decision tool to help utilities develop simultaneous compliance

Decision Tool to Help Utilities Develop Simultaneous Compliance Strategies

Subject Area: Water Quality

About the Water Research Foundation

The Water Research Foundation (formerly Awwa Research Foundation or AwwaRF) is a member-supported, international, 501(c)3 nonprofit organization that sponsors research to enable water utilities, public health agencies, and other professionals to provide safe and affordable drinking water to consumers.

The Foundation’s mission is to advance the science of water to improve the quality of life. To achieve this mission, the Foundation sponsors studies on all aspects of drinking water, including resources, treatment, distribution, and health effects. Funding for research is provided primarily by subscription payments from close to 1,000 water utilities, consulting firms, and manufacturers in North America and abroad. Additional funding comes from collaborative partnerships with other national and international organizations and the U.S. federal government, allowing for resources to be leveraged, expertise to be shared, and broad-based knowledge to be developed and disseminated.

From its headquarters in Denver, Colorado, the Foundation’s staff directs and supports the efforts of more than 800 volunteers who serve on the board of trustees and various committees. These volunteers represent many facets of the water industry, and contribute their expertise to select and monitor research studies that benefit the entire drinking water community.

The results of research are disseminated through a number of channels, including reports, the Web site, Webcasts, conferences, and periodicals.

For its subscribers, the Foundation serves as a cooperative program in which water suppliers unite to pool their resources. By applying Foundation research findings, these water suppliers can save substantial costs and stay on the leading edge of drinking water science and technology. Since its inception, the Foundation has supplied the water community with more than $460 million in applied research value.

More information about the Foundation and how to become a subscriber is available on the Web at www.WaterResearchFoundation.org.



Jointly sponsored by:Water Research Foundation6666 West Quincy Avenue, Denver, CO 80235-3098

and

U.S. Environmental Protection AgencyWashington, DC 20460-0001

Published by:

Prepared by:David B. Schendel, Zaid K. Chowdhury, and Christopher P. HillMalcolm Pirnie, Inc., Phoenix, AZ 85008-6945

R. Scott Summers, Erin Towler, and Rajagopalan BalajiDepartment of Civil, Environmental and Architectural EngineeringUniversity of Colorado-Boulder, Boulder, CO 80309-0428

and

Robert S. Raucher and John CromwellStratus Consulting, Inc., Boulder, CO, 80302-5148


DISCLAIMER

This study was jointly funded by the Water Research Foundation (Foundation) and the U.S. Environmental Protection Agency (USEPA) under Cooperative Agreement No. CR-83110401.

The Foundation and USEPA assume 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

either the Foundation or USEPA. This report is presented solely for informational purposes.

Copyright © 2009by Water Research Foundation

ALL RIGHTS RESERVED. No part of this publication may be copied, reproduced

or otherwise utilized without permission.

ISBN 978-1-60573-064-6

Printed in the U.S.A.


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CONTENTS LIST OF TABLES ........................................................................................................................ vii FOREWORD ................................................................................................................................. ix ACKNOWLEDGMENTS ............................................................................................................. xi EXECUTIVE SUMMARY ......................................................................................................... xiii

CHAPTER 1 - INTRODUCTION .................................................................................................. 1 Background and Purpose .................................................................................................... 1 Previous Research ............................................................................................................... 2 Project Objectives ............................................................................................................... 6

CHAPTER 2 - IDENTIFYING THE POTENTIAL FOR SIMULTANEOUS

COMPLIANCE CONFLICTS AND UNINTENDED CONSEQUENCES ............................. 7 Overview of Project Approach ........................................................................................... 7

Regulatory and Literature Review .......................................................................... 7 Identify Regulatory Conflicts and Utility Impacts .................................................. 8 Identify and Evaluate Technology Solutions .......................................................... 8 Develop Decision Framework ................................................................................ 8 Perform Economic Analysis of Alternatives .......................................................... 9 Develop Decision Tools .......................................................................................... 9

SCTool Content Development ............................................................................................ 9 Water Quality Issues and Treatment Solutions ....................................................... 9 Technology-Based Rules ...................................................................................... 10 Impacts of Variable Water Quality ....................................................................... 11

CHAPTER 3 - RECOMMENDATIONS TO UTILITIES ........................................................... 13

APPENDIX A – SCTOOL USER MANUAL ........................................................................... A-1

APPENDIX B – SCTOOL TREATMENT TECHNOLOGY DESCRIPTIONS ....................... B-1

APPENDIX C - TECHNOLOGY-BASED RULE LOGIC ....................................................... C-1

APPENDIX D - CAPTURING VARIABILITY IN SOURCE WATER ................................... D-1 APPENDIX E - MODELING TOC, ALKALINITY, AND PH FROM RAW WATER

TO THE SEDIMENTATION BASIN USING THE ICR DATABASE ............................... E-1

REFERENCES ........................................................................................................................... R-1

ACRONYMS AND ABBREVIATIONS .............................................................................. ABR-1


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TABLES

1.1 Simultaneous compliance issues ..........................................................................................3 1.2 Summary of unintended consequences which may impact lead and copper corrosion .......4 1.3 Summary of potential secondary impacts and unintended consequences ...........................5 2.1 Top-ranked technologies and operational responses with potential unintended

consequences..........................................................................................................10


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FOREWORD

The Water Research Foundation (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 a process 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 the 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 amount of water the 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. David E. Rager Robert C. Renner, P.E. Chair, Board of Trustees Executive Director Water Research Foundation Water Research Foundation


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ACKNOWLEDGMENTS

The authors of this report are indebted to the following water utilities and individuals for their cooperation and participation in this project:

American Water, Voorhees, N.J., Orren Schneider City of Cleveland Division of Water, Cleveland, Ohio, Margaret Rodgers Greater Cincinnati Water Works, Cincinnati, Ohio, Jeff Swertfeger Massachusetts Water Resources Authority, Boston, Mass., Stephen Estes-Smargiassi Pennichuck Water Works, Inc., Merrimack, N.H., Rebecca McEnroe City of Englewood Water Department, Englewood, Colo., Joe Pershin City of Newport News Waterworks, Newport News, Va., Mike Hotaling City of Portland Bureau of Waterworks, Portland, Ore., Yone Akagi City of Austin Water and Wastewater Utility, Austin, Texas, Charlie Maddox Washington Aqueduct Division, U.S. Army Corps of Engineers,

Washington, D.C., Anne Spiesman Zone 7 Water Agency, Livermore, Calif., Angela O’Brien California Domestic Water Company, Whittier, Calif., Jim Byerrum City of Ripon, Ripon, Calif., Matt Machado In addition, the advice and help offered by the Project Advisory Committee (PAC) – including, Michael J. Finn, U.S. Environmental Protection Agency, Office of Ground Water and Drinking Water, Washington, D.C.; Michael Hotaling, Newport News Waterworks, Newport News, Va.: Russell Ford, Montgomery Watson Harza, Saddlebrook, N.J.; Ronald Hunsinger, East Bay Municipal Utility District, Oakland, Calif.; and Eva Nieminski, Utah Department of Environmental Quality, Salt Lake City, Utah – are appreciated.


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EXECUTIVE SUMMARY Utility managers and staff are required to make decisions about competing water quality

objectives in the context of rapidly changing regulations and increasingly rigorous customer expectations. Moreover, these utility decisions frequently involve regulatory compliance actions which themselves can increase the potential for conflicts and unintended consequences. Simultaneous compliance conflicts and unintended consequences are frequently associated with modifying existing treatment operations and new technology implementation. Treatment changes can also create new challenges for residuals management and wastewater discharges, as well as aesthetics such as taste, odor and color. Without careful planning and proper implementation, utility actions originally intended to improve compliance can instead produce adverse unintended consequences.

As more drinking water regulations become effective, it will be increasingly difficult for public water systems to navigate the maze of potential simultaneous compliance issues. Much of the previous research on simultaneous compliance has focused on only a handful of simultaneous compliance issues such as disinfection, disinfection byproduct (DBP) precursor removal, DBP formation, and corrosion control. However, there are a significant number of other regulatory and non-regulatory water quality objectives that water utilities must continually address and for which adequate guidance is not currently available. These include regulations on radionuclides, arsenic, groundwater disinfection, and aesthetic issues emanating from taste and odor causing compounds or dissolved solids. Customer-driven water quality requirements often impose real constraints on how utilities plan and implement regulatory compliance strategies. RESEARCH APPROACH

The research focus of this project was to develop a web-deployed decision-making

assistance tool which allows utilities to more simply indentify and assess potential simultaneous compliance conflicts and other negative unintended consequences. The Simultaneous Compliance Tool (SCTool) developed under this project, is intended to assist utilities in evaluating appropriate technology choices to comply with multiple and/or conflicting water quality goals. The research approach generally proceeded as follows:

Identify and prioritize compliance conflicts and technology consequences routinely

encountered by water utilities; Sort conflicts and adverse unintended consequences around treatment technology

scenarios under which they most commonly arise; Develop a technology-based rule framework to assist utilities in making more informed

decisions, and to more quickly discern the potential for simultaneous compliance conflicts or negative unintended consequences; and

Develop a user-friendly web-based electronic tool which embodies the framework, and which will frame key issues needed to inform water quality planning decisions and avoid such conflicts.


xiv

During 2006 and 2007, a diverse group of drinking water utilities, regulatory agencies and engineering consultants were assembled to identify a broad range of potential regulatory and water quality objectives. Treatment and operational responses to address those objectives and potential simultaneous compliance and unintended consequences resulting from those responses were also identified and prioritized for inclusion in the decision tool framework. Project workshop participants identified approximately 40 potential technologies for possible inclusion in the SCTool. The technologies represented a wide range of optimization techniques, modified operational strategies and treatment techniques – and were selected in multiple break-out sessions focusing on various classes of contaminants, regulatory requirements, and water quality objectives.

The technologies and operational responses were then prioritized by utility participants and Project Advisory Committee (PAC) members based upon the significance and severity of simultaneous compliance issues, and also more subjective opinions about the frequency that each type of response might be employed by different size utilities. These efforts produced a prioritized list of technologies and optimization strategies with high potential for use and/or significant simultaneous compliance challenges.

TECHNOLOGY-BASED RULES FOR DECISION-MAKING

The heart of the decision-making framework was the creation and testing of the technology-based rules that serve to identify the potential for unintended consequences or simultaneous compliance issues. The functional aspects of these rules were gleaned from the research done in this study, the empirical experience of the engineers and scientists represented on the project team, and the project workshops. When combined with system data/information and water quality data provided by the utility user, the rules automatically flag issues or conflicts associated with the use of specific treatment technologies or O&M practices. Each technology included in the web-based SCTool is accompanied by a treatment technology description or “primer” – a brief summary of the technology, its effectiveness, costs, and associated unintended consequences or simultaneous compliance issues.

SCTool DESCRIPTION

The Simultaneous Compliance Tool (SCTool) is intended to assist utilities in making appropriate technology choices to comply with multiple water quality goals, with particular attention toward challenges posed by the source water, treatment and distribution system conditions/limitations. The SCTool utilizes a framework of technology-based rules to identify potential unintended consequences and simultaneous compliance conflicts associated with a particular solution in the context of system specific water quality, treatment, operational, and management characteristics. The SCTool allows utility users to modify anticipated future system conditions to eliminate or minimize the potential for compliance conflicts, and thereby facilitate more informed decisions about the suitability and implications of new treatment technology or operational practices. In addition to providing direct technology based “on-ramps,” the SCTool also allows the user to pick from a list of potentially applicable technologies based on a utility-specific water quality planning issue or compliance concern. Additional system-specific testing and demonstration may be needed once a short list of suitable technologies is identified using the SCTool.


xv

The URL to the website is: http://www.SimultaneousComplianceTool.org. The SCTool website has been designed to enable users to assess the impact of modifying existing treatment or selecting new treatment technologies to resolve “flagged” water quality and compliance issues. It provides the users with a summary of potential unintended consequences resulting from selection of a particular technology in the context of system-specific inputs on water quality, treatment configurations, operational data, and management characteristics. The SCTool allows users to evaluate the way changes in these system input characteristics contribute to or resolve conflicts and consequences which possibly may occur.

The simultaneous compliance conflicts and unintended consequences suggested by the SCTool represent the opinions of the Research Team based upon current available literature and empirical experience. The SCTool output and suggestions are intended to inform utility decision-making, but should not be used in place of system-specific data collection and economic analysis. In many cases, a more complete assessment of selected treatment technology will require the assistance of a licensed engineering professional. In some cases pilot plant or full-scale treatability data will need to be collected in accordance with state primacy agency requirements.

FINDINGS AND CONCLUSIONS

• It was necessary during development of the SCTool to carefully discriminate between relevant simultaneous compliance issue system inputs and “design criteria.” The SCTool is not a design tool! The participating utilities felt that the level and complexity of data entry should not approach that of a design tool, but rather be only that which is necessary to work within the framework of rules for each candidate technology.

• The SCTool is intended to be usable by utility personnel who are not “water quality experts.” While the input pages and data requirements are intentionally simplified, users nevertheless will need to have some degree of system knowledge and treatment technology to make effective use of the SCTool. During the project workshops it was evident that participants believed a wide disparity in utility compliance planning knowledge and expertise exists and that in general smaller and medium-sized utilities will approach decision-making differently than systems with greater knowledge and resources. Workshop participants and PAC members suggested that distinctly different compliance tools might be needed to best serve the needs of smaller systems, and other utilities with limited financial resources and knowledge of water quality planning and/or treatment technology.

• Research into decision-making has also shown that assessment of costs and benefits is always a utility-specific exercise, and no guidance manual or decision-making tool will likely be able to replicate this process. Often the process itself relies as much on instinct as quantitative rigor. The success of any decision-making framework is based in significant measure upon the quality of information and knowledge brought forward by the user(s). The SCTool developed under AwwaRF 3115 is no different.

• System specific costs/benefits are problematic to develop in such a generic device as the SCTool, and the use of cost as a decision-making criteria was abandoned early in the project. Utility participants expressed a general mistrust of “cost estimates generated by such tools.” It was feared that attempting to provide site specific costs had the potential


xvi

to create confusion or even foster bad judgment. A list of critical factors that influence costs was instead incorporated into the primer documents of each technology.

• The SCTool will not generate final solutions or recommendations. Rather, the interface prompts the user to enter water quality data and system attributes sufficient to identify when conflicts and consequences are in play. Problematic conflicts/consequences are organized around cross-reference pick-lists of viable treatment options for target compliance issues. In this manner the SCTool is intended to reflect the way utilities bring their individual value propositions to the decision-making.

• The primers that were developed for the treatment technologies should serve as references and help in the initial screening tool in evaluation of new treatment technologies.

FUTURE RESEARCH

The SCTool developed herein only covers certain candidate technologies. Future research efforts would likely be able to increase the number of technologies and related decision frameworks included in the SCTool.

Practical treatment models do not exist for many of the technologies addressed in the SCTool, and the development of such treatment simulation engines was beyond the scope of this work. Future versions of the SCTool may well be able to leverage advances in public-domain treatment simulation models. Incorporation of such models or engines would likely increase the complexity of data entry significantly and demand greater user sophistication.

Participating utilities and other members of the research team generally believed that distinctly different compliance tools might be necessary to best serve the needs of smaller systems, and other utilities with limited financial resources and knowledge of water quality planning and/or treatment technology. Such tools might make use of more default values based upon a limited number of water system categories, or might make use of a more limited number of candidate treatment technologies. It was also recognized that in general there is no direct correlation between system size and user sophistication.


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CHAPTER 1 INTRODUCTION

BACKGROUND AND PURPOSE

Utility managers and staff are required to make decisions about competing water quality objectives in the context of rapidly changing regulations and increasingly rigorous customer expectations. Moreover, these utility decisions frequently involve regulatory compliance actions which themselves can increase the potential for conflicts and unintended consequences. Simultaneous compliance conflicts and unintended consequences are frequently associated with modifying existing treatment operations and new technology implementation. Treatment changes can also create new challenges for residuals management and wastewater discharges, as well as aesthetics such as taste, odor and color. Without careful planning and proper implementation, utility actions originally intended to improve compliance can instead produce adverse unintended consequences.

Recent water utility concerns surrounding Lead and Copper Rule (LCR) compliance and conflicts with rules such as the Long-Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR), Stage 2 Disinfectants and Disinfection Byproducts Rule (Stage 2 D/DBP Rule) have amplified the need for practical guidance and solutions. In September 2008, the United States Environmental Protection Agency (USEPA) and water industry stakeholders reached an “Agreement in Principle” that will result in Revisions to the Total Coliform Rule, and USEPA has further identified a wide array of issues which may be regulated under a future Distribution System Rule. The Unregulated Contaminants Monitoring Rule 2 (UCMR2) identifies 25 potential newly-regulated contaminants, including N-nitrosodimethylamine (NDMA) which is already regulated in some, but not all states. Other drinking water contaminants, such as methyl-tert-butyl ether (MTBE) and perchlorate, are also currently regulated in some states and remain on the USEPA Candidate Contaminant List (CCL). Finally, endocrine-disrupting compounds (EDCs) and pharmaceuticals and personal care products are being viewed with increased scrutiny and are already controlled in some states. New DBPs are being identified and their health effects are being evaluated.

As more drinking water regulations become effective, it will be increasingly difficult for public water systems to navigate the maze of potential simultaneous compliance issues. There is a great need for technical tools to assist utilities in discerning possible conflicts and devising responsive strategies. Toward this end, in March 2007 the United States Environmental Protection Agency (USEPA) published a Simultaneous Compliance Guidance Manual for the LT2ESWTR and Stage 2 D/DBP Rule. USEPA’s Simultaneous Compliance Guidance provides an abundance of technical information on the conflicts and consequences of various treatment technologies.

Much of the previous research on simultaneous compliance has focused on only a handful of simultaneous compliance issues such as disinfection, DBP precursor removal, DBP formation, and corrosion control. However, there are a significant number of other regulatory and non-regulatory water quality objectives that water utilities must continually address and for which adequate guidance is not currently available. These include regulations on radionuclides, arsenic, groundwater disinfection, and aesthetic issues emanating from taste and odor causing compounds or dissolved solids. Changes in the concentrations of other water quality parameters,


2

such as total dissolved solids (TDS), dissolved oxygen (DO) and other dissolved gases, chloride, sulfate, and sodium, also have the potential to have significant impacts on the distribution system and finished water quality; however, proper guidance is not currently available to address possible consequences of changes in these parameters.

Conflicts can also arise between regulatory-driven water quality objectives and other goals specific to a utility and its customers. For example, the regulatory need to reduce disinfection byproduct concentrations might result in the use of chloramines for secondary disinfection. Customer concerns regarding the use of chloramines, properly founded or not, may result in barriers to implementation or require the water service provider to consider other alternatives. Customer-driven water quality requirements often impose real constraints on how utilities plan and implement compliance strategies.

PREVIOUS RESEARCH

More stringent drinking water quality regulations and a more informed customer base have resulted in a need and desire for more complex treatment processes and water quality that exceeds regulatory standards. These sometimes competing objectives have the potential to result in simultaneous compliance issues (i.e., compliance with one regulatory requirement at the expense of another requirement) or other unintended consequences. For example, in the 1990s water utilities were faced with the challenge of providing additional protection against Cryptosporidium while balancing the need to reduce disinfection byproduct (DBP) concentrations. The Water Research Foundation (Foundation), USEPA and many engineers and researchers worked diligently during that time to indentify a broad array of compliance alternatives for water utilities. However, the advantages of treatment changes were often touted without clearly identifying the potential for negative unintended consequences, making such choices all the more difficult.

With many new regulations pending, the focus during the mid-1990s was on optimizing existing treatment technology. It was a time when many treatment technologies and analytical techniques were in the midst of rapid evolution, and it was difficult for utilities to keep up with all the implications of process modification and new process implementation. One of the most comprehensive explorations of simultaneous compliance challenge facing utilities is a Foundation report entitled Balancing Multiple Water Quality Objectives (Daniel, 1998). A summary of the major observations from the 1998 study is provided in Table 1.1.


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Table 1.1 Simultaneous Compliance Issues

Area of Conflict Conflict Disinfection ■ Increased disinfectant doses result in increases in DBP formation

■ Chlorine and ozone dose can impact filtration efficacy ■ Ozone may increase biodegradable organic matter entering distribution

system resulting in increased microbial regrowth ■ Change in disinfectant type or dose may result in taste and odor (T&O) issues.

Disinfection byproducts

■ Reduced disinfectant dose may be insufficient to control T&O ■ Changes in finished water quality from enhanced precursor removal may

impact corrosion ■ Strategies to control bacterial regrowth may be at odds with DBP control.

Regrowth mitigation

■ Filtration rates needs to control regrowth may not be cost-effective. ■ Nitrification control strategies may increase T&O.

Corrosion control

■ Corrosion control pH may be at odds with DBP control strategy. ■ Addition of lime prior to filtration may decrease filter efficiency and impact

finished water turbidity. ■ Phosphate-based inhibitors may provide nutrient source for regrowth. ■ Increased phosphorous and other metals (Zn) may jeopardize NPDES

compliance. Source: Daniel, 1998

An American Water Works Association (AWWA) report, also completed in 1998, Secondary Impacts of Enhanced Coagulation and Enhanced Softening, identified the potential impacts of changes in treatment to enhance DBP precursor removal, including increases in finished water manganese or aluminum concentrations, changes in finished water chloride/sulfate/sodium concentrations, and increases in consumer tap lead and copper concentrations.

Under the auspices of the AWWA Water Utility Council Water Industry Technical Action Fund (WITAF), members of this project team assisted in development of an assessment protocol and management framework to assist utilities in avoiding potential unintended consequences associated with changes corrosion control treatment. Managing Change and Unintended Consequences: Lead and Copper Rule Corrosion Control Treatment (AWWA, 2005) remains one of the most up-to-date and comprehensive discussions of the potential impacts of change on corrosion control and Lead and Copper Rule (LCR) compliance. The document outlined a wide array of unintended consequences and simultaneous compliance issues associated with changes in source water, treatment practices, and distribution system operations for the purposes of complying with various drinking water regulations. Summaries of those potential impacts to LCR compliance and other water quality objectives are provided in Tables 1.2 and 1.3, respectively.


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Table 1.2 Summary of Unintended Consequences Which May Impact Lead and Copper Corrosion

Changes in corrosion-related water quality parameters

Potential impacts on existing scale

Other corrosion-related impacts

Cha

nge

in

finis

hed

wat

er

pH

Cha

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hed

wat

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alka

linity

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4

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olve

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s con

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n C

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ty/s

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ility

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s

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of p

artic

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to sy

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ing

Source of Supply Changes

Change in source water quality ● ● ● ● ● ● ●

Addition of a new source of supply ● ● ● ● ● ● ●

Blending of different source waters ● ● ● ● ● ● ●

Treatment Changes

Change in free chlorine dose ● ● ● ● Change from chlorine gas to hypochlorite ● ● ● ●

Addition of other oxidants/disinfectants (O3, KMnO4, ClO2)

● ● ●

Conversion from free chlorine to chloramines ● ● ● ●

Enhanced coagulation for NOM removal ● ● ● ● ●

Change coagulant type or dose ● ● ● ● ● ●

Change in finished water pH ● ● ● ● ● ●

Change in finished water alkalinity ● ● ● ● ● ●

Addition of a corrosion inhibitor ● ● ●

Change in inhibitor type or dose ● ● ● Addition/discontinuation of softening ● ● ● ● ● ● ● ●

Addition of NF/RO ● ● ● ● ● ● ● ●

Addition of GAC ● ●

Use of bio-filtration ● ● ●

Distribution Operations and Maintenance Activities

Lead service line replacement ● ● ● Meter and other device replacement ● ●

Blending of different finished waters ● ● ● ● ●

Flushing ●

Storage tank/reservoir maintenance ●

Source: AWWA, 2005.


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Table 1.3 Summary of Potential Secondary Impacts and Unintended Consequences

Aesthetic water quality

parameters

Secondary Compliance issues

Distribution system

issues

Red

w

ater

or

ot

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colo

r iss

ues

Tast

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ater

Incr

ease

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Cha

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ater

age

Cha

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flo

w,

dire

ctio

n, a

nd p

ress

ure

Nitr

ifica

tion

Source of Supply Changes

Change in source water quality ● ● ● ●

Addition of a new source of supply ● ● ● ●

Blending of different source waters ● ● ● ●

Treatment Changes

Change in free chlorine dose Change from chlorine gas to hypochlorite ●

Addition of other oxidants/ disinfectants (O3, KMnO4, ClO2)

● ● ●

Conversion from free chlorine to chloramines ● ● ●

Enhanced coagulation for NOM removal ●

Change coagulant type or dose

Change in finished water pH ● ● ●

Change in finished water alkalinity ●

Addition of a corrosion inhibitor ● ● ● ● ●

Change in inhibitor type or dose ● ● ● ● Addition/discontinuation of softening ● ● ●

Addition of NF/RO ● ● Addition of granular activated carbon

Use of bio-filtration

Distribution Operations and Maintenance Activities

Lead service line replacement ●

Meter and other device replacement Blending of different finished waters ● ●

Flushing ● ● ● ●

Storage tank/reservoir cleaning ● ● ● ● ● ● System and tank inspection and maintenance ● ● ● ● ● ●

Source: AWWA, 2005.


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PROJECT OBJECTIVES

Under this research, a team of engineers and scientists have developed a tool which leverages available data and knowledge to assist utilities in evaluating potential technology choices to comply with multiple and/or conflicting water quality goals.

The project objectives were to:

Identify and prioritize compliance conflicts and technology consequences faced by water utilities;

Sort conflicts and adverse unintended consequences around using the treatment optimization and technology scenarios under which they most commonly arise;

Develop a decision framework to assist utilities in making more informed decisions, and to more quickly discern the potential for simultaneous compliance conflicts or negative unintended consequences; and

Develop a web-based electronic tool to assist utilities in assessing their potential for simultaneous compliance conflicts, and frame key issues needed to inform water quality planning decisions to avoid such conflicts.

Develop awareness in water utility personnel about the value of monitoring water quality parameters that may influence the treatability of raw water.

The Simultaneous Compliance Tool (SCTool) is intended to assist utilities in making

appropriate technology choices to comply with multiple water quality goals, with particular attention toward challenges posed by the source water, treatment and distribution system conditions/limitations. The SCTool utilizes a framework of technology-based rules to identify potential unintended consequences and simultaneous compliance conflicts associated with a particular solution - in the context of system-specific water quality, treatment, operational, and management characteristics. The SCTool allows utility users to modify anticipated future system conditions to eliminate or minimize the potential for compliance conflicts, and thereby facilitate more informed decisions about the suitability and implications of new treatment technology or operational practices. Additional system-specific testing and demonstration may be needed once a short list of suitable technologies is identified using the SCTool.

The SCTool website has been designed to enable users to assess the impact of modifying existing treatment or selecting new treatment technologies to resolve “flagged” water quality and compliance issues. It provides the users with a summary of potential unintended consequences resulting from selection of a particular technology in the context of system-specific inputs on water quality, treatment configurations, operational data, and management characteristics. The SCTool allows users to evaluate the way changes in these system input characteristics contribute to or resolve conflicts and consequences which may occur. The simultaneous compliance conflicts and unintended consequences suggested by this SCTool represent the opinions of the Research Team, PAC members, and utility participants based upon current available literature and their collective experiences. The SCTool output and suggestions are intended to inform utility decision-making, but should not be used in place of system-specific data collection and economic analysis. In many cases a more complete assessment of selected treatment technology will require the assistance of a licensed engineering professional. In some cases pilot plant or full-scale treatability data will need to be collected in accordance with state primacy agency requirements.


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CHAPTER 2 IDENTIFYING THE POTENTIAL FOR SIMULTANEOUS COMPLIANCE

CONFLICTS AND UNINTENDED CONSEQUENCES OVERVIEW OF PROJECT APPROACH This project consisted of 6 main research components:

Regulatory and Literature Review Identify Regulatory Conflicts and Utility Impacts Identify and Evaluate Technology Solutions Develop Decision Framework Perform Economic Analysis of Alternatives Develop Decision Tools

The approach also relied heavily on input from the project utility partners. Three project

workshops were organized to identify regulatory conflicts and utility impacts, technology solutions to those conflicts, and beta-test the SCTool. Each of these workshops, as well as the findings and recommendations from those workshops are discussed in the following sections.

Regulatory and Literature Review

Prior to any of the project workshops, the project team conducted a review of existing, proposed, and potential future drinking water regulations to identify possible simultaneous compliance issues, as well as other resulting unintended consequences. This review also included identification of potential future regulated contaminants, for which regulations have not been proposed, but for which utilities may decide to implement treatment to address customer concerns, such as pharmaceuticals and other trace contaminants.

“Simultaneous compliance” issues refer specifically to incidences where changing treatment to comply with one regulation or meet another water quality objective potentially results in non-compliance with an existing State or federal drinking water regulation. “Unintended consequences” refer to upsets to water quality or existing treatment processes that do not result in non-compliance, but rather impact aesthetics such as taste and odor or color, or jeopardize the ability of a water service provider to meet other non-regulatory objectives.

Results from the literature review were sorted, grouped, and prioritized to determine which potential water quality compliance issues and objectives were most likely to result in simultaneous compliance conflicts or other unintended consequences. This preliminary ranking was used to help determine which conflicts and consequences were essential to development of the decision tool. The preliminary groupings and priority were used to facilitate discussion with the utility partners during Project Workshop 1.


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Identify Regulatory Conflicts and Utility Impacts

This task used information gathered during the literature review to facilitate a workshop designed to identify potential regulatory conflicts and unintended consequences and the resulting utility impacts. Project Workshop 1 resulted in the foundation upon which the SCTool was built.

Water quality compliance issues and objectives identified in Task 1 were expanded and refined based on input from the utility partners and Project Advisory Committee. For each of the identified regulatory requirements and water quality objectives, the workshop participants identified primary regulatory conflicts, unintended consequences and utility impacts associated with achieving those requirements and objectives. In addition, the workshop participants helped to identify and categorize secondary water quality planning issues and constraints not directly associated with simultaneous compliance, for example taste and odor, post-precipitation of solids, residuals management, wastewater treatment limitations, and other practical limitations can be intimately related to compliance techniques and technologies. Identify and Evaluate Technology Solutions

After compiling the results of the first project workshop, a second project workshop was used to identify technology and operational strategies to address the regulatory and other water quality objectives identified. Project Workshop 2 focused not only on identifying technology and operational strategies to meet the identified regulatory and water quality objectives, but also helped to establish the criteria or other constraints on the application of those solutions that might result in or prevent a resulting simultaneous compliance conflict or other unintended consequence.

The workshop participants again included the utility partners and Project Advisory Committee. The participants helped to identify water quality, operational, and other constraints that may result in simultaneous compliance conflicts or other unintended consequences when the technology solutions were utilized to address one or more regulatory or water quality objective. These constraints became the basis for the decision framework which ultimately made up the “brains” behind the SCTool.

Develop Decision Framework

A preliminary decision framework was developed using the technology solutions and constraints identified by the project team in the second workshop. The draft decision framework was then reviewed with the utility partners and Project Advisory Committee during Project Workshop 3. The project team identified four possible types of constraints: water quality, treatment, distribution, and utility management. For each technology solution, the project team identified factors in these four areas that could result in a simultaneous compliance conflict or other unintended consequence. A framework was then developed around those factors that identified the potential for the conflict or consequence to occur. For example, high water temperature and poorly mixed storage facilities are likely to result in nitrification in chloraminated systems. On the other hand, if water temperature is relatively low and adequate volume turnover and mixing occur in the tanks, then nitrification is still possible but not as likely.


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Perform Economic Analysis of Alternatives

The degree of economic analysis contained in the SCTool is intended to assist utilities in making informed decisions regarding modifications to source, treatment, or distribution practices for the purposes of meeting future water quality objectives, including regulatory requirements. It does not provide utility-specific cost estimates based on the information entered into the tool by a user for the various technology solutions; however, it does provide order of magnitude estimates for the purposes of comparing the various alternatives in each of the technology primers contained within the tool. The technology primers are accessible on the main page of the tool. Develop Decision Tools

The SCTool was developed using the decision framework prepared by the project team in Task 4. Extensive beta-testing was performed by the utility partners and Project Advisory Committee to test the user-interface, functional and technical requirements, and SCTool performance and accuracy. Each of the technology-based rules contained in the tool were tested to verify performance. The user input and tool output were scrutinized and refined in an attempt to maximize the user-friendliness of the tool. For example, does the tool provide reasonable direction to assist in eliminating potential conflicts and consequences. SCTOOL CONTENT DEVELOPMENT Water Quality Issues and Treatment Solutions

During 2006 and 2007, a diverse group of drinking water utilities, regulatory agencies

and engineering consultants was assembled to review and prioritize regulatory requirements or other water quality objectives and appropriate treatment or operational modifications to address those objectives. Break-out sessions focusing on specific objectives (e.g., disinfection byproduct control or LT2ESWTR compliance) then identified the technologies and operational responses most appropriate for addressing those issues. As a result of these sessions, project workshop participants identified approximately 40 potential technologies for possible inclusion in the SCTool. The technologies represented a wide range of optimization techniques, modified operational strategies and treatment techniques.

The technologies and operational responses were then prioritized by utility participants and Project Advisory Committee (PAC) members based upon the significance and severity of simultaneous compliance issues, and also more subjective opinions about the frequency that each type of response might be employed by different size utilities. These technology-based changes were “ranked” based on the criticality of simultaneous compliance and unintended consequences identified, as well as the team’s perception that a technology will be used for compliance with existing, future, or anticipated drinking water regulations. These efforts produced a prioritized list of technologies and optimization strategies with high potential for use and/or significant simultaneous compliance challenges. The top utility actions, technologies and operational responses which generate simultaneous compliance issues are presented in Table 2.1.


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Table 2.1 Top-Ranked Technologies and Operational Responses

with Potential Unintended Consequences

Technology-Based Rules

The heart of the decision-making framework was the creation and testing of the

technology-based rules that serve to identify the potential for unintended consequences or simultaneous compliance issues. The functional aspects of these rules were gleaned from the research outlined above, the empirical experience of the engineers and scientists represented on the project team, and the project workshops. When combined with system data/information and water quality data provided by the utility user, the rules automatically flag issues or conflicts associated with the use of specific treatment technologies or O&M practices. The technology-based rule logic for the SCTool is presented in Appendix C. Each technology included in the web-based SCTool is accompanied by a synopsis or “primer” – a brief summary of the technology, its effectiveness, costs, and associated unintended consequences or simultaneous compliance issues. The technology summaries are provided in Appendix B.

During SCTool development it became apparent that a few of the top-ranked operational responses required excessively detailed system design details as inputs, which was in conflict with the research goal of simplifying the SCTool for use by non-experts. The technologies evaluated and ultimately selected for inclusion in the SCTool are listed below, although this does not preclude the development of tool logic for additional technologies in future research efforts.

Rank Technology or Operational Response 1 Chloramination 2 Non-GAC Biological Filtration 3 GAC Filter Adsorbers 4 Enhanced or Modified Coagulation 5 pH adjustment (exclusive of CCT) 6 Ozonation 7 Advanced oxidation (Ozone or UV plus peroxide) 8 Nanofiltration/Reverse Osmosis 9 Change in Disinfectant Type/Dose

Chlorine Dioxide Move Point of Chlorination Initiate Chlorination (GWR compliance) Switch from Chlorine Gas to Hypochlorite (or vice-versa)

10 GAC Adsorption (post-filtration) 11 Membrane Filtration

(microfiltration/Ultrafiltration) 12 UV Disinfection for inactivation of chlorine-

resistant pathogens 13 Ion Exchange / Non-GAC Adsorption


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Similar technologies with comparable consequences were grouped (e.g. ozone and advanced oxidation processes). The treatment technologies that are covered by the SCTool include:

Biologically Active (Non-GAC) Filters Conversion to Chloramines Chlorine Dioxide Enhanced Coagulation GAC Filter Adsorber Post-filtration GAC Contactors Ion exchange/ Adsorption Microfiltration/Ultrafiltration Nanofiltration/ Reverse Osmosis Optimized Chlorination Practices Ozone & Ozone-related AOPs Ultraviolet Disinfection

Research into decision-making has shown that assessment of costs and benefits is always

a utility-specific exercise, and no guidance manual or decision-making tool will likely be able to replicate this process. Often the process itself relies as much on instinct as quantitative rigor. The success of any decision-making framework is based in significant measure upon the quality of information and knowledge brought forward by the user(s). The SCTool developed under AwwaRF 3115 is no different. The SCTool will not generate final solutions or recommendations. Rather, the Tool prompts the user to enter water quality data and system attributes sufficient to identify when conflicts and consequences are in play. Problematic conflicts/consequences are organized around pick-lists of viable treatment options for target compliance issues. In this manner the SCTool is intended to reflect the way utilities bring their individual value propositions to the decision-making.

Impacts of Variable Water Quality

The SCTool requires users to enter approximate values for a number of raw and treated

water quality parameters. However, variability in those parameters may impact SCTool results. Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, the research team developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing Information Collection Rule (ICR) data. In this technique, first an appropriate “feature vector” is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. This approach is described in greater detail and demonstrated by applying it to simulate monthly ensembles of total organic


12

carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities in Appendix D.

The SCTool does not model drinking water treatment process performance. Rather, it applies “rules” developed by the research team in conjunction with the Project Advisory Committee. However, SCTool users may wish to evaluate the potential impact of source water variability on SCTool results. For example, predicting the behavior of natural organic matter (NOM), alkalinity, and pH during drinking water coagulation is difficult because of the heterogeneous chemical nature of NOM and the complexity of carbonate chemistry. Parametric and nonparametric statistical regression methods were utilized to model the removal of NOM, as measured by total organic carbon (TOC), from raw water by conventional surface water treatment and to track the behavior of pH and alkalinity. Again, the ICR database was sampled for raw water and post-sedimentation data from conventional surface water plants. All models were evaluated in terms of their fit and predictive capability, and for all variables explored, the nonparametric local polynomial models outperformed their parametric linear least-squares counterparts. This was most pronounced with the pH model, and was attributed to the nonlinear relationship found between pH and one of the predictors. Between the sedimentation basin and the plant effluent, alkalinity was found to remain relatively constant, TOC decreased by 12% by filtration, and pH increased, consistent with chemical additions required to minimize corrosion in the distribution system. The modeling efforts presented in Appendix E are intended to be complementary to the SCTool and previous chemical and process models of water treatment.


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CHAPTER 3 RECOMMENDATIONS TO UTILITIES

It was necessary during development of the SCTool to carefully discriminate between

relevant simultaneous compliance issue system inputs and “design criteria.” The SCTool is not a design tool! The participating utilities felt that the level and complexity of data entry should not approach that of a design tool, buts rather be whatever is minimal to work within the framework rules of each candidate technology.

The SCTool is intended to be usable by utility personnel who are not “water quality experts.” While the input pages and data requirements are intentionally simplified, users nevertheless will need to have some degree of system knowledge and treatment technology to make effective use of the SCTool (i.e. the user actions guide the result). During the project workshops it was evident that participants believed a wide disparity in utility compliance planning knowledge and expertise exists, and that in general smaller and medium-sized utilities will approach decision-making differently than systems with greater knowledge and resources.

Workshop participants and PAC members suggested that distinctly different compliance tools might be needed to best serve the needs of smaller systems, and other utilities with limited financial resources and knowledge of water quality planning and/or treatment technology. Research into decision-making has also shown that assessment of costs and benefits is always a utility-specific exercise, and no guidance manual or decision-making tool will likely be able to replicate this process. Often the process itself relies as much on instinct as quantitative rigor. The success of any decision-making framework is based in significant measure upon the quality of information and knowledge brought forward by the user(s). The SCTool developed under AwwaRF 3115 is no different.

System specific costs/benefits are problematic to develop in such a generic device as the SCTool, and the use of cost as a decision-making criteria was abandoned early in the project. In Workshop #2 utility participants expressed a general mistrust of “cost estimates generated by such tools.” It was feared that attempting to provide site specific costs had the potential to create confusion or even foster bad judgment. A list of critical factors that influence costs was instead incorporated into the primer documents of each technology.

The SCTool will not generate final solutions or recommendations. Rather, the interface prompts the user to enter water quality data and system attributes sufficient to identify when conflicts and consequences are in play. Problematic conflicts/consequences are organized around cross-reference pick-lists of viable treatment options for target compliance issues. In this manner the SCTool is intended to reflect the way utilities bring their individual value propositions to the decision-making. It is recommended that the user employ the tool to evaluate different scenarios side by side and compare the results of the various treatment technologies to aid in the decision making process.


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APPENDIX A

SCTOOL USER MANUAL


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SCTOOL USER MANUAL

TABLE OF CONENTS

CHAPTER 1 – USING THE SCTOOL ...................................................................................... A-3 Purpose of the SCTool .................................................................................................... A-3 SCTool Navigation ......................................................................................................... A-4

Accessing the SCTool ......................................................................................... A-4 Website Menus.................................................................................................... A-5 New User or Profile ............................................................................................ A-7 Existing User or Profile ...................................................................................... A-9 Water Quality Issues On-Ramp ........................................................................ A-11 Treatment Technologies On-Ramp ................................................................... A-12 Output Pages ..................................................................................................... A-17 Summary Menu ................................................................................................. A-19 Help Menu ........................................................................................................ A-22

CHAPTER 2 – TECHNOLOGY INPUTS .............................................................................. A-23 Conversion to Chloramines SCTool Inputs ...................................................... A-24 Ozone and Ozone-Related Advanced Oxidation Processes SCTool Inputs ..... A-27 Nanofiltration and Reverse Osmosis SCTool Inputs ........................................ A-30 Ion Exchange and Adsorption Technologies SCTool Inputs ............................ A-33 Post-Filtration GAC Contactors ........................................................................ A-36 GAC Filter Adsorbers SCTool Inputs............................................................... A-39 Enhanced Coagulation SCTool Inputs .............................................................. A-42 Microfiltration and Ultrafiltration SCTool Inputs ............................................ A-45 Chlorine Dioxide SCTool Inputs ...................................................................... A-46 Ultraviolet Disinfection and UV-Related AOPs SCTool Inputs ...................... A-47 Modified Chlorination SCTool Inputs .............................................................. A-48


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CHAPTER A.1 USING THE SCTOOL

PURPOSE OF THE SCTOOL

The Simultaneous Compliance Tool (SCTool) is intended to assist utilities in making

appropriate technology choices to comply with multiple water quality goals, with particular attention toward challenges posed by the source water, treatment and distribution system conditions/limitations. The SCTool utilizes a framework of technology-based rules to identify potential unintended consequences and simultaneous compliance conflicts associated with a particular solution - in the context of system specific water quality, treatment, operational, and management characteristics. The SCTool allows utility users to modify anticipated future system conditions to eliminate or minimize the potential for compliance conflicts, and thereby facilitate more informed decisions about the suitability and implications of new treatment technology or operational practices. Additional system-specific testing and demonstration may be needed once a short list of suitable technologies is identified using the SCTool.

The URL to the website is: http://www.SimultaneousComplianceTool.org. The SCTool website has been designed to enable users to assess the impact of modifying existing treatment or selecting new treatment technologies to address regulatory and other water quality objectives. The SCTool allows the user to pick from a list of potentially applicable technologies based on a utility-specific regulatory or other water quality objective. The water quality planning issues addressed in the SCTool include:

DBP Control Taste & Odor Control Arsenic Removal DBP Precursor Removal Improve Disinfection Improve TDS removal Reduce THMs Reduce HAAs Reduce Bromate Comply with Stage 2 DBP Rule Comply With LT2 Rule EDC/PPCP Control SOC Control VOC Control Emerging Organic Compound Control Emerging Inorganic Compound Control Radionuclide Removal


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The SCTool also also provides direct technology-based “on-ramps.” The treatment technologies that are included in the SCTool include:

Biologically Active (Non-GAC) Filters Conversion to Chloramines Chlorine Dioxide Enhanced Coagulation GAC Filter Adsorber Post-filtration GAC Contactors Ion exchange/ Adsorption Microfiltration/Ultrafiltration Nanofiltration/ Reverse Osmosis Optimized Chlorination Practices Ozone & Ozone-related AOPs Ultraviolet Disinfection

After a user enters a specific treatment technology, the SCTool provides a summary of

potential unintended consequences resulting from selection of a particular technology in the context of system-specific inputs on water quality, treatment configurations, operational data, and management characteristics. The SCTool allows users to evaluate the way changes in these system input characteristics contribute to or resolve conflicts and consequences which may occur.

The simultaneous compliance conflicts and unintended consequences suggested by the SCTool represent the opinions of the Research Team based upon current available literature and empirical experience. The SCTool output and suggestions are intended to inform utility decision-making, but should not be used in place of system-specific data collection and economic analysis. In many cases a more complete assessment of selected treatment technology will require the assistance of a licensed design professional. In some cases, pilot plant or full-sale treatability data will need to be collected in accordance with state primacy agency requirements. SCTOOL NAVIGATION

Accessing the SCTool:

Using your browser, preferably Internet Explorer 6.0 or later, users can access the website by typing the URL: http://research.pirnie.com/SCTool. This should bring up the home page of the website as shown in Figure A.1.


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Figure A.1 SCTool Home Page

Website Menus:

The website has six basic menu items (Figure A.2) associated with the SCTool. These are represented by the six buttons on the top of the page. These buttons are available on every page of the website and act as the common navigation vehicle for the tool.


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Figure A.2 Menus

The purpose of each of the six menu items are:

Home – This menu item provides a link to the home page of the SCTool. This page also provides an overview of the Simultaneous Compliance Tool website, and has the user login/new user section embedded.

Issues – There are two ways of using the SCTool. This menu item (button) would be used when the user knows about the water quality issues that they are facing, but need help in selecting a technology. Clicking this button will take the user to a matrix table which provides a broad cross-reference of issues and treatment technologies. So, this screen allows the user to select candidate compliance technology options based on known or suspected water quality issues and continue on to screens for evaluating the technology.

Technologies – This menu item (button) represents the second way of using the SCTool. It identifies the issues for which a particular compliance technology may be effective and then allows the user to evaluate that technology for potential simultaneous compliance issues or other unintended consequences.

Menus


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Summary – This menu item (button) allows user to view a summary of the analysis completed to date, including the baseline conditions and any treatment or operational modifications. This screen can be accessed at any time after accessing the Tools module; however, the information contained in the summary will only be updated after a user has clicked the Submit button at the bottom of the appropriate Tools page.

Help – This menu item (button) provides access to Simultaneous Compliance Tool users guide and other program documentation, including the logic used by the Tools module to assist users in evaluation of technology options.

Change User – This menu item (button) can be used to change the Simultaneous Compliance Tool user or to access a different utility or water treatment plant profile. The tool stores information by each user name and as such allows users to change to a new profile by clicking on this menu.

New User or Profile

A new user or one with a new profile will have to first register their information with the SCTool. The system stores profile information by user name. So any new profile would need a new user name. So, if a person needs to use the SCTool for more than one utility/facility, they will need as many usernames in the system. The link to create a new username is in the “User Validation” field in the upper right corner of the Home Page, below the “Login” button, as shown in Figure A.3.


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Figure A.3 New User/Profile Link The link will open the “Add New User” data form which should be completed to create

an account. The user data form is shown in Figure A.4. The user may enter almost any user name in the screen textbox and enter login/submit; however, it needs to be unique for the system. If the username is already taken, then the system will prompt the user to use another name along with a suggested username. Once the name is validated and registered, the system allows the user to access the application Home Page. The user/profile is now available for subsequent access. Otherwise a message indicating “Invalid Login” is displayed and the user is asked to login again.

New User


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Figure A.4 Add New User Page

Existing User or Profile

Existing users can access their profile information by providing their username in the

“User Validation” section on the home page, as shown in Figure A.5. Once the name is validated, the system allows the user to access the application Home Page. Otherwise a message indicating “Invalid Login” is displayed and the user is asked to login again.


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Figure A.5 Login Page

Once the User successfully logs in, the Home Page appears as shown in Figure A.6. This page is nearly identical to the login screen, with the exception that the login fields have been replaced by two new menu options. Clicking on the name of a technology in the lower left will open the “primer” document describing that technology in a new window.

Login


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Figure A.6 Home Page

The home page provides the user two on-ramps for using the SCTool:

1. Specific water quality issue; or 2. Selecting a technology of interest.

Links to the USEPA Simultaneous Compliance Guidance Manual for the LT2ESWTR

and Stage 2 D/DBP Rule and other documents are also provided. Water Quality Issues On-Ramp

Selecting the water quality issues button on the home page takes the user to a matrix which provides a broad cross-reference of issues and treatment technologies, as shown in Figure A.7. The cross-reference matrix provides the first step in helping the user to identify and screen which technologies to consider to meet specific regulatory or other water quality objectives. Additional technical information for each candidate technology can also be accessed by clicking the technology name, which also serves as a link back to the technology primer documents. “Radio” buttons are provided to help the user select the treatment options that are applicable for the selected water quality issue.

On -Ramps


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Figure A.7 Water Quality Issues Page Clicking “Submit” after selecting the “radio button” for a technology will bring up the Input Screen for the selected treatment technology.

The application stores the first values provided by a user to any question on a parameter, as the original values applicable for the utility/facility. Subsequently, as the user modifies the value of the parameter while evaluating different technologies to mitigate unintended consequences of using these technologies, the latest value of the parameter is stored as the most current modified value. The results for each technology that the system determines are based on the most current value of the parameters.

The “Delete” button will delete all of the data entered for this facility by the current user, and allow the user to start over again. So, even the original values can be entered again.

The “Reset” button will reset the facility data to original values for current user. So, the user will not be able to modify the original values with this option. Treatment Technologies On-Ramp

Selecting either of the two “Technologies” buttons on the Home Page will take the user to another matrix which provides a cross-reference of regulatory compliance issues and treatment technologies, as shown in Figure A.8. Once again, this provides the user with an opportunity to identify and screen technologies which may be appropriate to utility specific planning issue and decision-making. As with the earlier matrix, additional information of each technology can be accessed by clicking the link to the technology primer document.


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Figure A.8 Treatment Technologies Page

Users may select a technology for assessment by choosing the appropriate radio button on the right-hand side of the display and clicking “Submit.” This will bring up the Input Screen for the selected treatment technology. This input screen is for the selected technology is the same screen for both on-ramps and the tool functions the same way from this point onwards.

Utility Information entered during creation of the User Account and a link to the “primer” for the technology under consideration are displayed automatically as shown in Figure A.9. Utility information and treatment facility water production data can be updated on this page.


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Figure A.9 Example Treatment Technology Input Page – Utility Information

There are three broad categories of technology inputs located below the utility information on every technology page (Figure A.10):

Water Quality; Treatment System/Distribution System Configuration; and Utility Management Practices.

The tool determines an appropriate set of questions for each technology and presents in

the input page. Users are required to answer all questions on the Treatment Technology Input Page. The technology-based rule logic will not run unless all input fields are populated. Every effort has been made to minimize the complexity of the data inputs in order to make the SCTool friendly for the widest array of users.


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Figure A.10 Example Treatment Technology Input Page – User Inputs Portion


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Each input or question represents a key parameter required to characterize the technology solution in terms of known or suspected simultaneous compliance conflicts and unintended consequences. For some questions, required input data has defined units, e.g., mg/L or percent. Users must avoid entering other characters like commas (,) or hyphens (-) inputting this information. Help is also available on certain questions, and additional explanation can be accessed by clicking on “?” and minimized by clicking on “-” as shown in Figure A.11.

Figure A.11 Example Treatment Technology Input Page – Help Screen

A key point to note is that the same question may be asked for multiple technologies. Once a user enters a value given question for the first time while evaluating any technology, they do not need to re-enter them again for the next technology’s input form. These values are pre-populated. Also, the values entered/selected the first time are stored as the original values for that parameter. Any subsequent modification of the parameter is stored as a modified value. The modified values can be changed as many times as necessary. However, the original values cannot be changed. So, it is critical that first values are entered with proper care. The only way to handle the need to reset original values is to use the “Delete” button on the “Issues” page. But the use of this button erases all entries.

Help Icons

Help Text


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Treatment Technology Input Lists

Since the tool requires that the users enter values for all questions in the input page for a technology, it is important that the user have all answers available to enter. A comprehensive list of all data inputs and questions for each technology is provided in Chapter 2. If a user does not have the necessary information, it is strongly recommended that the user begin collecting data and estimate the required values based on knowledge of the system. Output Pages

Once the user provides the answers to all questions on the technology’s input page and

clicks the “Submit” button, the application applies the built-in algorithms and displays the results of the evaluation on the screen. Figure A.12 provides an example of the output screen for the evaluation of conversion to chloramines.

Figure A.12 Example Treatment Technology Output Page


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The output page is sub-divided into three categories:

Summary of Utility Information as entered by the user Results of Analysis Configuration Summary

Interpreting Results

The results of the SCTool analysis are organized around a simple red flag/green flag

approach. A green flag suggests that a compliance or conflict is not expected to occur under the input configuration. A red flag means a compliance issue or unintended consequence is likely and should be reviewed or addressed. When a red flag appears it will be accompanied by the reason that the SCTool flagged the issue as a potential problem. In most cases, an advisory commentary will also be provided which explains why the issue is important, and ways to address it through modifying the treatment configuration or other system inputs. In addition, other general advisories are provided which are intended to help utilities with their planning and decision-making. General advisories are used to note particular planning issues frequently associated with the technology under consideration, even if the configuration provided does not suggest a negative conflict or water quality problem.

The Configuration Summary shows the original and the most recently submitted technology configuration, so that the user can compare outputs based on various scenarios of a given technology. Note that the SCTool is configured so that modified configurations of the same technology can be evaluated; however, the user cannot compare two different technologies. To compare the results of multiple technologies the user must print or save their results and do so manually. Figure A.13 an example of screen display for Configuration Summary.


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Figure A.13 Example Treatment Technology Output Page – Configuration Summary Iterations

The last section of the output pages include the “Input” page questions for the technology being evaluated. It provides the user with the option to change the values of the parameters based on the advisory comments. The application recalculates and presents the output results again based on the new values of the changed parameters. The user can continue this action any number of times. An important point to note is that changes to the values of input parameters affect all technologies and not just the technology for which it was changed. So, the effect of changes to the parameter values should be reviewed on other technologies that were evaluated earlier with the old values. This can be done by clicking on the “Summary” button. Summary Menu

The user can review the status of all the technologies that the user has evaluated by clicking

on the “Summary” menu button. The tool provides a summary page that shows the status for each technology that was evaluated with the latest values of the relevant input parameters as shown in Figure A.14. It also shows the changes to the plant configuration that have been recorded by the users while responding to the questions during the iterative use of the tool.


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Figure A.14 Summary Page


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Help Menu At any point, the help menu (Figure A.15) is available:

Figure A.15 Help Page


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CHAPTER A.2 TECHNOLOGY INPUTS

The following tables provide a listing of the data inputs and questions associated with each treatment technology included in the SCTool:


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fer i

nten

sity

w

hich

impa

cts s

ensi

tivity

to

pote

ntia

l cor

rosi

on im

pact

s.

• N

itrifi

catio

n is

mor

e lik

ely

to o

ccur

at

hig

her w

ater

tem

pera

ture

s.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in c

onse

rvat

ive

estim

ates

of p

oten

tial

unin

tend

ed c

onse

quen

ces.

Col

lect

the

data

as

soon

as p

ossi

ble,

and

re-r

un th

e SC

Tool

.

• Ta

rget

fini

shed

wat

er p

H =

7.

• Fi

nish

ed w

ater

alk

alin

ity =

15

mg/

L •

Max

imum

dis

tribu

tion

syst

em w

ater

te

mpe

ratu

re =

25

degr

ees C

Wat

er T

reat

men

t Sys

tem

Inpu

ts

• C

hlor

ine

diox

ide

(ClO

2)

used

in th

e w

ater

tre

atm

ent p

lant

? (y

es, n

o)

• O

rthop

hosp

hate

use

d?

(yes

, no)

• Ta

nk tu

rnov

er in

day

s?

Why

is it

impo

rtant

? •

Syst

ems t

hat u

se c

hlor

ine

diox

ide

are

less

pro

ne to

nitr

ifica

tion.

• O

rthop

hosp

hate

can

hel

p to

m

inim

ize

the

pote

ntia

l for

co

rros

ion-

rela

ted

impa

cts.

• N

itrifi

catio

n is

mor

e lik

ely

to o

ccur

in

stor

age

tank

s with

hig

her

turn

over

rate

s.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in c

onse

rvat

ive

estim

ates

of p

oten

tial

unin

tend

ed c

onse

quen

ces.

Col

lect

the

data

as

soon

as p

ossi

ble,

and

re-r

un th

e SC

Tool

.

• C

hlor

ine

diox

ide

= “n

o”

• O

rthop

hosp

hate

= “

no”

• Ta

nk tu

rnov

er =

7 d

ays


A-24

Con

vers

ion

to C

hlor

amin

es S

CT

ool I

nput

s (co

ntin

ued)

W

ater

Dis

tribu

tion

Syst

em In

puts

•

Max

imum

wat

er a

ge in

the

area

of t

he d

istri

butio

n sy

stem

serv

ed b

y th

is

plan

t in

days

? •

Dis

tribu

tion

syst

em

incl

udes

met

allic

(e.g

., ca

st o

r duc

tile

iron)

pip

es?

(asb

esto

s cem

ent,

cem

ent-

mor

tar-

lined

duc

tile

iron

, du

ctile

iron

, un-

lined

cas

t ir

on, p

last

ic, l

ead

serv

ice

lines

) •

Are

a of

the

dist

ribut

ion

syst

em se

rved

by

this

pl

ant r

ecei

ve w

ater

from

an

othe

r sou

rce/

plan

t? (y

es,

no)

• D

escr

ibe

the

dist

ribut

ion

syst

em d

isin

fect

ion

prac

tices

of t

he se

cond

ary

sour

ce/tr

eatm

ent p

lant

. (n

ot a

pplic

able

, fre

e ch

lori

ne, c

hlor

amin

es –

go

od C

l 2:N

H3 c

ontr

ol,

chlo

ram

ines

– p

oor

Cl 2:

NH

3 con

trol

)

Why

is it

impo

rtant

? •

Hig

her w

ater

age

lead

s to

incr

ease

d po

tent

ial f

or n

itrifi

catio

n.

•

Dis

tribu

tion

pipe

mat

eria

ls im

pact

th

e ty

pe o

f cor

rosi

on a

nd p

oten

tial

wat

er q

ualit

y is

sues

, suc

h as

lead

re

leas

e or

red

wat

er c

once

rns.

•

Ble

ndin

g w

ater

from

mul

tiple

tre

atm

ent p

lant

s can

cau

se

disr

uptio

n in

dis

tribu

ted

wat

er

qual

ity.

•

Ref

ers t

o th

e di

sinf

ectio

n pr

actic

es

of th

e “s

econ

dary

” tre

atm

ent p

lant

or

sour

ce in

the

prev

ious

que

stio

n –

if on

e ex

ists

. B

lend

ing

free

ch

lorin

e an

d ch

lora

min

es c

an re

sult

in lo

ss o

f res

idua

l and

tast

e an

d od

or is

sues

. Po

or c

ontro

l of t

he

chlo

rine-

to-a

mm

onia

feed

ratio

in

chlo

ram

inat

ed sy

stem

s inc

reas

es

the

pote

ntia

l for

resi

dual

dec

ay a

nd

nitri

ficat

ion.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in c

onse

rvat

ive

estim

ates

of p

oten

tial

unin

tend

ed c

onse

quen

ces.

Col

lect

the

data

as

soon

as p

ossi

ble,

and

re-r

un th

e SC

Tool

.

• M

axim

um w

ater

age

= 1

5 da

ys

• Se

lect

“un

-line

d ca

st-ir

on”

and

“lea

d se

rvic

e lin

es”.

•

Are

a se

rved

by

anot

her t

reat

men

t pl

ant o

r sou

rce

= ye

s •

Des

crib

e th

e di

sinf

ectio

n of

the

seco

ndar

y so

urce

= c

hlor

amin

es –

po

or C

l 2:N

H3 c

ontro

l


A-25

Con

vers

ion

to C

hlor

amin

es S

CT

ool I

nput

s (co

ntin

ued)

U

tility

Man

agem

ent I

nput

s •

Dis

tribu

tion

syst

em h

as

flush

ing

prog

ram

(yes

, no)

• C

hara

cter

ize

the

exis

ting

dist

ribut

ion

stor

age

tank

m

ixin

g? (g

ood,

bad

, un

know

n)

Why

is it

impo

rtant

? •

A w

ell-o

rgan

ized

and

wel

l-ex

ecut

ed fl

ushi

ng p

rogr

am c

an

min

imiz

e th

e po

tent

ial f

or

nitri

ficat

ion.

•

Poor

ly m

ixed

stor

age

tank

s can

in

crea

se th

e po

tent

ial f

or

nitri

ficat

ion.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in c

onse

rvat

ive

estim

ates

of p

oten

tial

unin

tend

ed c

onse

quen

ces.

Col

lect

the

data

as

soon

as p

ossi

ble,

and

re-r

un th

e SC

Tool

.

• D

istri

butio

n flu

shin

g pr

ogra

m =

no

• D

istri

butio

n st

orag

e ta

nk m

ixin

g =

unkn

own.

*

The

SCTo

ol r

equi

res

the

user

to

ente

r al

l of

the

req

uire

d In

puts

. I

f yo

u do

not

hav

e th

e re

quire

d da

ta,

begi

n co

llect

ing

the

appr

opria

te d

ata

as s

oon

as p

ossi

ble.

Im

prop

er e

ntry

of t

he re

quire

d in

puts

, or u

sing

the

sugg

este

d de

faul

t val

ues

may

resu

lt in

the

SCTo

ol o

utpu

t ind

icat

ing

pote

ntia

l uni

nten

ded

cons

eque

nces

that

do

not e

xist

, or m

ay n

ot o

utpu

t a c

onse

quen

ce th

at d

oes e

xist

.


A-26

O

zone

and

Ozo

ne-R

elat

ed A

dvan

ced

Oxi

datio

n Pr

oces

ses S

CT

ool I

nput

s W

ater

Qua

lity

Inpu

ts

• R

aw w

ater

bro

mid

e co

ncen

tratio

n in

ug/

L?

•

Man

gane

se p

rese

nt in

raw

w

ater

? (y

es, n

o)

•

Max

imum

fini

shed

wat

er

TOC

in m

g/L?

• pH

at t

he p

oint

of o

zone

ad

ditio

n?

•

Max

imum

tem

pera

ture

in

the

dist

ribut

ion

syst

em in

de

gree

Cel

sius

? •

Fini

shed

wat

er a

lkal

inity

in

mg/

L as

CaC

O3?

•

Targ

et fi

nish

ed w

ater

pH

?

Why

is it

impo

rtant

? •

The

pres

ence

of b

rom

ide

can

lead

to

the

form

atio

n of

bro

mat

ed –

a

regu

late

d di

sinf

ectio

n by

prod

uct.

• D

isco

ntin

uing

pre

-oxi

datio

n to

im

plem

ent o

zona

tion

can

lead

to

rele

ase

of m

anga

nese

from

exi

stin

g fil

ter m

edia

. •

Ozo

ne c

an in

crea

se th

e bi

odeg

rada

bilit

y of

org

anic

mat

ter

in th

e fin

ishe

d w

ater

, lea

ding

to

incr

ease

d po

tent

ial f

or b

iolo

gica

l re

grow

th in

the

dist

ribut

ion

syst

em.

• pH

at t

he p

oint

of o

zona

tion

impa

cts t

he p

oten

tial f

or b

rom

ate

form

atio

n.

• B

iolo

gica

l reg

row

th is

mor

e lik

ely

at h

ighe

r tem

pera

ture

s.

• A

lkal

inity

impa

cts t

he p

oten

tial f

or

red

wat

er in

the

dist

ribut

ion

syst

em.

• W

ith lo

w a

lkal

inity

, red

wat

er is

m

ore

likel

y to

occ

ur a

t low

fini

shed

w

ater

pH

in u

n-lin

ed c

ast i

ron

pipe

.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in

cons

erva

tive

estim

ates

of p

oten

tial u

nint

ende

d co

nseq

uenc

es.

Col

lect

the

data

as s

oon

as p

ossi

ble,

an

d re

-run

the

SCTo

ol.

•

Raw

wat

er b

rom

ide

= 10

0 ug

/L

• R

aw w

ater

man

gane

se =

yes

•

Max

imum

fini

shed

wat

er T

OC

= 2

mg/

L •

pH a

t poi

nt o

f ozo

natio

n =

7

• M

axim

um d

istri

butio

n sy

stem

tem

pera

ture

=

15 d

egre

es C

elsi

us

• F

inis

hed

wat

er a

lkal

inity

= 1

5 m

g/L

• Ta

rget

fini

shed

wat

er p

H =

7


A-27

Ozo

ne a

nd O

zone

-Rel

ated

Adv

ance

d O

xida

tion

Proc

esse

s SC

Too

l Inp

uts (

cont

inue

d)

Wat

er T

reat

men

t Sys

tem

Inpu

ts

• D

esig

n oz

one

dose

of t

he

syst

em in

mg/

L?

•

TOC

at t

he p

oint

of

ozon

atio

n in

mg/

L?

• W

ill th

is p

lant

pra

ctic

e bi

olog

ical

ly a

ctiv

e fil

tratio

n? (y

es, n

o)

• Ph

osph

ate

inhi

bito

r typ

e fo

r cor

rosi

on c

ontro

l tre

atm

ent (

if ap

plic

able

) (n

one,

ort

ho p

hosp

hate

, po

ly p

hosp

hate

, ort

ho/p

oly

phos

phat

es b

lend

, oth

er)

• Pr

e-ox

idan

t use

d in

the

WTP

? (n

one,

chl

orin

e,

KM

nO4,

ClO

2)

• Se

dim

enta

tion

empl

oyed

at

the

WTP

? (y

es, n

o)

•

Chl

orin

e us

ed p

rior t

o fil

tratio

n in

the

plan

t?

(yes

, no)

Why

is it

impo

rtant

? •

The

ozon

e do

se im

pact

s the

po

tent

ial f

or D

BP

form

atio

n –

spec

ifica

lly b

rom

ate.

•

The

ozon

e:TO

C ra

tio a

lso

impa

cts

the

form

atio

n of

ozo

ne D

BPs

. •

Bio

logi

cally

act

ive

filte

rs c

an

redu

ce th

e po

tent

ial f

or d

istri

butio

n sy

stem

regr

owth

. •

Phos

phat

e ad

ditio

n ca

n m

inim

ize

the

pote

ntia

l for

red

wat

er

prob

lem

s.

• D

isco

ntin

uatio

n of

pre

-oxi

datio

n ca

n le

ad to

man

gane

se re

leas

e fr

om

exis

ting

filte

rs.

• Pr

e-ox

idat

ion

with

sedi

men

tatio

n ca

n m

inim

ize

man

gane

se b

uild

-up

on fi

lters

and

redu

ce th

e po

tent

ial

for y

ello

w o

r pin

k w

ater

. •

Pre-

chlo

rinat

ion

can

help

to re

mov

e m

anga

nese

in se

dim

enta

tion

basi

ns

or fi

lters

.

Wha

t sho

uld

I do

if I d

on’t

have

this

dat

a?*

Ente

r the

follo

win

g va

lues

, whi

ch w

ill re

sult

in

cons

erva

tive

estim

ates

of p

oten

tial u

nint

ende

d co

nseq

uenc

es.

Col

lect

the

data

as s

oon

as p

ossi

ble,

an

d re

-run

the

SCTo

ol.

•

Des

ign

ozon

e do

se =

5 m

g/L

• TO

C a

t poi

nt o

f ozo

natio

n =

2.5

mg/

L •

Bio

logi

cally

act

ive

filte

rs =

no

• Ph

osph

ate

= no

ne

• Pr

e-ox

idan

t = n

one

• Se

dim

enta

tion

= n

o •

Chl

orin

e pr

ior t

o fil

tratio

n =

no


A-28

Ozone and Ozone-Related Advanced Oxidation Processes SCTool Inputs (continued) Utility Management Inputs

• Distribution system has flushing program? (yes, no)

• Characterize the existing distribution storage tank mixing? (good, bad, unknown)

Why is it important? • A well-organized and well-

executed flushing program can minimize the potential for biological regrowth.

• Poorly mixed storage tanks can increase the potential for biological regrowth.

What should I do if I don’t have this data?* Enter the following values, which will result in conservative estimates of potential unintended consequences. Collect the data as soon as possible, and re-run the SCTool.

• Distribution flushing program = no • Distribution storage tank mixing =

unknown.

* The SCTool requires the user to enter all of the required Inputs. If you do not have the required data, begin collecting the appropriate data as soon as possible. Improper entry of the required inputs, or using the suggested default values may result in the SCTool output indicating potential unintended consequences that do not exist, or may not output a consequence that does exist.


A-29

Nanofiltration and Reverse Osmosis SCTool Inputs

Water Quality Inputs • Target finished water pH?

• Raw water alkalinity in mg/L as CaCO3?

• Barium present in source water? (yes, no)

• Silica present in source water? (yes, no)

Why is it important? • Finished water pH impacts the

potential for distribution system corrosion issues.

• Nanofiltration and reverse osmosis remove alkalinity, which impacts potential distribution system corrosion.

• Barium is a strong membrane foulant.

• Silica is a strong membrane foulant.


• Target finished water pH = 7 • Raw water alkalinity = 100 mg/L • Barium = yes • Silica = yes

Water Treatment System Inputs • Current finished water

TDS in mg/L?

• Current finished water chloride concentration in mg/L?

• Percentage recovery of the NF/RO system?

Why is it important? • Changes in finished water TDS can

impact corrosion and taste and odor. Nanofiltration and reverse osmosis also produce a concentrated TDS waste stream that can be difficult to dispose.

• Nanofiltration and reverse osmosis also produce a concentrated chloride waste stream that can be difficult to dispose.

• Recovery is the ratio of the feed water volume to the product water volume and impacts how concentrate TDS and chloride concentrations.


• Current finished water TDS = 500 mg/L • Current finished water chloride = 20 mg/L • Percent recovery = 80 percent


A-30

Nanofiltration and Reverse Osmosis SCTool Inputs (continued) Water Treatment System Inputs

• Concentrate disposal method most likely to be used? (drying beds, ocean, river/stream, sewer, deep well injection)

• TDS limit for concentrate discharge for the selected disposal method in mg/L?

• Chloride limit for concentrate discharge for the selected disposal method in mg/L?

• Finished water pH

• Finished water alkalinity in mg/L as CaCO3?

• Target Langelier Saturation Index (LSI) in finished water.

• Target finished water CCPP?

• Orthophosphate used? (yes, no)

Why is it important? • The disposal method impacts the

concentrate water quality requirements.

• TDS limit for waste streams will impact selection of an appropriate concentrate disposal method.

• Chloride limit for waste streams will impact selection of an appropriate concentrate disposal method.

• Finished water pH impacts the potential for distribution system corrosion issues.

• Finished water alkalinity impacts the potential for distribution system corrosion issues.

• LSI can be an indicator of the potential for corrosion-related water quality problems in the distribution system.

• Calcium carbonate precipitation potential can be an indicator of the potential for corrosion-related water quality problems in the distribution system.

• Orthophosphate can minimize the potential for corrosion-related water quality problems.


• Concentrate disposal method = river/stream • TDS limit = 1500 mg/L • Chloride limit = 100 mg/L • Finished water pH = 7 • Finished water alkalinity = 15 mg/L • Target LSI = 0.0 • Target CCPP = 1 • Orthophosphate = no • Select “un-lined cast-iron” and “lead service

lines”.


A-31

Nanofiltration and Reverse Osmosis SCTool Inputs (continued) Water Treatment System Inputs

• Distribution system includes metallic (e.g., cast or ductile iron) pipes? (asbestos cement, cement-mortar-lined ductile iron, ductile iron, un-lined cast iron, plastic, lead service lines)

Why is it important?

• Certain distribution system piping materials are more likely to result in color and other corrosion problems.


• Select “un-lined cast-iron” and “lead service lines”.

* The SCTool requires the user to enter all of the required Inputs. If you do not have the required data, begin collecting the

appropriate data as soon as possible. Improper entry of the required inputs, or using the suggested default values may result in the SCTool output indicating potential unintended consequences that do not exist, or may not output a consequence that does exist.


A-32

Ion Exchange and Adsorption Technologies SCTool Inputs

Water Quality Inputs • Feed water hardness?

• Current finished water sulfate concentration in mg/L?

• Current finished water chloride concentration in mg/L?

• TDS concentration in spent brine in mg/L?

• Chloride concentration in spent brine in mg/L?

• Feed water sodium in mg/L?

Why is it important? • Feed water hardness impacts the

exchange process; higher values may result in higher finished water sodium concentrations.

• Ion exchange may cause a shift in the chloride-to-sulfate mass ratio, which may impact corrosion in the distribution system.

• Ion exchange may cause a shift in the chloride-to-sulfate mass ratio, which may impact corrosion in the distribution system.

• The spent brine TDS concentration will determine how it is able to be disposed. You should be able to get this value from an ion exchange equipment provider.

• The spent brine chloride concentration will determine how it is able to be disposed. You should be able to get this value from an ion exchange equipment provider.

• Ion exchange processes may result in an increase in finished water sodium concentrations.


• Feed water hardness = 300 mg/L • Finished water sulfate = 20 mg/L • Finished water chloride = 10 mg/L • Spent brine TDS = 5,000 mg/L • Spent brine chloride = 500 mg/L • Feed water sodium = 20 mg/L


A-33

Ion Exchange and Adsorption Technologies SCTool Inputs (continued) Water Treatment System Inputs

• Treatment process (ion exchange, adsorption)

• For Ion exchange treatment process, specify type (not applicable, anion, cation, anion-cation)

• Percentage flow treated

• Type of resin is used for ion exchange? (type I, type II)

• Chlorine used prior to filtration in the plant? (yes, no)

• Initial rinsing done for ion exchange? (yes, no)

Why is it important? • The potential unintended

consequences vary depending on which process is being used.

• Anion and cation exchange processes impact treated water quality differently.

• These systems frequently include a bypass stream to minimize cost.

• Type II resins are more prone to nitrosamine release. Check with the ion exchange manufacturer.

• Use of chlorine prior to a Type II ion exchange resin can cause a release of nitrosamines.

• Initial rinse of a Type II ion exchange resin can cause a release of nitrosamines.


• You must know whether you are evaluating ion exchange or adsorption.

• For hardness and radium removal, select "cation exchange", for arsenic, perchlorate, nitrate, natural organic matter, fluoride, uranium, and selenium, select "anion exchange".

• Type of resin = Type II • Chlorine used prior to filtration = yes • Initial rinse = yes


A-34

Ion Exchange and Adsorption Technologies SCTool Inputs (continued) Water Treatment System Inputs

• Concentrate disposal method most likely to be used? (drying beds, river/Stream, sewer discharge, ocean, deep well injection)

• TDS limit for concentrate discharge for the selected disposal method in mg/L?

• Chloride limit for concentrate discharge for the selected disposal method in mg/L?


• The disposal method impacts the concentrate water quality requirements.

• TDS limit for waste streams will impact selection of an appropriate concentrate disposal method.

• Chloride limit for waste streams will impact selection of an appropriate concentrate disposal method.


• Concentrate disposal method = river/stream • TDS limit = 1,500 mg/L • Chloride limit = 100 mg/L




A-35

Post-Filtration GAC Contactors SCTool Inputs

Water Quality Inputs • Raw water iron in mg/L?

• Raw water manganese in mg/L?

• Coagulation / filtration pH value?

Why is it important? • Discontinuation of pre-oxidation

may result in release of iron from existing filters or passage or iron through the process if sufficient treatment is not provided.

• Discontinuation of pre-oxidation may result in release of iron from existing filters or passage or iron through the process if sufficient treatment is not provided.

• The coagulation/filtration pH influences iron and manganese speciation and removal. Low pH may result in passage of iron and manganese to the treated water and colored water complaints in the presence of iron and manganese in the finished water.


• Raw water iron = 0.3 mg/L • Raw water manganese = 0.05 mg/L • Coagulation/filtration pH = 6.5

Water Treatment System Inputs • Pre-coagulation oxidation

present? (yes, no)

• Pretreatment time in hours?

• On-site reactivation furnace present? (yes, no)



• Removal of iron and manganese is better at treatment times greater than 4 hours.

• On-site reactivation may require air permits.


• Pre-coagulation oxidation = no • Pretreatment time = 1 hour • On-site reactivation = yes


A-36

Post-Filtration GAC Contactors SCTool Inputs (continued) Water Treatment System Inputs

• Type of on-site reactivation furnace present? (not applicable, gas, electric)

• Off-gas treatment done? (yes, no)

• Primary disinfection used? (ozone, chlorine dioxide, chlorine)

• Location of the disinfectant application with respect to post-filtration contractors? (not applicable, before future GAC, after future GAC)

• Location of the secondary disinfection? (before future GAC, after future GAC)

• Contact time for free chlorine in minutes?

Why is it important? • Use of gas may be more cost-

effective, but may require off-gas treatment or more extensive permitting.

• Permitting requirements may be lessened if off-gas treatment is provided.

• GAC may remove some oxidants, or some oxidants may prevent biological activity in GAC contactors.



• Biological activity in the filter may slough off. If inadequate contact time is provided, biological activity may occur in the distribution system.


• Type of regeneration = gas • Off-gas treatment = no • Primary disinfection = chlorine • Location of the disinfectant application

point = before future GAC • Location of secondary disinfection = after

future GAC • Contact time = 2 minutes


A-37

Post-Filtration GAC Contactors SCTool Inputs (continued) Water Treatment System Inputs

• Primary disinfectant residual on GAC? (yes, no)

• Post-Filtration GAC contractor treat 100% of water? (yes, no)


• GAC may remove some disinfectants (such as chlorine or chloramines).

• Treating less than 100 percent of the flow requires careful monitoring of breakthrough to assure blended concentrations do not exceed water quality targets. Treating less than 100 percent may also limit disinfection/removal credits for some contaminants, such as Cryptosporidium.


• Primary residual on GAC = yes • Treat 100% = no



A-38

GAC Filter Adsorbers SCTool Inputs



• Coagulation/filtration pH value?




• The coagulation/filtration pH influences iron and manganese speciation and removal. Low pH may result in passage of iron and manganese to the treated water and colored water complaints in the presence of iron and manganese in the finished water.


• Raw water iron = 0.3 mg/L • Raw water manganese = 0.05 mg/L • Coagulation/filtration pH = 6.5

Water Treatment System Inputs • Pre-coagulation oxidation

present? (yes, no)

• Pretreatment time in

hours?

• On-site reactivation furnace present? (yes, no)




• On-site reactivation may require air permits.


• Pre-coagulation oxidation = no • Pretreatment time = 1 hour • On-site reactivation = yes


A-39

GAC Filter Adsorbers SCTool Inputs (continued) Water Treatment System Inputs

• Type of on-site reactive furnace present? (not applicable, gas, electric)

• Off-gas treatment done? (yes, no)

• Primary disinfectant used? (ozone, chlorine dioxide, chlorine)

• Primary disinfectant residual on GAC? (yes, no)

• Location of the disinfectant application with respect to filter adsorber? (not applicable, before future GAC, after future GAC)

• Contact time for free chlorine in minutes?

• Location of the secondary disinfection? (before future GAC, after future GAC)

Why is it important? • Use of gas may be more cost-

effective, but may require off-gas treatment or more extensive permitting.

• Permitting requirements may be lessened if off-gas treatment is provided.




• Biological activity in the filter may slough off. If inadequate contact time is provided, biological activity may occur in the distribution system.

• GAC may remove some disinfectants (such as chlorine or chloramines).


• Type of regeneration = gas • Off-gas treatment = no • Primary disinfection = chlorine • Location of the disinfectant application

point = before future GAC • Location of secondary disinfection = after

future GAC • EBCT = 5 minutes


A-40

GAC Filter Adsorbers SCTool Inputs (continued) Water Treatment System Inputs

• Empty bed contact time?


• EBCT less than 10 minutes may not be sufficient for some contaminants.


• EBCT = 5 minutes




A-41

Enhanced Coagulation SCTool Inputs



• Arsenic present in raw water? (yes, no)

• Radionuclides present in raw water? (yes, no)

• Raw water TOC in mg/L?

• Raw water alkalinity in mg/L as CaCO3?

• Finished water alkalinity in mg/L as CaCO3?

• Target finished water pH?

• Weekly variation in finished water pH? (less than 0.5, (+/-) 0.5, (+/-) 1.0 or more)




• Arsenic removal may drive the required coagulant dose.

• Radionuclides may accumulate in process residuals causing difficulty for disposal.

• The Stage 1 DBPR has specific TOC removal requirements.

• Alkalinity impacts TOC removal requirements and coagulant dose.

• Finished water alkalinity is a key corrosion control parameter.

• Finished water pH influences potential corrosion impacts.

• Variation in finished water pH may result in corrosion-related problems.


• Raw water iron = 0.3 mg/L • Raw water manganese = 0.05 mg/L • Arsenic present = yes • Radionuclide present = yes • Raw water TOC = 3.5 mg/L • Raw water alkalinity = 50 mg/L • Finished water alkalinity = 15 mg/L • Target finished water pH = 7 • Weekly variation in finished water pH = 1.0

or more


A-42

Enhanced Coagulation SCTool Inputs (continued) Water Treatment System Inputs

• Primary coagulant (none, alum, PACl, ferric salt, other)

• New Primary coagulant chemical? (n/a, yes, no)

• Coagulant does in mg/L?

• Primary coagulant dose under enhanced coagulation anticipated to increase by 20% or more? (yes, no)

• Pre-treatment pH value?

• Pretreatment time in hours?

• Preoxidation stopped in conjunction with enhanced coagulation? (yes, no)

• Anticipated % reduction in TOC?

Why is it important? • The coagulant type impacts process

pH and other requirements. It may also impact finished water quality.

• Changes in coagulant can impact corrosion in the distribution system.

• Too high or too low dose may impact residuals, treated water quality, and corrosion.

• Significant increases in coagulant dose may cause disruptions in distribution water quality and corrosion.

• pH impacts solubility of aluminum and other coagulant byproducts.

• Removal of iron and other contaminants is better with longer pretreatment times.

• Discontinuation of pre-oxidation can result in release of iron and manganese from filter media.

• The Stage 1 DBPR requires certain TOC removals based on raw water TOC and alkalinity.


• Primary coagulant = alum • New primary coagulant? = yes • Primary coagulant dose increase by 20% or

more = yes • Pretreatment pH = 6.5 • Pretreatment time = 2 hours • Preoxidation stopped = yes • Anticipated TOC reduction = 10% •


A-43

Enhanced Coagulation SCTool Inputs (continued) Water Treatment System Inputs

• Phosphate inhibitor type for corrosion control treatment (if applicable). (none, orthophosphate, polyphosphates, ortho/polyphosphates blend, other)

• Use of phosphate inhibitor represent a new practice? (n/a, yes, no)


• Phosphate addition can minimize the potential for red water and other corrosion-related problems.

• Use of phosphate may cause problems with phosphorous limits at local wastewater treatment plants.


• Phosphate addition = no • New practice? = n/a




A-44

Microfiltration and Ultrafiltration SCTool Inputs Water Quality Inputs

• Raw water silica in mg/L? • Algae Level? (low,

medium, high) • Air entrained? (yes, no)

Why is it important? • Silica is a significant foulant and

can cause operational issues in membrane systems.

• Algae is a foulant and can lead to biological fouling of membrane systems.

• Entrained air may cause fouling of membrane systems.


• Silica = 50 mg/L • Algae level = high • Air entrained = yes

Water Treatment System Inputs

• Preoxidant? (yes, no) • Preoxidant Type? (n/a,

chlorine, other) • Metal salt coagulant

dosage? (0-20 mg/L, > 20 mg/L)

• Iron salt coagulant used? (yes, no)

• Synthetic organic polymer used as a coagulant or coagulant aid? (yes, no)

Why is it important? • Membrane materials may not be

compatible with chlorine or other oxidants.

• Membranes may have limited or no tolerance to chlorine for extended periods of operation.

• Iron salts, such as ferric chloride, may promote fouling of some membranes

• Membranes are generally not tolerant of polymers.


• Preoxidant = yes • Preoxidant type = chlorine • Iron salt used = yes • Polymer used = yes




A-45

Chlorine Dioxide SCTool Inputs Water Treatment System Inputs

• ClO2 dose in mg/L? • Chlorine dioxide

application before coagulation? (yes, no)

• Ferrous salts applied? (yes, no)

• ClO2 generation yield > 95%? (yes, no)

Why is it important? • Chlorine dioxide decay produces

chlorite – a regulated DBP. At doses greater than approximately 1.4 mg/L, chlorite may exceed the MCL of 1.0 mg/L

• Impurities in chlorine dioxide may cause formation of TTHM and HAA5 when applied prior to coagulation.

• Addition of ferrous salts can reduce chlorite concentrations.

• Low chlorine dioxide yields can result in high concentrations of free chlorine and increased formation of TTHM and HAA5.


• Chlorine dioxide dose = 1.5 mg/L • Applied before coagulation = yes • Ferrous salts applied = no • Chlorine dioxide generation yield = 90%




A-46

Ultraviolet Disinfection and UV-Related AOPs SCTool Inputs Water Quality Inputs

• Raw water iron concentration in mg/L?

• Raw water manganese concentration in mg/L?





• Raw water iron = 0.3 mg/L • Raw water manganese = 0.05 mg/L

Water Treatment System Inputs • Preoxidation practiced?

(yes, no)

• Preoxidation contact time in hours?





• Preoxidation practiced = yes • Preoxidation contact time = 2 hours




A-47

Modified Chlorination SCTool Inputs

Water Quality Inputs • Raw water iron

concentration in mg/L? • Raw water manganese

concentration in mg/L?





• Raw water iron = 0.3 mg/L • Raw water manganese = 0.05 mg/L

Water Treatment System Inputs • Preoxidation practiced?

(yes, no)

• Preoxidation contact time in hours?

• Primary disinfectant type? (chlorine, chlorine dioxide, ozone)

• Primary disinfectant application point? (pre-filtration, post-filtration)

• Primary disinfectant applied prior to GAC? (yes, no)




• Use of anthracite or GAC in filters or contactors may remove some oxidants.




• Preoxidation practiced = yes • Preoxidation contact time = 2 hours • Primary disinfectant type = chlorine • Primary disinfectant application point = pre-

filtration • Applied prior to GAC = yes


A-48

Modified Chlorination SCTool Inputs (continued) Water Treatment System Inputs

• Secondary disinfectant type? (chlorine, chloramines)

• Secondary disinfectant application point? (pre-filtration, post-filtration)


• GAC may remove some oxidants, or some oxidants may prevent biological activity in GAC contactors. Depletion of secondary disinfectant may lead to biological regrowth in the distribution system.



• Secondary disinfectant type = chlorine • Secondary disinfectant application point =

pre-filtration



B-1

APPENDIX B

SCTOOL TREATMENT TECHNOLOGY DESCRIPTIONS


B-2

BIOLOGICALLY ACTIVE FILTERS AND FILTER ADSORBERS

Biologically active filters (BAF) remove pollutants by three main mechanisms: biodegredation, adsorption of micropollutants, and filtration of suspended solids. The microbial growth attached to the filter media (biofilm) consumes the organic matter that would otherwise flow through the treatment plant and ultimately into the distribution system. The end products are carbon dioxide, water, biomass, and simpler organic molecules. Particle filtration takes place on the bare filter media as well as the biofilm.

BAF is often used by systems that use ozone as adding a strong oxidant appears to convert some of the dissolved organic materials in water into more readily biodegradable compounds. To promote biological activity ozone is added upstream to the filter beds.

Key factors in controlling BAF performance include temperature, contact time, backwash operations, and water quality (pH, alkalinity, turbidity, TSS, BDOC, and AOC). The water quality parameters impacting performance are the amount of biodegradable NOM, pH, and dissolved oxygen concentration. In particular, temperature controls biogrowth kinetic.

Several studies report that biological activity is high during the summer when temperature is above 20 oC and decreases during the cooler months with relatively insignificant activity during the winter (10 oC and below). Filter loading rates are similar to those used in rapid sand filtration (2 to 4 gpm/sf).

Maintaining biologically active filters will require those facilities that practice pre-chlorination to discontinue that practice. This has the potential to impact removal of dissolved inorganic species, such as iron and manganese. The use of an alternative pre-oxidant, such as potassium permanganate can help to eliminate this concern. Similarly, because pre-chlorination is not possible with BAF, it is necessary to consider the point of primary and secondary disinfectant addition (to assure sufficient contact time remains) when considering BAF.

Sloughing of bacteria can occur in biologically-active filters. In systems using free chlorine

for both primary and secondary disinfection there should be sufficient free chlorine contact time to inactivate any bacteria dislodged from the filter media. In systems utilizing chloramines for secondary disinfection, it may be necessary to provide a minimum of 5 minutes of free chlorine contact time prior to ammonia addition. CONVERSION TO CHLORAMINES

Chloramines are a family of oxidants formed by the reaction of chlorine and ammonia. In water treatment, chloramines are primarily used as a secondary disinfectant to provide a residual in the distribution system; however, chloramines are occasionally used as a primary disinfectant.

Rapid Mix Flocculation/Sedimentation

Biologically ActiveFilter

CausticCaustic

Ozone Generator &Contact Basin

CoagulantCoagulant


B-3

Chloramination is often an attractive alternative to chlorine for secondary disinfection because it is more persistent in the distribution system and minimizes the formation of trihalomethanes and haloacetic acids.

The ratios at which chlorine and ammonia are fed control the species of chloramines present. Monochloramine (NH2Cl) is the preferred species, as it is a more powerful oxidant and is less likely to cause taste and odor problems in the distribution system than dichloramine (NHCl2) and trichloramine (NCl3 – also known as nitrogen tri-chloride). Although a weaker oxidant than chlorine, monochloramine oxidizes precursors of disinfection byproducts, inactivates microorganisms, and controls biofilm. In fact for its persistence, chloramines are often more effective in controlling biofilms in distribution system. The effectiveness of chloramination is dependent on dose, contact time, pH, and temperature. There are a number of potential unintended consequences related to conversion to chloramines for secondary disinfection. The most significant include nitrification and destabilization of existing pipe scales (i.e., corrosion impacts). Nitrification occurs when free ammonia is oxidized by ammonia oxidizing bacteria (AOB) to nitrite (partial nitrification), and nitrite is subsequently oxidized by nitrite-oxidizing bacteria (NOB) to nitrate. Some free ammonia is normally present in chloraminated distribution systems as a result of the chloramination process; however, excess free ammonia can be present as a result of 1) poor control of the chlorine to ammonia ratio at the treatment plant and 2) chloramine degradation in the distribution system. Maintaining good control of the chlorine to ammonia feed ratio at the treatment plant is essential to preventing nitrification. A chlorine to ammonia mass ratio of 4.5:1 is generally recommended. Automatic, rather than manual, control of chlorine and ammonia feed systems will also help to reduce the amount of free ammonia entering the distribution system. Research has demonstrated that nitrification is less likely to occur in systems that use chlorine dioxide for oxidation or primary disinfection at the treatment plant due to the toxicity of chlorite to AOB. Systems with high water age (> 14 days), poorly mixed storage facilities, low storage facility volume turnover, and warm water temperatures (> 25° C) are more susceptible to nitrification. Flushing of system dead ends to minimize water age and maintain a chloramine residual can help to reduce the potential for nitrification. Improving volume turnover and mixing in distribution system storage facilities can substantially reduce the potential for nitrification. It is generally recommended that storage volume be turned over at least once every 5 days. In poorly mixed storage facilities, volume turnover may not be sufficient to prevent nitrification due to dead or stagnant zones. Improving mixing to eliminate these areas in the tank will also help to minimize the potential for nitrification and increase distributed water quality from these storage facilities. In poorly buffered waters (i.e., those with low alkalinity), nitrification can also result in increased corrosion. The nitrification process consumes alkalinity (as bicarbonate) and produces carbonic acid. In low alkalinity waters, this has the potential to cause localized depression of pH and increase iron, lead, and copper corrosion. It may also lead to dissolution of cement-mortar linings in distribution system piping. Chloramination can also impact existing pipe scale stability due to its lower oxidation-reduction potential (ORP) relative to free chlorine. Free chlorine, particularly at higher doses, has a higher ORP than monochloramine which can impact the oxidation state of existing metal pipe scales. At higher ORP values, iron is more likely to be present in ferric forms (Fe3+), which are generally harder and more stable than ferrous iron (Fe2+) species. Similarly, at higher ORP values lead scales are more likely to be present as Pb4+ species, which are harder and more stable than Pb2+


B-4

scales. Conversion to chloramines may reduce the distributed water ORP causing a shift in existing metallic scale species and result in increases in dissolved metal concentrations.

The use of an orthophosphate-based corrosion inhibitor (e.g., phosphoric acid or zinc orthophosphate) changes the metallic (i.e, iron and lead) precipitates on pipe surfaces and can help to minimize the potential for increased metals release as a result of conversion to chloramines.

Blending of chloraminated and chlorinated waters in the distribution system is not recommended. Such operation results in a shift in the local chlorine to ammonia ratio and can result in the formation of di- and trichloramine, as well as localized breakpoint chlorination. Any of these consequences is likely to result in increased taste and odor complaints, as well as a reduction in disinfection efficacy.

Other potential consequences of chloramination include the production of unregulated disinfection byproducts, including N-nitrosodimethylamine (NDMA) and iodoacids.

Chloramination is a low-cost disinfection byproduct control strategy. Approximate capital and operations and maintenance (O&M) costs are provided in Table B.1. Capital costs include the addition of an ammonia feed system, with associated piping and valves, and instrumentation and controls. O&M costs include ammonia, power, replacement parts, and maintenance labor. Conversion to chloramines also requires a public education program, may require conversion of booster chlorination or satellite production facilities (i.e., wellfields), and is likely to require some additional level of distribution system O&M. These costs are not included.

Table B.1 Approximate Costs of Conversion to Chloramines Design Flow (mgd) 0.01 0.1 1.0 10 100

Average Flow (mgd) 0.005 0.03 0.35 4.4 50

Capital Cost ($/gal)1 $4.00 $0.50 $0.10 $0.01 <$0.01

Annual O&M Cost ($/kgal)2 $3.00 $0.20 $0.05 $0.01 <$0.01 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

conversion to chloramines at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $40,000 ($4/gal × 10,000 gal = $40,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For example, annual O&M costs for a system with an average daily flow of 5,000 gallons (5 kgal) would be approximately $5,475 ($3/kgal × 5 kgal/day × 365 days/year = $5,475).

CHLORINE DIOXIDE

Chlorine dioxide is a chlorine compound in the +IV oxidation state and is therefore, a powerful oxidant and disinfectant. It is frequently used to improve the removal of iron and manganese, arsenic, color, taste and odor compounds (phenolic, decaying vegetation and algal-related compounds), and inactivation of chlorine-resistant microorganisms such as Cryptosporidium. Chlorine dioxide oxidizes natural organic matter (NOM) reducing disinfection byproduct (DBP) precursor concentrations and can be used as an alternative primary disinfectant to reduce the total chlorine dose, both of which may result in a reduction in the formation of chlorinated DBPs, such as trihalomethanes and haloacetic acids.

Chlorine dioxide can be applied at several points during treatment: the raw water as a preoxidant, the sedimentation tank, post-sedimentation or the filtered water as a primary


B-5

disinfectant. Chloramine or chlorine must be used for secondary disinfection following chlorine dioxide application.

Pathogen inactivation with chlorine dioxide is much less affected by pH than chlorine. Consequently, chlorine dioxide is a much more effective disinfectant than chlorine at higher pH levels. Iron concentration, manganese concentration, sunlight exposure, and aeration are among the parameters that exert additional chlorine dioxide demand. Greater dose and contact time, as well as increased temperature correlate with greater oxidation and disinfection with chlorine dioxide application.

Chlorine dioxide gas is explosive under pressure and must therefore be generated on-site. The generation process, chemicals, model, and capacity vary depending on the application. However, chlorine dioxide is typically formed in the reaction of sodium chlorite (NaClO2) solution with gaseous chlorine (Cl2) or hypochlorous acid (HOCl). Improper generation conditions can lead to feeding of excess free chlorine at the application point and the potential formation of regulated DBPs. New generators have been developed that replace the solution sodium chlorite with a solid form for minimized byproduct formation; and electrolysis of sodium chlorite has recently been introduced in the U.S. for low-dose applications.

Chlorine dioxide yields lower levels of chlorinated byproducts in comparison to free chlorine. However, approximately 70% of the chlorine dioxide applied in water treatment is converted to chlorite (ClO-) – a regulated disinfection byproduct with a maximum contaminant level of 1 mg/L. Chlorate is also a byproduct of chlorine dioxide decay. The maximum recommended sum of chlorine dioxide, chlorite and chlorate in the distribution system should be less than 1.0 mg/L.

CCllOO22

Rapid Mix Flocculation/ Sedimentation

Filtration Storage

CCaauussttiicc

Approximate capital and operations and maintenance (O&M) costs for a chlorine dioxide

system are provided in Table B.2. Capital costs include the chlorine dioxide generation and feed system, with associated piping and valves, and instrumentation and controls. O&M costs include chemicals, power, replacement parts, and maintenance labor.

Table B.2 Approximate Costs Chlorine Dioxide

Design Flow (mgd) 0.1 1.0 10 100

Average Flow (mgd) 0.03 0.35 4.4 50

Capital Cost ($/gal)1 $0.50 $0.05 $0.03 $0.01

Annual O&M Cost ($/kgal)2 $1.70 $0.15 $0.03 <$0.01 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of chlorine dioxide at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $50,000 ($0.50/gal × 100,000 gal = $50,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For example, annual O&M costs for a system with an average daily flow of 30,000 gallons (30 kgal) would be approximately $18,615 ($1. 70/kgal × 30 kgal/day × 365 days/year = $15,056).


B-6

ENHANCED COAGULATION Enhanced coagulation is now widely practiced for removing DBP precursors, and can also removes inorganic, particulate, and color causing compounds. It includes several optimization strategies: ■ Increase coagulant dose. ■ Reduce coagulation pH. ■ Reduce coagulation pH and increase dose. ■ Change coagulant with/without the above.

In coagulation, a positively charged coagulant (usually an alumimum or iron salt) is added to raw water and mixed in the rapid mix chamber. The coagulant alters or destabilizes negatively charged particulate, dissolved, and colloidal contaminants. The optimal pH range for coagulation is 6-7 when using alum and 5.5-6.5 when using iron. For high alkalinity water, excessive amounts of coagulant may be needed to lower the pH to the optimal pH range. In these cases, it may be beneficial to use acid in addition to the coagulant to reduce the amount of coagulant needed and effectively lower chemical costs.

Approximate capital and operations and maintenance (O&M) costs for enhanced coagulation are provided in Table B.3. Costs are based on an additional coagulant (alum or ferric) dose of 56.5 mg/L and additional caustic dose of 25 mg/L (to adjust the finished water pH to its original level). Costs assume a coagulant and caustic feed system are present and only need upgraded. Capital costs include upgrades to existing chemical feed systems, piping and valves, and instrumentation and controls. O&M costs include chemicals, power, replacement parts, and maintenance labor.

Reducing the coagulation pH, increasing the coagulant dose, or changing coagulants can affect finished water stability and corrosion control effectiveness. It is important that any reduction in coagulation pH be adequately re-adjusted prior to distribution to minimize the potential for increased lead, copper, or iron corrosion.

Increased alum or ferric salt doses can result in increased concentrations of dissolved aluminum and iron in the distribution system. Excess iron may precipitate in the distribution system causing red water. Residual aluminum can cause formation of aluminum precipitates, reducing hydraulic capacity, and increasing operations costs. Residual aluminum has also been demonstrated to be a factor in increased copper corrosion in home plumbing.

Coagulant changes from alum to ferric chloride may cause an increase in the finished water chloride-to-sulfate ratio which has been demonstrated to increase lead corrosion in some distribution systems. Using ferric sulfate, and/or the use of a corrosion inhibitor, has been shown to minimize these impacts.

Enhanced coagulation may increase or change the characteristics of the residuals generated. Coagulation sludge will contain elevated concentrations of contaminants removed during the treatment process. Depending on the source water concentration of a particular contaminant and any disposal limitations, it may be necessary evaluate the disposal of process solids with respect to state and local hazardous waste regulations.


B-7

Table B.3 Approximate Costs of Enhanced Coagulation

Design Flow (mgd) 0.01 0.1 1.0 10 100

Average Flow (mgd) 0.005 0.03 0.35 4.4 50

Capital Cost ($/gal)1 $0.20 $0.10 $0.05 $0.02 $0.01

Annual O&M Cost ($/kgal)2 $0.60 $0.30 $0.25 $0.20 $0.20 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of enhanced coagulation at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $2,000 ($0.20/gal × 10,000 gal = $2,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For example, annual O&M costs for a system with an average daily flow of 5,000 gallons (5 kgal) would be approximately $1,095 ($0.60/kgal × 5 kgal/day × 365 days/year = $1,095).

GAC ADSORPTION

Granular activated carbon (GAC) is commonly used in drinking water treatment to adsorb synthetic organic chemicals and natural organic compounds that cause taste and odor, color, and can react with chlorine to form disinfection byproducts (DBPs). Adsorption is both the physical and chemical process of accumulating a substance at the interface between liquid and solids phases. GAC is an effective adsorbent because it is a highly porous material and provides a large surface area to which contaminants may adsorb.

The two most common options for locating a GAC treatment unit in water treatment plants are: (1) post-filter adsorption, where the GAC unit is located after the conventional filtration process (as shown above); and (2) filter adsorbers, in which some or all of the filter media in a granular media filter is replaced with GAC.

In post-filter applications, the GAC contactor receives the highest quality water and, thus, has as its only objective the removal of dissolved organic compounds. Backwashing of these adsorbers is usually unnecessary, unless excessive biological growth occurs. This option provides the most flexibility for handling GAC and for designing specific adsorption conditions.

In addition to dissolved organics removal, the filter-adsorber configuration uses the GAC for turbidity and solids removal, and biological stabilization. Retrofitting existing high rate granular media filters can significantly reduce capital costs, however, filter-adsorbers have shorter filter run times and must be backwashed more frequently than post-filter adsorbers.


Filtration GAC FilterRapid Mix Flocculation/Sedimentation

Filtration GAC Filter


B-8

The empty bed contact time (EBCT) and the design flow rate define the size of and amount of carbon in a GAC contactor. The EBCT is a measure of the length of time in which water is in contact with the carbon. As the carbon adsorption sites become used up, breakthrough occurs and the GAC needs to be regenerated or replaced. A longer EBCT can delay breakthrough and reduce the GAC replacement/regeneration frequency. Typical EBCTs for water treatment applications range between 5 to 20 minutes. For filter adsorbers to be effective a EBCT of at least 5 minutes is generally required. Shorter EBCTs are likely to require much more frequent replacement or regeneration of the GAC media and the application is likely to become cost prohibitive.

GAC is more effective for the removal of DBP precursors than DBPs themselves. As a result, when used for control of DBPs, facilities that have practice pre-chlorination are likely to discontinue that practice. This has the potential to impact removal of dissolved inorganic species, such as iron and manganese. The use of an alternative pre-oxidant, such as potassium permanganate can help to eliminate this concern.

GAC will remove chlorine and chloramines. Therefore, it is necessary to consider the point of primary and secondary disinfectant application when adding GAC to assure the disinfection process is not compromised.

Sloughing of bacteria can occur in biologically-active GAC filter adsorbers and post-filter GAC contactors. In systems using free chlorine for both primary and secondary disinfection there should be sufficient free chlorine contact time to inactivate any bacteria dislodged from the GAC media. In systems utilizing chloramines for secondary disinfection, it may be necessary to provide a minimum of 5 minutes of free chlorine contact time prior to ammonia addition.

Depending on the economics, facilities may have on-site or off-site regeneration systems or may waste spent carbon and replace it with new. Spent GAC must be in accordance with state and federal laws. On-site regeneration will likely require the facility to acquire air permits.

Post-filter GAC contactors are considered secondary filtration under the Long Term 2 Enhanced Surface Water Treatment Rule. However, receiving a Cryptosporidium removal credit requires that 100 percent of the flow be treated by the GAC contactors.

Approximate capital and O&M costs for post-filter GAC adsorbers are provided in Table B.4. Capital costs are based on a 20-minute EBCT and 90-day regeneration frequency and include the addition of GAC contactors, initial load of carbon, associated piping and valves, and instrumentation and controls. O&M costs include spent GAC reactivation, power, replacement parts, and maintenance labor.

Table B.4 Approximate Costs of GAC Adsorption

Design Flow (mgd) 0.01 0.1 1.0 10 100 Average Flow (mgd) 0.005 0.03 0.35 4.4 50 Capital Cost ($/gal)1 $5.00 $1.50 $1.25 $0.75 $0.40 Annual O&M Cost ($/kgal)2 $9.00 $5.25 $1.40 $0.30 $0.20 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of GAC at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $50,000 ($5.00/gal × 10,000 gal = $50,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For example, annual O&M costs for a system with an average daily flow of 5,000 gallons (5 kgal) would be approximately $16,425 ($9.00/kgal × 5kgal/day × 365 days/year = $16,425).


B-9

ION EXCHANGE/ADSORPTION

Ion exchange (IX) and adsorption processes are used to remove dissolved ions and other charged species from water. IX processes are reversible chemical reactions that remove dissolved ions from solution and replacing them with other similarly charged ions. Adsorption processes rely on surface charges to adsorb charged ionic species.

Most IX and adsorption processes in water treatment operate in a continuous mode. Ion exchange or adsorption occur as water flow (typically in a down-flow mode) through a packed-bed of IX resin or adsorption media.

In water treatment, the most common IX process is cation exchange softening in which calcium and magnesium are removed. Radium can also be removed from drinking water by cation exchange. Anion exchange processes can be used for the removal of contaminants such as nitrate, fluoride, perchlorate, uranium, selenium, arsenic, sulfate, and natural organic matter (NOM), as well as others. Adsorption process, such as activated alumina and granular ferric hydroxide, are used to remove arsenic and similar species.

Contaminated Influent

Resin or Adsorption Media Bed

Upper Distributor

Underdrain Treated Influent

When the capacity of the IX resin is exhausted, it is necessary to regenerate the resin using

a saturated solution of the exchange ion (e.g., Na+ or Cl-) to restore the capacity of the resin and return the resin to its initial condition. Adsorption media must also be either be regenerated or replaced when the adsorptive capacity of the bed is exhausted.

Competition for ion exchange or adsorption sites can greatly impact a given system’s efficiency in removing contaminants. Generally, ions with higher valence, greater atomic weights and smaller radii are preferred by IX resins and adsorption media.

Anion exchange processes will generally preferentially remove sulfate over other target contaminants. Removal of sulfate and increased chloride concentrations (as a result of the exchange) can cause an increase in the chloride-to-sulfate ratio, which has been demonstrated to causes increases in lead corrosion in some distribution systems.

Strong acid anion exchange resins are generally available in two types: Type I and Type II. Type I resins contain trialkyl ammonium chloride or hydroxide, and Type II contain dialkyl 2-hydroxyethyl ammonium chloride or hydroxide. Type II resins have been demonstrated to release nitrosamines when preceded by chlorination in the treatment process or by an initial rinse following installation or regeneration.


B-10

Cation exchange functional group includes sodium can result in an increase in finished water sodium concentrations. Although there is no maximum contaminant level (MCL) for sodium, USEPA has established a Drinking Water Equivalence Level (i.e., guidance level) for sodium of 20 mg/L.

Depending on the TDS and other contaminant concentrations in the spent regenerant, it may be necessary to evaluate the impacts on wastewater treatment plant discharges and National Pollutant Discharge Elimination System (NPDES) requirements. Similarly, disposal of spent media with high concentrations of removed contaminants may require disposal as a hazardous waste.

Approximate capital and operations and maintenance (O&M) costs for IX and adsorption are provided in Table B.5. Capital costs include the addition of IX or adsorption beds, chemical storage, associated piping and valves, and instrumentation and controls. O&M costs include regenerant/chemicals, power, replacement parts, and maintenance labor. Costs do not include pH adjustment (which can enhance the treatment process and can represent a significant O&M expenditure).

Table B.5 Approximate Costs of Ion Exchange and Adsorption Design Flow (mgd) 0.01 0.1 1.0 10 100

Average Flow (mgd) 0.005 0.03 .035 4.4 50

Capital Cost ($/gal)1 $2.50 $0.75 $0.50 $0.50 $0.50

Annual O&M Cost ($/kgal)2 $3.00 $1.00 $0.75 $0.50 $0.50 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of IX at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $25,000 ($2.50/gal × 10,000 gal = $25,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For examples, annual O&M costs for a system with an average daily flow of 5,000 gallons (5 kgal) would be approximately $5,475 ($3.00/kgal × 5 kgal/day × 365 days/year = $5,475).

MEMBRANE FILTRATION

Microfiltration (MF) and ultrafiltration (UF) are membrane filtration processes commonly used in water treatment. MF and UF are typically applied for the removal of particulate and microbial contaminants, and are frequently used as an alternative to rapid sand filtration in conventional treatment and softening applications.

The primary difference between MF and UF is the pore size of the membranes. Both MF and UF membranes are primarily used for particulate and microbiological contaminant removal. Particulates removed include suspended solids, turbidity, some colloids, bacteria, protozoan cysts, and viruses (only UF has been demonstrated to remove viruses to any significant degree). Inorganic chemicals (e.g., phosphorus, hardness and metals) may be removed with suitable pre-treatment. Limited dissolved organics removal may also occur by either of these processes.

MF and UF membrane systems frequently require some type of source water pretreatment to prevent membrane fouling. The type of pretreatment required depends on the feed water quality and membrane type. Generally, surface water requires more extensive pretreatment than groundwater due to higher suspended solids and biological matter content.


B-11

Water temperature has a significant impact on water density and viscosity, which impacts MF and UF membrane performance. As the viscosity and density increase, the transmembrane pressure required to pass the water through the membrane also increases.

Both MF and UF membrane systems include routine backwashing to remove foulants from the membrane. Backwash frequency and duration depend on the membrane system and specific feed water quality conditions and treatment requirements. Chemical clean-in-place (CIP) is used periodically to control membrane fouling.

Approximate capital costs for MF and UF systems are provided in Table B.6. Capital costs do not include pre-treatment or post-treatment processes because they are highly dependent on the specific source water quality. O&M costs include power, replacement parts, membrane replacement, CIP chemicals, and maintenance labor. Residuals generated from MF and UF systems include the spent backwash and spent cleaning solutions. Spent backwash may be recycled to the process to increase system recovery, reduce chemical doses, and improve overall treatment performance. Otherwise, disposal of spent backwash is generally accomplished by discharge to a sanitary sewer or receiving stream, much the way spent backwash from a rapid sand filter would be handled. Spent cleaning solutions are generally acidic in nature and require neutralization prior to disposal.

Table B.6 Approximate Costs of Membrane Filtration Design Flow (mgd) 0.01 0.1 1.0 10 100 Average Flow (mgd) 0.005 0.03 0.35 4.4 50 Capital Cost ($/gal)1 $18.00 $4.30 $1.60 $1.10 $0.85 Annual O&M Cost ($/kgal)2 $4.25 $1.10 $0.60 $0.30 $0.25 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of membrane filtration at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $180,000 ($18.00/gal × 10,000 gal = $180,000).



B-12

NANOFILTRATION AND REVERSE OSMOSIS

Nanofiltration (NF) and reverse osmosis (RO) are membrane separation technologies that reverse the natural osmotic process by applying a feed pressure which forces water through a membrane against the natural osmotic gradient. This increases the dissolved contaminant concentrations on one side of the membrane. The primary difference between NF and RO is the size of dissolved contaminants that can be removed. NF membranes are typically used for hardness and organics (i.e. DBP precursors) removal. RO membranes are typically used for TDS and monovalent ion removal (e.g., seawater and brackish water desalting, F- and Cl- removal).

NF and RO processes include three basic flow streams: the feed, permeate or product, and concentrate or waste streams. A treatment process generally consists of multiple stages, wherein the concentrate from the prior stage becomes the feed for the subsequent stage. The permeate from each stage is blended together for the final product stream. The concentrate from the final stage is usually wasted (Figure B.1).

Figure B.1 NF and RO membrane systems always require some type of pretreatment to prevent

membrane fouling. The type of pretreatment required depends on the feed water quality and membrane type. For surface waters, pretreatment may be extensive and include coagulation, sedimentation, pH adjustment, microfiltration, GAC filtration, etc.

Residuals generated from NF and RO systems include the concentrate from the membrane processes and the spent cleaning chemicals. Concentrate disposal can be challenging as it is highly regulated by government agencies. Concentrate is typically a relatively high volume, high TDS waste stream and requires a comparatively large body of water for discharge or must be discharged to a wastewater treatment plant or deep well injection. Spent chemical cleaning solutions are generally acidic in nature and require neutralization prior to disposal.

NF and RO remove bicarbonate and alkalinity to varying degrees causing depression of the treated water pH, which can impact corrosion control and scale stability in the distribution system. For this reason, pH and/or alkalinity adjustment may be necessary in post-treatment to maintain effective corrosion control downstream of these processes.


B-13

To prevent corrosion of cement-mortar linings in distribution piping it is recommended that a finished water Langlier Saturation Index (LSI) value of 0.2 or greater be maintained. Values in excess of 0.5 will not cause any corrosion problems by may result in excessive precipitation of calcium carbonate in the distribution system. Similarly, a finished water calcium carbonate precipitation potential (CCPP) value of 4 to 10 will help to prevent dissolution of cement-mortar linings, but values in excess of 10 may result in excessive carbonate precipitation.

Significant decreases in finished water alkalinity (> 15%), particularly in low alkalinity waters may cause increased corrosion of iron, lead, and copper. The use of an orthophosphate-based corrosion inhibitor (e.g., phosphoric acid or zinc orthophosphate) can help to minimize the potential for increased metals release.

Approximate capital and O&M costs are provided in Table B.7. Capital costs do not include pre-treatment and post-treatment processes because these are highly depended on the specific source water quality. Capital costs include membranes, feed pumps, associated chemical feed equipment, and electrical and instrumentation. O&M costs include power, replacement parts, membrane replacement, and maintenance labor.

Table B.7 Approximate Costs of Nano Filtration and Reverse Osmosis

Design Flow (mgd) 0.01 0.1 1.0 10 100 Average Flow (mgd) 0.005 0.03 0.35 4.4 50 Capital Cost ($/gal)1 $8.25 $1.75 $1.00 $1.00 $0.75 Annual )&M Cost ($/kgal)2 $5.00 $1.50 $0.90 $0.65 $0.55 1. Capital costs are based on $ per gallon of treatment plant capacity. For example,

addition of NF or RO at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $82,500 ($8.25/gal × 10,000 gal = $82,500).


OPTIMIZED CHLORINATION Optimizing chlorination practices can be an effective strategy to reduce disinfection byproducts. It includes several strategies: ■ Eliminate pre-chlorination. ■ Move the point of chlorine addition. ■ Optimize chlorine dose. ■ Reduce chlorination pH.

Eliminating pre-chlorination can be particularly effective for the control of disinfection byproducts (DBPs). However, failure to replace chlorine with an alternative pre-oxidant, such as potassium permanganate, can result in several process upsets. Algae, taste and odor, iron and manganese, and other contaminants for which pre-chlorination was effective may now pass through the treatment process. Further, failure to maintain oxidizing conditions in the filters can result in desorption of manganese dioxide and cause pink or yellow water problems in the


B-14

distribution system. Where primary disinfection credit is awarded for pre-chlorination, alternate disinfection strategies need to be installed prior to elimination of pre-chlorination.

Moving the point of chlorination further downstream in the treatment processes, for example, after sedimentation/clarification, can also be an effective strategy for reducing DBPs. Delaying chlorine addition until after coagulated material, including natural organic matter (NOM), is removed from the process minimizes the concentration of DBP precursors present and reduced DBP formation. Again, where primary disinfection credit is awarded for pre-chlorination or chlorination helps to control other contaminants, this practice should be carefully evaluated.

Optimizing the chlorine dose can also be an effective way to reduce DBP levels in the distribution system. There are several strategies to do this, including: ■ Minimizing the pre-chlorine dose to that needed for pre-oxidation and applying what is

needed for primary disinfection after the filters rather than adding all of the chlorine prior to the filters.

■ Reducing the secondary chlorine dose (that prior to the system) to maintain minimal (0.2 – 0.5 mg/L) chlorine levels in the distribution system.

Optimizing the chlorine dose, particularly by reducing the dose entering the distribution

system can be difficult. Reducing the dose by too much may result in areas of the distribution system with no residual causing taste and odor, color, biofilm growth, corrosion, or other water quality problems.

With free chlorine, the dose required for primary disinfection is directly proportional to the chlorination pH. That is, lower CT values are required at lower pH values. Thus, by reducing the chlorination pH, the primary chlorine dose can be reduced and may result in reductions in DBP levels. It may be necessary to re-adjust the pH prior to distribution to eliminate the potential for any negative impacts on system corrosion. It also should be noted that changes in the chlorination pH may cause a shift in the speciation of DBPs – specifically an increase in haloacetic acid formation.

Optimizing chlorination practices can be an effective strategy to reduce DBPs for many systems. However, prior to implementing any such changes, the effects on treatment efficacy, primary disinfection, corrosion control effectiveness, and shifts in DBP speciation should be evaluated. OZONE AND ADVANCED OXIDATION PROCESSES

Ozone (O3) is one of the strongest disinfectants and oxidants available in drinking water treatment. Combinations of ozone and hydrogen peroxide (O3/H2O2) and ultraviolet light and hydrogen peroxide (UV/H2O2) are also being used more frequently as advanced oxidation processes (AOPs). The O3/H2O2 and UV/H2O2 processes enhance formation of the hydroxyl radical (•OH) which is a more powerful oxidant than molecular ozone and other oxidants and thus has the capability of oxidizing a variety of organic and inorganic contaminants.

Ozone is widely used in drinking water treatment for its oxidation (color, taste and odor, iron and manganese) and disinfection capabilities (Cryptosporidium). AOPs have been demonstrated to be effective for the removal/destruction of compounds not readily oxidized by ozone, or which may require higher than normal ozone doses (e.g., PCE, TCE, atrazine, taste and


B-15

odor compounds such as MIB and geosmin) and not be cost-effective. AOPs may make oxidation of these contaminants more economical.

Ozone can be applied at various points in the treatment train, although it is usually applied prior to coagulation (reduces coagulant demand) or filtration (causes micro-flocculation which improves filterability).

Ozone is typically added to water in a contactor consisting of several enclosed chambers via a diffused bubble system. In the first chamber, water flows downward against rising bubbles while in second chamber, water flows upward. Additional chambers are added to ensure sufficient contact time between ozone and water to achieve the desired treatment objective.

The most efficient operational use of H2O2/O3 is to add peroxide into the second chamber of an ozone contactor. This configuration allows the utility to obtain disinfection credits for ozonation while achieving the benefit of AOP for destruction of micropollutants. The most common point of application for an UV/H2O2 system is after filtration (lower turbidity, reduced obstruction/shielding of UV light, etc.).

Approximate capital and operations and maintenance (O&M) costs for ozone are provided in Table B.8. Capital costs include the addition of an ozone feed system, and contactor (12 minutes), ozone destruction equipment, associated piping and valves, and instrumentation and controls. O&M costs are based on an ozone dose of 7 mg/L and include chemicals, power, replacement parts, and maintenance labor. Costs do not include pH adjustment (which can enhance the oxidation process and can represent a significant O&M expenditure).

Ozone disinfection by-products include aldehydes, ketones, carboxyl acids, epoxides, peroxides, quinine phenols, and brominated organics as well as increased assimilable organic carbon (AOC). If untreated (typically by GAC filter), it may cause biological growth in the distribution system. Ozonation of water containing bromide can lead to the formation of bromate (BrO3), which must be maintained below the regulated 10 μg/L level. Major byproducts formed by O3/H2O2 are expected to be similar to those formed by ozonation alone. Studies of UV/H2O2 systems have shown the production of unknown partial oxidation byproducts (not yet regulated). Their impacts to human health are not yet known.

Table B.8 Approximate Costs of Ozone Design Flow (mgd) 0.1 1.0 10 100

Average Flow (mgd) 0.03 0.35 4.4 50

Capital Cost ($/gal)1 $4.00 $1.00 $0.50 $0.25

Annual O&M Cost ($/kgal)2 $6.50 $0.50 $0.25 $0.20 1. Capital costs are based on $ per gallon of treatment plant capacity. For

example, addition of ozone at a treatment facility with a capacity of 100,000 gpd would be expected to cost approximately $400,000 ($4.00/gal × 100,000 gal = $400,000).



B-16

UV DISINFECTION

Ultraviolet (UV) light can be used for the inactivation of drinking water pathogens or the oxidation of micropollutants. In the latter capacity, it is commonly used in combination with hydrogen peroxide as a part of an advanced oxidation process. UV disinfection or oxidation is a physical process that utilizes UV light and does not require addition of any chemicals. This technology is known for its germicidal power in inactivating microorganisms (i.e. bacteria, viruses, algae, etc.) including chlorine-resistant pathogens, such as Cryptosporidium.

UV disinfection uses UV light to inactivate pathogens by disrupting their DNA strands, inhibiting cell reproduction and causing the organism to die. UV light is generated by flowing electrons from an electrical source through ionized mercury vapor. Mercury is contained within a UV lamp. UV lamps commonly used in drinking water treatment are classified as low-pressure (LP) lamps, low-pressure high output (LP-HO) lamps, and medium-pressure (MP) lamps. MP lamps produce 10 to 20 times higher UV radiation outputs than LP and LP-HO lamps; thereby requiring fewer lamps and decreased maintenance. However, power requirements are significantly higher and higher temperatures generated can cause scaling of sleeves in some waters. LP and LP-HO systems are typically better suited for small and medium sized systems because of their reliability associated with operating with multiple lamps.

The UV dosage applied for inactivation is a function of UV irradiance and exposure time (intensity × time, IT) and is analogous to the CT term used in for chemical disinfectants. Water quality parameters such as turbidity and suspended solid (SS) can lower UV transmittance by screening/shielding UV light from the microorganisms. Water quality parameters such as pH, alkalinity, iron, hardness, and temperature can affect scaling of UV lamp sleeves. UV lamp sleeves require periodic cleaning to remove biological and chemical fouling materials which can decrease the intensity of the lamps.

Approximate capital and operations and maintenance (O&M) costs for a UV disinfection system are provided in Table B.9. Capital costs are based on a dose of 40 mJ/cm2 and include an uninterruptible power supply (UPS), electrical and instrumentation and controls. O&M costs include power, replacement lamps and sleeves, and maintenance labor.

UV disinfection has a low potential to form by-products because the intensities used are less than those required to cause photochemical reactions. UV irradiation has the advantage that it does not add or remove anything to change the water chemistry and only requires a very short contact time; however, because UV irradiation does not leave a residual, a second disinfectant must be added to leave a residual in the distribution system.


Filtration

CausticCaustic

StorageUV Light

CoagulantCoagulant

InterstagePumping


Filtration

CausticCaustic

StorageUV LightRapid Mix Flocculation/Sedimentation

Filtration

CausticCaustic

StorageUV Light

CoagulantCoagulant

InterstagePumping


B-17

Table B.9 Approximate Costs of Ultraviolet Disinfection

Design Flow (mgd) 0.01 0.1 1.0 10 100 Average Flow (mgd) 0.005 0.03 0.35 4.4 50 Capital Cost ($/gal)1 $1.5 $0.30 $0.25 $0.10 $0.07 Annual O&M Cost ($/kgal)2 $2.00 $0.50 $0.10 $0.05 $0.01 1. Capital costs are based on $ per gallon of treatment plant capacity. For example, addition

of UV disinfection at a treatment facility with a capacity of 10,000 gpd would be expected to cost approximately $15,000 ($1.50/gal × 10,000 gal = $15,000).

2. Annual O&M costs are based on $ per thousand gallons treated. For example, annual O&M costs for a system with an average daily flow of 5,000 gallons (5 kgal) would be approximately $3,650 ($2.00/kgal × 5 kgal/day × 365 days/year = $3,650). O&M costs do not include the current $0.015/1000 gal patent fee.


C-1

APPENDIX C

TECHNOLOGY-BASED RULE LOGIC


Con

vers

ion

to C

hlor

amin

es

Wat

er Q

ualit

y Pa

ram

eter

s In

put:

Cur

rent

fini

shed

wat

er p

H, c

urre

nt fi

nish

ed w

ater

alk

alin

ity, s

ourc

e w

ater

tem

pera

ture

Gen

eral

Out

put A

dvis

orie

s

NIT

RIF

ICA

TIO

N In

puts

: Fin

ishe

d w

ater

TO

C, c

hlor

ine

diox

ide

use

(Y/N

), ta

nk tu

rnov

er, t

ank

mix

ing

(goo

d, b

ad, u

nkno

wn)

, wat

er a

ge, f

lush

ing

prog

ram

(goo

d, a

vera

ge, p

oor)

Gen

eral

Out

put

Adv

isor

ies

For t

ank

turn

over

> 5

day

s, ta

nk m

ixin

g =

good

, poo

r, or

unk

now

n, a

nd fl

ushi

ng =

no.

Wat

er A

geC

lO2

= N

oW

ater

T <

10

CC

lO2

= N

o10

C <

Wat

er T

< 2

5 C

ClO

2 =

No

Wat

er T

> 2

5 C

< 3

days

Gre

en1

Gre

en2

Gre

en2

3 <

Wat

er A

ge <

14

days

Gre

en1

Red

1R

ed3

> 14

day

sG

reen

3R

ed2

Red

4

(ALL

) C

hlor

amin

es c

an re

sult

in th

e fo

rmat

ion

of o

ther

cur

rent

ly n

on-r

egul

ated

DB

Ps,

incl

udin

g n-

Nitr

osod

imet

hlam

ine

(ND

MA

) and

oth

er

nitro

geno

us D

BP

s. U

tiliti

es c

onsi

derin

g co

nver

sion

to c

hlor

amin

es a

re e

ncou

rage

d to

pro

perly

exp

lore

thes

e im

pact

s pr

ior t

o im

plem

entin

g ch

lora

min

atio

n.

(ALL

) C

hlor

amin

es m

ay im

pact

dia

lysi

s ce

nter

s an

d pa

tient

s, a

quar

ium

ow

ners

and

pet

sto

res,

and

can

impa

ct p

lum

bing

(ela

stom

er) m

ater

ials

. C

onve

rsio

n to

chl

oram

ines

re

quire

s an

effe

ctiv

e pu

blic

edu

catio

n pr

ogra

m to

not

ify n

ot o

nly

thes

e gr

oups

of c

usto

mer

s, b

ut th

e en

tire

affe

cted

ser

vice

are

a. A

WW

A h

as g

uida

nce

avai

labl

e fo

r dev

elop

ing

a pu

blic

edu

catio

n pr

ogra

m p

rior t

o co

nver

sion

to c

hlor

amin

es.

(Chl

orin

e di

oxid

e =

Yes)

The

use

of c

hlor

ine

diox

ide

has

been

sho

wn

to m

inim

ize

the

pote

ntia

l for

nitr

ifica

tion

due

to th

e ba

cter

icid

al p

rope

rties

of c

hlor

ite -

a de

grad

ate

of c

hlor

ine

diox

ide.

Whi

le th

e po

tent

ial f

or n

itrifi

catio

n is

min

imal

, util

ities

util

izin

g ch

lorin

e di

oxid

e an

d pr

actic

ing

chlo

ram

ines

sho

uld

still

exc

erci

se g

ood

dist

ribut

ion

syst

em o

pera

tions

pr

actic

es, i

nclu

ding

effe

ctiv

e ta

nk m

ixin

g an

d tu

rnov

er, a

nd ro

utin

e flu

shin

g.(T

ank

turn

over

<5

days

) A

tank

turn

over

tim

e of

3-5

day

s (2

0-30

per

cent

of v

olum

e da

ily) i

s ge

nera

lly re

com

men

ded.

It i

s re

com

men

ded

that

ste

ps b

e ta

ken

to m

aint

ain

tank

tu

rnov

er a

t the

leve

ls in

dica

ted.

(Tan

k tu

rnov

er >

5 da

ys)

A ta

nk tu

rnov

er ti

me

of 3

-5 d

ays

(20-

30 p

erce

nt o

f vol

ume

daily

) is

gene

rally

reco

mm

ende

d. I

t is

reco

mm

ende

d th

at s

teps

be

take

n to

impr

ove

tank

tu

rnov

er in

this

sys

tem

.(T

ank

mix

ing-

ALL

) Ta

nk m

ixin

g is

a c

ritic

al fa

ctor

in th

e co

ntro

l of n

itrifi

catio

n. I

t is

nece

ssar

y to

ens

ure

not o

nly

good

vol

ume

turn

over

, but

that

tank

s ar

e w

ell-m

ixed

dur

ing

fill a

nd

draw

cyc

les.

(ALL

) C

ontro

lling

the

chlo

rine-

to-a

mm

onia

feed

ratio

is c

ritic

al to

min

imiz

ing

the

pote

ntia

l for

nitr

ifica

tion.

Whe

n co

nsid

erin

g ch

lora

min

es, p

rope

r sel

ectio

n an

d co

ntro

l of c

hlor

ine

and

amm

onia

feed

rate

s is

crit

ical

to m

inim

izin

g fre

e am

mon

ia c

once

ntra

tions

ent

erin

g th

e sy

stem

and

pre

vent

ing

nitri

ficat

ion.

(ALL

) It

is im

porta

nt to

est

ablis

h a

chlo

ram

ine

resi

dual

targ

et (t

ypic

ally

1.0

mg/

L or

gre

ater

) for

all

area

s of

the

dist

ribut

ion

syst

em a

nd th

en im

plem

ent d

istri

butio

n m

anag

emen

t st

rate

gies

, suc

h as

flus

hing

and

tank

man

agem

ent,

to e

nsur

e th

ose

goal

s ar

e m

et.

Are

as o

f low

chl

oram

ine

resi

dual

are

par

ticul

arly

sus

cept

ible

to n

itrifi

catio

n.

Gre

en 1

) Bas

ed o

n th

e fin

ishe

d w

ater

tem

pera

ture

and

low

wat

er a

ge, t

he p

oten

tial f

or n

itrifi

catio

n in

th

is s

yste

m is

min

imal

. 2) B

ased

on

syst

em w

ater

age

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

m

inim

al, h

owev

er, s

yste

m o

pera

tion

to e

nsur

e go

od ta

nk m

ixin

g an

d a

rout

ine

flush

ing

prog

ram

are

re

com

men

ded,

par

ticul

arly

dur

ing

war

m w

ater

mon

ths.

3) B

ased

on

the

low

wat

er te

mpe

ratu

re, t

he

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

min

imal

. H

owev

er, g

ood

tank

turn

over

and

mix

ing,

as

wel

l as

flush

ing

shou

ld s

till b

e a

prio

rity

give

n th

e w

ater

age

in th

is s

yste

m.

Red

1) T

o re

duce

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m im

plem

ent a

rout

ine

wat

er q

ualit

y m

onito

ring

and

flush

ing

prog

ram

. Ta

ke s

teps

to e

nsur

e ef

fect

ive

tank

turn

over

and

mix

ing.

2) T

his

syst

em is

sus

cept

ible

to n

itrifi

catio

n, p

artic

ular

ly in

war

m w

ater

mon

ths.

Tak

e st

eps

to im

prov

e ta

nk

turn

over

and

mix

ing

and

impl

emen

t a fl

ushi

ng p

rogr

am to

redu

ce th

e im

pact

s of

wat

er a

ge.

3) D

ue to

th

e hi

gh fi

nish

ed w

ater

tem

pera

ture

, thi

s sy

stem

is s

usce

ptib

le to

nitr

ifica

tion.

Im

plem

ent a

rout

ine

wat

er q

ualit

y m

onito

ring

prog

ram

, tak

e st

eps

to im

prov

e ta

nk tu

rnov

er a

nd m

ixin

g, a

nd im

plem

ent a

flu

shin

g pr

ogra

m to

min

imiz

e th

e po

tent

ial f

or n

itrifi

caito

n. 4

) B

ased

on

wat

er a

ge a

nd te

mpe

ratu

re,

this

sys

tem

is h

ighl

y su

scep

tible

to n

itrifi

catio

n. I

mpl

emen

t a ro

utin

e w

ater

qua

lity

mon

itorin

g pr

ogra

m, t

ake

step

s to

impr

ove

tank

turn

over

and

mix

ing,

and

impl

emen

t a fl

ushi

ng p

rogr

am to

m

inim

ize

the

pote

ntia

l for

nitr

ifica

iton.

Mor

e ex

trem

e m

easu

res,

suc

h as

ann

ual f

ree

chlo

rine

burn

out

or a

dditi

on o

f chl

orin

e di

oxid

e at

the

WTP

may

be

need

ed to

com

plet

ely

elim

inat

e th

e po

tent

ial f

or

nitri

ficat

ion.

C-2©2009 Water Research Foundation. ALL RIGHTS RESERVED

For t

ank

turn

over

> 5

day

s, ta

nk m

ixin

g =

good

, poo

r, or

unk

now

n, fl

ushi

ng =

yes

.

Wat

er A

geC

lO2

= N

oW

ater

T <

10

CC

lO2

= N

o10

C <

Wat

er T

< 2

5 C

ClO

2 =

No

Wat

er T

> 2

5 C

< 3

days

Gre

en1

Gre

en2

Gre

en2

3 <

Wat

er A

ge <

14

days

Gre

en1

Red

1R

ed3

> 14

day

sG

reen

3R

ed2

Red

4

For t

ank

turn

over

<=

5 da

ys, t

ank

mix

ing

= go

od, a

nd fl

ushi

ng =

no.

Wat

er A

geC

lO2

= N

oW

ater

T <

10

CC

lO2

= N

o10

C <

Wat

er T

< 2

5 C

ClO

2 =

No

Wat

er T

> 2

5 C

< 3

days

Gre

en1

Gre

en2

Gre

en2

3 <

Wat

er A

ge <

14

days

Gre

en1

Red

1R

ed3

> 14

day

sG

reen

3R

ed2

Red

4

Gre

en 1

) Bas

ed o

n th

e fin

ishe

d w

ater

tem

pera

ture

and

low

wat

er a

ge, t

he p

oten

tial f

or n

itrifi

catio

n in

th

is s

yste

m is

min

imal

. 2) B

ased

on

syst

em w

ater

age

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

m

inim

al, h

owev

er, s

yste

m o

pera

tion

to e

nsur

e go

od ta

nk m

ixin

g an

d a

rout

ine

flush

ing

prog

ram

are

re

com

men

ded,

par

ticul

arly

dur

ing

war

m w

ater

mon

ths.

3) B

ased

on

the

low

wat

er te

mpe

ratu

re, t

he

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

min

imal

. H

owev

er, g

ood

tank

turn

over

and

mix

ing,

as

wel

l as

flush

ing

shou

ld s

till b

e a

prio

rity

give

n th

e w

ater

age

in th

is s

yste

m.

Red

1) T

o re

duce

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m im

plem

ent a

rout

ine

wat

er q

ualit

y m

onito

ring

and

flush

ing

prog

ram

. Ta

ke s

teps

to e

nsur

e ef

fect

ive

tank

turn

over

and

mix

ing.

2) T

his

syst

em is

sus

cept

ible

to n

itrifi

catio

n, p

artic

ular

ly in

war

m w

ater

mon

ths.

Tak

e st

eps

to im

prov

e ta

nk

turn

over

and

mix

ing

to re

duce

the

impa

cts

of w

ater

age

. 3)

Due

to th

e hi

gh fi

nish

ed w

ater

te

mpe

ratu

re, t

his

syst

em is

sus

cept

ible

to n

itrifi

catio

n. I

mpl

emen

t a ro

utin

e w

ater

qua

lity

mon

itorin

g pr

ogra

m, a

nd ta

ke s

teps

to im

prov

e ta

nk tu

rnov

er a

nd m

ixin

g to

min

imiz

e th

e po

tent

ial f

or n

itrifi

caito

n.4)

Bas

ed o

n w

ater

age

and

tem

pera

ture

, thi

s sy

stem

is h

ighl

y su

scep

tible

to n

itrifi

catio

n. I

mpl

emen

t a

rout

ine

wat

er q

ualit

y m

onito

ring

prog

ram

and

take

ste

ps to

impr

ove

tank

turn

over

and

mix

ing

to

min

imiz

e th

e po

tent

ial f

or n

itrifi

caito

n. M

ore

extre

me

mea

sure

s, s

uch

as a

nnua

l fre

e ch

lorin

e bu

rnou

t or

add

ition

of c

hlor

ine

diox

ide

at th

e W

TP m

ay b

e ne

eded

to c

ompl

etel

y el

imin

ate

the

pote

ntia

l for

ni

trific

atio

n.

Gre

en 1

) Bas

ed o

n th

e fin

ishe

d w

ater

tem

pera

ture

and

low

wat

er a

ge, t

he p

oten

tial f

or n

itrifi

catio

n in

th

is s

yste

m is

min

imal

. 2) B

ased

on

syst

em w

ater

age

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

m

inim

al, h

owev

er, s

yste

m o

pera

tion

to e

nsur

e go

od ta

nk m

ixin

g an

d a

rout

ine

flush

ing

prog

ram

are

re

com

men

ded,

par

ticul

arly

dur

ing

war

m w

ater

mon

ths.

3) B

ase

on th

e lo

w w

ater

tem

pera

ture

, the

po

tent

ial f

or n

itrifi

catio

n in

this

sys

tem

is m

inim

al.

How

ever

, goo

d ta

nk tu

rnov

er a

nd m

ixin

g, a

s w

ell a

sflu

shin

g sh

ould

stil

l be

a pr

iorit

y gi

ven

the

wat

er a

ge in

this

sys

tem

.

Red

1) T

o re

duce

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m im

plem

ent a

rout

ine

wat

er q

ualit

y m

onito

ring

and

flush

ing

prog

ram

. Ta

ke s

teps

to e

nsur

e ef

fect

ive

tank

turn

over

and

mix

ing

are

mai

ntai

ned.

2) T

his

syst

em is

sus

cept

ible

to n

itrifi

catio

n, p

artic

ular

ly in

war

m w

ater

mon

ths.

Tak

e st

eps

to e

nsur

e ef

fect

ive

tank

turn

over

and

mix

ing

are

mai

ntai

ned

and

impl

emen

t a fl

ushi

ng p

rogr

am

to re

duce

the

impa

cts

of w

ater

age

. 3)

Due

to th

e hi

gh fi

nish

ed w

ate

tem

pera

ture

, thi

s sy

stem

is

susc

eptib

le to

nitr

ifica

tion.

Im

plem

ent a

rout

ine

wat

er q

ualit

y m

onito

ring

prog

ram

, tak

e st

eps

to

ensu

re e

ffect

ive

tank

turn

over

and

mix

ing

are

mai

ntai

ned,

and

impl

emen

t a fl

ushi

ng p

rogr

am to

m

inim

ize

the

pote

ntia

l for

nitr

ifica

iton.

4)

Bas

ed o

n w

ater

age

and

tem

pera

ture

, thi

s sy

stem

is h

ighl

y su

scep

tible

to n

itrifi

catio

n. I

mpl

emen

t a ro

utin

e w

ater

qua

lity

mon

itorin

g pr

ogra

m, t

ake

step

s to

en

sure

effe

ctiv

e ta

nk tu

rnov

er a

nd m

ixin

g ar

e m

aint

aine

d, a

nd im

plem

ent a

flus

hing

pro

gram

to

min

imiz

e th

e po

tent

ial f

or n

itrifi

caito

n. M

ore

extre

me

mea

sure

s, s

uch

as a

nnua

l fre

e ch

lorin

e bu

rnou

t or

add

ition

of c

hlor

ine

diox

ide

at th

e W

TP m

ay b

e ne

eded

to c

ompl

etel

y el

imin

ate

the

pote

ntia

l for

ni

trific

atio

n.


For t

ank

turn

over

<=

5 da

ys, t

ank

mix

ing

= go

od, a

nd/o

r flu

shin

g =

yes

Wat

er A

geC

lO2

= N

oW

ater

T <

10

CC

lO2

= N

o10

C <

Wat

er T

< 2

5 C

ClO

2 =

No

Wat

er T

> 2

5 C

< 3

days

Gre

en1

Gre

en2

Gre

en2

3 <

Wat

er A

ge <

14

days

Gre

en1

Gre

en4

Gre

en4

> 14

day

sG

reen

3G

reen

4G

reen

5

Gre

en 1

) Bas

ed o

n th

e fin

ishe

d w

ater

tem

pera

ture

and

low

wat

er a

ge, t

he p

oten

tial f

or n

itrifi

catio

n in

th

is s

yste

m is

min

imal

. 2) B

ased

on

syst

em w

ater

age

the

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

m

inim

al, h

owev

er, s

yste

m o

pera

tion

to e

nsur

e go

od ta

nk m

ixin

g an

d a

rout

ine

flush

ing

prog

ram

are

re

com

men

ded,

par

ticul

arly

dur

ing

war

m w

ater

mon

ths.

3) B

ased

on

the

low

wat

er te

mpe

ratu

re, t

he

pote

ntia

l for

nitr

ifica

tion

in th

is s

yste

m is

min

imal

. H

owev

er, g

ood

tank

turn

over

and

mix

ing,

as

wel

l as

flush

ing

shou

ld s

till b

e a

prio

rity

give

n th

e w

ater

age

in th

is s

yste

m. 4

) M

aint

aini

ng e

ffect

ive

tank

tu

rnov

er a

nd m

ixin

g, a

s w

ell a

s co

ntin

uanc

e of

the

rout

ine

flush

ing

prog

ram

are

crit

ical

to re

duci

ng th

e po

tent

ial f

or n

itrifi

catio

n. 5

) Mai

ntai

ning

effe

ctiv

e ta

nk tu

rnov

er a

nd m

ixin

g, a

s w

ell a

s co

ntin

uanc

e of

th

e ro

utin

e flu

shin

g pr

ogra

m a

re c

ritic

al to

redu

cing

the

pote

ntia

l for

nitr

ifica

tion.

How

ever

, in

this

sy

stem

, bec

ause

of t

he h

igh

wat

er te

mpe

ratu

res

and

wat

er a

ge, m

ore

extre

me

mea

sure

s su

ch a

s an

nual

free

chl

orin

e bu

rnou

t or a

dditi

on o

f chl

orin

e di

oxid

e at

the

wat

er tr

eatm

ent p

lant

may

be

requ

ired

to c

ompl

etel

y pr

even

t nitr

ifica

tion.


CO

RR

OSI

ON

CO

NTR

OL/

DS

SCA

LE S

TAB

ILIT

Y

Gen

eral

Out

put A

dvis

orie

s

Fini

shed

wat

er A

lkal

inity

Nitr

ifica

tion

= R

edO

rtho

= Y

esN

itrifi

catio

n =

Red

Orth

o =

No

Nitr

ifica

tion

= R

edLS

L =

Yes

, O

rtho=

No

Nitr

ifica

tion

= R

edU

nlin

ed-C

IP =

Yes

Nitr

ifica

tion

= R

ed

Pla

stic

=Y

es

< 30

mg/

L as

CaC

O3

Red

Red

Red

1R

ed2

Gre

en5

30 m

g/L

< A

LK <

80

mg/

LG

reen

1R

edR

ed1

Gre

en6

Gre

en5

> 80

mg/

LG

reen

1,2

Gre

en2

Gre

en2,

3G

reen

2,4

Gre

en5

(Nitr

ifica

tion

= G

reen

) B

ased

on

the

oper

atin

g an

d w

ater

qua

lity

cond

ition

s en

tere

d, th

e po

tent

ial f

or c

orro

sion

-rel

ated

wat

er q

ualit

y pr

oble

ms

du

Red

(All)

- N

itrifi

catio

n m

ay c

ause

loca

lized

pH

de

pres

sion

resu

lting

in c

orro

sion

-wat

er q

ualit

y pr

oble

ms.

Wat

er q

ualit

y pr

oble

ms

may

incl

ude

incr

ease

d ta

p co

pper

con

cent

ratio

ns.

1) D

ue to

the

pres

ence

of l

ead

serv

ice

lines

, nitr

ifica

tion

may

cau

se

incr

ease

d ta

p le

ad c

once

ntra

tions

. 2)

Wat

er q

ualit

y pr

oble

ms

may

als

o in

clud

e re

d w

ater

in a

reas

of u

n-lin

ed c

ast i

ron

pipe

.

Gre

en (A

ll) -

Pot

entia

l for

cor

rosi

on-r

elat

ed im

pact

s re

sulti

ng fr

om n

itrifi

catio

n is

min

imal

. 1) U

se o

f or

thop

hosp

hate

min

imiz

es th

e po

tent

ial f

or c

orro

sion

-re

late

d w

ater

qua

lity

prob

lem

s re

late

d to

nitr

ifica

tion.

2)

Hig

h fin

ishe

d w

ater

alk

alin

ity a

lso

min

imiz

es th

e po

tent

ial f

or c

orro

sion

-rel

ated

wat

er q

ualit

y pr

oble

ms

due

to n

itrifi

catio

n. 3

) The

pot

entia

l for

incr

ease

d co

nsum

er ta

p le

ad c

once

ntra

tions

due

to n

itrifi

catio

n i s

min

imal

at y

our e

nter

ed fi

nish

ed w

ater

alk

alin

ity. 4

) Th

e po

tent

ial f

or re

d w

ater

inci

dent

s du

e to

nitr

ifica

tion

is a

lso

min

imal

at y

our e

nter

ed fi

nish

ed w

ater

al

kalin

ity.5

)Due

to th

e pr

esen

ce o

f onl

y pl

astic

pip

e th

epo

tent

ial f

or c

orro

sion

pro

blem

s in

the

dist

ribut

ion

syst

em is

min

imal

. How

ever

, nitr

ifica

tion

can

caus

e co

rros

ion

of h

ome

plum

bing

mat

eria

l. 6)

Due

to th

e fin

ishe

d w

ater

alk

alin

ity, t

he p

oten

tial f

or re

d w

ater

pr

oble

ms

in a

reas

ser

ved

by u

n-lin

ed c

ast i

ron

pipe

s is

min

imal

.

Inpu

ts: F

inis

hed

wat

er a

lkal

inity

, ort

hoph

osph

ate

use

(Y/N

), pi

pe m

ater

ials

(Asb

esto

s ce

men

t, ce

men

t-mor

tar-

lined

duc

tile

iron,

duc

tile

iron,

un-

lined

cas

t iro

n, p

last

ic, l

ead

serv

ice

lines

- se

lect

all

that

app

ly)

(LSL

= Y

es)

Cha

ngin

g fro

m fr

ee c

hlor

ine

to c

hlor

amin

es fo

r sec

onda

ry d

isin

fect

ion

can

caus

e re

duct

ions

in fi

nish

ed w

ater

oxi

datio

n-re

duct

ion

p o


LOC

ALI

ZED

BR

EAK

POIN

T C

HLO

RIN

ATI

ON

Inpu

ts: S

econ

dary

fini

shed

wat

er s

ourc

e (Y

/N),

seco

ndar

y so

urce

dis

infe

ctio

n (c

hlor

ine,

chl

oram

ines

- go

od C

l 2/NH

3 con

trol

, chl

oram

ines

- po

or C

l 2/N

H3 c

ontr

ol)

Issu

es a

ssoc

iate

d w

ith lo

caliz

ed b

reak

poin

t chl

orin

atio

n or

dill

utio

nG

roun

dwat

er w

ells

- w

ithou

t dis

infe

ctan

t, w

ith c

hlor

ine,

or w

ith c

hlor

amin

es

Gen

eral

Out

put A

dvis

orie

s

Sec

onda

ry S

ourc

eC

hlor

ine

Chl

oram

ine

- goo

d co

ntro

lC

hlor

amin

e - p

oor c

ontro

l

No

Gre

en1

Yes

Red

1G

reen

2R

ed2

Gre

en 1

) Ble

ndin

g of

wat

er fr

om d

iffer

ent s

ourc

es in

the

dist

ribut

ion

syst

em, p

artic

ular

ly w

hen

one

sour

ce u

ses

free

chlo

rine

and

anot

her u

ses

chlo

ram

ines

, can

lead

to lo

caliz

ed b

reak

poin

t chl

orin

atio

n,

loss

of d

isin

fect

ant r

esid

ual,

tast

e an

d od

or, n

itrifi

catio

n an

d ot

her w

ater

qua

lity

prob

lem

s. B

ased

on

the

data

ent

ered

, thi

s sy

stem

is n

ot u

sing

a s

econ

dary

sou

rce

of fi

nish

ed w

ater

; how

ever

, the

se

fact

ors

shou

ld b

e co

nsid

ered

in fu

ture

wat

er s

uppl

y pl

anni

ng. 2

) Ble

ndin

g of

wat

er fr

om d

iffer

ent

sour

ces

in th

e di

strib

utio

n sy

stem

can

lead

to lo

caliz

ed b

reak

poin

t chl

orin

atio

n, lo

ss o

f dis

infe

ctan

t re

sidu

al, t

aste

and

odo

r, ni

trific

atio

n an

d ot

her w

ater

qua

lity

prob

lem

s. B

ased

on

the

data

ent

ered

, th e

seco

ndar

y so

urce

in th

is in

stan

ce is

util

izin

g ch

lora

min

es a

nd e

xcer

cise

s go

od c

ontro

l of t

he c

hlor

ine-

to-a

mm

onia

feed

ratio

, and

the

pote

ntia

l for

wat

er q

ualit

y pr

oble

ms

is m

inim

al.

Wat

er q

ualit

y m

onito

ring

to e

valu

ate

the

impa

cts

of b

lend

ing

in th

e di

strib

utio

n sy

stem

is re

com

men

ded

upon

co

nver

sion

to c

hlor

amin

es, a

nd w

ater

qua

lity

goal

s re

gard

ing

chlo

ram

ine

resi

dual

and

free

am

mon

ia

conc

entra

tions

sho

uld

be re

-eva

luat

ed.

Red

1) B

lend

ing

of c

horin

ated

and

chl

oram

inat

ed w

ater

s in

the

dist

ribut

ion

syst

em w

ill le

ad to

lo

caliz

ed b

reak

poin

t chl

orin

atio

n, lo

ss o

f res

idua

l, ta

ste

and

odor

, and

oth

er w

ater

qua

lity

prob

lem

s.

Ste

ps s

houl

d be

take

n to

isol

ate

thes

e tw

o so

urce

s, o

r the

sou

rce

usin

g fre

e ch

lorin

e sh

ould

be

conv

erte

d to

chl

oram

ines

. 2)

Alth

ough

the

seco

ndar

y so

urce

in th

is in

stan

ce is

usi

ng c

hlor

amin

es,

poor

con

trol o

f the

chl

orin

e-to

-am

mon

ia fe

ed ra

tio m

ay re

sult

in n

itrifi

catio

n, ta

ste

and

odor

, or o

ther

w

ater

qua

lity

issu

es.

It is

sug

gest

ed th

at th

e se

cond

ary

sour

ce b

e op

timiz

ed to

impr

ove

cont

rol o

f the

ch

lorin

e-to

-am

mon

ia fe

ed ra

tio a

nd to

redu

ce th

e po

tent

ial f

or w

ater

qua

lity

prob

lem

s as

a re

sult

of

blen

ding

in th

e di

strib

utio

n sy

stem

.


Ozo

ne a

nd O

zone

-rel

ated

AO

PsIs

sues

ass

ocia

ted

with

the

use

of o

zone

and

ozo

ne b

ased

AO

P

Targ

et C

onta

min

ants

/ W

Q P

lann

ing

Issu

es (B

ulle

ts in

Issu

es M

atrix

Pag

e)

Bro

mat

e Fo

rmat

ion

For O

3:TO

C R

atio

< 1

Bro

mid

e (u

g/L)

pH <

or =

6.5

pH >

6.5

< 50

Gre

enG

reen

50-1

00G

reen

Red

> 10

0G

reen

Red

For O

3:TO

C R

atio

> 1

Bro

mid

e (u

g/L)

pH <

or =

6.5

pH >

6.5

< 50

Gre

enG

reen

50-1

00G

reen

Red

> 10

0R

edR

ed

Adv

isor

y O

utpu

t Ver

biag

e (fl

ag s

peci

fic)

Ant

icip

ated

Bro

mid

e le

vels

50

ug/L

or m

ore

and

pH >

6.5

.B

r-1

Ant

icip

ated

Bro

mid

e le

vels

of 1

00 u

g/L

or m

ore.

Br-

1

Reg

row

th P

oten

tial

(Res

idua

l Ins

tabi

lity)

WQ

(in

Dis

t sy

stem

)W

Q (F

inis

hed

Wat

er)

WTP

DS

DS

UM

max

tem

pTO

Cbi

ofilt

erFl

ushi

ngTa

nk M

anag

emen

tG

reen

<10

Gre

en<2

Gre

enye

sG

reen

nogo

odgo

odR

ed

Any

thin

g el

seR

P-1

Emer

ging

DB

Ps

O3:

TOC

Rat

ioW

QW

TPTO

Cbi

ofilt

erG

reen

<0.5

Gre

en<2

yes

Red

Any

thin

g E

lse

Red

Wat

erW

TP

pHA

lkP

oly

Pho

spha

teG

reen

>7.5

>=60

Gre

en7.

0-8.

0an

yY

esR

edan

y<6

0N

oR

W-1

Red

pH<7

.5N

oR

W-2

Rea

son

for R

ed (i

f App

licab

le)

RFR

: TO

C is

≥ 2

.0 a

nd

biol

ogic

al fi

ltrat

ion

is n

ot

bein

g pr

actic

ed.

AO

: U

se o

f ozo

ne a

nd o

zone

-rel

ated

AO

Ps

may

resu

lt in

fo

rmat

ion

of o

ther

DB

Ps

incl

udin

g al

dehy

des

and

keto

nes.

Fini

shed

Wat

er

Use

of o

zone

and

ozo

ne-A

OP

s m

ay p

rodu

ce r

egro

wth

in d

istri

butio

n sy

stem

with

out

biol

ogic

ally

-act

ive

filte

rs a

nd/o

r sup

erio

r dis

tribu

tion

syst

em m

anag

emen

t pra

ctic

es.

Bro

mat

e fo

rmat

ion

may

be

prob

lem

atic

at o

zone

dos

ages

ass

ocia

ted

with

inac

tivat

ion

of

chlo

rine-

resi

sten

t pat

hoge

ns.

Bro

mat

e fo

rmat

ion

may

be

prob

lem

atic

at o

zone

dos

ages

ass

ocia

ted

with

inac

tivat

ion

of

chlo

rine-

resi

sten

t pat

hoge

ns.

alka

liniti

es a

re <

60 m

g/l a

s C

aCO

3.p

< 7.

5 un

less

a s

eque

ster

ing

chem

ical

is a

dded

.


Mn

rele

ase

from

filte

rsIn

put:

Mn

WTP

WTP

Is M

anga

nese

pr

esen

t in

Raw

w

ater

?P

re-O

xida

tion

With

S

ettli

ng

Chl

orin

e pr

ior t

o fil

tratio

nG

reen

Yes

Yes

Gre

enye

sno

yes

Red

yes

Eve

ryth

ing

else

MN

-1

If M

N=n

o th

en n

o lo

gic

shou

ld ru

n he

re!!

Corro

sion

Cont

rol

Fini

shed

pH

Fini

shed

pH

va

riatio

nFi

nish

ed

Alk

alin

ityP

O4

Add

ition

Adv

isor

y O

utpu

ts

Red

<7.5

<40

No

CC

T-1

Gre

en7.

2 to

7.8

Yes

No

AO

Red

any

±1.0

or m

ore

Any

any

CC

T-2

Red

7.5

to 8

±0.5

<60

No

CC

T-1

Gre

en7.

5 to

8±0

.5<6

0Y

esN

o A

OG

reen

7.5

to 8

±0.5

≥60

No

No

AO

Red

≥7.8

any

any

Yes

CC

T-3

Gre

en≥8

.0±0

.5>4

0N

oN

o A

OP

O4

is N

ew

Pra

ctic

e

Yes

CC

T-4

CC

T-1

Ele

vate

d ta

p w

ater

lead

leve

ls a

nd/o

r dis

colo

red

wat

er m

ay re

sult

from

inad

equa

te c

orro

sion

con

trol t

reat

men

t.

CC

T-2

CC

T-3

Orth

opho

spha

te c

orro

sion

inhi

bito

rs a

re le

ss e

ffect

ive

at p

H le

vels

abo

ve 7

.8.

gy

unle

ss p

re-o

xida

tion

is p

ract

iced

ahe

ad o

f sed

imen

tatio

n; o

r

Ele

vate

d ta

p w

ater

lead

leve

ls a

nd/o

r dis

colo

red

wat

er m

ay re

sult

from

inco

nsis

tent

pH

in th

e di

strib

utio

n sy

stem

. E

xplo

re h

ow to

min

imiz

e fin

ishe

d w

ater

pH

va

riatio

ns.


Nan

ofilt

ratio

n an

d R

O

Wat

er Q

ualit

y Pa

ram

eter

s In

put:

Cur

rent

fini

shed

wat

er p

H, c

urre

nt fi

nish

ed w

ater

alk

alin

ity, s

ourc

e w

ater

bar

ium

(Y/N

), so

urce

wat

er s

ilica

(Y/N

)

Adv

isor

y O

uput

Bar

ium

= Y

esB

ariu

m s

alts

can

cau

se s

igni

fican

t fou

ling

and/

or re

duce

sys

tem

reco

very

. V

erify

app

licat

ion

pote

ntia

l with

equ

ipm

ent m

anuf

actu

rer/s

uppl

ier.

Sili

ca =

Yes

Sili

ca c

an c

ause

sig

nific

ant f

oulin

g an

d/or

redu

ce s

yste

m re

cove

ry.

Ver

ify a

pplic

atio

n po

tent

ial w

ith e

quip

men

t man

ufac

ture

r/sup

plie

r.

RES

IDU

ALS

DIS

POSA

L (B

RIN

E/C

ON

CEN

TRA

TE)

Inpu

ts: M

embr

ane

feed

TD

S, m

embr

ane

feed

chl

orid

e, %

reco

very

, dis

posa

l met

hod,

effl

uent

TD

S an

d ch

lorid

e lim

its fo

r oce

an, r

iver

/str

eam

, and

/or s

ewer

dis

char

ge, d

eep

wel

l inj

ectio

n,dr

ying

bed

s

O

ther

NPD

ES li

mita

tions

Cal

cula

te T

DS

and

chlo

ride

in b

rine/

conc

entr

ate

- ass

ume

a co

nser

vativ

e va

lue

of 9

9% re

ject

ion

for b

oth.

Cal

cula

tion

for s

pent

rege

nera

nt :

Feed

Con

cent

ratio

n / (

1 - %

reco

very

)

Dis

posa

l Met

hod

TDS

in s

pent

re

gene

rant

> TD

S L

imit

TDS

in s

pent

re

gene

rant

< TD

S L

imit

Cl- in

spe

nt

rege

nera

nt>

Cl-

Lim

it

Cl- in

spe

nt

rege

nera

nt <

Cl-

Lim

itO

cean

Red

1G

reen

Red

2G

reen

For a

ll:

Riv

er/S

tream

Red

1,3

Gre

enR

ed2,

3G

reen

Sew

erR

ed1,

4G

reen

Red

2,4

Gre

en

Dee

p W

ell I

njec

tion

Gre

enG

reen

Gre

enG

reen

Dry

ing

Bed

sG

reen

Gre

enG

reen

Gre

en

4)

Con

firm

loca

l dis

char

ge li

mita

tions

.

CO

RR

OSI

ON

CO

NTR

OL/

DS

SCA

LE S

TAB

ILIT

Y In

puts

: Fin

ishe

d w

ater

pH

, fin

ishe

d w

ater

alk

alin

ity, f

inis

hed

wat

er L

SI, f

inis

hed

wat

er C

CPP

Ort

hoph

osph

ate

use

(Y/N

), Pi

pe m

ater

ials

(Asb

esto

s ce

men

t, ce

men

t-mor

tar-

lined

duc

tile

iron,

duc

tile

iron,

un-

lined

cas

t iro

n, p

last

ic, l

ead

serv

ice

lines

- se

lect

all

that

app

ly)

Cal

cula

te: D

pH, D

ALK

LSI

pH <

7.5

7.5

<= p

H <

= 8.

5pH

>8.

5<0

Red

1R

ed1

Red

1

0< L

SI <

0.2

Red

2R

ed2

Gre

en

0.2

< LS

I < 0

.5R

ed2

Gre

enG

reen

Gre

en -

Pot

entia

l for

cor

rosi

on re

late

d pr

oble

ms

due

to p

H a

nd L

SI c

ombi

natio

n sp

ecifi

ed is

min

imal

.LS

I > 0

.5G

reen

Gre

enG

reen

Fini

shed

wat

er p

HO

rtho

= Y

esD

pH <

-20%

, Orth

o =N

oD

pH >

-20%

, Orth

o =

No

< 7.

5R

ed1

Red

2G

reen

7.5

< pH

< 8

.0G

reen

Red

2G

reen

Gre

en -

Pot

entia

l for

cor

rosi

on re

late

d pr

oble

ms

resu

lting

from

cha

nge

in p

H is

min

imal

.pH

> 8

.0G

reen

Red

2G

reen

Cur

rent

fini

shed

wat

er

Alk

alin

ityO

rtho

= Y

es∆

ALK

< 0

%∆

ALK

> 0

%∆

ALK

< -

15%

∆ A

LK >

- 15

%D

ALK

< -1

0 m

g/L

D A

LK >

- 10

mg/

L

< 15

mg/

L as

CaC

O3

Gre

enR

edG

reen

For a

ll:

15 m

g/L

<= A

LK <

= 80

mg/

LG

reen

Red

Gre

en

> 80

mg/

LG

reen

Red

Gre

en

CC

PP

Orth

o =

Yes

A-C

Pip

e =

Yes

Unl

ined

-CIP

=Y

esLS

L =

Yes

, Orth

o =

No

Cem

ent-M

orta

r lin

ed

DIP

= Y

ES

< 4

Gre

en*

(onl

y if

pipe

=

Pla

stic

or d

uctil

e iro

n)R

ed1

Red

2R

ed3

Red

1

4 <

CC

PP

< 1

0G

reen

*G

reen

*G

reen

*G

reen

*G

reen

**

Pot

entia

l for

cor

rosi

on-r

elat

ed p

robl

ems

as a

resu

lt of

CC

PP

is m

inim

al.

CC

PP

> 1

0G

reen

Gre

enG

reen

Gre

enG

reen

CC

PP

is h

ighe

r tha

n op

timal

rang

e fo

r cor

rosi

on c

ontro

l. E

xces

sive

car

bona

te p

reci

ptita

tion

and

scal

ing

may

occ

ur.

Red

- 1)

TD

S c

once

ntra

tion

in re

ject

stre

am e

xcee

ds a

llow

able

dis

char

ge li

mits

.

2) C

hlor

ide

conc

entra

tion

in re

ject

stre

am e

xcee

ds a

llow

able

dis

char

ge li

mits

.

3) D

ispo

sal t

o riv

ers

and

stre

ams

is d

epen

dent

upo

n flo

ws

avai

labl

e fo

r dilu

tion.

Con

firm

that

the

limit

e

nter

ed a

ccou

nts

for d

ilutio

n by

the

river

/stre

am.

Gre

en -

Bas

ed o

n th

e op

tions

and

crit

eria

sel

ecte

d, re

ject

dis

posa

l is

perm

issi

ble.

Ple

ase

conf

irm w

ith th

e ap

prop

riate

Sta

te o

r lo

cal a

genc

y.

Red

- 1)

Wat

er e

xibi

ting

nega

tive

LSI v

alue

hav

e th

e po

tent

ial t

o cr

eate

cor

rosi

on re

late

d pr

oble

m. 2

)Bas

ed o

n pH

and

LS

I val

ues

indi

cate

d, th

e po

tent

ial

for c

orro

sion

rela

ted

wat

er q

ualit

y pr

oble

m e

xist

.

Red

- 1)

Fin

ishe

d w

ater

pH

is o

utsi

de c

omm

on e

ffect

ive

rang

e fo

r orth

opho

spha

te e

ffect

iven

ess.

2) D

ecre

ase

in fi

nish

ed w

ater

pH

pre

sent

s th

e po

tent

ial

for c

orro

sion

-rel

ated

wat

er q

ualit

y pr

oble

ms.

Red

- D

ecre

ase

in fi

nish

ed w

ater

alk

alin

ity m

ay re

sult

in

corr

osio

n re

late

d pr

oble

ms.

Gre

en -

Pot

entia

l for

cor

rosi

on-r

elat

ed im

pact

s du

e to

ch

ange

in fi

nish

ed w

ater

alk

alin

ity is

min

imal

.

Cal

cula

tion

for ∆

pH: (

finis

hed

wat

er p

H -

Cur

rent

fini

shed

wat

er p

H)/C

urre

nt fi

nish

ed w

ater

pH

, ∆A

LK: (

fini

shed

wat

er a

lkal

inity

- C

urre

nt fi

nish

ed w

ater

alk

alin

ity)/C

urre

nt fi

nish

ed w

ater

alk

alin

ity ,

D A

LK :

finis

hed

wat

er a

lkal

inity

- C

urre

nt fi

nish

ed w

ater

al

kalin

ity

CC

PP

is o

utsi

de o

ptim

al ra

nge

for c

orro

sion

con

trol.

1) D

eter

iora

tion

of c

emen

t pip

es a

nd li

ning

s is

po

ssib

le.

2) T

here

is a

n in

crea

sed

pote

ntia

l for

red

wat

er o

ccur

renc

e in

are

as s

erve

d by

un-

lined

cas

t iro

n pi

pe. 3

) Inc

reas

ed le

ad c

orro

sion

is p

ossi

ble.


Ion

exch

ange

and

ads

orpt

ion

Trea

tmen

t Pro

cess

Par

amet

ers

Inpu

t: Tr

eatm

ent p

roce

ss (i

on e

xcha

nge

or a

dsor

ptio

n), e

xcha

nge

type

(if "

proc

ess

= io

n ex

chan

ge",

ani

on/c

atio

n,an

ion-

catio

n)

CO

RR

OSI

ON

CO

NTR

OL/

DS

SCA

LE S

TAB

ILIT

Y (

This

onl

y ap

plie

s to

ani

on e

xcha

nge

proc

esse

s - n

ot a

dsor

ptio

n.)

Inpu

t: %

of f

low

trea

ted,

feed

wat

er h

ardn

ess,

feed

wat

er c

hlor

ide(

mg/

L), f

eed

wat

er s

ulfa

te(m

g/L)

Cal

cula

te:

Initi

al C

l:SO

4, fi

nal C

l:SO

4, %

Cl:S

O4

Cl:S

O4

< 0.

58 §

Cl/S

O4

> 0.

58 §

<20%

Gre

enG

reen

>20%

Gre

enR

ed

NIT

RO

SAM

INES

REL

EAS E

Inpu

t: R

esin

Typ

e, P

rech

lorin

e (y

/n),

Initi

al R

inse

(y/n

)

Res

in T

ype

Pre

chlo

rine

Initi

al R

inse

War

ning

?

Type

IN

oY

esG

reen

Type

IY

esY

esG

reen

Type

IN

oN

oG

reen

Type

IY

esN

oG

reen

Type

IIN

oY

esR

ed

Type

IIY

esY

esR

ed

Type

IIN

oN

oG

reen

Type

IIY

esN

oR

ed

Gre

en -

Bas

ed o

n th

e re

sin

type

sel

ecte

d, n

itros

amin

es re

leas

e is

not

ant

icip

ated

to b

e a

conc

ern.

Ple

ase

conf

irm w

ith th

e re

sin

man

ufac

ture

r tha

t nitr

osam

ines

are

not

ant

icip

ated

to b

e a

prob

lem

und

er th

e se

lect

ed o

pera

ting

cond

ition

s.

Gre

en -

Bas

ed o

n th

e re

sin

type

sel

ecte

d, n

itros

amin

es re

leas

e is

not

ant

icip

ated

to b

e a

conc

ern.

Ple

ase

conf

irm w

ith th

e re

sin

man

ufac

ture

r tha

t nitr

osam

ines

are

not

ant

icip

ated

to b

e a

prob

lem

und

er th

e se

lect

ed o

pera

ting

cond

ition

s.

Inst

ruct

ions

to u

ser f

or e

xcha

nge

type

, "Fo

r har

dnes

s an

d ra

dium

rem

oval

, sel

ect "

catio

n ex

chan

ge",

for a

rsen

ic, p

erch

lora

te, n

itrat

e, n

atur

al o

rgan

ic m

atte

r, flu

orid

e, u

rani

um, a

nd s

elen

ium

, sel

ect "

anio

n ex

chan

ge".

Oth

erw

ise,

se

lect

the

exch

ange

type

you

are

inte

rest

ed in

eva

luat

ing.

"

Cal

cula

tion

- ini

tial C

L:SO

4 =

feed

wat

er c

hlor

ide/

feed

wat

er s

ulfa

te,fi

nal C

L co

ncen

trat

ion=

(fee

d w

ater

har

dnes

s *(

71/1

00)*

%tr

eate

d) +

(1- %

trea

ted)

* fee

d w

ater

chl

orid

e, fi

nal S

O4

conc

entr

atio

n =

(0.1

* fe

ed w

ater

sul

fate

* %tr

eate

d) +

((1

- %tr

eate

d)*f

eed

wat

er s

odiu

m),

final

CL:

SO4

(§) =

fina

l CL/

fina

l SO

4, %

CL:

SO4

= (fi

nal C

L:SO

4 - i

nitia

l CL:

SO4)

/initi

al C

L:SO

4

Gre

en (f

or a

ll) -

Bas

ed o

n th

e da

ta p

rovi

ded,

no

adve

rse

corr

osio

n-re

late

d im

pact

s ar

e an

ticip

ated

due

to th

e ch

ange

in

the

treat

ed w

ater

chl

orid

e-to

-sul

fate

mas

s ra

tio.Io

n ex

chan

ge p

roce

sses

can

cau

se s

igni

fican

t shi

fts in

the

treat

ed

wat

er c

hlor

ide-

to-s

ulfa

te m

ass

ratio

. In

crea

ses

in th

is ra

tio, i

n pa

rticu

lar,

have

bee

n de

mon

stra

ted

to c

ause

in

crea

ses

in c

onsu

mer

tap

lead

con

cent

ratio

ns.

Ple

ase

verif

y th

e im

pact

of t

he s

elec

ted

proc

ess

on th

e fin

ishe

d w

ater

chl

orid

e-to

-sul

fate

mas

s ra

tio.

Red

- Th

e tre

atm

ent a

nd w

ater

qua

lity

para

met

ers

ente

red

may

resu

lt in

a c

hang

e in

the

treat

ed w

ater

chl

orid

e-to

-su

lfate

mas

s ra

tio th

at h

as b

een

dem

onst

rate

d to

cau

se a

n in

crea

se in

con

sum

er ta

p le

ad c

once

ntra

tion

in s

ever

al

natio

nal c

ase

stud

ies.

Con

sult

with

a re

sin

man

ufac

ture

r to

conf

irm th

e re

sults

.

Gre

en -

Bas

ed o

n th

e re

sin

type

sel

ecte

d, n

itros

amin

es re

leas

e is

not

ant

icip

ated

to b

e a

conc

ern.

Ple

ase

conf

irm w

ith th

e re

sin

man

ufac

ture

r tha

t nitr

osam

ines

are

not

ant

icip

ated

to b

e a

prob

lem

und

er th

e se

lect

ed o

pera

ting

cond

ition

s.

Gre

en -

Bas

ed o

n th

e re

sin

type

sel

ecte

d, n

itros

amin

es re

leas

e is

not

ant

icip

ated

to b

e a

conc

ern.

Ple

ase

conf

irm w

ith th

e re

sin

man

ufac

ture

r tha

t nitr

osam

ines

are

not

ant

icip

ated

to b

e a

prob

lem

und

er th

e se

lect

ed o

pera

ting

cond

ition

s.

Red

- Th

e us

e of

a p

re-o

xida

nt o

r ini

tial r

inse

of a

Typ

e II

ion

exch

ange

resi

n ha

s be

en d

emon

stra

ted

to

rele

ase

nitro

sam

ines

into

the

treat

ed w

ater

.

Red

- Th

e us

e of

a p

re-o

xida

nt o

r ini

tial r

inse

of a

Typ

e II

ion

exch

ange

resi

n ha

s be

en d

emon

stra

ted

to

rele

ase

nitro

sam

ines

into

the

treat

ed w

ater

.

Gre

en -

Bas

ed o

n th

e m

ode

of o

pera

tion,

nitr

osam

ines

rele

ase

is n

ot a

ntic

ipat

ed to

be

a co

ncer

n. P

leas

e co

nfirm

with

the

resi

n m

anuf

actu

rer t

hat n

itros

amin

es a

re n

ot a

ntic

ipat

ed to

be

a pr

oble

m u

nder

the

sele

cted

ope

ratin

g co

nditi

ons.

Red

- Th

e us

e of

a p

re-o

xida

nt o

r ini

tial r

inse

of a

Typ

e II

ion

exch

ange

resi

n ha

s be

en d

emon

stra

ted

to

rele

ase

nitro

sam

ines

into

the

treat

ed w

ater

.


RES

IDU

ALS

DIS

POSA

L (S

PEN

T B

RIN

E) In

puts

: Fee

d TD

S, fe

ed c

hlor

ide,

% fl

ow T

reat

ed, d

ispo

sal m

etho

d, e

fflue

nt T

DS

and

chlo

ride

limits

for o

cean

, riv

er/s

trea

m, a

nd/o

r sew

er d

isch

arge

, Dee

p W

ell I

njec

tion,

Dry

ing

beds

Dis

posa

l Met

hod

Feed

TD

S>

TDS

Lim

i tFe

ed T

DS

< TD

S L

imi t

feed

chl

orid

e>

Cl-

Lim

i tfe

ed c

hlor

ide

< C

l- Li

mi t

Oce

anR

ed1

Gre

enR

ed2

Gre

enFo

r all:

Riv

er/S

tream

Red

1,3

Gre

enR

ed2,

3G

reen

Red

- 1)

TD

S c

once

ntra

tion

in s

pent

rege

nera

nt e

xcee

ds a

llow

able

dis

char

ge li

mits

.

Sew

erR

ed1,

4G

reen

Red

2,4

Gre

en

2)

Chl

orid

e co

ncen

tratio

n in

spe

nt re

gene

rant

exc

eeds

allo

wab

le d

isch

arge

lim

its.

Dee

p W

ell I

njec

tion

Gre

enG

reen

Gre

enG

reen

Dry

ing

Bed

sG

reen

Gre

enG

reen

Gre

en

4)

Con

firm

loca

l dis

char

ge li

mita

tions

.

INC

REA

SE IN

FIN

ISH

ED W

ATE

R S

OD

IUM

(Cat

ion

exch

ange

onl

y)In

put:

% o

f flo

w tr

eate

d, fe

ed w

ater

sod

ium

Cal

cula

te:

Fina

l sod

ium

= (f

eed

hard

ness

* (4

6/10

0)* %

trea

ted)

+ ((

1- %

trea

ted)

* fee

d so

dium

Feed

sod

ium

Fi

nal S

odiu

m >

20

mg/

LFi

nal S

odiu

m <

20

mg/

L

> 20

mg/

LG

reen

1

< 20

mg/

LR

edG

reen

2

Gre

en: 2

) No

adve

rse

incr

ease

s to

the

finis

hed

wat

er s

odiu

m le

vel a

re a

ntic

ipat

ed a

t the

trea

tmen

t con

ditio

ns s

elec

ted.

U

SE

PA

has

est

ablis

hed

a D

rinki

ng W

ater

Equ

ival

ent L

evel

(or g

uida

nce

leve

l) of

20

mg/

L fo

r sod

ium

. Le

vels

abo

ve th

is

conc

entra

tion

may

affe

ct c

erta

in "a

t-ris

k" c

onsu

mer

s, in

clud

ing

thos

e w

ith h

eart

dise

ase

and/

or h

igh

bloo

d pr

essu

re.

Out

put a

dvis

ory

- all

resi

dual

s ou

tput

s. S

pent

rege

nera

nt w

aste

stre

ams

cont

aini

ng e

leva

ted

leve

ls o

f reg

ulat

ed c

onta

min

ants

may

be

subj

ect t

o st

ate,

loca

l, or

fede

ral i

ndus

trial

pre

-trea

tmen

t gu

idel

ines

prio

r to

disp

osal

. D

ispo

sal o

f spe

nt e

xcha

nge

med

ia c

onta

inin

g el

evat

ed le

vels

of r

egul

ated

con

tam

inan

ts m

ay b

e cl

assi

fied

as "h

azar

dous

was

te" a

nd re

quire

dis

posa

l in

a pe

rmitt

ed

land

fill.

3) D

ispo

sal t

o riv

ers

and

stre

ams

is d

epen

dent

upo

n flo

ws

avai

labl

e fo

r dilu

tion.

Con

firm

that

the

limit

ente

red

acco

unts

for d

ilutio

n by

the

river

/stre

am.

Gre

en -

Bas

ed o

n th

e op

tions

and

crit

eria

sel

ecte

d, s

pent

rege

nera

nt d

ispo

sal i

s pe

rmis

sibl

e. P

leas

e co

nfirm

with

the

appr

opria

te S

tate

or l

ocal

age

ncy.

Gre

en: 1

) The

feed

wat

er s

odiu

m c

once

ntra

tion

exce

eds

the

US

EP

A D

rinki

ng W

ater

Equ

ival

ence

Lev

el (i

.e.,

guid

ance

leve

l) fo

r sod

ium

. C

atio

n ex

chan

ge w

ill fu

rther

incr

ease

the

sodi

um c

once

ntra

tion

beyo

nd th

is le

vel.

Red

- U

nder

the

treat

men

t con

ditio

ns s

elec

ted,

the

finis

hed

wat

er s

odiu

m c

once

ntra

tion

is li

kely

to in

crea

se to

> 2

0 m

g/L.

U

SE

PA

has

est

ablis

hed

a D

rinki

ng W

ater

Equ

ival

ent L

evel

(or g

uida

nce

leve

l) of

20

mg/

L fo

r sod

ium

. Le

vels

abo

ve th

is

conc

entra

tion

may

affe

ct c

erta

in "a

t-ris

k" c

onsu

mer

s, in

clud

ing

thos

e w

ith h

eart

dise

ase

and/

or h

igh

bloo

d pr

essu

re.


Post-filtration GAC Contactors

SC Issue: Compromised Removal of Iron and ManganeseInput: Raw water iron concentration

Raw water manganese concentrationPresence of absence of pre-coagulation oxidationpH during coagulation/flocculation/sedimentationDetention time in coagulation/flocculation/sedimentation

√ Raw Water Iron Raw Water Manganese

Preoxidation Pretreatment pH

Pretreatment Time

Advisory Output

Red > 0.3 > 0.05 No Fe-Mn 1Green > 0.3 > 0.05 Yes No AOGreen > 0.3 > 0.05 No > 7.5 > 4 hours No AO

SC Issue: Need for additional permitting requirements for GAC furnace operationInput: Presence or absence of on-site reactivation furnace

Type of furnace used for reactivationPresence or absence of off-gas treatment unit

√ Reactivation Gas Furnace Off-gas Treatment

Advisory Output

Red On-site gas No React-1Red On-site electric No React-2Green On-site gas Yes React 3Green On-site electric Yes React 3Green Off-site No AO

SC Issue: Potential compromise in primary disinfectionInput: Primary disinfection placement

Primary disinfectant typeSecondary disinfection placementSecondary disinfectant type

Primary Disinfection Placement

Primary Disinfectant Type

Primary Disinfectant Residual on

GAC

Secondary Disinfection Placement

Secondary Disinfectant

TypeAdvisory Output

√ Green After Future GAC No AO

GreenBefore Future

GACOzone No After Future

GACNo AO

√ RedBefore Future

GACOzone No Before Future

GACChlorine PD-1


GACOzone No Before Future

GACChloramines PD-2

GreenBefore Future

GACOzone Yes After Future

GACPD-3


GACOzone Yes Before Future

GACChlorine PD-1 and PD-3


GACOzone Yes Before Future

GACChloramines PD-2 and PD-3

√ GreenBefore Future

GACChlorine Dioxide No After Future

GACPD-4



GACChlorine Dioxide No Before Future



GACChlorine Dioxide No Before Future



GACChlorine Dioxide Yes After Future

GACPD-5


GACChlorine Dioxide Yes Before Future



GACChlorine Dioxide Yes Before Future


√ GreenBefore Future

GACChlorine No After Future

GACNo AO


GACChlorine No Before Future

GACChlorine PD-1


GACChlorine No Before Future

GACChloramines PD-2


GACChlorine Yes After Future

GACPD-6


GACChlorine Yes Before Future



GAC Chlorine Yes Before Future GAC Chloramines PD-2 and PD-6

SC Issue: Sloughing off of bacteria from GAC contactorInput: Secondary disinfection placement

Secondary disinfectant type

√


Secondary Disinfectant Type

Free Chlorine Contact Time

Advisory Output

RedBefore Future

GACHPC 1

RedBefore Future

GACChlorine HPC 1

RedBefore Future

GACChloramines HPC 1

Green After Future GAC No AO

Green After Future GAC Chlorine No AO

Red After Future GAC Chloramines < 5 min HPC 2

SC Issue: LT2 Rule ComplianceInput: Percent of Flow Treated

√

Percent of Flow to Post filter GAC Contactor

Advisory Output

Red <100% Crypto 1

Advisory Outputs:

Fe-Mn 1

React 1

React 2

React 3

Discontinuation of pre-oxidation practice may compromise removal of Fe and/or Mn at coagulation pH below 7.5 and pretreatment times less than 4 hours. Iron and/or manganese may pass through the plant and create coloroed water complaints in the distribution system. Implementation of a preoxidation step before filtration may be needed to avoid potential colored water complaints from the distribution system.

Use of gas furnace for reactivation and not having any off-gas treatment system may pose significant challenge in obtaining air emission permit for the reactivation facility. Installing off-gas treatment system may be necessary based upon direction from local permitting authority.Use of electric furnace for reactivation and not having any off-gas treatment system may pose some challenge in obtaining air emission permit for the reactivation facility, although the emissions are expected to be less severe compared to the use of gas furnace. Installing off-gas treatment system may be necessary based upon direction from local permitting authority.An air-emission permit may be required. The presence of an off-gas treatment system improves the chances of obtaining a permit from local permitting authority.


PD 1

PD 2

PD 3

PD 4

PD 5

PD 6

HPC 1

HPC 2

Crypto 1

Residual chloramines will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Residual ammonia from the prior chloramine application may promote nitrification in the GAC contactors. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water.

One of the microbial toolbox options under the LT2 Rule allows for an additional 0.5-log cryptosporidium removal credit for sequential disinfection with a second granular media filtration options. Post filter GAC contactors may qualify for this credit.

Ozone residual present in the water applied to GAC will be removed by the GAC which may result in the depletion of biological activity on the top layers of the media.Chlorite residual from prior chlorine dioxide application will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.Chlorine dioxide residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in the formation of additional chlorite concentration. Residual chlorine dioxide may also reduce biological activity in the top layers of the contactor. Chlorite residual will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.Chlorine residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in a reduction of biological activity in the top layers of the contactor and some degradation of adsorption capacity of the GAC.

Residual disinfecant will be depleted in the GAC contactor which may result in biological activity in the filter media. Depending on the extent of growth, bacteria may slough off in to the effluent water from the GAC contactor. Residual disinfectant need to be applied after GAC contactors.

Biological activity in the filter media may cause some bacteria to slough off in to the effluent water from the GAC contactor. May need to consider adding at least 5 minutes of free chlorine contact time prior to ammonia addition to effectively control the HPC bacterial that may slough off from the GAC contactors.

Residual chlorine will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water.


GAC Filter AdsorberSC Issue: Compromised Removal of Iron and ManganeseInput: Raw water iron concentration


Raw Water Iron Raw Water Manganese Preoxidation Pretreatment

pHPretreatment

Time Advisory Output


SC Issue: Need for additional permitting requirements for GAC furnace operationInput: Presence or absence of on-site reactivation furnace

Type of furnace used for reactivationPresence or absence of off-gas treatment unit

Reactivation Gas Furnace Off-gas Treatment

Advisory Output

Red On-site gas No React-1Red On-site electric No React-2Green On-site gas Yes React 3Green On-site electric Yes React 3Green Off-site No AO






GAC



TypeAdvisory Output

Green After Filters No AOGreen Before Filters Ozone No After Filters No AORed Before Filters Ozone No Before Filters Chlorine PD-1Red Before Filters Ozone No Before Filters Chloramines PD-2Green Before Filters Ozone Yes After Filters PD-3Red Before Filters Ozone Yes Before Filters Chlorine PD-1 and PD-3Red Before Filters Ozone Yes Before Filters Chloramines PD-2 and PD-3Green Before Filters Chlorine Dioxide No After Filters PD-4Red Before Filters Chlorine Dioxide No Before Filters Chlorine PD-1 and PD-4Red Before Filters Chlorine Dioxide No Before Filters Chloramines PD-2 and PD-4Red Before Filters Chlorine Dioxide Yes After Filters PD-5Red Before Filters Chlorine Dioxide Yes Before Filters Chlorine PD-1 and PD-5Red Before Filters Chlorine Dioxide Yes Before Filters Chloramines PD-2 and PD-5Green Before Filters Chlorine No After Filters No AORed Before Filters Chlorine No Before Filters Chlorine PD-1Red Before Filters Chlorine No Before Filters Chloramines PD-2Red Before Filters Chlorine Yes After Filters PD-6Red Before Filters Chlorine Yes Before Filters Chlorine PD-1 and PD-6Red Before Filters Chlorine Yes Before Filters Chloramines PD-2 and PD-6







Advisory Output

Red Before Filters HPC 1Red Before Filters Chlorine HPC 1Red Before Filters Chloramines HPC 1Green After Filters No AOGreen After Filters Chlorine No AORed After Filters Chloramines < 5 min HPC 2

SC Issue: Practical limitation to GAC replacementInput: Empty Bed Contact Time

EBCTAdvisory Output

Green > 10 min No AOGreen 5 to 10 min PL 1Red < 5 mn PL 2

Advisory Outputs:

Fe-Mn 1

React 1

React 2

React 3

PD 1

PD 2

PD 3

PD 4

PD 5

PD 6

HPC 1

HPC 2

Residual chloramines will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Residual ammonia from the prior chloramine application may promote nitrification in the GAC contactors. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water. Ozone residual present in the water applied to GAC will be removed by the GAC which may result in the depletion of biological activity on the top layers of the media.Chlorite residual from prior chlorine dioxide application will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.Chlorine dioxide residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in the formation of additional chlorite concentration. Residual chlorine dioxide may also reduce biological activity in the top layers of the contactor. Chlorite residual will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.

Chlorine residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in a reduction of biological activity in the top layers of the contactor and some degradation of adsorption capacity of the GAC.




Use of gas furnace for reactivation and not having any off-gas treatment system may pose significant challenge in obtaining air emission permit for the reactivation facility. Installing off-gas treatment system may be necessary based upon direction from local permitting authority.Use of electric furnace for reactivation and not having any off-gas treatment system may pose some challenge in obtaining air emission permit for the reactivation facility, although the emissions are expected to be less severe compared to the use of gas furnace. Installing off-gas treatment system may be necessary based upon direction from local permitting authority.An air-emission permit may be required. The presence of an off-gas treatment system improves the chances of obtaining a permit from local permitting authority.Residual chlorine will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water.


PL 1

PL 2

Depending on the concentration level of the target compound in the settled water, the GAC replacement frequency may be too high making the reliance on adsorption process for removal impractical. Generally a minimum interval of 90 to 120 days between replacement is considered acceptable

The Empty Bed Contact Time in the filter adsorber may be too short for practical use of adsorption process. However, biological removal on this type of shallow filter adsorbers may still occur and could possibly relied upon for biolgical stabilization of the treated water and some small degreee of removal of some of this contaminants.


Non GAC Biological Filters




pHPretreatment








Filters



TypeAdvisory Output

Green After Filters No AOGreen Before Filters Ozone No After Filters No AORed Before Filters Ozone No Before Filters Chlorine PD-1Red Before Filters Ozone No Before Filters Chloramines PD-2Green Before Filters Ozone Yes After Filters PD-3Red Before Filters Ozone Yes Before Filters Chlorine PD-1 and PD-3Red Before Filters Ozone Yes Before Filters Chloramines PD-2 and PD-3Green Before Filters Chlorine Dioxide No After Filters No AORed Before Filters Chlorine Dioxide No Before Filters Chlorine PD-1Red Before Filters Chlorine Dioxide No Before Filters Chloramines PD-2Red Before Filters Chlorine Dioxide Yes After Filters PD-4Red Before Filters Chlorine Dioxide Yes Before Filters Chlorine PD-1 and PD-4Red Before Filters Chlorine Dioxide Yes Before Filters Chloramines PD-2 and PD-4Green Before Filters Chlorine No After Filters No AORed Before Filters Chlorine No Before Filters Chlorine PD-1Red Before Filters Chlorine No Before Filters Chloramines PD-2Red Before Filters Chlorine Yes After Filters PD-6Red Before Filters Chlorine Yes Before Filters Chlorine PD-1 and PD-6Red Before Filters Chlorine Yes Before Filters Chloramines PD-2 and PD-6






Advisory Output



Advisory Outputs:

Fe-Mn 1

PD 1

PD 2

PD 3

PD 4

PD 6

HPC 1

HPC 2




Considerations for moving the point of secondary disinfectant to after biological filters will be necessary to allow biological activity on the filter media and to maintain residual in the distributed water.

Residual ammonia from the prior chloramine application may promote nitrification in the filters. Considerations for moving the point of secondary disinfectant to after filters will be necessary to maintain residual in the distributed water.

Ozonation prior to filter adsorber is expected to enhance biological activity in the filters improving removal through biodegradation. A positive residual of ozone in the water applied to the filters may cause some depletion of biological activity on the top layers of the media.A positive residual of chlorine dioxide in the water applied to the filters may cause some depletion of biological activity on the top layers of rhe media.Chlorine residual present in the water applied to the filters will reduce biological activity in the top layers of the media.


MF-

UF

Preo

xida

tion

Prac

tices

Will

pre

-oxi

datio

n be

pra

ctic

ed?

Pre

-oxi

dant

=

chlo

rine?

Pre

-oxi

dant

= o

ther

pr

e-ox

idan

t?G

reen

No

n/a

n/a

Gre

enY

esY

esN

oA

chl

orin

e -r

esis

tant

mem

bran

e m

ust b

e sp

ecifi

ed.

Gre

enY

esN

oY

esV

erify

that

sel

ecte

d m

embr

ane

is c

ompa

tible

with

pre

-oxi

dant

dos

ages

.

Coa

gula

tion

Prac

tices

Ahe

ad o

f Mem

bran

es

Met

al s

alt

coag

ulan

t do

sage

?

Wou

ld a

n iro

n sa

lt co

agul

ant b

e us

ed?

Wou

ld a

syn

thet

ic

orga

nic

poly

mer

be

used

as

a co

agul

ant

or c

oagu

lant

aid

?

Gre

en>2

0 m

g/L

No

No

Gre

en0-

20 m

g/L

No

No

Gre

en0-

20 o

r > 2

0 m

g/L

Yes

No

Red

0- 2

0 m

g/L

No

Yes

Red

>20

mg/

LN

oY

es

Adv

isor

y: H

igh

met

al s

alt c

oagu

lant

dos

ages

may

requ

ire a

dditi

onal

m

embr

ane

pre-

treat

men

t to

mai

ntai

n ac

cept

able

flux

rate

s.

Adv

isor

y: I

ron

salt

coag

ulan

ts c

an p

rom

ote

foul

ing

of s

ome

mem

bran

es.

Hig

h co

agul

ant d

osag

es u

sage

may

requ

ire a

dditi

onal

mem

bran

e pr

e-tre

atm

ent t

o m

aint

ain

acce

ptab

le fl

ux ra

tes.

Adv

isor

y: S

ynth

etic

pol

ymer

s m

ay re

duce

mem

bran

e flu

x ra

tes,

cau

se

irrev

ersi

ble

foul

ing,

or o

ther

wis

e be

inco

mpa

tible

with

spe

cific

mem

bran

es.

Con

side

r alte

rnat

e co

agul

atio

n ap

proa

ches

.

Adv

isor

y: S

ynth

etic

pol

ymer

s m

ay re

duce

mem

bran

e flu

x ra

tes,

cau

se

irrev

ersi

ble

foul

ing,

or o

ther

wis

e be

inco

mpa

tible

with

spe

cific

mem

bran

es.

Con

side

r alte

rnat

e co

agul

atio

n ap

proa

ches

. H

igh

met

al s

alt c

oagu

lant

do

sage

s m

ay re

quire

add

ition

al p

re-tr

eatm

ent t

o m

aint

ain

acce

ptab

le fl

ux

rate

s.


Perf

orm

ance

of M

embr

anes

Alg

ae L

evel

s?S

ilica

Ent

rain

ed A

ir

Red

med

.or h

igh

>50

Yes

Any

one

fact

or s

houl

d ca

use

a re

d.

Pre

treat

men

t for

al

gae

rem

oval

m

ust b

e pr

ovid

ed

to a

void

un

acce

ptab

le

mem

bran

e fo

ulin

g.

Coa

gula

tion

or o

ther

si

lica

rem

oval

mus

t be

pro

vide

d ah

ead

of m

embr

anes

to

avoi

d un

acce

ptab

le

foul

ing.

Sul

fer r

emov

al

shou

ld b

e pr

ovid

ed

ahea

d of

m

embr

anes

to a

void

un

acce

ptab

le

foul

ing.

Air

strip

ping

or d

iffus

ed

aera

tion

shou

ld b

e pr

ovid

ed a

head

of

mem

bran

es to

avo

id

unac

cept

able

foul

ing.

Adv

isor

y O

utpu

t Bul

lets

(Alw

ays

show

in o

utpu

t)M

F/U

F pr

ovid

es fo

r hig

h re

mov

al o

f pro

tozo

an p

atho

gens

and

bac

teria

. M

F/U

F di

sinf

ectio

n cr

edit

unde

r LT2

ES

WTR

may

allo

w fo

r low

er d

isin

fect

ant d

osag

es a

nd re

duce

d D

BP

form

atio

n.

Util

ities

sho

uld

cont

act t

heir

stat

e pr

imac

y ag

ency

for s

peci

fic M

F/U

F de

sign

, dem

onst

ratio

n an

d op

erat

or tr

aini

ng re

quire

men

ts.

Dis

solv

ed c

onst

ituen

ts in

MF/

UF

feed

wat

er, s

uch

as ir

on a

nd m

anga

nese

, may

requ

ire p

re-tr

eatm

ent t

o pr

even

t ser

ious

mem

bran

e fo

ulin

g. I

t is

reco

mm

ende

d th

at u

tiliti

es c

onsu

lt w

ith m

ultip

le m

embr

ane

equi

pmen

t sup

plie

rs to

det

erm

ine

appr

opria

te p

re-tr

eatm

ent r

equi

rem

ents

.

MF/

UF

gene

rally

pro

duce

s m

ore

was

hwat

er re

sidu

als

than

gra

nula

r med

ia fi

ltrat

ion.

The

impa

ct o

f MF/

UF

on re

sidu

als

quan

tity,

cha

ract

er a

nd re

cycl

e pr

actic

es s

houl

d be

car

eful

ly

eval

uate

d.


Enhanced Coagulation

Stage 1 D/DBP Rule Enhanced Coagulation RequirmentsInputs: RW TOC; RW Alkalinity; Expected % Reduction in TOC

RW TOC RW AlkalinityExpected TOC % reduction Advisory Outputs

Green <2.0 any any EC-1Green 2 to 4.0 <60 35 or more EC-2Green 2 to 4.0 60 to <120 25 or more EC-2Green 2 to 4.0 ≥120 15 or more EC-2Green >4 to 8 <60 45 or more EC-2Green >4 to 8 60 to <120 35 or more EC-2Green >4 to 8 ≥120 25 or more EC-2Green >8 <60 50 or more EC-2Green >8 60 to <120 40 or more EC-2Green >8 ≥120 30 or more EC-2

Red 2 to 4.0 <60 <35 EC-3Red 2 to 4.0 60 to <120 <25 EC-3Red 2 to 4.0 ≥120 <15 EC-3Red >4 to 8 <60 <45 EC-3Red >4 to 8 60 to <120 <35 EC-3Red >4 to 8 ≥120 <25 EC-3Red >8 <60 <50 EC-3Red >8 60 to <120 <40 EC-3Red >8 ≥120 <30 EC-3

Removal of Iron and Manganese - Input: Raw Water Iron and Manganese, pH, Preoxidation

Raw Water Iron Raw Water Manganese

Stopped Preoxidation Due to EC

Pretreatment pH Pretreatment Time

Red > 0.3 > 0.05 Yes Fe-Mn1-1Green > 0.3 > 0.05 No No outputGreen > 0.3 > 0.05 Yes > 7.5 > 4 hours No output

Finished pH Finished pH variation

Finished Alkalinity

PO4Addition

Red <7.5 <40 No CCT-1Green 7.2 to 7.8 Yes No AO

Red any ±1.0 or more Any any CCT-2Red 7.5 to 8 ±0.5 <60 No CCT-1Green 7.5 to 8 ±0.5 <60 Yes No AO

Green 7.5 to 8 ±0.5 ≥60 No No AO

Red ≥7.8 any any Yes CCT-3Green ≥8.0 ±0.5 >40 No No AO

Residuals Handling Inputs: Change in Primary Coagulant Dosage; Change in Coagulant; Arsenic in RW; Rads in RW

Change in Coagulant?

Change in Coagulant Dose

Green Yes < 20% RH-1, RH-3Red No > 20% RH-2, RH-3Red Yes > 20% RH-1, RH-2, RH-3

As in RW? Rads in RW?Red Yes RH-4Red Yes RH-5

Residual Aluminum in Finished Water

Coagulant

Coagulation/-Filtration pH

Red Alum < 6.5 Al-1Red PACl < 6.2 Al-1

Inplant CorrosionCoagulation/Filtr

ation pHGreen < 6.2 IPC-1

Removal of Arsenic Inputs: As; Primary Coagulant?; Primary Coagulant Dosage?

As in RW?Primary

Coagulant?Coagulant Dosage?

Red Yes Alum or PACl < 25 As-1Green Yes Alum or PACl >25 As-2Red Yes Ferric <10 As-3Green Yes Ferric >10 As-4Green No No Output

Advisory Outputs

Corrosion Control Treatment/Scale stability - Inputs: Finished water pH, Weekly variation in Finished Water pH, finished water alkalinity, PO4 additionAdvisory Outputs

Advisory Commentary

Advisory Outputs


Advisory Outputs Coded to UC/Issues above:

EC-1 Stage 1 DBP Rule enhanced coagulation requirements do not apply when TOC is less than 2.0 mg/L.EC-2 Expected TOC removal meets Stage 1 DBPR enhanced coagulation requirements.EC-3 Expected TOC removal inconsistent with Stage 1 DBPR enhanced coagulation requirements.

FeMn-1

CCT-1 Elevated tap water lead levels and/or discolored water may result from inadequate corrosion control treatment.CCT-2 Elevated tap water lead levels and/or discolored water may result from inconsistent pH in the distribution system. Explore how to minimize finished water pH variations.CCT-3 Orthophosphate corrosion inhibitors are less effective at pH levels above 7.8.

Al-1 Residual aluminum in filtered water may cause post-precipitation of aluminum hydroxide. Consider increasing pH upstream of filtration.IPC-1 A coagulation/filtration pH less than 6.5 may accelerate corrosion of plant infrastructure.

RH-1 Switching metal salt coagulants may change residuals quantity/characteristics and impact dewatering or disposal.RH-2 Additional solids handling and dewatering may be required.RH-3 Corrosion control treatment may need to be re-optimized.RH-4 Arsenic content of residuals may pase a disposal problem. Consult with solids disposal regulations in your area.RH-5 Radionuclide content of residuals may pose a disposal problem. Consult with solids disoposal regulation in your area.

As-1 Alum or PACl dosage may not be sufficient for As removal.As-2 Alum or PACl dosage may provide up to 60-80% removal of As.As-3 Ferric dosage may not be sufficient for As removal As-4 Ferric dosage may provide up to 60-80% removal of As

General Advisory Outputs that always come up for Enhanced Coagulation

Any reduction in finished water pH may cause disruption or dislodgement of protective pipe scales, and could lead to temporary red water or dirty water complaints.Changing coagulants can change chloride-to-sulfate ratios and may negatively impact lead and copper corrosion in some systems.

Consider desk-top or demonstration studies as necessary to re-optimize corrosion control treatment whenever coagulation practices are significantly changed.Initiation of phosphate corrosion inhibitor addition will impact regional wastewater treatment facilities, and may cause temporary upsets in scale stability, red water occurrence,

Enhanced coagulation may alter DBP speciation. While beneficial for DBP control, enhanced coagulation may diminish that degree of corrosion inhibition sometimes provided by NOM.

Discontinuation of pre-oxidation practice may compromise removal of Fe and/or Mn at coagulation pH below 7.5 and pretreatment times less than 4 hours.


Chlorine Dioxide

SC Issue: Violation of chlorite MCLInput: Chlorine dioxide dose

Chlorine dioxide application locationApplication of Ferrous salts

ClO2 Feed Location ClO2 Dose Ferrrous Salt

ApplicationAdvisory Output

Green < 1.4 mg/L No AOGreen Before Coag. > 1.4 mg/L Yes No AORed > 1.4 mg/L No Chlorite 1

SC Issue: Formation of THM/HAA from free chlorine present as impurity in ClO2Input: ClO2 generation yield

ClO2 Generation Yield Advisory Output

Green > 95% No AORed < or = 95% DBP 1

Advisory Outputs:

Chlorite 1

DBP 1

Approximately 70 percent of the applied chlorine dioxide is converted to chlorite ion. The MCL for chlorite at 1.0 mg/L can be superceeded if ClO2 dose is higher than 1.4 mg/L. Higher than 1.4 mg/L of ClO2 can be applied if there is a provision to removal chlorite by the addition of a reducing agent such as ferrous sulfate.

A chlorine dioxide generation yield of less than or equal to 95 percent indicates that there will be significant amounts of free chlorine present in the chemical stream containing chlorine dioxide. The free chlorine could react with the NOM to form THMs and HAAs. Generator yield can be improved by adjusting the ratio of sodium chlorite and chlorine.


Chlorine Optimization




pHPretreatment








GAC



TypeAdvisory Output

Green After Filters No AOGreen Before Filters Ozone No After Filters No AORed Before Filters Ozone No Before Filters Chlorine PD-1Red Before Filters Ozone No Before Filters Chloramines PD-2Green Before Filters Ozone Yes After Filters PD-3Red Before Filters Ozone Yes Before Filters Chlorine PD-1 and PD-3

Red Before Filters Ozone Yes Before Filters Chloramines PD-2 and PD-3Green Before Filters Chlorine Dioxide No After Filters PD-4Red Before Filters Chlorine Dioxide No Before Filters Chlorine PD-1 and PD-4Red Before Filters Chlorine Dioxide No Before Filters Chloramines PD-2 and PD-4Red Before Filters Chlorine Dioxide Yes After Filters PD-5Red Before Filters Chlorine Dioxide Yes Before Filters Chlorine PD-1 and PD-5Red Before Filters Chlorine Dioxide Yes Before Filters Chloramines PD-2 and PD-5Green Before Filters Chlorine No After Filters No AORed Before Filters Chlorine No Before Filters Chlorine PD-1Red Before Filters Chlorine No Before Filters Chloramines PD-2Red Before Filters Chlorine Yes After Filters PD-6Red Before Filters Chlorine Yes Before Filters Chlorine PD-1 and PD-6Red Before Filters Chlorine Yes Before Filters Chloramines PD-2 and PD-6

SC Issue: Sloughing off of bacteria from Biologically Active FiltersInput: Secondary disinfection placement





Advisory Output



Advisory Outputs:

Fe-Mn 1

PD 1

PD 2

PD 3

PD 4

PD 5

PD 6

HPC 1

HPC 2Biological activity in the filter media may cause some bacteria to slough off in to the effluent water from the GAC contactor. May need to consider adding at least 5 minutes of free chlorine contact time prior to ammonia addition to effectively control the HPC bacterial that may slough off from the GAC contactors.


Residual chlorine will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water.Residual chloramines will be depleted in the GAC contactors which may also compromise adsorption capacity of the GAC media. Residual ammonia from the prior chloramine application may promote nitrification in the GAC contactors. Considerations for moving the point of secondary disinfectant to after GAC contactor will be necessary to maintain residual in the distributed water. Ozone residual present in the water applied to GAC will be removed by the GAC which may result in the depletion of biological activity on the top layers of the media.Chlorite residual from prior chlorine dioxide application will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.Chlorine dioxide residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in the formation of additional chlorite concentration. Residual chlorine dioxide may also reduce biological activity in the top layers of the contactor. Chlorite residual will be removed for a short period of time by the GAC contactors, however, a breakthrough of the chlorite concentration equal to the influent chlorite level may be reached prior to the exhaustion of adsorption capacity of the GAC for natural organic matter or other contaminants.

Chlorine residual present in the water applied to GAC will be depleted in the top layers of the GAC contactor resulting in a reduction of biological activity in the top layers of the contactor and some degradation of adsorption capacity of the GAC.



UV Disinfection



√ Raw Water Iron Raw Water Manganese Preoxidation Pretreatment

pHPretreatment

TimeAdvisory Output


Advisory Outputs:

Fe-Mn 1



UV AOP



√ Raw Water Iron Raw Water Manganese Preoxidation Pretreatment

pHPretreatment

TimeAdvisory Output


Advisory Outputs:

Fe-Mn 1



D-1

APPENDIX D

CAPTURING VARIABILITY IN SOURCE WATER


D-2

CAPTURING VARIABILITY IN SOURCE WATER ABSTRACT The water quality of surface waters demonstrates significant variability because of climatological, geological, and water management impacts. This variability poses problems for drinking water utilities that must meet rigorous finished water quality objectives. To this end, a K-NN bootstrap technique was developed to generate ensembles of influent water quality conditioned on a “feature vector” that includes annual average concentration and location, i.e., latitude and longitude. The main strength of the approach is its simplicity in implementation and the ability to use a large amount of spatial data with limited temporal data to provide variability estimates in time for a given location. The approach is applied to simulate monthly ensembles of total organic carbon for two utilities in the United States (U.S.) with very different watersheds and to alkalinity and bromide at two other U.S. utilities. The simulations provide a rich variability, capture the historical observations well, and are viewed in light of recent available data. INTRODUCTION Drinking water utilities face complex decisions when balancing new and changing regulatory requirements with competing finished water quality objectives. Tools are needed to help utilities better understand treatment plant performance in light of changing regulations. These tools must assess the influent water quality and treatment process performance. Influent water quality can demonstrate a high degree of variability, especially surface waters, which constitute a major source for most of the U.S. population, and can exert a major impact on finished water quality. Variability of most source water quality parameters is not normally distributed and typically is positively skewed (Helsel and Hirsch, 1995). To adequately capture influent water quality, approaches are needed that are probabilistic in nature with robust quantification of variability. Several existing databases contain national water quality information, however, most are limited in some way, e.g., sample location geography, historical timeframe, sample parameters, etc. (Frey et al., 2004). The USEPA’s ICR database is the most comprehensive drinking water-relevant dataset, yet it is limited to an 18 month sample period (McGuire et al., 2002). Traditional methods for modeling uncertainty involve fitting a probability density function (PDF) to the data and using it to simulate “scenarios” that capture the variability, i.e., the Monte Carlo approach. There is a very rich history of this approach especially in hydrology and water resources management (Rajagopalan and Lall, 1999; Rajagopalan, et al., 1997; and references therein). However, this traditional approach has several drawbacks including (i) limited choice of the PDF that can be fit to the data, especially if the data exhibits bimodal distribution, (ii) no choice other than normal distribution for more than one variable, (iii) not portable across sites – i.e, a single PDF cannot be prescribed for all the locations, and (iv) greatly influenced by outliers. Recent developments in nonparametric methods (see Lall, 1995, for an overview of these methods and their applications to hydrologic problems) alleviate these drawbacks to a large extent and offer an attractive alternative. Within this, the K-NN bootstrap technique (Lall and Sharma, 1996) and its variations have been developed and applied successfully to generate scenarios of daily weather (Yates et al., 2003; Buishand and Brandsma,


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2001; Rajagopalan and Lall, 1999) and streamflows (Lall and Sharma, 1996; Prairie et al., 2006, Grantz et al., 2005) for water management and salinity (Prairie et al., 2005). The objective of this paper is to develop a simple, robust, and flexible framework to generate influent water quality values and associated variability at locations that may have limited or no observed data. To this end, the K-NN bootstrap technique is adapted and developed to simulate influent water quality using the ICR database (USEPA, 2000). The ICR database has 18 monthly values covering July 1997 through December 1998. While this is temporally limiting for most traditional models, it is spatially robust with observations at 500 treatment plants within 296 water systems across the U.S. (McGuire et al., 2002). A strength of this approach is its ability to use the spatial information to capture the temporal variability, which can be challenging for conventional methods. To demonstrate the technique, the methodology is applied to two utilities in the U.S. with very different watersheds for TOC and to alkalinity and bromide at two other U.S. utilities. METHODOLOGY Any attempt to generate scenarios is a conditional PDF simulation problem. For example, a utility might be interested in generating an ensemble of TOC concentration values that capture the variability for a given month conditioned on a suite of variables such as the annual average TOC concentration and location, i.e., latitude and longitude. This can be represented by the generalized formula as

)(),()|(

yhyxfyxf r

rr= (1)

where ),( yxf r is the joint probability distribution and )(yh r is the marginal probability distribution. Here, x is the monthly TOC concentration and yr is the “feature vector” of conditioning variables – in this case the annual average TOC concentration, latitude, and longitude. For a given user-specified feature vector, K-nearest neighbors are identified from the database that are nearest in the feature vector space. For example, if the feature vector consisted of only latitude and longitude, then the K-nearest neighbors will be the physically closest locations. Distances between the user’s feature vector and all of the eligible entries in the ICR database are calculated using a Mahalanobis distance metric. This has an advantage over other distance metrics in that the components of the feature vector do not need to be scaled (Davis, 1986; Yates et al., 2003). Different weights can be applied to each variable of the feature vector, thus allowing flexibility in selection of the neighbors, and this is demonstrated in the following section. The K-nearest neighbors are then weighted so that the nearest neighbor (i.e. location) is given the maximum weight and the farthest the least. With these weights, one of the nearest neighbors is selected at random, i.e., bootstrapped. The monthly TOC value from the selected site forms a simulated value for the user’s location. This is repeated a large number of times for each month, thus generating an ensemble of TOC values. This nearest neighbor re-sampling technique is akin to fitting the conditional PDF in equation (1) and simulating from it. The only parameter required is ‘K’, the size of the neighborhood. There are several methods for selecting this, but the heuristic rule, NK = , where N is the number of data points, with its theoretical justifications (Fukunaga, 1990; Lall and Sharma, 1996) seems to work well and has been used by all the earlier applications in the aforementioned references. The steps of the algorithm for implementation are provided below.


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Suppose a user wishes to generate ensembles of TOC concentration for all the months at a given location with surface source water, also suppose that there are m surface source water data available (from the ICR database). In this application, source waters are included only if eight or more months of data from 1998 exists, and annual average values are calculated from that monthly 1998 data. The “feature vector” consists of p(=3) variables which are, TOC = annual average TOC, Lat = latitude, and Lon = longitude. The steps of the algorithm are as follows:

1. The user will input the feature vector values for the p variables. In this case the feature vector is:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

user

user

user

user

LonLat

TOCy

2. The feature vector of all m source waters from the ICR database is constructed as:

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=

mmm

iiiICR

LonLatTOC

LonLatTOC

LonLatTOC

y.........

.........111

The user’s feature vector can be one of the entries in the ICRy matrix if the data at the user location is to be included.

3. The covariance matrix, S, of the ICRy matrix is computed.

4. Weights for the three variables of the feature vector are assigned as

[ ]LonLatTOC WWWW =

5. Weighted Mahalanobis distances id are computed between the usery vector and the

vector of the ith eligible source water in the ICRy matrix as

))(())(( 1iuser

TTiuseri yyWSyyWd −×−×= −

for all i=1 to m. T is the transpose of the vector.

6. The distances id are sorted and the K-nearest neighbors are chosen. The neighbor with

the smallest id value is considered the first or “nearest” neighbor, and so on until the Kth

neighbor. In this example, the K is chosen to be 18323 ≈== mK . As mentioned


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earlier, this heuristic rule has theoretical justifications and seems to work quite well in all the previous applications and also here. However, this can be chosen using objective methods as well (Lall and Sharma, 1996).

7. A probability metric with the weights obtained as

∑=

= K

ii

jjp

1

1

1

for all j = 1 to K is created. The cumulative sum of the probability metric p is then taken as for all i = 1 to K.

∑=

=i

jji pcp

1

8. Let the simulation begin for the month of January. A random number, u, between 0 and 1 is generated. If u ≤ 1cp , then a January value corresponding to the first neighbor is selected. If 1cp < u ≤ 2cp , then a January value corresponding to the second neighbor is selected, and so on. For example, with 18 nearest neighbors, 1cp = 0.2861, 2cp = 0.4292, … 18cp = 1.0000. Thus, if u = .3520, which is between 1cp and 2cp , then a January value corresponding to 2cp is selected. From this, one can see that the first neighbor is chosen about 29% of the time, the second neighbor about 14% of the time, and the percentages continue to decrease to the Kth neighbor.

9. Repeat step 8 to generate simulations for all the twelve months. The same is repeated to

obtain as many ensembles as required.

10. Steps 1 through 9 are repeated for a different user specified feature vector. RESULTS Results of applying the model with the feature vector including annual average concentration, latitude, and longitude to four utilities are shown in the following section. Simulation of monthly influent TOC concentrations for the City of Boulder’s Betasso Water Treatment Plant (CO) was first considered. The influent water quality to this plant is impacted by snow melt during spring runoff. The K-NN technique was used to generate 500 simulations of monthly influent TOC values. The statistics of the simulations were compared with that of the observed data at this location to evaluate the performance of the technique. Two simulation strategies were considered to show the versatility and flexibility of the method: (i) the data from the Boulder utility were dropped from the potential pool of neighbors and (ii) the data from the Boulder utility were included in the pool. The first strategy represents a scenario where a utility is considering a new plant that uses source water without a water quality parameter history. Furthermore, to demonstrate the flexibility of the method in neighborhood selection, the first simulation strategy was performed under two conditions – one in which only the annual average TOC concentration was considered in the feature vector by setting the weights of the latitude and longitude to zero in the distance calculation (shown in Figure D.1a) and in the other all three


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variables in the feature vector were given equal weights (Figure D.1b). This illustrates the impact of weighting the components of the feature vector on the resulting neighborhood. The weights in the neighbor selection can be optimized (Young, 1994; Yakowitz and Karlsson, 1987) or prescribed by the user relevant to the situation. For instance, if the variability of the water quality parameter is similar longitudinally, then the weights on the latitude can be reduced or eliminated, thus constraining the neighborhood to the longitudinal direction.

Figure D.1 Nearest neighbors (black dots) for the Boulder utility being simulated (black outlined triangle) where weights for latitude and longitude are equal to zero (a) and all weights are equal to one (b).

The simulations from the first strategy where the data from the Boulder utility was dropped from the pool, under the two conditions described above, are shown in Figure D.2 and Figure D.3. The simulations for each month and their annual average (Ann) are shown as box plots in which the box represents the 25th and 75th percentile, the whiskers show the 5th and 95th percentiles, points are values outside this range, and the horizontal line represents the mean. The box plots show the range of uncertainty, with a wider box indicating larger uncertainty. The triangles connected by dotted or solid lines correspond to the observed ICR data: 6 months in 1997 and 12 months in 1998. Also shown in the figures, as squares, are the utility’s three year average (3YA) values, which are averaged monthly and annual values from 2003 to 2005. If the observed value falls within the box (between the 25th and 75th percentile) it suggests that the simulations well-capture the historical properties. As shown in Figure D.2, when the simulations do not include the latitude and longitude in the neighbor selection, the seasonality present in the ICR and 3YA TOC data is not well simulated. Only 2 of the 18 monthly ICR values fall into the inner quartile simulation. However, the 1998 annual average value is well captured by the simulations even though the Boulder utility’s data was not used. Simulations using all three of the conditioning variables, as shown in Figure D.3, better capture the seasonality. Here, the simulations overestimate the TOC values from the ICR and only 7 of the 18 monthly ICR values fall into the inner quartile prediction. However, seasonality is better captured and the 3YA monthly and annual values are much better represented than when the latitude and longitude are not utilized (Figure D.2). The simulations are quite good considering that the data from the Boulder utility was excluded.

(a) (b)


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01

23

45

TOC

(mg/

L)

199719983YA

J F M A M J J A S O N D Ann

TOC

(m

g/L)

0

1

2

3

4

5

Figure D.2 Monthly TOC concentration simulations and their annual average (Ann) for the Boulder utility where the neighborhood was based only on the annual average TOC. 1997 and 1998 ICR data from the Boulder utility were not included in the pool of data for bootstrapping. 3YA represents the three year average of monthly values from 2003-2005.

01

23

45

TOC

(mg/

L)

199719983YA


TOC

(m

g/L)

0

1

2

3

4

5

Figure D.3 Monthly TOC concentration simulations and their annual average (Ann) for the Boulder utility where the neighborhood was based equally on all the three variables of the feature vector. 1997 and 1998 ICR data from the Boulder utility were not included in the pool of data for bootstrapping. 3YA represents the three year average of monthly values from 2003-2005. A simulation was also performed in which the neighborhood was selected only on the basis of latitude and longitude. The neighborhood in this case (Figure D.4) was similar to the case where equal weights were applied to all the variables (Figure D.1b) and consequently the


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simulations (Figure D.5) were similar to Figure D.3. However, this may not be the case for other utilities.

Figure D.4 Nearest neighbors (black dots) for the Boulder utility being simulated (black outlined triangle) where weight for annual average TOC was equal to zero. 1997 and 1998 ICR data from the Boulder utility were not included in the pool of data for bootstrapping.

01

23

45

6

TOC

(mg/

L)


199719983YA

Figure D.5 Monthly TOC concentration simulations and their annual average (Ann) for the Boulder utility where the neighborhood was based only on the latitude and longitude. 1997 and 1998 ICR data from the Boulder utility were not included in the pool of data for bootstrapping. 3YA represents the three year average of monthly values for 2003-2005.

Simulations from the second strategy where the ICR data from the Boulder utility were included in the pool and the weights for all the three variables in the feature vector were equal are shown in Figure D.6. As expected, the 1997 and 1998 ICR observed values fall within the inner quartile range for 14 of the 18 months and the seasonality is very well captured. The inner quartile range increases in the winter months (November to March) compared to the first strategy (Figure D.3) and decreases in the other months when the spring runoff impacts the water quality. In addition, data representing monthly and annual average observed values from 2003, 2004, and 2005 are overlaid on the figure. Here, the monthly variability for different years is well captured


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by the simulations. To quantify the comparison of values predicted by the model to observed monthly values from 2003, 2004, and 2005, the frequency of the observed monthly values within the predicted inner quartiles (box), 75th to 95th and 25th to 5th (whisker), and outliers (>95th and <5th) is plotted in Figure D.10. The expected outcome with a large data set would be that 50% of the observed data would fall into the inner quartiles (box), 40% into whiskers, and 10% into the outliers. For the simulations without the Boulder TOC data (Figure D.3) almost 20% of the observed values qualified as outliers (>95th and <5th), while when the Boulder TOC data was included in the simulations (Figure D.6) the frequency of observed data in the outlier group was reduced to that expected (about 10%) and the frequency of observed data in the inner quartile was increased to a higher than expected (83%). Thus, including the Boulder ICR data in the model development improved the predictive nature of the model. For this case, the improvement was a result of the model’s ability to better capture the seasonal differences.

01

23

45

TOC

(mg/

L)

19971998200320042005

TOC

(m

g/L)

0

1

2

3

4

5


Figure D.6 Monthly TOC concentration simulations and their annual average (Ann) for the Boulder utility where the neighborhood was based equally on all the three variables of the feature vector. 1997 and 1998 ICR data from the Boulder utility were included in the pool of data for bootstrapping. Monthly observed values for 2003, 2004, and 2005 are overlaid. To illustrate the extension of the method to other locations and other water quality parameters, six other simulations were run, each where all members of the feature vector were equally weighted, using both strategies, with and without the utility’s ICR data. Figure D.7 shows the simulation of influent TOC concentration values for the City of Birmingham’s Carson Filter Plant (AL) using the utility’s ICR data. The raw water source, Inland Lake, is a large


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reservoir that attenuates the impact of changes in runoff water quality. While the observed annual TOC values, with a mean of 1.93 mg/L, are not that dissimilar from those of Boulder, with a mean of 2.23 mg/L, there is much less seasonal variation in the monthly values and the range of the monthly inner quartile values is less than that for the Boulder data. Figure D.10 shows the frequency distribution of the observed 2003, 2004, and 2005 monthly values relative to the model predictions for models developed with (Figure D.7) and without utility ICR data. Again both models well capture the distribution of the observed data. The observed Birmingham data show a wider distribution compared to that predicted by the model when the ICR data was included in the model development: 37% in the inner quartile compared to 49%. The reason for this is that in this case, the 25th to 75th percentile (box) widened when the new neighbor replaced the data from the location.

01

23

45

TOC

(mg/

L)

19971998200320042005

TOC

(m

g/L)

0

1

2

3

4

5


Figure D.7 Monthly TOC concentration simulations and their annual average (Ann) for the City of Birmingham’s Carson Filter Plant where the neighborhood was based equally on all the three variables of the feature vector. 1997 and 1998 ICR data from the utility were included in the bootstrapping. Monthly observed values for 2003, 2004, and 2005 are overlaid.

Figure D.8 shows simulations of the source water alkalinity data for the New Jersey American Water Swimming River Treatment Plant (NJ) using the utility’s ICR data. The alkalinity varies seasonally with low values in the spring and high values in the fall. The simulation captures the seasonality of the alkalinity data. Observed monthly data from 2002 to 2005 tend to be under-predicted by the model simulation. However, as the frequency distribution in Figure D.8 shows, when the data is included, about 40% fall in the inner quartile range, and


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98% are within the 5th and 95th percentile of the simulation. There was little impact of including the ICR data from this utility in the development of the model.

++

+ ++ +

++

++ + + +

+

199719982002200320042005

Alk

alin

ity (a

s m

g/L

of C

aCO

3)

0

10

2

0

30

4

0

50

6

0

70


Figure D.8 Monthly alkalinity concentration simulations and their annual average (Ann) for the New Jersey American Water Swimming River Treatment Plant where the neighborhood was based equally on all the three variables of the feature vector. 1997 and 1998 ICR data from the utility were included in the bootstrapping. Monthly observed values for 2002, 2003, 2004, and 2005 are overlaid. Figure D.9 shows the simulation of the influent bromide concentration in the Ohio River at the Greater Cincinnati (OH) Water Works Richard Miller Water Treatment Plant using the ICR data in the model development. The bromide varies seasonally with high values occurring in the fall. The simulation captures this seasonality of the ICR bromide data and predicts high monthly variability, especially in the fall. Some (28%) of the observed monthly data from 2002 to 2005 were below the detection limit (which varied between 10 to 20 μg/L) and were therefore set to zero. The fit of the model to the observed data while good, was poorest for this case with respect to its ability to simulate the observed “outliers”. The frequency distribution in Figure D.10 shows about 40% fall in the inner quartile range, but about 25% are either less than the 5th or greater than the 95th percentile of the simulation. There was little impact of including the ICR data from this utility in the development of the model.


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+

++

+ + + +

+

+

+

+

+

199719982002200320042005

Bro

mid

e (μ

g/L)

0

50

1

00

1

50


Figure D.9 Monthly bromide concentration simulations and their annual average (Ann) for the Greater Cincinnati Water Works Richard Miller Water Treatment Plant where the neighborhood was based equally on all the three variables of the feature vector. 1997 and 1998 ICR data from the utility were included in the bootstrapping. Monthly observed values for 2002, 2003, 2004, and 2005 are overlaid.


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TOC (CO) TOC (AL) Alkalinity (NJ) Bromide (OH)

Freq

uenc

y of

Occ

urre

nce

(%)

0

20

4

0

60

80

100

W/O W W/O W W/O W W/O W

56

83

49

3744

40 40 40

25

6

34

46

5458

3136

1117 17

2 2

2924

19

Box Whisker Outlier

W/O = Without data from the utility

W = With data from the utility

Box Whisker Outlier

W/O = Without data from the utility

W = With data from the utility

Figure D.10 Frequency that recent data (from 2002-2005) was within the range of the 25th to 75th percentile (box), between the 75th and 95th or 5th and 25th percentile (whisker), or above the 95th or below the 5th percentile (outlier) of the simulations created from the ICR data.

DISCUSSION A K-NN based bootstrap technique was developed for simulating the variability of influent water quality at a location conditioned on a feature vector. The method is akin to estimating the conditional PDF based on the K-nearest neighbors and simulating from it. This “local” estimation is the main difference from the traditional methods, as well as its ability to capture any variability structure present in the data. As is common in environmental data, the generated PDFs with this technique showed many of the water quality simulations to be positively skewed. The simulated PDFs showed such variation in structure that conventional fits, such as normal or lognormal PDFs, would be unlikely to suffice. The methodology is capable of providing realistic variability of the influent water quality even when data from the location is not included in the approach; this is of use when variability is required at locations without data. This was demonstrated by application to TOC at the Boulder utility, and extension


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of the method was also shown by examining simulations from different locations and water quality parameters. The main strength of the technique is its capability to use large number of spatial data to capture the temporal variability when there is very limited temporal data – such as the case with the ICR data. Furthermore, the framework has flexibility in selecting a wide range of neighborhood depending on the site specific features. This can include varying the weights associated with each component of the feature vector in order to get the optimal neighbors for a location. Extensions to generate several water quality variables simultaneously and to include additional variables in the feature vector are straightforward. However, it should be noted that there is a tradeoff to including more variables in the feature vector. In this approach, the only components were latitude, longitude, and the average annual concentration of the variable of interest. It was not sufficient to only include latitude and longitude, as geographically close locations can have significantly different average annual values (Figure D.11). All three were included in an attempt to keep the algorithm simple and because they are all assumed to be readily available inputs. Including a fourth component, such as a TOC variability term, might improve the neighborhood selection, but is unlikely to be an easy value for a user to quantify. In addition, the algorithm could have been constructed so as to have a different feature vector for each month, where instead of average annual concentration, each would include average monthly concentration. This would be likely to improve the monthly simulations, but it would have required additional inputs.

Figure D.11 Average annual TOC concentration values in mg/L for surface waters in Ohio.

The K-nearest neighbors selected remain the same for all the months but each monthly simulation comes from any of the neighbors, thus providing a rich variety in the monthly sequences. Since the simulations are based on re-sampling of the historical observations, “new”


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values are not generated, but this can be addressed by adding some perturbations (Prairie et al., 2006). In this application, the box plots show the rich variety resulting from the technique. REFERENCES Buishand, T. A. & Brandsma, T. (2001). Multisite Simulation of Daily Precipitation and

Temperature in the Rhine Basin by Nearest-Neighbor Resampling. Water Resour. Res., 37, 2761– 2776.

Davis, J. (1986). Statistics and Data Analysis in Geology. Hoboken, NJ: John Wiley. Frey, M.F., Seidel, C., Edwards, M., and Parks, J.L. (2004). Occurrence Survey of Boron and

Hexavalent Chromium. Denver, CO: AwwaRF. Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. San Diego, CA:

Academic. Grantz, K., Rajagopalan, B., Clark, M., & Zagona, E. (2005). A Technique for Incorporating

Large-Scale Climate Information in Basin-Scale Ensemble Streamflow Forecasts. Water Resour. Res., 41 (W10410), 1-13.

Helsel, D. R. & Hirsch, R. M. (1995). Statistical Methods in Water Resources. New York: Elsevier.

Lall, U. (1995). Recent Advances in Nonparametric Function Estimation: Hydraulic Applications. Rev. Geophys., 33, 1093-1102.

Lall, U. & Sharma, A. (1996). A Nearest Neighbor Bootstrap for Resampling Hydrologic Time Series. Water Resour. Res., 32, 2803-2823.

McGuire, M. J., McLain, J.L., & Obolensky, A. (2002). Information Collection Rule Data Analysis. Denver, CO: AwwaRF.

Prairie, J., Rajagopalan, B., Fulp, T., & Zagona, E. (2006). Modified K-NN Model for Stochastic Streamflow Simulation. J. Hydrol. Eng., 11(4), 371-378.

Prairie, J., Rajagopalan, B., Fulp, T., & Zagona, E. (2005). Statistical Nonparametric Model for Natural Salt Estimation. ASCE J. Environ. Eng., 131(1), 130-138.

Rajagopalan, B. & Lall, U. (1999). A Nearest Neighbor Bootstrap Resampling Scheme for Resampling Daily Precipitation and Other Weather Variables, Water Resour. Res., 35 (10), 3089-3101.

Rajagopalan, B., Lall, U., Tarboton, D.G., & Bowles, D.S. (1997). Multivariate Nonparametric Resampling Scheme for Simulation of Daily Weather Variables. Stoch. Hydrol. Hydraul., 11, 65-93.

USEPA. (2000). ICR Auxiliary 1 Database Version 5.0. Query Tool Version 2.0 (CD-ROM). EPA 815-C-00-002, Washington, D.C.

Yates, D., Gangopadhyay, S., Rajagopalan, B., & Strzepek, K. (2003). A Nearest Neighbor Bootstrap Technique for Generating Regional Climate Scenarios for Integrated Assessments. Water Resour. Res., 39, 1199-1214.

Young, K.C. (1994). A Multivariate Chain Model for Simulating Climatic Parameters from Daily Data. J. Appl. Meteorol., 33, 661–671.

Yakowitz, S. & Karlsson, M. (1987). Nearest Neighbor Methods with Application to Rainfall/Runoff Prediction. In Stochastic Hydrology, edited by Macneil, J.B. & Humphries, G.J. Norwell, MA: D. Reidel.


E-1

APPENDIX E MODELING TOC, ALKALINITY, AND PH FROM RAW WATER TO THE

SEDIMENTATION BASIN USING THE ICR DATABASE


E-2

MODELING TOC, ALKALINITY, AND PH FROM RAW WATER TO THE SEDIMENTATION BASIN USING THE ICR DATABASE

ABSTRACT The removal of NOM by drinking water treatment processes is difficult because of the heterogeneous chemical nature of NOM. Parametric and nonparametric statistical regression methods were implemented to model the removal of NOM, as measured by TOC from raw water by conventional surface water treatment and to track the behavior of pH and alkalinity. The USEPA ICR data base was sampled for raw water and post-sedimentation data from surface water plants. All models were evaluated in terms of their fit and predictive capability, and for all variables explored, the nonparametric local polynomial models outperformed their parametric linear least-squares counterparts. This was most pronounced with the pH model, and was attributed to the nonlinear relationship found between it and one of the predictors. Finally, input uncertainty was incorporated into the TOC model to see output scenarios and the probability of exceeding a given limit. INTRODUCTION Modeling drinking water treatment processes can often be challenging because of complex chemical and physical nature of organic and inorganic constituents. The removal of NOM by coagulation, in particular, is difficult because of the heterogeneous chemical nature of NOM. Thus, utilities normally rely on operator experience and trial and error approaches in order to adjust process conditions. The inefficiency of this approach compromises the ability of plants to plan for future scenarios, such as estimating the economic impacts of proposed regulations or understanding the feasibility of inserting additional processes. Improvements on this have come from theoretically motivated models, such as with Edwards’ (1997) Langmuir-based semi-empirical model to predict NOM, as measured by dissolved organic carbon (DOC) removal during coagulation. However, use of this model is limited as it requires the pH of coagulation for input. The USEPA’s original water treatment plant model used an empirical equation that included initial TOC, alum dose, and pH (Harrington et al., 1992) and was modified by Solarik and colleagues (2000) to include Edwards’ model, which is coupled with a model of the carbonate system that allows the pH of coagulation to be predicted based on the initial pH, alkalinity, and the coagulant dose. In addition, Gupta and Shrivastava (2006) coupled a Monte Carlo simulation based approach with an integrated process model in order to assess uncertainty in water treatment plant performance. In this chapter, two statistical regression methods, traditional linear regression and nonparametric regression, are offered as additional attractive tools for modeling the overall coagulation/flocculation/sedimentation process (from raw water through sedimentation basin) in conventional surface water treatment plants. The variables considered are TOC, a measure of NOM, and two water quality variables which impact NOM removal by coagulation: pH and alkalinity. The change in these water quality parameters from the sedimentation basin to finished water quality will also be examined. The regression models are constructed using monthly water quality and chemical addition data recorded at conventional surface water plants monitored under the USEPA’s ICR. Influent TOC simulations, generated in Chapter 3, are incorporated into the TOC model to view the probability of exceeding a given limit.


E-3

DATA SET AND PREDICTORS The data used to develop and validate the models in this investigation were obtained entirely from the USEPA’s ICR database (USEPA, 2000). The ICR database covers 18 monthly intervals covering July 1997 through December 1998 (McGuire et al., 2002). The predictors for the variables considered included raw water quality variables and selected chemical additions between raw water and the sedimentation basin for conventional surface water utility plants in the continental U.S. For modeling the post-sedimentation TOC (TOCsed), the following predictors were considered: influent TOC (TOCin), influent pH (pHin), influent alkalinity (alkin), influent turbidity (turbin), influent temperature (tempin), influent total hardness (t-hardin), influent TSUVA (TSUVAin), and coagulant dose. The sample size for this modeling effort was 2291. For post-sedimentation pH and alkalinity (pHsed and alksed), the following predictors were considered: pHin, alkin, turbin, tempin, coagulant dose, lime dose, and chlorine dose. The sample size was 2997 and 3019, respectively, for pH and alkalinity. MODEL DEVELOPMENT In this investigation, both parametric linear regression and nonparametric local polynomial methods were employed to model water quality variables through the coagulation/sedimentation process. Because both of these techniques have been well documented in the literature, this chapter is limited to a brief overview of the main points of each technique. The reader is referred to the references throughout the following section for a detailed review. Statistical prediction models can be represented as:

exfy += )(

where f is a function fit to a set of predictor variables )(x , y is the dependent variable of interest, and e is the associated estimation error, generally assumed to be Normally distributed (with mean of 0 and variance 2σ ) and independent. There are two approaches to estimating the function f , which are described below. Linear Regression Traditionally, a linear relationship between the predictors and the dependent variable is assumed and is fit, of the form:

exxy kk +×++×+= βββ ...110

where there are k independent variables and the β coefficients are estimated from the data so as to minimize the mean squared errors. The variance of the errors are also obtained from theory (Helsel and Hirsch, 1995). The theory behind such linear regression models are well developed with several packages to implement them easily, hence, they are widely used (Helsel and Hirsch, 1995; Rao and Toutenburg, 1999). Higher orders of the variables (e.g., squares and cubes) can


E-4

be included in the above equation to fit nonlinear functional forms. The fitted equation is used to estimate the value of the dependent variable at future independent variable values. However, this traditional approach has several drawbacks including (i) the assumption of a Normal distribution of data and errors, (ii) the assumption of a linear relationship between the predictors and the dependent variable, (iii) higher order fits (e.g., quadratic or cubic) require large amounts of data for fitting, (iv) the models are not portable across data sets, and (v) model parameters are greatly influenced by outliers (Rajagopalan et al., 2005). Nonparametric Regression Nonparametric methods offer an attractive alternative to alleviating the drawbacks of the traditional linear regression approach. In this approach, the estimate of the function at any point, say, x*, is influenced by the data points within a small neighborhood of x*. Thus, no single equation is fit to the entire data, as in the case of traditional linear regression approach. This ‘local’ fitting provides the capability to capture any nonlinear features that might be present locally in the data. As will be seen, the nonparametric methods are more computationally intensive than their linear counterpart, but with the enormous increase in computation power in recent years, this is no longer an issue. Lall (1995) provides an excellent review of nonparametric methods and their various applications to hydrologic applications. There are several local functional estimation approaches, including kernel-based (Bowman and Azzalini, 1997), splines, K-NN local polynomials (Rajagopalan and Lall, 1999; Owosina, 1992); and locally weighted polynomials (Loader, 1999). The locally weighted polynomials, henceforth referred to as LOCFIT, are used in this application, as they are computationally efficient, easy to implement, and robust. Furthermore, with the availability of the powerful LOCFIT library (Loader, 2004) in the statistical software R, the implementation is made easy. This has been successfully used for salinity and flow modeling (Prairie et al., 2005, 2006), streamflow forecasting (Grantz et al., 2005; Regonda et al., 2006a) and in other hydrologic applications. The implementation steps of LOCFIT are as follows: For any point of interest, say, x*

(1) )( NK ×= α nearest neighbors (K-NN) from the observational data are identified, where α is the fraction of the observational data and N is the sample size.

(2) A polynomial of order P is fit to the identified K-NN. (3) The fitted polynomial is used to estimate the value of the dependent variable, Y(x*), at

x*. (4) The residuals from the polynomial fitted to the K-NN are used to obtain the standard

error variance (σle2) of the estimate (Loader, 1999, page 29-30).

(5) Repeat (i) through (iv) for all points of interest.

The polynomial coefficients are estimated by minimizing the weighted mean squared errors – as opposed to the mean squared errors in the traditional linear regression. The K-nearest neighbors are weighted based on their proximity to x* with highest weights to the nearest neighbors and zero weights to the farthest. Any weight function can be used to provide the weights and the approach is insensitive to the choice of the weight function. Notice that if K is set to N (i.e., all the available observation data), P is set to 1, and all the neighbors are given


E-5

equal weights, this approach collapses to the traditional linear regression. Thus, the local polynomial approach offers a general framework with the traditional linear regression model being a subset. The two parameters of the approach, K and P have to be identified for a given observation data. This is obtained using the generalized cross validation (GCV) function. The combination of K and P that minimizes the GCV function is chosen as the best set of parameters for the LOCFIT. The GCV function is defined as,

21

2

1

)ˆ(

),(

⎟⎠⎞

⎜⎝⎛ −

−

=∑=

NmN

yy

PKGCV

N

i

ii

where ii yy ˆ− is the residual (error) between the observed and predicted values, N is the number of data points, and m is the degrees of freedom of the fitted polynomial (Loader, 1999, page 31). If all of the points are used (i.e. 1=α so NK = ) and weighted equally and, the polynomial order is one, then the GCV for the parametric linear regression is calculated. The GCV has been found to be a good estimate of the predictive risk of the model, unlike other functions which are goodness of fit measures (Craven and Whaba, 1979). A step-by-step overview of this process can be seen in Prarie et al. (2005). In this application, the LOCFIT package of the statistical software R, developed by Loader (2004), was employed. The GCV measure can also be used to identify the best subset of predictors (Regonda et al., 2005; Regonda et al., 2006b). This would entail finding the combination of predictors (and the associated parameters K and P) that results in a minimum GCV value. This has been used in modeling water quality variables and shown to improve upon the traditional stepwise regression methods (Zachman et al., 2007). MODEL VALIDATION For each dependent variable explored in this investigation, a suite of linear and local polynomial regression models were fit with different variable combinations ranging from 2 to 6. The ‘best’ variable subset for both the methods was chosen based on the GCV score. For the ‘best’ models, R2 and root mean squared error (RMSE) were calculated as a way to quantify and compare their performances with other studies. R2 is defined as:

SSTSSER −= 12 , where ∑

=

−=N

iii yySSE

1

2)ˆ( and ∑=

−=N

iii yySST

1

2)(

and y is the predicted value and y is the mean of y. RMSE is defined as:

2

1

)ˆ(1 ∑=

−=N

iii yy

NRMSE


E-6

Mean errors (ME) were reported which is defined as:

∑=

−=N

iii yy

NME

1)ˆ(1

The above measures, in addition to the GCV score, provided a comprehensive

quantification of internal validation. In practice, the skill of these models in a predictive setting is desired. For this purpose, it is common to fit the model on a portion of the data and predict the withheld data and compute the skills. While this is an acceptable approach, the predictive skill depends on the data withheld. To address this the following approach is proposed whose steps are:

(1). Ten percent of the data are randomly selected and withheld. (2). The models are fit to the remaining dataset (3). The fitted models are used to estimate the values at the withheld points and skill

measures computed. (4). Steps 1 through 3 are repeated several number of times (100 in this case).

The skill scores from the simulations from the two methods are compared as box plots. A sensitivity analysis was also run for each of the models. In this analysis, a “baseline” case for the model was computed using the median value for each independent variable. Then, holding all other variables at their median value, the 25th percentile for each water quality variable was input into the best model. This was repeated with the 75th percentile value for each variable. In addition, nonparametric confidence intervals representing the 5th and 95th percentiles were added to each point. This was achieved by running each input (as described above) through 100 different models (using the same K and P as the “best” model) created from bootstrap samples (with replacement) of the original data. RESULTS The results from the investigation are presented in the following sections. TOC Model

The modeling of post-sedimentation TOC was first considered. Table E.1 shows the

variable combinations, GCV scores, and R2 values for the top five local polynomial (referred henceforth as, NP-1 through NP-5) and linear (referred henceforth as, L-1 through L-5) models. In the table, the regression coefficients for each independent variable were reported for the linear model, but were designated as X’s for the local polynomial models since the coefficients change depending on where the prediction is being estimated. In terms of variable selection, all 10 “best” models chose alkin, TOCin, and coagulant dose to be included. pHin was chosen the least, with only one of the linear models including it. Tempin was chosen by all of the linear models, but by only two of the local polynomial models. Turbin, TSUVAin, and t-hardin were chosen in only some of the models for both methods. In terms of GCV score and R2 value, the top five local polynomial models performed better than the top five linear models. However, within each respective method (NP or L), there was little difference in the GCV and R2 values among the top


E-7

five models. This is common in real data sets and often ignored in traditional stepwise regression approaches. In such cases, multi-model approaches that combine estimations from all the top models have been advocated and shown to improve the predictions (e.g., Regonda et al., 2006b). A multi-model approach was investigated in this paper, but did not effectively improve the model results, thus, only the top model within each category (i.e. NP-1 and L-1) was used. Figure E.1 shows the scatterplots of the observed and estimated values for the TOCsed concentrations from L-1 and NP-1 models. It can be seen that estimates from NP-1 showed a tighter scatter around the one-to-one relationship (straight lines in the figure) compared to the linear model. Also, the RMSE score was lower for NP-1 (0.39) than for the L-1 (0.50). It was interesting to note that for NP-1, the bestα was found to be 0.5 (i.e. half of the points were used in each local estimation) and a second degree polynomial (P=2) was utilized.


E-8

Table E.1 Selected predictor variables, designated with X’s for the nonparametric (NP) fits and with regression coefficient values for the linear (L) fits, and goodness-of-fit statistics for five best-fit NP and L models for predicting post-sedimentation

TOC (TOCsed) concentrations.

Model

Regression Coefficient Values

GCV R2 Intercept pHin

Alkin (as mg/L CaCO3)

Turbin (NTU)

Tempin (C)

TOCin (mg/L)

TSUVAin (L/m-mg)

Coagulant Dose

(mmol/L) T-Hardin (mg/L)

NP-1 X X X X X X 0.1780 0.8288 NP-2 X X X X X 0.1822 0.8187 NP-3 X X X X 0.1837 0.8102 NP-4 X X X X X 0.1838 0.8129 NP-5 X X X X X X 0.1850 0.8208 L-1 0.2109 0.0030 -0.0053 0.0095 0.5462 -0.8654 0.0013 0.2506 0.7149 L-2 0.2852 0.0027 0.0095 0.5423 -0.0320 -0.8349 0.0013 0.2515 0.7138 L-3 0.2018 0.0027 0.0098 0.5390 -0.8870 0.0014 0.2524 0.7125 L-4 0.1281 0.0102 0.0026 0.0098 0.5395 -0.8825 0.0014 0.2526 0.7125 L-5 0.2655 0.0048 -0.0049 0.0097 0.5463 -0.0224 -0.8249 0.2530 0.7121


E-9

0 1 2 3 4 6

RMSE = 0.50

0 1 2 3 4 5 6 7

Observed

(a)

0 1 2 3 4 5 6 7

Observed

(b)

Fitte

d

0

1

2

3

4

5

6

7

Fitte

d

0

1

2

3

4

5

6

7

RMSE = 0.39

Figure E.1 Scatterplot of observed versus fitted TOCsed concentration data from the best linear model (a) and the best local polynomial model (b). The straight line represents where Observed = Fitted.

The sensitivity analysis, which utilized the 25th and 75th percentile influent data shown in Table E.2, revealed that TOCin exerted the largest influence on TOCsed for both NP-1 (Figure E.2) and L-1 (Figure E.3). This was consistent with the TOCin coefficient (0.55), which had the


E-10

largest magnitude of all the coefficients found for L-1 (Table E.1). Figure E.2 also showed that the NP-1 model was slightly sensitive to the values of alkin and coagulant dose. Figure E.3 showed that none of the other variables included in L-1 exerted much influence on the model, but the direction of each coefficient was still examined for their consistency with prior knowledge. The alkin and t-hardin coefficients were positive, consistent with the fact that higher alkalinity (and higher total hardness) waters are associated with characteristics that make them less amenable to coagulation (Archer and Singer, 2006). The turbin coefficient was negative, which was consistent with findings that turbidity can serve as a nucleus for floc, which would decrease TOCsed (Letterman et al., 1999). The coefficient of tempin being positive was counterintuitive, as higher rates of reaction, and therefore more TOC removal, would be expected in association with higher temperatures. The TOCin had a positive coefficient since higher raw water TOC values will likely yield relatively higher TOCsed values. The coagulant dose was negative, underscoring the fact that TOC was removed during coagulation. It should be noted that the coefficients were obtained by minimizing the sum of squares of the model errors and no constraints are placed on the coefficients – hence, they can result in signs that might not be physically consistent all the time.

Table E.2 Summary statistics for influent water quality parameters, as well as TOCsed values, for the TOCsed model.

Percentile

5th 25th 50th 75th 95th pHin 6.52 7.22 7.70 8.10 8.40 Alkin

(as mg/L CaCO3)

9.00 28.0 73.0 110 178

Turbin (NTU) 0.690 1.70 3.90 8.60 27.0

Tempin (C) 5.00 11.7 17.0 23.0 28.0

TOCin (mg/L) 1.45 2.05 2.70 3.70 5.93

TSUVAin (L/m-mg) 1.19 1.91 2.50 3.13 4.68

Coagulant Dose (mmol/L) 0.0143 0.0405 0.0700 0.128 0.304

T-Hardin (mg/L) 11.0 38.0 98.0 148 284

TOCsed (mg/L) 1.15 1.60 2.05 2.80 4.05


E-11

25th Percentile50th Percentile75th Percentile

TOC

sed

0

1

2

3

4

Alk

in

Turb

in

Tem

p in

TOC

in

TSU

VA

in

Coa

g. D

ose

Figure E.2 Sensitivity of NP-1 predicted TOCsed concentrations to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case.

Alk

in

Turb

in

Tem

p in

TOC

in

T-H

ard in

Coa

g. D

ose


TOC

sed

0

1

2

3

4

Figure E.3 Sensitivity of L-1 predicted TOCsed concentrations to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case.


E-12

Alkalinity and pH Models Table E.3 shows the variable combinations, GCV scores, R2, and RMSE values for the best-fit models for post sedimentation alkalinity (alksed) and pH (pHsed). The NP-1 model for alksed performed better than its L-1 counterpart in terms of GCV and RMSE, but was virtually the same in terms of the fitted R2. In addition, scatterplots of the NP-1 and L-1 estimates with the observed values were very similar (Figure E.4), with the L-1 model exhibiting only slightly more spread, but no systematic pattern of over or under-estimation.

Table E.3 Selected predictor variables, designated with X’s for the top nonparametric (NP-1) and with regression coefficient values for the top linear (L-1) fits, and goodness-of-fit statistics for predicting pH and alkalinity in the sedimentation basin (pHsed and alksed).

Alksed pHsed NP-1 L-1 NP-1 L-1

Regression Coefficient

Values

Intercept 17.5 4.90 pHin -2.48 X 0.274 Alkin

(as mg/L CaCO3)

X 0.972 X 0.00476

Turbin (NTU) X -0.135 X -0.00892 Tempin (C) -0.133 -0.00875

Coagulant Dose (mmol/L) X -23.4 X

Lime Dose (ppm) X 0.229 X 0.0466

Chlorine Dose (ppm) X X -0.0306

GCV 70.9 139 0.172 0.479 R2 0.978 0.950 0.819 0.351

RMSE 7.8 11.8 0.366 0.691


E-13

0 50 100 150 200 250 300

Observed

(a)

Fitte

d

0

50

1

00

150

200

25

0 3

00

0 50 100 150 200 250 300

Observed

(b)

Fitte

d

0

50

1

00

150

200

25

0 3

00

Figure E.4 Scatterplot of observed versus fitted alksed concentrations (mg/L as CaCO3) data for the alksed model of the best linear model (a) and the best local polynomial model (b). The straight line represents where Observed = Fitted

The sensitivity analysis, which utilized the 25th and 75th percentile influent data shown in

Table E.4, revealed that alkin exerted the largest influence on alksed for both NP-1 (Figure E.5)


E-14

and L-1 (Figure E.6). This was consistent with the positive alkin coefficient (0.97) found for L-1 shown in Table E.3. Figure E.5 also showed that the NP-1 model was relatively sensitive to the values of coagulant dose. In addition, there was a large confidence interval around the lime dose 25th percentile value, but it should be noted from Table E.4 that the 5th, 25th, and 50th percentile input values were all zero (i.e. no lime dose was added). Figure E.6 showed that none of the other variables included in L-1 exerted much influence on the model and that the confidence intervals were very tight. Nevertheless, the linear regression coefficients were examined. The linear model for alksed had a negative coefficient for pHin, which was counterintuitive since high pHs are often closely connected with high alkalinities; however, as previously mentioned, pHin had a very small contribution on the overall model (Figure E.6). Turbin and coagulant dose both had negative coefficients, as higher turbidity necessitates more coagulant dose, and coagulant hydrolysis consumes alkalinity. The coefficient for lime dose was positive, since lime addition will add alkalinity to water, while the tempin coefficient was negative, indicating that higher temperatures allowed more of the alkalinity to be consumed in a given residence time of the coagulation/flocculation/sedimentation system.

Table E.4 Summary statistics for influent water quality parameters, as well as alksed values, for the alksed model.

Percentile

5th 25th 50th 75th 95th pHin 6.52 7.20 7.70 8.10 8.40 Alkin

(as mg/L CaCO3) 8.00 26.0 73.0 112 178

Turbin (NTU) 0.660 1.71 3.90 8.80 27.9

Tempin (C) 5.00 11.7 17.2 23.0 28.1


Lime Dose (ppm) 0.00 0.00 0.00 1.21 19.6

Chlorine Dose (ppm) 0.00 0.00 1.40 2.80 6.00

Alksed (as mg/L CaCO3)

5.00 19.0 62.0 105 168


E-15

Alk

sed

0

2

0

40

60

8

0

10

0

120

Alk

in

Turb

in

Coa

g. D

ose

Lim

e D

ose

Chl

orin

e D

ose


Figure E.5 Sensitivity of NP-1 alksed concentrations (mg/L as CaCO3) to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case.

pHin

Alk

in

Turb

in

Tem

p in

Coa

g. D

ose

Lim

e D

ose


Alk

sed

0

2

0

40

60

8

0

10

0

120

Figure E.6 Sensitivity of L-1 alksed concentrations (mg/L as CaCO3) to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case. The NP-1 model for pHsed performed much better than its L-1 counterpart in terms of GCV, R2, and RMSE and with a tighter scatter (Figure E.7) in all pH ranges, but especially for


E-16

observed pHsed values below 7 and above 8. For pHsed values of about 9.5, there was substantial under-estimation by the NP-1 model, but above this value the model fit was better. The L-1 model did a relatively good job at estimating observed values between 6 and 8, but showed a systematic bias with observed values below 6 being over-estimated and values above 8 under-estimated. This was investigated further by examining the scatterplots of each of the predictor variable with the pHsed. Figure E.8 shows the scatterplot of alkin versus pHsed – a strong nonlinearity can be observed. This nonlinearity could be contributing to the ineffectiveness of the linear model in this case. It was interesting and consistent to note that for both pHsed and alksed models the bestα for the NP-1 model was found to be 0.2 (i.e. twenty percent of the points were used in each local estimation) and a second degree polynomial ( P =2) was utilized. The sensitivity analysis for the pHsed model, which utilized the 25th and 75th percentile influent data shown in Table E.5, revealed that none of the influent variables exerted much influence on the value of pHsed for NP-1 (Figure E.9) or L-1 (Figure E.10). Similar signs in the coefficients of L-1 model for the pH were expected, since pH and alkalinity are inextricably linked through carbonate equilibrium. Indeed, this was the case with alkin, turbin, tempin, and lime. However, the coefficient of pHin was found to be positive as to be expected, unlike what was found for alkalinity. Coagulant dose was found not to be a contributor to post sedimentation pH, somewhat surprising since coagulants consume alkalinity and can lead to lower pH values. The chlorine dose was found to contribute with a negative coefficient, as would be expected when chlorine is added as an acid.


E-17

2 4 6 8 10

2 4 6 8 10

Observed

(a)

Fitte

d

2

4

6

8

1

0

2 4 6 8 10

Observed

(b)

Fitte

d

2

4

6

8

1

0

Figure E.7 Scatterplot of observed versus fitted pHsed data for the pHsed model of the best linear model (a) and the best local polynomial model (b). The straight line represents where Observed = Fitted.


E-18

pHse

d

4

6

8

1

0

0 50 100 150 200 250 300

Alkin (as mg/L CaCO3)

Figure E.8 Scatterplot of influent alkalinity (alkin) versus pH in the sedimentation basin (pHsed).

Table E.5 Summary statistics for influent water quality parameters, as well as pHsed values, for the pHsed model

Percentile 5th 25th 50th 75th 95th

pHin 6.52 7.20 7.70 8.10 8.40 Alkin

(as mg/L CaCO3)

8.00 25.0 72.0 112 178

Turbin (NTU) 0.670 1.74 3.93 8.80 28.0

Tempin (C) 5.00 11.5 17.2 23.0 28.2


Lime Dose (ppm) 0.00 0.00 0.00 1.32 19.7

Chlorine Dose (ppm) 0.00 0.00 1.38 2.75 6.00

pHsed 5.92 6.70 7.21 7.64 8.56


E-19

pHse

d

0

2

4

6

8

10

1

2

14

pHin

Alk

in

Turb

in

Coa

g. D

ose

Lim

e D

ose

Chl

orin

e D

ose


Figure E.9 Sensitivity of NP-1 pHsed to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case.

pHse

d

0

2

4

6

8

10

1

2

14

pHin

Alk

in

Turb

in

Tem

p in

Lim

e D

ose

Chl

orin

e D

ose


Figure E.10 Sensitivity of L-1 pHsed to changing each variable value to the influent 25th and 75th percentile value while leaving all other variables at their median value. Confidence intervals representing the 5th and 95th percentiles are included for each case.


E-20

Validation: TOC, Alkalinity, and pH Models The results of skill measures by witholding 10% of the data one hundred times are shown in Figure E.11 (R2) and Figure 12 (RMSE) as box plots. The box represents the 25th and 75th percentile (inner quartile range), the whiskers show the 5th and 95th percentiles, points are values outside this range, and the horizontal line represents the median. The box plots show the range of uncertainty, with a wider box indicating larger uncertainty. The overlaid red dot shows the values from the fit based on the entire data. In general, the NP-1 model exhibited higher R2 and lower RMSE, more dramatically for pHsed.

TOCsed Alksed pHsed

NP-1 L-1 NP-1 L-1 NP-1 L-1

R2

0.0

0.

2

0.4

0.

6

0.8

1.0

Figure E.11 Simulations of R2 values when 10% of the data is dropped for TOCsed, alksed, and pHsed models. NP-1 and L-1 are the best models for the nonparametric and linear cases, respectively, for each dependent variable being considered. Red dots are R2 value from the fitting.


E-21

NP-1 L-1 NP-1 L-1 NP-1 L-1

TOCsed Alksed pHsed

RM

SE

0.0

0.5

1.

0

1.

5

2

.0

Figure 12 Simulations of RMSE values when 10% of the data is dropped for TOCsed (mg/L), alksed (as 10 x mg/L CaCO3), and pHsed (pH units). NP-1 and L-1 are the best models for the nonparametric and linear cases, respectively, for each dependent variable being considered. Red dots are RMSE value from the fitting.

Incorporating Input Uncertainty: TOC Model These models can be run with input uncertainty to evaluate various output scenarios. Because the sensitivity analysis showed that the TOC model was mainly affected by influent TOC, the best TOCsed model (NP-1), was run with input scenarios generated for influent TOC in Chapter 3 for the City of Boulder’s Betasso Water Treatment Plant (CO). The input TOC and predicted TOCsed scenarios were shown side-by-side in Figure E.13. As would be intuitively expected, the box plots that represent the simulations shift down from the influent to the sedimentation basin. Figure E.14 only shows the TOC model predictions, and validated the model by overlaying the actual measured value at Boulder, as well as what the model predicted for that value. Ideally, the measured and predicted values would be right on top of one another, and they would be within the inner quartile range of the box. Although this was not the case for every month, the output scenarios quantify the uncertainty of the TOC in the sedimentation basin. In the Stage 2 D/DBP Rule, one method of compliance relies on the TOC in the sedimentation basin being less than 2 mg/L. Thus, the output scenarios allowed for the calculation of the probability of exceeding this limit, which is shown in Table E.6. Here it can


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be seen that November only has a 12% chance of being out of compliance, but May has a 73% chance of being out of compliance.


InfluentSedimentation Basin

TOC

(mg/

L)

0

1

2

3

45

Figure E.13 Monthly influent TOC simulations and TOCsed predictions for the Boulder utility.


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MeasuredPredictedLimit


TOC

sed

(mg/

L)

0

1

2

3

4

Figure E.14 Monthly TOCsed predictions for the Boulder utility with actual measured and predicted values overlaid. Dotted line represents the threshold for exceedence.

Table E.6 Probability of exceeding the 2 mg/L TOC limit based on the output scenarios.

Period Probability of Exceedence

Jan 19% Feb 21% Mar 20% Apr 26% May 73% Jun 58% Jul 64%

Aug 40% Sep 25% Oct 31% Nov 12% Dec 35%

Jan-Dec 35% Ann 18%


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WATER QUALITY CHANGES FROM POST-SEDIMENTATION TO FINISHED WATER Scatterplots of post-sedimentation TOC, alkalinity , and pH with their corresponding values in the finished water are shown in Figure E.15, Figure E.16, and Figure E.17, respectively. It can be seen that TOCsed and alksed had a tight scatter around the one-to-one line (Figure E.15 and Figure E.16). However, the mean error (ME) was slightly negative for TOC, reflecting some removal of particulate organic matter by the filter, and positive for alkalinity, reflecting some base addition for corrosion control. The slope of the TOC plot was 0.88 (with intercept set to zero), revealing that an additional 22% of TOC was removed between the sedimentation basin and the finished water. The slope of the alkalinity plot was 1.0 (with the intercept set to zero), indicating that there was no change in alkalinity. Figure E.17 shows no apparent relationship between pHsed and the finished water pH (pHfin), other than the pHfin values being higher when the pHsed values were below 7, consistent with utilities modifying the pH after sedimentation to minimize corrosion in the distribution system. Thus, while the pHsed model is useful if a process was going to be added after sedimentation, a new model should be fit, including the relevant chemical additions, to capture the relationship from sedimentation to finished water.

0 2 4 6 8 10

TOCsed

TOC

fin

0

2

4

6

8

10 ME = -0.2416

N = 2210

Figure E.15 Scatterplot of finished TOC (TOCfin) versus TOC of sedimentation (TOCsed). Straight line represents where TOCfin = TOCsed.


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0 50 100 150 200 250 300

Alksed

Alk

fin

0

50

100

150

200

250

300 ME = 4.827

N = 2553

Figure E.16 Scatterplot of finished alkalinity (alkfin) versus alkalinity of sedimentation (alksed). Straight line represents where alkfin = alksed.

4 6 8 10 12

pHsed

pHfin

4

6

8

1

0

12 ME = 0.5335

N = 2938

Figure E.17 Scatterplot of finished pH (pHfin) versus pH of sedimentation (pHsed). Straight line represents where pHfin = pHsed.


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DISCUSSION Statistical methods of regression are useful in modeling efforts, especially when the underlying relationships are complex. Nonparametric regression methods have shown promising results in a variety of water management applications (e.g. Prairie et al., 2005; Grantz et al., 2007), and this paper extends their influence to modeling conventional drinking water treatment (coagulation/flocculation/sedimentation). In this investigation, the nonparametric local polynomial models outperformed their parametric linear least-squares counterparts in terms of fit and predictive capability. This was most pronounced with the pH model, and was attributed to the nonlinear relationship found between it and one of the predictors. However, nonparametric models are not without shortcomings, including that they require substantial computer power and are not trivial to implement. As such, parametric models, despite their previously mentioned drawbacks, can provide a practical alternative. As water quality standards heighten, being able to estimate intermediate and finished water quality is important for decision making and planning in water treatment. In many cases, decision-making tools are developed to help utilities weigh various options, such as additional processes, as they plan for their future. Predictive models, such as those developed in this chapter, could be useful in estimating variables of consequence that could be inputs in a decision-making tool. This could be useful when a decision tool is trying to limit its required user inputs. The statistical model for TOC that was presented in this paper incorporated input uncertainty in order to examine the probability of the output exceeding a given limit. Input uncertainty arises from the inherent variability in source waters, whether from climate, geology, or water management factors. Nevertheless, there are other sources of uncertainty that were not considered in this chapter. The measurement error of the ICR database was not considered in this analysis. NOTATION The following symbols and abbreviations are used in this document: alkin = influent alkalinity (as mg/L CaCO3); alkfin = finished water alkalinity (as mg/L CaCO3); alksed = alkalinity in the sedimentation basin (as mg/L CaCO3); e = error term; i = index term; K = number of neighbors; m = number of predictors; N = sample size; P = order of the polynomial; pHin = influent pH; pHfin = finished water pH; pHsed = pH in the sedimentation basin; tempin = influent temperature (C); turbin = influent turbidity (NTU); TOC in = influent TOC (mg/L) TOCfin = finished water TOC (mg/L)


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TOCsed = TOC in the sedimentation basin (mg/L); TSUVAin = influent total specific ultraviolet absorbance (L/m-mg); t-hardin = influent total hardness (mg/L); x = independent variable; y = dependent variable; α = fraction of sample size used in neighborhood; and β = vector of model parameters. REFERENCES Archer, A.D. and Singer, P.D. (2006). An evaluation of the relationship between SUVA and

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Rajagopalan, B., Grantz, K., Regonda, S., Clark, M., and Zagona, E. (2005). Ensemble streamflow forecasting: methods and applications. In U. Aswathanarayana, Ed., Advances in Water Science Methodologies, Netherlands: Taylor and Francis.

Rajagopalan, B., and Lall, U. (1999). A nearest neighbor bootstrap resampling scheme for resampling daily precipitation and other weather variables. Water Resour. Res. 35, 3089.

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Regonda, S.K., Rajagopalan, B., Lall, U., Clark, M., and Moon, Y. (2005). Local polynomial method for ensemble forecast of time series. Nonlinear Proc. Geoph. 12, 397.

Solarik, G., Summers, R.S., Soh, J., Swanson, W.J., Chowdhury, Z.K., and Amy, G.L. (2000). Extensions and verification of the water treatment plant model for disinfection by-product formation. In S.E. Barrett, S.W. Krasner, and G.L. Amy, Eds., ACS Symposium Series 761: Natural Organic Matter and Disinfection By-Products, Characterization and Control in Drinking Water, Washington, D.C.: American Chemical Society.

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Zachman, B., Rajagopalan, B., and Summers, R.S. (2007). Modeling NOM breakthrough in GAC adsorbers using nonparametric regression techniques. Eviron. Eng. Sci. 24, 1280.


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Boulay, N. and M. Edwards (2001). “Role of Temperature, Chlorine and Organic Matter on Copper Corrosion Byproduct Release in Soft Waters.” Water Research, 35:3:683-690.

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Carlson, K., S. Via, B. Bellamy and M. Carlson (2000). “Secondary Effects of Enhanced Coagulation and Softening,” J. AWWA, 92:6:63-75.

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ABR-1

ACRONYMS AND ABBREVIATIONS

Al aluminum AO advisory output AOC assimilable organic carbon AOP advanced oxidation process AWWA American Water Works Association BDOC biodegradable dissolved organic carbon CCL Candidate Contaminant List CCPP calcium carbonate precipitation potential CCT corrosion control treatment CT disinfectant residual concentration (mg/L) × effective contact time (min) DBPs disinfection byproducts DO dissolved oxygen DOC dissolved organic carbon DS distribution system EBCT empty bed contact time EC enhanced coagulation EDC endocrine disrupting compound ES effective size GAC granular activated carbon gpm gallons per minute GWR Groundwater Rule HAAs haloacetic acids HPC heterotrophic plate count ICR Information Collection Rule IFE individual filter effluent IPC in-plant corrosion LCR Lead and Copper Rule LSI Langlier Saturation Index LT2ESWTR Long Term 2 Enhanced Surface Water Treatment Rule MCL maximum contaminant level MF microfiltration MGD million gallons per day MTBE methyl-tertiary butyl ether NDMA N-nitrosodimethylamine


ABR-2

NF nanofiltration NOM natural organic matter NPDES National Pollutant Discharge Elimination System NTU nephelometric turbidity unit O&M operations and maintenance PAC powdered activated carbon PACl polyaluminum chloride PCPs personal care products PD primary disinfection PL practical limitation (to GAC replacement) PSW Partnership for Safe Water RH residuals handling RO reverse osmosis RP regrowth potential RW red water SC simultaneous compliance SCTool Simultaneous Compliance Tool sf square foot Secondary MCL secondary maximum contaminant level SWTR Surface Water Treatment Rule SOC synthetic organic chemical Stage 1 D/DBPR Stage 1 Disinfectants and Disinfection Byproducts Rule Stage 2 D/DBPR Stage 2 Disinfectants and Disinfection Byproducts Rule SUVA specific ultraviolet absorbance (UV254 ×100/TOC) T&O taste and odor TDS total dissolved solids THMs trihalomethanes TOC total organic carbon UCMR2 Unregulated Contaminants Monitoring Rule 2 UF ultrafiltration UM utility management USEPA United States Environmental Protection Agency UV ultraviolet disinfection UV254 ultraviolet absorbance at a wavelength of 254 nanometers VOC volatile organic chemical WITAF Water Industry Technical Action Fund WQ water quality WTP water treatment plant


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