic River 

water 36 

4.1 Effect of chlorination conditions on RSSCT TTHM 

breakthrough for Ohio River water 41 

4.2 Effect of chlorination conditions on RSSCT HAA6 


4.3 Effect of chlorination conditions on RSSCT TOX 



breakthrough for Lake Gaillard water 42 






breakthrough for Mississippi River water 44 





4.10 Effect of chlorination conditions on RSSCT CH 


5.1 Seasonal variation of nonhumic and humic fractions for 

Ohio River raw water 63 

5.2 Specific TTHM yields of nonhumic and humic fractions for 

Ohio River seasonal raw water samples 63 

5.3 Seasonal variation of nonhumic and humic fractions for 

Passaic River raw water 64 

5.4 Specific TTHM yields of nonhumic and humic fractions for 

Passaic River seasonal raw water samples 64 

5.5 Full-scale nonhumic and humic fraction breakthrough for 

Ohio River water 65 


Lake Gaillard water 65 


Mississippi River water 66

Figures xvii 

5.8 Pilot-scale nonhumic and humic fraction breakthrough for 

Passaic River water 66 

5.9 Pilot-scale BDOC breakthrough for Mississippi River water 67 

5.10 Example curve showing sampling points for NOM 

characterization at bench scale 67 

5.11 Bench-scale nonhumic and humic fraction 


5.12 Bench-scale specific TTHM yields of humic 

and nonhumic fractions for Ohio River water 68 



5.14 Bench-scale specific TTHM yields of humic 

and nonhumic fractions for Lake Gaillard water 69 



5.16 Bench-scale specific TTHM yields of humic and 

nonhumic fractions for Mississippi River water 70 


breakthrough for Passaic River water 71 

5.18 Bench-scale specific TTHM yields of nonhumic 

and humic fractions for Passaic River water 71 


breakthrough for Salt River Project water 72 


and humic fractions for Salt River Project water 72 


breakthrough for Florida groundwater 73 


and humic fractions for Florida groundwater 73 

5.23 Molecular size fraction breakthrough for Ohio River water 74 

5.24 Specific TTHM yields of MS fractions for Ohio River water 74 

5.25 Molecular size fraction breakthrough for Lake Gaillard water 75 

5.26 Specific TTHM yields of MS fractions for Lake Gaillard water 75 

5.27 Molecular size fraction breakthrough for Mississippi River 

water 76 

5.28 Specific TTHM yields of MS fractions for Mississippi River 

water 76 

5.29 Molecular size fraction breakthrough for Passaic River water 77 

5.30 Specific TTHM yields of MS fractions for Passaic River water 77 

5.31 Molecular size fraction breakthrough for Salt River Project 

water 78 

5.32 Specific TTHM yields of MS fractions for Salt River Project 

water 78 

5.33 Molecular size fraction breakthrough for Florida groundwater 79 

5.34 Specific TTHM yields of MS fractions for Florida groundwater 79

xviii Removal of DBF Precursors by GAC Adsorption 

5.35 Effect of BR~:DOC on DOC fractions for GAC influent for 

six waters 80 

6.1 Model simulation of the effect of EBCT on TOC breakthrough 

for the Ohio River water 92 

6.2 Model simulation of the effect of EBCT on normalized TOC 


6.3 Effect of EBCT on TOC breakthrough for Ohio River water 93 

6.4 Effect of EBCT on normalized TOC breakthrough for Ohio 


6.5 Effect of EBCT on normalized UV254 breakthrough for Ohio 


6.6 Effect of EBCT on normalized UFC-TOX breakthrough for 


6.7 Effect of EBCT on normalized UFC-TTHM breakthrough for 


6.8 Effect of EBCT on normalized UFC-HAA6 breakthrough for 


6.9 Effect of EBCT on TOC breakthrough for Passaic River water 96 

6.10 Effect of EBCT on normalized TOC breakthrough for Passaic 




6.12 Effect of EBCT on TOC breakthrough for Salt River Project 

water 97 

6.13 Effect of EBCT on normalized TOC breakthrough for Salt 

River Project water 98 

6.14 Effect of EBCT on normalized UFC-TOX breakthrough for 

Salt River Project water 98 

6.15 Effect of EBCT on TOC breakthrough for Florida groundwater 99 

6.16 Effect of EBCT on normalized TOC breakthrough for Florida 

groundwater 99 


Florida groundwater 100 

6.18 Operation of multiple GAC contactors in parallel 100 

6.19 Individual breakthrough curves of multiple contactors operated 

in parallel 101 

6.20 Blended GAC effluent from multiple contactors operated in 

parallel 101 

6.21 Effect of blending on TOC breakthrough for Passaic River 

water 102 

6.22 Effect of blending on UFC-TTHM breakthrough for Passaic 


6.23 Effect of blending on TOC breakthrough for Salt River 

Project water 103

Figures xix 

6.24 Effect of blending on UFC-TTHM breakthrough for Salt River 

Project water 103 

6.25 Effect of blending on TOC breakthrough for Florida 


6.26 Effect of blending on UFC-TTHM breakthrough for Florida 


6.27 NOM desorption for Passaic River water: TOC levels 105 

6.28 NOM desorption for Passaic River water: UV254 levels 105 

6.29 Impact of backwashing on TOC breakthrough for Passaic 


6.30 Impact of backwashing on UV254 breakthrough for Passaic 


6.31 Impact of backwashing on UFC-TTHM breakthrough for 


7.1 Effect of coagulation pretreatment on TOC breakthrough for 


7.2 Effect of coagulation pretreatment on UV254 breakthrough for 


7.3 Effect of coagulation pretreatment on TTHM breakthrough for 


7.4 Effect of coagulation pretreatment on HAA6 breakthrough for 


7.5 Effect of coagulation pretreatment on TOX breakthrough for 


7.6 Effect of coagulation pretreatment on CH breakthrough for 


7.7 Effect of coagulation pretreatment on TOC breakthrough for 


7.8 Effect of coagulation pretreatment on UV254 breakthrough for 


7.9 Effect of coagulation pretreatment on TTHM breakthrough for 


7.10 Effect of coagulation pretreatment on HAA6 breakthrough for 


7.11 Effect of coagulation pretreatment on TOX breakthrough for 


7.12 Effect of coagulation pretreatment on CH breakthrough for 


7.13 Effect of coagulation pretreatment on the relationship between 

TOC and TTHM formation during GAC breakthrough for 


7.14 Four possible outcomes based on regression analysis with 

categorical variables 128

xx Removal of DBF Precursors by GAC Adsorption 


TOC and HAA6 formation during GAC breakthrough for 



TOC and TOX formation during GAC breakthrough for Ohio 


7.17 Effect of coagulation pretreatment on the relationship beween 

TOC and TTHM formation during GAC breakthrough for 



TOC and HAA6 formation during GAC breakthrough for 



TOC and TOX formation during GAC breakthrough for 


7.20 Effect of coagulation pretreatment on humic and nonhumic 

fraction breakthrough for Ohio River water 131 

7.21 Effect of coagulation pretreatment on the specific UFC-TTHM 

yields of humic-nonhumic fractions for Ohio River water 132 

7.22 Effect of coagulation pretreatment on humic and nonhumic 

fraction breakthrough for Salt River Project water 132 

7.23 Effect of coagulation pretreatment on molecular size fraction 



yields of molecular size fractions for Ohio River water 133 

7.25 Effect of coagulation pretreatment on molecular size fraction 

breakthrough for Salt River Project water 134 


yields of molecular size fractions for Salt River Project water 134 

8.1 Effect of ozonation and biotreatment on normalized TOC 


8.2 Effect of ozonation and biotreatment on TOC breakthrough for 


8.3 Effect of ozonation and biotreatment on UV254 breakthrough for 


8.4 Effect of ozonation and biotreatment on UFC-TOX breakthrough 

for Ohio River water 149 

8.5 Effect of ozonation and biotreatment on UFC-TTHM 


8.6 Effect of ozonation and biotreatment on UFC-HAA6 



breakthrough for Passaic River water 151

Figures xxi 





8.10 Effect of ozonation and biotreatment on UFC-TOX 












8.16 Effect of ozonation and biotreatment on UFC-TOX breakthrough 

for Florida groundwater 155 





8.19 Effect of ozonation and biotreatment on the relationship 

between TOC and TOX formation during GAC 



between TOC and TTHM formation during GAC 



between TOC and HAA6 formation during GAC 



between TOC and TOX formation during GAC breakthrough 

for Passaic River water 158 








between TOC and TOX formation during GAC breakthrough 

for Florida groundwater 160

xxii Removal of DBF Precursors by GA C Adsorption 







8.28 Effect of ozonation and biotreatment on humic-nonhumic 

fraction breakthrough for Ohio River water 161 


fraction specific yields for Ohio River water 162 


fraction breakthrough for Passaic River water 162 


fractionation for Florida groundwater 163 


fraction specific yields for Passaic River water 163 


fraction specific yields for Florida groundwater 164 

8.34 Effect of ozonation and biotreatment on MS fraction 


8.35 Effect of ozonation and biotreatment on specific yields for MS 

fractions for Ohio River water 165 





8.38 Effect of ozonation and biotreatment on specific yields for 

MS fractions for Passaic River water 166 

9.1 GAC breakthrough of THM species in Ohio River water after 

conventional treatment 173 

9.2 GAC breakthrough of THM species in Ohio River water after 

optimized coagulation 173 

9.3 GAC breakthrough of THM species in Salt River Project water 

after conventional treatment 174 

9.4 GAC breakthrough of HAA species in Salt River Project water 

after optimized coagulation 174 

9.5 GAC breakthrough of THM species in Passaic River water after 


9.6 GAC breakthrough of THM species in Passaic River water after 

ozonation and biotreatment 175 

9.7 GAC breakthrough of HAA species in Florida groundwater after 


9.8 GAC breakthrough of HAA species in Florida groundwater after 

ozonation and biotreatment 176

Figures xxiii 

9.9 Effect of treatment on THM bromine incorporation factor (n) 

forORW 177 

9.10 Effect of treatment on THM bromine incorporation factor (n) for 

SRPW 177 

9.11 Impact of pretreatment on THM bromine incorporation factor (n) 


9.12 Impact of pretreatment on THM bromine incorporation factor (n) 


9.13 Effect of treatment on HAA bromine incorporation factor (n') for 

ORW 179 

9.14 Effect of treatment on HAA bromine incorporation factor (n') for 


9.15 Effect of pretreatment on HAA bromine incorporation factor (n') 


9.16 Effect of pretreatment on HAA bromine incorporation factor (n') 


10.1 Comparison of GAC adsorption capacity for DCE under different 

matrix conditions of Passaic River water 185 

10.2 Impact of preloading of Ohio River water at full and small scale 

on DCE adsorption capacity 185 

10.3 Impact of preloading of Lake Gaillard water at pilot and small 

scale on DCE adsorption capacity 186 

10.4 Impact of preloading of Mississippi River water at small scale 


10.5 Impact of preloading of Salt River Project water at small scale 


10.6 Impact of preloading of Florida groundwater at small scale on 

DCE adsorption capacity 187 

10.7 Correlation between solid phase DCE concentration and 

throughput in bed volumes for six sites 188 

11.1 Comparison of relative breakthroughs for NOM and UFC-DBPs 

for Ohio River water 196 


for Mississippi River water 196 




for Salt River Project water 197 



12.1 Schematic of process components for GAC 216 

12.2 Schematic of process components for optimized coagulation 216

xxiv Removal of DBF Precursors by C AC Adsorption 

12.3 Process schematic for ozonation and biofiltration treatment 217 

12.4 Cost curves for GAC facilities 218 

12.5 Cost curves for on-site reactivation 219 

12.6 Cost curves for off-site reactivation 220 

12:7 Cost curves for optimized coagulation 221 

12.8 Cost curves for ozonation 222

Foreword 

The AWWA Research Foundation is a nonprofit corporation that is 

dedicated to the implementation of a research effort to help utilities respond to 

regulatory requirements and traditional high-priority concerns of the industry. The 

research agenda is developed through aprocess of consultation with subscribers and 

drinking water professionals. Under the umbrella of a Strategic Research Plan, the 

Research Advisory Council prioritizes the suggested projects based upon current 

and future needs, applicability, and past work; the recommendations are forwarded 

to the Board of Trustees for final selection. The foundation also sponsors research 

projects through the unsolicited proposal process; the Collaborative Research, 

Research Applications, and Tailored Collaboration programs; and various joint 

research efforts with organizations such as the U.S. Environmental Protection 

Agency, the U.S. Bureau of Reclamation, and the Association of California Water 

Agencies. 

This publication is a result of one of these sponsored studies, and it is hoped 

that its findings will be applied in communities throughout the world. The following 

report serves not only as a means of communicating the results of the water 

industry' s centralized research program but also as a tool to enlist the further support 

of the nonmember utilities and individuals. 

Projects are managed closely from their inception to the final report by the 

foundation's staff and large cadre of volunteers who willingly contribute their time 

and expertise. The foundation serves a planning and management function and 

awards contracts to other institutions such as water utilities, universities, and 

engineering firms. The funding for this research effort comes primarily from the 

Subscription Program, through which water utilities subscribe to the research 

program and make an annual payment proportionate to the volume of water they 

deliver and consultants and manufacturers subscribe based on their annual billings. 

The program offers a cost-effective and fair method for funding research in the 

public interest. 

A broad spectrum of water supply issues is addressed by the foundation's 

research agenda: resources, treatment and operations, distribution and storage, 

water quality and analysis, toxicology, economics, and management. The ultimate 

purpose of the coordinated effort is to assist water suppliers to provide the highest 

possible quality of water economically and reliably. The true benefits are realized 

when the results are implemented at the utility level. The foundation's trustees are 

pleased to offer this publication as a contribution toward that end. 

Granular activated carbon (GAC) has been specified by the United States 

Environmental Protection Agency (USEPA) as a best available technology (BAT) 

for removing many contaminants in drinking water. It is known that GAC has the 

potential to remove precursor compounds for disinfection by-products (DBFs), an 

important classification of contaminants that are currently being regulated, and 

therefore it is important to improve the industry's current understanding of the 

XXV

xxvi 

Removal of DBF Precursors by GA C Adsorption 

capability of GAC to remove precursor compounds. This report examines the 

capacity of GAC to adsorb DBF precursors for several source waters, provides 

information relative to the impact of pretreatment on GAC adsorption, and discusses 

the relationship between DBF precursor characteristics and the precursors' ability 

to be removed by GAC treatment. 

Julius Ciaccia Jr. 

Chair, Board of Trustees 


James F. Manwaring, P.E. 

Executive Director 

AWWA Research Foundation

Acknowledgments 

The authors of this report wish to thank the following participants for their 

contributing efforts to this project: 

A WWARF Project Managers 

Jeffrey L. Oxenford and Joseph Roccaro 

Participating Utilities 

Cincinnati Water Works, Cincinnati, Ohio 

City of Phoenix Water and Wastewater Department, Phoenix, Ariz. 

Florida Cities Water Company, Sarasota, Fla. 

Jefferson Parish Department of Water, Jefferson Parish, La. 

Passaic Valley Water Commission, Clifton, N.J. 

South Central Connecticut Regional Water Authority, New Haven, 

Conn. 

Other Participants 

Kimberley A. Gray, University of Notre Dame, Notre Dame, Ind. 

David A. Reckhow, University of Massachusetts-Amherst, Amherst, 

Mass. 

John Tobiason, University of Massachusetts-Amherst, Amherst, Mass. 

Project Advisory Committee 

Robert Beaurivage, Manchester, N.H. 

Pierre La France, City of Laval, Que., Canada 

Benjamin Lykins Jr., USEPA, Cincinnati, Ohio. 

Technical Review Committee 

John C. Crittenden, Michigan Technological University, Houghton, 

Mich. 

Jack DeMarco, Cincinnati Water Works, Cincinnati, Ohio 

Philip C. Singer, University of North Carolina, Chapel Hill, N.C. 

XXV'II

Executive Summary 

Background 

Granular activated carbon (GAC) adsorption is universally recognized as 

one of the most effective processes for the removal of organic matter. GAC 

adsorption has demonstrated multiple uses in water treatment, such as removal of 

(1) taste-and-odor-causing compounds, (2) synthetic organic chemicals (SOCs) 

that may be present in industrially impacted source waters, and (3) natural organic 

matter (NOM), which serves as a precursor to the formation of disinfection by 

products (DBFs). In many instances GAC is also considered to be a preferred 

medium to support biodegradation of some fractions of the NOM matrix. 

Various aspects of the GAC adsorption process have been studied in 

isolation by many researchers, focusing on key aspects of this treatment process and 

developing great insights into the NOM removal processes. A comprehensive and 

systematic study addressing wide variations of raw water characteristics and the 

effect of pretreatment on the removal of NOM by GAC, however, has not been 

undertaken thus far. In addition, conclusions regarding design criteria and costs 

have often been based upon bench- or pilot-scale studies composed of singlecolumn 

experiments evaluating individual compounds. In full-scale applications, 

multiple GAC contactors are routinely employed to produce the desired water 

volume, resulting in significant cost savings from the blending of effluents from 

GAC contactors in different stages of maturation with respect to the breakthrough 

of organic material. The goal of this study was to investigate optimization of the 

GAC adsorption process utilizing the most advanced analytical protocols over a 

wide range of raw water qualities. 

Regulatory Implications_________________ 

The role of GAC for water treatment has been made prominent by recent 

regulatory activities that are expected to lower the maximum contaminant level 

(MCL) for some DBFs and establish new MCLs for other compounds. These 

regulations also recognize the GAC adsorption process as a best available technol 

ogy (BAT) for DBF precursor removal. As a part of the newly promulgated 

Information Collection Rule (ICR), the United States Environmental Protection 

Agency (USEPA) will require larger utilities treating relatively poorer quality 

source water to collect information on GAC treatability by conducting bench-scale 

or pilot-scale GAC adsorption studies. Clearly, greater scrutiny is being placed on 

GAC as a treatment process for controlling DBFs.

xxx Removal of DBF Precursors by GA C Adsorption 

The results presented in this report in terms of both adsorption behavior 

and cost implications provide a preview of the national database that will be 

developed as a result of these USEPA activities. In addition, the results will provide 

a more thorough understanding of the adsorption characteristics of GAC adsorption, 

with and without pretreatment optimization. 

Approach_________________________ 

There are several factors that affect the design of a GAC process, including 

(1) the breakthrough characteristics of the naturally occurring DBF precursors, (2) 

the mode of operation of multiple GAC contactors, (3) the type of pretreatment 

before the adsorption process, and (4) the presence of other, competing adsorbing 

material. All of these factors are addressed in this study. In addition, a protocol was 

developed for evaluating the formation of DBFs on a uniform basis when multiple 

source waters are being compared. This protocol provides a means for directly 

comparing precursor material among different sources while at the same time using 

criteria that are typical of many treatment and distribution systems. Previous to this 

study, DBF formation potential (DBPFP) was routinely used for comparing precur 

sor materials among multiple sources, whereas simulated distribution system (SDS) 

conditions were used to assess the conditions expected for a given system. Tests 

using the uniform formation conditions (UFC) that were developed during this study 

provide a method for addressing both issues simultaneously. 

The adsorption behavior of NOM onto GAC was evaluated by conducting 

both bench-scale and pilot-scale experiments for a wide variety of source waters 

from participating utilities. Some raw water characteristics for the participating 

utilities are shown in Table ES.l. The NOM before and after GAC treatment was 

characterized by such traditional parameters as NOM surrogates (e.g., total organic 

carbon [TOC] and ultraviolet adsorption at 254 nm [UV254]) and DBF formation, 

as well as by more advanced techniques, including humic-nonhumic fractionation, 

molecular weight fractionation, and pyrolysis gas chromatography-mass spectrometry 

(PY-GC-MS). 

In addition to an evaluation of the removal of NOM by GAC treatment, the 

fate of organic matter through pretreatment (e.g., optimized coagulation; ozonation 

and biological filtration) and pretreatment's impact on GAC adsorption were 

characterized. The effects of various process parameters on GAC adsorption, such 

as empty bed contact time (EBCT), backwashing (which often results in 

restratification of adsorption zones), and the competitive adsorption of SOCs and 

NOM were also evaluated. Finally, experimental and modeling techniques were 

developed to use single-column experiments to determine breakthrough character 

istics resulting from the blending of multiple contactors operating in parallel in a 

full-scale facility. 

Table ES.2 summarizes the testing program evaluated during this research. 

The costs of GAC treatment with and without optimized coagulation and biological 

pretreatment were estimated to provide guideline to utilities that are considering 

GAC adsorption as a treatment process for DBF precursor removal.

Executive Summary xxxi 

Table ES. 1 Participating utilities and source water characteristics 

Raw water quality 

Utility 

name 

Cincinnati Water Works 

Source 

water 

Ohio River (ORW) 

TOC UFC-THM UFC-HAA6 

(mg/L) (ug/L) (ug/L) 

2.2 87 41 

South Central Connecticut 

Regional Water Authority 

Lake Gaillard 

(LGW) 

2.9 131 I09* 

Jefferson Parish Water Department 

Mississippi River 

(MRW) 

5.2 102 133 

Passaic Valley Water Commission 

Passaic River 

(PRW) 

5.0 135 171 

City of Phoenix 

Salt River Project 

(SRPW) 

2.5 76 36 

Florida Cities Water Company 

Groundwater 

(FGW) 

13 135 457 

HAAS = sum of five haloacetic acids 

HAA6 = sum of six haloacetic acids 

THM = trihalomethane 

* UFC-HAA5 

Table ES.2 Summary of experimental 

Program stage 

Field-scale GAC (full- or pilot-scale) 

RSSCT verification 

Design and operational considerations 

Impact of EBCT 

Impact of blending 

Impact of backwashing 

Impact of pretreatment 

Optimized coagulation 

Ozonation and biotreatment 

SOC adsorption 

program 

ORW LGW 

X X 

X X 

X 

X 

X 

X X 

Source 

MRW PRW SRPW FGW 

X X 

X X 

XXX 

XXX 

X 

X 

X X 

X XXX 

RSSCT = rapid, small-scale column test

xxxii Removal of DBF Precursors by GA C Adsorption 

Conclusions and Recommendations 

The following are the significant conclusions and recommendations devel 

oped as a result of the experimental program conducted during this research: 

Based upon visual and statistical comparisons, bench-scale 

experiments using the rapid small-scale column test (RSSCT) were 

found to adequately predict NOM breakthrough behavior observed 

during pilot- or full-scale applications. Collection of a representative 

sample was found to be a key to the success of RSSCTs. For sources 

that show significant seasonal variation in NOM content, multiple 

RSSCT experiments are recommended to understand adsorption 

behavior subject to seasonal variability. It is also recommended that 

RSSCTs be conducted as rapidly as possible for waters with 

significant concentrations of biodegradable dissolved organic carbon 

(BDOC). Any delay due to shipping or other concerns may result in 

breakthrough characteristics that will not be similar to those in pilotor 

full-scale tests. 

The UFC test developed during this research is represented by the 

parameters in Table ES.3. This protocol can be used for comparing 

DBF formation across unique waters and can be used to predict DBF 

formation in a distribution system if the system characteristics are 

similar to the UFC. 

The raw waters evaluated during this study were found, in general, 

to be slightly more humic than nonhumic and dominated by 

intermediate molecular size (500 to 3,000 D) fractions. The aromatic 

and aliphatic fractions varied widely among sources. Some of the 

sources that are believed to be industrially impacted also showed 

signatures of chlorinated fragments, a telltale sign of industrial 

activities. 

Breakthrough of dissolved organic carbon (DOC) during GAC 

adsorption was found to be a conservative predictor for the 

breakthrough of DBF precursors. The early GAC effluent samples 

containing the nonadsorbable NOM were found to be almost entirely 

nonhumic, aliphatic, and of small molecular size. Samples later in 

the breakthrough curve progressively showed the appearance of 

humic and aromatic fractions and larger molecular size fractions. 

The DBF yields of the humic fractions were found to be slightly 

higher compared to those of the nonhumic fractions. No clear 

differences in DBF yield were observed with respect to molecular 

size. 

Pretreatment with coagulation was found to be almost equally 

effective on the humic and nonhumic fractions whereas the 

intermediate molecular size fractions were preferentially removed. 

Ozonation and biofiltration preferentially removed larger and humic 

material over smaller and nonhumic material. 

Evaluations of various EBCTs indicated that the breakthrough curve 

could be prolonged with longer EBCTs; however, the production of 

water per unit mass of GAC (water throughput, or number of bed

Executive Summary xxxiii 

Table ES.3 Parameters for the UFC test 

Parameter Value 

Incubation time (hours) 

Incubation temperature (°C) 

PH 

24-hour chlorine residual (mg/L) 

24±1 

20.0±1.0 

8.0±0.2 

1.0+0.3 

volumes) was not affected by EBCTs between 10 and 20 minutes. 

Consequently, shorter EBCTs could be preferred in contactor design 

because they will result in lower capital costs. Shorter EBCTs will, 

however, require more frequent replacement or regeneration of 

carbon. 

Based upon experimental and modeling results, blending of effluents 

from multiple GAC contactors was found to significantly prolong 

the breakthrough curves and substantially lower the operational 

costs. The modeling approach used in this study was found to 

adequately predict the blended effluent breakthrough characteristics 

based upon experimental results from a single contactor. 

Based upon simulated experiments, completely restratifyirrg the 

adsorption zone during backwashing may shorten the useful life of 

GAC in an adsorption contactor. The capacity of GAC for adsorbing 

SOCs was also found to be reduced as a result of NOM adsorption. 

The reduction in SOC adsorption was proportional to the amount of 

NOM adsorbed. 

Optimizing coagulation pretreatment may result in longer GAC life, 

whereas the use of ozonation and biological filtration prior to GAC 

adsorption may not improve GAC adsorption this latter 

pretreatment causes a shift to less adsorbable fractions in the GAC 

influent in addition to some removal of precursors due to 

biodegradation. However, it is recognized that there may be other 

advantages to this pretreatment (relative to GAC adsorption alone) 

that were not evaluated in this work. 

The conceptual cost estimates developed during this study suggest 

that lower EBCTs may result in lower overall system costs. Based 

on these cost estimates, it was also concluded that optimization of 

coagulation pretreatment may result in lower system costs if the 

CUR can be reduced by 50 percent at a coagulation cost of 5 cents 

or less per 1,000 gal (3,785 L). Pretreatment with ozonation and 

biological filtration also lowered the overall system costs for the 

utility with the highest source water NOM content, suggesting that 

this treatment strategy may be more effective for GAC adsorption as 

the concentration of organic material in the source water increases.

Chapter 1 

Introduction 

Granular activated carbon (GAC) adsorption is a process that has been used 

extensively throughout the world for adsorption of organic compounds. Some of 

these compounds are naturally occurring in the environment, such as taste-andodor-causing 

compounds, while other compouds known as synthetic organic 

chemicals (SOCs) are anthropogenic. Taste-and-odor-causing compounds affect 

the aesthetic perception of water quality, while certain SOCs in drinking water 

are reported to be associated with adverse health effects. Because of the wellrecognized 

efficiency of GAC for removing taste-and-odor-causing compounds 

and SOCs, GAC's use in the water industry has focused almost exclusively on the 

removal of these compounds. 

More recently, however, utilities have been focusing on the cost-effective 

control of disinfection by-products (DBFs), which also are reported to have 

potential adverse health effects. Utilities have a wide variety of options available to 

reduce the formation of DBFs, each with their associated costs and efficiencies. In 

general, DBFs can be reduced by three methods: 

1. Reduce the concentration of DBF precursor material prior to adding 

a disinfectant. 

2. Use alternative disinfectants to free chlorine to reduce the formation 

of halogenated DBFs. 

3. Remove the DBFs after they are formed. 

The first two methods have been shown by the water industry to be costeffective 

in the majority of applications. DBF precursor removal strategies include 

increasing coagulant dosages in conventional treatment, as well as applying 

advanced precursor removal processes such as the use of GAC or membranes. 

Alternative disinfectants to free chlorine include chloramines, typically used as a 

residual disinfectant in distribution systems, as well as stronger disinfectants such 

as ozone or chlorine dioxide. Although these latter disinfectants substantially 

reduce the formation of halogenated organic DBFs (such as bromate and chlorate), 

there are other DBFs that are formed by their use. 

Consequently, GAC may be cast in a new role in water treatment, and this 

report focuses on the optimization of GAC adsorption for the removal of DBF 

precursors. Such a study is timely in light of the current regulatory agenda.

2 Removal of DBF Precursors by GAC Adsorption 

Regulatory Agenda 

At the time of this writing, only one group of DBFs is regulated. A 

maximum contaminant level (MCL) of 100 \ig/L has been established by the United 

States Environmental Protection Agency (USEPA) fortotal trihalomethane (TTHM), 

which is composed of four individual chemical compounds. Recently, however, the 

USEPA proposed a Disinfectants-Disinfection By-Products (D-DBP) Rule de 

signed to control the use of disinfectants and the formation of DBPs (USEPA 1994). 

This rule has two proposed stages In the first stage, the MCL for TTHM is lowered 

to 80 ug/L, and an MCL of 60 ug/L has been proposed for the sum of five haloacetic 

acid (HAAS) species. In the second stage, it is proposed to reduce these two DBP 

groups by one-half, to 40 and 30 ug/L for TTHM and HAA5, respectively. Although 

this Stage 2 regulation is subject to revision, the intent of the regulatory community 

is clear: to reduce exposure of the public to the potential adverse health effects of 

the DBPs. 

Furthermore, USEPA is focusing on the removal of precursor material as 

an initial approach to reducing DBP production. In the D-DBP Rule, a treatment 

technique is proposed for conventional treatment plants to remove DBP precursors 

by improving the efficiency of existing coagulation processes. This process, termed 

enhanced coagulation, is also a best available technology (BAT) along with GAC. 

Although many systems may be able to comply with the Stage 1 requirements 

without implementing advanced precursor removal strategies or such alternative 

disinfectants as ozone or chlorine dioxide, Stage 2 compliance will be more difficult 

with conventional water treatment processes. In recognition of this difficulty, a 

companion regulation, the Information Collection Rule (ICR), has been proposed 

to evaluate the removal of DBP precursors and the formation of DBPs in large water 

treatment systems. A subset of these systems will also have to evaluate the removal 

of DBP precursors by GAC or membranes on a bench or pilot scale to assist in 

understanding the efficiency and costs associated with advanced precursor removal 

processes. Consequently, there is a great interest in optimizing GAC treatment to 

improve efficiency and reduce overall implementation costs. 

GAC Treatment issues__________________ 

Adsorbability of Natural Organic Matter 

DBP precursor compounds are a subset of natural organic matter (NOM), 

found in natural waters. NOM is ubiquitous in source waters and is composed of 

organic substances that vary in chemical nature and complexity. This heterogeneity 

is also evident from the differences in adsorbability of these compounds by GAC 

(Sontheimer et al. 1988). Normally, 80 to 90 percent of the NOM measured in raw 

water sources can be removed by GAC adsorption (Roberts and Summers 1982). 

Recent work has also shown that NOM adsorbed on GAC does not desorb as a result 

of a decrease in the liquid-phase concentration (Summers and Roberts 1988; 

Summers et al. 1989, 1992). This may also contribute to the loss of GAC capacity 

for target SOCs that has been found to occur after pre-exposure of GAC to NOM 

(Arora 1989; Sontheimer et al. 1988; Summers et al. 1989, 1992). This GAC

Introduction 3 

"fouling" has not been evaluated on a wide range of NOM sources (1) with the same 

SOC or (2) under similar preloading circumstances. 

Blending of Contactor Effluents 

In the full-scale application of GAC at utilities treating more than 1 mgd 

(3.785 ML/d), multiple contactors will be used, with the effluents from the 

contactors blended together. The contactors are placed on-line in a staggered 

manner (i.e., starting times are different); thus, the effluent from each contactor has 

a different concentration, and the blended water concentration is the average of 

individual effluents. For single solute compounds, blending can be effectively 

modeled (Roberts and Summers 1982; Sontheimer et al. 1988). However, DBF 

formation in blended GAC effluents has not been investigated or modeled. As a 

result of the nonlinear nature of the reaction of chlorine and NOM, the modeling may 

not be as straightforward as with individual compounds. 

DBF Speciation and Formation Conditions 

Recently, considerable attention has been given to the brominated species 

of TTHM and the haloacetic acids (HAAs). The speciation is known to be a function 

of the relative concentrations of bromide ion, dissolved organic carbon (DOC), and 

applied chlorine, as well as the reaction time. Because the NOM effluent from a 

GAC adsorber initially is low, the bromide-to-DOC ratio (Br:DOC) is higher than 

that in the influent. This leads to the production of a higher percentage of brominated 

DBFs in the GAC effluent. Depending on the chlorination approach, a similar 

pattern of more brominated DBFs may emerge following chlorination. If a constant 

chlorine dose is used, the C12:DOC ratio will decrease as the DOC incrases with 

time, which may not represent the conditions of practice when blended waters are 

used or when chlorine is dosed to achieve a constant residual. 

The variability in the conditions used to represent DBF formation has been 

a major obstacle to the uniform assessment of GAC and other treatment processes. 

Two approaches for DBF formation conditions have been taken in the past. The 

formation potential (FP) conditions are somewhat standardized but are thought to 

be too extreme to represent DBF formation conditions in practice and may 

misrepresent treatment efficiencies. The simulated distribution system (SDS) 

conditions are representative of practical conditions, but they normally vary from 

utility to utility, bcause they are meant to be site-specific. A controlled comparison 

of several water sources under uniform, realistic conditions is needed to allow a 

comprehensive assessment of the efficiency of GAC treatment. 

Evaluation of Process Variables 

To optimize GAC use, such process variables as empty bed contact time 

(EBCT) and pretreatment (i.e., coagulation; ozonation followed by biotreatment) 

need to be investigated in combination. For some waters, coagulation can be 

effectively optimized for DBF precursor removal, which can impact the perfor 

mance of the GAC system. If the GAC system is operated without a disinfectant 

residual in the influent, biological activity will occur before and during GAC 

treatment. This bioactivity can contribute to the removal of DBF precursors.

4 Removal of DBF Precursors by GA C Adsorption 

Evaluating the impact of optimized pretreatment and EBCT on GAC 

performance at a pilot scale is time-consuming and costly. The rapid small-scale 

column test (RSSCT) for the simulation of field-scale NOM removal by GAC was 

shown to be an effective and economical alternative for eight different waters by 

Summers and Crittenden (1989) and Crittenden et al. (1991). More recently, the 

success of RSSCT has been demonstrated for simulating removal of TTHM and 

total organic halide (TOX) precursors of a groundwater and a surface water 

(Summers et al. 1992). 

Study Objectives_____________________ 

The primary objective of this study is to evaluate the use of GAC for the 

control of DBF formation under uniform, representative conditions over a range of 

water sources. The project was planned to achieve the following specific objectives: 

1. Examine a wide range of water sources at both field scale and 

laboratory scale to assess GAC breakthrough behavior. 

2. Characterize the NOM in raw and GAC treated waters. 

3. -Evaluate the formation of DBFs under regulatory focus using 

representative but uniform conditions (i.e., uniform formation 

conditions [UFC]): 

4. Optimize GAC performance based upon 

empty bed contact time 

coagulation pretreatment 

ozonation-biofiltraion pretreatment 

5. Assess the impact of the preadsorption of NOM on GAC's capacity 

for SOCs. 

6. Establish relationships between NOM parameters and DBF 

formation with respect to GAC treatment. 

7. Produce optimization guidelines and cost estimates for GAC 

treatment.

Chapter 2 

Materials and Methods 

Five surface waters and one ground water were sampled for this study. The 

waters represent a wide range of water quality characteristics, as shown in Table 2.1 

for raw and treated water quality. The treatment processes used by the participating 

utilities that contributed the source waters are summarized in Table 2.2. 

Ohio River Water (ORW) was collected prior to the full-scale GAC 

contactor at Cincinnati Water Works (CWW) (Cincinnati, Ohio). ORW is an 

industrially impacted surface water with moderate color, medium alkalinity, 

medium hardness, average levels of organic matter, and intermittent presence of 

SOCs. The water was pretreated by alum coagulation (8 mg/L), sedimentation, and 

filtration. 

Lake Gaillard Water (LOW) was obtained from the South Central Con 

necticut Regional Water Authority (SCCRWA) (New Haven, Conn.), which 

operated a pilot-scale postfilter adsorber. This water represents a high-quality 

surface water with low color, low alkalinity, low hardness, medium levels of organic 

matter, and intermittent presence of taste-and-odor-causing compounds. The water 

was pretreated by direct filtration and ozonation. 

Mississippi River Water (MRW) was obtained from the Jefferson Parish 

Water Department (JPWD) (Jefferson Parish, La.), which operated a pilot-scale 

filter adsorber. MRW is an industrially impacted surface water with moderate color, 

high alkalinity, high hardness, medium to high levels of organic matter, and 

intermittent presence of SOCs. The water was pretreated by alum coagulation, 

sedimentation, and ozonation. 

Passaic River Water (PRW) was obtained from the Little Falls Water 

Treatment Plant in Totowa, N.J. The Passaic Valley Water Commission (PVWC) 

(Clifton, N.J.) operated a pilot-scale GAC postfilter adsorber. PRW, also an 

industrially impacted surface water, has moderate color, high alkalinity, high 

hardness, high levels of organic matter, and intermittent presence of taste-and-odorcausing 

compounds and SOCs. The water was sampled after alum coagulation (30 

mg/L), sedimentation, and filtration. 

Salt River Project Water (SRPW), a blend of the Salt and Verde Rivers, was 

sampled from the City of Phoenix Water and Wastewater Department (Phoenix, 

Ariz.) after alum coagulation (10.6 mg/L), sedimentation, and filtration. SRPW has 

low color, high dissolved solids, moderate hardness, moderate alkalinity, and 

medium levels of organic matter. 

Florida groundwater (FGW) was obtained from the Barefoot Bay Water 

Treatment Plant (Florida Cities Water Company, Sarasota, Fla.). FGW has high 

color, high hardness, high alkalinity, and high levels of organic matter. Raw water 

was sampled and batch coagulated (48 mg/L alum) and softened (280 mg/L lime)

Florida 

groundwater 

Raw 

13 

0.48 

276 

7.7 

240 

300 

135 

457 

1,070 

26 

GAC 

influent 

10 

0.29 

276 

8.6 

130 

80 

238 

142 

1,046 

7.8 

s? s 

o 

§ 

to 

5 n 

1 

5' 

Table 2.1 Summary of water quality parameters for water sources 

s 

2 

Ohio River 

Lake Gaillard 

Mississippi River 

Passaic River 

Salt River 

Project 

3 

•S" 

Parameter 

Raw 


influent 

Raw 


influent 

Raw 


ifluent 

Raw 


influent 

Raw 


influent 

TOC (mg/L) 

UV254 (1/cm) 

Br (ug/L) 

pH 

Alkalinity (mg/L as CaCO 3 ) 

Hardness (mg/L as CaCO 3) 

UFC-TTHM (ug/L) 

UFC-HAA6 (ug/L) 

UFC-TOX (ug CM.) 

UFC-CH (pg/L) 

2.2 

0.053 

106 

7.9 

40 

150 

87 

41 

NA 

9.3 

2.1 

0.044 

132 

7.8 

66 

163 

86 

26 

170 

3.9 

2.9 

0.093 

12 

8.1 

12 

21 

131 

109* 

490 

10 

1.5 

0.012 

14 

7.9 

NA 

NA 

31 

29* 

131 

2.8 

5.2 

0.115 

51 

8.2 

96 

132 

102 

133 

690 

5.9 

2.8 

0.022 

68 

7.9 

NA 

NA 

50 

22 

168 

7.9 

5.0 

0.10 

80 

7.8 

94 

143 

135 

171 

480 

15 

3.2 

0.076 

80 

7.7 

80 

142 

73 

70 

277 

15 

2.5 

0.055 

89 

8.2 

135 

178 

76 

36 

196 

4.4 

2.2 

0.047 

89 

7.8 

NA 

NA 

72 

28 

170 

4.1 

CH = chloral hydrate 

HAA6 = sum of six haloacetic acids 

NA = not analyzed 

UV254 = ultraviolet absorbance at 254 nm 

*UFC = HAAS

Materials and Methods 7 

Table 2.2 Field-scale GAC systems 

Source 

water 

Utility 

Pretreatment 


contactor size 

GAC EBCT 

contactor type (minutes) 

Ohio River 


Alum coagulation, 

sedimentation, 

filtration 

Full scale 

Postfilter 15 

adsorber 

Lake Gaillard South Central Connecticut Direct filtration, 

Regional Water Authority ozonation 

Pilot scale 

Postfilter 

adsorber 15 

Mississippi River Jefferson Parish Water 

Department 



ozonation 

Pilot scale 

Filter adsorber 6.25 

Passaic River Passaic Valley Water 

Commission 



filtration 

Pilot scale Postfilter 

adsorber 

20 

Salt River 

Project 

Florida 

groundwater 


Florida Cities Water 

Company 



filtration 

Prechlorination; 

lime softening and 

alum coagulation; 

sedimentation 

No field-scale GAC system at this site. 

at the University of Cincinnati (UC) (Cincinnati, Ohio) using the chemical doses of 

the utility at the day of sampling. 

Experimental Approach 

Testing Program 

A summary of the experimental program for all waters is presented in Table 

2.3. Four pilot- or full-scale GAC contactors were operated, and RSSCTs were 

conducted to verify the ability of the RSSCT for predicting NOM and DBF control 

under SDS conditions at the field-scale level. Experiments were conducted to 

examine the effects of design and operational parameters of EBCT, blending, and 

backwashing using the RSSCT and UFC. The impact of (1) optimized coagulation 

and (2) ozonation and biological filtration prior to the GAC column were investi 

gated using the RSSCT and UFC. To determine the impact of prior exposure to 

NOM on the residual adsorption of cis-l,2-dichloroethene (DCE) on GAC, batch


Table 2.3 Summary of experimental program 

Source 

Program stage______________ORW____LGW MRW PRW____SRPW FGW 

Field-scale GAC (full or pilot scale) X X X X 

RSSCT verification X XXX 

Design and operational considerations 

Impact of EBCT X XXX 

Impact of blending X X X 

Impact of backwashing X 

Impact of pretreatment 


Ozonation and biotreatment 

SOC adsorption 

X X 

X XX 

X X X X X X 

isotherms were run with GAC from the field and RSSCT columns after the 

completion of column operation. A matrix linking the experimental program 

analyses with the laboratories at which the analyses were performed is shown in 

Table 2.4. 

Sample Collection and Handling 

Treated ORW, MRW, PRW, and SRPW were sampled in 55-gal (208-L) 

drums and shipped to UC on the same day from utilities. These waters were used (1) 

as influent to RSSCTs performed at UC for verification studies and (2) to assess the 

impact of design and operational parameters. For ORW, PRW, and SRPW, a portion 

of the plant-treated water received was subjected to secondary bench-scale treat 

ment at UC, consisting either of coagulation with additional alum doses or of 

ozonation and biological filtration, to examine the effects of pretreatment. 

The verification RSSCT for LGW was performed on-site at SCCRWA. No 

other RSSCTs were conducted for this water. For FGW, raw water only was 

sampled and shipped to UC in 55 gal (208-L) drums. This water was batch treated 

at UC. For additional analyses, raw water from all six utilities was sampled on the 

same day as treated water. The raw water was shipped to UC by overnight courier. 

The delivery time for water in 55 gal (208-L) drums was 4 to 6 days for 

MRW, SRPW, and FGW. For PRW the water was delivered in 2 days, while for 

ORW the water was delivered on the same day because of the proximity of CWW 

toUC. 

Experimental Procedures 

Rapid Small-Scale Column Test 

The RSSCT was used to predict pilot- or full-scale NOM removal and DBP 

control. The development of the RSSCT for NOM and DBP control is described by 

Summers et al. (1995). The RSSCT utilizes small GAC particle sizes, typically 0.08


Table 2.4 Laboratories performing sample analyses for experimental program 

Water 

source 

Experiment 

TOC 

uv254 

SDS 

UFC 

Analysis 

TOX 

THM 

HAA 

CH 

MRW 

Pilot-scale GAC 


JPWD 

UC 

JPWD 

UC 

JPWD 

UC 

NA 

UC 

JPWD 

UC 

JPWD 

JPWD 

JPWD 

JPWD 

JPWD 

JPWD 

ORW 

Full-scale GAC 




Ozonation and 

biotreatment 

CWW 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

CWW 

UC 

NA 

NA 

NA 

NA 

UC 

UC 

UC 

UC 

CWW 

UC 

UC 

UC 

UC 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

CWW 

LGW 


RSSCT verification . 

UM 

UM 

NA 

NA 

UM 

UM 

NA 

UC 

UC 

UC 

UM 

UM 

UM 

UM 

UM 

UM 

PRW 




Ozonation and 

biotreatment 

Impact of backwashing 


UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

UC 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

PVWC 

UC 

UC 

UC 

UC 

UC 

MWL 

MWL 

MWL 

MWL 

NA 

NA 

MWL 

MWL 

MWL 

MWL 

UC 

UC 

MWL 

MWL 

MWL 

MWL 

NA 

NA 

MWL 

MWL 

MWL 

MWL 

NA 

NA 

SRPW 




UC 

UC 

UC 

UC 

UC 

NA 

NA 

NA 

NA 

UC 

UC 

UC 

UC 

UC 

NA 

CWW 

CWW 

UC 

CWW 

CWW 

NA 

CWW 

CWW 

NA 

FGW 


Ozonation and 

biotreatment 


UC 

UC 

UC 

UC 

UC 

NA 

NA 

NA 

NA 

UC 

UC 

UC 

UC 

UC 

NA 

CWW 

CWW 

UC 

CWW 

CWW 

NA 

CWW 

CWW 

NA 

CH = chloral hydrate 

MWL = Montgomery Watson Laboratories, Pasadena, Calif. 


TOC = total organic carbon 

UM = University of Massachusetts, Amherst, Mass. 

UV254 = ultraviolet absorbance at 254 nm 

to 0.2 mm in diameter, and is designed to decrease the time and effort needed to 

assess the concentration profile in a GAC column (Crittenden et al. 1986, 1987, 

1991). A proportional diffusivity design is used (Summers etal. 1989,1994b, 1995), 

so the EBCT and run time of the RSSCT are inversely proportional to the ratio of 

particle sizes in the field-scale and RSSCT columns, as described in Appendix A. 

A statistical procedure for comparing RSSCT to pilot- or full-scale breakthrough 

curves is described in Appendix B. 

To perform the tests, commercially available 8.0-, 11.0-, or 15.0-mm glass 

chromatography columns with polytetrafluoroethylene (PTFE) or stainless steel 

fittings were used. After being grinded to appropriate size (see Table 2.5) by mortar 

and pestle, the GAC was washed with laboratory clean water to remove fines. An


ultrasonic vibrator was used to aid in the removal of fines. Finally, a vacuum was 

applied to remove air from the pores of the GAC. The GAC was packed into the 

column to the appropriate bed length as determined by scaling equations (Summers 

et al. 1995), which are described in Appendix A. The RSSCT columns were fed from 

a 19-L glass carboy containing the prefiltered (1.0-um cartridge filter) influent at 

room temperature, which was refilled with the batch influent water as needed. The 

influent water was pumped into a stainless steel gas cylinder, which served to 

dampen the pulsing effect of the diaphragm pump used to push water through the 

columns. Fresh GAC was obtained from the full- or pilot-scale contactor and ground 

to smaller size at UC. Field-scale and RSSCT operational and design parameters are 

described in Table 2.5. 

Bench-Scale Coagulation and Softening 

For optimized coagulation pretreatment, the bench-scale alum coagulation 

process was conducted at UC for ORW and SRPW. It was repeated three to five 

times on conventionally treated water contained in different 55-gal (208-L) drums. 

Table 2.5 Field-scale and RSSCT verification operation parameters___________ 

Field scale 

________________ORW______LGW______MRW_____PRW_____SRPW* FGW* 

Carbon type F 400 F 400 GAC 40 F 400 F 400 F 400 

dLC (mm) 1.05 1.49t 1.05 1.05 1.05 1.05 

EBCTLC (minutes) 15 15 6.25 20 15 15 

LLC (m) 3.2 — 0.52 2.67 — — 

13 5.0 8.0 


Carbon type 

dsc (mm) 

SF 

EBCTSC (minutes) 

Lsc (m) 

° e SC,min 

vsc (m/h) 

Qsc (mL/min) 

DSC (mm) 

F400 

0.08 

12.6 

1.2 

0.193 

0.5 

9.7 

8.1 

8.0 

F400 

0.20 

7.4 

2.0 

0.127 

0.5 

4.1 

6.4 

11.0 

GAC 40 

0.11 

9.4 

0.66 

0.16 

1.0 

15 

23 

11.0 

F400 

0.08 

12.6 

1.6 

0.257 

0.5 

9.7 

8.1 

8.0 

F400 

0.11 

9.4 

1.6 

0.193 

0.5 

7.3 

6.1 

8.0 

F400 

0.20 

5.3 

2.8 

0.193 

0.5 

4.1 

12 

15.0 

d = particle diameter 

D = column diameter 

EBCT = empty bed contact time 

L = GAC bed length 

LC = large particle column 

Q = flow rate 

ReSCmin = minimum Reynold's number for small particle column 

SC ='small particle column 

SF = scaling factor 

v = superficial velocity 

— indicates not known 

* Field-scale column was not operated; RSSCT design was based on the given field-scale parameters, 

t Sieved to yield a larger size.


A variable speed shaft mixer was operated at high speed prior to alum addition and 

for 4 minutes after alum addition. The water was then flocculated for 12 minutes at 

low speed. After settling, the water was passed through a 1.0-um cartridge filter 

prior to GAC treatment. 

Bench-scale coagulation and softening of FGW were performed in a similar 

manner to bench-scale coagulation. Both alum and lime were added during rapid 

mix. If the pH did not reach 10.3 immediately after chemical addition, sodium 

hydroxide was added to achieve this pH level. This procedure was designed to 

simulate the procedure used for FGW at the full-scale treatment facility. The 

remaining procedure was the same as that for alum coagulation. 

Bench-Scale Ozonation and Biotreatment 

The impact of ozonation and biological filtration was investigated for 

ORW, PRW, and FGW. Conventionally treated water was passed through a benchscale 

ozonation system (Allgeier and Summers 1995). Ozonation was performed at 

room temperature. A 1 -L contactor with a detention time of 7 minutes was used as 

a reactor with a porous diffuser at the bottom. Ozonation was carried out in a 

countercurrent mode. Ozone was generated using an electric discharge ozonizer 

(Model 200, Sander Aquarientechnik, Vetze-Eltze, Germany). The gas flow rate 

used was 60 mL/min, and the liquid flow rate used was 143 mL/min. The dose was 

applied to produce a residual of 0.4 mg O3/L on average. Applied ozone and off-gas 

were measured by iodometry, Method 422 (APHA et al. 1995) and dissolved ozone 

was measured spectrophotometrically using the indigo trisulfonate method (APHA 

etal. 1995). 

After ozonation the water was passed through a biologically active sand 

filter. The bench-scale filter column had a diameter of 3 cm and a bed depth of 0.30 

m. The filter EBCT was 5 minutes and was designed to remove the quickly 

biodegradable fraction of NOM. The biologically active sand was acclimated 

(steady-state BDOC removal) with the water to be treated prior to the run. After 

biotreatment, the water was filtered through a 1 -urn cartridge filter and treated by 

GAC. 

Blending Experiments 

In practice, multiple GAC contactors are operated in a parallel configura 

tion, and the effluents from all contactors are blended prior to disinfection. Parallel 

contactors are operated in a staggered mode (i.e., starting times are different) 

because each contactor will have been in operation for a different length of time, 

yielding different levels of breakthrough. The RSSCT, however, represents only a 

single contactor. To simulate the effect of blending on GAC performance, an 

integral breakthrough experiment was performed. The effluent from the RSSCT 

was continuously collected in a large reservoir, from which samples were taken as 

the volume and NOM concentration in the reservoir increased. Blending experi 

ments were conducted for PRW, SRPW, and FGW. Five to six samples were taken 

and analyzed for TOC and THM formation under UFC. 

DCE Isotherms 

In order to evaluate the residual GAC capacity for removing DCE after prior 

exposure to NOM, bottle-point batch isotherm studies were performed using 

different carbon doses (Randtke and Snoeyink 1983). DCE was chosen because it


has intermediate adsorbability by GAC and is not biodegradable within a short 

period of time under ambient conditions. GAC was sampled both from RSSCTs 

performed for all six waters and from two field-scale columns after the completion 

of the adsorption run. The preloaded GAC was removed from the columns and dried 

in an oven at 100 C until there was no detectable weight change. The field-scale 

GAC was first dried and then ground to the same size as the respective RSSCT GAC. 

The dried carbon was weighed and placed in 250-mL bottles and filled headspacefree 

with the prepared DCE solution. To prepare the DCE solution, a highconcentration 

aqueous stock solution was added to 200 mL of low-organic-content 

water and mixed for 24 hours. The prepared bottles were placed on a tumbler at 12.5 

rpm for 7 days. Four controls were made without carbon for each set of isotherms. 

After 7 days, samples were taken from each bottle and filtered before analysis using 

a 25-mL glass syringe with 0.45-um-pore-diameter membranes (Millipore HV, 

Millipore Corp., Bedford, Mass.) 

Backwashing 

To investigate the impact of backwashing on GAC performance, an RSSCT 

column was backwashed and run in parallel to a nonbackwashed column treating 

PRW. The RSSCT column was backwashed once every 11 full-scale equivalent 

days by completely removing all carbon and mixing the carbon in a beaker using 

effluent water collected just prior to backwashing. This represents a worst-case 

scenario because, in practice, a carbon bed will not become completely restratified 

after backwashing. The column was then repacked and filled with influent water. 

After backwashing, the first 20 mL of water in the column and tubing was discarded. 

The total down time for each backwashing was 30 minutes. After backwashing, one 

bed volume of effluent was discarded before sampling. Samples were collected 

before and after backwashing and analyzed for total organic carbon (TOC) and 

ultraviolet absorbance at 254 nm (UV254). DBF formation was assessed by 

trihalomethane (THM) formation under UFC. 

Analytical Methods___________________ 

Standard Methods 

A summary of standard analytical methods used in this study is presented 

in Table 2.6. The following is a description of the analytical procedures followed 

at the University of Cincinnati. 

Total Organic Carbon 

Organic carbon is the most commonly used nonspecific parameter for 

quantitative measurements of NOM. TOC is composed of dissolved organic carbon 

and a fraction containing particulate and colloidal organic carbon. TOC analysis 

performed at UC utilized a Dohrmann Model DC-180 organic carbon analyzer 

(Rosemount Analytical Corp., Santa Clara, Calif.) that employed persulfate and 

ultraviolet light oxidation. The methodology followed in the operation of this 

instrument was in accordance with the manufacturer's recommendations and 

Method 5310 C (APHA et al. 1995). Samples were acidified to pH < 2 by


Table 2.6 Summary of analytical methods 

Parameter 

Laboratory 

Method 

Detection limit Precision (%) 

TOC 

UC 

JPWD 

CWW 

UM 

MWL 

SM 5310 C (APHA et al. 1995) 

— 

SM 5310 C (APHA et al. 1995) 

SM 5310 C (APHA et al. 1995) 

SM 5310 (APHA et al. 1995) 

0.1 mg/L


thoroughly rinsed with laboratory clean water (LCW) and prerinsed once with the 

sample to be filtered prior to use. 

Ultraviolet Light Absorbance at 254 nm 

NOM absorbs light in the range of 200 to 700 nm, and absorbance at 254 

nm has been the industry standard for representing NOM. UV254 measurements 

performed at UC utilized a Hewlett Packard (Palo Alto, Calif.) Model 8452A 

diode-array spectrophotometer. A 5-cm-path-length, 15-mL quartz cell was used for 

sample analysis. The absorbance at 254 nm was measured, and all reported values 

were expressed in units of I/cm. Method 5910 B (APHA et al. 1995) was followed 

except for ORW, MRW, and SRPW samples, which were acidified to pH < 2 with 

o-phosphoric acid and analyzed within 7 days. LCW acidified to pH < 2 with 

ophosphoric acid was used as a blank. For PRW and FGW, samples were not 

acidified and were analyzed within 24 hours. Nonacidified LCW was used to obtain 

a blank reading. In all cases, samples were stored at 4 C prior to analysis and were 

warmed to room temperature for analysis. 

Trihalomethanes 

THM analysis includes chloroform, bromodichloromethane, dibromochloromethane, 

and bromoform. After specified chlorination time, samples were quenched 

with 5-mg/L sodium sulfite and held headspace-free until analysis. Samples were 

stored at 4 C for up to 1 week. Analysis was performed following USEPA Method 

524.2 Revision 3.0 (USEPA 1992) using a gas chromatograph (Model 3400, Varian, 

Palo Alto, Calif.) equipped with a purge-and-trap system (LSC 2000, Tekmar, 

Cincinnati, Ohio) and an electrolytic conductivity detector (Model 1000, Tracer, 

Austin, Texas). A blank sample was run daily for baseline correction, and the 

instrument was calibrated daily with a single standard containing known concentra 

tions of the four THM species. Standard calibration curves were obtained for 

concentrations of 5, 10, 25, and 50 ug/L of each THM species, and r2 values were 

typically greater than 0.99. 

Haloacetic Acids and Chloral Hydrate 

HAA and chloral hydrate (CH) analyses were conducted using the methods 

outlined in Table 2.6. These analyses were not performed at UC. The HAA species 

analyzed that constitute HAAS are monochloroacetic acid, dichloroacetic acid, 

trichloroacetic acid, monobromoacetic acid, and dibromoacetic acid. HAA samples 

analyzed at the University of Massachusetts (UM), Amherst, Mass., for LOW were 

reported as HAAS. However, most analyses included bromochloroacetic acid and 

were reported as the sum of six haloacetic acids (HAA6). 

Total Organic Halide 

TOX is a surrogate measure of adsorbable halogenated DBPs measured as 

Cl~. TOX analysis at UC was conducted according to Standard Method 5320 

(APHA et al. 1995) using a TOX analyzer (Dohrmann DX-20, Rosemount Analyti 

cal Corp., Santa Clara, Calif.). After quenching of the sample with 5-mg/L sodium 

sulfite and acidifying to pH < 2 with concentrated nitric acid, the sample was stored 

headspace-free at 4 C for up to 2 weeks. The instrument was calibrated by direct 

injection of a known concentration of 2,4,6-trichloropenol, and standard recoveries 

ranged from 90 to 110 percent. Blank samples were also analyzed daily.


cis-l,2-Dichloroethene 

DCE analysis was performed according to USEPA Method 502.2, Revision 

2.0, using the same equipment as described earlier for THM analysis. Samples were 

held headspace-free in 15-mL vials until analysis, which was performed within 2 

weeks. Standard calibration curves at 5, 10, 25, and 50 ug/L were created daily. 

Nonstandard Methods 

Chlorination 

Chlorination was conducted under uniform formation conditions, de 

scribed in detail in Appendix C. The UFC test, developed as part of this study, 

provides constant Chlorination conditions that are representative of nationwide 

average distribution system conditions. These conditions are as follows: a 24-hour 

incubation time at 20.0 1.0 C, buffered at pH 8.0 0.2, and a 24-hour free chlorine 

residual of 1.0 0.3 mg/L as Clr UFC test conditions allow for DBF formation 

comparisons to be made between different waters or between different treatments 

performed on the same water. SDS test conditions were utilized in addition to UFC 

for the field-scale GAC operation and RSSCT verification study for MRW, ORW, 

and LGW. The SDS test conditions used for the RSSCT verification study were 

based on conditions utilized by each utility during the field-scale GAC operation 

and are summarized in Table 2.7. For the PRW study, UFC and SDS conditions were 

the same. 

Humic-Nonhumic Fractionation 

Humic-nonhumic fractionation was performed using XAD-8 resin (Rohm 

& Haas, Philadelphia, Pa.; see Thurman and Malcolm 1981). The resin was packed 

in an 8.0- or 11.0-mm-diameter glass chromatography column, and samples were 

pumped through the column at a flow rate of 4.0 mL/min. A k' (ratio of resin void 

volume to volume of sample passed) of 50 was used for all separation experiments. 

Prior to fractionation, samples were prefiltered through a 0.45-nm hydrophilic 

membrane filter (Millipore HV, Millipore Corp., Bedford, Mass.) and acidified 

with concentrated sulfuric acid to pH 2.0. The DOC measured in the effluent was 

operationally defined as the nonhumic fraction, while the resin retained the humic 

fraction. The DOC of the humic fraction was obtained by difference between the 

unfractionated sample and the nonhumic fraction. The resin was cleaned by passing 

one bed volume each of 0. IN sulfuric acid and 0.1/V sodium hydroxide three times. 

Table 2.7 UFC and SDS Chlorination conditions 

Parameter 

UFC 

MRW-SDS 

ORW-SDS 

LGW-SDS 

Incubation time (days) 


PH 

Chlorine residual (mg CI^L) 

1 

20 

8.0 

1.0 

5 

28,20 

8.4 

1.0 

3 

30,23, 10 

8.0 

1.0 

2 

10 

7.3 

Dose: 2.8* 

* Initial dose; test was not base on a target residual


A sample of the humic fraction retained by the resin was obtained by eluting 

with 0.1 N sodium hydroxide in an upflow mode. Mass balance calculations 

indicated that the measured humic fraction was within 90 to 110 percent of the 

calculated humic fraction. For chlorination, the unfractionated sample and the 

humic fraction were diluted to the DOC of the nonhumic fraction using low-organiccontent 

water (


DOC/100 mL by a combination of rotary vacuum evaporation and room temperature 

evaporation under N2 atmosphere. Using a Buchi rotavapor (Model R114withB480 

Waterbath), 1 L of sample was reduced to 30 mL at 22 C and under a vacuum 

pressure of approximately 27.5 in. Hg (70 mm Hg). This volume reduction was 

conducted in a successive manner whereby the concentrate was decanted from a 

larger vessel into a smaller flask, allowing the physical separation of precipitated 

salts. If the salt precipitate appeared colored, it was rinsed with water and this wash 

was combined with the concentrate. Upon reaching a volume of 30 mL, the 

concentrate was placed in small sample vials (50 mL); using ultra-high-purity N2 gas 

with a Pierce Reacti-Therm III Heating Stirring Module, the sample was concen 

trated to a final volume of approximately 100 mL. In this volume, the amount of 

DOC was targeted at 1 mg. If salt precipitation was visible and if the precipitate was 

colored, the precipitate was rinsed in an attempt to minimize DOC loss. DOC losses 

were typically less than 5 percent. 

Pyrolysis samples were prepared in quartz capillary tubes from this final 

concentrate by coating the tubes with 20 mL of the concentrate (200 mg DOC) and 

allowing them to dry overnight at room temperature. Care was taken to evenly 

distribute the sample along the surface area of the tubes and not to overload the tubes 

with sample. In order to produce reproducible and uniform pyrolytic conditions, 

thermal gradients within the sample tube were avoided (Irwin 1982). Overloaded 

tubes could lead to the formation of secondary pyrolysis product because of a 

recombination of primary fragments. When there was a high concentration of salt 

in the sample (i.e., hard water source or high-alkalinity water), the quartz tubes were 

placed in an oven at 50 C overnight. Once the tubes were dried, deactivated glass 

wool was placed in each end of the tube. 

Specifications for each component of the analysis are shown in Table 2.8. 

The quartz tubes of samples were placed inside a coiled platinum filament of the 

Pyroprobe 2000. This unit employed heated filament pyrolysis and had flexible 

temperature-ramping capabilities up to a maximum temperature of 1,500 C. This 

instrument also provided coupled temperature ramping of the interface and the 

pyrolysis filament. Often the first pyrolysis tube of a set of replicates was sacrificed 

in order to screen any special features of a particular sample, particularly salt 

influences. In the absence of salt effects (i.e., for low carbonate concentration), 

sample tubes were loaded at an initial interface temperature of 70 C. The interface 

was then ramped from 70 to 250 C at 30 C/s simultaneously with the temperature 

ramp of the pyrolysis filament. 

For carbonate salts, an additional effect was observed that involved a 

swamping effect by CO 2 gas as it sublimed from the pyrolysis sample. A method to 

minimize this effect was developed and involved loading the pyrolysis sample into 

the interface at an initial temperature of 120 C and holding the sample at this 

temperature for 90 minutes. This procedure drove off inorganic CO2 and associated 

water without loss of organic carbon. The interface was then ramped from 120 to 

250 C at 33.3 C/s simultaneously with the pyrolysis ramp as described earlier. 

Once the pyrolysis run was completed, the interface temperature was held at 250 C 

for 2 minutes and then reduced to 50 C. By a lowering of the temperature of the 

interface, CO2 sublimation was minimized. Although these steps help to eliminate 

salt effects, it was also necessary to increase the frequency of cleaning the mass 

spectrometry (MS) source and interface. Because all the first GAC breakthrough 

samples and Salt River samples had very high salt levels, the source was cleaned


Table 2.8 Specifications for PY-GC-MS analysis 

Analytical step Instrument Conditions 

Pyrolysis 

Chemical Data Systems 

Pyroprobe 2000 

Heated filament 

Temperature ramp: 100-720°C 

at 20°C/millisecond 

Hold 720°C for 20 seconds 

Internal temperature 625±5°C 

Gas chromatography Fison 8030 

Supelcowax 10, 60-m column 

Splitless injection 

45-260°C at 2°C/min; 

at 260°C hold 45 minutes 

Mass spectrometry Fison MD 800 

Operated at 70 eV, 

scanning 20-400 amu at 1 scan/s 

Data analysis 

Digital 433dxLp 

NIST Library 

NIST = National Institute of Standards and Technology 

after every two runs, and the interface was cleaned with a cotton swab after each run 

in order to avoid salt deposition throughout the instrument. 

The pyrolysis temperature was ramped resistively by a booster current from 

an initial temperature of 70 or 120 C to 725 C at 20 C/millisecond. The final 

temperature was held for 20 seconds. This internal temperature was critical for 

reproducibility of organic fragmentation consistent with predicted patterns. A minithermometer 

(Cole-Palmer 8508-45 Type K) was used to verify these conditions. 

Pyrolysis was conducted in an inert helium atmosphere (ultrahigh purity); 

with the temperature profile just described, flash pyrolysis of the parent organic 

compounds occurred. The pyrolysis fragments were quickly swept onto the gas 

chromatography (GC) column of the Fisons 8030 gas chromatograph. A Supelcowax 

10,60-m polar column was used with splitless injection (allowing use of very small 

sample mass). A 60-m length was chosen because better peak separation was 

achieved. The following temperature gradient was used: 45 C held for 15 minutes, 

45-260 C ramping at 2 C/min and 260 C held for 45 minutes. 

A Fisons MD 800 mass spectrometer served as the detector. It was operated 

at 70 eV and typically scans from 20-400 amu at 1 scan/s. 

Quality Assurance-Quality Control 

The quality assurance-quality control steps stipulated by the respective 

analytical methods were followed. Table 2.9 summarizes the analytical precision 

for the methods followed at UC and the laboratories of the participating utilities.

University 

of Cincinnati 

Detection % Samples Detection % Samples Detection % Samples Detection % 

limit recovery replicated (%) limit recovery replicated (%) limit recovery replicated (%) limit recovery 

Samples 

replicated (%) 

100 

100 

— 

10 

— 

— 

— 

— 

I 

c 

a 

ex. 

a- 

I 

Table 2.9 Summary of detection limits and precisions for analytical methods 

Cincinnati Jefferson Parish University 

Water Works Water Department of Massachusetts 

TOC or DOC 

uv254 

CD 

TOX 

0.2mg/L >90 10 10 pg/L 98 

— — — — — 

0.01 mg/L 98 — 0.07 mg/L — 

5M9/L 93-107 10 0.27 pg Cl~ 99.2 

100 50 pg/L ±5 

— 0.01/cm — 

— — — 

100 — — 

100 

100 

— 

— 

0.1 mg/L ±5 

0.01/cm — 

0.1 mg/L — 

3 M9/L 95-105 

THM 

0.1 ng/L — 10 0.001-0.04 (jg/L >95 

5 0.5 pg/L 90-110 

100 

1 pg/L >95 

HAA 

0.3-0.5 \ig/L — 10 — — 

— 0.5pg/L 90-110 

10 

— — 

CH 

Aldehydes 

Ketoacids 

0.5 Mg/L — 10 — — 

— — — — — 

— — — — — 

— 0.5pg/L 90-100 

— — — 

— 1 \ig/L 80-120 

5 

— 

5 

— — 

— — 

— — 

10-100 

CD = chlorine demand 

— indicates not known

Chapter 3___________ 

Using the RSSCT for Prediction of 

Field-Scale NOM Removal and DBP 

Control by GAG Adsorption 

Studies to ascertain and optimize GAC performance are typically con 

ducted at pilot-scale prior to full-scale application. Pilot-scale studies, however, are 

costly and time-consuming. Consequently, it is desirable to be able to predict GAC 

performance by alternative methods that may reduce both cost and time. Sontheimer 

et al. (1988) described both successful and unsuccessful applications of mathemati 

cal models to predict field-scale (both pilot- and full-scale) breakthrough. The 

modeling approach presents various difficulties. There are many limitations in 

accurately modeling a mixture such as NOM, and many model parameters are 

needed. 

An alternative to mathematical modeling is to use the RSSCT, which was 

developed to decrease the time and effort needed to assess the concentration 

breakthrough in a GAC column. To directly predict field-scale behavior, similitude 

to field-scale GAC systems has been maintained (Sontheimer et al. 1988). The 

relationship for the EBCT, hydraulic loading, column length, and operation time of 

the small-scale column and large-scale (pilot- or full-scale) column is determined 

through the use of dimensional analysis and is a function of the ratio of GAC particle 

sizes utilized in the two columns. The RSSCT utilizes smaller GAC particle sizes, 

which yield shorter EBCTs, column lengths, and operation times compared to the 

large-scale columns. Successful application of the RSSCT produces breakthrough 

curves that are equivalent to those of a full- or pilot-scale adsorber. The method was 

pioneered by Frick (1982) and further developed into the RSSCT and extensively 

applied by Crittenden and coworkers (Crittenden et al. 1986, 1987, 1989, 1991; 

Hineline et al. 1987). A survey of past comparisons of the RSSCT to field-scale data 

for many waters and a variety of parameters can be found in Summers et al. (1995). 

The RSSCT has a number of advantages over other methods used to predict 

or simulate full-scale GAC performance, such as pilot-scale tests or predictive 

models. Depending on the conditions, the RSSCT can be conducted in


capacity and kinetics are not required, nor is the use of numerical or analytical 

models. 

However, the RSSCT has drawbacks due to the nature of the influent water. 

Because there is often variability in the quality of the influent water, the use of a 

batch influent for the RSSCT may lead to results that are not representative of the 

long-term operation of full- or pilot-scale systems, which show the effects of 

seasonal water quality variability. Thus, the selection of a representative sample to 

serve as the influent to the RSSCT for representative comparisons is critical to the 

success of RSSCT simulations. Seasonal trends can be addressed by conducting a 

series of RSSCTs using batch influent water sampled at different times of the year. 

Furthermore, removal of DBF precursors in a GAC column due to long-term 

biodegradation is not simulated by the RSSCT, since the microorganisms may not 

have sufficient time to acclimate. 

Objectives and Approach________________ 

The objectives of the work presented in this chapter were to verify the use 

of the RSSCT to predict full- or pilot-scale NOM removal and DBF control by GAC 

adsorption. For this study, the appropriateness of the RSSCT was verified utilizing 

four waters from utilities operating either full- or pilot-scale GAC facilities: ORW, 

LGW, MRW, and PRW. Detailed information about each utility and pretreatment 

conditions is presented in Chapter 2. The RSSCT was designed based on either fullor 

pilot-scale operational conditions using the proportional diffusivity approach. 

This design approach, in which the scaling factor (SF) is the ratio of the particle 

diameter in the large column to the particle diameter in the small column, has been 

found appropriate for the scaling of columns used for NOM and DBF precursor 

adsorption (Summers et al. 1989, 1994c, 1995). Field-scale and RSSCT design 

parameters are presented in Table 3.1; RSSCT design equations are presented in 

Appendix A. 

The RSSCT and the full- or pilot-scale contactors were monitored for NOM 

removal by the surrogate parameters TOC and UV254 . DBF formation at both field 

and bench scale was assessed by the formation of TOX, TTHM, HAA6, and CH 

under utility-specific SDS conditions. During the RSSCT verification run, influent 

and effluent samples were also chlorinated under UFC. These uniform conditions 

Table 3.1 RSSCT and field-scale GAC contactor design parameters 


GAC type 

Particle diameter (mm) 

RSSCT, 

"so 

Field scale, 

"LC 

Scaling 

factor 

EBCT (minutes) 

RSSCT, 

EBCTSC 

Field scale, 

EBCTLC 

ORW 

MRW 

LGW 

PRW 

F400 

GAC 40 

F400* 

F400 

0.08 

0.11 

0.20 

0.08 

1.05 

1.05 

1.49 

1.05 

12.6 

9.4 

7.4 

. 12.6 

1.2 

0.66 

2.0 

1.6 

15 

6.25 

15 

20 

* Sieved to yield a larger size

Using the RSSCTfor Field-Scale Prediction 23 

were used for all other RSSCT runs. The comparison between DBF formation under 

SDS conditions and UFC is presented in Chapter 4. Full- or pilot-scale chlorination 

was performed by the utility, while bench-scale chlorination was performed at UC. 

Results and Discussion_________________ 

Ohio River Water 

The TOC results of the full-scale contactor and RSSCT are shown in Figure 

3.1 for ORW. For this figure and the remaining figures in this chapter, the full- or 

pilot-scale effluent values are normalized with respect to the influent concentration 

of the same sample day. The full- or pilot-scale influent values are normalized with 

respect to the average influent concentration. The RSSCT influent concentration is 

the average value of two or three measurements of the batch influent, and the RSSCT 

effluent values are normalized to that value. The RSSCT run time is expressed as 

that of the full-scale column by using the SF; i.e., scaled operation time = RSSCT 

run time x SF. 

The full-scale influent TOC concentration of ORW varied by nearly a factor 

of 2 over the course of the 210-day run, but the average influent concentration was 

the same as that of the batch RSSCT influent, which was collected on day 120. In 

spite of the influent concentration variation, the full-scale breakthrough behavior 

was extremely well predicted by the RSSCT. Biodegradation, which probably 

occurred in the full-scale GAC column and presumably not in the RSSCT, did not 

have a detectable impact on the comparison. The BDOC levels of the full-scale and 

RSSCT influents were only 10 percent of the TOC. 

Similar success was found for UV254 breakthrough behavior, another 

indicator of NOM breakthrough behavior, as shown in Figure 3.2. Figures 3.3 to 3.6 

illustrate breakthrough behavior at both bench and full scale for SDS-TOX, SDS- 

TTHM, SDS-HAA6, and SDS-CH, respectively. DBF formation breakthrough was 

similar to NOM breakthrough, and the RSSCT successfully predicted full-scale 

conditions for these parameters. 

Lake Gaillard Water 

The RSSCT and pilot-scale GAC performance for LOW is shown in 

Figures 3.7 through 3.10. The RSSCT was performed on-site at SCCRWA. Figure 

3.7 shows TOC breakthrough. The ozonated pilot-scale influent remained relatively 

constant over the course of the run, and the average pilot-scale and batch RSSCT 

influent TOC concentrations were essentially the same. Although the influent 

concentrations were the same, the pilot-scale contactor showed earlier break 

through. 

Figures 3.8 to 3.10 compare DBF control for SDS-TOX, SDS-TTHM, and 

SDS-HAA5, respectively. CH concentrations were at or below the detection limit. 

The RSSCT predicted DBF control at the pilot-scale level extremely well. For 

LGW, GAC very effectively removed DBF precursors: After 4 months the effluent 

concentration reached only approximately 30 percent of the influent concentration 

for the three parameters investigated.


Mississippi River Water 

The TOC and UV254 breakthrough behaviors for MRW are compared in 

Figures 3.11 and 3.12, respectively. The ozonated pilot plant influent was relatively 

constant over the 75-day run, but the average TOC and UV254 values were 30 per 

cent higher than those of the RSSCT. The batch sample for the RSSCT influent was 

taken on day 15, at a time when the influent TOC and UV254 values were 10 percent 

lower than for the average pilot plant influent. In addition, during the 6-day shipping 

time from JPWD to UC, the influent was biodegraded no BDOC was detected in 

the shipped influent, while the pilot plant influent had averaged about 25 percent 

BDOC following ozonation. This change in TOC and UV254 level and composition 

affected the NOM breakthrough, as shown in Figures 3.11 and 3.12. As expected, 

the higher influent values of the pilot plant column led to earlier breakthrough than 

for the RSSCT. Another factor possibly contributing to the earlier breakthrough in 

the pilot plant is that the pilot-scale column was operated as a filter adsorber and was 

therefore backwashed regularly, while the RSSCT was not backwashed. However, 

Hong and Summers (1994) have shown that backwashing normally does not impact 

NOM breakthrough behavior! After about 50 percent breakthrough, the RSSCT and 

pilot plant results coincided. Similar behavior was found for SDS-TOX, SDS- 

TTHM, SDS-HAA6, and SDS-CH, as shown in Figures 3.13 to 3.16, respectively. 

The higher influent concentrations to the pilot-scale contactor resulted in earlier 

breakthrough. 

Passaic River Water 

The TOC breakthrough behavior for PRW is shown in Figure 3.17. The 

sample for the RSSCT batch influent was collected on the first day of operation of 

the pilot plant column; the pilot plant influent TOC decreased for the first 100 days, 

then increased during the next 100 days. Still, the normalized breakthrough patterns 

match up extremely well. Given the 30 percent higher average TOC concentration 

in the RSSCT influent, an earlier RSSCT breakthrough was expected. For UV254 , 

shown in Figure 3.18, the pilot-scale showed earlier breakthrough. The results for 

DBF precursor breakthrough are shown in Figures 3.19 to 3.22. With the exception 

of SDS-TOX, the pilot-scale GAC contactor showed earlier breakthrough. 

Summary_________________________ 

The RSSCT can be successful in predicting full- or pilot-scale NOM 

removal and DBF control. The RSSCT predictions do not seem to be affected by the 

small amount of biodegradation that normally occurs in field-scale operation. 

However, the breakthrough behaviors discussed in this chapter illustrate the 

importance of gathering representative samples for the batch RSSCT influent. 

Influent water quality in river water sources can significantly change throughout the 

year; thus, several RSSCTs are needed, spaced to capture seasonal trends. For 

waters that are ozonated, the RSSCT should be performed on-site as quickly as 

possible after ozonation. Delaying the RSSCT because of shipping or for other 

reasons can lead to biodegradation and changes in the NOM because ozonated 

samples typically have higher levels of biodegradable fractions. If these limitations

Using the RSSCT for Field-Scale Prediction 25 

are recognized, the RSSCT can still be used to investigate operational consider 

ations and the impact of various pretreatment options under controlled conditions. 

In this study, RSSCT predictions were compared to field-scale behavior on 

a purely qualitative basis. To determine whether there is a significant difference 

between field-scale and RSSCT breakthrough behavior, however, it is desirable to 

statistically compare the data. A rigorous statistical analysis to compare RSSCT and 

field-scale data has been attempted in the past, but various statistical alternatives 

have not led to an accepted approach. As part of this project, a statistical method for 

the comparison of RSSCT and field-scale GAC breakthrough curves was developed 

and tested using the data presented in this chapter. 

The statistical method developed was based on the paired t-test. The 

procedure was applied to the four waters and range of parameters that were 

discussed qualitatively. A detailed description of the method and results is presented 

in Appendix B.

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Comparison of Breakthrough Patterns 

for Simulated Distribution System and 

Uniform Formation Condition DBFs 

In Chapter 3, SDS conditions were utilized to verify the RSSCT by com 

parisons of field-scale and RSSCT breakthrough curves for a given water. However, 

SDS conditions change from utility to utility, so results from different waters cannot 

be directly compared. To facilitate comparisons among different waters, the UFC 

chlorination test was developed. The UFC test can also be used to analyze the effects 

of pretreatment, EBCT, and other operational parameters on GAC breakthrough for 

a single water. 

The concentration of DBFs formed under SDS conditions may differ 

compared to that under UFC, because of variations in incubation time, temperature, 

pH, or chlorine dose and residual. After GAC treatment, however, the effluent DBF 

concentrations are often normalized by being divided by influent concentrations, 

thereby increasing the potential for a close match of the breakthrough patterns using 

UFC and SDS conditions. Of interest is whether normalized DBF breakthrough 

curve patterns under UFC are similar to those produced under SDS conditions. If 

there are no differences in the normalized breakthrough curves under UFC and SDS 

conditions, then studies conducted with UFC can be used by utilities to ascertain the 

adsorption breakthrough under SDS conditions. 

Experimental Conditions_________________ 

For three of the four waters used in the verification study (Chapter 3), GAC 

influent and effluent samples were chlorinated under both UFC and SDS conditions, 

allowing for comparisons of the normalized DBF breakthrough patterns observed 

under both chlorination tests. These waters were ORW, LOW, and MRW. The 

treatment conditions for these waters are described in Chapter 2. The three waters 

represent a range of water quality conditions. TOC concentrations prior to GAC 

treatment range from 1.7 to 2.7 mg/L,whileBr concentrations range from 14to 132 

ug/L. Thus, a range of DBF formation is expected. Table 4.1 compares UFC to 

water-specific SDS chlorination conditions. 

SDS incubation times were higher in all three cases, ranging from 2 to 5 

days. For both ORW and MRW, SDS incubation temperature was varied during 

breakthrough to better simulate field-scale conditions. The pH values used for the 

37


Table 4.1 UFC and SDS chlorination conditions 

Parameter 

UFC 

ORW-SDS 

LGW-SDS 

MRW-SDS 



PH 

Chlorine residual (mg CI2/L) 

1 

20.0 

8.0 

1.0 

3 

30, 23, 10 

8.0 

1.0 

2 

10 

7.3 

Dose: 2.8* 

5 

28,20 

8.4 

1.0 

* Initial dose; test was not based on a residual 

SDS tests were similar to UFC, ranging from 7.3 to 8.4. For ORW and MRW, the 

chlorine residual was the same as that required for UFC, while LGW used a chlorine 

dose-based test resulting in a different residual after the incubation period. 

Results and Discussion________________ 

For the three waters examined, the range of SDS chlorination conditions 

outlined in Table 4.1 led to differences in DBF formation in the influent to GAC as 

compared to UFC. Table 4.2 compares the TTHM, HAA6 or HAAS, TOX, and CH 

concentrations formed under both UFC and SDS conditions in the influent GAC 

samples for all three waters. 

It is evident that the SDS conditions used for ORW and MRW increased the 

formation of all DBFs analyzed as compared to UFC, because of longer incubation 

times and higher incubation temperatures. For LGW, SDS conditions resulted in 

lower DBF concentrations. Although incubation times were longer and chlorine 

residuals higher, the SDS incubation temperature was much lower than for UFC. 

The effects of incubation time, temperature, pH, and chlorine residual on DBF 

formation are discussed for ORW and SRPW in Appendix C. 

These differences in DBF yield were expected given the differences in 

chlorination between SDS conditions and UFC. After GAC treatment, weakly 

adsorbed DBF precursors show earlier breakthrough, followed by more strongly 

adsorbed precursors. It was important to determine whether the change in DBF 

precursor levels as a rsult of GAC treatment was proportionally different for UFC 

and SDS conditions. To evaluate this, a comparison of the GAC breakthrough 

patterns on a normalized basis was made for each DBF chlorinated under both types 

of conditions. If the pattern generated using UFC was representative of that using 

SDS conditions for each water, then the UFC breakthrough curves could be used to 

estimate breakthrough at different chlorination conditions. 

Ohio River Water 

Figures 4.1, 4.2, and 4.3 show the effect of chlorination conditions on the 

GAC breakthrough of TTHM, HAA6, and TOX, respectively, for ORW. CH 

concentrations were difficult to compare because most effluent samples chlorinated 

under both types of conditions were below the detection limit. The breakthrough 

patterns observed for TTHM and TOX were very similar for both UFC and SDS

Breakthrough for SDS and UFC DBFs 39 

Table 4.2 DBP formation in GAC influent under UFC and SDS conditions 

ORW LGW MRW 

Diff. Diff. Diff 

DBP UFC SDS UFC SDS UFC SDS 

TTHM (ug/L) 

HAA6 (ug/L) 

TOX (ug CI-/L) 

CH (ug/L) 

86.1 

25.5 

172 

3.9 

131 

50 

228 

11.3 

+52 

+96 

+33 

+290 

28.9 

31.6* 

132 

NA 

22.3 

18.1* 

109 

2.8 

-23 

-43 

-17 

NA 

50.2 

21.2 

169 

7.9 

85.1 

43.1 

255 

10.9 

+69 

+103 

+51 

+38 

Diff. = difference 

NA = not available 

*HAA5 

conditions. Conversely, a rapid increase in breakthrough was seen for HAA6 under 

UFC as compared to SDS conditions. The departure between these two curves may 

possibly be attributed to the fact that the HAAs were only partially quantified— 

analytical standards are available only for six of a total of nine HAA species. Those 

not analyzed in this evaluation are the mixed chloro-bromo species, which have 

been shown to represent a significant percentage of total HAA formation in waters 

containing high levels of bromide. This comparison is further confounded by the 

facts that (1) brominated DBFs typically form faster than their chlorinated analogs, 

which is relevant given that the UFC and SDS conditions represent different holding 

periods; (2) early in the breakthrough period, the bromide: DOC ratio (Br:DOC) 

and consequently bromide:chlorine (Br~:Cl 2) ratios were high, resulting in higher 

bromine substitution in DBFs; and (3) this particular water contained a relatively 

high bromide concentration (132 ug/L). These factors combine to hamper the 

comparison of normalized SDS and UFC HAA6 breakthrough curves. 

Lake Gailiard Water 

The influent DBP precursor concentrations measured for LGW were 

usually much lower than for either ORW or MRW. Still, overall breakthrough 

patterns generated by the two types of chlorination conditions compared extremely 

well (except for two points around 30 and 60 days for THM breakthrough), as shown 

in Figures 4.4,4.5, and 4.6 for TTHM, HAA5, and TOX, respectively. Is should be 

noted here that LGW has much lower bromide levels compared to ORW, and thus 

some of the confounding factors were not apparent. 

Mississippi River Water 

The effect of chlorination conditions on the GAC breakthrough patterns for 

MRW are shown in Figures 4.7,4.8,4.9, and 4.10 for TTHM, HAA6, TOX, and CH, 

respectively (except for four points on the CH breakthrough curve). Even though 

influent DBP formation was consistently much higher under SDS conditions than 

under UFC, effluent breakthrough curves, when normalized to the respective


influent values, showed very similar patterns for both types of conditions. Similar 

to LOW, MRW also had lower bromide levels compared to ORW. 

Summary_____________________________ 

This study showed that chlorination under UFC resulted in normalized 

GAC breakthrough patterns that were very similar to those generated under the sitespecific 

SDS conditions for three waters encompassing a range of water quality 

parameters. Although the SDS conditions differed from UFC, as shown by the 

differences of up to a factor of 4 in GAC influent DBF formation, the normalized 

DBF breakthrough curves generated under UFC were very similar to those gener 

ated under SDS conditions. In 9 of the 10 cases studied, including four DBFs and 

three waters, UFC and SDS normalized breakthrough patterns compared extremely 

well. This indicates that the DBF precursors remaining after GAC treatment reacted 

proportionally the same way as before GAC treatment under both types of chlori 

nation conditions. 

It should be noted here that, for waters with elevated bromide levels, the 

comparisons may not be adequate, especially if all the halogenated species of a 

particular DBF are not considered. For example, the use of six species out of a total 

of nine HAAs resulted in differences between UFC and SDS conditions for ORW, 

a source with elevated bromide levels. 

The impact of bromide concentrations on this finding is important. For 

water with relatively high bromide concentrations, the speciation of DBFs within 

a specific class of DBFs may vary depending on chlorination conditions because of 

varying Br~:Cl2 ratios between SDS conditions and UFC. The difference in HAA6 

breakthrough patterns between SDS conditios and UFC for ORW may be due in 

large part to the relatively high bromide concentrations in this water. For the other 

waters, which contained moderate to low bromide concentrations, the normalized 

breakthrough curves were more similar.

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Mississippi River water 

1.0 

0.8 - 

HAA6 C0 (ug/L) 



• UFC effluent 

0 SDS effluent 

10 20 30 


Figure 4.8 Effect of chlorination conditions on RSSCT HAA6 breakthrough for 

Mississippi River water

Breakthrough for SDS and UFC DBFs 45 

8 

o 

1.0 

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UFC influent 

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169 

255 

I 0.4 - 

o 

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0) 

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1 0.2 H 

o 

z 

0.0 

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Mississippi River water 

CD (ug/L) 






Figure 4.10 Effect of chlorination conditions on RSSCT CH breakthrough for 


Chapter 5 

NOM Characterization 

NOM is composed of a broad spectrum of organic substances with a wide 

range of chemical characteristics. Because of its heterogeneous nature, NOM 

cannot be measured directly; rather, it must be quantified using nonspecific surro 

gate parameters. This heterogeneity is also evident in the differing degrees of 

adsorbability of these compounds by GAC (Sontheimer et al. 1988). Owen et al. 

(1992) indicated that treatment can be tailored for targeted removal of NOM 

fractions. In general, the easily adsorbable (and thus easily removable) fraction is 

thought to be more nonpolar and more humic in nature than the nonadsorbable 

fraction, as well as having intermediate molecular size and specific ultraviolet (UV) 

absorbance. The nonadsorbable fraction of NOM is thought to be composed of the 

polar nonhumic NOM fraction that has a smaller molecular size and low specific U V 

absorbance (Jackson et al. 1993). Semmens and Staples (1986) showed that 70 to 

90 percent of organic compounds passing through GAC were predominantly 

hydrophilic (nonhumic) and had a small MS. Earlier work with isolated humic 

substances showed that adsorbability decreased with increasing MS because of size 

exclusion in the smaller pores (Roberts and Summers, 1982). In that work, the 

different MS fractions of the humic substances were shown to be chemically 

homogenous. For natural waters, NOM is a mixture of humic and nonhumic 

substances, and the small MS fractions may be nonhumic in nature (Koechling and 

Summers 1995). 

In addition to a characterization of NOM into DOC fractions, the DBF 

precursor content in each fraction is also of interest. Semmens and Staples (1986) 

showed that the THM precursors in raw MRW were almost evenly split between the 

hydrophobic (humic) and hydrophilic (nonhumic) fractions. Koechling and Sum 

mers (1995) and Shukairy (1994) demonstrated that the small MS fraction contained 

similar specific DBF yields as compared to the larger MS fraction when all fractions 

were adjusted to the same DOC. Dryfuse et al. (1995), however, showed lower 

specific DBF yields for the small MS compounds compared to those for the larger 

MS fractions when the DOC was not adjusted. 

Objectives________________________ 

The objectives of the work presented in this chapter are (1) to systematically 

characterize NOM in both raw and treated waters, as well as its removal by GAC, 

and (2) to relate it to DBF formation for a range of waters. The chapter is subdivided 

to discuss NOM characterization of raw water, field-scale treatment systems, and 

bench-scale treatment systems. 

47


Raw Water NOM Characterization 

Approach 

Raw water was sampled at the same time that water for the GAC influent 

was collected for ORW, LOW, MRW, PRW, SRPW, and FGW. Raw water NOM 

was characterized in terms of humic and nonhumic fractions, MS fractions, and 

biodegradability (as determined by BDOC). The unfractionated and fractionated 

waters were characterized in terms of DOC and specific UFC-TTHM yields accord 

ing to the methods outlined in Chapter 2. Seasonal water quality monitoring was 

performed for ORW and PRW. Raw water was collected four times, and samples 

were characterized in terms of humic and nonhumic fraction composition, biode 

gradability, and PY-GC-MS fractions. 

Results and Discussion 

Source Water Characteristics 

Table 5.1 summarizes the DOC fractionation and BDOC results for the six 

raw waters examined. The raw waters were more humic (53 to 75 percent) than 

nonhumic (25 to 47 percent) in nature. The relative percent composition of each 

fraction was dependent on the water source. FGW was the most humic in nature (75 

percent of the DOC was found in the humic fraction), while SRPW was the least 

humic in nature (53 percent). The intermediate MS fraction dominated the MS 

distribution, with most waters having an equal distribution between the largest and 

smallest MS fractions. The BDOC varied from below detection limit (

NOM Characterization 49 

Table 5.1 

Summary of raw water fractionation and BDOC results 

DOC (%) 

BDOC 


Sample 

date 

DOC 

(mg/L) 

Nonhumic 

Humic 

3,000 

MS 

(mg/L) 

% 

of DOC 

ORW 

1 1/4/93 

2.2 

45 

55 

19 

60 

21 

0.5 

23 

LGW 

MRW 

5/25/94 

10/30/93 

2.6 

3.7 

40 

34 

60 

66 

10 

14 

60 

76 

30 

10 

0.4 

0.9 

15 

24 

PRW 

11/16/94 

3.9 

44 

56 

16 

63 

21 

1.0 

26 

SRPW 

FGW 

8/10/94 

2/28/95 

13 

2.2 

47 

25 

53 

75 

20 

8 

65 

90 

15 

2 

BDL 

2.2 

NA 

17 

BDL = below detection limit 

NA = not applicable 

Table 5.2 


Specific TTHM yields of raw waters 

Br-:DOC 

(mg/mg) 

Specific Yield (\ig TTHM/mg DOC) 

Unfractionated 

Nonhumic 

Humic 

Br-:DOC 

(mg/mg) 

Specific Yield (ug TTHM/mg DOC) 



simpler for FGW, indicating the greater age of the FGW humic material compared 

tothatofLGW. 

Seasonal Variations 

The impact of seasonal variation on the unfractionated DOC and the humic - 

nonhumic distribution forORW is illustrated in Figure 5.1. The DOC varied from 

1.7 to 3.3 mg/L. As with the data in Table 5.1, the raw water NOM was more humic 

than nonhumic in nature for all four samples. The relative distributions of DOC did 

not change significantly at different times in the year. The humic fraction compo 

sition varied from 52 to 63 percent. Figure 5.2 shows the specific TTHM yields for 

the fractions and the unfractionated sample. The specific TTHM yields of the humic 

fraction were slightly higher than those of the nonhumic fraction in two of the four 

seasons. 

The humic-nonhumic DOC distribution as a function of seasonal variability 

is shown in Figure 5.3 for PRW. The raw water DOC varied from 2.8 to 5.4 mg/L. 

As with other raw waters, the NOM was more humic in nature, which contributed 

to the formation of more DBFs in all four samples. The humic fraction composition 

varied from 53 to 59 percent of the total DOC. The specific TTHM yields for the 

fractions are shown in Figure 5.4. The humic fraction showed higher TTHM yields 

in three of the four seasons. The seasonal BDOC values of both ORW and PRW are 

presented in Table 5.4 and show that the average BDOC was about 18 percent of the 

total DOC, with no apparent seasonal pattern. 

As shown in Table 5.5, the relative distribution of NOM fragments varied 

significantly among the sampling dates. More variations were observed for ORW 

than for PRW samples. Benzonitrile and benzoic acid, markers of industrial or 

wastewater influence, were found in all ORW samples. Also, the ORW chromatographs 

were relatively simpler, with fewer peaks—another indication of a lack of 

natural biological activity. PRW samples, although relatively more stable, showed 

a strong presence of halogen substituted fragments—another indication of indus 

trial influence. 

Table 5.4 Summary of seasonal water quality monitoring BDOC results_________ 

ORW 

Raw Raw Raw Raw 

sample 1 sample 2 sample 3 sample 4 

Sample date 11/4/93 7/29/94 3/24/95 8/2/95 

BDOC (mg/L) 0.5 0.4 0.2 0.5 

BDOC (% of DOC) 23 23 9 17 

PRW 

Sample date 11/16/94 2/16/95 5/16/95 8/2/95 

BDOC (mg/L) 1.0 0.6 0.4 1.0 

BDOC (% Of DOC) 22 17 15 18


Table 5.5 PY-GC-MS classification of seasonal samples 

Percent based upon peak height 


Sample 

date 

DOC 

(mg/L) 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

ORW 

1 1/4/93 

7/29/94 

3/24/95 

8/2/95 

2.2 

1.7 

2.2 

2.9 

34.5 

24.3 . 

24.6 

5.3 

15.8 

40.3 

51.5 

67.6 

35.9 

19.5 

9.0 

7.1 

13.9 

4.0 

6.0 

17.3 

BDL 

12.0 

8.9 

2.6 

PRW 

11/16/94 

2/16/95 

5/16/95 

8/2/95 

4.5 

3.5 

2.7 

5.5 

23.0 

24.3 

39.0 

34.3 

3.9 

4.0 

6.4 

16.6 

1.2 

6.6 

16.5 

12.8 

67.0 

61.5 

33.1 

31.2 

4.3 

3.7 

4.9 

5.0 


Field-Scale NOM Characterization 

Approach 

GAC influent and effluent were sampled from pilot- or full-scale GAC 

contactors for ORW, LOW, MRW, and PRW. NOM was characterized at each site 

for humic and nonhumic fractions and biodegradability. 



The GAC influent concentrations and the humic-nonhumic fractionation 

results for the pilot or full scale are shown in Table 5.6. In general, the GAC influent 

was more nonhumic than humic in nature. When these values are compared to the 

raw water results in Table 5.1 and Figures 5.1 and 5.3, it is evident that the 

pretreatment process at all four locations preferentially removed the humic fraction. 

This shift can be attributed to removal by coagulation for all waters except LGW, 

where the decrease in the humic fraction was most likely due to ozonation. The 

results presented in Table 5.6 also illustrate the variability found in field-scale GAC 

systems. The GAC influent DOC varied from water to water. For ORW and PRW, 

DOC varied over the run time of the pilot- or full-scale GAC contactor. The GAC 

influent DOC for ORW varied from 1.3 to 2.6 mg/L, and the nonhumic fraction 

ranged from 56 to 67 percent of the total DOC. The GAC influent for LGW remained 

very constant over the course of the run, as did the humic and nonhumic fractions. 

For MRW, the GAC influent was sampled only once and yielded a 60 percent 

nonhumic fraction (Figure 3.11 [p. 31] showed that the influent concentration did 

not vary much over the course of the run). PRW exhibited significant variability in 

DOC concentration in the GAC influent, from 1.8 to 4.3 mg/L, although the 

percentage of DOC that was nonhumic in nature varied from only 51 to 59 percent.


Table 5.6 Summary of field-scale GAC influent humic-nonhumic fractionation results 

for ORW, LGW, MRW, and PRW 

Nonhumic fraction 

Humic fraction 


Sample 

date 

Day of 

run 

DOC 

(mg/L) 

DOC 

(mg/L) 

% of total 

DOC 

DOC 

(mg/L) 

% of total 

DOC 

ORW 

9/3/93 

10/6/93 

64 

97 

2.6 

2.3 

1.4 

1.4 

56 

64 

1.2 

0.9 

44 

36 

1(3/29/93 

120 

2.3 

1.6 

67 

0.7 

33 

1/27/94 

Average 

210 

1.3 

0.9 

66 

63 

0.4 

36 

37 

LGW 

4/15/94 

4/1 9/94 

29 

43 

1.5 

1.5 

0.8 

0.8 

53 

53 

0.7 

0.7 

47 

47 

5/3/94 

57 

1.5 

0.8 

53 

0.7 

47 

5/17/94 

71 

1.5 

0.8 

53 

0.7 

47 

6/27/94 

113 

1.4 

0.8 

57 

0.6 

43 

Average 

54 

46 

MRW 

12/10/93 

49 

3.0 

1.8 

60 

1.2 

40 

PRW 

11/16/94 

1 

3.5 

2.0 

57 

1.5 

43 

1/21/95 

66 

1.8 

1.1 

59 

0.7 

41 

1/31/95 

76 

2.0 

1.0 

51 

1.0 

49 

2/27/95 

103 

2.5 

1.5 

59 

1.0 

41 

4/26/95 

162 

3.0 

1.5 

51 

1.5 

49 

5/24/95 

190 

4.3 

2.5 

59 

1.8 

41 

8/2/95 

Average 

239 

3.1 

1.8 

57 

56 

1.3 

43 

44 

The effect of pretreatment and GAC treatment on the NOM was very similar 

for all four waters. The humic-nonhumic fractionation results for the raw water, 

GAC influent, and effluents from the pilot- and full-scale contactors for ORW, 

LGW, MRW, and PRW are shown in Figures 5.5,5.6,5.7, and 5.8, respectively. The 

bar charts represent the raw water and GAC influent. The difference in the results 

between these two sets of bars represents the impact of full-scale treatment systems 

currently in place at the four sites. The effect of pretreatment on the DOC and the 

humic-nonhumic distribution is shown on these figures. DOC removal by the 

various pretreatment processes ranged from 15 to 45 percent and resulted in a major 

shift from humic to nonhumic material. As evidenced by the first GAC effluent 

samples, the nonadsorbable fractions for ORW, LGW, MRW, and PRW were 

composed nearly entirely of the nonhumic fraction; the humic fraction was virtually 

completely removed. With time, the nonhumic fraction broke through rapidly, 

though the humic fraction continued to be better removed. The nonhumic fraction 

breakthrough reached a plateau level after 40,60,30, and 160 days for ORW, LGW,


MRW, and PRW, respectively. Further increases in effluent DOC concentrations 

were a result of the humic fraction breaking through. The humic DOC fraction was 

still almost totally removed (below 0.1 mg/L) as of 60,50,4, and 80 days for ORW, 

LOW, MRW, and PRW, respectively. It is interesting to note that GAC treatment 

was in a filter adsorber mode for MRW, as compared to postfilter contactors for 

ORW, LOW, and PRW. The associated lower EBCT in the MRW filter adsorber 

may be related to MRW's significantly shorter period before humic breakthrough 

occurred compared to the other GAC applications. 

Biodegradability 

NOM was also characterized in terms of biodegradability (as shown by 

BDOC levels) for all four waters. Figure 5.9 shows the effect of GAC on BDOC for 

MRW. The BDOC of the GAC influent varied from 0.5 to 1.3 mg/L, while the 

influent DOC varied from 2.5 to 4.5 mg/L for this preozonated water. BDOC was 

initially well removed by GAC and began to break through after 14 days of 

operation. The measurement of relatively early BDOC breakthrough for MRW may 

again be attributed to the higher influent BDOC or the lower EBCT in the filter 

adsorber configuration, as well as to the fact that the water was ozonated prior to the 

GAC contractor. It was not possible to characterize the breakthrough behavior of 

BDOC for ORW, LGW, and PRW. The BDOC values for ORW and PRW are shown 

in Table 5.7. For ORW the BDOC of the influent varied from 0.1 to 0.8 mg/L; 

however, it was often below the method detection limit of 0.1 mg/L. The effluent 

BDOC was below the detection limit for all cases. Similar results were observed for 

PRW. The influent BDOC varied from 0.4 to 0.6 mg/L, and the effluent BDOC was 

below the detection limit for six samples. For one sample, an effluent BDOC of 0.3 

mg/L was detected. No BDOC was detected for LGW in either the influent or the 

GAC effluent. 

Table 5.7 Summary of ORW and PRW field-scale BDOC results 


Sample day 

Day of run 

GAC influent BDOC 

(mg/L) 

GAC effluent BDOC 

(mg/L) 

ORW 

8/9/93 

9/3/93 

10/6/93 

10/29/93 

1/27/94 

39 

64 

97 

120 

210 

0.80 

0.15 

BDL 

BDL 

0.12 

BDL 

BDL 

BDL 

BDL 

BDL 

PRW 

11/16/94 

1 

0.6 

BDL 

1/21/95 

66 

BDL 

BDL 

1/31/95 

76 

BDL 

BDL 

2/27/95 

103 

BDL 

BDL 

4/26/95 

162 

BDL 

BDL 

5/24/94 

8/2/95 

190 

239 

0.4 

0.4 

BDL 

0.3 

BDL = below detection limit of 0.1 mg/L


Bench-Scale NOM Characterization 

Approach 

NOM was characterized at bench scale with respect to humic-nonhumic 

fractionation, MS fractionation, biodegradability, and PY-GC-MS fractionation for 

ORW, LOW, MRW, PRW, SRPW, and FGW at four defined points on the 

breakthrough curve, as illustrated in Figure 5.10: 

1. GAC influent 

2. Point A, the nonadsorbable fraction 

3. Point B, intermediate (20-35 percent) breakthrough 

4. Point C, advanced (>50 percent) breakthrough 

These four points were chosen to represent critical points of a breakthrough curve. 

Comparison of the NOM compositions at these different points facilitates the 

assessment of NOM adsorbability.The raw water and GAC influent samples were 

taken from the field-scale plant at the same time. The DOC and the specific TTHM 

yields of the fractions were determined according to the methodology discussed in 

Chapter 2. 



Figure 5.11 shows the DOC of the unfractionated sample and the nonhumic 

and humic fractions of ORW. The bars represent the raw water and the GAC 

influent, and the line graph indicates the breakthrough of the fractions plotted versus 

scaled operation time. As shown in the previous section of this chapter, conven 

tional pretreatment predominantly removed the humic fraction from the raw ORW, 

yielding a GAC influent that was 52 percent nonhumic and 48 percent humic in 

nature. Initially, the humic fraction was completely removed by GAC and the 

nonadsorbable fraction (point A) was entirely nonhumic. The nonhumic fraction 

broke through rapidly and completely, while the humic fraction was better removed 

and exhibited slower breakthrough. At point C, which characterizes the later portion 

of the breakthrough curve, the humic fraction was still being removed by more than 

50 percent. 

The specific TTHM yields of the unfractionated sample and the nonhumic 

and humic fractions for the raw water, GAC influent, and the three effluent samples 

for ORW are shown in Figure 5.1 2. The fractions and the unfractionated portion of 

a given sample were chlorinated at constant Br~:DOC ratio to allow for comparison 

among them. However, the five separate samples cannot be compared to each other 

since the Br~:DOC ratio shifts, as indicated on the graph. The specific TTHM yields 

for the nonhumic and humic fractions were similar to each other and to the 

unfractionated sample for the raw water and at the GAC influent. It is difficult to 

compare the three effluent samples because the Br~ :DOC ratios were different. The 

specific TTHM yields for raw ORW were slightly higher than those for the GAC 

influent; these values can be compared since the chlorination conditions (i.e., 

Br:DOC ratios) were similar.


Figures 5.13 and 5.14 show the DOC fractionation and specific TTHM 

yields for humic-nonhumic fractions of LGW. Similar graphs for MRW, PRW, 

SRPW, and FGW are shown in Figures 5.15 through 5.22. 

The raw water DOC for all surface water cases was more humic than 

nonhumic in nature, ranging from 52 percent humic for SRPW to 63 percent for 

MRW. The raw water DOC from FGW (a groundwater) had a much higher humic 

content, 75 percent. In general, the pretreatment process (summarized in Table 2.2, 

[p. 7] for each water) resulted in a reduction of DOC, which was mainly due to a 

removal or alteration (in the case of ozonation) of the hurnic fraction. In the case of 

FGW, the removal was comparable for both fractions. This is summarized in Table 

5.8. For all waters, the nonadsorbable fraction was nearly entirely nonhumic in 

nature, indicating that the humic fraction was very well removed by GAC. With time 

the nonhumic fraction broke through rapidly, while the humic fraction broke 

through more slowly. Even for the last effluent sample, the effluent NOM was 

predominantly nonhumic in nature. These results are consistent with the field-scale 

behavior shown in the previous section (Figures 5.5 to 5.8). 

In terms of the specific TTHM yields (Figures 5.12 to 5.22), the humic 

fraction of five of the six waters had a slightly higher specific TTHM yield than did 

the nonhumic fraction for the raw and GAC influent waters. There were no apparent 

differences in DBP formation between the humic and nonhumic fractions in the 

GAC effluent at the advanced breakthrough points, although the results were 

confounded by the differing Br:DOC ratios. At the initial and intermediate 

breakthrough points, the humic fraction was too small to determine specific TTHM 

yields. 

Molecular Size Distribution 

Figure 5.23 shows the ORW DOC distribution of the 3,000 MS fractions for raw water, GAC influent, and three effluent 

samples. The dominant fraction in the raw water was the intermediate MS fraction, 

with the small and large MS fractions contributing approximately equal amounts of 

DOC. After GAC treatment, the nonadsorbable fraction was composed entirely of 


the latter portion of the breakthrough curve, the 500-3,000 MS fraction was still 

being removed by more than 50 percent. 

After conventional pretreatment, the >3,000 MS fraction was removed (or 

altered) appreciably in most cases and the other fractions were not significantly 

impacted. For all waters, the MS distribution in the GAC influent is summarized in 

Table 5.8. 

For ORW, the specific TTHM yields of the unfractionated sample and the 


in the GAC influent. The fraction was initially well removed and rapidly broke 

through to the same concentration of that in the GAC influent. 

For LGW, MRW, PRW, and SRPW the GAC effluent specific TTHM 

yields of the

58 Removal ofDBP Precursors by GAC Adsorption 

Table 5.10 Summary of BDOC results for PRW at bench scale 

Sample Scaled operation time (days) BDOC (mg/L) 

GAC influent 

Nonadsorbable 

Intermediate breakthrough 

Advanced breakthrough 

NA 

20 

76 

190 

0.4 

BDL 

BDL 

0.3 

BDL = below detection limit of 0.1 mg/L 


Table 5.11 PY-GC-MS classification of Ohio River water 


Stage 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

Raw 


Nonadsorbable 



34.5 

30.2 

76.5 

52.5 

47.8 

15.8 

1.6 

BDL 

1.0 

BDL 

35.9 

4.8 

3.5 

9.0 

18.3 

13.9 

52.0 

15.5 

36.6 

33.9 

BDL 

11.5 

4.4 

0.8 

BDL 


Table 5.12 PY-GC-MS classification of Lake Gaillard water 


Stage 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

Raw 


Nonadsorbabie 



16.3 

12.5 

3.0 

2.1 

11.3 

70.0 

77.7 

85.4 

91.2 

73.2 

7.7 

2.0 

5.3 

0.5 

5.3 

BDL 

BDL 

6.3 

1.9 

1.6 

5.8 

NA 

NA 

4.2 

8.4 

BDL = below detection level 


remained the dominant fragment at the intermediate and advanced breakthrough 

samples as well. In the first two breakthrough samples, the aromatic character was 

in the range of 85-90 percent of the total peak height. Typically, very simple 

fragmentation patterns were obtained at these early breakthrough points. In the final 

breakthrough sample, the fingerprint, while still predominantly aromatic (73 

percent), began to show the appearance of other fragments (i.e., acetic acid, 

propanoic acid, methyl furfural, and so on). A rather distinct feature of this water


was that typical aliphatic, possibly polysaccharide-derived fragments appeared to 

be correlated with humic material. 

Based upon the results shown in Table 5.13, it is evident that the relative 

aliphatic nature of MRW's organic matrix remained unchanged through pretreatment 

and GAC adsorption. The aromatic fraction and nitrogen-containing frag 

ments were, however, preferentially removed by GAC adsorption. The occurrence 

of halogenated fragments increased after GAC adsorption. This could also be 

attributed to the removal of other fragments, which made the chromatograms more 

susceptible to detection of smaller fragments that were earlier masked by the large 

presence of other peaks. Inspection of the PY-GC-MS fingerprints illustrates that 

acetic acid was the major peak at each breakthrough point and that other major peaks 

were formic acid, benzaldehyde, and benzonitrile or benzoic acid (suspected 

markers of industrial discharge). It is suspected that one source of the halosubstituted 

fragments was chlorinated wastewater discharge; a strong presence of 

such fragments may be observed at the breakthrough stages because the parent 

halogenated organic material has higher molecular weight and is not well adsorbed 

to GAC because of micropore exclusion. 

The general chemical nature of PRW was not significantly altered by 

pretreatment prior to GAC (Table 5.14). The predominant identified pyrolysis 

fragments after GAC adsorption were acetic acid and only various halogenated 

fragments. The chemical signature of NOM at the second GAC breakthrough point 

was predominantly aliphatic and dominated by a strong acetic acid peak. Although 

Table 5.13 PY-GC-MS classification of Mississippi River water 


Stage Aliphatic Aromatic 

Nitrogen 

containing 

Halogen 

substituted Unknown 

Raw 


Nonadsorbable 



37.7 

38.0 

39.0 

28.0 

33.0 

13.4 

12.6 

7.0 

3.03 

5.7 

40.7 

42.5 

6.3 

16.0 

21.0 

— 

— 

35.6 

18.8 

30.6 

8.3 

7 

12 

4 

9 

Table 5.14 PY-GC-MS classification of Passaic River water 


Stage Aliphatic Aromatic 

Nitrogen 

containing 

Halogen 

substituted Unknown 

Raw 23.0 

GAC influent 37.4 

Nonadsorbable 10.4 

Intermediate breakthrough 54.8 

Advanced breakthrough 17.2 

3.9 

5.5 

12.0 

4.0 

1.6 

1.6 

5.5 

67.0 

52.2 

13.3 

43.7 

58.4 

4.3 

6.4 

74.7 

13.4


much weaker relative to acetic acid, the other three predominant peaks (in descend 

ing order) were trichlorobenzene, tetrachlorobenzene, and formic acid. At complete 

breakthrough, the chemical character of the organic matrix was very similar to that 

of the raw and GAC influent water. The halo-substituted signature was dominant. 

The strongest peak was for dichloroacetonitrile; the other predominant peaks (all 

roughly comparable) were for acetic acid, trichlorobenzene, tetrachlorobenzene, 

and pentachlorobenzene. 

The pyrolysis fingerprint of GAC influent water for SRPW (Table 5.15) 

showed some change in the characteristics of the aliphatic signature. While the 

chemical nature of this water remained predominantly aliphatic, this signature was 

simplified and composed primarily of a strong acetic acid peak and a somewhat 

smaller peak of propanoic acid. Whereas in the raw water strong peaks of substituted 

furfurals and furans were detected, these peaks were diminished relative to acetic 

acid. The slight increase in percent aromaticity appeared to be related to a lack of 

removal of aromatic parent compounds compared to aliphatic material. There were 

no halogenated fragments detected in the GAC effluent. The nonadsorbable NOM 

could not be characterized by PY-GC-MS. The only peaks detected in the analysis 

of the second GAC breakthrough point were aliphatic fragments. Among these, 

acetic acid was the major peak, and a variety of carboxylic acid fragments were 

detected as minorpeaks. The chemical signature at complete breakthrough was very 

similar to the GAC influent, with a major acetic acid peak, a strong peak of pro 

panoic acid, and smaller furfural and formic acid fragments. 

The chemical classification of the PY-GC-MS data for FGW was essen 

tially unaltered by treatment, as shown in Table 5.16. Close to 200 fragments 

consisting primarily of substituted benzenes, naphthalenes, and phenols were 

identified in the treated water. In addition to a strong peak of phenol, major peaks 

of/?- and m-cresol, C2-phenol, and methyl naphthalene were observed. The pattern 

differences between the raw and treated water suggest that the organic material 

removed in coagulation and softening was resolved into a single, strong phenol 

fragment by pyrolysis, and with its removal more of the details of the matrix became 

visible. The chemical signature of the nonadsorbable NOM was dramatically 

different than either the raw or conventionally treated waters. As shown in Table 

5.16, the organic quality of the nonadsorbable fraction was predominantly aliphatic 

and was characterized by a very strong peak of acetic acid. The second GAC 

breakthrough sample was also predominantly aliphatic, and its pyrolysis finger 

print was composed primarily of acetic acid and formic acid. At complete break 

through, the pyrolysis fingerprint was very similar to that of the influent. 

Summary________________________ 

Samples of raw ORW, MRW, PRW, and SRPW showed very similar NOM 

fractionation results. These four waters were surface water. With respect to these 

four waters, LGW showed a similar humic and nonhumic DOC distribution, but the 

MS distribution showed much larger intermediate MS fractions. FGW showed the 

largest percentage of DOC in the intermediate MS range, and 75 percent of the 

DOC was found in the humic fraction. In general, specific TTHM yields of the raw 

water humic fractions and the


Table 5.15 PY-GC-MS classification of Salt River Project water 

Stage 

Nitrogen Halogen 

Aliphatic Aromatic containing substituted 

Unknown 

Raw 


Nonadsorbable 



57.9 

68.6 

— 

100 

93.4 

9.1 

18.0 

— 

— 

0.6 

9.2 

5.1 

— 

— 

2.2 

13.2 

1.1 

— 

— 

— 

10.5 

7.2 

— 

— 

3.9 

Table 5.16 PY-GC-MS classification of Florida groundwater 

Stage 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

Raw 


Nonadsorbable 



1.4 

1.7 

80.0 

64.4 

1.5 

97.0 

93.3 

— 

17.9 

93.5 

— 

1.7 

— 

17.7 

1.4 

— 

2.4 

20.0 

— 

0.2 

1.6 

0.9 

— 

— 

3.4 

For the field-scale analyses, the GAC influent (after pretreatment) was 

slightly more nonhumic than humic in nature, and the largest percentage of the DOC 

was found in the intermediate MS range. The influent DOC concentrations in two 

of the four waters tested in the field scale changed during the GAC runs; however, 

the NOM composition remained more nonhumic than humic. The humic fraction 

was initially well removed by GAC for all four waters (ORW, LGW, MRW, and 

PRW), and the nonadsorbable fraction was nonhumic in nature. The nonhumic 

fraction broke through rapidly, while the humic fraction exhibited slower break 

through behavior. The intermediate MS range was best removed by GAC, while the 


Table 5.17 Average DOC distribution of river waters with breakthrough: Nonhumic and 

humic fractions 

Nonhumic DOC (%) 

Humic DOC (%) 

Average 

Standard deviation 

Average 

Standard deviation 


Nonadsorbable 



55 

96 

87 

74 

4.8 

8.0 

15 

4 

45 

4 

13 

26 

4.8 

8.0 

15 

4 

Table 5.18 Average DOC distribution of river 

fractions 

waters with breakthrough: Molecular size 


3.5 

3.0 - 




c 2.0 

.o 

i= 1.5 -\ 

o> 

o 

3 1.0 -\ 

o 

Q 0.5 -\ 

0.0 

11/93 7/94 3/95 8/95 

Figure 5.1 Seasonal variation of nonhumic and humic fractions for Ohio River raw 

water 




11/93 7/94 3/95 8/95 

Figure 5.2 Specific TTHM yields of nonhumic and humic fractions for Ohio River 

seasonal raw water samples



__ Nonhumic fraction 

I I Humic fraction 

11/94 2/95 5/95 8/95 

Figure 5.3 Seasonal variation of nonhumic and humic fractions for Passaic River raw 

water 




11/94 2/95 5/95 8/95 

Figure 5.4 Specific TTHM yields of nonhumic and humic fractions for Passaic River 

seasonal raw water samples


0.0 

Average Average 0 50 100 150 200 250 

raw GAC 

influent Operation time (days) 

Figure 5.5 Full-scale nonhumic and humic fraction breakthrough for Ohio River 

water 

0.0 

Average Average 


influent 

Operation time (days) 

150 

Figure 5.6 Full-scale nonhumic and humic fraction breakthrough for Lake Gaillard 

water




influent 

10 20 30 40 


50 

Figure 5.7 Full-scale nonhumic and humic fraction breakthrough for Mississippi River 

water 



influent 

50 100 150 200 250 


Figure 5.8 Pilot-scale nonhumic and humic fraction breakthrough for Passaic River 

water


• GAC Influent 

0 GAC Effluent 

o 

50%) breakthrough 

B. Intermediate (20-36%) breakthrough 

A. Nonadsorbable fraction 

40 80 


Figure 5.10 Example curve showing sampling points for NOM characterization at bench 

scale


2.5 




o 1.5 H 

I ,.


Raw GAC 

Influent 

50 100 150 


Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. 

Figure 5.13 Bench-scale nonhumic and humic fraction breakthrough for Lake Gaillard 

water 





Influent 



Figure 5.14 Bench-scale specific TTHM yields of humic and nonhumic fractions for 

Lake Gaillard water


5.5 

5.0 

^ 4.5 

a. 4.0 

^ 3.5 

I 3.0 

§ 2 ' 5 

§ 2.0 

g 1.5 

1 • • ' i • • ' ' i • ' 




0.5 

0.0 


Influent 

10 20 30 40 50 



Figure 5.15 Bench-scale nonhumic and humic fraction breakthrough for Mississippi 

River water 





Influent 


Figure 5.16 Bench-scale specific TTHM yields of humic and nonhumic fractions for 




Influent 

0 50 100 150 200 



Figure 5.17 Bench-scale nonhumic and humic fraction breakthrough for Passaic River 

water 

60 

o 

8 

o> 

50- 

40- 

69 

211 




54 

30 - 

Br":DOC = 62 

(ng/mg) 

•I 20 

8. 10 

V) 


Influent 



Figure 5.18 Bench-scale specific TTHM yields of nonhumic and humic fractions for 

Passaic River water



0 Nonhumic fraction 

A Humic fraction 


Influent 

0 50 100 150 



Figure 5.19 Bench-scale nonhumic and humic fraction breakthrough for Salt River 

Project water 




Raw GAC A 

Influent 



Figure 5.20 Bench-scale specific TTHM yields of nonhumic and humic fractions for Salt 

River Project water


14 

12 - 

5 

!> 10 - 


° Nonhumic fraction 


I 6 ~ 

2 - 

0 


Influent 

10 20 


30 


Figure 5.21 Bench-scale nonhumic and humic fraction breakthrough for Florida 

groundwater 

60 



50- 


o> 

I 

§ 

40 - 

30 - 

2 

•I 20 H 

i 

8. 10 4 

CO 

Br":DOC = 21 

(ng/mg) 

102 

131 

130 


Influent 



Figure 5.22 Bench-scale specific TTHM yields of nonhumic and humic fractions for 

Florida groundwater



< 500 MS fraction 

500-3000 MS fraction 

> 3000 MS fraction 

0.0 


Influent 

50 100 150 



Figure 5.23 Molecular size fraction breakthrough for Ohio River water 

O 

g at 

X 

O) 

TJ 

0) 

& 

CO 

100 -r 

90 - 

80 - 

70 - 

60 - 

50 - 

40 - 

30 - 

20 - 

10 - 

Br":DOC = 257 

(H9/mg) 

452 

945 




585 

0 


Influent 


Figure 5.24 Specific TTHM yields of MS fractions for Ohio River water

I 

S-t 

f 5" 

3! s 

< C » ?! 

? I 

2 ® 32. 

> 

3 > 

in p ro 

en P3 

KJ g a> 

ro o) 

05 t a 

Specific yield (ug TTHM/mg DOC) 

01 

g 

^ 

* 2 

O_ CD 

o 

§. 






Raw GAC 0 10 20 30 40 

Influent Scaled operation time (days) 

50 


Figure 5.27 Molecular size fraction breakthrough for Mississippi River water 

80 

70 - Br:DOC = 141 

(Hg/mg) 




106 


Influent 

B 


Figure 5.28 Specific TTHM yields of MS fractions for Mississippi River water


4.0 

3.5 - 

§ 3.0 - 





c 2.5 H 

* 2.0 - 

§ 1 -5 ' 

o 

O 1.0 1 

0.5 - 

0.0 


Influent 

50 100 150 


200 


Figure 5.29 Molecular size fraction breakthrough for Passaic River water 





Influent 



Figure 5.30 Specific TTHM yields of MS fractions for Passaic River water


Raw GAC 0 50 100 150 

Influent Scaled operation time (days) 


Figure 5.31 Molecular size fraction breakthrough for Salt River Project water 

o 

80 




BrDOC = 196 

(Hg/mg) —i 200 

445 

Raw 


Influent 



Figure 5.32 Specific TTHM yields of MS fractions for Salt River Project water






I 

8 

8 

8 

o 

5 - 

Raw GAG 

Influent 

10 20 


30 

Note: ft,, B, and C denote initial, intermediate, and advanced breakthrough points. 

Figure 5.33 Molecular size fraction breakthrough for Florida groundwater 

120 

8 i°o 

Q 

Bf:DOC = 240 

(ng/mg) -^ 358 




493 

9B5 

NA 


Influent 

B 



Figure 5.34 Specific TTHM yields of MS fractions for Florida groundwater


o 

a> 

100 

80 - 

A unfractionated (XAD-8) 

o nonhumic 

a humic 

60 - 

o> 

40 - 

£ 

(0 

20- 

0 4 

• 

0 ° o • 

• unfractionated 

• < 500 MS 

• < 3000 MS 

100 

200 300 

BrDOC 

400 500 

Figure 5.35 Effect of BR~:DOC on DOC fractions for GAC influent for six waters

Chapter 6___________ 

Design and Operational 

Considerations 

The design, operation, and performance of a GAC contactor depends on 

GAC type, the water to be treated, and the mode of contactor operation. The 

objective of process optimization is to meet effluent criteria at the lowest cost. The 

most important design variables are the EBCT and bed operation (Sontheimer et al. 

1988). Consequently, optimizing GAC performance involves maximizing GAC bed 

lifetime while minimizing the size of the contactor. 

The EBCT dictates the contactor size and is a function of the filter type and 

the water to be treated. Filter adsorbers typically have lower EBCTs and require 

more frequent backwashing to reduce head loss buildup than do GAC contactors 

operated in the postfilter adsorber mode. For a given water, the effluent criteria can 

be met by (1) a short EBCT (low capital cost) and frequent replacement or 

regeneration of GAC (high operations and maintenance [O&M] costs) or (2) a long 

EBCT (high capital cost) and a less frequent replacement or regeneration of GAC 

(low O&M costs). For utilities where GAC is a viable treatment option, more than 

one contactor is usually operated. Depending on the treatment goal, the operation 

of the GAC adsorbers can occur either in series or in parallel (Sontheimer et al. 

1988). When very high removals are needed (e.g., greater than 90 percent), series 

operation is often best because it maximizes the use of the carbon in the first 

contactor in the series. An advantage of parallel operation is that the effluents of the 

contactors can be blended, which extends the life of a given contactor (Roberts and 

Summers 1982). 

Evaluation of various process configurations can lead to optimum GAC 

performance for a given plant capacity. To determine the optimum process configu 

ration, representative process performance data need to be generated. Although 

pilot-scale tests can be run, these are costly and time-consuming. A viable alterna 

tive is the RSSCT. In Chapter 3, the use of the RSSCT and its verification with fulland 

pilot-scale data for four waters were presented. The investigation of the impact 

of design and operational considerations using the RSSCT is presented in this 

chapter. The effect of EBCT on adsorption behavior, the impact of blending 

effluents from multiple contactors in parallel, and the effect of backwashing 

on NOM adsorption are discussed. The cost considerations are presented in Chap 

ter 12. 

81


Impact of EBCT on Adsorption Behavior 

The EBCT of a GAC contactor is a primary design factor; it determines the 

bed depth at a given hydraulic loading rate. The optimum selection of the EBCT of 

a GAC contactor is an important parameter in determining the effectiveness of GAC 

adsorption. Hong (1995) used the pore-surface diffusion model (Sontheimer, et al. 

1988) to simulate the breakthrough behavior for ORW. Based on the parameters 

from this simulation, the breakthrough behavior for ORW was simulated for EBCTs 

of 10, 15, and 20 minutes. 

As illustrated by the model simulations in Figure 6.1, increasing the EBCT 

increases the time it takes for the effluent to reach a given TOC concentration; thus, 

it decreases the reactivation frequency. However, it will also increase the bed depth 

and therefore the capital cost of the contactor. The increase in the EBCT may not 

be directly proportional to the increase in run time because the shape of the 

adsorption front may become "sharper" with longer EBCTs (Sontheimer et al. 

1988). This indirect proportionality is illustrated by the model simulations in 

Figures 6.1 and 6.2 and is attributable to the adsorption equilibrium relationship and 

mass transfer resistance. In Figure 6.2, the operation time is normalized by the 

EBCT to yield throughput measured in bed volumes (BV), where BV = operation 

time/EBCT. At a TOC effluent of 1.6 mg/L, there is very little difference in the 

throughput of the three different EBCTs; the bed life is directly proportional to the 

EBCT. One can also see this in Figure 6.1 by comparing the ratios of the operation 

times for this effluent criterion to the ratios of the respective EBCTs. At a TOC 

effluent of 0.8 mg/L, however, there is a 10 to 15 percent difference in the 

throughputs, with the shorter EBCTs yielding faster breakthroughs. The effect of 

EBCT on NOM adsorption has not been extensively evaluated. Sontheimer et al. 

(1988) showed that for an ozonated water with a high amount of biodegradable 

NOM, more bioactivity occurs in columns with higher EBCT, which in turn 

increases the overall removal of TOC. 

EBCTs between 10 and 20 minutes are representative of GAC contactors 

used for NOM control in previous studies. An EBCT of 10 minutes represents the 

upper range for a filter adsorber and the lower range for a postfilter adsorber. 

Increases in EBCT beyond 20 minutes for a single contactor do not increase the run 

time significantly (Sontheimer et al. 1988). 

Objectives and Approach 

The objectives of the work presented in this section were to evaluate the 

effect of EBCT on NOM adsorption for a range of water sources. Four convention 

ally treated waters were evaluated: ORW, SRPW, PRW, and FGW. Each water was 

evaluated at EBCTs of 10, 15, and 20 minutes. Conventionally treated water was 

used, and the RSSCT operation followed the same protocol outlined for the 

verification RSSCTs. 

The RSSCTs were monitored for NOM removal by analyzing for TOC and 

UV254 . DBF formation was assessed by UFC-TOX, -TTHM, -HAA6, and -CH. 

The breakthrough behaviors with respect to optimization parameters can be 

compared based on effluent criteria or water quality goals. Ideally, these treatment 

goals should reflect current or proposed future regulatory agendas. However, only

Design and Operational Considerations 83 

two of the DBFs have current or proposed MCLs, and no regulatory criteria have 

been set for TOC or UV254 . 

Proposed TTHM and HAA5 MCLs for Stages 1 and 2 of the D-DBP Rule 

were used: 80 and 40 ug/L for TTHM and 60 and 30 ug/L for HAAS. (Although the 

authors had HAA6 data, HAA5 data were used in these calculations.) For the other 

two DBFs, no regulatory basis exists. For TOX, an effluent criterion of 70 ug/L was 

chosen, because this would allow comparison for all waters. The breakthrough 

behaviors of CH were not compared, since in most cases the effluent concentrations 

were extremely low. 

Treatment goals for TOC and UV254 were chosen somewhat pragmatically. 

Enhanced coagulation is not required to remove DBF precursors if TOC concentra 

tion is


Table 6.1 Summary of bed lives for different effluent criteria_______ 

Run time until criterion is met (days) 

Source 

EBCT 

TOC TOX TTHM HAA6 

1.6 mg/L 0.8 mg/L 0.01/cm 70 ug CI-/L 64 ug/L 32 ug/L 48 ug/L 24 ug/L 

ORW 

10 

15 

20 

— 

— 

— 

66 

80 

139 

62 

117 

131 

126 

— 

— 

— 

— 

— 

64 

87 

146 

— — 

— — 

— — 

PRW 

10 

15 

20 

58 

128 

135 

35 

62 

70 

33 

65 

69 

38 

89 

153 

113 

— 

208 

50 

100 

90 

— 42 

— 112 

— 117 

SRPW 

10 

15 

20 

— 

— 

— 

42 

83 

103 

47 

85 

128 

70 

120 

162 

— 

— 

— 

56 

103 

132 

_ _ 

— — 

— — 

FGW 

10 

3.5 

— 

2 

4.5 

8.5 

8 

14 12 

15 

9.0 

4 

6 

9.5 

12 

12 

21 13 

20 

12 

8 

9 

10 

13 

19 

28 21 

— indicates effluent did not reach criterion 

systematically affect the normalized breakthrough of TTHM and HAA6 precursors 

for ORW. 

Figure 6.9 shows the effect of EBCT on effluent TOC concentrations for 

PRW. For an increase in EBCT from 10 to 20 minutes, the run time to a TOC 

breakthrough of 1.6 mg/L increased from 58 to 135 scaled operation days, which 

represents an increase in run time of 133 percent. The run time to a TOC 

breakthrough of 0.8 mg/L increased from 35 to 70 scaled operation days, which 

represents an increase in run time of 100 percent. The 15- and 20-minute-EBCT 

columns showed very similar behavior. When normalized by EBCT, the NOM 

adsorption behavior was found to be the same for all three EBCTs for PRW, as 

shown in Figure 6.10. All three EBCTs had the same impact on normalized DBP 

precursor removal for PRW, as illustrated for UFC-TTHM in Figure 6.11. 

Figure 6.12 shows the effluent TOC concentration as a function of scaled 

operation time for SRPW. As expected, the 10-minute-EBCT contactor broke 

through earliest in time, and the 20-minute contactor broke through last. For an 

increase in EBCT from 10 to 20 minutes, the run time to a TOC breakthrough of 0.8 

mg/L increased from 41 to 104 scaled operation days, which represents an increase 

in run time of 154 percent. Similar to the results seen for ORW and PRW, EBCT did 

not systematically affect normalized NOM adsorption behavior for SRPW, as 

indicated in Figure 6.13, although after 6,000 BV the effluent from the 10-minute- 

EBCT contactor tended to exhibit breakthrough first. The same effect of EBCT 

was found for normalized DBP precursor removal, as illustrated in Figure 6.14 for 

UFC-TOX.


The EBCT effect on TOC is shown in Figure 6. 1 5 for FGW. The results are 

similar to those of the three surface waters discussed previously. An increase in run 

time of 243 percent was observed from a 10- to 20-minute EBCT for a TOC goal of 

1 .6 mg/L, whereas the increase was 300 percent for a TOC goal of 0.8 mg/L. The 

EBCT did appear to affect normalized TOC adsorption behavior, as shown in Figure 

6. 1 6. In the first half of the curve, breakthrough occurred earliest in the 10-minute- 

EBCT contactor and latest in the 20-minute-EBCT contactor. The results are similar 

to those in Figure 6.2. EBCT did not have as clear of an impact on normalized DBF 

precursor adsorption, however, as illustrated in Figure 6.17 for UFC-TTHM. 

Impact of Blending ___________________ 

In most GAC applications of any significant size, multiple contactors will 

be operated in a parallel configuration and the number of contactors will depend on 

the plant size. Parallel GAC contactors are operated in a staggered mode wherein 

each contactor is in operation for different lengths of time. This is illustrated in 

Figures 6.18, 6.19, and 6.20. In this mode of operation, one contactor at a time is 

taken off-line when the blended effluent exceeds the target effluent concentration, 

and a column with fresh or reactivated GAC is then placed on-line. This will cause 

the blended effluent concentration to temporarily decrease, as illustrated in Figure 

6.20. The effluent from the contactor in operation the longest can have a higher 

breakthrough concentration than the target because it is blended with water from 

contactors that have effluent concentrations much lower than the target concentra 

tions (see time t in Figure 6.19). The effluents of parallel contactors are blended 

prior to disinfection. Thus, parallel operation in a multiple-contactor configuration 

will result in longer GAC bedlife, and the time between reactivations will be longer. 

Under ideal conditions, staged blending with multiple parallel contactors leads to 

near steady-state effluent concentration (Roberts and Summers 1982). 

The effluent concentration is controlled by the frequency of reactivation or 

the carbon use rate, i.e., how long a single adsorber is allowed to operate prior to 

reactivation. Because of blending of effluents from multiple contactors, the overall 

GAC plant performance will be better than predicted by single contactor results. The 

overall GAC plant performance for a system of n parallel contactors can be 

described by the following equation (Roberts and Summers 1982): 

c~ ifi •«*'"> 

! = 5- = M_ ———— (6.0 

which, for large values of n, becomes 

n f. Jn 

f=I-J- = -Ifi (6.2) 

i=i n n 1=1 

where 

fj = normalized concentration in the effluent of the ith (of n) 

contactor


f = the average normalized concentration of the blended effluent 

CE = the average effluent concentration of the blended water 

C0 = the influent concentration 

t D = the reactivation time 

K 

Roberts and Summers (1982) indicated that this approximation is valid when the 

number of parallel contactors exceeds 10. Equation 6.2 can be related to the 

definition of mathematical integration. The following equation defines mathemati 

cal integration over time (t): 

Mm n 

jf(t)-dt = I f^t). At (6.3) 

where 

At = - (6.4) 

n 

from Equations 6.3 and 6.4, it follows that 

- hm I fi(t) = -Jf(t)dt (6.5) 

n n — >°° 1=1 t 

If Equations 6.2 and 6.5 are combined, the blended effluent breakthrough curve 

can be represented by 

f = -Jf(t)dt (6.6) 

Equation 6.6 indicates that if one knows the mathematical description of a singlecontactor 

breakthrough curve, f. = f(t), the breakthrough curve for the blended 

effluent, f , can be derived by mathematical or numerical integration. Numerical 

integration, however, can be done without a mathematical equation being devel 

oped. 

A logistic equation of the following form was used to fit the experimental 

data from a single contactor: 

(6'7) 

where A, B, and C are fitting parameters and t is breakthrough time. Upon 

substituting Equation 6.7 for f(t) in Equation 6.6 and performing integration, one 

arrives at the following equation for the blended effluent breakthrough curve: 

f = A + -^-«]n(l+B.e"C") - — .ln(l + B) (6.8) 

Ot C


Equation 6.8 can be solved by trial and error for a target blended concentration to 

determine the breakthrough time to achieve the target. 

For mixtures like NOM, modeling has not been reported and is complicated 

by the impact of the Br:DOC ratio during chlorination on the formation of DBFs. 

Blending experiments are important because of the impact of changing the Br~:DOC 

ratio on DBF formation and speciation. At higher Br~:DOC ratios, more DBFs are 

formed and a higher percent of brominated DBFs occur. Because TOC in the GAC 

effluent is initially low and Br is not removed by GAC, the Br":DOC ratio is 

initially high. This phenomenon leads to a higher percentage of brominated 

compounds compared to that in the influent. This may be problematic if the 

brominated DBFs turn out to be individually regulated in the future D-DBP Rule. 

Previously published work on DBF formation, however, has not considered the 

impact of blending of GAC effluents on this phenomenon. Prior to disinfection, the 

plant water will be made up of blended effluent from multiple GAC contactors at 

various stages of NOM breakthrough. This blended water may have a different 

distribution of DBF precursors compared to a single contactor. Blending is also 

impacted by different molecular size and hydrophobic-hydrophilic effluent compo 

sitions from different contactors, as well as by different Br" :DOC ratios. 


The objectives of the work presented in this section were to investigate the 

effect of blending effluent water on the breakthrough behavior of NOM and DBF 

precursors. The goal was to experimentally simulate blending from multiple parallel 

contactors and evaluate the use of the integrated logistic function to predict NOM 

removal after blending. 

To simulate the blended water scenario, an integral breakthrough experi 

ment with the RSSCT was performed. The effluent from the RSSCT was continu 

ously collected in a large reservoir. With time, the volume and NOM concentration 

in this composite reservoir increased and the water was sampled. The DBFs formed 

in the chlorinated samples represent the effluent in a blended system, and the time 

at which a potential MCL is exceeded can be used to calculate GAC usage rate. The 

results of the integral breakthrough experiment were predicted based on the 

integrated logistic function model, and the fitting parameters were determined 

based on a fit of the logistic function model on the data from a single contactor. 

Blending experiments were conducted for PRW (EBCT = 15 minutes), 

SRPW (EBCT = 10 minutes), and FGW (EBCT = 20 minutes), which represent a 

range of water qualities (see Table 2.1 [p. 6]). Five or six blended effluent samples 

were collected for each RSSCT. The samples were analyzed for TOC and UFC- 

TTHM. The single-contactor breakthrough behavior was compared to the blended 

water scenario using TOC and TTHM target concentrations discussed earlier in this 

chapter. 


The impact of blending on effluent TOC concentrations for PRW is shown 

in Figure 6.21. The discrete effluent values of the single contactor are compared 

with those of the composite "blended" sample. The logistic model, Equation 6.7, 

was applied to the single-contactor breakthrough data, and a best-fit procedure


yielded values for the parameters A, B and C (this model simulation is shown by the 

solid line). Using these parameter values, Equation 6.8 was used to predict the 

blended effluent concentration (the model prediction is shown by the dashed line). 

The impact of blending is summarized for PRW, SRPW, and FGW in Table 6.2. 

For PRW the operating time to a TOC breakthrough of 0.8 mg/L was 

extended from 63 to 116 scaled operation days by blending the effluent water. The 

numerical model showed very good agreement with the experimental blended 

effluent data. Figure 6.22 shows the results for DBP precursor removal for PRW as 

illustrated by UFC-TTHM. The run time to the TTHM goal of 32 ug/L was 

increased from 94 to 199 scaled operation days. The effect of blending on TTHM 

concentration was also well predicted by the model. 

The impact of blending water from multiple contactors is shown for effluent 

TOC concentrations for SRPW in Figure 6.23. The run time to a TOC concentration 

of 0.8 mg/L was extended from 42 to 104 scaled operation days. The blending model 

slightly underpredicted the experimental TOC data. The results of blending GAC 

effluent are shown for UFC-TTHM for SRPW in Figure 6.24. The time to an effluent 

TTHM concentration of 32 ug/L was extended by blending from 56 to 281 scaled 

operation days. The blending model also underpredicted the TTHM concentration. 

The effluent TOC concentrations results from the blending experiments are 

shown in Figure 6.25 for FGW. As was the case with the previous two waters, the 

GAC run time was increased by blending the effluent from multiple GAC contactors. 

The time to a TOC concentration of 1.6 mg/L was extended from 13 to 28 scaled 

operation days. The time to a TOC concentration of 0.8 mg/L was extended from 

8 to 14 scaled operation days. The experimental blended data were slightly 

underpredicted by the numerical model. These results are mirrored in the results 

for TTHM precursors shown in Figure 6.26. The run time to the TTHM goal of 

64 ug/L was extended from 15 to 25 scaled operation days. The run time to the 

TTHM goal of 32 ug/L was extended from 11 to 19 scaled operation days. Similar 

to the modeled results for TOC, the TTHM results were slightly underpredicted in 

the latter part of the breakthrough by the numerical model. 

NOM Desorption and the Impact of Backwashing 

Recently, evidence has been gathered that indicates that NOM does not 

completely desorb from the surface of GAC when concentration gradients are 

applied. Once adsorbed, some of the NOM will not desorb from GAC back into 

solution if the liquid phase concentration is reduced. Summers and Roberts (1988) 

and Summers et al. (1989, 1992) showed for four source waters that less than 6 

percent of the total adsorbed NOM, as measured in terms of TOC and UV254 , 

desorbed when the influent concentration was reduced to the detection limit. Hong 

(1995) showed that less than 15 percent of previously adsorbed DOC was desorbed 

by a reverse in the concentration gradient for six natural waters. 

GAC filter adsorbers require frequent backwashing in order to reduce head 

loss accumulation. During backwashing, the GAC particles may become restratif ied, 

which can result in saturated particles moving from the top of the column (which has 

been exposed to high influent NOM concentrations) to the bottom of the column. 

If the liquid phase NOM concentration at the bottom of the column is low during this 

backwashing, the concentration gradient favors the desorption of NOM. Further, the


Table 6.2 Impact of blending on bed life___________________________ 

__ Run time (days)_____________ 

TOC TTHM 

Source 

configuration 1.6mg/L 

0.8 mg/L 

64 ug/L 32 ug/L 

PRW 

Single — 

Blended — 

63 

116 

— 94 

— 199 

SRPW 

Single — 

Blended — 

42 

104 

— 56 

— 281 

FGW 

Single 13 

Blended 28 

8 

14 

15 11 

25 19 

• indicates effluent did not reach criterion 

restratification of GAC particles may lead to a spreading out of the mass transfer 

zone, which can lead to earlier breakthrough (Sontheimer et al. 1988). However, 

Hong and Summers (1994) and Hong (1995) have shown that, in general, backwashing 

does not lead to significantly earlier NOM and DBF precursor breakthrough. 

The lack of desorption of NOM may have a major impact on the application 

of GAC for DBF precursor control. Concerns about the impact of backwashing on 

the mass transfer zone and premature breakthrough may not be relevant for NOM 

and DBF precursors, which would facilitate the use of filter adsorbers in situations 

where high removal efficiencies are not required. 


The objectives of the work presented in this section were to evaluate the 

impact of backwashing on the breakthrough behavior of NOM and DBF precursors 

and to determine the extent of NOM desorption from a column previously exposed 

to NOM. 

Desorption and backwashing experiments were run for PRW (EBCT =10 

minutes). Desorption experiments were run to evaluate NOM desorption as a result 

of a change in concentration gradient. An adsorption RSSCT with an influent TOC 

of 3 mg/L was run to 72 percent breakthrough. After completion of the RSSCT run, 

lab clean water (conductivity adjusted with KC1) containing less than 0.2 mg/L TOC 

was fed to the column at the same flow rate as during adsorption. The effluent was 

monitored for TOC and UV254 . The desorption experiment was conducted until the 

effluent TOC concentration was below 10 percent of the adsorption influent 

concentration, or 0.3 mg/L. 

To investigate the impact of backwashing, two identically designed RSSCTs 

(EBCT =10 minutes) were operated. A 10-minute EBCT was chosen because it 

represents the upper operation limit for a filter adsorber. One column was backwashedevery 

11 scaled operation days, while the other column was neverbackwashed.



The results for NOM desorption for PRW are shown in Figures 6.27 and 

6.28 for TOC and UV254, respectively. The run time for the desorption experiment 

was 18.5 scaled operation days. To determine the mass of NOM desorbed during 

that time, the breakthrough curve was integrated and compared to the integrated 

adsorption breakthrough curve. Eleven percent of the total adsorbed TOC and 5.8 

percent of the total removed UV254 were desorbed from the GAC by reversing the 

concentration gradient through a change in the influent TOC concentration of 3.0 

mg/L to 0.2 mg/L. Most of the NOM desorption for PRW occurred in the first 5 

scaled operation days, after which desorption was much slower. These results are 

similar to those reported by Hong (1995). 

Figures 6.29 and 6.30 show the impact of backwashing on NOM break 

through for PRW as measured by TOC and UV 254 , respectively. After backwashing, 

the effluent concentration initially increased by an average of 8 percent, and no 

trends were evident until the next backwashing. After 11 days the backwashed 

column broke through more rapidly than the nonbackwashed column. For the 

backwashed column, the total desorbed mass from solid phase to liquid phase as a 

result of backwashing was 13 percent of the adsorbed mass in the nonbackwashed 

column. The mass of TOC that desorbed because of backwashing was similar to the 

amount desorbed in the desorption experiment. Backwashing decreased the run 

time to 1.6 mg/L TOC from 58 to 48 days and to 0.8 mg/L from 34 to 24 days. 

A similar result is shown for UFC-TTHM in Figure 6.31. The backwashed 

column showed earlier breakthrough of TTHM precursors, 30 percent earlier at 32 

ug/L. However, after approximately 60 scaled operation days, the behavior of both 

columns was very similar. 

PRW breakthrough behavior showed a significant impact of backwashing. 

These results differ from those of Hong and Summers (1994) and Hong (1995), who 

reported that only one out of four waters studied showed minor evidence of early 

breakthrough due to backwashing. The results from the PRW desorption experi 

ment (Figures 6.27 amd 6.28) indicated that the mass of TOC desorbed was similar 

to that for other waters. PRW is a heavily industrially impacted surface water in 

which SOCs are known to be intermittently present. Other researchers (Sontheimer 

et al. 1988) have shown that backwashing leads to earlier breakthrough of SOCs. 

Thus, the nature of TOC may be an important factor in determining the impact of 

backwashing. 

Summary_________________________ 

Impact of EBCT on Adsorption Behavior 

Increasing the EBCT resulted in longer operating times to a specific 

breakthrough condition, as expected. For ORW, SRPW, and PRW, an increase in 

EBCT from 10 to 20 minutes resulted in a 100 to 154 percent increase in run time 

to a TOC breakthrough of 0.8 mg/L. Neither the NOM nor DBP precursor 

adsorption was found to be systematically affected by EBCT for these three waters 

when comparisons were based on throughput. For FGW, a 229 percent increase in


run time was observed to a TOC breakthrough of 1.6 mg/L, and a systematic impact 

of EBCT was found for TOC and to a lesser extent for the DBF precursors. 

A longer EBCT will result in longer run times to a given effluent criterion 

and yield lower GAC reactivation frequencies. The carbon usage rate (i.e., the 

amount of carbon that must be reactivated), however, may not be related to EBCT. 

Further, a longer EBCT will have higher costs associated with larger contactors. An 

economic analysis is needed to determine the optimum EBCT for a given system. 

Only the cost analysis can determine whether the greater capital expense associated 

with longer EBCTs compensates for a potential reduction in GAC reactivation 

costs. The cost analysis associated with these data is presented in Chapter 12. 

Impact of Blending 

The results presented in the blending section emphasize the advantage of 

operating multiple contactors in parallel. The time to GAC performance goals can 

be significantly extended by blending the effluent from multiple contactors. For the 

three waters examined (PRW, SRPW, and FGW), blending increased the run time 

by an average of 150 percent for both TOC and UFC-TTHM. The integral logistic 

model was found to predict blended effluent TOC concentrations well and reason 

ably predict those for UFC-TTHM. It is important for one to consider the added 

benefits of blending when analyzing GAC cost with respect to a performance goal. 

The impact of these extended run times on GAC use rate and costs will be also be 

presented in Chapter 12. 

NOM Desorption and the Impact of Backwashing 

For PRW, NOM did not desorb easily from GAC as a function of a change 

in the concentration gradient. Only 11 percent of the TOC was found to desorb from 

a column that was 75 percent exhausted. For PRW, backwashing led to earlier NOM 

and DBP precursor breakthrough behavior. Other researchers, however, have found 

this not to be the case. It is postulated that the impact found in this study is due to 

the nature of the NOM in PRW and the possible presence of SOCs, the removal of 

which has been demonstrated to be affected by backwashing.


2.0 

TOC 

C0 = 2 mg/L 

10 

u> 

1.5 - 

2 1.0 - 

I o 

O 

0.5 - 

EBCT (min) 

—— 10 

— -15 

........ 20 

0.0 

100 200 300 


400 500 

Figure 6.1 Model simulation of the effect of EBCT on TOC breakthrough for the Ohio 

River water 

2.0 

1.5 - 

TOC 

C0 = 2 mg/L 

20 ...--^ 

10 

o 

1e 1.0 - 

§ 

o 

0.5 - 

0.0 

10000 20000 30000 

Throughput, BV (bed volumes) 

EBCT (min) 

—— 10 

— - 15 

20 

40000 

Figure 6.2 Model simulation of the effect of EBCT on normalized TOC breakthrough for 

Ohio River water

s o 

s 

a. 

2 

5' 

2 5' 

•n 

•n 


0.025 

0.020 - 

d> 

o 

0.015 - 

TO 

-Q 0.010 - 

CO 

o 

0.005 - 

1 o 

EBCT(min) C0 (1/cm) 

0.000 

3000 6000 9000 12000 


15000 18000 

Figure 6.5 Effect of EBCT on normalized UV254 breakthrough for Ohio River water 

o 

5 

•o 

o 

O 

EBCT (min) C0 (ug CI7L) 

3000 6000 9000 12000 

15000 18000 


Figure 6.6 Effect of EBCT on normalized UFC-TOX breakthrough for Ohio River water


o> 

O 

4-' 

1 8 

O EBCT (min) C0 (\ig/L) 

3000 6000 9000 12000 

15000 18000 


Figure 6.7 Effect of EBCT on normalized UFC-TTHM breakthrough for Ohio River 

water 

o> 

•- 

I 

O O 

EBCT (min) C0 fog/L) 

3000 6000 9000 12000 

15000 18000 


Figure 6.8 Effect of EBCT on normalized UFC-HAA6 breakthrough for Ohio River 

water

s 


80 

70 - 

TTHM. 

60 - 

1 

o •JP 

50 - 

40 - 

a 

o 

-4» 

§ 

t) 

to 

13 

I 

2 

O 

^ 

8- 

o 

•3 

I 

 

m 

3} 

s a 

a. s 

3 

I aÎ1 

2 

m 

Concentration (mg/L) 

(C C 

(D 

O) 

O1 

Concentration (mg/L) 

o I-* 

a m CD 

o H 

O 3 

O 

i O 

m CD 

o 

o 

. _l . I I I . 1 . I . 1 , I 

SL N° 

(D a 

3 O 

o> 

S 

o c 

CD 

T] 

O 

^ 

a D) 

(Q 

O 

3 

a 

I 

Q) 

5 

o 

c 

(Q 

o^ 

Tl 

O 

a 

Q> 

(Q 

O 

Q. 

I


200 

_J 

a. 

o 

14>rf 

§ 

500 1000 1500 2000 2500 3000 

3500 


Figure 6.17 Effect of EBCT on normalized UFC-TTHM breakthrough for Florida 

groundwater 

n 

Figure 6.18 Operation of multiple GAC contactors in parallel


t 

Time 

Figure 6.19 Individual breakthrough curves of multiple contactors operated in parallel 

c 

o 

start-up 

normal operation 

I o 

O 

plant effluent criterion 

Time 

Figure 6.20 Blended GAC effluent from multiple contactors operated in parallel


o> 

2.5 

2.0 - 

TOG 

EBCT= 15min 

Logistic Model 

I 

I o 

O 

1.5 - 

1.0 - 

0.5 - 

Predicted Blended Effluent 

C0 (mg/L) 

• Discrete effluent samples 3.0 

o Blended effluent samples 3.0 

0.0 

50 100 150 200 


250 

Figure 6.21 Effect of blending on TOC breakthrough for Passaic River water 

80 

70 - 

60 - 

* Discrete effluent samples 85 

0 Blended effluent samples 85 

TTHM 

EBCT=15min 

o 

1 *-» 

I o 

O 


50 100 150 

200 250 


Figure 6.22 Effect of blending on UFC-TTHM breakthrough for Passaic River water

Design and Operational Considerations JOB 

1.6 

1.4 

1.2 

C0 (mg/L) 


o Blended effluent samples 2.2 

—— Model prediction 

TOO 

EBCT=10min 

o 

1 

Io 

o 

1.0 

0.8 

0.6 


0.4 

0.2 

0.0 

0 10 20 30 40 50 60 70 80 90 100 


Figure 6.23 Effect of blending on TOC breakthrough for Salt River Project water 

CD 

n. 

60 

50 - 

40 - 

3 30- 

£ 

c0 (ug/L) 

• Discrete effluent samples 72 

0 Blended effluent samples 72 


TTHM 

EBCT = 10min 

I o 

O 

20 - 

10 - 


10 20 30 40 50 60 70 80 90 


Figure 6.24 Effect of blending on UFC-TTHM breakthrough for Salt River Project water


o> 

§ 

o 

10 

9 - 

8 - 

7 - 

6 - 

5 - 

C0 (mg/L) 


° Blended effluent samples 10.0 


TOC 

EBCT = 20 min 

§ 

4 - 

3 - 

2 - 

1 - 


0 

—i— 

10 

—i— 

20 30 

—i— 

40 50 


Figure 6.25 Effect of blending on TOC breakthrough for Florida groundwater 

200 

150 - 

cn (ug/L) 

• Discrete effluent samples 238 

o Blended effluent samples 238 

— — Model prediction 

TTHM 

EBCT = 20 min 

o •a 

I 

8 

O 


Figure 6.26 Effect of blending on UFC-TTHM breakthrough for Florida groundwater


3.0 

2.5 - 

j TOC 

C0 (mg/L) 

Adsorption 3.0 

Desorption 0.2 

EBCT=10min 

co 

I 

o 

I o 

O 

2.0 - 

1.5 - 

1.0 - 

0.5 - 

0.0 

—i— 

10 


—i— 

15 20 

Figure 6.27 NOM desorption for Passaic River water: TOC levels 

o 

0.08 

0.07 - 

c 

0.06 - 

0.05 - 

UV. 

254 C0 (1/cm) 

Adsorption 0.072 

Desorption 0.001 

EBCT=10 min 

CO 

0.04 - 

o (A 

CO 

JB 

g 

0.03 - 

0.02 - 

I 

z> 

0.01 - 

0.00 

T~ 

5 

—i— 

10 

—i— 

15 20 


Figure 6.28 NOM desorption for Passaic River water: UV254 levels


3.0 

2.5- 

TOC EBCT=10min 

I 

g 

S 

2.0 - 

1.5 - 

1.0 - 

0.5 - 

C0 (mg/L) 

o Nonbackwashed 3.0 

n Backwashed 3.0 

0.0 

0 20 40 60 80 100 120 140 160 180 200 


Figure 6.29 Impact of backwashing on TOC breakthrough for Passaic River water 

0.07 

0.06 H 

UV- 254 EBCT=10 min 

8 

S 

-e 

% 

x> 

(0 

« 

o 

1 

0.05 

0.04 

0.03 

0.02 

0.01 

o Nonbackwashed 0.076 

o Backwashed 0.076 

0.00 

0 20 40 60 80 100 120 140 160 180 200 


Figure 6.30 Impact of backwashing on UV254 breakthrough for Passaic River water


O) 

.2 

1 

o Nonbackwashed 

n Backwashed 

20 40 60 80 100 120 140 160 180 200 


Figure 6.31 Impact of backwashing on UFC-TTHM breakthrough for Passaic River water

Chapter 7 

The Impact of Optimized Coagulation 

on NOM Removal and DBP Control by 

GAC Adsorption 

The capacity of GAC for NOM adsorption can be improved by a decrease 

in initial NOM concentration (Randtke and Jepsen 1982; Summers and Roberts 

1988; Sontheimer et al. 1988). GAC's capacity for adsorption of NOM is also 

affected by pH (McCreary and Snoeyink 1980; Weber et al. 1983). These research 

ers demonstrated that as the initial pH was lowered, the adsorption capacity of GAC 

was improved. This improvement is most often attributed to a decrease in the 

solubility of NOM at lower pH. 

Coagulation processes used as pretreatment to GAC can both reduce 

influent TOC concentrations and decrease the influent pH to the adsorber, thus 

leading to improved GAC performance. Coagulation also preferentially removes 

the large molecular size fraction and humic fraction of NOM (Semmens and Staples 

1986; Collins et al. 1986; Dryfuse et al. 1995). A study by Semmens et al. (1986) 

showed that coagulation pretreatment prior to GAC increased the run time of the 

contactor and that further improvements in GAC run time can be achieved at higher 

coagulant doses. 


The systematic evaluation of the impact of optimized coagulation pretreat 

ment on GAC performance for NOM removal and DBP control is discussed in this 

chapter. The GAC adsorption characteristics of two waters pretreated under 

conventional and optimized alum coagulation conditions were compared. Opti 

mized coagulation with respect to TOC is defined as the coagulant dose beyond 

which improvement in marginal TOC removal is not measurable. Changes in 

specific DBP yield in GAC effluent as a result of optimized coagulation pretreat 

ment were also investigated. Finally, to determine how the adsorbability of NOM 

characteristics was affected by coagulation pretreatment, a study of NOM charac 

teristics by two fractionatiori techniques, ultrafiltration and the use of XAD-8 resins, 

and PY-GC-MS was performed. 

709


Experimental Conditions 

Two waters were used in this study: ORW and SRPW. Raw water 

characteristics are summarized in Table 7.1. 

ORW was sampled at CWW after conventional treatment using 7.7 mg/L 

alum, 8.6 mg/L lime, pH 7.8, and rapid sand filtration. Conventionally treated water 

from this source was batch coagulated with an additional 80 mg/L of alum at pH 6.4. 

This dose was based on jar test results of Dryfuse et al. (1995). SRPW was a blend 

of the Verde River and Salt River (in Arizona). SRPW was conventionally treated 

by coagulation with alum, sedimentation, and sand filtration; the plant alum dose 

was 10.6 mg/L at pH 7.8. Based on jar test results, and as a result of the high 

alkalinity of the water, the pH of the water was first reduced to 6.9 with sulfuric acid, 

and an additional alum dose of 60 mg/L was then added. This yielded a pH of 6.5 

for optimized coagulation. 

Although high alum dosages were used, this study was designed to examine 

the impact on GAC performance of reductions in influent TOC concentrations after 

optimized coagulation. Lowering alum dosages for optimized coagulation similarly 

causes decreases in influent TOC concentration, but it was beyond the scope of this 

study to optimize GAC performance in terms of coagulant dosage. 

The batch coagulation process was similar for both ORW and SRPW. It was 

repeated three times on water contained in 55-gal (208-L) drums. A variable speed 

shaft mixer was operated at high speed prior to alum addition and for 4 minutes after 

alum addition. The water was then flocculated for 12 minutes at low speed. After 

settling, the water was passed through a 1.0-um cartridge filter. The treated water 

characteristics are summarized in Table 7.2. Also included in Table 7.2 are the Step 

1 enhanced coagulation requirements according to Stage 1 of the proposed D-DBP 

Rule (USEPA 1994), based on raw water characteristics. 


The results presented in this section involve a comparison of the relative 

effects of optimized coagulation and conventional pretreatment on GAC break 

through curves for DBP surrogate parameters (TOC and UV254) and formed DBPs 

(TTHM, HAA6, TOX, and CH). All DBP formation was conducted under UFC 

protocol (see Appendix C). As a result of the unsteady-state nature of the break 

through process, it can be difficult to compare breakthrough curves beyond a 

qualitative assessment. It is possible, however, to use breakthrough characteristics 

to systematically compare breakthrough behavior. In this study, the concentration 

of the immediate breakthrough (Cimm), which is representative of the nonadsorbable 

fraction, and the time to initial breakthrough (tjm), which represents the time at 

which the adsorbable fraction begins to show breakthrough, are used. Furthermore, 

comparisons are made by establishing GAC run time criteria and comparing the 

difference in run time obtained after conventional treatment with that obtained after 

optimized coagulation. The GAC run time criterion for TOC is defined as the time 

for reaching an effluent concentration of 0.8 mg/L. For UV154 , the criterion is an 

effluent absorbance of 0.01/cm. The criterion for TTHM is guided by Stage 2 of 

the D-DBP Rule and is 32 ug/L, a value 20 percent below the proposed MCL of 

40 ug/L. For HAA6, the run time criterion is set at 24 ug/L, based on a target

The Impact of Optimized Coagulation 111 

Table 7.1 Raw water characteristics for Chapter 7 experiments 

Water TOC (mg/L) Alkalinity (mg/L as CaCO3) 

ORW 

SRPW 

2.2 

2.5 

40 

140 

Table 7.2 Treated water characteristics for Chapter 7 experiments 


Alum 

dose 

(mg/L) 

Conventional 

treatment 

TOC 

(mg/L) 

PH 

TOC 

removal 

(%) 

Alum 

dose 

(mg/L) 

Optimized 

coagulation 

TOC 

(mg/L) 

PH 

TOC 

removal 

(%) 

Enhanced 

coagulation 

TOC removal 

requirement 

(%) 

ORW 

SRPW 

8 

11 

2.0 

2.2 

7.8 

7.8 

6 

9 

88 

71 

1.5 

1.7 

6.4 

6.5 

32 

31 

40 

20 

20 percent below the Stage 2 HAAS MCL, 30 ug/L. Finally, a value of 70 ug 

C1~/L is used for the TOX run time criterion. 

The GAC influent levels of TOC and UV254 and of the formed TTHM, 

HAA6, TOX, and CH for the influent waters are summarized in Table 7.3. The 

influent concentrations of CH were very low, and its formation was well controlled 

by GAC. Therefore, characterization of its breakthrough will not be discussed. 

GAC breakthrough behavior for ORW pretreated under conventional and 

optimized coagulation is shown in Figures 7.1 to 7.6 for TOC, UV 254 , and various 

DBFs. For each pair of breakthrough curves, the Cimm, tim , and respective run time 

criteria comparisons between the pretreatment types are summarized in Table 7.4. 

The run time criteria are also listed in Table 7.4. Although the Cimm was usually very 

low for the breakthrough curves of both pretreatment types, it is evident that 

optimized coagulation pretreatment dramatically improved GAC performance for 

the adsorption of DBF surrogates and DBFs. While the tint ranged from 30 to 50 days 

after conventional treatment, it was improved to 125 to 170 days after optimized 

coagulation. Furthermore, as defined by the criteria mentioned earlier, the GAC run 

time improved from a range of 80 to 125 days following conventional treatment to 

a range of 214 to 300 days following optimized coagulation. The comparison was 

not possible for HAA6 because effluent concentrations were very low. It is also clear 

that the effect of optimized coagulation on adsorption of TOC and UV254 was very 

similar to the effect on the DBFs analyzed. This is a good indication of the ability 

of TOC and UV254 to serve as surrogates for DBF precursor breakthrough. 

Figures 7.7 to 7.12 show the impact of optimized coagulation on GAC 

breakthrough of TOC and UV254 , as well as various DBFs, for SRPW. The 

breakthrough behavior comparison parameters (Cimm , tint, and run time criteria) are 

summarized in Table 7.4. In a manner similar to the case of ORW, optimized 

coagulation greatly improved GAC performance for all parameters analyzed. The 

DBF surrogate parameters (TOC and UV254) and the formed DBFs analyzed


Table 7.3 Summary of DBF surrogate and DBF concentrations prior to GAC treatment 

Water CT 

Formation under UFC 

TOC UV254 TTHM HAA6 TOX 

(mg/L) (1/cm) (ng/L) (ug/L) (ug CI'/L) 

OC CT OC CT OC CT 

OC CT OC 

CT 

CH 

(M9/L) 

OC 

ORW 2.0 

SRPW 2.2 

1.5 0.04 0.03 86 59 26 

1.7 0.05 0.03 72 46 28 

19 170 92 

17 170 130 

3.9 

4.1 

1.4 

2.6 

CT = conventional treatment 

OC = optimized coagulation 

showed similar improvements in their breakthrough behavior with optimized 

coagulation. Under conventional pretreatment conditions, initial breakthrough 

occurred after 40 and 55 days for TOC and UV254 , respectively, as compared to 

between 25 and 45 days for the DBF precursors. After optimized coagulation, initial 

breakthrough occurred after 100 days for both TOC and UV254 , while that for the 

DBF precursors occurred after 90 to 110 days. GAC run time improved from a range 

of 83 to 120 days to a range of 155 to 300 days, based on the run time criteria. 

The results indicate that for the two waters examined, GAC performance 

after optimized coagulation was significantly improved compared to that after 

conventional treatment. On average, the percent improvement in tint for all param 

eters was greatest for ORW, at 254 percent, while that for SRPW was still very large, 

at 179 percent. Because GAC reactivation times are controlled by run time, the 

comparison based on run time criterion is very important. For all parameters, the 

average percent increase in run times were 148 and 129 percent for ORW and 

SRPW, respectively. These results demonstrate that optimized pretreatment prior 

to a GAC contactor may provide significant cost reductions associated with GAC 

reactivation. Furthermore, GAC coupled with optimized coagulation may be a 

necessary step for many utilities to comply with future, more stringent DBP 

regulations. However, the alum dosages used in this study were high, and it was 

beyond the scope of this project to optimize GAC performance at more moderate 

coagulant dosages. 

It should be noted that these results are based on single RSSCT runs. In 

practice, multiple GAC contactors are operated in a parallel, staggered mode, and 

the effluent water quality is blended prior to disinfection. As shown in Chapter 6, 

the impact of blending further improves GAC run time. 

In this study, optimized coagulation resulted in both a decrease in influent 

TOC and a decrease in influent pH for both waters. Other research has shown that 

both factors can contribute to an increase in the performance of GAC for the control 

of NOM. A survey of 26 GAC runs utilizing 18 different water sources yielded a 

logarithmic relationship of run time (as measured by bed volumes to 50 percent TOC 

breakthrough) as a function of influent TOC (Hooper 1996; Summers et al. 1994b). 

The relationship indicated that as influent TOC increased, run time decreased. A 

controlled study performed on a single water source (Hooper et al. 1995) showed 

that the impact of influent TOC on GAC run time was similar to that observed in the 

relationship just described. That study also investigated the impact of influent pH

OC 

(days) 

214 

245 

241 

* 

300 

155 

245 

300 

* 

180 

Change 

(%) 

+168 

+96 

+177 

* 

+150 

+148 

+87 

+188 

+191 

* 

+50 

+129 

3 

a. 

I a" 

s 

Table 7.4 Effect of coagulation pretreatment on GAC breakthrough characteristics 

Immediate breakthrough 

concentration, C lmm 

Time to initial 

breakthrough, tjnt 

Run time based on 

established criterion 


Parameter 

Units 

Cone 

CT 

%lnf 

OC 

Cone 

%lnf 

CT 

(days) 

OC 

(days) 

Change 

(%) 

Criterion 

CT 

(days) 

ORW 

TOC 

uv254 

TTHM 

HAA6 

TOX 

Average 

mg/L 

1/cm 

M9/L 

H9/L 

pg cr/L 

0.2 

0.003 

3 

4 

5 

9.8 

6.8 

3.5 

15.4 

2.9 

8 

0.15 

BDL 

1 

4 

10 

10.1 

NA 

1.7 

21.1 

10.9 

11 

45 

50 

45 

30 

50 

125 

145 

150 

170 

150 

+178 

+190 

+233 

+467 

+200 

+254 

0.8 

0.01 

32 

24 

70 

80 

125 

87 

* 

120 

SRPW 

TOC 

UV254 

TTHM 

HAA6 

TOX 

Average 

mg/L 

1/cm 

H9/L 

M9/L 

ug CI-/L 

0.18 

0.001 

BDL 

2.5 

10 

8.1 

2.1 

NA 

8.8 

5.9 

6 

0.1 

0.001 

BDL 

2.5 

5 

6.0 

3.6 

NA 

15.0 

3.8 

6 

40 

55 

30 

45 

25 

100 

100 

90 

100 

110 

+150 

+82 

+200 

+122 

+340 

+179 

0.8 

0.01 

32 

24 

70 

83 

85 

103 

* 

120 


Cone = concentration 

CT = Conventional treatment 

NA = not available 

%lnf = percent influent 

OC = optimized coagulation 

* Effluent concentration did not reach criterion


on GAC breakthrough under controlled conditions and found that as influent pH 

increased from 5.5 to 8.5, GAC run time decreased. For the two waters investigated 

in the present study, most of the improvement in GAC performance found after 

optimized coagulation was attributed to the decrease in influent TOC concentration 

(64 and 96 percent for ORW and SRPW, respectively). 

Effect of Coagulation Pretreatment 

on Specific DBF Yield of GAC Effluent_________ 

Although the effectiveness of optimized coagulation pretreatment for the 

improved removal of DBF formation in GAC effluent has been shown, the 

comparisons were made with breakthrough curves plotted against scaled operation 

time. One can gain additional insight into the improvement of the RSSCT receiving 

optimized coagulated water (RSSCT-OC) by plotting DBF formation concentra 

tions against the corresponding TOC concentration for each sample and comparing 

these plots to those for the RSSCT receiving conventionally treated water (RSSCT- 

CT). These plots tend to have high linear regression correlations—as most showed 

an r2 value greater than 0.900. Two important features of these plots are the relative 

position and slope of each best-fit curve; significant changes in these parameters 

provide an indication of any changes in the specific DBF yield between RSSCT-CT 

and RSSCT-OC (Hooper 1996). 

Figure 7.13 shows the relationship between TTHM formation and TOC 

concentration for RSSCT-CT and RSSCT-OC for ORW. Both the slope and y-axis 

intercept were compared using regression analysis with categorical variables 

(Berthouex and Brown 1994). This method uses categorical variables to enable the 

entire data set (both pretreatments) to be fit to one model while still allowing the data 

sets to be distinguishable. The two categories were conventional pretreatment and 

optimized coagulation pretreatment. There are two null hypotheses: 

H Q : "The difference between the two slopes is zero." 

H0': "The difference between the two y-axis intercepts is zero." 

If the analysis shows that both null hypotheses should be accepted, then for the 

particular DBF examined, pretreatment by optimized coagulation did not signifi 

cantly affect the specific DBF yield after GAC treatment. If one or both hypotheses 

are rejected, then one or both of the following alternative hypotheses are accepted, 

and pretreatment significantly affected the specific DBF yield after GAC treatment: 

HA : "The difference between the two slopes is not zero." 

HA': "The difference between the two y-axis intercepts is not zero." 

TOC is 

A linear model for regression analysis of conventional pretreatment for 

VDBP= ao + a


for the DBF of interest, where y DBp and XTOC are the variables of the linear model 

and a0 and a { are constants. Similarly, for GAC effluent pretreated with optimized 

coagulation, 

where 60 and Bj are constants. A combined model can be created from these two 

models so that the model can be simultaneously fit to all the data. The combined 

model uses Z as a dummy variable: Z = 0 if the data are for the RSSCT-CT, and 

Z = 1 if the data are for the RSSCT-OC: 

y DBp = a0 + CC,XTOC + Z(y0 + y,xTOC) (7.3) 

60 = a0 + y0 (7.4) 

Bâ, +Y, (7.5) 

where y0 and y, are constants. The combined model can be rearranged: 

y DBp = oc0 + alXTOC + Zy0 + y,ZxTOC (7.6) 

The regression is performed with independent variables XTOC , Z, and ZxTOC to 

estimate parameters oc0, (Xj , y0 , and yt . Once the model is fit to the data and estimates 

have been obtained for all four parameters, the model is simplified. Simplification 

is possible when y0 or y, is estimated as zero. When this occurs, the parameter is 

eliminated from the model, and the simplified version is fit to the data. For example, 

if y0 = 0, then B0 = aQ . This method leads to four possible outcomes, as shown in 

Figure 7.14. The four possible outcomes have been labeled type 1, 2, 3, and 4. A 

statistical software package (SYSTAT Version 3.0 for Windows, Systat, Inc., 

Evanston, 111.) was used for the analysis. The estimated parameter values for the 

four-parameter curve fit were associated with t statistics. A parameter was not 

significantly different than zero when the value of the t statistic was less than the 

critical t statistic, t*. The t* was determined from atable of critical t statistics, based 

on the sample size, at the 95 percent confidence level. 

The regression sum of squares for the complete model and the simplified 

version can be compared to ensure that elimination of one or more parameters from 

the model has not significantly affected the goodness of fit. To compare the 

regression sum of squares, the F statistic was computed and compared to the critical 

F statistic, F*, at the upper 5 percent point of the F distribution, for a 95 percent 

confidence level test of significance (Berthouex and Brown 1994). 

For the data shown in Figure 7.13, no significant change was observed in 

the y-axis intercepts, but a significant decrease in slope occurred between conven 

tional treatment and optimized coagulation pretreatment. Two conclusions can be 

drawn from these type 3 results. First, pretreatment by optimized coagulation 

reduced the specific TTHM yield after GAC treatment for ORW at a given effluent 

TOC concentration. Second, the increase in TTHM formation for a given increase 

in TOC concentration decreased after pretreatment by optimized coagulation.


The results obtained for the formation of TTHM, HAA6, and TOX for 

ORW and SRPW are summarized in Table 7.5, together with a comparison of F to 

F*. The analysis of HAA6 formation for ORW also yielded type 3 behavior, as 

shown in Figure 7.15. The analysis performed on TOX formation yielded a 

significant increase in y-axis intercept and a significant decrease in slope with GAC 

treatment after optimized coagulation, resulting in a "crossing" pattern, as shown 

in Figure 7.16, which is a modified type 1 behavior. Therefore, at low TOC 

concentrations (below 0.5 mg/L), equivalent TOC concentrations showed a higher 

specific TOX yield in GAC effluent after optimized coagulation, while at TOC 

concentrations greater than 0.5 mg/L, a higher TOX yield was observed in GAC 

effluent after conventional treatment. Overall, however, the increase in TOX 

formation for a given increase in TOC concentration in GAC effluent was lower for 

the water pretreated by optimized coagulation. 

For SRPW, the effects of treatment on the relationship between TOC and 

the formation of TTHM and HAA6, respectively, are is shown in Figures 7.17 and 

7.18. Statistical analysis yielded type 3 results for these DBFs, similar to ORW 

results for the same DBFs. Both the specific DBF yield at a given TOC and the 

increase in DBF formation for a given increase in TOC concentration decreased for 

these DBFs with optimized coagulation pretreatment. 

The comparison involving TOX data for SRPW (Figure 7.19) showed that 

there was a significant difference in neither the y-axis intercepts nor the slopes, 

which corresponded to type 4 behavior. Thus, the specific TOX yield in GAC 

effluent was not significantly affected by optimized coagulation pretreatment for 

SRPW. 

The statistical analysis presented here allows for a direct comparison of the 

effect of optimized coagulation on the specific DBF yield of GAC effluent. In all 

cases, optimized coagulation yielded lower TOC concentrations in the GAC 

effluent at any given time, which also yielded lower formed DBFs. In most cases, 

DBF formation was reduced at a given TOC concentration in GAC effluent after 

optimized coagulation pretreatment. In cases for which DBF formation was not 

Table 7.5 Effect of coagulation pretreatment on relationship between GAC effluent TOC 

and DBF formation 

Slope Y-axis intercept 


Conventional Optimized 

treatment coagulation 

DBF (eg (B,) 



K) (Bo) F 

F* 

Behavior 

type 

ORW 

TTHM 

HAA6 

TOX 

55.1 

11.5 

90.8 

> 38.2 

> 7.3 

> 31.0 

-11.6 

1.3 

-24.9 

= -11.6 

1.3 

< 6.3 

2.01 

0.01 

NA 

4.67 

4.67 

NA 

3 

3 

1 modified 

SRPW TTHM 

HAA6 

TOX 

31.9 

8.7 

56.9 

> 24.6 

> 6.9 

56.9 

-3.1 

1.2 

-0.6 

-3.1 

1.2 

-0.6 

0.30 

3.28 

3.59 

4.60 

4.60 

4.10 

3 

3 

4 

NA = not applicable


reduced, there was no significant difference in specific DBF yield. Therefore, based 

on this statistical analysis for the two waters examined, optimized coagulation 

pretreatment to GAC not only reduced the influent TOC concentration but also 

reduced DBF formation at a given TOC in the GAC effluent. 

Impact of Pretreatment on NOM 

Fraction Characteristics After GAC____________ 


Figure 7.20 is a composite graph that shows the humic and nonhumic 

makeup of ORW at various stages of treatment: raw, conventional treatment, 

optimized coagulation, and three GAC effluent samples for RSSCT-OC. The bar 

graphs compare the humic and nonhumic content of the raw water sample to the 

fractionation of water samples after conventional treatment and optimized coagu 

lation. Table 7.6 summarizes the humic and nonhumic makeup of the samples 

analyzed. The results show that the raw water sample was 55 percent humic and 45 

percent nonhumic. A small decrease in total DOC was observed between raw and 

conventionally treated water, mostly because of a decrease in the humic fraction. 

Optimized coagulation decreased the total DOC by 32 percent relative to the raw 

water, with substantial removal occurring in both fractions. The humic and nonhumic 

fractions composed 58 and 42 percent, respectively, of the total DOC of the 

optimized coagulated water. 

The right side of Figure 7.20 shows the humic and nonhumic makeup of the 

GAC effluent samples after optimized coagulation pretreatment. The nonadsorbable 

fraction of DOC was composed entirely of nonhumic organic matter, while the 

humic fraction was initially nearly completely removed and did not show break 

through until after 150 days of operation. This behavior is similar to what was 

presented for the conventionally pretreated water in Chapter 5. For both columns, 

the concentration of the nonhumic fraction in the GAC effluent gradually increased 

over the course of the GAC operation. The nonhumic fraction showed a steeper 

breakthrough behavior in the RSSCT-CT effluent than in the RSSCT-OC effluent. 

Table 7.6 Impact of coagulation treatment on humic-nonhumic and molecular size 

fractionation 


source 

Treatment 

Humic 

Nonhumic 

Percent of total DOC 

3,000 MS 

ORW 

Raw 

Conventional treatment 


55 

52 

58 

45 

48 

42 

19 

28 

33 

60 

72 

60 

21 

0 

7 

SRPW 

Raw 

Conventional treatment 


53 

53 

65 

47 

47 

35 

20 

18 

20 

65 

76 

69 

15 

6 

11


The specific UFC-TTHM yields fortheunfractionated, humic, andnonhumic 

fractions of ORW are shown in Figure 7.21. Results shown are for raw water, both 

RSSCT-CT and RSSCT-OC influents, and RSSCT-OC effluent. The RSSCT 

effluent sample points (A, B, and C) correspond to those shown in Figure 7.20. As 

was stated previously, because of differences in the Br~:DOC ratio among different 

sample points, the specific UFC-TTHM yields are not comparable at different 

locations and times. For a given sample point, though, the results can be compared. 

The specific UFC-TTHM yields of the humic and nonhumic fraction of any given 

sample point or time were similar to each other and to the unfractionated sample. 

Thus, for ORW, both the humic and nonhumic fraction contributed equally as 

precursors to UFC-TTHM formation, and this was not affected by optimized 

coagulation or GAC treatment. 

Figure 7.22 shows the DOC results for humic-nonhumic fractionation in 

raw, conventionally treated, and optimized coagulated SRPW. RSSCT-OC effluent 

fractionation data are not presented as a result of analytical difficulties that affected 

all three samples. The raw SRPW DOC was 53 percent humic and 47 percent 

nonhumic, as shown in Table 7.6. No change in the relative composition of the 

SRPW was seen after conventional treatment. Optimized coagulation preferentially 

removed the nonhumic fraction, resulting in a 65 percent humic and 35 percent 

nonhumic makeup. The breakthrough behavior of the humic and nonhumic frac 

tions in the RSSCT-CT effluent was similar to that observed for ORW; this behavior 

was discussed in Chapter 5, where it was shown that the humic fraction was well 

adsorbed while the nonhumic fraction showed high levels of immediate break 

through. 

Molecular Size Fractionation 

The effect of treatment on the molecular size distribution of ORW as 

measured by DOC is shown in Figure 7.23. The molecular size distributions for all 

samples are summarized in Table 7.6. The raw sample of ORW was dominated by 

the 500-3,000 MS fraction, with the remaining DOC approximately evenly divided 

between 3,000 MS fractions. With both conventional and optimized 

coagulation, virtually all the >3,000 MS fraction was removed. For conventional 

treatment, there was no removal of either the


The specific UFC-TTHM yields for ORW molecular size fractions are 

shown in Figure 7.24 for the raw water and RSSCT-OC influent and effluent 

samples. The specific yield of the


Table 7.7 PY-GC-MS classification of ORW: Enhanced coagulation 


Stage 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

Raw 


Nonadsorbable 

Intermediate breakthrouogh 


34.5 

2.5 

15.6 

55.6 

51.1 

15.8 

BDL 

BDL 

0.1 

BDL 

35.9 

16.3 

0.3 

2.2 

1.9 

13.9 

66.5 

81 

37.5 

46.2 

BDL 

14.7 

3.1 

4.6 

0.9 


peaks of acetaldehyde, acetic acid, formic acid, and trichlorobenzene, was very 

similar in general character to that observed in the case of conventional pretreatment. 

As shown in Table 7.8, the pyrolysis fingerprint of the enhanced coagulated 

SRPW, was very similar to that produced by conventional coagulation (discussed 

in Chapter 5). The major peak was acetic acid, with minor peaks of furfural, 

propanoic acid, and methyl furfural. The chemical signature of the nonadsorbable 

NOM was similar to that of the influent, with a predominant acetic acid fragment 

produced. The organic matrix of the second breakthrough point was characterized 

by a strong acetic acid and methyl furfural and also a number of peaks that could not 

be identified. The complete breakthrough sample was more similar to the raw water. 

This fingerprint contained strong acetic acid, methyl furfural, propanoic acid, and 

furfural. 

Summary_________________________ 

Optimized coagulation improved GAC run times by 129 to 148 percent over 

conventional pretreatment for the two waters studied, as measured by breakthrough 

behavior of NOM surrogates and DBF formation. Although the optimized coagu 

lation alum doses used were high, the findings indicate the potential benefits of 

increasing coagulant doses prior to GAC adsorption. In addition, optimized coagu 

lation pretreatment significantly decreased specific DBF yield in the GAC effluent. 

The adsorbability of the NOM fractions as measured by XAD-8 fractionation and 

ultrafiltration was not affected by coagulation conditions. 

Because pretreatment conditions did not affect NOM characteristics and 

adsorbability, the significant improvement in GAC performance with optimized 

coagulation may be attributed to lower influent concentrations and lower influent 

pH values. It is difficult to separate these effects in a comparison of GAC 

performance under two coagulation conditions for these waters. 

For the two waters examined, optimized coagulation removed a fraction of 

the TOC present after conventional treatment. For ORW, both humic and nonhumic 

fractions were affected about equally, while for SRPW the nonhumic fraction was 

preferentially removed. Other research has shown that the humic fraction is usually 

preferentially removed over the nonhumic fraction by coagulation (Semmens and 

Staples 1986; Collins et al. 1986; Dryfuse et al. 1995). MS fractionation studies


Table 7.8 PY-GC-MS classification of SRPW: Enhanced coagulation 


Stage 

Aliphatic 

Aromatic 

Nitrogen 

containing 

Halogen 

substituted 

Unknown 

Raw 


Nonadsorbable 



57.9 

66.9 

87.1 

49.2 

59.7 

9.1 

25.3 

1.8 

6.6 

17.0 

9.2 

BDL 

5.7 

4.3 

2.6 

13.2 

BDL 

4.1 

10.4 

BDL 

10.5 

7.8 

1.2 

29.5 

20.7 


showed that the 500-3,000 MS fraction was reduced after optimized coagulation for 

both waters, while the


2.25 

Conventional treatment influent 

Optimized coagulation influent 

0 Conventional treatment effluent 

• Optimized coagulation effluent 

50 100 150 200 


250 300 

Figure 7.1 Effect of coagulation pretreatment on TOC breakthrough for Ohio River 

water 

Conventional treatment influent 0.044 

Optimized coagulation influent 0.031 

° Conventional treatment effluent 


0.000 

100 150 200 


Figure 7.2 Effect of coagulation pretreatment on UV254 breakthrough for Ohio River 

water






50 100 150 200 

250 300 


Figure 7.3 Effect of coagulation pretreatment on TTHM breakthrough for Ohio River 

water 




* Optimized coagulation effluent 

50 100 150 200 

250 300 


Figure 7.4 Effect of coagulation pretreatment on HAA6 breakthrough for Ohio River 

water


O 

o> 

120 

100 - 

80 - 

60 

.0 

I c 40 H 

8 

§ 

O 20 -\ 

TOX 





C0 (ug CI-/L). 

170 

92 

50 100 150 200 

250 300 


Figure 7.5 Effect of coagulation pretreatment on TOX breakthrough for Ohio River 

water 

o.u - 

• 

4.5 - 

4.0 - 

. 

3.5 - 

^j 

°> 3.0 - 

I 2.5- 

| 2.0- 

0) 

c 1-5 - 

o 

0 1.0- 

0.5 - 

CH 

1 .,,.,..,. 1 .... 1 ...,,,,., 





C0 (M9/L) - 

' >> - 

- 

- 

- 

0.0 - 

.,., I ..,, I ,.,,,,,,.,.,,,,.,., 

() 50 100 150 200 250 31DO 


Figure 7.6 Effect of coagulation pretreatment on CH breakthrough for Ohio River water


2.25 

2.00 - 

1.75 - 

1.50 - 

TOC 



Conventional treatment effluent 

Optimized coagulation effluent 

C0 (mg/L) 

2.2 

1.7 

50 100 150 200 

250 300 


Figure 7.7 Effect of coagulation pretreatment on TOC breakthrough for Salt River 

Project water 





0.000 

50 100 150 200 


250 300 

Figure 7.8 Effect of coagulation pretreatment on UV254 breakthrough for Salt River 

Project water


90 

80 

70 

60 

TTHM C0 (ug/L) 





50 

*•/ 

I 

0) 

o 

§O 

40 

30 

20 

10 

0 

100 150 200 


250 300 

Figure 7.9 Effect of coagulation pretreatment on TTHM breakthrough for Salt River 

Project water 

25 

20 - 

15 - 

HAA8 

CQ (ug/p 





I 

10 - 

o 

O 

—i— 

50 

100 150 200 


250 300 

Figure 7.10 Effect of coagulation pretreatment on HAA6 breakthrough for Salt River 

Project water


140 

120 - 

100- 

TOX 





C0 (ug CI-/L) 

170 

130 

50 100 150 200 

250 300 


Figure 7.11 Effect of coagulation pretreatment on TOX breakthrough for Salt River 

Project water 

5.0 

4.5 - 

4.0 - 

3.5 - 

g> 3.0 - 

CH C0 (ug/L) 





C •) r I 

o t.O 

£ 2.0 H 

8 15 , 

C '-5 n 

O 

0 1.0 H 

50 100 150 200 

250 300 


Figure 7.12 Effect of coagulation pretreatment on CH breakthrough for Salt River 

Project water


100 

s 

o 

I 8 


O) 

20 

16 - 

HAA6 

00=1.3 

a, = 11.5 

Pi » 7.3 

i——'——i——>——i——'——i———"~ 

—e-- Conventional treatment (OQ, a,) 

—•—Optimized coagulation ((30 , p,) 

8 

, 8H 

4 - 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 

1.4 

TOC concentration (mg/L) 

Figure 7.15 Effect of coagulation pretreatment on the relationship between TOC and 

HAA6 formation during GAC breakthrough for Ohio River water 

iuu - 

~ 8° " 

3" 

b 

I 60- 

? 

* 1 ' 1 ' 1 ' 1 • 1 ' 1 ' 

TOX o Conventional treatment (a0 , a.,) . 

• Optimized coagulation (p0, p,) 

a0 = -24.9 

o 

a, = 90.8 

0^31.0 ° 

• 

| 4°o 

O 20 - 

• o 

• * 

n - 

• o 

0 

0.0 0.2 0.4 0.6 0.8 1.0 

1.2 1.4 



TOX formation during GAC breakthrough for Ohio River water

130 Removal ofDBP Precursors by GA C Adsorption 

50 - ——— i ——— | ——— i ——— i ——— > ——— i —— i ——— i — • — i • i 

1 

I 

§ 

3 

40 - 

30 - 

20 - 

10 - 

TTHM o Conventional treatment (OQ, a,) . 

• Optimized coagulation (P0, p,) 

oto = P 0 = -3.05 

a, = 31.9 ° 

P, = 24.6 ° 

0 • 

0 » 

o 

0 • 

• 

n - 

0.0 0.2 0.4 0.6 0.8 1.0 


1.2 1.4 

Figure 7.17 Effect of coagulation pretreatment on the relationship beween TOC and 

TTHM formation during GAC breakthrough for Salt River Project water 

«u 

15 - 

s 

? 

| 10- 

c 

8 co 

o 5- 

HAA6 o Conventional treatment (OQ, a,) 

• Optimized coagulation (P0, p,) 

OQ = p0 = 1 .2 

a, = 8.7 

P, = 6-9 ° . 

o 

m • 

o 

QQ ^^^ 

o • 

0 0 

• 

• 

- 

n - 

0.0 0.2 0.4 0.6 0.8 1.0 

1.2 1.4 



HAA6 formation during GAC breakthrough for Salt River Project water


100 - 

o Conventional treatment (OQ, a.,) 

• Optimized coagulation (P0 , p,) 

o 

at 

80 - 

Og = Po = -0.6 

a, = p, = 56.9 

o 60 

| 

| 40 

o 

O 

20 -J 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 


1.4 


TOX formation during GAC breakthrough for Salt River Project water 

Raw Conventional Optimized 0 50 100 150 200 250 




Figure 7.20 Effect of coagulation pretreatment on humic and nonhumic fraction 

breakthrough for Ohio River water





Raw Conventional Optimized 


(GAC influent) 

GAC effluent samples 

Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. NA = not analyzed 

Figure 7.21 Effect of coagulation pretreatment on the specific UFC-TTHM yields of 

humic-nonhumic fractions for Ohio River water 

2.5 

1 • i • ' ' ' i ' ' > ' i ' • 




Raw Conventional Optimized 0 


50 100 150 200 250 300 


Note: Effluent sample fractionation not performed. 

Figure 7.22 Effect of coagulation pretreatment on humic and nonhumic fraction 

breakthrough for Salt River Project water


en 

2.5 


3000 MS fraction 

1.5 - 

8 

1.0 H 

0.5 - 

0.0 

Raw Conventional Optimized Q 

• treatment coagulation 

100 150 200 250 



Figure 7.23 Effect of coagulation pretreatment on molecular size fraction breakthrough 

for Ohio River water 

o> 

100 

80 - 

452 

IHH Unfractionated 


I I < 3000 MS fraction 

t 

o> 

2 40 - 

o 

60- 

20- 

Bf:DOC 257 

(ng/mg) 

427 

Raw Conventional Optimized A 

treatment coagulation _._ „ . . 

(GAC influent) GAC effluent samples 



molecular size fractions for Ohio River water


2.5 

~ 2.0- 


3000 MS fraction 

1.5 - 

O 

0.5 - 

0.0 

Raw Conventional Optimized 0 


50 100 150 200 250 300 



Figure 7.25 Effect of coagulation pretreatment on molecular size fraction breakthrough 

for Salt River Project water 


__ < 500 MS fraction 

I I < 3000 MS fraction 

Raw Conventional Optimized 


(GAC influent) 

A B 

GAC effluent samples 



molecular size fractions for Salt River Project water

Chapter 8___________ 

The Impact of Ozonation and 

Biological Filtration on NOM 

Removal and DBF Control by GAG 

The adsorption capacity of GAC is affected by the influent concentration 

and chemical nature of the water being treated. For natural waters, in general, lower 

initial concentrations yield longer GAC adsorption capacities and therefore longer 

run times to a given effluent concentration. Changes in composition jand organic 

matter structure, however, can also affect adsorption capacity. 

One GAC pretreatment option that may potentially improve adsorption 

behavior is ozonation with biological filtration. Ozonation of the NOM causes a 

shift to lower-molecular-weight compounds that have lower UV absorbance and are 

more easily biodegradable. Microorganisms attached to biologically active filters 

can then utilize some of these biodegradable compounds, reducing the overall NOM 

concentration as well as biostabilizing the water. Typically, the adsorbability of 

NOM decreases with ozonation because of the creation of more polar, hydrophilic 

compounds. Sontheimer et al. (1988) showed that biological filtration can compen 

sate for the negative effect of ozonation on adsorption by removing these weakly 

adsorbing compounds. Summers and Crittenden (1989) showed that ozonation and 

biological filtration reduced the organic concentration in filtered ORW and in 

creased the run time of a GAC contactor. The amount of reduction in NOM resulting 

from ozonation and biological filtration is a function of the ozone dose and the water 

being treated. Miltner and Summers (1992) reported a 20 to 40 percent TOC 

removal for ORW after ozonation and biological filtration, while Joselyn and 

Summers (1992) reported only a 15 percent TOC removal in settled ORW. 

Metz et al. (1993) compared GAC performance of ozonated, biologically 

filtered, ozonated and biologically filtered, and conventionally treated ORW. The 

case of ozonated and biologically filtered water produced the lowest DBF yields 

after GAC treatment for a given run time. 


The objective of the work presented in this chapter was to evaluate the 

impact of ozonation and subsequent biological filtration on NOM removal and DBF 

control by GAC. Conventionally treated water was ozonated and passed through an 

acclimated biofilter before being treated by GAC, as discussed in Chapter 2. GAC 

135


performance was monitored in terms of TOC and UV,54 , and DBF removal was 

assessed in terms of UFC-TOX, -TTHM, -HAA6, and -CH. The impact of ozonation 

and biological filtration on NOM characteristics was investigated in terms of the 

effects on humic and nonhumic composition, molecular size fractions, biodegradability, 

and PY-GC-MS results. 

Experimental Conditions________________ 

The impact of ozonation and biological filtration as pretreatment before 

GAC was investigated for ORW, PRW, and FGW. Conventionally treated water 

was passed through a bench-scale ozonation system with a contact time of 7 minutes. 

Details of the system are presented in Chapter 2. Table 8.1 summarizes the 

experimental conditions for all three waters. The ozone dose reported is the ratio of 

transferred ozone to DOC in mg/mg. The BDOC after ozonation was approximately 

10 percent of the TOC for both PRW and FGW, and it was 20 percent of the TOC 

for ORW. 

After ozonation the water was passed through a biologically active sand 

filter. The sand filter was designed to remove the quickly biodegradable fraction and 

had an EBCT of 5 minutes. The biologically active sand was acclimated with the 

water to be treated prior to the run, as discussed in Chapter 2. The TOC removals 

after biological filtration were 33, 26, and 17 percent for ORW, FGW, and PRW, 

respectively. After biological filtration, the water was filtered through a 1-um 

cartridge and treated by GAC. 

Impact of Pretreatment on GAC Breakthrough 

Behavior: Results and Discussion___________ 

The impact of ozonation and biological filtration on GAC breakthrough 

behavior was compared for ORW, PRW, and FGW. Table 8.2 summarizes the effect 

of ozonation and biological filtration on GAC influent. The GAC influent TOC 

concentrations were not significantly affected by ozonation alone. After biological 

filtration, however, they decreased 17 to 33 percent. The breakthrough behavior was 

compared using the concentration of immediate breakthrough, C imm , and the time 

to initial breakthrough, tint, as well as run times to the effluent criteria described in 

Chapter 6. The terms Cimm and tint represent the nonadsorbable concentration and 

the time at which the adsorbable fraction begins to break through, respectively. 

Table 8.3 summarizes the comparisons between Cimm , tjnt , and run times to effluent 

criteria for all three waters. The results for CH are not reported because concentra 

tions were often at or below the detection limit. 

Figure 8.1 shows the impact of ozonation and biological filtration on 

normalized TOC breakthrough for ORW. Because the influent concentrations were 

different, the normalized concentration was used to facilitate comparisons of 

breakthrough behavior. Ozonaticn and biological filtration reduced the influent 

TOC concentration from 2.1 to 1.4 mg/L. Based on the lower influent concentration, 

the column treating the ozonated and biologically filtered water would be expected 

to break through later. However, for ORW the ozonated and biologically filtered 

water initially broke through more quickly. After 40 percent breakthrough, both

The Impact of Ozonation and Biological Filtration 137 

Table 8.1 Summary of experimental conditions for ozonation and biological filtration 


After 

conventional 

TOC 

(mg/L) 

UV254 

(1/cm) 

Ozone dose 

(mg Cymg TOC) 

TOC 

(mg/L) 

After 

ozonation 

uv 254 

(1/cm) 

BDOC 

(mg/L) 

TOC 

(mg/L) 

After 

biological filtration 

UV254 

(1/cm) 

BDOC 

(mg/L) 

ORW. 

PRW 

FGW 

2.1 

3.0 

10.0 

0.048 

0.076 

0.29 

1.04 

1.58 

1.03 

2.0 

2.8 

9.8 

0.015 

0.041 

0.11 

0.4 

0.3 

0.8 

1.4 

2.5 

7.4 

0.016 

0.038 

0.10 

BDL 

BDL 

BDL 


Table 8.2 Summary of GAC influent characteristics after ozonation and biological 

filtration 

TOC 

(mg/L) 

UV254 

(1/cm) 

TOX 

(M9 CL-/L) 

TTHM 

(H9/L) 

HAA6 

(P9/L) 

CH 

(M9/L) 


Conv. 

Oj/bio 

Conv. 

O3/bio 

Conv. 

Oj/bio 

Conv. 

03/bio 

Conv. 

O^bio 

Conv. 

O3/bio 

ORW 

PRW 

FGW 

2.1 

3.0 

10.0 

1.4 

2.5 

7.4 

0.048 

0.076 

0.29 

0.016 

0.038 

0.10 

170 

277 

1,046 

56 

135 

645 

86 

73 

238 

38 

57 

205 

26 

70 

142 

13 

31 

102 

3.9 

15 

7.8 

0.5 

11 

9.5 

Conv. = conventional treatment 

O 3/bio = ozonation and biological filtration 

columns performed similarly. This behavior indicates that after ozonation and 

biological filtration, a higher percentage of the TOC was composed of more polar 

and less adsorbable compounds, which were created during ozonation but were not 

removed by biological filtration. 

Figure 8.2 compares the absolute effluent TOC concentrations from the 

columns treating conventionally treated ORW and ozonated and biologically 

filtered ORW. The effluent TOC concentrations of both columns were similar up 

to 60 scaled operation days, after which the column treating the ozonated and 

biologically filtered water performed better. The run time to an effluent TOC 

criterion of 0.8 mg/L was increased from 80 to 105 days by pretreatment. As 

indicated in Table 8.3, the nonadsorbable TOC concentration remained the same, 

but as a fraction of total TOC it increased from 9.8 to 14 percent. The time to initial 

breakthrough was reduced from 45 to 35 days. Figure 8.3 shows the breakthrough 

behavior of UV254 . The nonadsorbable fraction was lower for the ozonated and 

biologically filtered water. Between 30 and 90 scaled operation days, both columns 

behaved similarly. After 90 days, the column treating ozonated and biologically 

filtered water performed better. The tint decreased from 50 to 10 days for UV254 . It 

is recognized, however, that comparisons between conventional treatment and 

ozonation with biological filtration are complicated by ozonation's destruction of 

a water's capacity for adsorbing UV light at 254 nm.

120 

62 

65 

90 

95 

115 

9 

7.5 

1 

10 

9 

5 

30 

15 

112 

40 

49 

48 

105 

>150 

17 

13 

12 

6 

27 

15 

>40 

21 

31 

>20 

>25 

>70 

— 

-67 

-35 

-25 

-47 

11 

>30 

89 

73 

1,100 

-40 

200 

200 

>100 

40 

~ 

i ciuie o.«a ouuiiinai y ui in ipai*i wi u'^wiiauwn anu uivsiwyivai mil auuii uu vmo pel i ui mam 

Water Parameter 

ORW TOC 

• uv254 

TOX 

TTHM 

HAA6 

Units 

mg/L 

1/cm 

M9 CI-/L 

Mg/L 

Mg/L 

Cone. 

0.2 

0.003 

3 

4 

5 

Immediate breakthrough 

concentration, C lmm 

Conv. 

%lnf 

9.8 

6.8 

3.5 

15 

2.9 

Cone. 

0.2 

BDL 

10 

4 

5 

Cybio 

%lnf 

14 

— 

18 

11 

39 

Conv. 

(days) 

45 

50 

45 

30 

50 

Time to initial 

breakthrough, t 

O-/bio 

(days) 

35 

10 

85 

— 

— 

•Int 

Change 

-22 

-80 

89 

— 

— . 

rC 

Criterion 

0.8 

0.01 

70 

32 

24 

Run time based on 

established criterion 

Conv. 

(days) 

80 

125 

120 

88 

>150 

O3/bio Change 

(days) (%) 

105 

>150 

>150 

>150 

>150 

Oo 

ofDBP Removal 

"0 

s 

3o 

•i" 

d 

0 

1 o 

•3 

PRW TOC 

UV254 

TOX 

TTHM 

HAA6 

mg/L 

1/cm 

Mg CI-/L 

Mg/L 

Mg/L 

0.2 

0.003 

10 

5 

7.5 

6.7 

3.5 

3.6 

6.8 

11 

0.3 

0.005 

10 

5 

7.5 

12 

13 

7.4 

8.8 

24 

50 

45 

55 

55 

75 

20 

20 

40 

20 

20 

-60 

-56 

-27 

-64 

-73 

1.6 

0.8 

0.01 

70 

32 

24 

FGW TOC 

UV254 

TOX 

TTHM 

HAA6 

mg/L 

1/cm 

Mg CI-/L 

Mg/L 

Mg/L 

0.6 

0.01 

25 

The Impact ofOzonation and Biological Filtration 139 

Figures 8.4,8.5, and 8.6 compare the breakthrough behavior of UFC-TOX, 

-TTHM, and -HAA6 for ORW. The effluent concentrations in both columns were 

similar initially for all three DBFs; however, the ozonated and biologically filtered 

column performed significantly better than the column to which conventionally 

treated water was applied. The nonadsorbable concentration of TTHM and HAA6 

precursors remained the same, while that for TOX precursors increased from 3 to 

10 ug/L after ozonation and biological filtration. The time to initial breakthrough 

increased for TOX precursors, decreased for TTHM precursors, and remained the 

same for HAA6 precursors. The effluent concentrations of the column treating 

ozonated and biologically filtered water did not reach the effluent criteria used in 

this study; in fact, they had not reached 50 percent of the effluent criteria after 150 

days. 

Figure 8.7 compares normalized TOC breakthrough behavior for PRW. 

Ozonation and biological filtration resulted in a reduction of influent TOC from 3.0 

to 2.5 mg/L. TOC in the column treating ozonated and biologically filtered water 

broke through more rapidly than in the column treating conventionally treated 

water. Although the influent TOC concentration was decreased, ozonation and 

biological filtration affected the nature of the NOM, which could have resulted in 

earlier breakthrough. After approximately 65 percent breakthrough, the normalized 

effluents from both columns were similar. Figure 8.8 compares the effluent TOC 

concentrations on an absolute basis. The column treating ozonated and biologically 

filtered water broke through earlier. However, after 80 scaled operation days, the 

columns showed similar behavior. The TOC tint decreased from 50 to 20 days as a 

result of ozonation and biological filtration. The nonadsorbable concentration 

increased by 50 percent with ozonation and biological filtration. As with ORW, this 

adsorption behavior indicates that ozonation created polar, less adsorbable com 

pounds that were not entirely removed by biological filtration. These compounds 

led to earlier breakthrough. The run time to an effluent TOC criterion of 1.6 mg/L 

decreased from 120 to 112 scaled operation days with ozonation and biological 

filtration. The run time to aTOC criterion of 0.8 mg/L decreased from 62 to 50 scaled 

operation days. The UV254 results for PRW effluent are shown in Figure 8.9. The 

column treating ozonated and biologically filtered water broke through earlier. 

However, after 70 scaled operation days, the performance of this column was better 

than the column representing conventional treatment. The run time to an effluent 

criterion of 0.01/cm decreased from 65 to 49 scaled operation days, although this 

was likely significantly affected by the destruction of UV-absorbing compounds 

during ozonation prior to GAC treatment. 

The impact of ozonation and biological filtration on DBF control for PRW 

is shown in Figures 8.10, 8.11, and 8.12 for UFC-TOX, -TTHM and -HAA6, 

respectively. The behavior of the three formed DBFs was similar to that of UV254 . 

The column treating ozonated biologically filtered water initially broke through 

earlier than the column treating conventionally treated water, but after 80 to 100 

days the column treating ozonated and biologically filtered water showed better 

DBF removal. The nonadsorbable concentrations of TOX, TTHM, and HAA6 

precursors remained the same after ozonation and biological filtration but increased 

as a fraction of the total influent concentration. The run time for an effluent TOX 

criterion of 70 ug C1~/L decreased from 90 to 4.8 scaled operation days; the run time 

for a TTHM effluent criterion of 32 ug/L increased from 95 to 105 scaled operation 

days, and that for HAA6 increased from 115 to > 150 scaled operation days.


Figure 8.13 shows the impact of ozonation and biological filtration on 

normalized TOC breakthrough for FGW. The influent GAC concentration de 

creased from 10.0 to 7.4 mg/L after pretreatment, and the performance of the column 

treating ozonated and biologically filtered water was better than that of the column 

representing conventional treatment. The same impact is shown in Figure 8.14, 

which compares the effluent TOC concentrations. The nonadsorbable TOC concen 

tration after ozonation and biological filtration decreased and the tint doubled. The 

run time to an effluent TOC concentration of 1.6 mg/L increased from 9 to 17 scaled 

operation days. The run time to an effluent TOC concentration of 0.8 mg/L 

increased from 7.5 to 13 scaled operation days. The results for UV254 breakthrough 

in Figure 8.15 were similar to TOC results. The column treating ozonated and 

biologically filtered water lowered UV254 very well. The nonadsorbable matter 

measured by UV254 decreased by 50 percent. 

For FGW, ozonation and biological filtration prior to GAC resulted in 

better removal of TOX, TTHM, and HAA6 precursors by GAC, as shown in Figures 

8.16, 8.17, and 8.18, respectively. The nonadsorbable concentrations increased for 

TOX and HAA6 precursors. The run times to the given effluent criteria for TTHM 

and HAA6 increased as a result of ozonation and biological filtration by 300 and 40 

percent, respectively, while that for TOX decreased by 40 percent. For FGW, the 

TOX effluent criterion is very low compared to the influent TOX concentration. 

Also, fewer data points were taken for TOX than for the other formed DBFs. 

Effect of Ozonation and Biological Filtration on 

Specific DBF Yield of GAC________________ 

The results from the preceding section indicate that the effect of using 

ozonation and biological filtration as GAC pretreatment may be dependent on the 

water source. It is difficult to directly compare the GAC control of DBF formation 

in ozonated and biologically filtered water to that in conventionally treated water 

because the influent concentrations were different. To facilitate the comparison, 

DBF formation can be plotted as a function of TOC concentration. As discussed in 

Chapter 7, this method allows the DBF formations to be compared at the same TOC 

values. 

The plots of DBF formation versus TOC tended to be highly linear. A bestfit 

curve was applied to the DBFs formed and TOC concentration data of both 

columns. The slope and the y-axis intercept of the curves from the ozonated and 

biologically filtered water and conventionally treated water were compared using 

regression analysis with categorical variables. The method for comparison was 

discussed in detail in Chapter 7. The four parameters, a0 , a,, 80, and 6,, were 

estimated and used to compare the specific yields for the two treatments. The t-test 

was used to determine whether the parameters were significantly different than 

zero. The results were categorized into five possible outcomes— types 1, 1 modi 

fied, 2, 3, and 4—as discussed in Chapter 7 (see Figure 7.14, p. 128). 

Figure 8.19 shows the effect of biotreatment of ORW on TOX formation. 

These results were classified as type 3 behavior. The intercepts of the two curves 

were the same while the slopes were different. Ozonation and biological filtration 

decreased the TOX formed at a given TOC. The slope represents the specific DBF 

yield. The results for specific TOX, TTHM, and HAA6 yields for ORW, PRW, and


Table 8.4 Summary of the effect of ozonation and biotreatment on the relationship 

between TOC and DBF formation 

Slope Y-axis intercept 


Conventional O3/bio Conventional Oj/bio 

treatment, filtered, treatment, filtered, 

Parameter a, I3n 

«0 B 0 

F 

F* 

Behavior 

Type 

ORW 

TOX 

TTHM 

HAA6 

86 

59 

11 

> 

> 

> 

22 

19 

5.5 

1.0 

-15 

2.0 

1.0 

< -2.0 

2.0 

2.44 

* 

3.91 

5.99 

1c 

4.60 

3 

1 modified 

3 

PRW 

TOX 

TTHM 

HAA6 

67 

35 

18 

> 

> 

> 

55 

28 

9.3 

-11 

-11 

-1.8 

. = -11 

= -11 

< 2.1 

0.43 

0.07 

* 

4.49 

4.49 

* 

3 

3 

1 modified 

FGW 

TOX 

TTHM 

HAA6 

80 

19 

10 

> 

> 

> 

22 

12 

3.1 

-1.0 

12 

-7.0 

= -1.0 

= 12 

< 9.6 

* 

0.37 

* 

• 

4.67 

* 

3 

3 

1 modified 

* Full model was not simplified. 

FGW are summarized in Table 8.4. The comparisons between F and the critical F 

value, F*, are also summarized in Table 8.4. Figures 8.20 and 8.21 show the TTHM 

and HAA6 results, respectively. TTHM results showed type 1 behavior; the slopes 

and intercepts of the two curves were different. HAA6 results showed type 3 

behavior, indicating that after ozone and biological pretreatment the DBF formation 

was lower for a given TOC concentration above 0.4 mg/L. In addition, the increase 

in specific DBF yield for a given increase in TOC was less for the ozonated and 

biologically filtered water than for the conventionally treated water. 

The PRW results for TOX and TTHM formation indicate Type 3 behavior 

and are shown in Figures 8.22 and 8.23, respectively. The HAA6 results for PRW, 

shown in Figure 8.24, show modified type 1 behavior, i.e., the two curves intersect. 

Thus, at aTOC concentration below 0.5 mg/L, the H AA6 formation for the ozonated 

and biologically filtered water could be higher than for the conventionally treated 

water; however, there were no data in this range. . 

The results for TOX, TTHM, and HAA6 formation for FGW are shown in 

Figures 8.25,8.26, and 8.27, respectively. Similar to the results for PRW, the TOX 

and TTHM results showed type 3 behavior. The TTHM results of the ozonated and 

biologically filtered water in Figure 8.26 showed a lot of scatter, though, making it 

very difficult to classify the data. For a given TOC concentration, however, the 

specific yield was lower after ozonation and biological filtration. The HAA6 

formation data shown in Figure 8.27 exhibits modified type 1 behavior, again with 

a lot of scatter in the ozonated and biologically filtered data. 

In all cases, ozonation and biological filtration decreased the DBF forma 

tion for a given TOC concentration. Thus, the specific DBF yields were lower. Also, 

for a given increase in TOC concentration, the corresponding increase in specific 

DBF yield was lower after ozonation and biological filtration than for conventional


treatment. For all nine cases, the slopes of the two curves were significantly 

different, while the intercepts were significantly different in only three of the nine 

cases. Therefore, ozonation and biological filtration followed by GAC seemed to 

preferentially remove DBF precursors as compared to GAC alone. 

Impact of Ozonation and Biological Filtration 

on NOM Fraction Characteristics After GAC______ 

GAC influent and three effluent samples were fractionated into humic and 

nonhumic components and molecular weight components as described in Chapter 

5. Three effluent samples were collected to represent the nonadsorbable fraction, 

intermediate breakthrough, and advanced breakthrough, as shown in Figure 5.10 

(p. 67). The fractions were characterized in terms of TOC and UV254 , and the DBF 

formation was assessed by TTHM formation under UFC at constant Br~:DOC 

ratios. The fractionation results for the raw and conventionally treated water were 

discussed in detail in Chapter 5. 


Figure 8.28 shows the humic and nonhumic fractionation results for ORW. 

The bar graph region shows the raw water, the conventionally treated GAC influent, 

and the ozonated and biologically filtered GAC influent. The line graph region 

shows DOC composition in the GAC effluent from the column treating ozonated 

and biologically filtered water. Table 8.5 summarizes the DOC composition of the 

raw water and influent to the GAC columns. Conventional treatment of ORW 

reduced the DOC slightly, mainly as a result of a decrease in the humic fraction. 

Ozonation and biological filtration decreased the TOC by 33 percent, and the DOC 

removal was due to the removal of both fractions. The resulting GAC influent was 

59 percent nonhumic in nature. Other researchers (Koechling et al. 1996) have 

shown that ozonation caused a shift in DOC, creating a more nonhumic NOM; 

subsequent biological filtration reduced the nonhumic fraction. For this study, 

NOM was not characterized after ozonation but before biological filtration. Thus, 

it is not possible to determine whether the same effect occurred in this work. GAC 

treatment affected the DOC composition in a similar manner as was described in 

Chapter 5. The humic fraction was initially very well removed, and the nonadsorbable 

fraction was entirely nonhumic in nature. The humic fraction continued to be well 

removed. Even at advanced breakthrough, the DOC was still 73 percent nonhumic. 

The specific TTHM yields for the humic and nonhumic fractions are shown 

in Figure 8.29 for ORW. The Br:DOC ratio was adjusted to the same value for each 

unfractionated and fractionated sample group so that the TTHM formations from 

these samples could be compared. However, the raw, conventionally treated, 

ozonated and biologically filtered, and GAC effluent samples cannot be compared 

to each other since the Br:DOC ratio shifts, as indicated on the graph. The humic 

fraction of the ozonated and biologically filtered water had a slightly lower specific 

yield than the unfractionated water and nonhumic fraction. 

The humic-nonhumic DOC fraction results for PRW and FGW are shown 

in Figures 8.30 and 8.31, respectively. Ozonation and biological filtration decreased 

the total DOC concentration of the GAC influent by 10 and 25 percent for PRW and


Table 8.5 Impact of ozonation and biotreatment on humic-nonhumic and MS 

fractionation results 

Percent of total DOC 


Treatment 

Nonhumic 

Humic 

3,000 MS 

ORW 

Raw 

Conventional 

Ozone-biological 

45 

48 

59 

55 

52 

.41 

19 

28 

21 

60 

72 

65 

21 

0 

14 

PRW 

Raw 

Conventional 


44 

48 

70 

56 

52 

30 

16 

21 

20 

63 

79 

64 

21 

0 

16 

FGW 

Raw 

Conventional 


25 

25 

54 

75 

75 

46 

8 

7 

11 

90 

76 

84 

2 

10 

5 

FGW, respectively. This removal was due to a removal of the humic fraction. The 

nonhumic concentration of both waters increased after ozonation and biological 

filtration. The DOC composition of both waters after GAC treatment was similar to 

that for ORW. The nonadsorbable fraction was entirely nonhumic in nature. The 

humic fraction was very well removed in both waters, while the nonhumic fraction 

broke through quickly. 

The specific TTHM yields for the humic-nonhumic fractions for PRW and 

FGW are shown in Figures 8.32 and 8.33. For both PRW and FGW, the specific 

yields for the humic and unfractionated samples were similar for the raw and 

conventionally treated water, while those for the nonhumic fraction were lower. 

Ozonation and biological filtration did not result in a significantly lower specific 

yield for either of the two waters. There was no systematic effect of ozonation and 

biological filtration on specific yields of the humic-nonhumic fractions after GAC 

treatment for either PRW or FGW. 

Molecular Size Fractionation 

Figure 8.34 shows the effect of the ozonation and biological filtration 

pretreatment on MS fractions for ORW. The fractionation results are summarized 

in Table 8.5. The dominant fraction in raw ORW was the intermediate fraction 

(500-3,000 MS). Conventional treatment caused a slight reduction in DOC as a 

result of the removal of the >3,000 MS fraction. The decrease in DOC after 

ozonation and biological filtration was mainly attributed to removal in the small and 

intermediate MS fractions. The effect of GAC treatment on MS fractions was 

similar to that described in Chapter 5. The small MS fraction quickly yielded 

complete breakthrough, while the intermediate fraction was better removed.

144 Removal ofDBP Precursors by GA C Adsorption 

The effect of pretreatment on the specific TTHM yields of MS fractions for 

ORW is shown in Figure 8.35. The specific yields of the GAC influent and effluent 

showed no systematic difference among the MS fractions. 

The MS fractionation DOC results for PRW and FGW are shown in Figures 

8.36 and 8.37, respectively. The results for PRW and FGW were similar to those for 

ORW. The dominant fraction in the raw, conventionally treated, and ozonated and 

biologically filtered water was the intermediate MS fraction. For PRW, DOC 

removal by conventional treatment was largely due to the total removal of the large 

MS fraction, while for FGW it was mainly the intermediate MS fraction that was 

removed. As with ORW, the >3,000 MS fraction of PRW increased in the ozonated 

and biologically filtered water relative to conventional treatment. The effect of 

GAC treatment on the MS fractions of PRW and FGW was similar to that for ORW. 

The small MS fraction broke through quickly, while the intermediate MS fraction 

was much better removed. 

Figure 8.38 shows the specific TTHM yields for MS fractions of PRW. The 

specific yields of the


Table 8.6 PY-GC-MS classification of ORW: Ozonation and biotreatment 


Stage 

Aliphatic 


Aromatic containing substituted 

Unknown 

Raw 


Nonadsorbable 



34.5 

57.9 

50.3 

55.2 

48.1 

15.8 35.9 13.9 

0.4 6.1 30.3 

0.8 7.1 37.2 

BDL 3.2 38.3 

1.4 8.0 28.7 

BDL 

5.2 

4.6 

3.3 

13.9 


Table 8.7 PY-GC-MS classification 

of PRW: Ozonation and biotreatment 


Stage 

Aliphatic 



Unknown 

Raw 


Nonadsorbable 



23.0 

38.3 

BDL 

58.6 

9.04 

3.9 12.0 67.0 

5.5 8.7 40.0 

BDL BDL BDL 

BDL 1.1 • 36.6 

0.9 3.75 78.0 

4.3 

6.8 

BDL 

3.75 

8.2 


Table 8.8 PY-GC-MS 

classification 

of FGW: Ozonation and biotreatment 


Stage 

Aliphatic 



Unknown 

Raw 


Nonadsorbable 



1.4 

7.6 

94.3 

54.4 

56.7 

97.0 BDL BDL 

83.0 2.6 2.7 

BDL 2.3 3.0 

1.0 5.1 39.5 

14.5 8.6 10.5 

1.6 

4.1 

BDL 

BDL 

9.7 

BDL = below detection limit


fragments (dibromochloromethane>tribromomethane>dichloroacetonitrile). The 

organic matrix at complete GAC breakthrough remained predominantly aliphatic, 

although the aromatic fraction showed a small increase in comparison to the second 

breakthrough point. The pyrolysis fingerprint was characterized by a strong acetic 

acid peak and minor peaks of formic and propanoic acid. 

Summary_________________________ 

The impact of ozonation and biological filtration on GAC breakthrough 

behavior was water dependent. For ORW and PRW, ozonation likely created polar, 

less adsorbable compounds that were not removed entirely by biological filtration. 

The presence of these compounds in the GAC influent caused poorer performance 

in the initial part of breakthrough. During the latter stages of breakthrough, the GAC 

column treating ozonated and biologically filtered water showed better perfor 

mance, which is likely due to the overall lower influent concentration. For ORW and 

PRW, ozonation and biological filtration increased the nonadsorbable fraction (0.7 

percent and 37 percent, respectively) and decreased the time to initial breakthrough 

of the adsorbable fraction. For ORW, GAC performance based on the effluent 

criteria improved after ozonation and biological filtration. The improvement 

occurred after 70 days for the NOM surrogates, TOC and UV, 54 , and after 30 to 50 

days for the DBF precursors. For PRW, ozonation and biological filtration de 

creased the run time to effluent criteria for TOC, UV254 , and TOX and increased the 

run time to the effluent criteria for TTHM and HAA6. For FGW, ozonation and 

biological filtration resulted in a significant improvement in GAC performance; the 

run times to initial breakthrough and to the effluent criteria increased. Thus, 

biological filtration of FGW was effective in removing or altering the polar, less 

adsorbable compounds created by ozonation. 

For the three waters studied, ozonation and biological filtration resulted in 

a shift in NOM composition. Ozonation and biological filtration yielded NOM that 

was more nonhumic than humic in nature, especially for ORW and PRW, where the 

nonhumic fractions were 59 and 70 percent, respectively. This shift to a greater 

percentage of the less adsorbable compounds explains the earlier breakthrough after 

ozonation and biological filtration of ORW and PRW. 

Ozonation and biological filtration also resulted in a decrease in the TOC 

concentration of the GAC influent. If a decrease in TOC is not accompanied by a 

change in the chemical makeup of the water, the reduced influent concentration 

should increase the run time to a given effluent criterion. An increase in the 

nonhumic content of NOM will result in a decrease in adsorbability of the NOM. 

Thus, it is postulated that the change in NOM composition decreased the benefit 

associated with a decrease in influent TOC concentration. This effect was not 

observed for FGW, which was highly humic in nature prior to treatment. Even after 

ozonation and biological filtration, 46 percent of the DOC was still humic for FGW. 

Thus, decreasing the influent concentration outweighed the change in humicnonhumic 

composition. 

Ozonation and biological filtration resulted in a decrease in the intermedi 

ate MS fraction. Ozonation has been shown to cause a shift toward the small MS 

fraction (Koechling et al. 1996). These small MS compounds can then easily be 

degraded. Thus, ozonation likely caused a decrease in the intermediate and large MS


fraction, while biological filtration removed a substantial amount of the small MS 

fractions. The intermediate MS fraction was shown in Chapter 5 to be better 

adsorbed than the small and large MS fractions. Thus, decreasing this fraction and 

increasing the more poorly adsorbed large MS fractions could contribute to the 

earlier GAC breakthrough seen after ozonation and biological filtration.


-a 

o 

o. 

o 

I 

o o 

o 

1.0 - 

0.9 - 

0.8 - 

0.7 - 

0.6 - 

0.5 - 

0.4 - 

TOC C0 (mg/L) 

• Conventional treatment 2.1 

0 Ozonation + biotreatment 1.4 

EBCT= 15 min 

0.3 - 

0.2 - 

0.1 -: 

0.0 

50 100 

150 200 


Figure 8.1 Effect of ozonation and biotreatment on normalized TOC breakthrough for 

Ohio River water 

1.6 

1.2- 

TOC C0 (mg/L) 



EBCT = 15min 

I 

o •s 

I 

Io 

O 

0.8 

0.4 - 

0.0 

50 100 150 


200 

Figure 8.2 Effect of ozonation and biotreatment on TOC breakthrough for Ohio River 

water


o 

0.020 

0.018 - 

Conventional treatment 0.048 

Ozonation + biotreatment 0.016 


80 

70- 

60 - 

TTHM C0 (M9/L) EBCT = 15min 

* Conventional treatment 86 

0 Ozonation + biotreatment 38 

50 - 

I 

O 

40 

30 - 

20 - 

10 - 

—i— 

50 100 150 200 


Figure 8.5 Effect of ozonation and biotreatment on UFC-TTHM breakthrough for Ohio 

River water 

30 

HAA6 (H9/L) EBCT= 15 min 

25 - 

• Conventional treatment 26 


20 - 

c 

o 

2•s 

8 

o 

O 

15 - 

10 - 

5 - 

—i— 

50 100 150 200 


Figure 8.6 Effect of ozonation and biotreatment on UFC-HAA6 breakthrough for Ohio 

River water


5 

1 

§o 

8 

I 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

TOC C0 (mg/L) 


o Ozonation + biotreatment 2.5 

EBCT=15min 

0.2 

0.1 

0.0 

50 100 150 200 


250 


Passaic River water 

2.8 

2.4 - 

2.0 - 

TOC C0 (mg/L) 



i '' •^ 

EBCT=15min 

o 

1 § 


8 

ro 

-e 

o 10 

.a 

0.04 - 

0.03 - 

0.02 - 

(Q 

0.05 



0.01 - 

0.00 

50 100 150 


200 250 

Figure 8.9 Effect of ozonation and biotreatment on UV254 breakthrough for Passaic 

River water 

180 

160 - 

140 - 

TOX cr/L) 


0 Ozonatfon + biotreatment 135 

EBCT = 15 min 

o 

o> 

o 

•£ 

8 

o 

O 

120 - 

100 - 

80 - 

60 - 

40 - 

20 - 

50 100 150 

200 250 


Figure 8.10 Effect of ozonation and biotreatment on UFC-TOX breakthrough for Passaic 

River water


80 

TTHM C0 (M9/L) 

70 - 



60 - 

EBCT=15min 

50 

Io 

8 

o 

O 

40 -\ 

30 

20 

10 A 

—i— 50 

—i— 

100 150 


200 250 

Figure 8.11 Effect of ozonation and biotreatment on UFC-TTHM breakthrough for 


50 

40 - 

HAA6 C0 (M9/L) 

* Conventional treatment 70 


EBCT= 15 min 

o> 

o 

8 

o 

O 

30 - 

20 - 

10 - 

50 100 150 

200 250 


Figure 8.12 Effect of ozonation and biotreatment on UFC-HAA6 breakthrough for 

Passaic River water


o 

1.0 

0.9 

0.8 

0.7 

0.6 

TOC C0 (mg/L) 



EBCT=15min 

I 

a 

0.5 

0.4 

0.3 

o 

0.2 

0.1 

0.0 

10 20 


30 40 


Florida groundwater 

o> 

10 

9 - 

8 - 

7 - 

6 - 

TOC C0 (mg/L) 



EBCT=15min 

g 

5 - 

• 

4 - 

I o 

O 

3 - 

2 - 

1 

—i— 

10 

—i— 

20 

30 40 


Figure 8.14 Effect of ozonation and biotreatment on TOC breakthrough for Florida 

groundwater


0.30 

UV. 254 

u 

?= 0.20 - 

8 

OJ 

€ 

o 

in 

.Q 

(0 

0.25 - • Conventional treatment 0.29 


0.15 - 

IZD 

0.00 -^ 


Figure 8.15 Effect of ozonation and biotreatment on UV254 breakthrough for Florida 

groundwater 

800 -T 

TOX C0 (M9CIVL) 

700 - 



600 - 

EBCT = 15min 

o 

1 

g 

2 

*-• 

I o 

O 

500 - 

400 - 

300 - 

200 - 

100 - 

10 20 

30 40 


Figure 8.16 Effect of ozonation and biotreatment on UFC-TOX breakthrough for Florida 

groundwater


200 

180 

160 

TTHM C0 (M9/L) EBCT=15min 

Conventional treatment 238 

Ozonation + biotreatment 205 

140 

o 

^= 

2 

I o 

O 

120 

100 

80 

60 

40 

20 

0 


Figure 8.17 Effect of ozonation and biotreatment on UFC-TTHM breakthrough for 


100 

90 

80 

HAA6 c0 (ng/L) EBCT=15min 


° Ozonation + biotreatment 102 

O) 

o 

I 

70 

60 

50 

40 

30 

20 

10 

0 

—i— 

10 20 


—r— 

30 40 

Figure 8.18 Effect of ozonation and biotreatment on UFC-HAA6 breakthrough for 

Florida groundwater


100 

80 - 

Oo-P0 =1.0 

a, = 80 

'—-•-- Conventional treatment (a,,, a,) 

Ozonation/biotreatment (P 0 , p,) 

O 60 - 

O) 

o 

8 

o 

O 

0.0 0.2 0.4 0.6 0.8 1.0 

1.2 1.4 


Figure 8.19 Effect of ozonation and biotreatment on the relationship between TOC and 

TOX formation during GAC breakthrough for Ohio River water 

100 

0.2 0.4 0.6 0.8 1.0 1.2 


1.4 


TTHM formation during GAC breakthrough for Ohio River water


û - 

16 - 

i ' 

1 12 - 

o 

s 

Concent 

OB 

4 - 

HAA6 • Conventional treatment (a0 , a,) . 

o Ozone/biotreatment (P0 , p,) 

a, = 11 • 

Pl = 5.5 

* 0 00 0 

o ° 

* ° 0 

n - 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 



HAA6 formation during GAC breakthrough for Ohio River water 

o 

a 

o 

8 

o 

200 

175 

150 - 

125 - 

100 - 

75 - 

50 

25 

TOX 

=Po = - 

a, =67 

Conventional treatment (a0 , a,) 

Ozone/biotreatment (p0 , p,) 

0.0 0.5 1.0 1.5 2.0 

2.5 3.0 



TOX formation during GAC breakthrough for Passaic River water


s 

c 

o 

8 

o 

O 

100 

80 - 

60 - 

40 

20 - 

TTHM 

a, = 35 

Conventional treatment (a0 , a,) 

Ozone/biotreatment (P0 , p,) 

0.0 0.5 1.0 1.5 2.0 

2.5 3.0 



TTHM formation during GAC breakthrough for Passaic River water 

60 

50 - 

HAA6 

00 =-1.8 

' Conventional treatment (a0 , a,) 

" Ozone/biotreatment (p0 , p,) 

^- 40 - 

^ 

§ 30 H 

a, = 18 

Pi = 9-3 

20 - 

10 - 

0.0 0.5 1.0 1.5 2.0 

2.5 3.0 



HAA6 formation during GAC breakthrough for Passaic River water


£ 

b 

0) 

»uu - 

800 - 

700 - 

600 - 

500 - 

400 - 

' i ' i • i • i • i • i • i • i • i • 

TOX ~*~~ Conventional treatment (o^, a,) 

a, = 80 

— e — Ozone/biotreatment (P0 , p,) 

Pi=22 .//^ '. 

/• 

9 

c o1 

Io 

O 

300 - 

200 - 

100 - 

/ 

s 

/^ o 

s' o ^^__^— 

S 00 ^ —— — — 

0 T ——— ' ——— | ——— i ——— 1 ——— ' ——— 1 ——— ' ——— | ——— ' ——— 1 ——— ' ——— 1 ——— • ——— 1 ——— ' ——— 1 ——— ' ——— | ——— ' ——— 1 

01234567891 



TOX formation during GAC breakthrough for Florida groundwater 

Conventional treatment (OQ, a,) 

Ozone/biotreatment (P0 , p,) 

o> 

o •-C 

I0) 

o 

o 

234567 



TTHM formation during GAC breakthrough for Florida groundwater


100 

HAA6 

Conventional treatment (a,,, a,) 

Ozone/biotreatment (p0, p,) 

I 

8 

8 

60 - 

40 

20 - 

Pi =3.1 

234567 

TOO concentration (mg/L) 

9 10 


HAA6 formation during GAC breakthrough for Florida groundwater 

2.5 

" Unfractionated 



o 1.5 J 

1 

8 

8 

1.0 - 

80- 

2.0- 

0.5- 

0.0 

Raw Conventional OJBio „ ... .... ^_. 

treatment y 0 50 100 150 


Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. O3/Bio = ozonation and 

biological filtration. 

Figure 8.28 Effect of ozonation and biotreatment on humic-nonhumic fraction 

breakthrough for Ohio River water





CO 

Raw Conventional cyBio A 

treatment 

Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. NA = not analyzed. 

O 3/Bio = ozonation and biological filtration. 

Figure 8.29 Effect of ozonation and biotreatment on humic-nonhumic fraction specific 

yields for Ohio River water 




Raw Conventional CyBio Q 

treatment 

50 100 


150 

Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. O/Bio = ozonation and 


Figure 8.30 Effect of ozonation and biotreatment on humic-nonhumic fraction 

breakthrough for Passaic River water

The Impact ofOzonation and Biological Filtration 163 

i 

o 

14 

12 - 

10- 

8 - 

m Unfractionated 



8 6^ 

8 

2- 

0 

Raw Conventional Oj/Bio 0 

treatment 

10 20 30 


40 



Figure 8.31 Effect of ozonation and biotreatment on humic-nonhumic fractionation for 





Raw Conventional 

treatment 


O/Bio = ozonation and biological filtration. 


yields for Passaic River water





Br:DOC 21 

(Hg/mg) ,—, 

Raw Conventional (v/Bio A 

treatment 


O3/Bio = ozonation and biological filtration. 


yields for Florida groundwater 

2.5 

O) 





1.5 - 

8 

8 

1.0 - 

0.5- 

0.0 

Raw Conventional Oj/Bio rj 

treatment 

50 100 150 


Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. 0,/Bio = ozonation and 


Figure 8.34 Effect of ozonation and biotreatment on MS fraction breakthrough for Ohio 

River water





O) 

I 

2 

•I 

0 Q. 

to 

Raw Conventional cyBio 

treatment 

Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. CyBio = ozonation and 


Figure 8.35 Effect of ozonation and biotreatment on specific yields for MS fractions for 


4.0 

3.5 

I" 

| 2.0 

8 

I 1 -5 

O 1.0 


0 < 500 MS fraction 

* 500-3000 MS fraction 

0 > 3000 MS fraction 

B 

0.5 

0.0 

Raw Conventional O/Bio o 

treatment 

50 100 150 


Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. 0,/Bio = ozonation and 


Figure 8.36 Effect of ozonation and biotreatment on MS fraction breakthrough for 

Passaic River water


15 

10 - 


0 < 500 MS fraction 

A 500-3000 MS fraction 

0 > 3000 MS fraction 

I 

8 5 

8 

Raw Conventional cyBio 

treatment 




Figure 8.37 Effect of ozonation and biotreatment on MS fraction breakthrough for 





Raw Conventional O/Bio 

treatment 

Note: A, B, and C denote initial, intermediate, and advanced breakthrough points. CyBio = ozonation and 


Figure 8.38 Effect of ozonation and biotreatment on specific yields for MS fractions for 

Passaic River water

Chapter 9 

Impact of Treatment on DBP 

Speciation 

Changes in the ratio of Br to TOC (Br:TOC) as a result of preferential 

removal of TOC by a treatment process can strongly affect DBP speciation upon 

chlorination. DBP speciation is important because the proposed MCLs for TTHM 

and HAAS as part of the D-DBP Rule are mass-based levels. The atomic weight of 

the bromine atom is 2.25 times greater than that of the chlorine atom. Thus, an 

increase in bromine incorporation into DBP speciation, given the same molar yield, 

will lead to higher TTHM or HAAS mass concentrations. These increases in mass 

concentrations may not necessarily be reflected in changes in molar TTHM and 

HAAS concentrations. DBP speciation is also significant for health related issues 

because some of the more brominated species may be of a greater health concern 

than chlorinated species (Krasner et al. 1992). It is also important to consider that 

not all nine HAA species are measured by HAAS or HAA6. In fact, all four species 

not accounted for by HAAS are bromo-substituted species; therefore, increases in 

the concentrations of these species due to shifts in speciation after treatment are not 

accounted for by measurements of HAAS. The formed concentrations of HAA 

species not accounted for by HAAS or HAA6 measurements can constitute a 

significant fraction of total HAA formation at moderate to high Br~:TOC ratios 

(Cowman and Singer 1994). 


GAC Effluent DBP Speciation 

One can observe effect of GAC treatment on DBP speciation by plotting the 

breakthrough of each THM compound on one graph, as shown in Figure 9.1 for 

ORW after conventional pretreatment. After 65 days of scaled operation, concen 

trations in the chlorinated GAC effluent were highest for bromoform and 

dibromochloromethane and lowest for bromodichloromethane and chloroform. 

This can be contrasted with THMs measured in the influent upon chlorination, 

where bromoform was lowest and bromodichloromethane was highest. As GAC 

breakthrough continued, the bromoform concentration following chlorination of 

the GAC effluent reached a peak, and then the bromodichloromethane concentra 

tion increased above bromoform. Figure 9.2 shows THM species breakthrough in 

ORW after GAC treatment preceded by optimized coagulation. 

167


An example of the breakthrough of HAA species in chlorinated GAC 

effluent following conventional treatment of SRPW is shown in Figure 9. 3. Influent 

DBF formation was highest for dichloroacetic acid (DCAA) and trichloroacetic 

acid (TCAA), both nonbrominated species. During GAC breakthrough, two brominated 

species, dibromoacetic acid (DBAA) and bromochloroacetic acid (BCAA), 

showed concentrations that were higher than both DCAA and TCAA. The figure 

also shows results for monochloracetic acid (MCAA) and monobromoacetic acid 

(MBAA). Results observed for HAA species breakthrough in SRPW after opti 

mized coagulation are shown in Figure 9.4. 

The effect of GAC treatment on the breakthrough of THM precursors for 

PRW is shown in Figure 9.5. At 75 scaled operation days, dibromochloromethane 

concentrations were highest. At the end of the run, 190 scaled operation days, 

chloroform had the highest concentration. This corresponds to the influent speciation, 

where chloroform also had the highest concentration. The concentration of 

bromoform peaked at 75 scaled operation days, after which it decreased. Thus, after 

75 days, the Br~:TOC ratio decreased as a result of TOC breakthrough to an extent 

that the formation of more chlorinated species was favored. The effect of ozonation 

and biological filtration on THM species for PRW is shown in Figure 9.6. A shift 

in speciation occurred in the GAC influent, with dibromochloromethane concentra 

tions being the highest throughout the breakthrough period. After GAC treatment, 

the more brominated compounds broke through to a greater extent. At the end of the 

run at 1 50 scaled operation days, dibromochloromethane had the highest concentra 

tion in the effluent, while chloroform concentrations were only slightly higher than 

those of bromoform. 

Breakthrough of HAA species after conventional treatment is shown in 

Figure 9.7 for FGW. The highest GAC influent concentrations were for DCAA and 

TCAA. These two species also had the highest concentrations at the end of the run. 

DBAA peaked at 1 6 scaled operation days, after which its concentration in the GAC 

effluent decreased. The effect of ozonation and biological filtration on HAA species 

breakthrough is shown in Figure 9.8 for FGW. After pretreatment, the speciation in 

the GAC influent shifted. Large concentrations of MCAA and DBAA were 

detected, while the concentrations of DCAA and TCAA decreased. This is also 

reflected in the GAC effluent, where DBAA had the highest concentration at the end 

of the run. 

Bromine Incorporation Factors 

These speciation results after GAC treatment are similar to those reported 

by Summers et al. (1994b). It is difficult, however, to directly compare shifts in 

speciation as breakthrough occurs in chlorinated GAC effluent for different 

pretreatment scenarios, such as conventional pretreatment, optimized coagulation, 

or ozonation and biological filtration. To assist in making these comparisons, the 

bromine incorporation factor, n, can be calculated on a molar basis for THM 

speciation (Gould et al. 1983) using Equation 9.1 : 

„ = (9.1) 

TTHM

Impact of Treatment on DBF Speciation 169 

where THM-Br is defined as: 

THM-Br = Ii«CHCl,_.Br. 

j=0 311 

(9-2) 

Both THM-Br and TTHM have units of u.mol/L. The n value can range from 0 to 

3 depending on the extent of bromine incorporation; n = 0 if only chloroform is 

formed, while n = 3 if only bromoform is present. 

A similar calculation, Equation 9.3, can be made to calculate the bromine 

incorporation factor for HAA speciation, n' (Shukairy and Summers 1993): 

n = 

HAA - Br 

HAA 

(9.3) 

where HAA-Br is defined as the sum of the bromo-substituted HAA species 

analyzed. Within HAA6, not all bromo-substituted HAA species are included. 

Therefore, HAA-Br is defined by Equation 9.4: 

HAA-Br = (1 • MBAA) + MBAA) + (1 • BCAA) + (2 • DBAA) (9.4) 

where MBAA is monobromoacetic acid, BCAA is bromochloroacetic acid, and 

DBAA is dibromoacetic acid. Again, the units used for HAA-Br and HAA are 

umol/L. The n' value can range from 0 to 2 depending on the extent of bromine 

incorporation. 

Optimized Coagulation 

Table 9.1 lists TOC, bromide, Br:TOC, n, and n' for ORW and SRPW after 

conventional treatment and optimized coagulation. For ORW, optimized coagula 

tion increased the bromine incorporation for both THMs and HAAs as compared to 

conventional treatment. This increase was primarily caused by an increase in 

Br:TOC with optimized treatment, since TOC was reduced while the bromide 

concentration was assumed to be unaffected. The Br":TOC ratio increased 

38 percent with optimized coagulation for ORW, and n and n' increased 40 and 

Table 9.1 Effect of optimized coagulation on DBF speciation 

ORW SRPW 

Parameter 



Change Conventional Optimized Change 

(%) treatment coagulation (%) 

Influent TOC (mg/L) 

Bromide (ug/L) 

Br:TOC (ug/mg) 

n 

n' 

2.0 

132 

64.7 

0.92 

0.61 

1.5 

132* 

89.2 

1.28 

0.76 

-28 

NA 

38 

40 

24 

2.2 

89 

39.9 

0.75 

0.40 

1.7 

89* 

52.9 

0.98 

0.52 

-25 

NA 

33 

30 

31 


*Not measured; it was assumed that GAC treatment did not affect bromide concentrations.


24 percent, respectively. For SRPW, the 33 percent increase in Br:TOC was matched 

by 30 and 31 percent increases in n and n'. Thus, for these two waters, the change in 

BrrTOC with optimized coagulation was matched by similar changes in n and n'. 

Ozonation and Biological Filtration 

The effect of ozonation and biological filtration on GAC influent speciation 

is shown in Table 9.2. For ORW, ozonation and biological filtration decreased the 

Br:TOC ratio by 12 percent, while for PRW and FGW, the Br :TOC ratio increased 

by 20 and 31 percent, respectively. The bromine incorporation factors for both 

THMs and HAAs increased for all three waters after ozonation and biological 

filtration. For ORW, Br:TOC decreased by 12 percent, while 60 and 34 percent 

increases in n and n', respectively, were observed. For PRW, there was a 20 percent 

increase in the Br~:TOC, while n and n' increased by 177 and 135 percent, 

respectively. For FGW, n and n' values increased by 124 and 117 percent, 

respectively, after a 31 percent increase in the Br:TOC ratio. As discussed earlier, 

increases in n and n' values after ozonation and biological filtration were larger than 

after optimized coagulation for similar increases in the Br~:TOC ratio. An increase 

in the Br:TOC ratio is known to cause the shift to more brominated species. After 

ozonation and biological filtration, the characteristics of the organic matter changed 

as discussed in Chapter 8. This change in NOM characteristics could have caused 

the larger shift in speciation observed after pretreatment by ozonation and biological 

filtration. The results for ORW, where a decrease in the Br:TOC ratio resulted in 

an increase in n and n', would seem to support this. 

It is difficult to determine the effect of pretreatment on GAC effluent n and 

n' when these values are plotted as a function of scaled operation time because, at 

any given time, effluent TOC concentrations in the RSSCT effluent for the two 

pretreatments were not equal. Therefore, n and n' have been plotted against the 

measured TOC for each sample. In this manner, the behavior of n and n' with 

increasing TOC can be compared between the two types of pretreatment conditions 

for each water. Figure 9.9 shows the effect of pretreatment on n values for ORW. 

At TOC concentrations of 0.4 mg/L and higher, n decreased for both pretreatment 

types. Although at TOC concentrations below 0.8 mg/L the n values of the effluent 

samples from RSSCT-OC were slightly lower than those from RSSCT-CT, above 

0.8 mg/L there was little difference between the two pretreatments. At TOC 

concentrations below 0.4 mg/L, n increased with TOC for both RSSCTs. This trend 

would not have been expected based on the high Br:TOC ratio at very low TOC 

concentrations. It may be caused in part, however, by analytical difficulties at low 

TOC concentrations, as well as by the very low levels of substrate (TOC) available 

for reaction. A similar relationship illustrating n as a function of TOC for SRPW 

showed the same overall trends shown for ORW, as well as little to no effect of 

pretreatment on GAC effluent n values (Figure 9.10). 

Figure 9.11 shows the effect of ozonation and biological filtration on GAC 

effluent n values for PRW. The results seen in Figures 9.5 and 9.6 are again evident 

here. The n values after ozonation and biological filtration were larger, indicating 

a more brominated THM distribution. At TOC concentrations greater than 0.7 

mg/L, n decreased with increasing TOC. At TOC concentrations less than 0.7 

mg/L, n increased. Similar results were seen for FGW, shown in Figure 9.12. The 

n values after conventional treatment were in general lower than after ozonation and

FGW 

Ozonation/ 

biological 

filtration 

7.4 

268 

36.2 

0.75 

0.26 

Change 

% 

-26 

_g 

31 

124 

117 

13 

O 

n 5 

s 

o 

re 

S' 

Table 9.2 Effect of ozonation and biological filtration on DBF speciation 

ORW 

PRW 

Conventional 

Parameter 

treatment 

Influent TOC (mg/L) 2.0 

Bromide (ug/L) 

132 

Br:TOC (ug/mg) 

64.7 

n 0.92 

n' 

0.61 

Ozonation/ 

biological 

filtration 

1.4 

80 

57.1 

1.46 

0.82 

Change 

% 

-33 

-39 

-12 

60 

34 

Conventional 

treatment 

3.0 

80 

26.7 

0.47 

0.24 

Ozonation/ 

biological 

filtration 

2.5 

80 

32.0 

1.29 

0.56 

Change 

% 

-17 

0 

20 

177 

135 

Conventional 

treatment 

10.0 

276 

27.6 

0.34 

0.12


biological filtration. Ozonation and biological filtration increased the bromine 

incorporation factor. Similar results were reported in Shukairy et al. (1994) and 

Koechlingetal. (1996). 

The effect of pretreatment on GAC effluent n' values is shown in Figure 

9.13 for ORW. Conventionally pretreated GAC effluent sample n' values were 

slightly higher than those for GAC effluent samples after optimized coagulation. 

The comparison is confounded however, by the fact that three of the nine HAA 

species are not measured when HAA6 is being determined; shifts in speciation to 

these species are not incorporated by n'. Below a TOC concentration of 1.0 mg/L, 

a direct relationship between n' and TOC was found. Again, possible shifts to the 

nonanalyzed HAA species, which are all brominated, at low TOC concentrations 

could account for this relationship. For SRPW, pretreatment did not seem to have 

a large effect on GAC effluent n' values, as shown in Figure 9.14. 

The effect of ozonation and biological filtration on GAC effluent n' values 

for PRW is shown in Figure 9.15. At TOC concentrations below 1.6 mg/L, n' values 

for both pretreatments were similar. At TOC concentrations greater than 1.6 mg/L 

n' values were greater after ozonation and biological filtration. For FGW, shown in 

Figure 9.16, there was little effect of ozonation and biological filtration on GAC 

effluent n' values. Other researchers (Shukairy et al. 1994; Koechling et al. 1996) 

have shown that n' values increased after ozonation and biological filtration. 

Summary_________________________ 

It is evident that treatment by GAC after conventional pretreatment in 

creases the Br~:TOC ratio, as well as the Br~:Q 2 ratio when chlorinating takes place 

at a fixed Cl ,:TOC ratio, and therefore shifts DBF speciation to the more bromosubstituted 

species. Although an increase in bromo-substitution as measured by the 

bromine incorporation factors n and n' was observed with optimized coagulation as 

compared to conventional treatment, the percent increases in n and n' were well 

predicted by the increase in Br~:TOC ratio after optimized coagulation. This 

indicates that DBF speciation was not affected by changes in the chemistry of NOM 

after optimized coagulation, but that the increased bromo-substitution was mainly 

a result of the change in Br~:TOC resulting from the decrease in TOC after 

optimized coagulation. 

Ozonation with biological filtration resulted in significant increases in 

bromine incorporation for both THMs and HAAs that were not reflected by the 

changes in the Br~:TOC ratio between conventional treatment and ozonation with 

biological filtration. In fact, ORW showed a decrease in Br~:TOC but still showed 

increases in both n and n'. PRW and FGW yielded increases in n and n' by factors 

of 8 and 4, respectively, which were greater than the increases seen inBr~:TOC.This 

behavior is a strong indication that ozonation and biological filtration changed the 

character of NOM precursors to THMs and HAAs. 

The results also showed that differences in pretreatment to GAC, when 

normalized by the effluent TOC concentration, did not have a significant impact on 

effluent DBP speciation. For pretreatment with optimized coagulation and with 

ozonation and biological filtration, most n and n' curves showed an initial increase, 

which may have been caused by low substrate or analytical difficulties, followed by 

a decrease as TOC concentrations increased and yielded lower Br~:TOC ratios.

Impact of Treatment on DBP Speciation 173 


175 

Figure 9.1 

treatment 

GAC breakthrough of THM species in Ohio River water after conventional 

1 

o 

1 

V 

u 

o 

O 

5 - 

50 100 150 200 

250 300 


Figure 9.2 GAC breakthrough of THM species in Ohio River water after optimized 

coagulation


HAA species 

o MCAA 

a DCM 

• TCAA 

^5 *S 

2 - 

1 - 

25 50 75 


100 125 

Figure 9.3 GAC breakthrough of THM species in Salt River Project water after 

conventional treatment 

o 

100 150 200 


300 

Figure 9.4 GAC breakthrough of HAA species in Salt River Project water optimized 

coagulation


i 

•S3 o 

1 

8 O 

50 100 


200 

Figure 9.5 GAC breakthrough of THM species in Passaic River water after conventional 

treatment 

g 

"•4-1 

S 

O 

100 


150 200 

Figure 9.6 GAC breakthrough of THM species in Passaic River water after ozonation 

and biotreatment


i 

o MCAA 

Q DCAA 

TCAA 

o 

8 

o 

O 


Figure 9.7 GAC breakthrough of HAA species in Florida groundwater after 

conventional treatment 

2 

o 

1 

8 

10 20 30 


40 

Figure 9.8 GAC breakthrough of HAA species in Florida groundwater after ozonation 

and biotreatment


3.0 

c 2.5 H I" 

o Conventional treatment 

• Optimized coagulation 

? 2'°1 

o ••sa 

S 1.5 - 

0> 1.0 - 

2 

m 0.5 - 

0.0 

0.0 0.5 

1.0 1.5 

TOO (mg/L) 

2.0 2.5 

Wore: Data represent GAC effluent following pretreatment. 

Figure 9.9 Effect of treatment on THM bromine incorporation factor (n) for ORW 

2.5 

L.- 2.0 - 


• Optimized coagulation pretreatment 

3. 

1.5 H 

5 

I 

§ 1.0 - 

Q) 

1 

8 0.5 - 

m 

0.0 

0.0 

0.5 1.0 1.5 

TOC (mg/L) 

2.0 2.5 

Wore: Data represent GAC effluent following pretreatment. 

Figure 9.10 Effect of treatment on THM bromine incorporation factor (n) for SRPW


2.5 


• Ozonation and biotreatment 

2.0 - 

I C J 

1.5 1 

0) 

1.0 - 

m 0.5 - 

0.0 

0.0 0.5 1.0 

1.5 2.0 

TOC (mg/L) 

2.5 3.0 3.5 

Note: Data represent GAC effluent following pretreatment. 

Figure 9.11 Impact of pretreatment on THM bromine incorporation factor (n) for 


5.0 

4.5 - 

4.0 - 



3.5 - 

3.0 - 

2.5 - 

2.0- 

8. 

m 

1.5 - 

1.0 - 

0.5 - 

0.0 T- 

2 

T~ 

6 

TOC (mg/L) 


Figure 9.12 Impact of pretreatment on THM bromine incorporation factor (n) for Florida 

groundwater

Impact of Treatment on DBP Speciation 179 

1.6 

1.4 - 

1.2 - 

1.0 - 

n 1 



0.8- 

c 0.6 - 

o> 

E 0.4 - 

8 

CQ 

0.2 H 

0.0 

0.0 0.5 

1.0 1.5 

TOC (mg/L) 

2.0 2.5 


Figure 9.13 Effect of treatment on HAA bromine incorporation factor (n') for ORW 

1.6 

1.4 H 

n' 



g 1.0 H 

1 2 0.8 H 

c 0.6 - 

0) 

I 0.4 1 

8 

m 0.2 H 

0.0 

0.0 

0.5 1.0 1.5 

TOC (mg/L) 

2.0 2.5 


Figure 9.14 Effect of treatment on HAA bromine incorporation factor (n') for Salt River 

Project water


s 

o 

0.8 

0.6 - 



0.4 - 

Chapter 10 

The Impact of NOM Preloading on 

SOC Adsorption Behavior 

Even when designed and operated to remove NOM and control DBF 

formation, GAC can adsorb other compounds, such as SOCs, although at dimin 

ished capacities. Several researchers have evaluated the impact of prior exposure to 

NOM (preloading) on GAC's adsorption capacity for SOCs (Sontheimeret al. 1988; 

Summers et al. 1989; Cummings 1992). In the referenced research, the extent and 

kinetics of the loss of GAC adsorption capacity (fouling) have been investigated for 

a wide range of GACs and SOCs. Unfortunately, only a few sources of NOM have 

been examined, and most of them have been groundwaters. This chapter describes 

the impact of NOM preloading on the adsorption of SOCs for all six source waters 

used in this study. 


The objective of the work presented in this chapter was, for a range of water 

sources, to assess the loss of GAC adsorption capacity for a specific SOC as a result 

of prior exposure to NOM. The GAC was collected from the RSSCT columns at the 

end of the NOM adsorption runs for all six sources: LOW, MRW, ORW, PRW, 

SRPW, and FGW. Preloaded GAC was also collected from the pilot plant and fullscale 

adsorbers treating LGW and ORW, respectively, at the end of the operating 

life. Preloaded GAC was sampled from the bottom and top of each column so that 

the impact of bed depth could be examined. After the GAC was dry, and after 

grinding in the case of the pilot and full-scale GACs exposed to LGW and ORW, 

bottle-point batch isotherms were conducted, as described in Chapter 2, to assess 

the residual GAC adsorption capacity for DCE. The isotherm results were then 

compared to determine the influence of NOM source, GAC bed depth, time of 

preloading, and scale of preloading (RSSCT versus field scale) on adsorption of 

DCE. 

A summary of the operating conditions under which the GAC was preloaded 

is presented in Table 10.1. TheEBCT of the pilot- or full-scale contactors was'15 

minutes, and that of the RSSCTs was the equivalent of 15 minutes at the pilot or full 

scale. The exception was the RSSCT for MRW (because of pilot test design 

constraints for the utility), which was designed to simulate a pilot-scale EBCT of 6.2 

minutes. The influent TOC concentrations ranged from 1.5 to 10.0 mg/L, and the 

columns were operated until 60 to 80 percent TOC breakthrough was attained. The 

breakthrough curves associated with these studies are shown in previous chapters. 

181


Table 10.1 Summary of operating conditions for preloaded GAC 

Source 

Influent DOC 

Scale (mg/L) 

EBCT* 

(minutes) 

Operation time 

(days) 

Throughput 

(bed volumes) 

ORW 

Field 

RSSCT 

2.1 

2.0 

15.0 

1.19(15) 

210 

150 

20,160 

14,400 

LGW 

Field ' 

RSSCT 

1.5 

1.5 

15.0 

1.88 (15) 

127 

135 

12,190 

12,960 

MRW 

RSSCT 

2.7 

0.66 (6.2) 

47 

10,830 

PRW 

RSSCT 

3.0 

1.59(15) 

205 

19,500 

SRPW 

RSSCT 

2.2 

1.59(15) 

150 

14,500 

FGW 

RSSCT 

10.0 

2.85(15) 

31 

3,000 

* Values in parentheses represent equivalent large-scale EBCT. 

A single-solute isotherm was conducted to establish the adsorption capacity 

of the GAC for DCE without any competition from other cosolutes or preadsorbed 

compounds. In addition, a coadsorption isotherm was run with fresh GAC and DCE 

in the presence of PRW to determine the adsorption competition resulting from the 

background NOM. 


The results of the single-solute and coadsorption isotherms are shown in 

Figure 10.1. The results of these two isotherms, as well as the other 16 isotherms 

conducted for this study, were modeled using the Freundlich equation: 

q = KFC" (10.1) 

where q = the adsorbed or solid phase concentration 

C = liquid phase concentration 

Kp n = the Freundlich model coefficients 

Application of the Freundlich equation to the isotherm data is summarized in Table 

10.2. Nonlinear regression of the Freundlich equation to the data resulted in r2 values 

above 0.90 for all isotherms and 0.98 or above for 13 of the 18 isotherms. Theq, 00 

value represents the solid phase concentration at a DCE liquid phase concentration 

of 100 ug/L and is used to compare the isotherm results. The Freundlich coefficient 

KF was not used for comparison purposes because it represents the solid phase 

concentration at a DCE liquid phase concentration of 1 ug/L, which is not in the 

range of values tested in this research.

The Impact ofNOM Preloading on SOC Adsorption 183 

Table 10.2 Comparison of GAC residual capacity for DCE 

Source 

Scale 

Location 

c 

(M9/L) 

KF 

n 

r2 

9100 

(ug DCE/g GAC) 

ORW 

Field 

RSSCT 

Top 

Bottom 

Top 

Bottom 

507 

507 

507 

507 

32 

63 

29 

95 

0.73 

0.65 

0.73 

0.63 

0.98 

0.97 

0.99 

0.99 

923 

1,237 

836 

1,730 

LGW 

Field 

RSSCT 

Top 

Bottom 

Top 

Bottom 

507 

507 

507 

507 

73 

69 

31 

92 

0.69 

0.68 

0.75 

0.57 

0.99 

0.98 

0.96 

0.99 

1,751 

1,581 

947 

1,393 

MRW 

RSSCT 

Top 

Bottom 

450 

450 

29 

66 

0.77 

0.57 

0.98 

0.95 

1,006 

911 

PRW 

RSSCT 

Top 

Bottom 

450 

450 

92 

108 

0.42 

0.47 

0.90 

0.94 

636 

932 

SRPW 

RSSCT 

Top 

Bottom 

450 

450 

103 

148 

0.55 

0.50 

0.99 

0.99 

1,284 

1,470 

FGW 

RSSCT 

Top 

Bottom 

450 

450 

121 

126 

0.58 

0.67 

0.99 

0.99 

1,749 

2,757 

As shown by the results in Figure 10.1 and Table 10.2, the presence of the 

background organic matter in PRW decreased the adsorption capacity for DCE by 

37 percent. Other researchers have shown that in some cases the background NOM 

will decrease the adsorption capacity for SOCs in a coadsorption isotherm (Sontheimer 

et al. 1988; Cummings 1992), while in other cases the background NOM has no 

negative impact (Sontheimer et al. 1988; Summers et al. 1989). These differences 

are likely due to the strength of adsorption of the target compound relative to that 

of components in the background NOM. 

The effect of operating the RSSCT column with PRW for the equivalent of 

205 days on the residual DCE adsorption capacity is also shown in Figure 10.1. The 

adsorption capacities for DCE were reduced by 84 and 77 percent for the GAC taken 

from the top and bottom of the RSSCT column, respectively. The residual adsorp 

tion capacity of the GAC from the top of the column was lower than that from the 

bottom because more of the "fouling" substances are removed from the background 

NOM at the top of the column (Summers et al. 1989). 

The isotherm results for GAC preloaded with ORW and LGW are shown 

in Figures 10.2 and 10.3, respectively. In addition to results from GAC preloaded 

in the RSSCT, the results from GAC preloaded in the field columns are shown. The 

average DCE capacity reductions were 70 and 65 percent because of preloading by 

ORW and LGW, respectively. The GAC from the top of the LGW field column 

yielded unusually high values. With the exception of the pilot-scale GAC from


LOW preloading, the GAC from the top of the column generally had a lower 

adsorption capacity for these two waters (similar to the GAC preloaded with PRW). 

The preloaded GAC from the RSSCT yielded results that were very similar to those 

for the GAC preloaded in the field. Cummings (1992) also found this for GAC from 

a Florida groundwater. 

Figures 10.4,10.5, and 10.6 show the preloaded isotherm results for MRW, 

SRPW, and FGW, respectively. The GAC in all three cases was collected from the 

RSSCT. For SRPW and FGW, lower capacities were found for GAC from the top 

of the column resulting in a depth effect. The GAC preloaded with MRW was taken 

from a short column (EBCT = 6.2 minutes); thus, the GAC from the bottom of this 

column would be more equivalent to GAC from the middle of the other columns, 

which had EBCTs equivalent to 15 minutes. Thus, the lack of a depth effect for the 

GAC preloaded with MRW is understandable. 

To assess the impact of the time of preloading and NOM source water on 

the residual adsorption capacity for DCE, the relative q, 00 values were plotted as a 

function of throughput for the GACs from the RSSCTs. The results are shown in 

Figure 10.7. The q loo values were normalized to that of the single-solute isotherm, 

q ss , 00, and are shown for GAC from the top of the column, as are average q )00 values 

of GAC from the top and bottom. For both types of q |00 values, a trend of decreasing 

value with increasing throughput was found. Thus, for a given contactor, longer 

operation times with the background NOM would yield lower residual adsorption 

capacities for the target SOC. Similar results have been found by others (Sontheimer 

etal. 1988; Summers etal. 1989; Cummings 1992). Of particular interest, this trend 

was independent of NOM source, which has not been shown by other researchers. 

The column run time or throughput was found to be dependent on influent 

concentration, as discussed in the other chapters. 

The trend shows a quick decrease in DCE capacity followed by a slow 

gradual decrease with time. A second-order polynomial was used to fit the preloaded 

isotherm results, and the value from the coadsorption isotherm was plotted with a 

throughput value of 700 bed volumes, which is the equivalent of the length of time 

the isotherm was run in terms of a 15 minute-EBCT contactor. For the average q, 00 

values, the results from MRW (throughput = 10,830; see Table 10.1) were lower than 

expected because of the short EBCT of that column. When the q, 00 values from the 

GAC at the top of the column are compared, the results for MRW are similar to the 

trend found for the other waters. No relationship between the relative adsorption 

capacity and operation time or mass of TOC removed was found. 

Summary_________________________ 

Exposure of GAC to the background water during the removal of NOM and 

DBP precursors led to a loss of the GAC residual adsorption capacity for DCE. The 

loss in capacity was higher for preloaded GAC from the top of the column. The 

results from preloaded GAC from the RSSCT were similar to those found for GAC 

from the pilot- and full-scale contactors. The residual capacity was found to 

decrease with operation time, expressed as throughput, and was not directly 

dependent on the source water.


o 10000 - 

s 

o> 

PRW 

A Single solute 

T Coadsorption 

0 Preloaded- top 

D Preloaded- bottom 

I 1000 - 

§ 


O 10000 - 

LGW 

Single solute 

c 1000 - 

1 

8 

8 

• F|ekJ- top 

• Field- bottom 

0 RSSCT- top 

0 RSSCT- bottom 

Q. 100 

•o 

=5 1 

CO 

i i i i 111 

10 

1 ' I 

100 

Liquid phase concentration, C (ng DCE/L) 

1000 

Figure 10.3 Impact of preloading of Lake Gaillard water at pilot and small scale on DCE 

adsorption capacity 

o 10000 - 

o 

MRW 

O 

o 

I 

§ 1000 

I 

§8 

0) 

0 RSSCT- top 

D RSSCT- bottom 

o. 100 

"o 

CO 

10 100 


1000 

Figure 10.4 Impact of preloading of Mississippi River water at small scale on DCE 

adsorption capacity


O 10000 

I u? 

O 

Q 

O) 

SRPW 

Single solute 

1000 - 

I 

8 

m CO 

O 

(O 

100 

10 .100 


1000 

0 RSSCT- top 


Figure 10.5 Impact of preloading of Salt River Project water at small scale on DCE 

adsorption capacity 

O 10000 d 

< 

O 

FGW 

O 

Q 

S cr 

c 

.2 

1000 - 

8 

8 

o> 

CD 

Q. 

100 

0 RSSCT- top 


10 100 


1000 

Figure 10.6 Impact of preloading of Florida groundwater at small scale on DCE 

adsorption capacity


Single solute 

T Coadsorption 

- Preloaded-top 

— Preloaded- average 

0.0 

5000 10000 


20000 

Figure 10.7 Correlation between solid phase DCE concentration and throughput in bed 

volumes for six sites

Chapter 11 

Relationship Among NOM 

Characteristics, Removal of DBF 

Precursors by GAC Treatment, and 

DBP Formation 

NOM is ubiquitous in source waters and is composed of a broad spectrum 

of organic compounds, most of which cannot be measured directly, but rather must 

be quantified by surrogate measures. A subset of NOM is composed of precursor 

compounds to DBFs; these DBFs form upon the addition of a disinfectant, such as 

free chlorine. Consequently, it is of great interest to understand the relationship 

among NOM characteristics, removal of DBP precursors by treatment, and DBP 

formation so that the drinking water community can better understand the potential 

for successful application of treatment processes to reduce DBPs based upon an 

understanding of source water characteristics. A study sponsored by AWWARF 

and completed in 1992 (Owen et. al. 1992) began to address the relationship among 

these variables across abroad range of source waters. As an outgrowth of that effort, 

this project was developed to specifically address the removal of DBP precursors 

by GAC adsorption. During the conduct of this work, NOM characterization was 

performed to assist in elucidating the interrelationship among the characteristics of 

DBP precursors, their removal by GAC adsorption, and ultimately the resulting 

formation of DBPs. 

Overview of Characterization Techniques_______ 

The characterization work performed in this study included humic-nonhumic 

fractionation, molecular size fractionation, and pyrolysis GC-MS. The DBP precur 

sor analyses included surrogates commonly used in the water industry (TOC and 

UV254), and the DBPs analyzed included a host of compounds of regulatory interest, 

as well as those that assist in understanding the broad spectrum of halogenated DBPs 

that may be formed upon chlorination (e.g., TOX). Therefore, there is a significant 

amount of data collected in this work that may provide meaningful insight into 

source water characteristics and successful treatment by GAC. To account for the 

preferential removal of NOM compared to bromide, various fractions (humicnonhumic 

and molecular size fractions) were equilibrated with respect to Br~:TOC 

ratio in this research to minimize the impact of variations in that ratio. 

189


As discussed in Chapter 2, pyrolysis GC-MS is a relatively new analytical 

technique that directly measures the fragments of compounds that have been 

pyrolyzed. An evaluation of the pyrolyzed fragments lends insight into the nature 

and composition of the parent compounds. When coupled with DBF analyses 

resulting from the chlorination of the parent material, the pyrolysis GC-MS 

technique has the potential to evaluate "markers" that may indicate parent material 

that is responsible for specific DBF formation. 

Detailed discussions of the effect of pretreatment and GAC adsorption on 

humic-nonhumic fractionation and molecular size fractionation are presented in 

Chapters 5 and 7. A summary of these results will be presented here. In addition, the 

results of pyrolysis GC-MS analyses on various samples of raw and treated water 

will be discussed in this chapter. These results, together with data on DBF formation 

in treated and untreated waters, provide insight into the relationship among NOM, 

DBF precursor removal by GAC treatment, and DBF formation. 

Molecular Size Fractionation___________________ 

The intermediate MS fraction (500 to 3,000) dominated the MS distribution 

in the raw water samples analyzed during this research. Most waters were equally 

distributed between the largest (MS >3,000) and the smallest (MS 3,000 MS fraction was removed and the other 

fractions were not effectively impacted. After GAC treatment, the nonadsorbable 

fraction was composed entirely of

NOM Characteristics, Removal of DBF Precursors, and DBF Formation 191 

Ozonation and biptreatment resulted in a decrease in the intermediate MS 

fraction. Ozonation has been shown to cause a shift toward the small MS fraction 

(Koechling et al. 1996). These small MS compounds are then easily degraded. Thus, 

ozonation likely caused a decrease in the intermediate and large MS fractions, while 

biotreatment removed the more substantial small MS fraction. The intermediate MS 

fractions was shown (in Chapter 5) to be better adsorbed than the small and large 

MS fraction. Thus, decreasing this fraction and increasing the more poorly adsorbed, 

large MS fractions could contribute to the earlier GAC breakthrough seen after 

ozonation and biotreatment. 

Humic-Nonhumic Fractionation______________ 

DOC fractionation for the six raw waters examined during this research 

indicated humic fractions in the range of 53 to 75 percent (a slight dominance over 

the nonhumic fraction). The relative percent composition of each fraction was 

dependent on the water source. FGW was the most humic in nature (75 percent of 

the DOC was found in the humic fraction), while SRPW was the least humic in 

nature (53 percent). The effect of pretreatment and GAC treatment on the NOM was 

very similar for all six waters. DOC removal by the various pretreatment processes 

ranged from 15 to 45 percent and resulted in a major shift from humic to nonhumic 

material (the nonhumic fraction of the treated waters ranged from 56 to 67 percent 

of the total DOC). As evidenced by the GAC effluent samples collected during the 

initial portion of the breakthrough curves, the nonadsorbable fractions were 

composed nearly entirely of the nonhumic fraction; the humic fraction was virtually 

completely removed. With time, the nonhumic fraction broke through rapidly, while 

the humic fraction continued to be better removed. The nonhumic fraction break 

through reached a plateau, while further increases in effluent DOC concentrations 

were observed as a result of the breakthrough of the humic fraction. 

The specific TTHM yield of the humic fraction of either the raw or 

conventionally treated water was only slightly higher than that of the nonhumic 

fraction. After GAC treatment, no systematic differences could be found. This is 

significant in that most researchers have historically considered the humic fraction 

to be more problematic from a standpoint of DBF yield; consequently, more 

attention has been focused on removal of the humic fraction. Recent work (Owen 

et. al., 1992) has recognized the importance of the nonhumic fraction in DBF 

formation, but attempts to quantify the comparison of DBF yields between humic 

and nonhumic fractions was confounded by varying Br~:TOC ratios. Consequently, 

this research represents the first work indicating comparable yields between humic 

and nonhumic fractions under equilibrated Br":TOC ratios. 

For the two waters examined (ORW and SRPW), optimized coagulation 

removed a fraction of the TOC present after conventional treatment. For ORW, both 

humic and nonhumic fractions were affected about equally, while for SRPW the 

nonhumic fraction was preferentially removed. Other research has shown that 

coagulation usually preferentially removes the humic fraction over the nonhumic 

fraction (Semmens and Staples 1986; Collins et al. 1986; Dryfuse et al. 1995). 

For the three waters studied (ORW, PRW, and FGW), ozonation and 

biotreatment resulted in a shift in NOM composition. Ozonation and biotreatment 

yielded NOM that was more nonhumic than humic in nature, especially for ORW


and PRW, for which the nonhumic fractions were 59 and 70 percent, respectively. 

This shift to more of the less adsorbable compounds explains the earlier break 

through for ORW and PRW after ozonation and biotreatment. This type of 

pretreatment resulted in a decrease in the TOC concentration of the GAC influent. 

If the decrease in TOC is not accompanied by a change in the chemical makeup of 

the water, the reduced influent concentration should increase the run time before a 

given effluent criterion is reached. An increase in the nonhumic content of NOM 

will result in a decrease in adsorbability of the NOM. Thus, it is postulated that the 

change in NOM makeup decreased the benefit associated with a decrease in influent 

concentration. This effect, however, was not observed for FGW. FGW was highly 

humic in nature prior to treatment. Even after ozonation and biotreatment, 46 

percent of the DOC was still humic in nature. Thus, decreasing the influent 

concentration outweighed the change in humic-nonhumic composition. 

The Role of TOC and UV254________________ 

The comparisons of relative breakthrough curves for TOC, UV., 54 , and DBF 

precursors for ORW, MRW, PRW, SRPW, and FGW are shown in Figures 11.1 to 

11.5. The results shown are for the RSSCT. TOC and UV254 were used to 

characterize NOM breakthrough, and TOX, TTHM, HAA6, and CH precursor 

breakthrough were assessed under UFC. For ORW, HAA6 precursors had the 

earliest breakthrough of the measured DBP precursors; CH precursors broke 

through latest, and the other two parameters were found in between. For the 

remaining four waters, TTHM precursors broke through earliest, and the relative 

breakthrough patterns for DBP precursors were similar to those for ORW. 

For ORW, TOC can be used as a conservative indicator of the breakthrough 

of DBP precursors after 35 percent breakthrough. In the early portions of the 

breakthrough curve, HAA6 precursor breakthrough was more rapid than TOC 

breakthrough. For PRW, SRPW, and FGW, TOC broke through earliest and thus 

can also be used as a conservative indicator of DBP precursor breakthrough. An 

exception to this was observed for MRW, for which a DBP precursor broke through 

earlier than TOC (Figure 11.2). One possible reason for this anomaly could be the 

short EBCT used during the testing of this water compared to others. 

Pyrolysis GC-MS_____________________ 

Six source waters and associated influents and effluents of GAC have been 

analyzed by PY-GC-MS. The following is a summary of observations related to PY- 

GC-MS analysis. These analyses showed in general that the humic fraction of NOM 

is not necessarily aromatic in nature as is commonly believed. Only two of the six 

waters analyzed (LGW and FGW) indicated a more aromatic nature of the humic 

fractions. In many cases in this study, the humic and nonhumic fractions had similar 

percentages and showed similar chemical signatures.


Raw Water Characteristics 

Three of the six surface waters evaluated by PY-GC-MS were found to be 

industrially impacted: MRW, ORW, and PRW. Although each water possessed 

distinct features, there were three features as characterized by PY-GC-MS that were 

common to these waters. In each of these raw waters, benzonitrile and benzoic acid 

were detected at relatively significant levels. These pyrolysis fragments are thought 

to be markers of industrial discharge and may be derived from aromatic polyamides 

or phthalic-based polymers. Each of these waters showed relatively strong halogenated 

signatures. These results suggest that, in general, industrially impacted waters 

had much higher levels of halogenated organic material than nonindustrially 

impacted waters and that halo-substituted fragments in combination with benzonitrile 

and benzoic acid may be good markers for industrial and wastewater discharge. The 

pyrolysis fingerprints of all of these waters lacked a biological signature, such as 

fragments associated with biopolymers. 

The other three source waters—LGW, SRPW, and FGW—were not ex 

pected to show signs of industrial impact. LGW represented a high-quality surface 

water. The proposed industrial markers of benzonitrile and benzoic acid were not 

detected in any of these waters. Although minor peaks of halo-substituted fragments 

were detected in these samples, only in SRPW were they identified in the raw water. 

In the two surface water samples, LGW and SRPW, biological signatures were 

evident based on the variety of fragments identified and the presence of fragments 

typical of biopolymeric parent material (e.g., polysaccharides, proteins). 

Seasonal samples indicated similar organic characteristics; however, the 

relative proportions of different markers varied from season to season. All of the 

seasonal samples were characterized by very strong halogenated signatures. For the 

most part, the chemical signatures were dominated by markers of industrial or 

wastewater discharge and there was a general absence of biological markers. Halosubstituted 

fragments were the more prominent fragments in the winter samples, 

and benzonitrile became a major industrial marker in the spring and summer 

samples. 

Effect of Coagulation 

The effect of enhanced or optimized coagulation on GAC performance and 

DBP yield was studied for two waters: ORW and SRPW. For ORW, enhanced 

coagulation increased TOC removal to 31 percent (compared to 5 percent with 

conventional treatment). The residual organic matrix of the sample pretreated by 

enhanced coagulation was predominantly halogenated, as was the organic matrix of 

the conventionally pretreated sample. There were, however, a number of notable 

differences between the chemical fingerprints of the two pretreatment types. The 

enhanced coagulated water appeared to have very little acetic acid and acetaldehyde 

present, a slightly greater percentage of brominated fragments, and mostly earlyeluting 

fragments in contrast to the conventionally treated sample. The organic 

quality of the GAC breakthrough samples in the enhanced coagulation experiments 

also showed differences with respect to conventional pretreatment. Enhanced 

coagulation pretreatment produced a matrix in the GAC breakthrough samples 

having a different chemical signature, as evidenced by the strong presence of 

acetaldehyde and lesser relative evidence of acetic acid and formic acid. Significant


improvement in GAC performance with optimized coagulation pretreatment was 

observed and could be partially attributed to quality differences revealed by PY- 

GC-MS. 

In general, the nonadsorbable fraction of the NOM was very difficult to 

analyze by PY-GC-MS for a number of reasons. The organic material present at this 

early breakthrough point is typically characterized by low molecular weights and 

thus is poorly suited to a method tailored to nonvolatile, macromolecular material. 

These samples had very low TOC values, which required concentrating large 

volumes (10-20 L) of water to obtain sufficient organic material to analyze. This 

also resulted in concentrating large quantities of inorganic salts, which can possibly 

result in a variety of interferences. Despite these difficulties, some very general 

trends are visible among the data for the nonadsorbable fraction. For most samples, 

the nonadsorbable NOM was characterized by a major peak of acetic acid. A notable 

exception to this was LGW, which was primarily aromatic but showed a shift in 

fragment dominance from phenol top-cresol. In a number of instances, a significant 

halogenated signature emerged. It is thought that PY-GC-MS is particularly 

sensitive to halogenated macromolecular material, especially in the absence of other 

macromolecular compounds. 

Effect of Ozone and Biofiltration 

The effect of ozone and biofiltration on GAC performance and DBP yield 

was studied for three waters: ORW, PRW, and FGW. For ORW, the chemical 

characteristics of the organic matrix pretreated by ozonation and biofiltration were 

remarkably altered in comparison to conventional treatment or enhanced coagula 

tion. This pretreatment strategy produced a pyrolysis fingerprint bearing a much 

reduced halogenated character. The major fragments were acetaldehyde and acetic 

acid, and the predominant chemical class was aliphatic. These same characteristics 

were carried throughout the various GAC breakthrough samples such that the 

halogenated fraction was never the predominant fraction of pyrolysis fragments. 

For PRW, the chemical classification of PY-GC-MS data for ozonation and 

biofiltration pretreatment, however, was similar to that of conventional pretreat 

ment. Although there was a decrease in the number and proportion of halosubstituted 

pyrolysis fragments with ozonation and biotreatment, the group of major 

peaks (dichloroacetonitrile, acetic acid, trichlorobenzene) remained unchanged. 

The relative increase in the nonhumic fraction may be associated with the relative 

decrease in the halogenated class of fragments. Although the TOC removals 

produced by ozonation and biofiltration were similar for ORW and PRW, the 

behavior of the halogenated fraction differed. While in the case of both waters the 

halo-substituted signature was diminished and simplified at the first two break 

through points, a single halogenated fragment, dichloroacetonitrile, composed most 

of the halo-substituted character of the PRW samples; this peak was the major peak 

at complete breakthrough. 

For FGW, the chemical nature remained primarily aromatic, but in com 

parison to conventional treatment it had slightly greater aliphatic character. With 

GAC adsorption, the chemical signature of this water became aliphatic. Yet, even 

at complete breakthrough, the organic quality of the GAC effluent produced by 

ozonation and biotreatment was greatly modified in comparison to the influent 

quality and in contrast to that observed in the case of conventional pretreatment; in


the latter case, at complete breakthrough, the water was very similar to the GAC 

influent and highly aromatic. This observation suggests that the humic material of 

FGW broke through less readily than in the case of conventional pretreatment; this 

may correspond to the reported significant improvement in GAC performance for 

ozonation and pretreatment of FGW.

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Chapter 12___________ 

Evaluation of Design Criteria 

and Costs 

The operation of a GAC process in a treatment facility is primarily 

controlled by the breakthrough characteristics of the contaminant of concern and the 

relevant treated water quality goal. NOM breakthrough, in terms of DBF precursor 

compounds and chlorinated DBFs, has been discussed in previous chapters of this 

report, as have the effects of pretreatment and operating characteristics (i.e., EBCT, 

blending, and backwashing) on GAC adsorption. 

This chapter presents design criteria and conceptual treatment costs for 

implementing GAC at four of the participating utilities treating FGW, ORW, PRW, 

and SRPW, respectively. Conceptual cost estimates for pretreatment to optimize 

GAC contactor operation are also presented in this chapter. The chapter concludes 

with a discussion of cost implications related to the benefits of pretreatment in 

reducing overall facility costs for implementing GAC treatment. 

GAC Design Parameters_________________ 

A GAC treatment process includes several facility components, including 

GAC contactors, media backwashing, storage for fresh and spent carbon, and 

pumping to the GAC contactors. Depending on the size of the treatment plant, 

carbon reactivation also may be required. In full-scale applications, reactivation is 

achieved by thermal methods, typically with multiple-hearth incinerators. Conse 

quently, on-site reactivation requires facilities for transporting spent and reacti 

vated carbon, carbon dewatering, and off-gas treatment. 

The capital cost for the GAC process is a function of the treatment plant 

flow and the relative size of the contactor (i.e., EBCT). Operation and maintenance 

costs for the GAC facility also depends on the plant production rate and the rate at 

which spent carbon needs to be replaced or reactivated (i.e., carbon usage rate). 

Because the CUR determines the amount of carbon that must be reactivated, this 

parameter also determines the capital cost for the GAC reactivation facilities. 

Pretreatment (e.g., optimized coagulation; ozone and biofiltration) can 

reduce the CUR, thereby reducing the capital and O&M costs for the GAC 

reactivation facility, as well as O&M costs for the GAC facility. The capital and 

O&M costs for pretreatment are functions of plant production rate and the design 

criteria for pretreatment. 

199


Empty Bed Contact Time 

The EBCT is a measure of the volume of a carbon contactor, expressed in 

terms of the retention time in an empty contactor at design flow. Consequently, the 

EBCT is in direct proportion to the volume of carbon in a contactor, and longer 

EBCTs translate to larger contactors. Short EBCTs can be advantageous for welladsorbing 

specific micropollutants to reduce competition effects from other organics 

that may adsorb deeper in the GAC. Longer EBCTs are necessary to capture 

organic material that is more poorly adsorbed, which may nevertheless be problem 

atic. Because of the heterogeneous nature of NOM and the potential for DBF 

precursor material to be composed of a range of organics with varying adsorption 

characteristics, research for and application of GAC adsorption for DBF precursor 

material have typically focused on EBCTs between 10 and 20 minutes. As discussed 

in Chapter 6, experimental studies as a part of this research were limited to three 

EBCTs: 10 minutes, 15 minutes, and 20 minutes. 

The selection of EBCT may also be dependent upon the desired frequency 

of reactivation. For specific source water conditions, longer EBCTs will typically 

result in longer operation cycles and less frequent reactivation. Consequently, a 

system may increase EBCT to reduce the reactivation frequency and replacement 

of carbon in a contactor. The reactivation frequency, however, should not be 

confused with CUR. The CUR measures the capacity of carbon to adsorb organics 

from a water; consequently, it is related to both the EBCT and reactivation 

frequency as described in the following section. Therefore, although increasing the 

EBCT may decrease the reactivation frequency, it is entirely possible that the CUR 

will not change and that consequently the amount of carbon to be reactivated can 

remain constant. On the other hand, increasing the EBCT may decrease the CUR, 

thus permitting a reduction in reactivation frequency proportionally greater than the 

ratio of the EBCTs. Under these circumstances, cost trade-offs can be considered 

between increasing EBCT and reducing the size of the reactivation facilities. 

Carbon Usage Rate 

As stated previously, the CUR represents the amount of GAC needed to 

treat a unit volume of water satisfying the finished water quality goal. CUR can be 

determined using the following formula which is based upon the length of time 

along the breakthrough curve for which the finished water quality is at or below the 

target level: 

CUR = EBCT. 28. 1,000,000 

T. 1,440 .7.48 

where CUR = carbon usage rate (Ib/mil gal) 

EBCT = empty bed contact time (minutes) 

28 = density of GAC (Ib/ft3)(= 0.45 g/cm 3 ) 

1,000,000 = conversion factor (1,000,000 gal = 1 mil gal [3,785 m 3]) 

T = breakthrough time (days) 

1,440 = conversion factor (1,400 minutes = 1 day) 

7.48 = conversion factor (7.48 gal = 1 ft3 [0.028 m 3])

Evaluation of Design Criteria and Costs 201 

Relevant breakthrough times for a given source water can be estimated from 

the results of a single-contactor RSSCT experiment. To represent a realistic mode 

of operation, however, the single-contactor results can be transformed into a 

blended effluent breakthrough curve. Mathematically simulated blended effluent 

breakthrough curves can be used to estimate CUR values that are more representa 

tive of those found in full-scale applications. 

Effect of Pretreatment 

Removal of contaminants prior to GAC treatment is expected to result in 

longer contactor operation prior to reactivation. Pretreatment of the source water to 

remove TOC and DBF precursors is also expected to result in lower CUR values 

compared to water that has not been pretreated. It should be noted, however, that the 

breakthrough curves developed during this study were based upon settled or filtered 

water from the participating utilities. For this reason, all the waters used for the 

RSSCT evaluation were pretreated using the existing treatment scheme (conven 

tional coagulation, flocculation, sedimentation, and filtration). Further optimiza 

tion for the removal of TOC and DBF precursors was studied by additional treatment 

of the settled or filtered water through the application of coagulant or by treat 

ment of the water with ozone and biotreatment, as discussed in Chapters 7 and 8, 

respectively. 

From a cost standpoint, the benefit of pretreatment is dependent upon 

whether the cost of the pretreatment is offset by the reduction in GAC facility costs. 

In this chapter, the GAC facility costs with conventional pretreatment alone are 

compared with the costs of a GAC facility plus optimized coagulation or ozone with 

biotreatment, as practiced in this research effort, to determine whether pretreatment 

lowers overall facility costs for implementing GAC treatment. 

Determination of Design Criteria____________ 

Breakthrough characteristics for TOC and chlorinated DBFs for various 

source waters were used to determine the CUR estimates for the participating 

utilities. Three different EBCT values (10, 15, and 20 minutes) were evaluated 

during the testing under pretreatment conditions currently used at each utility. The 

effect of pretreatment was evaluated for selected utilities for one EBCT value (15 

minutes). The following text presents a discussion of the water quality goals 

selected for the development of the design criteria and an estimate of CURs for 

various GAC treatment scenarios at the participating utilities. 

Water Quality Goals 

Water quality goals were selected to reflect the regulatory agenda of the 

USEPA. Proposed THM and HAA5 MCLs for Stage 2 of the D-DBP Rule were 

used as goals for the chlorinated DBFs. The Stage 2 goals were selected for two 

reasons: (1) the treatability studies in the ICR are based, in part, upon determining 

the design criteria and associated costs for systems to meet potential Stage 2 

requirements using GAC treatment; and (2) the Stage 1 MCLs are sufficiently high


that operating times to breakthrough could not reliably be determined for three of 

the four waters tested. The Stage 2 proposed MCLs for TTHM and HAAS are 

40 ug/L and 30 ug/L, respectively. It is recognized, however, that these MCLs are 

"placeholders" subject to renegotiation in the future. 

The selected goal for TOC is 1.0 mg/L. Again, a value of 2.0 mg/L has more 

regulatory relevance for the Stage 1 D-DBP Rule, in that enhanced coagulation, a 

treatment technique to remove DBF precursors in conventional treatment, is not 

required if the treated water TOC prior to continuous disinfection is less than 2.0 

mg/L. Similar to the case with DBFs, however, a treatment goal of 2.0 mg/L did not 

permit reliable estimates of operating times for three of the four waters evaluated. 

A 20 percent factor of safety was used to determine the maximum allow 

able values of these parameters. This safety factor, typical of what is used in 

practice, allows for operational variation and was endorsed by the water industry as 

an acceptable margin of safety during the regulatory negotiations for the D-DBP 

Rule. As a result, the following were selected as the target parameter values after 

GAC treatment: 

• TOC = 0.8 mg/L 

• TTHM =32 ug/L 

• HAA5 =24 ug/L 

Single Contactor Breakthrough Curves 

Table 12.1 summarizes the single-contactor breakthrough times for reach 

ing the target TOC, THM, and HAA5 concentrations for the participating utilities. 

A logistic equation of the following form was used to fit the experimental data: 

f"(t) - ——— -r (12.2) 

" 

where A, B, and C are fitting parameters and t is breakthrough time. 

Equation 12.2 can be rearranged to show breakthrough time (t) as a function 

of effluent concentration (f(t)), which can be used to determine the breakthrough 

time in (days) for a given effluent concentration. The restructured equation is as 

follows: 

t = -_ «in 

C 

1 ( A 

-1 (12.3) 

B lf(t) 

Equation 12.3 was used to estimate the value oft, shown in the "scaled days" 

column in Table 12.1, for the single-contactor breakthrough curves. Corresponding 

estimates of CUR (using Equation 12.1) are also shown in Table 12.1.


Table 12.1 Estimates of CURs for the participating utilities 

Utility name 

Pretreatment 

type 

Controlling 

parameter and 

influent concentration 

EBCT 

(minutes) 

Scaled days 

Single 

Blended 

CUR (Ib/mil gal) 

Single 

Blended 

Cincinnati Water 

Works 

Conventional 

coagulation 

TOC: 2.0 mg/L 

THM: 86 ug/L 

10 

15 

20 

10 

15 

20 

66 

80 

139 

64 

87 

146 

149 

169 

328 

248 

183 

876 

393 

490 

375 

404 

447 

357 

175 

231 

158 

105 

213 

59 

Optimized 

coagulation 

TOC: 1 .5 mg/L 

THM: 58 ug/L 

15 

15 

214 

241 

348 

1,046 

182 

162 

112 

37 

Ozone and 

biotreatment 


15 

104 

505 

377 

77 


Conventional 

coagulation 

TOC: 2.3 mg/L 

THM: 71 ug/L 

10 

15 

20 

10 

15 

42 

83 

103 

56 

103 

104 

178 

208 

281 

363 

615 

470 

503 

463 

378 

250 

219 

251 

93 

107 

Optimized 

coagulation 


15 

155 

382 

251 

102 

Passaic Valley 


Commission 

Conventional 

coagulation 

TOC: 3.2 mg/L 

10 

15 ' 

20 

34 

63 

68 

63 

116 

128 

756 

616 

762 

415 

335 

408 

THM: 85 ug/L 

10 

15 

20 

50 

94 

94 

99 

199 

188 

525 

417 

553 

264 

196 

277 

HAAS: 57 ug/L 

10 

15 

144 

143 

394 

401 

181 

272 

66 

97 

Ozone and 

biotreatment 

TOC: 2.5 mg/L 

THM: 57 ug/L 

15 

15 

45 

172 

87 

487 

874 

226 

450 

80 

Florida Cities 

Conventional 

coagulation 

TOC: 7.4 mg/L 

15 

20 

4 

8 

7 

14 

10,056 

6,695 

5,410 

3,774 

THM: 238 ug/L 

10 

15 

20 

4 

6 

11 

8 

12 

19 

5,999 

6,003 

4,605 

3,284 

3,293 

2,712 

HAAS: 128 ug/L 

10 

15 

20 

12 

15 

21 

21 

24 

36 

2,125 

2,685 

2,476 

1,243 

1,593 

1,439 

Ozone and 

biotreatment 

TOC: 10 mg/L 

15 

13 

21 

3,062 

1,826


Effect of Blending From Multiple Contactors 

As discussed earlier, blending of effluent from multiple parallel GAC 

contactors decreases the CUR values for satisfying a given water quality target. The 

mathematical approach described in Chapter 6 was used to transform the singlecontactor 

breakthrough curves (Equation 12.2) into blended effluent curves. As 

discussed in Chapter 6, blended effluent breakthrough curve can be represented by 

f - -Jf(t) dt (12.4) 

Substituting Equation 12.2 for f(t) in the preceding equation and performing 

integration, one obtains the following equation for the blended effluent break 

through curve results: 

f = A + -^- • ln(l + B • e~C * l ) - — • ln(l + B) (12.5) 

Equation 12.5 is transcendental (no direct solution for t); at very small values of t, 

which are not representative of GAC applications, it will predict an increase in the 

blended effluent concentrations (resulting from the presence of t in the denomina 

tor). This equation can be solved by trial and error for a target blended concentration 

to determine the breakthrough time to achieve the target. Breakthrough times and 

CURs for the blended effluent curve, calculated using these equations, are summa 

rized in Table 12.1. 

It should be noted that the underlying assumption in the derivation of the 

blended effluent equation is that the multiple contactors will be placed on-line (with 

fresh GAC) at equal intervals. If the frequency of placing a new contactor on-line 

is held constant, the actual concentration from the set of parallel contactors will 

fluctuate around the value of the target concentration with a mean value equal to the 

target concentration. The amplitude of fluctuation will depend on the number of 

contactors. 

Cost Curve Development________________ 

A computer program generated by Culp-Wesner-Culp (CWC Engineering 

Software, San Clemente, CA) was used to develop costs for GAC and pretreatment 

processes. The process components for the GAC and pretreatment options, along 

with the design parameters and the appropriate assumptions required for executing 

the CWC software, are outlined in the following subsections. 

Process Components for GAC 

Figure 12.1 shows a schematic of the assumed components for the GAC 

process. These components include an influent pumping station, GAC contactors, 

backwash pumping station, carbon storage, and multiple-hearth incineration 

with off-gas treatment for on-site reactivation. It was assumed that in a retrofit


application, plant filtered water will be pumped into the GAC adsorbers, which can 

be operated as either a gravity-flow or pressure-driven process. 

On-site reactivation of spent GAC was assumed for total carbon usage rates 

of 4,000 Ib/d (1,815 kg/d) or higher. A standby furnace was assumed for reactivation 

facilities as a result of the significant maintenance requirement. Storage facilities 

for spent, reactivated, and makeup carbon were included. For smaller facilities 

(requiring less than 4,000 Ib GAC/d), off-site disposal and replacement of spent 

GAC were assumed. Adequate storage facilities for the spent GAC were provided. 

Process Components for Pretreatment 

Two types of pretreatment were considered during this study: (1) optimized 

coagulation and (2) ozonation with biological filtration. Optimized coagulation was 

achieved either by an increase in the amount of alum addition or by a combination 

of lower coagulation pH at elevated alum dosage. Figure 12.2 shows a schematic of 

the process components for the optimized coagulation option. Acid storage and 

pumping facilities were included for the optimized coagulation method that requires 

lowering the pH. Both methods for achieving optimized coagulation require 

additional alum storage, chemical feed facilities, and residuals-handling facilities. 

Sludge-drying beds were assumed for the residuals-handling process, although it is 

recognized that these may not be feasible in facilities with site constraints and 

climatic limitations. Nevertheless, sludge-drying beds can be land-intensive, and 

costs were included for land purchase, thereby increasing the cost of this disposal 

option. For both optimized coagulation methods, caustic storage and pumping 

facilities were required to adjust the low coagulation pH to an acceptable distribu 

tion system pH. 

Figure 12.3 presents a schematic of the assumed process components for 

the ozonation and biofiltration process. Settled water was assumed to be pumped 

into the ozone contactor. On-site generation of ozone from ambient air was assumed 

during the cost computations. No additional costs were attributed to the filtration 

process. 

Design Parameters 

Conceptual costs were calculated using the CWC model for eight distinct 

flow rates ranging from 0.7 to 270 mgd (2.65 to 1,022 ML/d). The selected flow rates 

represent USEPA flow categories 5 through 12. As shown in Table 12.2, these flow 

categories represent service area populations of 3,000 to greater than 1,000,000. 

A total dynamic head of 30 ft (9.1 m) was assumed for pumping filtered 

water to the GAC adsorbers. EBCTs used for developing the cost curves were 10, 

15, and 20 minutes. For carbon amounts less than 80,000 Ib (36,300 kg), standard 

pressure contactors were used. For larger carbon installations, gravity contactors 

were used. The minimum number of contactors was four for both types of 

contactors, and the maximum number of pressure contactors was six. The maximum 

number of gravity contactors was determined by the number of contactors with a 

surface area less than 1,950 ft2 (180 m2). A hydraulic loading rate of 5 gpm/ft2 (12.2 

m/h) was assumed. 

Off-site reactivation was assumed for plants with average design flow less 

than 2.1 mgd (8 ML/d). Off-site reactivation was also assumed for spent carbon


Table 12.2 Treatment plant flows considered for cost curves 

Category 

Population range 

Median population 

Average flow (mgd) 

5 

6 

7 

8 

9 

10 

11 

12 

3,301-10,000 

10,001-25,000 

25,001-50,000 

50,001-75,000 

75,001-100,000 

100,001-500,000 

500,001-1,000,000 

Greater than 1 ,000,000 

5,500 

15,500 

35,500 

60,000 

88,100 

175,000 

730,000 

1,550,000 

0.7 

2.1 

5 

8.8 

13 

27 

120 

270 

loading rates less than 4,000 Ib/d (1,815 kg/d) based upon available furnace sizes. 

For higher loadings, on-site carbon reactivation was used. CURs used for develop 

ing cost curves were 100, 300, and 500 Ib/mil gal (12, 36, and 60 g/m 3). 

The design of facilities for optimized coagulation was based upon the 

following chemical dosages: 

• 65 mg/L of additional alum for the alum-only option 

• 45 mg/L of additional alum and 15 mg/L of additional sulfuric acid 

for the alum-and-acid option 

• 20 mg/L of caustic 

These dosages were selected to approximate the range of dosages used in the 

research. It is recognized that these dosages are relatively large, and it is uncertain 

whether smaller dosages could have achieved the same effect as that described in 

Chapter 7. Such an optimization was beyond the scope of this research. 

In the alum-only option, the selected alum dosage is expected to lower the 

coagulation pH to approximately 7.0 for source waters with alkalinity of approxi 

mately 120 mg/L as CaCO 3 . For the alum-and-acid option, the combination of alum 

and acid dosages is expected to effect a similar pH depression. A 20-mg/L dosage 

of caustic is expected to raise the coagulation pH to a distribution system pH of 8.0 

for the same source. The design parameters for additional sludge-drying beds were 

based upon a loading rate of 12.5 lb/ft2 (61 kg/m2) with a filling cycle of 100 days. 

The design of ozonation facilities included in-plant pumping to provide an 

additional 30 ft of total dynamic head. The ozone contactor was assumed to have a 

contact time of 10 minutes. Costs were calculated for three different applied ozone 

dosages: 3, 5, and 10 mg/L. 

Cost Factors 

July 1995 cost indices were used in developing the cost curves (Engineer 

ing News Record 1995). Table 12.3 summarizes the relevant cost indices. Various 

cost factors were applied to the construction costs predicted by the CWC model to 

determine the overall capital cost of the facilities. These cost factors are summarized 

in Table 12.4. Table 12.5 summarizes the unit costs for various building and 

operating materials.


Table 12.3 Indices used in developing cost curves 

Cost Index 

ENR building cost 

ENR skilled labor 

ENR material prices 

Housing cost ($/ft2) 

Index value 

3,114 

4,945 

1,996 

125 

ENR = Engineering News Record 

Table 12.4 Factors applied to construction cost estimates 

Cost parameter Factor 

Sitework (%) 

Contractor's overhead and profit (%) 

Contingencies (%) 

Engineering and design (%) 

Mobilization and bonding (%) 

Legal and administration (%) 

Interest rate (%) 

Number of years 

Land cost ($/acre) 

15 

12 

15 

15 

3 

10 

7 

30 

25,000 

Source: Engineering News Record 1995 

Table 12.5 Unit costs for building and operating materials 

Cost parameter Unit cost 

Electricity ($/kW»h) 

Labor ($/hour) 

Diesel fuel ($/gal) 

Natural gas ($/ft3 ) 

Building energy use (kW«h/ft2/year) 

Carbon attrition at 10 percent ($/lb) 

GAC ($/ton) 

Alum—liquid ($/ton) 

Sulfuric acid ($/ton) 

Sodium hydroxide—50% ($/ton) 

0.12 

30 

1.25 

0.006 

102.6 

0.9 

1,600 

230 

100 

350 

Source: Engineering News Record 1995


Cost Curves 

Figure 12.4 shows capital and O&M costs for the GAC facilities for EBCTs 

of 10,15, and 20 minutes. An exponential model was used to fit the costs presented 

in this and the subsequent figures. The fitting parameters for the exponential model 

are shown on the figures. The capital costs shown on Figure 12.4 do not include 

carbon reactivation. The O&M cost for the GAC facilities is primarily associated 

with the pumping costs. The capital and O&M costs for on-site and off-site 

reactivation are presented on Figures 12.5 and 12.6, respectively. These costs were 

generated for carbon usage rates of 100, 300, and 500 Ib/mil gal (12, 36, and 60 

g/m 3). It is evident from the cost curves that a majority of the capital cost is 

associated with the carbon contactors. In addition, the O&M cost for carbon 

reactivation is less than that for facility pumping (included in the GAC facility 

O&M) at lower CURs. 

Figure 12.7 shows capital and O&M costs for the optimized coagulation 

facilities. Costs for both methods for achieving optimized coagulation are illus 

trated on this figure. Figure 12.8 presents capital and O&M costs for the ozonation 

facilities for applied ozone dosages of 3, 5, and 10 mg/L. 

Verification of Costs 

Capital and O&M costs from the CWC model were compared with actual 

costs incurred during the construction and operation of the GAC facilities for the 

California Water Treatment Plant (WTP) at Cincinnati, Ohio. The California WTP 

consists of presedimentation, coagulation with alum, rapid sand filtration, GAC 

adsorption, and disinfection with chlorine. The GAC adsorption facilities consist of 

12 gravity contactors with an EBCT of 15 minutes and a hydraulic loading rate of 

5 gpm/ft2 (12.2 m/h) at design flow. Spent carbon is reactivated by an on-site 

reactivation system. This system includes storage facilities for spent, reactivated, 

and makeup carbon and the reactivating furnace. The reactivating furnace consists 

of two multiple furnaces (both in service) designed to operate at a spent carbon rate 

of 80,000 Ib/d (36,300 kg/d). Results of the comparison of capital and O&M costs 

between the CWC model and the California WTP are shown in Table 12.6. From 

this table, it is evident that the CWC model was able to accurately predict the O&M 

costs for the Cincinnati facilities. The difference in the capital costs can be attributed 

to additional facilities, beyond the construction of the GAC contactor and reactiva 

tion facilities alone, that were included in the bid estimate. The $60 million bid 

estimate for the California WTP also included other processes, such as improve 

ments in the clearwell, addition of new maintenance area, modifications to the 

chemical feed facilities, and modifications to the pumping stations. 

Capital and O&M costs based on cost curves for optimized coagulation 

facilities were compared with cost estimates for the South WTP at Tempe, Ariz. 

These latter costs were generated during a preliminary engineering cost estimate 

(Malcolm Pirnie, Inc. 1994). The South WTP consists of presedimentation, coagu 

lation with alum, filtration, and disinfection with chlorine. Bench-scale studies 

indicated that in order for the WTP to comply with Stage 1 of the D-DBP Rule, the 

alum dosage needed (1) to be increased from 25 to 30 mg/L with pH lowered to 7.3, 

or (2) to be increased to 60 mg/L without any pH adjustment. Sulfuric acid was used 

at a concentration of 15 mg/L to adjust the pH to 7.3. Caustic soda was used at a


Table 12.6 Comparison of costs for the GAG processes_______________ 

___ Cost California WTP* CWC modelf__________ 

Capital costs (million $) 60 51 

O&M costs (cents/1,000 gal) 10 10 

* Design flow for California WTP is 175 mgd; average flow for California WTP is 120 mgd; the CUR for California WTP is 228 

Ib/mil gal for design flow and 333 Ib/mil gal for average flow, 

t A CUR of 300 Ib/mil gal and EBCT of 15 minutes were used in the CWC model. 

Table 12.7 Comparison of costs for optimized coagulation _______________ 

Capital cost (million $) O&M Cost (cents/1,000 gal) 

Treatment South WTP* Cost curves South WTP* Cost curves 

Alum only 0.93 1.71 9.9 11.3 

Alum at low pH 0.95 1.57 8.5 9.8 

* Source: Malcolm Pirnie, Inc. (1994). Additional chemical dosages were adjusted to the selected dosages for this study. 

Design flow for South WTP is 40 mgd. Average flow for South WTP is 25 mgd. 

concentration of 20 mg/L to raise the coagulation pH to a distribution system pH of 

8.0. At average plant flow, the increase of alum dose to 60 mg/L generated 3,960 

Ib/d (1,800 kg/d) additional solids, whereas the increase of alum dose to 30 mg/L 

with pH lowered to 7.3 generated 990 Ib/d (450 kg/d) additional solids. The 

additional sludge production was assumed to be accommodated by new drying beds 

constructed next to the existing drying beds. Results of the comparison of capital and 

O&M costs predicted by the cost curves and the South WTP are shown in Table 12.7. 

This table indicates that the cost curves overpredicted both capital and O&M costs 

for the South WTP. It should be noted, though, that the costs generated for the South 

WTP were very site specific and assumed use of some available facilities that were 

not actually in use. For example, at this facility, two sodium chlorite tanks that had 

been abandoned were converted to alum tanks to minimize the costs to the utility. 

The costs curves, on the other hand, assumed the construction of all new facilities. 

Capital and O&M costs based on cost curves for ozonation facilities were 

compared with costs presented in the document entitled Technology and Costs for 

the Control of Disinfectants and Disinfection By-Products (USEPA 1993), which 

was peer reviewed and validated during the D-DBP Rule regulatory negotiation. 

Results from this comparison are shown in Table 12.8. It is evident from the table 

that cost curves very accurately predicted the capital cost from the report. However, 

the CWC model appeared to overpredict the O&M cost.


Table 12.8 Comparison of ozone facilities costs 

Capital cost (million $) O&M cost (cents/1,000 gal) 

Flow (mgd) USEPA report* Cost curves USEPA report Cost curves 

11 

210 

2.3 

19 

2.5 

21.6 

3.9 

3.2 

8.8 

8.8 

Note: The ozone concentration was 5 mg/L 

" * Source: US ERA (1993) 

GAC Treatment Costs for Participating Utilities____ 

The costs curves presented in the previous section were used to determine 

the cost of retrofitting the participating utilities with GAC treatment process. Costs 

were developed for GAC treatment with or without optimized pretreatment at these 

facilities. A summary of all the costs for the participating utilities is included in 

Table 12.9. The following is a discussion of the additional treatment cost for each 

of the participating utilities. 


Cincinnati Waterworks treats Ohio River water in a 175-mgd (660-ML/d) 

WTP. The average production rate at this facility is 120 mgd (450 ML/d). The 

treatment facility at this utility consists of conventional treatment followed by GAC 

adsorption in a 15-minute-EBCT contactor (at design flow). GAC is reactivated onsite. 

The RSSCT results with Ohio River water under conventional pretreatment 

suggested that the CUR estimates were not significantly affected by the EBCTs (see 

Table 12.1). Consequently, although the reactivation frequency was lower as EBCT 

increased, the total amount of carbon to be reactivated was similar. Therefore, there 

is little savings in reactivation capital for longeer EBCTs, and O&M costs and the 

overall treatment costs were lowest for the 10-minute EBCT, directly because of 

the lower capital costs associated with this EBCT. It is important to recognize that 

the extensive studies performed at Cincinnati to arrive at the design criteria for the 

full-scale GAC facility were based upon considerations other than the removal of 

DBF precursor material. The GAC process was installed to provide removal of 

SOCs, which can intermittently be measured in the Ohio River as a result of spills. 

Consequently, the optimization for DBF precursor removal in this research cannot 

be compared directly to the constraints at the full-scale facility. 

Both optimized coagulation and ozone with biotreatment were studied as 

pretreatment for this source water. Estimated CUR values were significantly 

lowered by both pretreatment schemes—by one-half and two-thirds for optimized 

coagulation and ozone with biotreatment, respectively. The overall treatment costs 

with the pretreatment, however, were found to be higher because of the costs 

associated with the pretreatment. Therefore, the reduction in costs for GAC 

treatment, primarily in capital and O&M for reactivation, were not sufficient to 

offset the increase in costs associated with the pretreatment. It is important to


Table 12.9 Estimates of GAC treatment costs for the participating utilities 

Utility name 

Controlling 

Pretreatment parameter and 

type influent concentration 

EBCT 

(minutes) 

Treatment cost (cents/1 ,000 gal) 

Pretreatment GAC Total 

Cincinnati Water 

Works 

Conventional 

coagulation 

TOC: 2.0 mg/L 

THM: 86 ug/L 

10 

15 

20 

10 

15 

20 

17.7 

20.7 

21.9 

16.5 

20.4 

20.3 

17.7 

20.7 

21.9 

16.5 

20.4 

20.3 

Optimized 

coagulation 


THM: 58 ug/L 

15 

15 

11.2 18.6 

11.2 17.4 

29.8 

28.6 

Ozone 

and biotreatment 


15 

10.8 18.0 

28.8 


Conventional 

coagulation 

TOC: 2.3 mg/L 

THM: 71 ug/L 

10 

15 

20 

10 

15 

21.4 

23.4 

27.0 

18.8 

21.6 

21.4 

23.4 

27.0 

18.8 

21.6 

Optimized 

coagulation 


15 

11.6 21.5 

33.1 

Passaic Valley 


Commission 

Conventional 

coagulation 

TOC: 3.2 mg/L 

THM: 85 ug/L 

HAA5: 57 ug/L 

10 

15 

20 

10 

15 

20 

10 

15 

28.6 

30.2 

35.7 

25.5 

27.6 

33.0 

17.5 

25.9 

28.6 

30.2 

35.7 

25.5 

27.6 

33.0 

17.5 

25.9 

Ozone and 

biotreatment 

TOC: 2.5 mg/L 

THM: 57 ug/L 

15 

15 

13.2 32.7 

13.2 25.6 

45.9 

38.8 

Florida Cities 

Conventional 

coagulation 

and softening 

TOC: 7.4 mg/L 

THM: 238 ug/L 

HAAS: 1 28 ug/L 

15 

20 

10 

15 

20 

10 

15 

20 

228.3 

212.5 

152.9 

194.7 

195.7 

120.6 

167.8 

175.5 

228.3 

212.5 

152.9 

194.7 

195.7 

120.6 

167.8 

175.5 

Ozone 

and biotreatment 

TOC: 10 mg/L 

15 

57.3 171.5 

228.8

2/2 Removal ofDBP Precursors by GAC Adsorption 

recognize that a relatively high coagulant dosage was used in this research. 

Consequently, if CURs could be similarly improved at lower coagulant dosages, 

the cost-effectiveness of optimized coagulation pretreatment may improve. Further, 

it should be noted that the benefit of ozone with biotreatment was measured only in 

terms of costs associated with DBF reduction in subsequent treatment by GAC. 

Other benefits of ozone with biotreatment, such as disinfection and improvement in 

biostability, could not be factored into this conceptual cost analysis. 

The treatment costs were also observed to be lower for satisfying the DBF 

goals rather than satisfying the TOC goal. Because the TOC goal is not related to 

any specific regulatory constraint at this time, meeting the Stage 2 DBF goals may 

be a more relevant comparison for GAC costs. 


The City of Phoenix operates five different surface water treatment facili 

ties, four of which use water from the Salt River Project (SRP). The average design 

capacity of SRP plants is 130 mgd (490 ML/d), and the average annual water 

production rate at these facilities is 75 mgd (280 ML/d). RSSCTs were performed 

for 10-, 15-, and 20-minute EBCTs using filtered water (see Table 12.1). Conven 

tional treatment at these utilities includes coagulation for particulate removal 

followed by dual media filtration. 

Similar to ORW, CUR estimates for the blended effluent curve using Salt 

River Project water were found to be relatively unaffected by EBCT. Consequently, 

the 10-minute EBCT exhibited the lowest conceptual cost. 

Optimized coagulation was found to reduce the CUR for the Salt River 

Project water by approximately 50 percent. The overall treatment cost with 

optimized coagulation, however, again was found to be higher because of the 

relatively high coagulation cost under the selected conditions. As shown on Table 

12.9, the treatment cost estimates are slightly lower if only the THM target is to be 

satisfied. 

Passaic Valley Water Commission 

The Passaic Valley Water Commission treats Passaic River water in a 

110-mgd (415-ML/d) capacity WTP. The average production rate at this utility is 

50 mgd (190 ML/d). Three alternative EBCT values were studied using this source 

water. The CUR estimate was found to be lower at a 15-minute EBCT, than at either 

a 10- or 20-minute EBCT (see Table 12.1). The overall treatment costs, however, 

were relatively unaffected by the differences in the CUR estimate because the 

majority of the capital costs were associated with the carbon contactors. The lowest 

overall GAC treatment costs at this utility were found to be associated with a 10- 

minute EBCT. 

Ozonation followed by biological filtration was studied as a pretreatment 

for the GAC process for this source water. No improvement in CUR was observed 

as a result of this pretreatment scheme. As a result of the additional cost of 

ozonation, the pretreatment-GAC process trains were found to be the most expen 

sive. As with the Cincinnati and Phoenix data, the treatment costs for satisfying the 

DBP goals were lower compared to that for TOC.


Florida Cities Water Company 

A groundwater sample from Florida Cities Water Company was used in the 

RSSCT experiments to determine the breakthrough characteristics. This water 

sample was relatively high in NOM, as indicated by the high TOC and DBF 

formation potentials. The participating utility for this source water is a small facility 

with a design capacity of 1 mgd (3.78 ML/d) and an average production rate of 0.7 

mgd (2.65 ML/d). The CUR estimates for this source water were much higher than 

the CUR ranges evaluated to prepare the cost curves. For this reason, the costs 

shown for this utility were extrapolated and may not be realistic. 

As a result of the small size of the treatment facility and the high NOM 

content in the source water, the estimated overall cost of GAC treatment is very high. 

Ozonation and biofiltration of this water prior to GAC treatment, however, resulted 

in a similar overall treatment cost. The treatment costs were found to be significantly 

lower if the process is controlled by effluent DBF concentration rather than the 

effluent TOC concentration. 

Implications for Process Optimization_________ 

The results presented in the previous section yield several implications 

associated with process optimization. The following text discusses these implica 

tions in terms of optimizing EBCT and the impact of pretreatment on GAC 

efficiency for the removal of DBF precursors. 

Empty Bed Contact Time 

The cost of GAC treatment process is primarily controlled by the size of the 

contactors (a function of EBCT) and the reactivation process (a function of the 

CUR). Of these two components, the bulk of the costs, in terms of both capital and 

unit costs (after amortizing the capital investment), is in the contactors. Conse 

quently, reducing the size of the contactors has the greatest impact on the overall cost 

reduction of the GAC facility. 

The experimental observation of NOM breakthrough curves, as indicated 

by the breakthrough of TOC and DBF precursors, suggested that a longer EBCT 

does not necessarily improve the CUR. Therefore, the size of the reactivation 

process does not decrease as EBCT increases, thereby negating any potential cost 

benefit. Because the CUR is relatively unaffected by EBCT in the 10 to 20 minute 

range evaluated in this study for these waters, it is most economical to minimize the 

EBCT. It is recognized, however, that a utility may place a practical minimum value 

on the duration of contactor operation prior to reactivation; the duration of contactor 

operation will decrease as the EBCT decreases for a constant CUR. Therefore, the 

EBCT should not be reduced to such a degree that the period of contactor operation 

is so short as to result in other O&M related constraints. 

Further, it is recognized that as EBCT continues to decrease, eventually the 

CUR will begin to increase as premature breakthrough of difficult-to-adsorb NOM 

and DBF precursors occurs. This was not found for the waters in this research at an 

EBCT of 10 minutes; at some value below 10 minutes, though, it would be expected 

to occur. Therefore, it is recommended that utilities consider evaluating EBCTs


lower than 10 minutes to determine the EBCT at which the CUR begins to increase 

as a result of premature breakthrough. As long as the period of contactor operation 

is acceptable at this minimum EBCT before CUR begins increasing, this EBCT will 

yield the most economical facility. 

Finally, because the majority of the cost is associated with the contactors, 

it still may be advantageous from a cost standpoint to allow the CUR to increase a 

certain amount as EBCT decreases. At some point, the CUR will increase to an 

extent that the capital and O&M costs associated with the reactivation furnaces will 

offset the cost savings of reducing the EBCT. This is the true optimization point 

from a cost standpoint, but it was not reached at an EBCT as low as 10 minutes for 

the waters used in this research. 

Pretreatment Optimization 

Pretreatment is expected to increase the length of operation of a GAC 

facility as it reduces the CUR. The overall cost of precursor removal via enhanced 

pretreatment and GAC adsorption, however, must be considered to determine the 

benefit of the pretreatment. The following paragraphs discuss implications of 

optimized coagulation and ozone with biotreatment in terms of cost-effectively 

improving GAC performance for the removal of DBF precursors. 

Optimized Coagulation 

The experimental studies conducted during this research focused on rela 

tively extreme conditions for optimizing precursor removal via coagulation; in 

addition, the "optimized" coagulation conditions used in this study should not be 

confused with the regulatory requirements of "enhanced" coagulation in Stage 1 of 

the D-DBP Rule. Although a 50 percent reduction in CUR was achieved under the 

selected pretreatment conditions, the overall costs were higher as a result of the cost 

associated with the high dosages used to define "optimized" coagulation conditions. 

If the 50 percent reduction in CUR yielded under the optimized coagulation 

conditions used in this study could be achieved using lower dosages, an overall 

system cost reduction could be achieved if the cost of additional coagulation could 

be reduced to less than approximately 5 cents/1,000 gal (approximately 10 cents per 

1,000 gal [3,785 L] was the estimated cost for the treatment in this study). 

Alternatively, the CUR could be held constant by pretreatment, and the 

EBCT could be decreased. Although this effort was beyond the scope of work for 

this research, lowering EBCT at a constant CUR may have a greater potential to 

reduce the overall system costs because the majority of the cost is associated with 

the contactors. It is recommended that utilities, when evaluating the benefits of 

pretreatment, consider both (1) reducing CUR while holding EBCT constant and (2) 

reducing EBCT while reducing CUR. Finally, it is important for a utility to find the 

breakpoint at which only marginal reductions in CUR and EBCT are achieved for 

large increases in costs associated with coagulation. At this point, it is not necessary 

to further enhance the pretreatment processes to improve GAC performance. 

Ozone With Biotreatment 

Pretreatment using ozone with biotreatment was found to be effective in 

lowering the CUR values for removal of DBP precursors with postfilter contactors 

in two of the three waters evaluated: ORW and FGW. Further, the reduction in


reactivation costs associated with this reduction in CUR was sufficient to justify the 

cost of ozone with biotreatment for FGW. It must also be recognized that other 

benefits associated with ozone and biotreatment, such as biostability of the treated 

water and disinfection benefits, were not factored into this analysis. The costbenefit 

analysis of ozone with biotreatment considered only the improvemed GAC 

adsorption for the removal of DBF precursor compounds. 

Cost comparisons at FGW implied that the higher the CUR, the better the 

opportunity for any pretreatment to become cost-effective if the percentage reduc 

tion in CUR remains constant. That is, if a 50 percent reduction in CUR can be 

achieved for two different waters, one with an initial CUR of 100 Ib/mil gal (12 

kg/ML) and the other with an initial CUR of 500 Ib/mil gal (60 kg/ML) the cost 

benefit will be greater for the latter. Therefore, optimizing pretreatment provides 

better potential for lowering overall system cost as the CUR increases. Similarly, if 

an equivalent reduction in EBCT can be achieved as the CUR is lowered, then 

potentially even greater benefits can be achieved by pretreatment.


Spent 

Carbon Storage 

Off-Site 

Reactivation 

Filtered Water- 

Spent/Reactvated/Makeup 

Carbon Storage 

Plant 

Pumping Station 

Multiple 

Hearth Furnace 

GAC Adsorber 

Backwash 


Figure 12.1 Schematic of process components for GAC 

Acid Storage 

Acid 


Raw Water 

Flocculaton/Sedimentatjon Basin 

Fitters 

-\ I 

Alum Storage 

Solar Drying Beds 

Alum 

! Pumping Station 

I Caustic Storage 

Caustic 


Figure 12.2 Schematic of process components for optimized coagulation


BiofStratton 

Settled Water 

Plant Pumping Station Ozone Contactor 

Ozone Generator 

Figure 12.3 Process schematic for ozonation and biofiltration treatment


80 

70 ^ 

I 6M 

5 50 ^ 

I « 

"5. 30 - 

O 

20 - 

y=-338.4-exp(-0.0008894-x)+340.1 

y=-320.8-exp(-0.0007332-x)+322.4 

y=-208.0-exp(-0.0008516-x}+209.3 

• EBCT = 10 min 

• EBCT =15 min 

+ EBCT = 20 min 

Capital Cost 

10 - 

0 - 

70 

3 10 30 100 300 1000 

Plant Capacity (mgd) 

O&M Cost 

co 

_o 

§> 

8 

in 

o 

O 

60 - 

50 - 

40 - 

30 - 

20 - 

y=79.16-exp(-0.5031 

y=74.75-exp(-0.512-x)+5.315 

y=41.23'exp(-0.7425->)+5.64 

• EBCT =10 min 



10 - 

3 10 30 100 


300 1000 

Figure 12.4 Cost curves for GAC facilities


25 

20 - 

CUR = 100lb/milgal 

CUR = 300 Ib/ma gal 

CUR = 500 Ib/mil gal 

Capital Cost 

(0 

O 

= 15 -\ 

y=-98.18-exp(-0.0005836-xJ+103.4 

y=-552.2-exp(-0.00006584-^+557.1 

Tn 

o 

O 

I 

Q. 

ra 

O 

10 - 

5 - 

y=0.0084-x+5.682 

in 

jO 

15 

O) 

25 

20 - 

15 - 

10 30 100 300 


O&M Cost 

• CUR =100 Ib/mil gal 

• CUR = 300 Ib/mil gal 


y=18.46-exp(-0.06274o$+8.425 

1000 

10 - 

y=14.53-exp(-0.06216 

w 

o 

O 

5 - 

0 - 

y=-0.0032-x*-2.584 

10 30 100 300 


1000 

Figure 12.5 Cost curves for on-site reactivation



• CUR = 300 Ib/ma gal 


Capital Cost 

2 - 

(O 

5 H 

Q. 

CO 

O 

0 - 

y=-162.4-exp(-0.0004874-^+162.5 

i.149-exp(0.03111-x)-5.977 

y=29.09-exp(0.01386-x)-29.06 

~l———————————————I— 

1 3 10 


30 

O&M Cost 

7 - 

in 

£"co 

o> 

6- 

y=7.28 

36 




4 

u 

3 3 H 

08 

° 2-\ 

y=1.46 

-•—•- 

3 10 


30 

Figure 12.6 Cost curves for off-site reactivation


10 

8 - 

Alum-only 

Alum-at low pH 

Capital Cost 

ID 

i 6 

y=-15.21 -exp(-0.003189->)-H 5.50 - 

y=-12.68-exp(-0.003523-x)+12.96 

(A 

O 

To 

'a. 

CO 

O 

4 - 

2 - 

0 - 

10 30 100 300 1000 


20 

18 - 

O&M Cost 

Alum-only 

Alum-at low pH 

W 

O 

1 16 

O) 

y=14.23-exp(-0.8034-x)+11.33 

^ 14 - 

y=14.30-exp(-0.7244-x)+9.782 

I 

9f 12 - 

O 

5 

o 10 - 

8 

1 3 10 30 100 


300 1000 

Figure 12.7 Cost curves for optimized coagulation

co 

en 

O 

o> •o 

O 

o 

w 

§ 

CO 

8- 

o 

•3 

5' 

(Q 

JO 

CO 

a 

o 

I 

en 

08M Cost (cents/1,000 gallons) 

o (ft 

to 

o 

o o 

Capital Cost ($-millions) 

ro 

o 

ro 

en 

O Q) 

T3 

01 

O 

CA> 

O 

M en 

O 

0) 

T> 

01 

O. 

*< ?v 

O 

S 

O 

Q> 

3 

co _>. 

B 8 

CO 

Q. 

CO 

o 

o 

O 

o 

0) o

Chapter 13 

Conclusions and Recommendations 

This chapter summarizes the conclusions and recommendations reached 

based upon the work conducted in this research effort. 

Verification of RSSCT Results_____________ 

• A comparison of breakthrough curves from full-scale application, 

pilot-scale testing, and RSSCTs indicated that the rapid test 

procedure can be successfully used to predict NOM breakthrough 

behavior in terms of TOC, UV254, and several chlorinated DBFs. 

The importance of representative sample collection, however, was 

highlighted during this study. In order to use RSSCTs for the 

prediction of breakthrough characteristics as a function of seasonal 

change, several RSSCTs should be performed with batches of 

influent waters that represent the seasons of interest. 

• Most raw and conventionally treated waters evaluated during this 

study contained small amounts of biodegradable organic matter. For 

this reason, the biodegradation occurring in the full-scale and pilotscale 

applications did not result in any significant differences with 

the RSSCT applications. Further, breakthrough behavior between 

the RSSCT and the pilot or full scale could be compared on the 

basis of GAC effluent concentration as a proportion of GAC influent 

concentration (C/C0), thereby allowing a comparison of the relative 

adsorbability of a constituent rather than the absolute concentration 

if the source water varied significantly at full or pilot-scale during 

the period of comparison. GAC treatment following ozonation, 

however, is expected to include a significant biodegradation 

component. For this type of pretreatment, RSSCTs should be 

performed on-site to evaluate breakthrough characteristics. The time 

required to ship samples to a laboratory for RSSCTs may 

significantly alter the NOM characteristics and hence the adsorption 

behavior. 

• A statistical method has been developed during this study to 

compare breakthrough curves developed from RSSCTs with the 

curves from full- and pilot-scale tests. The statistical test was based 

upon the paired t-test procedure. The qualitative inferences were 

adequately matched by the statistical tests, although one must take 

223


certain precautions when interpreting values from statistical tests in 

the absence of visual comparison of the breakthrough curves. 

• The formation of DBFs was evaluated under two sets of chlorination 

conditions: SDS conditions and UFC. SDS conditions were used to 

conduct experiments to simulate the impact of distribution system 

conditions, specific to a given utility, on DBF formation. 

Alternatively, the UFC were used to study fundamental adsorption 

behavior as a function of source water variation and were therefore 

applied uniformly across all waters for comparison. Because SDS 

conditions reflected the conditions at a particular water utility, these 

results were significantly different, in some cases, relative to the 

UFC. The adsorption behavior, however, was found to be predicted 

similarly by either condition type when the effluent concentrations 

were reported on a normalized basis with respect to the influent 

concentrations. 

Impact of GAC Treatment on 

NOM Characteristics and DBF Yield_________ 

• For the raw source waters analyzed during this study, humic 

fractions slightly dominated the NOM content as characterized by 

DOC and UV,54 . The specific DBF yield of the humic fraction was 

also observed to be slightly higher than that of the nonhumic 

fraction under equalized Br:DOC ratios. Conventional treatment 

removed a greater amount of the humic fraction, resulting in a 

predominantly nonhumic NOM in the GAC influent water. 

Additional optimized coagulation of the conventionally coagulated 

water removed both humic and nonhumic fractions in approximately 

equal proportions. Ozonation followed by biotreatment of the 

conventionally treated source waters resulted in a relatively more 

predominant nonhumic fraction. 

• In the adsorption experiments, the initial samples of the 

nonadsorbable fraction of NOM were observed to be almost 

completely nonhumic in nature. The nonhumic fraction broke 

through rather quickly and reached a plateau after a short period of 

operation, while the humic fraction was removed more efficiently 

during GAC adsorption for a significant portion of the breakthrough 

curve. The humic fraction broke through toward the end of the 

adsorption experiments, when C/C0 exceeded 0.5 to 0.6 for TOC. 

• Molecular size fractionation of the source water samples indicated 

that the majority of the DOC was in the intermediate range (500- 

3,000 MS) with the remaining fractions approximately evenly 

distributed between 3,000 MS fractions. Conventional 

treatment was found to effectively remove the >3,000 MS fraction, 

whereas the other fractions remained relatively unaffected. 

Optimized coagulation of the conventionally treated water removed 

DOC mostly from the intermediate MS range, whereas ozonation

Conclusions and Recommendations 225 

followed by biotreatment resulted in a shift toward smaller MS 

fractions. 

• The initial samples from GAC adsorption experiments indicated that 

the nonadsorbable fraction was almost entirely composed of the 


• Increasing EBCT resulted in longer run times for the GAC 

contactor. The CUR, however, was not significantly affected by 

increasing EBCT between 10 and 20 minutes, implying that the total 

amount of carbon used (and, hence, carbon requiring reactivation) is 

equivalent in this EBCT range. Breakthrough curves in which the 

effluent concentrations were plotted as a function of number of bed 

volumes processed at the time of sample collection indicated no 

significant differences with respect to EBCT values. 

• Additional optimized coagulation of the conventionally treated 

water resulted in significant removal of NOM and increased GAC 

run times by 129 to 148 percent. It should be noted, however, that 

the optimized coagulation dosages examined during this study were 

significantly greater than those required for enhanced coagulation as 

a part of the D-DBP Rule. 

• Ozonation followed by biotreatment resulted in a shift of the NOM 

toward more nonhumic fractions and smaller MS fractions. The 

benefit of this pretreatment scheme in lowering the influent NOM 

concentration was somewhat offset by the shift toward the 

nonadsorbable fraction. The ultimate effect of this pretreatment 

scheme was that the CUR values were not significantly lowered. In 

two of the source waters (i.e., FGW and ORW), this pretreatment 

scheme resulted in lower CUR values. It should be noted, however, 

that the benefits of ozone with biotreatment were evaluated only in 

terms of their impact on the removal of DBF precursors by GAC 

adsorption. An evaluation of other benefits, such as disinfection and 

biological stability, was not a part of this work. 

Estimation of Design Criteria and 

Conceptual Costs for GAC Treatment__________ 

• A mathematical procedure was developed during this study to 

predict breakthrough curves in a multiple-contactor GAC adsorption 

system based on data from a single-contactor breakthrough curve. 

Multiple-contactor operation is much more representative of fullscale 

applications. Verification of the procedure using actual 

blending experiments indicated the success of this approach. The 

blended effluent curves resulted in significantly longer periods of 

operation prior to an effluent criterion being reached, thereby 

resulting in significantly lower CURs compared to single contactors. 

• Conceptual treatment cost estimates for the GAC process indicated 

that minimizing the EBCT lowered the unit costs. This was 

attributed to the fact that the CUR did not change as EBCT 

decreased, and therefore capital costs reduction associated with the 

contactors were reduced at lower EBCTs without consequent cost 

increases associated with carbon reactivation. The lowest EBCT 

evaluated in this study was 10 minutes.

Conclusions and Recommendations 227 

Extrapolations based upon these results indicate that even lower 

EBCTs (less than 10 minutes) could further reduce unit costs for 

GAC treatment. At some lower value of EBCT, however, CUR 

would begin to increase as a result of premature breakthrough, and 

consequent increases in reactivation costs would offset the reduction 

in capital cost yielded by the lower EBCT. It is also recognized that 

a practical limitation may exist in operational complexity resulting 

from the increase in reactivation frequency that accompanies a 

lower EBCT. 

Optimized coagulation resulted in an approximately 50 percent 

reduction in CUR, thereby reducing the cost of the GAC facility. 

The overall system cost (i.e., the cost of pretreatment and GAC), 

however, was not reduced the high cost of the relatively high 

coagulant dosages used in this study outweighed the facility cost 

reduction. If a similar reduction in CUR could be achieved with a 

smaller increase in coagulant dosage, optimized coagulation 

potentially could lower the overall system cost. A unit cost lower 

than 5 cents per 1,000 gal (3,785 L) (including sludge handling and 

disposal) for optimized coagulation was the breakpoint at which the 

system cost was reduced in this study. 

Ozonation followed by biotreatment resulted in lower CUR 

estimates for two of the three waters evaluated during this study. 

The overall cost of treatment was found to be lower for one of the 

source waters. Again, lowering EBCT while maintaining a constant 

CUR may result in overall lower system costs when pretreatment is 

being applied. 

The extent to which pretreatment can reduce overall system costs is 

partially dependent upon the CUR. As the CUR for a given water 

increases, the cost benefits for a specific percent reduction in CUR 

by pretreatment also increase. Because of the relatively large 

proportion of unit costs associated with the capital cost of the GAC 

contactors, a more efficient method of reducing cost by pretreatment 

may be to reduce EBCT while maintaining the CUR constant. Such 

an optimization study was beyond the scope of this research.

Appendix A 

RSSCT Design 

The scaling equations that were used to design the RSSCT were developed 

based on similitude and dimensional analysis (Crittenden et al. 1986, 1987, 1989, 

1991; Hineline et al. 1987; Sontheimer et al. 1988). The scaling relationship is a 

function of the carbon particle sizes used in the small-scale (RSSCT) and large-scale 

(pilot- or full-scale) columns. A problem with RSSCT design is the possible 

dependence of the intraparticle diffusivity on GAC particle size. The design equa 

tions were originally developed with the assumption of constant diffusivity (CD). 

However, a number of small column tests and batch kinetic tests have shown that 

the diffusion coefficient decreases proportionally with decreasing particle size, and 

a proportional diffusivity (PD) design approach has been developed (Crittenden et 

al. 1991; Sontheimer et al. 1988; Benz 1989). The proportional diffusivity design 

was used for all RSSCTs conducted as part of this project. 

The critical RSSCT design parameters are the EBCT and the hydraulic 

loading rate, or superficial velocity (v). Also of interest is the operation time (t). The 

scaling factor (SF) can be defined as the ratio of particle diameters (d) of the large 

particle column (LC) to that of small particle column (SC): 

SF = dLC/dsc (A.D 

The dependency of the intraparticle diffusion coefficient (D) on particle size can be 

expressed as 

Dsc = (dsc/dLC)X DLC = SF"XDLC (A-2) 

where X is the diffusivity factor. The following three equations express the 

relationships for EBCT, v, and t: 

EBCTSC/EBCTLC = (dsc/dLC)2-x = SFX-2 (A.3) 

lsAc = (dsc/dLC)2~X = SF*-2 (A.5) 

When similitude prevails and the intraparticle diffusion coefficient is not a function 

of carbon particle size, then the CD design equations with X = 0 are used. 

For several studies, especially with NOM as measured by TOC and UV254 , 

a linear relationship between D and d has been found (e.g., as shown by Benz 1989). 

229


This dependency, with X = 1 , leads to the proportional diffusivity design approach 

in which the EBCT and operation time of the RSSCT are defined as 

EBCTSC = (dsc/d LC) EBCTLC = EBCTLC/SF 

(A.6) 

The similitude velocity in the RSSCT, v*sc , is directly related to the velocity in the 

large-scale column by the following equation: 

The design of an RSSCT based on Equations A.6 and A.8 will yield an RSSCT 

column with the same length (1) as the large- or full-scale column. 

lsc = v*sc • EBCTSC = (V LC • SF) • (EBCTLC/SF) 

(A.9) 

lsc = V LC ' EBCTLC = ILC (A' 10) 

RSSCTs designed with such long lengths will likely produce excessive head loss 

and are difficult to operate at the bench scale. 

To shorten the RSSCT bed length, the dominance of internal mass transfer 

over external mass transfer is utilized. The design velocity of the RSSCT, vsc , can 

be set to a lower value than that of the similitude velocity, as long as it is above the 

minimum velocity at which the RSSCT can be operated such that internal mass 

transfer still dominates. This minimum velocity is defined with the minimum 

Reynolds number, ReSCmjn , which ranges from 0.02 to 0.13 depending on the 

molecular weight of the compound (Crittenden et al. 1 986). The lack of impact of 

Resc on the breakthrough curve has been previously shown, where reducing the 

Reynolds number from 5 in the large-scale column, d LC =1.1 mm, to 0.05 in the 

RSSCT, dsc = 0.1 1 mm, had very little impact on the modeled breakthrough curve 

(Summers et al. 1994b). 

Using a value of Resc in the RSSCT that is lower than that in the full-scale 

system allows a lower vsc in the RSSCT to be used. As shown in Equation A.8, lower 

vsc values will result in shorter columns and lower flow rates, both of which are 

desirable for bench-scale operation. The design equation for the RSSCT hydraulic 

loading thus becomes 

V = SFvLc(Resc.mi,/Re Lc> (A- 11 ) 

Values of Resc min in the range of 1 .0 to 0.5 have been successfully used and are 

recommended. 

Equations A.6 and A.I 1 were used for the RSSCT design in this study. 

These equations are based on proportional diffusivity and have been found to yield 

results that are comparable to those of pilot- and full-scale systems. Details on the 

design, construction, operation, and sampling of RSSCTs and pilot plants are 

provided in the guidance manual for GAC treatment studies for compliance with the 

ICR requirements (USEPA 1996).

Appendix B 

A Statistical Method for the 

Comparison of RSSCT and 

Field-Scale GAC 

Breakthrough Curves 

Many studies performed on a variety of waters have shown that the RSSCT 

is a good predictor of field-scale GAC performance for control of NOM and DBF 

precursors when representative samples are collected for the RSSCT influent 

(Summers et. al. 1995). Comparisons are usually made qualitatively, using descrip 

tors such as "well-predicted," "well-simulated," and "good predictability." A quan 

titative method of comparing RSSCT and field-scale breakthrough patterns using 

statistical analysis has not been rigorously developed. Such a method would be used 

on a case-by-case basis to quantitatively determine whether the RSSCT is a good 

predictor of field-scale GAC performance. A statistical method of breakthrough 

curve comparison can strengthen the foundation upon which the RSSCT is com 

pared to field-scale data. 

Paired t-Test Procedure__________________ 

To systematically and quantitatively evaluate the RSSCT prediction of 

field-scale GAC performance, the paired t-test was utilized. A typical use of the 

paired t-test is to compare two analytical methods. Several samples that are uniquely 

matched and related, or paired, are split and analyzed by each method to obtain a 

series of paired measurements (Berthouex and Brown 1994). The average differ 

ence between the two methods is calculated, and the paired t-test is used to determine 

whether, at some confidence level, the average difference between paired points is 

significantly different from zero. The paired t-test assumes that the series of 

differences are independent and normally distributed (Daniel 1987). 

To be used in a paired t-test, GAC breakthrough data sets must be in the form 

of a time series of normalized effluent concentrations (effluent values reported as 

fractions of influent values, C/C0). Since sample points of the RSSCT and fieldscale 

adsorber are not normally paired in time, one data set is interpolated to obtain 

values corresponding to the sample times of the second data set. Linear interpolation 

is performed after the RSSCT breakthrough data have been scaled to equivalent 

field-scale time. To minimize error, the data set chosen for interpolation is that with 

231


the highest average density of sample points present within the time range that both 

sets share. This data set is termed the interpolated data set. Therefore, the location 

in time of each sample point within the data set with a lower density of points (the 

reference data set) is used to obtain a point on the interpolated data set. Sample 

points from either data set lying outside of the range that both share are discarded, 

except for two points located at each end of the interpolated data set. These two 

points must be retained in order to perform the first and last interpolations. Once two 

sets of paired sample points are generated, the difference between each pair, or the 

residual, is calculated. The average difference across the entire data set is an 

indicator of how well the RSSCT predicts field-scale performance. 

The null hypothesis, HQ, is defined as follows: 

HQ : 

"The difference between the two breakthrough curves is zero," 

or udo = 0. 

where udo is the hypothesized population mean difference. 

The alternate hypothesis, HA , is defined as: 

H A : 

"The difference between the two breakthrough curves is not zero," or 

The test statistic, t, is calculated by Equation B.I, assuming the differences are a 

random sample of a normally distributed population of differences (Daniel 1987): 

where 

« - * (B.1) 

( S SA) 

_ 

d = the sample mean difference 

s^ = the standard deviation of the difference 

n = the number of observations 

A level of significance, a, is chosen for acceptance or rejection of the null 

hypothesis. The test statistic is compared to the critical test statistic, t* (obtained 

from a table of probabilities of student's t distribution), to determine whether, at the 

(1 - a ) x 100 percent confidence level, the null hypothesis is accepted. 

Important assumptions of the paired t-test are that the series of differences 

are independent and that their distribution is normal. An empirical test of serial 

dependence is the autocorrelation function (ACF), available in statistical software 

packages, which measures the correlation between differences at certain units apart 

in the series (West and Hepworth 1991). 

To test whether a series of differences fulfills a normal distribution, a 

probability plot is generated. The plotting position of the ranked differences is 

calculated using Equation B.2 (Berthouex and Brown 1994): 

Appendix B 233 

where p is the probability and i is the rank. Based on their calculated probability, the 

differences are plotted against the position they would occupy in a normal distribu 

tion. A straight line indicates a normal distribution. Deviations from a straight line 

are an indication that the distribution is not normal, but results of the paired t-test 

procedure are still valid for deviations that are not drastic (Daniel 1987). This 

procedure is not quantitative and was not used to distinguish non-normal distribu 

tions in this study. 

As many sample points as possible should be used for comparisons of 

breakthrough curves by the paired t-test procedure. When the method is applied to 

small sample sizes, especially those containing fewer than 10 points, results may be 

negatively affected by the presence of one or two outliers in the original break 

through curves, even when the breakthrough curves show similar overall trends. 

Results obtained for small sample sizes should be accepted with caution. A 

statistical method that eliminates outliers prior to the test is recommended for small 

sample sizes. 

Application_____________________________ 

The paired t-test method has been applied to data obtained from field-scale 

GAC columns and RSSCTs for the four waters presented in Chapter 3. The waters 

studies were Ohio River water, Lake Gaillard water, Mississippi River water, and 

Passaic River water. For ORW, MRW, and PRW, the breakthrough curve compari 

son method was applied to TOC, UV254 , TTHM, HAA6, and TOX. For LOW, the 

breakthrough curve comparison method was applied to TOC, TTHM, and HAAS. 

For this study, a was chosen as 0.05 to yield a 95 percent confidence level. 

Figure B.I shows the RSSCT and field-scale GAC breakthrough behavior 

for TOC obtained using ORW. The RSSCT data set contained fewer points within 

the range that both data sets occupy and was used as the reference data set. The 

RSSCT and interpolated field-scale GAC breakthroughcurves are shown in Figure 

B.2. The comparison of the paired points showed that d = 0.0167, s^- = 0.0595, and 

n = 34, which yields t = 1.637. The critical test statistic, t*, for a = 0.05, obtained 

from a table of probabilities of t distributions with 33 degrees of freedom, is 2.036. 

Thus, t is less than t*, and the null hypothesis (i.e., no difference between the 

breakthrough curves) can be accepted once the assumption that the series of 

differences is independent is verified. The results of the ACF analysis are presented 

in Figure B .3, which is a plot of autocorrelations against each lag, or autocorrelogram. 

A significant autocorrelation would be represented as a bar extending beyond the 

plotted parentheses, which represent a 95 percent confidence level for a serial 

autocorrelation of the data at a particular lag. The probability plot, which qualita 

tively assesses the normality of the distribution, is shown in Figure B.4. Because the 

ACF analysis showed that there were no serial correlations, and since t is less than 

t*, the null hypothesis was accepted: TOC breakthrough behavior for ORW was not 

significantly different between the RSSCT and field-scale adsorber at the 95 percent 

confidence level. A summary of the results of this method applied to all RSSCT 

verification studies is shown in Table B.I. 

The comparison between field-scale and RSSCT UV254 breakthrough for 

PRW (Figure B.5) yielded a t value of 1.947. The critical t statistic, t*, value was


Table B.1 Summary of field-scale and RSSCT breakthrough curve comparisons 


Parameter 

n 

Average 

difference, 

d 

Independent 

series of 

differences? 

Significant 

difference in 

d at a = 0.05? 

Variance, 

s^ 2 

ORW 

TOC 

uv254 

TTHM 

HAA6 

TOX 

34 

13 

8 

8 

5 

0.017 

0.057 

0.037 

0.101 

0.094 

yes 

yes 

yes 

yes 

yes 

no 

yes 

no 

yes 

no 

0.0035 

0.0045 

0.0055 

0.013 

0.010 

LGW 

TOC 

TTHM 

HAAS 

7 

7 

7 

0.010 

0.030 

0.051 

yes 

yes 

yes 

no 

no 

no 

0.0070 

0.017 

0.010 

MRW 

TOC 

uv254 

TTHM 

HAA6 

22 

11 

9 

9 

0.075 

0.149 

0.072 

0.066 

no 

yes 

yes 

no 

NA* 

yes 

no 

NA 

0.011 

0.019 

0.071 

0.026 

TOX 

5 

0.058 

yes 

no 

0.0070 

PRW 

TOC 

uv254 

TTHM 

HAA6 

TOX 

32 

31 

9 

10 

10 

0.008 

0.014 

0.038 

0.048 

0.026 

no 

no 

yes 

yes 

yes 

NA 

NA 

no 

no 

no 

0.0054 

0.0017 

0.0083 

0.0060 

0.0038 

NA = not applicable; series of differences is independent. 

2.042. Since t is less than t*, the null hypothesis was accepted: The UV254 

breakthrough curves for PRW were not significantly different. 

Figure B.6 shows the field-scale and RSSCT TTHM breakthrough for 

LGW. For these breakthrough curves, the results showed a t value of 0.617, which 

is less than the t* value of 2.447. The RSSCT and field-scale TTHM breakthrough 

curves for LGW were not significantly different at the 95 percent confidence level. 

The results of the statistical method of comparing breakthrough curves for 

these three preceding examples do not contradict a qualitative assessment of how 

well the RSSCT predicted field-scale GAC breakthrough. However, this is not 

always the case, as illustrated by the following example. Figure B.7 shows fieldscale 

and RSSCT H AA6 breakthrough curves obtained for MRW. It is clear that the 

field-scale curve had a high immediate breakthrough as compared to the RSSCT, 

which showed a very low immediate breakthrough. The RSSCT data then showed 

a steeper breakthrough than did the field-scale data, and at about 19 scaled operation 

days the two curves crossed. Qualitatively, the RSSCT did not provide a good fit of 

field-scale breakthrough. The results of the statistical method yielded a t value of 

1.234, which is less than the t* value of 2.306. Thus, according to the method, the 

two curves were not significantly different. These results are caused by the addition


of large positive and negative differences, which led to a mean value of the 

differences near zero. A similar effect was seen when TTHM breakthrough curves 

were compared for MRW, as shown in Figure B.8. For these cases, the calculated 

variance, s^2 , may be a useful indicator of the occurrence of this effect. Figure B.9 

shows the variance for all RSSCT and field-scale breakthrough curve comparisons. 

The highest variances seen were those generated from MRW HAA6 and TTHM 

breakthrough curve comparisons (Figures B.7 and B.8). A high variance was also 

associated with MRW UV254 breakthrough (Figure B.I7), which showed the same 

breakthrough effect seen for HAA6 and TTHM breakthrough but for which the null 

hypothesis was rejected. A high variance was also found for LOW TTHM break 

through (Figure B.6) and was due to scatter in the data. All remaining breakthrough 

curve comparisons are shown in Figures B. 10 through B .22, and the results obtained 

are summarized in Table B.I. 

Summary_________________________ 

A statistical method has been developed, based on paired t-test analysis, to 

quantitatively compare RSSCT and field-scale GAC breakthrough curves. The 

procedure has been applied to four waters for a range of parameters, and the results 

are summarized in Table B.I. Of the 18 cases studied, 14 yielded an independent 

series of differences. Of these, 11 were not significantly different at the a = 0.05 

confidence level. Assumptions of independent and normally distributed differences 

should be verified. Certain breakthrough patterns that contain large positive and 

negative differences and that qualitatively do not show a good fit may be incorrectly 

assessed by this test. However, a high variance associated with the average 

difference can be used as an indicator that the results of the test should be accepted 

with caution. A variance greater than 0.015 is recommended as the criterion based 

on these results. 

The drawback of any statistical method to quantitatively compare break 

through curves is that the procedure results in acceptance or rejection of the fit. A 

statistical method's "acceptance" of the null hypothesis that there is no significant 

difference between two breakthrough curves does not mean the RSSCT was 

successful. On the other hand, when the same method determines that the two curves 

are significantly different, it does not mean that the RSSCT results are unusable. 

Either of the two conclusions reached using a statistical method must be combined 

with sound engineering judgment and common sense before a final evaluation of the 

data is made. When it is expected that the RSSCT breakthrough data "well predict" 

field-scale breakthrough data, it is useful to have a method available that provides 

a statistical basis for the comparison, beyond a simple qualitative statement based 

on visual inspection. However, certain factors can affect the comparison made using 

the statistical method, leading to a result that contradicts that based on sound 

engineering judgment. It is extremely important that when a statistical method is 

used for breakthrough curve comparisons, it should not be used blindly, without a 

thorough study of the data available.

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a 

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1.0 

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0.8 - 

8 

o 0.6 -\ 

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25 50 75 100 

* Pilot-scale effluent 


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Lake Gaillard water


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25 50 75 100 

• Pilot-scale effluent 


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water 




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water


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1.0 

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8 

TOX 

i(0 

o 0.2- 

0.0 

10 20 30 



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40 50 

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water


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100 125 

9 Pilot-scale effluent 



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water 

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TTHM 

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0.6 - 

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Appendix C__________ 

Development of a New Test 

for Disinfection By-Product 

Formation Assessment: 

Uniform Formation Conditions 

Until recently, two approaches have been used to assess the formation of 

chlorination disinfection by-products in drinking water: the formation potential test 

and the simulated distribution system test. Under the FP test, waters are chlorinated 

at high doses and long incubation times to maximize DBF formation and to 

indirectly assess the presence of DBF precursors (Stevens and Symons 1977). 

Typically, these conditions are a 7-day incubation period, a temperature of 25°C, a 

pH of 7, and a chlorine residual of 3 to 5 mg/L at the end of the incubation period 

(APHA et al. 1992). These high chlorine doses and long incubation times lead to the 

formation of more chloro-substituted than bromo-substituted DBFs (Symons et al. 

1993; Shukairy et al. 1994) compared to the speciation of DBFs formed under the 

conditions common to a treatment plant and distribution system. Moreover, a utility 

cannot directly use the FP test results to evaluate how changes in a given treatment 

will affect compliance with DBF regulations. 

To more accurately represent DBF formation in a specific distribution 

system, utilities can perform SDS tests, where samples are chlorinated under sitespecific 

conditions of time, temperature, pH, and chlorine dose or residual that 

reflect the conditions found in the distribution system. This test was initiated by J. 

DeMarco and A. Stevens at the USEPA for five field investigations in 1978 (Miller 

et al. 1982), and the results from SDS tests have been shown to represent DBF 

formation found in distribution systems well (Koch et al. 1991). A limitation of the 

SDS test is that chlorination conditions vary among distribution systems, compli 

cating DBF formation comparisons among different waters. 

A new test has been developed in an effort to provide a means for direct DBF 

formation comparisons among different waters, and it utilizes chlorination under 

constant conditions that are representative of average distribution system condi 

tions. This approach has been termed the uniform formation conditions test for DBF 

formation (Summers et al. 1996). The UFC test can also be used to study the effect 

of treatment conditions on subsequent DBF formation. 

247


Objectives 

The objectives of this appendix are (1) to describe the UFC test conditions 

and the rationale for their selection, (2) to present the results of chlorine demand 

studies performed on various waters, and (3) to present the results of sensitivity 

analyses performed on each UFC parameter for three waters. 

Uniform Formation Conditions_____________ 

The chlorination conditions selected for the UFC test are as follows: 24±1 

hours, 20.0+1.0°C, pH 8.0±0.2, and 1.0±0.4 mg/L free chlorine residual after 24 

hours, as outlined in Table C. 1. 

A complete description of the UFC test procedure can be found in Summers 

et al. (1996). The process of selecting the conditions for the UFC test began with the 

distribution of a comprehensive survey to utilities and researchers who perform the 

SDS and FP tests. A range of conditions for the SDS test were obtained from data 

generated by the survey. Together with data made available from the AWWA Water 

Industry Database (WIDE), based on information gathered from 318 large utilities 

that indicated free chlorine as the only residual disinfectant, a preliminary UFC set 

was developed. An important difference with respect to previous approaches was 

the adoption of a target chlorine residual rather than the use of a chlorine dose based 

on TOC. After discussions with several researchers and utility representatives, 

proposed conditions for the UFC test were agreed upon, as shown in Table C.I. 

Incubation Time 

Incubation times of 1 to 3. days were initially considered. Data from the 

WIDE showed that the average maximum time in the distribution system is 3.0 days, 

and that the average mean time is 1.3 days. Initially, an incubation time of 3 days 

was proposed, representing average maximum conditions in the distribution sys 

tem. From a practical viewpoint, however, a 1-day incubation time yields faster 

results and allows for more tests to be conducted. In a 5-day work week, four tests 

could be conducted if the incubation time were 1 day, whereas a 3-day incubation 

time would allow for only three tests to be conducted per week. Additionally, if a 

chlorine demand study were required for a given sample, then the complete test 

could be completed in 2 days with a 1-day incubation time, as opposed to 6 days if 

a 3-day incubation time were used. An investigation of DBF formation kinetics 

(described later) showed that the increase in DBF formation from 1 to 3 days 

Table C.1 UFC test conditions 

Parameter Value 

Incubation time (hours) 24±1 

Incubation temperature (°C) 20.0±1.0 

pH 8.0±0.2 

24-hour chlorine residual (mg/L) 1.0±0.4

Appendix C 249 

averaged 22 percent for two waters examined and three DBFs analyzed. Ultimately, 

an incubation time of 1 day was chosen for the UFC test. 

Incubation Temperature 

Initially, a temperature of 22±2°C was proposed, allowing labs to conduct 

the test at ambient temperatures, without an incubator. After further discussion, it 

was determined that temperature extremes commonly found in labs were greater 

than those acceptable for the UFC test and that, as such, many labs would require 

a temperature-controlled incubator anyway. In view of this information, a lower 

temperature with a narrower range of acceptable deviation was proposed: 20.0± 1.0°C. 

pH Value 

The pH value for the test has not changed throughout the discussions. A pH 

of 8.0 was chosen to reflect the impact of the Lead and Copper Rule on distribution 

system pH. Both samples and the chlorine dosing solution were buffered at pH 8.0 

with a borate buffer. Approximately 2 mL/L pH 8.0 borate buffer (1 .OM boric acid 

and 0.26M sodium hydroxide) was added to each sample before dosing. The dosing 

solution was buffered to pH 8.0 by addition of pH 6.7 borate buffer (1 .OM boric acid 

and 0.1 \M sodium hydroxide) based on the method described by Koch et al. (1987). 

Chlorine Residual 

A target chlorine residual after 24 hours of 1.0 mg/L free chlorine was 

chosen. This value is supported by the WIDE data, which reported an average mean 

residual of 0.90 mg/L. The average minimum and maximum chlorine residuals 

supplied by the WIDE are 0.39 and 1.57 mg/L, respectively. The acceptable range 

for the UFC test was decreased from ±0.5 to ±0.4 mg/L, after concerns were 

expressed that low residuals after 24 hours would not lead to detectable residuals 

after 3 to 5 days. The minimum acceptable chlorine residual, 0.7 mg/L, was thought 

still to yield a detectable residual after 3 to 5 days in most waters. Some concern was 

also expressed that for waters with very high TOC concentrations, a residual would 

not be detectable after 5 days. To address these issues, chlorine demand studies were 

performed, the results are presented in the next section. 


Chlorine Demand 

One of the primary objectives in the establishment of the UFC test is that 

the chlorination conditions be representative of average distribution system condi 

tions. As stated earlier, the UFC test requires a 1.0-mg/L chlorine residual after 24 

hours. Another concern is the maintenance of a residual for periods that reflect 

maximum times in a distribution system. Therefore, chlorine decay kinetics were 

investigated for a range of waters to determine whether the selected chlorine 

residual of 1.0 mg/L after 1 day would still yield detectable residuals after 3 to 5 

days, as would be necessary in a distribution system. Figure C.I shows chlorine


decay kinetics for several raw waters with moderate to high dissolved organic 

carbon concentrations (Dugan et al. 1995). For a chlorine dose that yielded a 24-hour 

chlorine residual near 1.0 mg/L, a chlorine residual was detected on day 3 and day 

5 for both Little Miami River (Ohio) water (DOC = 3.3 mg/L) and Winton Lake 

(Cincinnati, Ohio) water (DOC = 5.2 mg/L). Harsha Lake (Cincinnati, Ohio) water 

(DOC = 6.1 mg/L) showed no detectable residual after n day 3, and an extreme 

example, Hillsborough River (Florida) water (DOC = 16.4 mg/L), showed no 

detectable residual after day 2. 

The chlorine demand of all four raw waters should be significantly lowered 

by treatment. Chlorine decay studies performed on finished waters in which a 

1.0-mg/L residual was measured after 1 day have consistently yielded detectable 

chlorine residuals after 3 to 5 days. Results for three finished waters, Lake Gaillard 

water, Ohio River water, and Salt River Project water, with TOC concentrations 

ranging from 1.4 to 2.2 mg/L, are shown in Figure C.2. All three waters yielded 

detectable residuals on days 3 and 5 when the chlorine residual after 1 day was near 

1.0 mg/L. 

Because the UFC test accepts a range of 0.3 mg/L above and below the 

target 1.0-mg/L 24-hour chlorine residual, the lower limit of the target residual was 

investigated to demonstrate that, after a 24-hour residual near 0.7 mg/L, a chlorine 

residual would still be detectable after 3 and 5 days. Figure C.3 shows a comparison 

of two chlorine doses on SRPW that yielded two residuals after 24 hours (1.0 and 

0.6 mg/L). A detectable residual was measured for both waters on day 5. As shown 

in Figure C.4, similar results were found with Ohio River water for three chlorine 

doses yielding 24-hour residuals as low as 0.6 mg/L. A chlorine residual was 

detectable after 3 to 5 days for all three doses. For waters with higher TOC 

concentrations, however, the 24-hour residual needed to be higher to yield a 5-day 

detectable residual, as illustrated in Figure C.5 for Manatee Lake (Brandenton, Fla.) 

water (MLW). Dosed to yield the target 24-hour residual of 1.0 mg/L, this water 

yielded a detectable residual on day 3 but not on day 5. Increasing the dosage to yield 

a 1.5 mg/L residual after 24 hours resulted in a 0.5 mg/L 5-day residual. 

Sensitivity Analysis 

An investigation of the sensitivity of DBF formation to small and large 

deviations from UFC was performed on three waters. For the sensitivity analysis, 

one parameter was varied over a range of interest while the remaining parameters 

were held constant at the proposed UFC. This procedure was repeated for all 

parameters for three waters: ORW, SRPW, and MLW. All three waters were 

sampled after conventional treatment, which included alum coagulation, sedimen 

tation, and filtration. ORW is an industrially impacted source water with low TOC 

concentration, medium bromide concentration, and medium alkalinity. SRPW is a 

water with moderate TOC concentration, high bromide concentration, and high 

alkalinity. MLW is a surface water with high TOC concentration, low alkalinity, and 

high bromide concentration. General water quality parameters are summarized in 

Table C.2. 

The range of parameter values examined in the sensitivity analysis is shown 

in Table C.3. The values were selected to represent the range commonly found in 

distribution systems.


Table C.2 Water quality of waters used in sensitivity analysis 


Parameter 

ORW 

SRPW 

MLW 

Treatment 


Bromide concentration (ug/L) 

Alkalinity (mg/L as CaCO 3) 

Total hardness (mg/L as CaCO 3) 

PH 

Conventional 

1.3 

35 

.60 

• 128 

8.2 

Conventional 

.2.2 

89 

113 

176 

8.1 

Conventional 

4.1 

107 

12 

92 

7.8 

Table C.3 Sensitivity analysis experimental matrix 

Parameter 

UFC test 

value 

UFC test 

window 

ORW 

Sensitivity analysis 

range examined 

SRPW 

MLW 

Incubation time (hours) 


24 

20.0 

±1 

±1.0 

4-120 

6-36 

24-120 

9-30 

12-120 

9-33 

PH 


8.0 

1.0 

±0.2 

±0.3 

6.5-8.7 

0.3-3.1 

7.0-8.7 

0.5-1.5 

7.2-8.8 

0.5-1.6 

DBF formation was characterized in terms of total trihalomethane, the sum 

of six haloacetic acids, and total organic halide. The HAA species included in HAA6 

were monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, monobromoacetic 

acid, dibromoacetic acid, and bromochloroacetic acid. All error bars in the 

remaining figures represent relative percent differences (RPD) based on analyses of 

duplicate chlorination tests. Pairs of vertical dashed lines shown in the figures 

indicate the limits of the UFC test window for each parameter. 

Effect of Incubation Time 

Figures C.6, C.7, and C.8 show the effect of incubation time on the 

formation of THM, HAA, and TOX, respectively, for ORW. Figures C.9, C. 10, and 

C.ll show the equivalent data obtained for SRPW. Figures C.I2, C.I3, and C.I4 

show the data obtained for MLW. Incubation time was varied from 4 hours to 5 days 

for ORW, from 1 to 5 days for SRPW, and from 12 hours to 5 days for MLW. For 

all three waters, most of the 3-day DBF formation was already present after the first 

24 hours of reaction: 24-hour formation averaged 84 percent of 3-day formation 

(range: 71 -93 percent) for TTHM, HAA6, and TOX. After 24 hours, the increased 

formation of both TTHM and HAA6 was largely driven by the formation of the more 

chloro-substituted species, while the more bromo-substituted species showed fast 

initial rates of formation within the first 24 hours and smaller increases after 24


hours. This phenomenon occurred with all three waters examined and has been 

reported by others (Symons et al. 1993). 

Effect of Incubation Temperature 

The effect of incubation temperature on DBF formation was examined over 

a temperature range of 6 to 36°C for ORW (Figures C. 15, C. 16, and C. 17), 9 to 30°C 

forSRPW (Figures C.I8, C.I9, and C.20), and 9 to 33°C for MLW (Figures C.21, 

C.22, and C.23). While all DBFs analyzed showed an increase in formation with 

increasing temperature, the effect was more pronounced for TTHM and HAA6. 

This increase in formation after 24 hours at the higher temperatures can be attributed 

to an increase in reaction rates at the higher temperatures. The more chlorosubstituted 

THMs and HAAs showed larger increases in formation with increasing 

temperature than did the more bromo-substituted species. For instance, while 

DCAA and MCAA formation in SRPW increased by more than 95 percent each 

between incubation temperatures of 9 and 30°C, DBAA and MBAA formation 

showed 40 and 30 percent increases, respectively, over the same temperature range 

(Figure C. 19). This impact of temperature on speciation was likely due to the slower 

formation kinetics of the chloro-substituted DBFs (Krasner et al. 1992; Symons et 

al. 1993). As shown in Figures C.9 and C. 10, most of the bromo-substituted species 

were already formed after 24 hours, while the chloro-substituted species were still 

forming and thus were more sensitive to temperature changes. Similar effects were 

observed in the ORW and MLW studies. 

Effect of pH 

Figures C.24, C.25, and C.26 show the effect of pH on THM, HAA, and 

TOX formation, respectively, for ORW. The effect of pH on THM, HAA, and TOX 

formation for SRPW is shown in Figures C.27, C.28, and C.29, respectively, and that 

for MLW is shown in Figures C.30, C.31, and C.32. DBF formation was examined 

for ORW over a pH range of 6.7 to 8.7, for SRPW over a pH range of 7.0 and 8.7, 

and for MLW over a pH range of 7.2 and 8.8. A base catalyzed reaction, TTHM 

formation increased with increasing pH, driven by an increase in CHC13 formation, 

for all waters. For all waters, changes in HAA6 formation with increasing pH were 

similar. An increase in pH up to 8.2 did not affect HAA6 formation, and between 

8.2 and 8.7 there was a decrease in HAA6 formation for all waters, which is 

consistent with results reported by others (Stevens et al. 1989; Reckhow and Singer 

1985). For all waters, the decrease in HAA6 formation at high pH values was driven 

by a decrease in TCAA formation. TOX formation was not affected by pH within 

the range examined, which could represent a balance between the formation of base 

catalyzed and acid catalyzed DBFs. 

Effect of 24-Hour Chlorine Residual 

The effect of 24-hour chlorine residual was investigated from 0.3 to 3.1 mg 

C1 2/L for ORW (Figures C.33, C.34, and C.35), from 0.5 to 1.5 mg CL/L for SRPW 

(Figures C.36, C.37, and C.38), and from 0.5 to 1.6 mg C1,/L for MLW (Figures 

C.39, C.40, and C.41) . For ORW, TTHM formation sho'wed an increase with 

increasing residual only above 1.3 mg C12/L, while HAA6 formation was more 

strongly affected, showing a linear increase over the entire residual range. The 

increase in formation was largely driven by increases in TCAA and DCAA. There 

was a slight increase in TOX formation with increasing chlorine residual. For


SRPW, both TTHM and HAA6 showed slight increases in formation with increas 

ing chlorine residual, driven by increases in the more chloro-substituted species. For 

MLW, there was little change in TTHM and HAA6 formation over this chlorine 

residual range. TOX formation showed no change in formation for all three waters 

within the ranges examined. 

DBF Formation Sensitivity Within UFC Test Window 

The goal in providing an acceptable window for each UFC test parameter 

was to establish limits on the acceptance of data for the test; this goal was intended 

to allow for some deviation from the required test conditions in a manner that would 

result in little change in DBF formation. From the data collected for the sensitivity 

analysis, the percent change in DBF formation within the UFC test window was 

calculated. Table C.4 summarizes the DBF formation sensitivity to UFC. Within the 

acceptable window, DBF formation was least sensitive to incubation time (average 

2.3 percent change) and most sensitive to pH and 24-hour chlorine residual (average 

5.4 and 5.0 percent change, respectively). For ORW, the average change in DBF 

formation within all parameter windows was 3.0 percent, with a minimum of 1 

percent and a maximum of 9 percent. The average change in DBF formation within 

all parameter windows for SRPW was 3.8 percent, with a minimum and maximum 

of 1 and 8 percent, respectively. For MLW, the average change in DBF formation 

was 4.3 percent, with a minimum and maximum of


Chlorine demand tests showed that UFC should yield a 3-5 day detectable 

chlorine residual in most treated waters. Future work should include chlorine 

demand tests on finished waters with higher TOC concentrations than those 

presented here (to determine the presence of a residual after 3 and 5 days) and on 

finished waters with low alkalinity (to determine the robustness of the buffer 

system). 

DBF formation under UFC was not drastically affected by variations within 

the acceptable windows given for the waters used in this study: Ohio River water, 

Salt River Project water, and Manatee Lake water. The average change in DBF 

formation for all three DBFs, all three waters, and at the extremes of all UFC 

parameter windows was 3.7 percent. For any given DBF, formation at the limits of 

the UFC parameter windows did not exceed 13 percent. Within the UFC test 

window, TTHM formation was most sensitive to changes in pH, HAA6 formation 

was most sensitive to changes in 24-hour chlorine residual, and TOX was most 

sensitive to changes in pH for SRPW and 24-hour chlorine residual for ORW. 

Overall, for the waters examined, DBF formation was not overly sensitive to the 

conditions chosen for the UFC test or to slight changes that are still acceptable for 

the test.


o Hillsborough River 16.4 

a HarshaLake 6.1 

O Winton Lake 5.2 

Little Miami River 3.3 

DOC (mg/L) Chlorine dose (mg/L) 

26 

7.7 

6.1 

5.5 

0 - 

0123456 


Source: Data from Dugan et al. (1995) 

Figure C.1 Chlorine decay kinetics for raw waters with moderate to high DOC 

concentrations 

3.0 

—i———•———i———'———i———•- 

TOC (mg/L) Chlorine dose (mg/L) 

2.5 - 

O) 

e 2.0 - 

"3 

O Lake Gaillard 

o Ohio River 

a Salt River Project 

1.4 

1.9 

2.2 

2.2 

2.6 

2.5 

1 1.5-1 

£ 

o 

I 1'°"1 

O 

0.5 - 

0.0 

—r~ 

2 

T~ 

4 

T- 

5 


Figure C.2 Chlorine decay kinetics for three finished waters


Salt River Project water 24-hour chlorine 

TOC = 2.2 mg/L residual (mg/L) 

0.0 

234 


Figure C.3 Effect of chlorine dose on chlorine decay kinetics for Salt River Project water 


TOC = 1.3 mg/L 

24-hour chlorine 

residual (mg/L) 

a 1.3 

O 0,8 

A 0.6 

o.o 

234 


Figure C.4 Effect of chlorine dose on chlorine decay kinetics for Ohio River water


Manatee Lake water 

TOC = 4.1 mg/L 

24-hour chlorine 

residual (mg/L) 

234 


Figure C.5 Effect of chlorine dose on chlorine decay kinetics for Manatee Lake water 

12345 


Note: Incubation temperature, 22°C; pH, 7.95±0.02; 24-hour chlorine residual, 1.32±0.03 mg/L 

Figure C.6 Effect of incubation time on THM formation for ORW


012345 



Figure C.7 Effect of incubation time on HAA formation for ORW 

300 

o -4 

012345 



Figure C.8 Effect of incubation time on TOX formation for ORW


120 

100- 

80 - 

TO 

60 - 

40 - 

20 - 

234 


Note: Incubation temperature, 20°C; pH, 8.03±0.03; 24-hour chlorine residual, 1.00+0.05 mg/L 

Figure C.9 Effect of incubation time on THM formation for SRPW 

50 

I § 

40- 

30- 

20 - 

10 - 

§ — 

6 

—————— 5 

- —— *r 

——————-a—————— 

———————————— Q——— •————————— 

———:——? 

§ 

HAA6 

ORWHAA6 

5 DCAA 

TCAA 

BCAA 

OBAA 

MCAA 

MBAA 



Figure C.10 Effect of incubation time on HAA formation for SRPW

txj 

Ox 

so 

I 1 

I 

i 

C * 

3 g 

P & 

_L 0> 

ro 5 

rn = 

=* § 

(D 3 

a 1 

o a 

-t. C 

5' P 

O ro 

& 6 

O I 

3 a, 

=•• b 

3-. 0 1+ 


120 

234 



Figure C.13 Effect of incubation time on HAA formation for MLW 

800 

234 


Note: Incubation temperature, 20°C; pH, 8.00+0.02; 24-hour chlorine residual, 1.03±0.07 mg/L 

Figure C.14 Effect of incubation time on TOX formation for MLW


20 30 


40 

Note: Incubation time, 24 hours; pH, 8.04±0.02; 24-hour chlorine residual, 0.99±0.09 mg/L 

Figure C.15 Effect of incubation temperature on THM formation for ORW 

20 30 


40 


Figure C.16 Effect of incubation temperature on HAA formation for ORW


250 

10 20 

30 



Figure C.17 Effect of incubation temperature on TOX formation for ORW 

0 10 20 30 


Note: Incubation time, 24 hours; pH, 8.01 ±0.02; 24-hour chlorine residual, 1.08±0.11 mg/L 

Figure C.18 Effect of incubation temperature on THM formation for SRPW


50 

HAA6 

3- 40- 

1 

I 30- 

(D 

ORW 

HAA6 

20 - 

DCAA 

10 - 

TCAA 

BCAA 

DBAA 

~ MCAA 

O MBAA 

10 . 20 30 

40 


Note: Incubation time, 24 hours; pH, 8.01±0.02; 24-hour chlorine residual, 1.08+0.11 mg/L 

Figure C.19 Effect of incubation temperature on HAA formation for SRPW 

200 - 

..-• ORW 

150 - 

..H 

100 - 

50- 

10 20 30 

40 



Figure C.20 Effect of incubation temperature on TOX formation for SRPW


250 

20 30 


40 


Figure C.21 Effect of incubation temperature on THM formation for MLW 

120 

20 30 


40 

Note: Incubation time, 24 hours; pH, 8.01 ±0.02; 24-hour chlorine residual, 0.97+0.05 mg/L 

Figure C.22 Effect of incubation temperature on HAA formation for MLW


800 

700 - 

600 - 

O 

S» 500 

*-' / 

f 20° - 

—!•"—' 

.__..— SRPWTOX 

8 

x 100- 

O 

ORW TOX 

10 20 


30 

40 


Figure C.23 Effect of incubation temperature on TOX formation for MLW 

80 

60 - 

TTHM 

O 

t 40 - 

CHCIj 

O 

20 - 

CHBrCI 

O CHBr2CI 

6.5 7.0 7.5 8.0 

PH 

8.5 9.0 

9.5 

Note: Incubation time, 24 hours; incubation temperature, 20°C; 24-hour chlorine residual, 1.03±0.03 mg/L 

Figure C.24 Effect of pH on THM formation for ORW


40 

0 MCAA 

D DBAA 

O MBAA 

'13 q 

20 - 

HAA6 

DCAA 

BCAA 

TCAA 

6.5 7.0 7.5 8.0 

PH 

8.5 9.0 9.5 


Figure C.25 Effect of pH on HAA formation for ORW 

£*JW 

d 200 - 

O 

- 

0 150 - 

1 

c 100 - 

8 

X 

0 

K 50 - 

T__r 

. - 

" 

0- 

————— i ————— | ————— i ————— | ————— r-1 

6. 5 7.0 7.5 

8.0 

PH 

-i ————— | ———— , ————— ! ————— , ————— 

8.5 9.0 9. 

Note: Incubation time, 24 hours; incubation temperature, 20°C; 24-hour chlorine residual,1.03±0.03 mg/L 

Figure C.26 Effect of pH on TOX formation for ORW

00 

O 

Oo 

P & 

IO B* 

« 5' 

fl> CD 

a "ro 

O t 

§ 

I & 

> a 

O 5 

3 

HAA concentration (pg/L) 

ro 

o 

to 

o 

p & 

ro £ 

-4 o 

ro 

-a | 

X 55 

§ if 

H & 

I £ 

5 ! 

O CD 

3 1 

0) n> 

r+ Q) 

5' c 

cn 

In 

THM concentration (M9/L) 

O 

I 

CT> 

O 

O 3 

ro 

o V) O 

JJ 

TJ 

o 

c 

= CD 

O g 

(/) O 

TJ 

ro 

o 

o 

o" 

CD 

o 

CD 

5 

CD 

cn 

CL 

c 

0) 

o 

03 

H- 

o 

CL 

SO 

O 

CO 

H- 

O 

O 

3 

CQ 

CO 

in 

§ 

CO 

"0 

I 

2 

I 

'


250 

O 

o> 

1 

200- 

150- 

100- 

X 

e 50. 

ORW 

6.5 7.0 

7.5 8.0 

PH 

8.5 9.0 9.5 


Figure C.29 Effect of pH on TOX formation for SRPW 

200 

160 - 

TTHM 

•I 120 - 

CHCI, 

80 - 

SRPW TTHM 

ORW TTHM 

40 - 

A CHBrCI2 

O CHBfjCI 

6.5 

7.0 7.5 8.0 

PH 

8.5 9.0 9.5 


Figure C.30 Effect of pH on THM formation for MLW

. 

^ 

& 

2 

8- 

•53 

5' 

•n 


80 

TTHM 

s § «o 

CHCt, 

20H 

60- 

-£-45- 

-5-42- 

ACHBfCI, 

-O CHBr, 

0-0 0.5 1.0 1.5 2.0 2.5 


3.0 3.5 

Note: Incubation time, 24 hours; incubation temperature, 20°C; pH, 7.97±0.02 mg/L 

Figure C.33 Effect of 24-hour chlorine residual on THM formation for ORW 

0.5 1.0 1.5 2.0 2.5 


3.0 3.5 


Figure C.34 Effect of 24-hour chlorine residual on HAA formation for ORW


250 

,-» 200- 

C 

O 

8 100- 

8 

0.0 0.5 1.0 1.5 2.0 2.5 


3.0 

3.5 


Figure C.35 Effect of 24-hour chlorine residual on TOX formation for ORW 

0.5 1.0 1.5 


2.0 


Figure C.36 Effect of 24-hour chlorine residual on THM formation for SRPW

i A 

. 

i.' 

1 

ra 

s 

SI S' 

O 

NJ 

•~* T" r^ < Tl ' > ^- 

1 i t i 

(D 03 2 5=- 

P| 0 | 

t» | TOX concentration (MS CI7L) !ij |. HAA concentration (ug/L) 

HI ^-f -* -* K> Rj ni .-. — h K5 tJ 

=63 0888 §8-3 i 0088 

o -° P 

2. IV) 0 

a £ 

K i 

• y>. 

I 3 

C c 

~« IT 

o Si 

3" o p 

o -' i" 

§. | K 

33 ^ 

"> ^ ? 

J5 a ^ 

S2. S o 

a ® Z 

C M 0 

2. °* §• _ 

o P * b - 

3 ^ 3 

Hi 52. 

g « & 

?s 0 o 

•* ro — 

W rt IT 14- *-~^ ._ 

29 3 

3 ° *S. 

1 3° C 

%% in' 

«^» 

O^ 

(/) 

3 

i 

1 , 1 . 1 . 1 

. h H 

1 H 

( 

gj —.—, 1 —. ,— i ,—.,— , I — .—, I 

(O 

—,—, 0 - 

" 

£ * § 1 

-* ro 

a ? 

£ s 

f* 3 

0 I' 

C g 

-« cr 

o a o 

Z §' in ' 

O W 

2. 0 D _. 

""• o ffl . 

0 P * o 

3 (0 

-a w 

ii s 

S e i 

-* 1^3 ^ 

S £ I 

1 s c 

tt 3 r* - 

0 (0 en 

3 r- 

••it 

0 -t 

w 71 

•o 

i . i 1 i i, 

P f 9? f 

T\ : \ 

h \ \ 

l\\ '• \ 

tli il ' 

JuJUlJ '• 1 _ 1 

r~l ' 1 1 

II \ 

^ll 

1 1 

IR 1 r*HM r- 

1 0 

no S°Q o o < 

^^^^^ | 

1 . 1 . 1 

0, 

1— 1 

*~f 

I 

a 

6 S 

1


200 - 

———————— 1 ————————————— | ————————— -T—— 

,..., _____ , ,.,.. —— . 

d 150- 

I 

1 

A concenti 8 i 

I 50- 

*—— 

D —— - 

ORW TTHM __ 

-— — $ ———— 

_- —— 5 ———— 

^- 

—— J TTHM 

ṛ.._ 

7T fûrî 

.^ SRPW TTHM 

I ——————**—————————————————— 

A ——— 

| ——-* CHBrCI2 

———————— 0 —————————— __.^ CHBr2CI 

ft - 

0.0 0.5 1.0 1.5 


2.0 

Note: Incubation time, 24 hours; incubation temperature, 20°C; pH, 8.01*0.02 mg/L 

Figure C.39 Effect of 24-hour chlorine residual on THM formation for MLW 

0.5 1.0 1.5 


2.0 

Note: Incubation time, 24 hours; incubation temperature, 20°C; pH, 8.01 ±0.02 mg/L 

Figure C.40 Effect of 24-hour chlorine residual on HAA formation for MLW


/uu - 

1 

i 

i 

• 

650- 

g«: 

I ———— i 

1 

T 

f ^ 

o 250- 

/ 

• 

> 

- 

| 2°°- 

8 150- 

- SRPW TOX 

ORWTOX 

- 

" 

%oh- 100- 

50- 

- 

n - 

0.0 0.5 1.0 1.5 


2.0 

Note: Incubation time, 24 hours; incubation temperature, 20°C; pH, 8.01 ±0.02 mg/L 

Figure C.41 Effect of 24-hour chlorine residual on TOX formation for MLW

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Abbreviations 

ACF autocorrelation function 

amu atomic mass unit 

a level of significance 

aQ, a,, BO, Cj parameters used in regression 

analysis modeling of 

pretreatment 

Avg. average 

AWWA American Water Works 

Association 

AWWARF American Water Works 

Association Research Foundation 

BAT best available technology 

BCAA bromochloroacetic acid 

BDOC biodegradable dissolved organic 

carbon 

Br:DOC bromide-to-DOC ratio 

BrrTOC bromide-to-TOC ratio 

BV bed volumes 

C concentration 

C. concentration of immediate 

imm 

breakthrough 

CQ influent concentration 

C Q average influent concentration 

CD constant diffusivity 

CH chloral hydrate 

°C degrees Celsius 

cm centimeter 

I/cm per centimeter 

CT conventional treatment 

CUR carbon usage rate 

CWC Culp-Wesner-Culp 

CWW Cincinnati Water Works 

D dalton 

d day 

D-DBP Rule Disinfectants-Disinfection By- 

Products Rule 

DBAA dibromochloroacetic acid 

DBF disinfection by-products 

283 

DBPFP disinfection by-product formation 

potential 

DCAA dichloroacetic acid 

DCE cw-l,2-dichloroethene 

DOC dissolved organic carbon 

EBCT empty bed contact time 

eV electron volt 

FGW Florida groundwater 

FP formation potential 

ft foot 

g gram 

GAC granular activated carbon 

gal gallons 

GC gas chromatography 

gpm gallons per minute 

h hour 

HAA haloacetic acid 

HAAS sum of five haloacetic acids 

HAA6 sum of six haloacetic acids 

ICR Information Collection Rule 

in. Hg inch mercury 

JPWD Jefferson Parish Water District 

k' ratio of resin void volume to 

volume of sample passed 

kW'h kilowatt-hour 

L liter 

LC large column 

LCW laboratory clean water 

LGW Lake Gaillard water 

Ibs pounds 

M molar 

m meter


MBAA monobromoacetic acid 

MCAA monochloroacetic acid 

MCL maximum contaminant level 

mg milligrams 

mgd million gallons per day 

jig microgram 

uL microliter 

mil gal million gallon 

min minutes 

mL milliliter 

MLW Manatee Lake water 

mm millimeter 

MRW Mississippi River water 

MS molecular size (synonymous to 

apparent molecular weight, 

AMW); mass spectrometry 

N normal 

n bromine incorporation factor for 

THM speciation 

n' bromine incorporation factor for 

HAA speciation 

nm nanometer 

NOM natural organic matter 

O&M operation and maintenance 

OC optimized coagulation 

ORW Ohio River water 

PD proportional diffusivity 

pH negative logarithm of hydrogen 

ion concentration 

PRW Passaic River water 

PTFE polytetrafluoroethylene 

PVWC Passaic Valley Water Commission 

PY-GC-MS pyrolysis gas chromatographymass 

spectrometry 

rpm 

RSSCT 

s 

SC 

SCCRWA 

SDS 

SF 

SOC 

SRP 

SRPW 

TCAA 

THM 

'in, 

TOC 

TOX 

TTHM 

UC 

UFC 

UM 

USEPA 

UV 

UV 254 

WIDB 

WTP 

solid phase concentration 

correlation coefficient 

revolutions per minute 

rapid small-scale column test 

second 

small column 

South Central Connecticut 

Regional Water Authority 

simulated distribution system 

scaling factor 

synthetic organic chemical 

Salt River Project 

Salt River Project water 

trichloroacetic acid 

trihalomethane 

time at which adsorbable fraction 

begins to show breakthrough 

total organic carbon 

total organic halide 

total trihalomethane 

University of Cincinnati 

uniform formation conditions 

University of Massachusetts 

United States Environmental 

Protection Agency 

ultraviolet 

ultraviolet light absorbance at 254 

nanometers 

Water Industry Data Base 

Water treatment plant


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